Genomic Analysis of Complex Disease in the Norfolk Island Bounty Mutineer Descendents
Author Cox, Hannah Claxton
Published 2011
Thesis Type Thesis (PhD Doctorate)
School School of Medical Science
DOI https://doi.org/10.25904/1912/1806
Copyright Statement The author owns the copyright in this thesis, unless stated otherwise.
Downloaded from http://hdl.handle.net/10072/365525
Griffith Research Online https://research-repository.griffith.edu.au Genomic Analysis of Complex Disease in the Norfolk Island Bounty Mutineer Descendents
Hannah Claxton Cox BBiomedSci (Hons)
School of Medical Science Griffith Health Griffith University
Submitted in fulfilment of the requirements of the degree of Doctor of Philosophy
June 2011
Abstract
The beginning of the new millennium has witnessed the completion of the first DNA reference sequence for Homo sapiens (Human Genome Project) and the establishment of a human SNP haplotype map (International HapMap Project). In particular, these advancements have revolutionised our understanding of the role of genes and genetic variation in the study of human traits and disease. Although a plethora of genes underlying complex phenotypes have been characterised, many are family specific or only explain a portion of the total underlying genetic component. Increasingly, researchers are turning to population isolates to dissect the genetic and non-genetic components underlying common, complex human disorders.
Isolates are populations that have expanded in severe geographical or cultural isolation from a limited number of original founders. Genetic and non-genetic heterogeneity are limited or even reduced in isolates due to the presence of various genetic, environmental and societal factors. Norfolk Island is a young, South Pacific population isolate whose origins are intertwined with the fate of Her Majesty’s Armed
Ship, the Bounty. The majority of permanent residents are descended from 9 Isle of
Man, Bounty Mutineers and 6 Tahitian women who colonised Pitcairn Island (then uninhabited) in 1790 and relocated to the then uninhabited Norfolk Island in 1856.
These historical origins have been confirmed with ancestry informative markers.
Medical and epidemiological data and DNA is available for 600 participants from the
Norfolk Island Health study. Of these individuals, 377 are related through a complex,
11-generation pedigree with unbroken lineage to the Bounty mutineer founders.
Demographic investigations indicate the Norfolk Island community is a high risk
Page I genetic isolate for cardiovascular disease (CVD), particularly hypertension, obesity and hyperlipidemia. Genetic studies thus far have attempted to define the genetic component underlying CVD risk and subsequently identified susceptibility regions on chromosomes 1, 2, 18, and 20 segregating with various CVD phenotypes. The
Norfolk population presents a novel and interesting cohort for further characterisation of CVD risk and evaluation of another complex multifactorial disease, migraine. The central aims of this thesis relate to the use of this population to identify genetic and environmental factors that contribute to CVD risk and migraine in this cohort.
The first aim of this research was to further explore the demographic and molecular aetiology of CVD-risk traits in the Norfolk pedigree by multivariate analysis in terms of a genome-wide microsatellite scan. The CVD component of this thesis assessed the
CVD-risk traits of systolic and diastolic blood pressure, total cholesterol, triglycerides, high density lipoprotein cholesterol, low density lipoprotein cholesterol, and body mass index and was extended to include additional obesity related traits such as hip circumference, waist circumference, percentage body fat and weight.
Using multivariate analysis methods, these 11 highly correlated CVD risk variables were reduced to 4 variables (principal components) that explained 83% of the variation in the 11 original variables. These 4 variables explained ‘obesity and
Syndrome X’ risk (principal component 1), ‘essential hypertension’ risk (principal component 3) and ‘stroke and heart attack’ risk (principal component 4) in the extended Norfolk Island pedigree. A significant genetic component was detected for principal components 1, 2 and 4. The highest detected LOD score, 1.85 resulted from principal component 2 on chromosome 5q35. The highest LOD scores detected for heritable principal components 1 and 4 occurred on chromosomes 10p11.2
Page II (LOD=1.27) and 12q13 (LOD=1.63), respectively. These loci were undetected using univariate analysis methods.
Another central aim of this research was to assess another multifactorial disorder, migraine, by investigating this discrete phenotype for the first time in the Norfolk
Island Health Study Cohort. Phenotypic data for migraine and genome wide microsatellite data was assessed in 600 individuals, the Norfolk pedigree (96 migraineurs and 281 migraine-free individuals) and the unrelated subset (54 migraineurs and 165 migraine-free individuals) of this cohort. Heritability estimation supported a significant genetic component for migraine (h2=0.53, P<0.05) in this cohort. In addition, linkage peaks were detected for a region on chromosome 13q33.1
(LOD=1.6; P=0.003) and chromosome 9q22.32 (LOD=1.26; P=0.008), both of which were replicated (P<0.05) in the unrelated Norfolk Island sub-group.
The final component of the research described in this thesis centred on a genome wide scan of SNP markers aimed at identifying genomic variants associated with migraine using the Illumina BeadChip platform in core pedigree members (N=285). Genome wide analysis identified marginal evidence for association for 7 SNPs (P<1x10-5). The most significantly associated SNP (P=1.96x10-6) occurred in an intronic region of the
ADAMTSL1 gene on 9p22.2-p22.1. In addition, candidate gene analysis identified variants in 8 novel migraine genes that were significant at the gene wide threshold.
Four of these genes, ADARB2, HTR7, GRM7 and SLC17A8 were related through their roles in serotonin and glutamate pathways.
Lastly, we undertook a systematic analysis of previously known migraine candidate genes as well as genes identified in genome wide association scans, and familial genes implicated in migraine with aura (MA) and familial hemiplegic migraine (FHM) Page III subtypes. The strongest associated SNP, rs2813554 (P=0.0011), was significant at the gene wide level. This SNP was located in an intronic region of the estrogen receptor
(ESR1) gene. Interestingly, 9 other SNPs were significant at the nominal level
(P<0.05) and formed 2 distinct haplotypes blocks extending 41kb and 39kb across the
ESR1 gene transcript. No evidence of association in the Norfolk pedigree was evident for the recently identified migraine gene candidates, MTHD and NGFR, (P>0.05).
However, nominal level association was detected for rs3858331 (p=0.0012) in the
MA gene KCNK18 and rs8104676 (P=0.016) in the FHM1 gene CACNA1A. Overall results provided compelling evidence for the involvement of ESR1 gene in migraine susceptibility in the Norfolk pedigree.
Overall, genomic analysis methods identified several CVD susceptibility loci and migraine candidate genes in the Norfolk Island Bounty mutineer pedigree. The progression of the Norfolk Island Health Study will rely on the on-going recruitment of pedigree members, increasing current marker density and further replication in independent populations. In 2009 a second wave of participant recruitment on Norfolk
Island was commenced and a new repository of phenotypic and biological data (DNA and RNA) established. These new samples will allow a focus on characterising functional variants and their effects on gene expression to further characterise the genetic basis to complex disease in this unique population.
Page IV Acknowledgements
The work described in this thesis was completed in the laboratory of Professor Lyn R
Griffiths, the Genomics Research Centre (GRC), from 2006 to 2011 as fulfilment of the Doctor of Philosophy Program in the School of Medical Science and the Griffith
Health Institute, Griffith University, Gold Coast, Australia. I commenced study and casual employment in Lyn’s laboratory in 2004. Since this moment Lyn pushed me to achieve beyond my limits, believing in my abilities when I did not. I have grown personally and professionally under Lyn’s guidance and gained many valuable skills I will use throughout my personal and professional life. Lyn generously provided additional scholarship support throughout my study as well as opportunities to attend numerous professional development activities both nationally and internationally. I will always remember viewing the Dead Sea Scrolls at the American Society of
Human Genetics (ASHG) Annual Meeting, San Diego, USA and my first seminar presentation and visit to Miramare Castle at the 4th International Meeting on Genetics of Complex Diseases and Isolated Populations, Trieste, Italy. First and foremost all the work described in this thesis is dedicated to Professor Griffiths. Thank you Lyn for everything.
I extend my appreciation to my co-supervisors Dr Rod Lea and Dr Dale Nyholt of the
GRC, Griffith University, Gold Coast Australia and the Neurogenetics Laboratory,
Queensland Institute of Medical Research (QIMR), Brisbane, Australia, respectively, for their supervision and contributions to this thesis. Dr Lea has been essential for the progression and completion of the migraine genome wide association study described in chapter 8 and for the continued growth of the Norfolk Island Health study. I thank him for his patience and guidance in these undertakings. Page V A National Health and Medical Research Council (NHMRC) of Australia Biomedical
Post-graduate Scholarship (428268) from 2007-2009, a Griffith University Post- graduate Travel Grant in 2009, and a GRC funded Scholarship from 2006-2010 on behalf of Professor Lyn Griffiths have provided financial support for my candidature.
An NHMRC grant and a Medical Bioinformatics Genomics Proteomics Program
(MGBPP) grant have funded the Norfolk Island Health Study. This funding is all gratefully acknowledged.
The Department of Genetics, Texas Biomedical Research Institute (TBRI), San
Antonio, Texas, USA has been an integral part of the completion of all work detailed in this thesis. I would like to acknowledge the genotyping support provided by Dr
Joanne Curran, Dr Claire Bellis and Dr Melanie Carless for the Illumina Infinium
DNA analysis BeadChips. Also, special thanks to Dr Thomas D Dyer, Dr Jac
Charlesworth and especially, Dr John Blangero for their exhaustive contributions to the SOLAR-based analyses detailed throughout this thesis. I am also extremely grateful to Dr Blangero and Dr Curren, for generously hosting me at TBRI in
November 2007 to undertake statistical training in the SOLAR software package.
Griffith University, Gold Coast support services have been crucial for the progression of the Norfolk Island Health Study. My gratitude to the Research Computing Services team at Griffith University for providing support on Griffith's Sun Solaris HPC cluster, particularly Paul Jardine for making all SOLAR-based analyses possible at
Griffith University.
I would like to thank the many members of the GRC with whom I shared the laboratory, office spaces and many morning coffees during my time at Griffith
University. In particular, thank you to the following individuals: Claire (who provided Page VI inspiration for the principal components chapter), Emma, Learne, Emily, Bridget,
Lizzie, and Sherin. A special mention to Dr Larisa Haupt, Dr Dianne Moses, Rachel
Mackay and Sharon Quinlan, four truly amazing women who provided guidance and support at various stages throughout my candidature.
This thesis is dedicated to many special people in my life: my dear friends Rachel,
Erin and Tara, my parents Geoff and Mary, my siblings Marty and Caity, my extended family (particularly Uncle Hans, Uncle Peter, Marney and Poppy), and my partner Stuart. I thank you all for your input to this thesis and for providing me with unconditional love and encouragement these past five years.
Lastly, I extend my appreciation to all the individuals from Norfolk Island who volunteered for this study and for their continued participation in the Norfolk Island
Health Study.
Page VII Statement of Originality
The material presented in this thesis has not previously been submitted for a degree or diploma in any university and to the best of my knowledge contains no material previously published or written by another person except where due acknowledgement is made in the thesis itself.
______
Hannah Claxton Cox
Page VIII Table of Contents
Abstract ...... I Acknowledgements ...... V Statement of Originality ...... VIII Table of Contents ...... IX List of Figures ...... XIII List of Tables ...... XIV List of Abbreviations ...... XV Publication List ...... XVIII General Introduction ...... 1 The Aims of This Research ...... 3 The Significance of This Research...... 4
Chapter 1: Isolated Population Genetics ...... 9 1.1 Overview ...... 9 1.1.1 Founder Effect ...... 10 1.1.2 Migration ...... 12 1.1.3 Consanguinity ...... 12 1.1.4 The Environment ...... 13 1.1.5 Population Bottlenecks ...... 14 1.1.6 Random Genetic Drift: The Impact on Allelic Diversity and LD ...... 15 1.2 Gene Mapping in Isolated Populations ...... 18 1.3 The Norfolk Island Isolate ...... 22 1.3.1 The Descendents of the Bounty Mutineers ...... 22 1.3.2 Norfolk Island: Molecular Genetic Characterisation ...... 25 1.3.3 Close Resemblances: The Island Isolate of Kosrae ...... 26 1.4 Genetic Mapping Techniques ...... 28 1.4.1 Association ...... 28 1.4.2 Linkage Analysis ...... 29 1.5 Summary ...... 31
Chapter 2: General Disease Background ...... 33 2.1 Overview ...... 33 2.2 Migraine Introduction ...... 33 2.3 Definition ...... 34 2.4 Clinical Classification and Diagnosis ...... 34 2.4.1 Migraine without Aura ...... 36 2.4.2 Migraine with Aura ...... 37 2.4.3 Probable Migraine ...... 39 2.4.4 A Severe Subtype of MA: Familial Hemiplegic Migraine ...... 40 2.4.5 The Stages of a Typical Migraine Attack ...... 41 2.5.1 Incidence ...... 42 2.5.2 Prevalence ...... 44 2.5.3 Migraine in The United States of America ...... 44 2.5.4 Migraine in Europe ...... 45 2.5.5 Global Estimates ...... 46 2.5.6 Burden of migraine ...... 46 Page IX 2.6 Pathophysiology ...... 47 2.6.1 Cortical Spreading Depression ...... 47 2.6.2 The Trigeminovascular System ...... 48 2.6.3 A Typical Migraine Gene ...... 49 2.6.4 Pathological insights from Hemiplegic Migraine ...... 50 2.6.5 Pathophysiology and pharmacology ...... 51 2.6.6 Comorbidity ...... 52 2.7 Evidence of Genetic Susceptibility ...... 53 2.7.1 Twin Concordance and Familial Aggregation ...... 53 2.7.2 Heritability ...... 55 2.7.3 FHM and Common Migraine ...... 57 2.8 Molecular Genetics ...... 58 2.8.1 Familial Hemiplegic Migraine ...... 58 2.8.2 Familial Hemiplegic Migraine Type 1: The CACNA1A Gene ...... 61 2.8.3 Familial Hemiplegic Migraine Type 2: The ATP1A2 Gene ...... 62 2.8.4 Familial Hemiplegic Migraine Type 3: The SCN1A Gene ...... 62 2.8.5 Additional FHM loci ...... 63 2.8.6 Migraine: Positional Cloning ...... 63 2.8.7 Migraine: Association-Based Approaches ...... 69 2.8.8 Migraine: GWAS ...... 72 2.8.9 Migraine: A Familial Gene ...... 73 2.9 Cardiovascular Disease Risk ...... 74 2.9.1 The Correlations Between CVD Risk Traits ...... 77 2.9.2 Hypertension ...... 78 2.9.3 Obesity ...... 80 2.9.4 Dyslipidemia ...... 81 2.10 Summary ...... 82
Chapter 3: Materials and Methods ...... 83 3.1 Overview ...... 83 3.2 Materials ...... 84 3.3 Sample Ascertainment ...... 84 3.4 Demographics and Phenotyping ...... 85 3.5 Standard Protocol Approvals ...... 87 3.6 DNA Extraction ...... 87 3.6.1 DNA Isolation ...... 87 3.6.2 Ethanol Precipitation of DNA Stocks ...... 88 3.7 Genome Wide Linkage Scan ...... 89 3.8 Genome Wide Association Scan ...... 91 3.9 The Norfolk Genealogy ...... 93 3.10 Pedigree Cleaning ...... 96 3.11 Data Screening ...... 97 3.12 Principal Component Analysis ...... 97 3.13 Quantitative Genetic Analysis ...... 99 3.13.1 Heritability Estimation ...... 101 3.13.2 Variance Component Linkage Methods ...... 104 3.13.3 The LOD Score ...... 106 3.13.4 Variance Component Association Methods ...... 107
Page X Chapter 4: Principal Component and Linkage Analysis of Cardiovascular Risk Traits in the Norfolk Isolate ...... 109 4.1 Overview ...... 109 4.2 Background ...... 110 4.3 Materials and Methods ...... 111 4.3.1 Sample Ascertainment ...... 111 4.3.2 Phenotyping ...... 112 4.3.3 Pedigree Structure ...... 113 4.3.4 Genome Wide Scan ...... 114 4.3.5 Statistical Analysis ...... 114 4.4 Results ...... 116 4.5 Discussion ...... 125 4.6 Conclusion ...... 129
Chapter 5: Genome Wide Linkage Analysis of Migraine in the Norfolk Genetic Isolate ...... 130 5.1 Overview ...... 130 5.2 Background ...... 131 5.3 Materials and Methods ...... 132 5.3.1 Sample Ascertainment ...... 132 5.3.2 Genotyping ...... 134 5.3.3 Statistical Analyses ...... 135 5.3.4 Replication Cohort ...... 136 5.4 Results ...... 136 5.4.1 Pedigree ...... 136 5.4.2 Replication Cohort ...... 141 5.5 Discussion ...... 143 5.6 Conclusion ...... 147
Chapter 6: Genomic Analysis of ‘Bounty’ Descendents Implicates a Novel Neurotransmitter Pathway in Migraine Susceptibility ...... 149 6.1 Overview ...... 149 6.2 Background ...... 150 6.3 Methods...... 152 6.3.1 Sample Ascertainment ...... 152 6.3.2 Genealogical Structure ...... 152 6.3.3 SNP genotyping ...... 153 6.3.4 Statistical analysis: Heritability and Pedigree-Based Association ...... 154 6.3.5 Candidate Gene Analysis ...... 155 6.4 Results ...... 156 6.4.1 Genome Wide Association Analysis ...... 156 6.4.2 Candidate Gene Association Analysis ...... 164 6.5 Discussion ...... 167 6.5.1 pGWAS Summary ...... 167 6.5.2 Candidate Gene Analysis ...... 167 6.5.3 Linkage versus Association ...... 170 6.6 Conclusion ...... 172
Page XI Chapter 7: A Systematic Analysis of Putative Migraine Susceptibility Genes in the Norfolk ‘Mutiny on the Bounty’ Pedigree Implicates the Estrogen Receptor Gene (ESR1) ...... 173 7.1 Overview ...... 173 7.2 Background ...... 174 7.3 Methodology ...... 176 7.3.1 Norfolk Island Cohort ...... 176 7.3.3 Ethics Statement ...... 177 7.3.4 Gene Selection ...... 178 7.4 Results ...... 179 7.4.1 Putative Migraine Genes ...... 179 7.4.2 Norfolk Pedigree ...... 185 7.5 Discussion ...... 190 7.6 Conclusion ...... 193
Chapter 8: Conclusions and Future Directions...... 194 8.1 Overview ...... 194 8.2 CVD Risk ...... 194 8.3 Migraine: Genome Wide Linkage Scan ...... 197 8.4 Migraine: Genome Wide Association Scan ...... 199 8.5 Systematic Analysis of Putative Migraine Susceptibility Genes ...... 202 8.6 Norfolk Island Health Study: Future Directions ...... 204 8.6.1 Quantitative Traits: pGWAS ...... 204 8.6.2 Expression Analysis ...... 204
References ...... 207
Page XII List of Figures
Figure 1.1 The consequences of founder effect……………………………………...11 Figure 1.2. Magnitude and distribution of linkage disequilibrium in population isolates………………………………………………………………………………...16 Figure 2.1. The phases of a typical migraine attack………………………………… 41 Figure 4.1. Plot of rotated principal components…………………………………...121 Figure 4.2. Genome wide multipoint linkage results for principal components 2 on chromosome 5……………………………………………………………………….123 Figure 5.1. Power to detect a significantly (P<0.05) non-zero estimate of the heritability for a discrete phenotype in the 1,078-member Norfolk pedigree……… 137 Figure 5.2. Multipoint variance component linkage results for chromosome 9 and 13 for migraine………………………………………………………………………… 139 Figure 6.1. Manhattan Plot of autosomal genome-wide associations for migraine in the Norfolk Island pedigree………………………………………………………… 156 Figure 6.2. Comparison of linkage and association findings in the Norfolk pedigree for chromosome 9 (a) and chromosome 13 (b)………………………………...... 170 Figure 7.1. Migraine ESR1 haplotypes in the Norfolk pedigree…………………... 188
Page XIII List of Tables
Table 1.1. A comparison of the utility of isolated and oubred populations for complex disease gene mapping………………………………………………………………... 18 Table 1.2. Select examples of genes causing non-syndromic deafness in consanguineous and isolated pedigrees……………………………………………… 19 Table 1.3. Select examples of complex disease loci and genes identified in Isolated Populations…………………………………………………………………………... 21 Table 2.1. ICHD-II diagnostic criteria for migraine without aura (1.1.)……………. 36 Table 2.2. ICHD-II diagnostic criteria for migraine with aura (1.2)………………... 38 Table 2.3. ICHD-II diagnostic criteria for the probable migraines (1.6.)…………… 39 Table 2.4. ICHD-II diagnostic criteria for familial hemiplegic migraine (1.2.4)…… 40 Table 2.5. A summary of FHM linkage and gene studies…………………………... 60 Table 2.6. A comparison of migraine positional study design……………………… 67 Table 2.7. Summary of positive gene associations with migraine…………………...70 Table 2.8 American Heart Association age adjusted prevalence rates from National Health Interview Survey of adults aged 18 or over in 2008…………………………. 74 Table 2.9. Comparison of the prevalence of CVD risk factors in the Norfolk population (N=600) and mainland Australia………………………………………… 76 Table 2.10. Preliminary univariate genome wide linkage scan results for CVD risk traits in the Norfolk pedigree…………………………………………………………76 Table 2.11. Risk factors for risk of CVD development and progression…………….77 Table 2.12. Summary of multivariate analysis studies of CVD risk, obesity, insulin resistance and Metabolic Syndrome…………………………………………………. 79 Table 2.13. Classification of adults according to BMI……………………………… 80 Table 2.14. Adult Treatment Panel III (ATP III) classification of serum cholesterol levels…………………………………………………………………………………. 81 Table 3.1. Number of relative pairs within the Norfolk Island linkage pedigree…… 95 Table 4.1. Phenotypic characteristics of participants (mean +/- standard deviation).118 Table 4.2. Pearson’s correlation coefficient matrix of the traits after normalisation and blood pressure adjustments…………………………………………………….. 119 Table 4.3. Coefficients and variances of principal components satisfying the Eigen value > 1 criterion…………………………………………………………………...120 Table 4.4. Summary of PCA genome scan results………………………………… 123 Table 5.1. Migraine Demographics in the Norfolk Island cohort and pedigree…... 133 Table 5.2. Multipoint genome wide results exceeding the nominal threshold (LOD>0.59; P<0.05) for linkage…………………………………………………… 139 Table 5.3. GENEPOP HWE Exact Test for microsatellites on chromosome 9 and 13 ……………………………………………………………………………………… 140 Table 5.4. CLUMP tests of allelic association for microsatellites on chromosome 9 and 13………………………………………………………………………………. 142 Table 5.5. A comparison of the Norfolk Island genome wide linkage scan with known migraine loci………………………………………………………………………... 145 Table 6.1. Summary of the top 0.05% of SNPs (n=172) detected in the Norfolk study…………………………………………………………………………………157 Table 6.2. Candidate SNPs (n=12) selected from the Norfolk Island cohort..…….. 165 Table 7.1. Migraine genes identified through candidate gene studies …………….. 181 Table 7.2. Migraine gene findings in the Norfolk Island pedigree………………… 185 Page XIV List of Abbreviations
5-HT Serotonin ABI Applied Biosystems ACE Angiotensin I-Converting Enzyme ADORA2A Adenosine A2 Receptor AGRF Australian Genome Research Facility AMM-I American Migraine Study I AMM-II American Migraine Study II AMMP American Migraine Prevalence and Prevention study APOE Apolipoprotein E ATP1A2 ATPase, Na+/K+ Transporting, Alpha-2 Polypeptide BMI Body Mass Index CACNA1A Calcium Channel, Voltage-Dependent, P/Q Type, Alpha-1a Subunit CADASIL Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy CDH Chronic Daily Headache CHD Chronic Heart Disease Cl Chloride cM centi Morgans CM Complicated Migraine CNR1 Cannabinoid Receptor 1 CPH Chronic Paroxysmal Hemicrania CVD Cardiovascular disease DBH Dopamine Beta-Hydroxylase, Plasma DDC Dopa Decarboxylase dNTP Deoxyribo Nucleoside Triphosphate DRD2 Dopamine Receptor D2 DRD4 Dopamine Receptor D4 DZ Dizygotic EDTA Ethylenediaminetetraacetic acid ENDRA Endothelin Receptor, Type A ESR1 Estrogen Receptor 1 ESR2 Estrogen Receptor 2 Fanny Grade of membership 'fuzzy' clustering FHM Familial Hemiplegic Migraine FHM1 Familial Hemiplegic Migraine Type 1 FHM2 Familial Hemiplegic Migraine Type 2 FHM3 Familial Hemiplegic Migraine Type 3 GoM Grade of Membership GNAS GNAS Complex Locus GRIA1 Glutamate Receptor 1 GRIA3 Glutamate Receptor, Iopnotropic AMPA 3 GWS Genome Wide Scan HDLc High Density Lipoprotein Cholesterol HLA-DRB1 Major Histocompatibility Complex, Class II, Dr Beta-1 HM Hemiplegic Migraine HMAS His Majesty’s Armed Ship Page XV HTR1A 5-Hydroxytryptamine Receptor 1A HTR1B 5-Hydroxytryptamine Receptor 1B HTR2A 5-Hydroxytryptamine Receptor 2A HTR2B 5-Hydroxytryptamine Receptor 2B HTR2C 5-Hydroxytryptamine Receptor 2C IBD Identity By Descent ICHD-I International Classification of Headache Disorders, Edition 1 ICHD-II International Classification of Headache Disorders, Edition 2 IHS International Headache Society INSR Insulin Receptor K Potassium KCNK18 Potassium Channel, Subfamily K, Member 18 LCA Latent Class Analysis LD Linkage Disequilibrium LDLc Low Density Lipoprotein Cholesterol LOD Logarithm of the odds LTA Lymphotoxin-Alpha (Tumor Necrosis Factor Beta) MA Migraine with Aura MAOA Monoamine Oxidase A MgCl2 Magnesium Chloride MGR Migraine Locus MIBD Multipoint Identity By Descent MIM Mendelian Inheritance in Man min Minute μL Micro Litres mL Milli Litre mM Milli Molar MMP3 Matrix Metalloproteinase 3 MO Migraine without Aura MTDH Metadherin MTHFD1 Methylenetetrahydrofolate Dehydrogenase MTHFR 5,10-Methylenetetrahydrofolate Reductase MZ Monozygotic Na Sodium NaCl Sodium Chloride NDPH New daily-persistent headache NIDDM Noninsulin Dependent Diabetes Mellitus NIHS Norfolk Island Health Study NKM Sodium-Potassium-Magnesium ng Nanogram nm Nanometre NMH Non-Migrainous Headache NOS3 Nitric Oxide Synthase 3 NOTCH3 Notch, Drosophila, Homolog of, 3 NP Non-Parametric NPL Non-Parametric LOD OD Optical Density PC Principal Component PCA Principal Component Analysis PGR Progesterone Receptor
Page XVI pH potential Hydrogen pmol pico mol PREST Pedigree RElationship Statistical Test QIMR Queensland Institute of Medical Research QTL Quantitative Trait Loci RFLP Restriction Fragment Length Polymorphism rpm Revolutions Per Minute RR Relative Risk s second SCN1A Sodium Channel, Neuronal Type I, Alpha Subunit SFBR Southwest Foundation for Biomedical Research SHM Sporadic Hemiplegic Migraine sibpair sibling pair SLC6A3 Solute Carrier Family 6 (Neurotransmitter Transporter, Dopamine), Member 3 SLC6A4 Solute Carrier Family 6 (Neurotransmitter Transporter, Serotonin), Member 4 SHM Sporadic Hemiplegic Migraine SOLAR Sequential Oligogenic Linkage Analysis Routines STR Short Tandem Repeat STX1A Syntaxin 1A SUNCT Short-lasting Unilateral Neuralgiform headache attacks with Conjunctival injection and Tearing TAC Trigeminal Autonomic Cephalalgias TBRI Texas Biomedical Research Institute TC Total Cholesterol TCA Trait Component Analysis TDT Transmission Disequilibrium Test TE Tris-EDTA TG Total Triglycerides TNF Tumor Necrosis Factor TPH1 Tryptophan Hydroxylase TTH Tension Type headache TYMS Thymidylate Synthetase U Units UK United Kingdom US United States V Volts VC Variance Component
Page XVII Publication List
Publications Arising From This Research
Bellis C., Cox H., Dyer T., Charlesworth J., Begley K., Quinlan S., Lea. R., Heath S., Blangero J., and Griffiths L. (2008) Linkage mapping of CVD risk traits in the isolated Norfolk Island population. Human Genetics. 124(5);543-552.
Bellis C., Cox H.C., Ovaric M., Begley K.N., Lea R.A., Quinlan S., Burgner D., Heath S.C., Blangero J., and Griffiths L.R. (2008) Linkage disequilibrium analysis in the genetically isolated Norfolk Island population. Heredity. 100;366-373.
Macgregor S., Bellis C., Lea R.A., Cox H.C., Tiang T., Blangero J., Visscher P., and Griffiths L.R. (2010) Legacy of mutiny on the Bounty: Founder effect and admixture on Norfolk Island. European Journal of Human Genetics.18;67-72.
McEvoy B.P, Zhao Z.Z., Macgregor S., Bellis C., Lea R.A., Cox H., Montgomery G.W., Griffiths L.R. and Visscher P.M. (2010) European and Polynesian Admixture in the Norfolk Island Population. Heredity. 105;229-234.
Cox H.C., Bellis C., Lea R.A., Quinlan S., Hughes R., Dyer T., Charlesworth J., Blangero J. and Griffiths L.R. (2008). Principal Component and Linkage Analysis of Cardiovascular Risk Traits in the Norfolk Isolate. Human Heredity. 68;55-64.
Cox H., Lea, R., Bellis C., Carless M., Dyer T., Charlesworth J., Curran J., Blangero J., & Griffiths L (2010) Variants in the human potassium channel gene (KCNN3) are associated with migraine in a high risk genetic isolate. Journal of Headache and Pain. (Accepted 15th November 2010)
Cox H.C., Bellis C., Lea R.A., MacGregor S., Nyholt D.R., Dyer T.D., Charlesworth J., Matovinovic E., Blangero J., and Griffiths L. (2011) Migraine linkage analysis of Norfolk Island 'Bounty' descendants implicates chromosomes 13q and 9p. Human Genetics (Under Review)
Cox H., Lea, R., Bellis C., Carless M., Dyer T., Charlesworth J., Curran J., Blangero J., & Griffiths L. (2011) Genomic analysis of 'Bounty' descendants implicates a novel neurotransmitter pathway in migraine susceptibility. PLoS One (Under Review)
Cox H.C., Lea R.A., Bellis C., Carless M., Dyer T. D., Charlesworth J., Curran J., Blangero J., Griffith L. R. (2011) A review of putative migraine genes implicates the hormone gene ESR1 in the Norfolk ‘Mutiny on the Bounty’ pedigree. (In Preparation)
Page XVIII Related Publications
Liu A., Menon S., Colson N.J., Quinlan S., Cox H.C., Peterson M., Tiang T., Haupt L.M., Lea R.A. and Griffiths, L.R. (2010) Analysis of the MTHFR C677T variant with migraine phenotypes. BMC Research Notes. 3(1);213.
Menon S., Quinlan S., Cox H., Kuwahata M., MacMillan J.C., Lea R.A., Haupt L.M. & Griffiths L.R. (2011) Notch 3 Polymorphisms and Migraine. Cephalalgia. 31;264- 270.
Mackey D.A., Sherwin J.C., Kearns L.S., Ma Y., Kelly J., Chu B-S., MacMillan R., Barbour J.M., Wilkinson C.H., Matovinovic E., Cox H.C., Bellis C., Lea R.A., Quinlan S., Griffiths L.R., and Hewitt A.W. (2011) The Norfolk Island Eye Study (NIES): Rationale, Methodology and distribution of ocular biometry (Biometry of the Bounty). Twins Research and Hum Genetics. 14(1);42-52.
Presentations
Cox, H.C. Characterisation of migraine prevalence, heritability and symptom phenotypes in the Norfolk Island population 4th International Meeting on Genetics of Complex Diseases and Isolated Populations, Trieste, Italy, Jun 2009.
Cox, H.C. Exploring the genetics of cardiovascular disease risk in the descendents of the Bounty Mutineers International Student Research Forum, Griffith University, Gold Coast, Australia, November 2009.
Other Peer Reviewed Conference Papers (Poster)
Cox H.C., Bellis C., Lea R.A., Quinlan S., Blangero J., and Griffiths L.R. Investigation of the Xq24-28 migraine gene locus in the unique Norfolk Island genetic isolate. AHMRC, Melbourne, Vic, Australia, November 2006.
Cox H.C., Bellis C., Begley K.N., Hughes R.M., Quinlan S., Lea R.A., Heath S.C., Blangero J., and Griffiths L.R. Streamlining genealogy, genetics and clinical data analysis with information technology. E-Health Research Colloquium, Health ICT Innovations: Making Impacts, Brisbane Qld, Australia, March 2007.
Cox H.C., Bellis C., Begley K.N., Quinlan S., Lea R.A., Blangero J., Griffiths L.R. Heritability and prevalence of migraine in the Norfolk Island population isolate. Australasian Human Gene Mapping Meeting (“GeneMappers”), Brisbane, Qld, Australia, October, 2007.
Cox H.C., Bellis C., Begley K.N., Quinlan S., Lea R.A., Blangero J., Griffiths L.R. Heritability and prevalence of migraine in the Norfolk Island population isolate. American Society of Human Genetics (ASHG) Annual Meeting, San Diego, California, United States of America, October, 2007.
Page XIX Cox H.C., Bellis C., Quinlan S., Dyer T., Lea R., Blangero J., Griffiths L. Principal Component and Linkage Analysis of Cardiovascular Disease Risk Traits in Norfolk Islanders. Australian Society for Medical Research (ASMR) Postgraduate Student Conference, Brisbane, Qld, Australia, May, 2008.
Cox H.C., Bellis C., Lea R.A., Quinlan S., Hughes R., Dyer T., Charlesworth J., Blangero J., and Griffiths L.R. Cardiovascular risk traits in the Norfolk Isolate. Australian Health & Medical Research Congress (AHMRC), Brisbane, Qld, Australia, November 2008.
Cox H.C., Bellis C., Lea R.A., Nyholt D. R.., Charlesworth J., Dyer T., Blangero J. and Griffiths L.R. Investigating migraine in the descendents of the Bounty mutineers. Australasian Human Gene Mapping Meeting (“GeneMappers”), Katoomba, NSW, Australia, April 2009.
Cox H.C., Bellis C., Lea R. A., Quinlan S., Hughes R., Dyer T., Charlesworth J., Blangero J. and Griffiths LR. Investigating the Genetics of CVD Risk in the Norfolk Isolate. Gold Coast Health & Medical Research Conference (GCHMR), Gold Coast, QLD, Australia, December 2009.
Peer Reviewed Conference Papers
Griffiths L.R., Bellis C., Lea R.A., Cox H., Macgregor S., Visscher P., Quinlan S., Dyer T. and Blangero J. Genetic legacy of the mutiny on the Bounty: Use of the Norfolk Island population for CVD and migraine gene mapping. 4th International Meeting on Genetics of Complex Diseases and Isolated Populations, Trieste, Italy, June 2009.
Matovinovic E., Lea R., Cox H., Herruer M., Hanna M., Sherwin J. C., Hewitt A. W., Kearns L. S., Kelly J., Ma Y., Mackey L., and Griffiths L. Heritability of ocular phenotypes in the genetic isolate of Norfolk Island. ASHG, Washington DC, USA, November 2010.
Griffiths L. R., Cox H. C., Lea R. A., Bellis C., Carless M., Dyer T. D., Charlesworth J., Matovinovic E., MacGregor S., and Blangero J. Genome-wide association analysis of the Norfolk Island isolate implicates novel variants in migraine susceptibility. ASHG, Washington DC, USA, November 2010.
Benton M., Lea R., Cox H., Coxson D. M., Eccles D., Hanna M., Chambers G., and Griffiths L. A phenome scan for heritable traits associated with metabolic syndrome in the genetic isolate of Norfolk Island. Gold Coast Health & Medical Research Conference (GCHMR), Gold Coast, QLD, Australia, December 2010.
Page XX General Introduction
I am now to relate one of the most atrocious and consummate acts of
piracy ever committed. At dawn of day Fletcher Christian, Officer of the
Master, Charles Churchill, Ships Corporal, Thomas Burkitt, Seaman and
several others came into my Cabin while I was asleep seized and tied my
hands behind my back with a strong cord, and with cutlasses and a
bayonet fixed at my breast threatened instant death if I spoke or made the
least noise…. I was now hauled upon deck in my shirt and hands tied
behind me held by Fletcher Christian…... I was told by Christian, your
officers are now in the boat and you must go with them. I was then taken
hold of under a guard and forced on the gangway into the boat, which
waited only for me.
Vice-Admiral William Bligh, October 13th 1789
This excerpt from a letter written by Captain William Bligh vividly describes the infamous mutiny that occurred on His Majesty’s Armed Ship (HMAS) the Bounty in the early hours of April 28th, 1789. This mutiny catalysed a series of unusual and fascinating historical events that resulted in the founding of a unique isolated population on Pitcairn and later, Norfolk Island. The original letter, addressed to Sir
Joseph Banks is held in archive at the State Library of New South Wales, Australia.
Isolated populations, such as Pitcairn and Norfolk Island, exist in many geographically remote areas of the world. Famous examples of such communities include the Old Order Amish of Lancaster County, Pennsylvania (USA), the
Hutterites of South Dakota (USA), the Pima Indians of Arizona (USA), the Early and
Page 1 Old Finish Settlements (Finland), the villages of Talana, Sardinia (Italy), the Bedouins of South Sinai (Egypt) and Island communities such as Kosrae (Federated States of
Micronesia). These communities have expanded in severe geographical, cultural and genetic isolation from a small number of original founding individuals. Features such as these limit genetic and environmental variation within and between households and may result in gene enrichment. Due to these phenomena, many gene-finding expeditions recruit individuals from isolated communities to study Mendelian and complex inherited human traits and disorders. The identification of causative genes aims to improve understanding of genetic predisposition, pathophysiology and in the case of disease, facilitate development of diagnostic tests and therapeutics.
The isolated population of Norfolk Island is of great interest for molecular genetic studies of human traits and disorders. Approximately 80% of the current permanent residents are descended from Pitcairn Islanders relocated to Norfolk Island (then uninhabited) after living conditions became unsustainable on Pitcairn Island. The
Pitcairn Islanders were founded by a small number of original Bounty mutineers
(including the Ship’s acting Lieutenant, Fletcher Christian) and female Tahitian consorts. The descendents of the Bounty mutineers, now inhabiting Norfolk Island are well suited for gene-finding expeditions as individuals are linked through an extensive pedigree derived from a limited number of founders with all generations exposed to genetic, geographical and cultural isolation. Using individuals from the
Norfolk isolate, this thesis endeavours to further knowledge of the genetic component and molecular aetiology of complex disorders such as migraine and cardiovascular disease phenotypes.
Page 2 The Aims of This Research
This study aimed to investigate the molecular genetic basis of complex disorders in an extended pedigree from the Norfolk genetic isolate with a specific focus on migraine, but also cardiovascular disease (CVD) risk. Complex disorders are the result of variation in the human genome, non-genetic variation and interaction effects. It is hypothesised that the coupling of genetic linkage analysis with the unique characteristics of the Norfolk Island ‘Bounty’ mutineer descendents should aid in elucidating novel genetic loci and replicate known genetic loci involved in the pathogenesis of these two complex disorders.
Specific aims of the project in terms of CVD risk traits are as follows:
1. Undertake principal component analysis to discern the pattern of CVD risk trait
clustering in related individuals (N=377) comprising the Norfolk Island pedigree.
2. Estimate the heritable component of extracted principal components.
3. Undertake a genome-wide linkage scan to detect quantitative trait loci (QTLs)
influencing the derived principal components.
Specific aims of the project in terms of migraine are as follows:
4. Estimate the heritable component of migraine in the Norfolk pedigree (N=377).
5. Analyse genome wide linkage scan data to detect QTLs for migraine.
6. Undertake a genome wide association scan to identify genomic variants
associated with migraine.
7. Examine genetic variation within positional candidate genes for association with
migraine.
Page 3 The Significance of This Research
The beginning of the 21st century has seen an influx of high throughput, cost effective genotyping and sequencing technologies in the field of molecular genetics. The beginning of the new millennium has also witnessed the completion of the first DNA reference sequence for Homo sapiens (Human Genome Sequencing, 2004) and the establishment of a human haplotype map detailing the distribution and frequency of
SNPs across human populations (The International HapMap Consortium, 2005). The
Human Genome Project (http://www.sanger.ac.uk/HGP/) and the International
HapMap Project (http://hapmap.ncbi.nlm.nih.gov/) have revolutionised the understanding of the molecular genetics of human traits, diseases, phylogeny, and the role of genes and genetic variation. Whilst some genes underlying complex phenotypes have been identified, they are often family-specific or only account for a portion of the overall genetic component.
Complex gene mapping may be enhanced in isolated populations where genetic and non-genetic heterogeneity is limited or even reduced. Isolated populations exist in many regions of the world, from the Adriatic Islands of Croatia (Rudan et al., 1999;
Vitart et al., 2006) to countries such as Finland, located on the European continent
(Norio, 2003). Extreme geographical and cultural isolation, limited immigration and migration, elevated levels of endogamy and/or consanguineous unions, and the presence of extended genealogies are all common characteristics of population isolates. These unique features impact on genetic drift. This may result in a higher incidence of particular traits and/or disorders than that observed in general, outbred populations. This phenomena is evidenced by the high frequency of Mendelian disorders in both Dutch (Zeegers et al., 2004) and Finnish isolates (Norio, 2003a, b, Page 4 c). Extreme isolation exposes individuals to a common environment and promotes a uniform lifestyle, which acts to limit and even minimise non-genetic variation. The presence of large genealogies and detailed municipal records allows construction of exhaustive pedigrees, which are powerful resources for gene mapping (Blangero and
Almasy, 1997).
Norfolk is a small, isolated volcanic island situated in the South Pacific Ocean, approximately 1,500 kilometres east of Brisbane, Australia. The majority of permanent residents are descended from 9 Isle of Man, ‘Bounty’ Mutineers and 6
Tahitian women who colonised Pitcairn Island (then uninhabited) in 1790 and 2
European whalers (male) who joined the small colony in the early 19th century
(Hoare, 1999; Macgregor et al., 2010). In 1856, the small community was relocated to the small, geographically isolated Norfolk Island (then uninhabited) as population growth became unsustainable on Pitcairn Island. Population characteristics include admixture, a small number of original founders, minimal migration and immigration after founding, population bottlenecks, endogamy and consanguineous unions in early generations, detailed genealogical records, and the availability of an extended pedigree. Furthermore, the limited geographical area, limited import and export industry, heavy reliance on local primary produce and community centred culture promotes uniform lifestyle within and between households. Overall, the Norfolk population presents a novel and interesting study cohort for the study of complex disorders such as migraine and CVD risk.
Migraine is a complex neurovascular disorder. Individuals typically experience recurrent attacks of headache that is of variable intensity, frequency and duration. The headache is typically unilateral, of pulsating quality and associated with nausea and
Page 5 vomiting, and photophobia and phonophobia. In some individuals, the headache is preceded by transient neurological disturbances (aura).
Migraine affects a significant proportion of the global population. Global prevalence estimates indicate 11.7% (males 5.6%; females 17.1%) of individuals suffer migraine throughout their lifetime (Diamond et al., 2007; Lipton et al., 2007). Incidence varies by age, gender, geographic location, ethnicity and socioeconomic status (Lipton and
Bigal, 2005). Migraine significantly impacts upon quality of life. Sufferers often report impairment from daily activities (Lipton et al., 2007). In fact, the World Health
Organisation (WHO) recognises migraine among the 20 leading causes of years lived with disability worldwide (WHO, 2001). This disorder incurs a heavy burden on society through healthcare expenditure, lost productivity and absenteeism (Lipton and
Bigal, 2005). In 2003 headache cost the United States economy $18 billion per year in lost productivity at work (Stewart et al., 2003).
Migraine has a significant genetic component. First-degree relatives of migraine probands have a 2 to 4-fold increased risk of developing migraine compared to individuals from the general population (Cologno et al., 2003; Stewart et al., 2006).
Whilst population based twin studies report heritability estimates ranging from 0.34 to
0.57 (Mulder et al., 2003; Svensson et al., 2003). Many significant loci and positive candidate gene associations are described, however convincing evidence of involvement is limited to a common risk variant (rs1835740) significantly correlated with transcript levels of a gene involved in glutamate homeostasis, metadherin
(MTDH; MIM 610323) (Anttila et al., 2010) and a frame shift mutation in the potassium channel gene, KCNK18 (Gene ID: 338567) (Tikka-Kleemola et al., 2010).
Given the complex aetiology of migraine, it is likely other modifying genetic factors
Page 6 convey susceptibility. Gene mapping in unique populations like Norfolk Island aim to further characterise the genetic component of this complex disorder.
In contrast to migraine, which has plagued individuals since early human existence,
CVD is more an affliction of modern day living. CVD involves a range of clinical phenotypes. Development and progression is highly complex and influenced by environmental exposures, lifestyle choices and genetic factors. Major risk factors include tobacco use, physical inactivity, unhealthy diet, obesity, dyslipidemia, hypertension, diabetes mellitus, metabolic syndrome and a positive family history
(Expert Panel on Detection, 2001). Incidence varies by age, gender, ethnicity and geographical location (AIHW et al., 2006; AIHW: et al., 2006; Bello and Mosca,
2004; Yusuf et al., 2001b). CVD and associated phenotypes are a leading cause of morbidity and mortality in developed nations of the world (Mackay and Mensah,
2004; Yusuf et al., 2001a). CVD is the leading cause of death in Australia and the
United States (AHA, 2003; AIHW et al., 2006). The economic impact of CVD is substantial. In 2004, a study in the United Sates reported a total of $368.4 billion dollars in health expenditure and lost productivity as a result of CVD (AHA, 2003).
Whilst genes have been identified for CVD related disorders including familial hypertension (Hansson et al., 1995; Shimkets et al., 1994), obesity (Rankinen et al.,
2006), and coronary artery disease (Wang et al., 2003; Wang, 2005) they do not explain the entire genetic component of CVD risk. To further characterise the genetic component of CVD risk this study employed a gene-mapping approach in an extended pedigree from a population isolate, Norfolk Island.
The Norfolk community has an increased prevalence of hypertension, obesity and other CVD risk factors compared to outbred Caucasian populations (Bellis et al., Page 7 2008). Genome wide linkage analysis has identified suggestive evidence of linkage to several chromosomal regions by univariate analysis of individual risk traits i.e. systolic blood pressure (SBP), total triglycerides (TG), and high density lipoprotein cholesterol (HDLc) (Bellis et al., 2008). However, as CVD risk phenotypes are often highly correlated further analysis using multivariate methods may potentially locate additional QTLs undetectable by univariate analysis ( Cai et al., 2004; He et al., 2008;
Shmulewitz et al., 2001; Tang et al., 2003). The research described in this thesis aims to undertake multivariate testing of CVD risk traits to localise susceptibility loci influencing multiple CVD risk traits.
Migraine and CVD risk traits are heritable phenotypes influenced by genetic and environmental factors. Gene-mapping studies have identified susceptibility loci and genes, however the majority of the genetic component underlying these phenotypes are as yet, undiscovered. The use of an extended pedigree, coupled with genetic, geographical and cultural isolation should enhance QTL mapping efforts. The identification of susceptibility genes will contribute to a better understanding of disease pathophysiology and aid in the development of diagnostic tests, high-efficacy pharmaceuticals, and preventative therapies.
Page 8 Chapter 1: Isolated Population Genetics
1.1 Overview
Population genetics is the quantitative study of genetic variation in populations and how allele and genotype frequencies are maintained or change over time (Nussbaum et al., 2007). Population genetics is concerned with genetic, environmental and societal factors that dictate the frequency and distribution of alleles and genotypes in families and communities. When a small number of individuals are separated from the parent population due to factors such as migration or population bottlenecks and sustain isolation, marked changes in allele and genotype frequencies may result. Such communities are commonly referred to as isolated populations. In population genetics, isolated populations are a unique resource to study Mendelian and complex inherited human traits as genetic and non-genetic heterogeneity is expected to be limited or even reduced due to the presence of genetic, environmental and societal factors.
Many isolated populations exist worldwide including the Island of Kosrae, Federated
States of Micronesia (Shmulewitz et al., 2006), the European Island country of
Iceland (Moises et al., 1995), the Islands of Dalmatia, Croatia, Old order Amish of
Lancaster County, Pennsylvania, USA (Ginns et al., 1996), Hutterite communities of
Canada (Ober et al., 1998a), Bedouin isolates of South Sinai (Guilford et al., 1994),
Paisa Community of Antioquia, Colombia (Arcos-Burgos and Muenke, 2002), Pima
Indians of Arizona, USA (Hanson et al., 1998) and the European country of Finland
(Pajukanta et al., 1999a) to name a few. Isolated populations are defined as having a small number of founding individuals and geographical or linguistic, religious or other cultural barriers that prohibit migration and interbreeding with other groups, Page 9 thus restricting genetic diversity. One such example is the Ashkenazi Jewish population, which has remained separate from surrounding European populations due to religious and cultural practices of endogamy (Bray et al., 2010). Over 20 rare recessive diseases are reported in this isolate.
Genetic drift and founder effect are commonly present in isolated populations, both major evolutionary forces that determine the fluctuation in allele frequencies
(Nussbaum et al., 2007). The magnitudes of these effects depend on population size, mating and the time elapsed since the initial founding event. Consequently, the genetic structure of each isolated population may differ depending on the evolutionary history of the population and extent of genetic drift and founder effect. In summary, isolated populations may be characterised by one or more of the following characteristics: limited number of initial founders, prolonged isolation, limited immigration and/or migration, high levels of endogamy and consanguineous unions, large genealogies, uniform environment and lifestyle factors, and presence and frequency of population bottlenecks. All of these factors impact upon allelic diversity and linkage disequilibrium (LD), forging unique genetic profiles that may enhance positional cloning of Mendelian and complex human disorders.
1.1.1 Founder Effect
Founders are a small number of unrelated individuals (<200) who randomly mate and undergo population expansion. If a trait allele(s) is introduced into a population by one of the original founders it is termed a “founder effect”. Many rare Mendelian disorders in isolated populations can be traced to one or more of the original population founders. Founder effects are particularly common in Dutch and Finnish
Isolates (Norio, 2003a, b, c; Zeegers et al., 2004). The change in allele frequencies Page 10 associated with founder effects is referred to as random genetic drift. Due to this drift, founder populations often have increased incidence of genetic disorders as in the case of Bardet-Biedl syndrome in Newfoundland, which has a 11-fold increase in frequency compared to the general European population (Green et al., 1989).
When a small number of individuals are isolated from a large population, the allele frequencies may differ from the parent population (Figure 1.1) (Nussbaum et al.,
2007). The small group will contain a random sample of the alleles from the parent population, which by chance may differ from the original parent population. If one of the founders carries a rare allele, the frequency of this allele may be pushed to a higher frequency due to the small population size (Figure 1.1). One example of this is the high frequency of Ellis van Creveld syndrome (MIM225500), an autosomal recessive form of short-limbed dwarfism caused by a mutation in the EVC gene
(EVC; MIM604831) in the Old Order Amish, in Lancaster County, Pennsylvania
(Nussbaum et al., 2007).
Figure 1.1. The consequences of founder effect.
Page 11 1.1.2 Migration
When individuals move from one area to another they carry their genes with them.
Migrants may settle uncolonised areas or migrate into established populations. The introduction of genes into a population by recent migrants can alter allele and genotype frequencies. An example of this is homozygosity of a mutant allele, a 32 base-pair deletion, in the chemokine receptor 5 gene (CCR5; MIM601373) providing strong resistance against infection by human immunodeficiency virus (HIV) (Lucotte,
2001). The Vikings disseminated this mutant allele across Europe during the eighth to tenth centuries. Gene flow has altered allele and genotype frequencies for this variant across European populations.
In the case of population isolates, limited migration and immigration restrict the gene pool diversity. A limited number of migrants settling an isolated, uncolonised area promotes genetic drift, which may led to positive selection of alleles and an increased incidence of rare traits compared to outbred populations. Limited immigration also promotes non-random mating, increasing the frequency of consanguinity and endogamy, which is also reported in may population isolates.
1.1.3 Consanguinity
Consanguinity is a union between a couple that is second cousin or closer related.
This is equivalent to a coefficient of inbreeding in their progeny of 0.0156 (Bittles A.
H., 2001). A limited number of founders combined with isolation produce high rates of consanguineous marriages, which acts to conserve trait alleles and haplotypes. This increases the frequency of rare autosomal recessive disorders by increasing the chance of mating between carriers of the recessive allele (Nussbaum et al., 2007). As a result,
Page 12 uncommon alleles may become homozygous. In isolated populations the frequency of homozygosity may be even higher as seen in Tay-Sacs disease (MIM272800) in the isolated Ashkenazi Jewish population. The birth rate is estimated as 1 in 4,100 for those of Jewish ancestry compared to 1 in 340,000 for those of non-Jewish ancestry
(Kaback et al., 1993). Consanguinity changes the genetic structure of a population in terms of genotype frequencies, increasing the frequency of homozygote genotypes and reducing the number of heterozygote genotypes (Nussbaum et al., 2007).
1.1.4 The Environment
The combination of geographical and cultural isolation promotes a common, shared environment between individuals (e.g. diet, exercise, sanitation). The homogeneous environment shared by individuals is of great significance for the study of complex diseases such as obesity, CVD, metabolic syndrome, and noninsulin dependent diabetes mellitus (NIDDM, MIM125853) where there appears to be a thresh-hold effect for the disease that is heavily influenced by non-genetic factors such as diet.
For instance, the prevalence of NIDDM is 38% in US Pima Indians, 6.9% in Mexican
Pima Indians and 2.6% in non-Pima Mexicans (Schulz et al., 2006). The differences in NIDDM prevalence between 2 genetically similar populations, US and Mexican
Pima Indians indicates that predisposition is largely the result of environmental factors, in this example, change from a traditional to a western lifestyle. The reduction in environmental heterogeneity within and between households in population isolates reduces the signal-to-noise ratio and may result in complex disease gene mapping being more robust to detect ‘true’ effects.
Page 13 1.1.5 Population Bottlenecks
A severe reduction in population size due to natural disasters such as disease, famine, typhoons and war is referred to as a population bottleneck. Bottlenecks are generally followed by rapid population expansion by a small number of random surviving individuals. If a severe bottleneck occurs in an already isolated population, inbreeding and genetic drift may result. Population bottlenecks of short duration will have little effect on heterozygosity, but are expected to severely reduce the number of alleles present in the population (Nussbaum et al., 2007). In the case of genetic disease, this may cause an allele(s) to be lost or pushed to a much higher frequency compared to outbred populations as observed for colour-blindness among the Pingelapese Islanders
(Sundin et al., 2000).
Pingelap is a coral atoll in the Western Pacific Ocean that belongs to the Federated
States of Micronesia. In approximately 1775, Typhoon Lengkieki devastated
Pingelap. The population was reduced to 20 survivors who re-established the population during 2 centuries of isolation. Approximately 5% of the population is affected by achromatopsia-2 (MIM262300), a rare form of colour-blindness (Hussels and Morton, 1972), that is the result of a mutation in the beta subunit of the cone cyclic nucleotide-gated cation channel (CNGB3; MIM605080) gene (Sundin et al.,
2000). This mutation has been traced to a single male typhoon survivor, heterozygous for the mutation. The severe population bottleneck caused by the typhoon, combined with prolonged isolation has led to positive selection of this allele and increased disease incidence in subsequent generations (Figure 1.1). Population bottlenecks are not limited to the Pingelapese islanders. The evolutionary history of many isolated populations include bottlenecks, including the European Roma/Gypsies (Kalaydjieva
Page 14 et al., 2005), the Hutterites (Pichler et al., 2010), the Ashkenazi Jewish (Behar et al.,
2004) and the Kosraean Islanders (Lowe et al., 2009).
1.1.6 Random Genetic Drift: The Impact on Allelic Diversity and LD
Random changes in allele frequencies are referred to as genetic drift. Drift acts to erode genetic variability in populations. Although the Icelandic population is not a conventional isolate, genetic drift has reduced the amount of variation relative to the rest of Europe (Helgason et al., 2003). Typically, small populations are more susceptible to drift than large populations due to the already restricted genetic diversity. Reduced genetic diversity restricts the number of haplotypes, resulting in linkage disequilibrium (LD) that extends over large distances.
The co-inheritance of a trait allele with adjacent DNA markers at the population level is known as LD. The extent of LD around a particular allele is the result of selection and population history such as population size, genetic drift, admixture, mutation and recombination events. These factors act to degrade the founder chromosome containing a trait allele(s). LD is proportional to the number of meioses since founding. There is currently no way to predict the pattern of LD across the genome due to its heterogeneous nature; it can only be assessed through sample analysis
(Palmer and Cardon, 2005). Several studies have attempted to characterise the genome wide distribution of LD in outbred and isolated populations worldwide
(Conrad et al., 2006; Gu et al., 2007). One study compared the pattern of LD of SNPs across chromosome 22 in 12 world wide populations; 11 population isolates and 1 outbred European-derived sample (Service et al., 2006). Results indicated young population isolates possessed higher levels of LD and fewer regions of very low LD
(‘holes’) than the outbred sample (Figure 1.2), suggesting that genome wide Page 15 association mapping in young population isolates requires fewer SNPs to detect
disease loci than outbred populations.
a. Afrikaner Antioquia, Columbia Ashkenazi Jewish Azores Central Valley of Costa Rica Early-Settlement Finland Finland (Nationwide) Kuusamo, Finland Newfoundland Nuoro Province, Sardinia, Italy Outbred European Village in Southwestern Netherlands 0 100 200 300 400 500 600 700 800 900 1000 Length of LD map in LD units (LDU) b. Afrikaner Antioquia, Columbia Ashkenazi Jewish Azores Central Valley of Costa Rica Early-Settlement Finland Finland (Nationwide) Kuusamo, Finland Newfoundland Nuoro Province, Sardinia, Italy Outbred European Village in Southwestern Netherlands 0 10 20 30 40 50 60 70 80 90 Number of LD holes
Figure 1.2. Magnitude and distribution of linkage disequilibrium in population
isolates. (a) LD metric map length by population. Error bars represent the standard
error of the mean (SEM) length of LD (b) The number of LD holes (defined as a gap
in the LD map of >2.5 LDU) across chromosome 22 by population. Data adapted
from Service et al. (2006) (Service et al., 2006).
Page 16 Founder effect combined with isolation may result in a select number of susceptibility genes over time with unique patterns of haplotype sharing and LD, particularly in the vicinity of disease. This phenomenon is exemplified by studies of the isolated population of Kosrae, which has increased LD and reduced haplotypic diversity compared to International HapMap populations (Bonnen et al., 2006a). In terms of gene mapping, a lower density marker map should be more informative in Kosrae and other young founder isolates with long-ranging LD compared to old outbred populations (Peltonen et al., 2000).
Like Kosrae, a genome-wide comparison of LD between unrelated Hutterites and unrelated Europeans (HapMap CEU) has been recently completed (Thompson et al.,
2010). The Hutterites, an Anabaptist religious group established in the Tyrolean Alps,
Italy in 1528 is a young population isolate that has been studied extensively in the past 20 years. Many of the individuals in this population are related to each other in a
13-generation pedigree with 62 founders (average inbreeding coefficient =0.03) (Ober et al., 1998b). The results of genome wide SNP assessment revealed LD to extend further in the Hutterites than Europeans for some marker pairs. However, overall the genome wide pattern of LD and minor allele frequency (MAF) were similar between the 2 populations. Results suggested common alleles for complex disease might be shared between these two populations, that data from the international HapMap data can be used for imputation in Hutterites and that gene discovery may be enhanced in
Hutterites as they share a communal lifestyle, reducing environmental heterogeneity.
Compared to the Kosraen population, the Hutterite study highlights the degree of variation in LD that may be observed in isolated populations and the need for individual population assessment.
Page 17 1.2 Gene Mapping in Isolated Populations
The success of Mendelian disease gene mapping in population isolates is clearly evident in Finland and The Netherlands (Norio, 2003a, b, c; Zeegers et al., 2004).
However, genomic studies are not restricted to rare disorders. Increasingly, population isolates are being employed to dissect the genetic and non-genetic factors underlying common, complex human disorders and their component traits. The utility of population isolates to identify genes underlying common, complex diseases has been debated at length (Bourgain and Genin, 2005; Eaves et al., 2000; Kristiansson et al.,
2008; Peltonen, 2000; Sheffield et al., 1998). Many population isolates with diverse demographic histories exist worldwide. The advantages isolates may offer for genome wide complex disease mapping compared to general outbred populations are detailed in Table 1.1.
Table 1.1. A comparison of the utility of isolated and outbred populations for complex disease gene mapping (Kristiansson et al., 2008).
Advantages of Isolated Populations Advantages of Outbred Populations Large regions of LD Genetic markers are highly polymorphic Fewer areas of low LD Large number of affected individuals Map recessive genes Availability of large sample sizes Decreased number of causative alleles Greater opportunity for replication Large genealogies Good genealogical records Homogenous environment Limited immigration and/or migration Potential for founder effect Potential for genetic drift Increased levels of endogamy and consanguineous unions High participation rates
Page 18 One of the most cited examples of complex disease-mapping successes in isolated populations is the case of autosomal recessive non-syndromic sensorineural hearing impairment (ARNSHI). At least 91 loci (DFNB91; MIM613453) and at least 35 causative genes have been identified. Most of these genes were identified in consanguineous pedigrees from population isolates, some of which are detailed in
Table 1.2.
Table 1.2. Select examples of genes causing non-syndromic deafness in consanguineous and isolated pedigrees.
Chromosome Gene Population Reference 1p13.3 GPSM2 Palestinian (Walsh et al., 2010) 1p36.3-p36.1 ESPN Pakistani (Naz et al., 2004) 3p21 TMIE India (Naz et al., 2002) 5q13.1 MARVELD2 Pakistani (Riazuddin et al., 2006a) Pakistani (Chishti et al., 2008) 6p25 SERPINB6 Turkish (Sirmaci et al., 2010) 7q21.1 HGF Pakistani & Indian (Schultz et al., 2009) 9q34.3 TPRN Moroccan & Dutch (Li et al., 2010) Pakistani (Rehman et al., 2010) 10q21-q22 CDHR23 Pakistani (Bork et al., 2001) 10q21-q22 PCDH15 Dutch (Doucette et al., 2009) 11q13.3-q13.4 LRTOMT Iranian (Du et al., 2008) 11q22-q24 TECTA Iranian & Pakistani (Naz et al., 2003) 11q23 RDX Pakistani (Khan et al., 2007) Iranian (Shearer et al., 2009) 13q11-q12 GJB2 Israeli (Carrasquillo et al., 1997) Ashkenazi Jews (Morell et al., 1998) Spanish-Romani (Alvarez et al., 2005) 17p11.2 MYO15A Balinese (Wang et al., 1998a) Pakistani (Liburd et al., 2001) Turkish (Kalay et al., 2007) Brazilian (Lezirovitz et al., 2008) 22q13.1 TRIOBP Pakistani & Indian (Riazuddin et al., 2006b) Palestinian (Shahin et al., 2006) CDHR23=Cadherin 23 (MIM605516); ESPN=Mouse Homolog of ESPIN (MIM606351); GJB2=Gap Junction Protein, Beta-2 (MIM121011); GPSM2=G-Protein Signalling Modulator 2 (MIM609245); HGF=Hepatocyte Growth F(MIM142409); LRTOMT=Leucine-Rich Transmembrane O- Methyltransferase (MIM612414); MARVELD2=Marvel Domain-Containing Protein 2 (MIM610572); MYO15A=Myosin XVA (MIM602666); PCDH15=Protocadherin 15 (MIM605514); RDX=Radixin (MIM179410); SERPINB6=Serpin Peptidase Inhibitor, Clade B (Ovalbumin), Member 6 (MIM173321); TECTA=Tectorin, Alpha (MIM602574); TMIE=Transmembrane Inner Ear-Expressed Gene (MIM607237); TRIOBP=Trio- and F-Actin Binding Protein (MIM609761); TPRN=Taperin (MIM613354)
Page 19 Examples such as ARNSHI have been met with scepticism by some (Eaves et al.,
2000). Do the mutations described in isolated populations represent rare Mendelian forms of a complex disorder, hearing loss or do they represent common variants that may be generalised to other general populations? Although the extent and effect of genetic drift and LD varies across populations, and may impact upon the diversity and frequency of alleles, there is evidence suggesting that findings in isolated populations may be relevant to outbred populations. In the Genetic Research in Isolated
Population (GRIP) program, genetic variants with frequencies higher than 1% were present in young population isolates as well as the general HapMap CEPH population
(Pardo et al. 2005). This suggests that the results of genome wide studies in young isolates may be generalised to other demographic contexts. In fact, many loci and even some genes underlying complex disorders are reported in young and old population isolates with varying population demographics and histories (Table 1.3). In particular, many studies of CVD and complex neurological disorders in population isolates that have proved highly successful. Such results suggest that complex disease mapping in other young population isolates, such as Norfolk Island, may also aid in characterising the genetic component underlying these disorders.
Page 20 Table 1.3. Select examples of complex disease loci and genes identified in Isolated Populations.
Isolate Disorder Loci/Gene Detected Reference Amish Hypertension 2q31-34 (Hsueh et al., 2000) Amish Bipolar 6, 13, 15 (Ginns et al., 1996) Ashkenazi Jews Parkinson’s Disease GBA (Gan-Or et al., 2008) Basques Schizophrenia DRD2 (Parsons et al., 2007) Bedouins Nonsyndromic Deafness 13q12 (Guilford et al., 1994) Campora (Italy) Essential Hypertension 1q42–43, 4p16, 8q22–23 (Ciullo et al., 2006) Costa Rican Schizophrenia 5q (DeLisi et al., 2002) Eastern Finland Schizophrenia 1, 2q, 5q (Paunio et al., 2001) Finland Psychotic and bipolar spectrum disorders TSNAX, DISC1 (Palo et al., 2007) Finland (17th Century settlement) Schizophrenia 1q32.2-q41 (Hovatta et al., 1999) Finland (Kainuu Province) Preeclampsia 2p25, 9p13 (Laivuori et al., 2003) Finland (Kainuu Province) Asthma-related traits 7p14-15 (Laitinen et al., 2001) Finland (Koilliskaira Region) Coeliac disease 15q11-q13 (Woolley et al., 2002) Finland/Sardinia/Amish Height GDF5, UQCC (Sanna et al., 2008) Finland/Scandinavia Fasting Glucose Levels G6PC2, ABCB11 (Chen et al., 2008) Hutterites Asthma CHI3L1 (Ober et al., 2008) Hutterites Asthma-related traits 5p, 5q, 8p, 14q, 16q, 19q (Ober et al., 2001) Iceland Exfoliation glaucoma LOXL1 (Thorleifsson et al., 2007) Iceland Type II Diabetes Mellitus CDKAL1 (Steinthorsdottir et al., 2007) Iceland Schizophrenia 6p (Moises et al., 1995) Kosrae, Federated States of Micronesia Syndrome X 1q31–1q43, 2p24–25, 4q23, 5q35, (Shmulewitz et al., 2006) 10q25, 12q24, 16qter, 18p11, 18q21, 19p13, 20p12, 20q13 Netherlands Type II Diabetes Mellitus 18p (Aulchenko et al., 2003) Paisa families (Antioquia, Colombia) Attention-deficit/hyperactivity disorder 4q13.2, 5q33.3, 11q22, 17p11 (Arcos-Burgos and Muenke, 2002) Pima Indians Type II Diabetes Mellitus and BMI 11q, 1q (Hanson et al., 1998) Sardinia Asthma IRAK3 (Balaci et al., 2007) Sardinia Obesity FTO, PFKP (Scuteri et al., 2007) Talana (Sardinia, Italy) Essential Hypertension 2q24, 11q23.1–25, 13q14.11–21.33 (Mocci et al., 2009) Western Finland Non−insulin dependent diabetes mellitus 12 (Mahtani et al., 1996) Whole Finland Familial Combined Hyperlipidemia 10p11.2 (Pajukanta et al., 1999b) Whole Finland Familial Combined Hyperlipidemia 1q21-q23 (Pajukanta et al., 1999a) ABCB11=ATP-Binding Cassette, Subfamily B, Member 11 [MIM603201]; CDKAL1=CDK5 Regulatory Subunit-Associated Protein 1-Like 1 [MIM611259]; CHI3L1=Chitinase 3-Like 1 [MIM601525]; DISC1=Disrupted In Schizophrenia 1 [MIM605210]; DRD2=Dopamine Receptor D2 [MIM126450]; FTO=Fat Mass and Obesity Associated Gene [MIM610966]; GBA= Acid Beta-Glucosidase [MIM606463]; GDF5=Growth/Differentiation F5 [MIM601146]; G6PC2=Glucose-6-Phosphatase, Catalytic, 2 [MIM608058]; IRAK3=Interleukin 1 Receptor Associated Kinase 3 [MIM604459], LOXL1=Lysyl Oxidase-Like 1 [MIM153456]; PFKP=Phosphofructokinase, Platelet Type [MIM171840]; TSNAX=Translin-Associated FX [MIM602964]; UQCC=Ubiquinol-Cytochrome C Reuctase Complex Chaperone [MIM611979]
Page 21 1.3 The Norfolk Island Isolate
Norfolk Island is a small, volcanic Island located almost 1,500 kilometres west of Brisbane,
Australia in the South Pacific Ocean. The island lies between New Caledonia, New Zealand, and Australia along the Norfolk Ridge and has an area of 34.6 square kilometres. Captain
James Cook discovered Norfolk Island on October 10, 1774 and claimed it under the British
Crown. The first penal settlement was established in 1788, but was abandoned in 1814 due to convict uprisings. In 1825 the then uninhabited Island of Norfolk was re-occupied as a convict station under the British Empire for the most violent felons. Once more, murder, convict uprising and prisoner brutality forced the station to be closed. All inhabitants were relocated to Hobart, Tasmania. Norfolk remained uninhabited until June 1856.
1.3.1 The Descendents of the Bounty Mutineers
The tale of the Bounty is truly captivating. Originally commissioned for a breadfruit expedition to Tahiti from England, the voyage ended when 24 seamen led by Acting
Lieutenant Fletcher Christian revolted against Captain William Bligh in the early hours of
April 28th, 1789. The mutiny occurred near the Tongan Island of Tofua. Reports indicate the seamen had become enchanted with the Tahitian women and were reluctant to return to
England. The mutineers seized control of the Bounty and returned to Tahiti.
Upon reaching their destination, 16 crewmen deserted the ship to inhabit Tahiti. All of these individuals were eventually captured by the HMAS Pandora and trialled in England
(Luoukakis, 1984). Now fugitives of the British crown, Acting Lieutenant Fletcher Christian along with Edward Young (midshipman), John Mills (gunner’s mate), William Brown
(assistant botanist), William McCoy (seaman), Matthew Quintal (seaman), John Adams
Page 22 (seaman), John Williams (seaman) and Isaac Martin (seaman) fled Tahiti to seek refuge.
However, before leaving Tahiti the Bounty Mutineers acquired Tahitian women and men.
The crew consisted of the 9 British sailors, 12 Tahitian women (9 of whom were acquired as wives for the English sailors), 6 Tahitian men (to whom the other 3 women belonged) and a baby girl (Hoare, 1999).
After sailing to Tonga and Fiji, the ship landed on the uninhabited Pitcairn Island January
1790. The men stripped and set fire to the Bounty to prevent discovery and desertion, and to ensure isolation. The details of the destruction of the Bounty, dynamics of the settlement, day-to-day happenings and records of births and deaths on Pitcairn Island are detailed in the
Pitcairn Island Register Book, compiled by various Pitcairn inhabitants from January 1790 to
1854. The burning of the Bounty is commemorated annually in January as a public holiday known as Pitcairn Bounty Day. The early years of the Pitcairn settlement were extremely violent. All the Tahitian men and 7 of the mutineers met brutal deaths. Williams, Martin and
Brown and all the Tahitian men left no children. Due to the limited number of founders and extreme geographical isolation, early stages of population growth were characterised by complex relationships with high levels of endogamy and consanguineous unions.
The population remained undiscovered until 1808, when the ship Topaz stumbled across the small English-speaking Island community. After their discovery, the Islanders had extremely limited contact with visiting ships and whalers. Of the few visitors, only 3 male immigrants were permitted to join the settlement; Englishmen John Buffett and John Evans in 1823, both crewmen of the whaling vessel the Cyrus, and an Irish man George Hunn Nobbs. During expansion the already small population suffered bottlenecks in the form of epidemics, drought, and food shortages. Eventually the Island’s natural resources diminished, population
Page 23 growth became unsustainable and relocation was required. To aid the population, Queen
Victoria gifted the people Norfolk Island (then uninhabited).
On the morning of June 8th 1856, 194 settlers landed at Kingston, Norfolk Island (Hoare,
1999). The population included 40 men, 47 women, 53 boys, 53 girls and a baby boy born on the relocation voyage (27 islanders left Norfolk to resettle Pitcairn Island in 1863). The anniversary of the landing on Norfolk Island is celebrated to this present day as a public holiday, known as Norfolk Bounty Day. Strict immigration laws allowing only those of
Pitcairn descent to occupy Norfolk Island were established.
The population currently residing on Norfolk Island are largely descended from Pitcairn
Islander’s who originated from the Bounty Mutineers and their Tahitian consorts. The isolation and lack of immigration is evidenced by the high frequency of the surnames Adams,
Christian, Young, Quintal, McCoy, Evans, Buffett and Nobbs among the Islanders, which has resulted in the only telephone directory in the world that includes nicknames to differentiate between individuals with the same name (Squires, 2006). In 2001 the Islands permanent population totalled 1574 individuals of whom 756 claimed to be of Pitcairn decent
(Matthews, 2001). The population supports itself from local produce, however as a result of both isolation and small land mass the population is highly dependent on imports of primary produce and manufactured goods. The islanders live a relatively homogeneous lifestyle due to their isolation, strict quarantine and immigration laws, and community centred culture.
Given lifestyle, geographic location and population history, environmental and genetic variation between individuals should be limited.
Page 24 1.3.2 Norfolk Island: Molecular Genetic Characterisation
In 2000, the Norfolk Island Health Study was commenced and a phenotypic and biological repository was established (Bellis et al., 2005). From genealogical records and ancestry specific questionnaires, an 11-generation, 6537-member pedigree was compiled for genetic studies. As the study progressed, ancestry informative markers (AIMS) and autosomal genome wide STR markers were used to validate the historical origins of the population.
Analysis of AIMs revealed 9 Bounty Mutineer and 6 Tahitian lineages to be conserved in the present day population (Macgregor et al., 2010; McEvoy et al., 2010). Autosomal AIMs support 88% European and 12% Polynesian ancestry in founder descendents. Nearly all Y- chromosomes are of European origin and at least 25% of mtDNAs are of Polynesian origin.
Demographic investigations support increased prevalence of hypertension, obesity and other
CVD risk factors compared to outbred Caucasian populations, some of which are directly influenced by founder effect and extreme admixture (Bellis et al., 2008; Macgregor et al.,
2010). Linkage disequilibrium (LD) extends up to 9.5–11Mb suggesting that the Norfolk pedigree is a powerful resource for the localisation of complex disease genes (Bellis et al.,
2007).
Initial assessment of the Norfolk pedigree estimated 80% power to obtain a LOD score of 3 when considering a QTL accounting for 20% of the variation in the trait, which is sufficient power for gene mapping (Bellis et al., 2005). After pedigree trimming and validation, power was again approximated. A QTL accounting for approximately 30% of the variation would be detected with 50% power and the threshold for suggestive linkage a LOD score of 1.675
(Bellis et al., 2008). Autosomal genome wide linkage analysis of CVD risk traits identified regions of suggestive linkage on chromosome 2q37.1 for total cholesterol, chromosomes
18q22.3 and 20p12.3 for high density lipoprotein cholesterol and chromosomes 1p36.22 and
Page 25 8p12 for systolic blood pressure (Bellis et al., 2008). Overall this pedigree presents a unique opportunity for disease gene mapping.
1.3.3 Close Resemblances: The Island Isolate of Kosrae
One isolate that closely resembles Norfolk Island is the Island isolate of Kosrae, Federated
States of Micronesia. Micronesia is a country of over 600 islands in the Central Western
Pacific. Kosrae is an island population of 7,700 individuals (Bonnen et al., 2010). The population was settled over 2,000 years ago and has experienced prolonged geographical and cultural isolation. The source of settlement is currently undetermined, but is believed to be by
Melanesians or people from continental Asia. Over the period of a decade 3,150 subjects have been recruited from the population and a pedigree compiled for the entire island
(Shmulewitz et al., 2006). An extensive multigenerational pedigree containing over 2000 individuals has been constructed from genetic data, genealogical records, and meetings with village elders and is characterised by endogamy (Lowe et al., 2009a; Shmulewitz et al.,
2006). For analysis purposes 4,854 sibling pairs in 750 nuclear families with 885 trios are available.
Like Norfolk, this island population is admixed. Ancestry analysis using genome wide SNP data indicate 39% of approximately 3,200 ascertained subjects possess some European ancestry (Bonnen et al., 2010). Approximately 77% of admixed subjects possess less than
10% European alleles and that the majority of the European admixture can be traced to a single male in late 19th Century. By comparison, the Norfolk isolate experienced European admixture in the form of 9 male Bounty mutineers in the late 18th century and 2 male whalers in the early 19th century. Norfolk differs, as founder descendents are admixed 88% European and 12% Polynesian ancestry (Macgregor et al., 2010; McEvoy et al., 2010).
Page 26 Micronesia, including the island of Kosrae was isolated for a long period of time until the last
100 years. During World War II (1945) the United States (US) occupied the Islands. This led to significant lifestyle and economic changes, particularly a shift to western diets, which is believed to have led to a current obesity epidemic on Kosrae (88% of adults over 20 have a
BMI>25) (Casseles, 2006). Like Kosrae, the Norfolk population has also experienced recent exposure to western diet with a high incidence of overweight and obese adults (Bellis et al.,
2005).
Like many isolates worldwide, Kosrae is characterised by reduced genetic heterogeneity. A comparison of 113,240 genome wide SNPs support longer ranging LD and reduced haplotype diversity in the Kosraean population compared to the International HapMap populations
(Bonnen et al., 2006b). More than 98% of Kosraean haplotypes are also present in HapMap populations. The extended LD and reduced allelic diversity suggest this isolate is a powerful resource for gene mapping and that future study designs may incorporate HapMap data.
Due to the qualities of admixture, prolonged geographical and cultural isolation, reduced genetic heterogeneity, endogamy, and the presence of large genealogies, Kosrae has been the focus of epidemiological and genetic studies of complex disorders. Epidemiological studies are reported for schizophrenia and syndrome X (Shmulewitz et al., 2001; Waldo, 1999).
Candidate gene, genome wide linkage analysis and GWAS approaches for Syndrome X, metabolic traits, anthropometric measures, CVD risk, schizophrenia and electrocardiographic conduction measures in Kosrae replicate many known and several novel loci (Han et al.,
2002; Kenny et al., 2011; Lowe et al., 2009b; Shmulewitz et al., 2006; Wijsman et al., 2003).
Positive associations of common variations in a cardiac voltage gated sodium channel gene
(SCN5A; MIM600163) are reported for electrocardiographic conduction measures (Smith et al., 2009). Associations are also reported between low density lipoprotein cholesterol and
Page 27 variants in the 3-Hydroxy-3-Methylglutaryl-CoA Reductase HMG-CoA reductase gene
(HMGCR; MIM142910) (Burkhardt et al., 2008). Overall, current findings from complex disease gene mapping efforts prove promising and support genetic studies in similar isolated populations, like Norfolk Island.
1.4 Genetic Mapping Techniques
Historically, gene mapping endeavours have utilised short tandem repeats (STRs) due to their high information content (highly polymorphic), fast mutational rate and high density in the genome for linkage analysis. Association approaches have recently gained much attention.
With the advent of the International HapMap and new genotyping technologies it has become feasible to perform high density, whole genome single nucleotide polymorphism (SNP) scans in large case-control cohorts and even families (Wang et al., 1998b). Further advances in technology such as DNA hybridisation arrays, high-throughput sequencing and the availability of genome wide copy number variants (CNVs) maps are revolutionising approaches to gene mapping. Gene mapping can be broadly classed into 2 approaches, linkage and association analysis. Many factors have to be considered in order to choose an optimal approach for gene discovery, particularly for complex phenotypes.
1.4.1 Association
Association analysis compares the differences in disease allele frequencies between affected and unaffected individuals. Traditionally, this approach uses a case-control approach to assess allele frequencies in independent (unrelated) unaffected and affected individuals matched for age, sex, gender and ethnicity. Families and affected sib-pairs can also be used in this approach. The distribution of alleles is tested using standard non-parametric Chi-Square analysis, logistic regression and odds ratios. Association of a marker and trait can indicate
Page 28 several possibilities; the variant directly causes the trait; the variant is indirectly associated with the trait due to association with a nearby causal variant (linkage disequilibrium); or the association is the result of population substructure.
Association studies have a greater power to detect genes of small effect; however this is offset by the need for a greater marker density and large cohort numbers. Other limitations include the presence of population substructure (stratification), which can generate false positive associations (Hoggart et al., 2003). Population stratification can arise if the total population is derived from a combination of 2 or more sub-population leading to racial admixture, as is the case with African American, African Caribbean, and Hispanic American populations (Hoggart et al., 2003). Studies have shown substantial differences in the frequencies of common polymorphisms and LD in different ethnic populations. Such admixture can be measured accurately and controlled if specific allele frequencies for each sub-population are known for a set of markers. Results may be misinterpreted if adjustments are not made to account for population stratification.
1.4.2 Linkage Analysis
Linkage analysis tests for co-segregation of a genetic marker and phenotype within a pedigree to determine whether a marker and a disease-predisposing gene are in close physical proximity (‘linked’) (Lander and Schork, 1994). When large, mutigenerational pedigrees are available, linkage analysis is a powerful method to localise disease genes (Blangero and
Almasy, 1997). This approach is particularly well suited for mapping Mendelian traits such as Cystic Fibrosis or variants of complex traits following simple Mendelian inheritance such as familial hemiplegic migraine.
Page 29 Generally, highly polymorphic STRs of known chromosomal location are amplified by polymerase chain reaction (PCR), and the amplicon is genotyped using electrophoresis. A genome wide panel of approximately 400 STRs evenly distributed throughout the genome is genotyped. Linkage analysis has two basic assumptions: each parental allele has an equal chance of transmission to offspring at every locus, and random recombination occurs during meiosis. The identification of linkage at a particular locus is indicative of a susceptibility gene in the absence of type I or II errors. There are two statistical models of linkage analysis, parametric and non-parametric.
Parametric analysis evaluates the transmission of marker alleles under a defined disease inheritance model to produce a logarithm of the odds (LOD) score. LOD scores are calculated to measure the likelihood that inheritance of an allele at a specific marker in affected individuals is the result of linkage opposed to chance. LOD scores are calculated using logs to the base 10 and a parameter θ, known as the recombination fraction in 2-point analysis or map position in multipoint analysis (Teare and Barrett, 2005). For X-linked markers, significant evidence of linkage is indicated by a LOD score equal or greater than 2.
For autosomal markers, significant evidence of linkage is indicated by a LOD score equal or greater than 3. A LOD score less than negative 2 excludes linkage.
Non-parametric analysis reports allele-sharing frequencies in affected relatives without any assumed parameters; it is model free. This analysis is ideal for complex traits with an undefined genetic model such as migraine. Non-parametric analysis uses all the genotype data for markers simultaneously to estimate the proportion of alleles shared at any point along the chromosome. Excess allele sharing between affected relatives is indicative of linkage to the trait. This method assumes that in the presence of a disease locus, affected relatives will exhibit excess sharing of haplotypes that are identical-by-descent (IBD)
Page 30 regardless of whether a disease model is specified. Hence this form of analysis is also referred to as allele-sharing methods. This analysis can be extended to include both affected and unaffected individuals. Linkage is reported as a likelihood ratio using natural logarithms.
This score can be converted to a traditional LOD score by dividing by 4.6 (i.e. 2xloge10)
(Teare and Barrett, 2005).
1.5 Summary
Isolated populations arise when a small number of individuals become geographically, linguistically, religiously or culturally segregated from a larger population. When isolation is severe and prolonged marked changes in allelic diversity may result. Factors such as the number of original founders, the severity and frequency of population bottlenecks, the duration and severity of isolation, extent of immigration and/or migration, the presence of admixture, and degree of consanguinity all impact upon the overall genetic profile of the population. Consequently, isolated populations can possess diverse genetic profiles and the extent and effects of genetic drift and LD may differ markedly.
The expected reduction in allelic diversity, longer-ranging LD, and the homogenous environment shared between individuals in isolated populations may enhance gene mapping.
Many examples of successful gene mapping for Mendelian and complex disorders are reported in diverse genetic isolates, including but not limited to non-syndromic deafness,
CVD, schizophrenia, asthma and obesity. Given these advantages, this study aims to undertake gene mapping of migraine and CVD risk in a young population isolate originating from 18th Century Bounty Mutineers and their Tahitian consorts, currently residing on
Norfolk Island.
Page 31 Norfolk is a geographically isolated island in the South Pacific Ocean with many of the permanent inhabitants related through a single 11-generation pedigree. The population displays many features that may facilitate complex disease mapping, including extended LD, large genealogy, good genealogical records, homogeneous environment, limited immigration and migration, potential for founder effect, potential for genetic drift, historical bottlenecks, severe isolation, increased levels of consanguinity, and admixture. A slightly older island population isolate, Kosrae, shares many of these characteristics and has proved successful in complex disease mapping. Present findings from Norfolk Island and similar isolates like
Kosrae suggest future complex disease gene mapping efforts to be promising. The following chapter will address the major topic of this thesis, complex disease, with a strong emphasis on migraine.
Page 32 Chapter 2: General Disease Background
2.1 Overview
Complex diseases are influenced by multiple genetic and non-genetic (environmental) factors and their interactions. Genetic studies remain challenging because of the multitude of genes of varying effect sizes and environmental factors underlying complex phenotypes, which may be further complicated by the presence of phenocopies, genetic heterogeneity, variable clinical expression, incomplete penetrance, epistasis, and polygenic inheritance. This chapter focuses on 2 complex phenotypes, migraine and CVD risk, which will be investigated using the Norfolk Island Pedigree for the purpose of this thesis.
2.2 Migraine Introduction
Migraine and headache related disorders have plagued individuals since early human existence. Trepanation, the ritual perforation of the skull and removal of a piece of cranium to relieve headache, epilepsy and psychosis is evident in Neolithic human skulls from circa
7,000 BC ., 2003). Ancient Egyptian writings (circa 1200 BC) detail clinical descriptions of migraine and treatment by remedies such a placing a clay crocodile on one’s head ., 2003). Hippocrates, the renowned Greek Physician, described visual aura and its cure by purging in 400 BC ., 2003). In modern society there are many instances of writers and artists making reference to migraine in their works. Signs of negative scotomas (blanks in the visual field) and scintillating scotomas (zig-zags and/or dancing lines), both characteristic of visual aura, are depicted in many twentieth century works of art (Fuller and Gale, 1988).
Page 33 One of the most famous MA sufferers is Charles L. Dodgson, who under the pseudonym
Lewis Carroll (1832-1898) authored Alice in Wonderland and the sequel, Through the
Looking Glass. The adventures of Alice are thought to reflect Dodgson’s own migraine aura experiences, which included symptoms of micropsia, macropsia, negative scotomas and other visual disturbances, a condition often referred to as Alice in Wonderland Syndrome (Kew et al., 1998). The following sections define migraine and describe current epidemiological, physiological, genetic and molecular genetic knowledge.
2.3 Definition
Derived from the Greek word hemicrania, the term migraine describes a recurring, episodic neurological disorder, characterised by unilateral, throbbing headache. The length of the episode varies between 4 and 72 hours. Headache is of moderate to severe pain intensity and may be accompanied by nausea, vomiting as well as hypersensitivity to light and sound.
About 30% of patients may also experience transient sensory or visual symptoms referred to as aura (Russell et al., 1996). Aura symptoms include reversible numbness and tingling, speech disturbances, or positive or negative visual impairment. Clinical diagnosis is established by fulfilment of symptom-based criteria defined by the International Headache
Society (IHS) International Classification of Headache Disorders (ICHD).
2.4 Clinical Classification and Diagnosis
Head pain is an extremely common ailment that displays a spectrum of clinical phenotypes and is symptomatic of many disorders. A formal, global standard for headache diagnosis was established with the first edition of the International Classification of Headache Disorders
(ICHD-I) in 1988, released by the IHS (ICHD-I, 1988). A revised edition, ICHD-II was
Page 34 released in 2004 (ICHD-II, 2004). This criterion standardised clinical diagnosis in the absence of laboratory based tests and facilitated research study comparisons.
ICHD-II describes a hierarchical system for headache diagnosis and classification. In 3 parts, the criterion addresses primary headaches, secondary headaches and cranial neuralgias, central and primary facial pain and other headaches. Primary headaches are distinct clinical disorders that have no apparent underlying cause, secondary headaches are attributable to an underlying disorder such as trauma, infection or psychiatric ailments, while the third category attempts to classify neuralgic pain due to disease of the cranium, neck and facial structures and subtypes of headaches described for the first time or for which not enough information is yet available (ICHD-II, 2004).
Four primary headaches are described by the diagnostic criterion;
1. Migraine (1.0.),
2. Tension-type headache (TTH) (2.0.),
3. Cluster headache (CH) and other trigeminal autonomic cephalalgias (TACs) (3.0.), and
4. Other headaches (4.0.).
Migraine is further divided into 6 categories. The 2 main categories and the primary focus of this thesis are migraine without aura (1.1.) and migraine with aura (1.2.). Also of interest are a further migraine category, probable migraine (1.6.) and a sub-set of migraine with aura, familial hemiplegic migraine (1.2.4.). Descriptions of these 4 primary headache classifications follow.
Page 35 2.4.1 Migraine without Aura
Migraine without aura (MO) is a syndrome characterised by headache and associated features. Patients present with recurrent headaches lasting 4-72 hours, accompanied by any 2 of the following symptoms: unilateral location, pulsating quality, moderate to severe intensity, and/or aggravation by physical activity (Table 2.1). Patients also experience nausea and/or vomiting or photophobia and phonophobia. A minimum of 5 such attacks is required to confirm diagnosis.
Table 2.1. ICHD-II diagnostic criteria for migraine without aura (1.1.) (ICHD-II, 2004).
Code Description A. At least 5 attacks1 fulfilling criteria B-D B. Headache attacks lasting 4-72 hours (untreated or unsuccessfully treated) C. Headache has at least two of the following characteristics: 1. unilateral location 2. pulsating quality 3. moderate or severe pain intensity 4. aggravation by or causing avoidance of routine physical activity (e.g., walking, climbing stairs) D. During headache at least one of the following: 1. nausea and/or vomiting 2. photophobia and phonophobia E. Not attributed to another disorder
Page 36 2.4.2 Migraine with Aura
Migraine with aura (MA) is distinguished by the presence of reversible focal neurological symptoms (aura) preceding or accompanying the headache phase. Neurological symptoms manifest as fully reversible, visual, sensory and/or dysphasic speech disturbances (Table 2.2).
Visual symptoms may include positive symptoms such as flickering lights, spots and lines or negative symptoms such as loss or blurring of central vision. Sensory symptoms may include pins and needles (positive) or numbness (negative). Symptoms gradually develop over 5-20 minutes and last for up to an hour. Headache analogous to MO may occur during or within one hour of aura (typical aura with migraine headache). In some cases the headache may not meet MO criteria (typical aura with non-migraine headache) or be completely absent (typical aura without headache). A minimum of 2 such attacks must be experienced to confirm diagnosis.
Unlike MO, MA is further clinically sub-divided. The 6 categories of MA are as follows:
1. Typical aura with migraine headache (1.2.1.)
2. Typical aura with non-migraine headache (1.2.2.)
3. Typical aura without headache (1.2.3.)
4. Familial Hemiplegic Migraine (1.2.4.)
5. Sporadic Hemiplegic Migraine (1.2.5)
6. Basilar-type migraine (1.2.6.)
For the purposes of this study, the term MA will refer to any of the typical aura categories
(1.2.1., 1.2.2., and 1.2.3.).
Page 37 Table 2.2: ICHD-II diagnostic criteria for migraine with aura (1.2) (ICHD-II, 2004).
Code Classification/Description 1.2. Diagnostic criteria for migraine with aura A. At least 2 attacks fulfilling criterion B B. Migraine aura fulfilling criteria B and C for one of the sub forms 1.2.1.-1.2.6. C. Not attributed to another disorder 1.2.1. Typical aura with migraine headache A. At least 2 attacks fulfilling criteria B–D B. Aura consisting of at least one of the following, but no motor weakness: 1. fully reversible visual symptoms including positive features (e.g., flickering lights, spots or lines) and/or negative features (i.e., loss of vision) 2. fully reversible sensory symptoms including positive features (i.e., pins and needles) and/or negative features (i.e. numbness) 3. fully reversible dysphasic speech disturbance C. At least two of the following: 1. homonymous visual symptoms and/or unilateral sensory symptoms 2. at least 1 aura symptom develops gradually over >5 minutes and/or different aura symptoms occur in succession over >5 minutes 3. each symptom lasts >5 and <60 minutes D. Headache fulfilling criteria B-D for 1.1. Migraine without aura begins during the aura or follows aura within 60 minutes E. Not attributed to another disorder 1.2.2. Typical aura with non-migraine headache A. At least 2 attacks fulfilling criteria B–D B. As per 1.2.1. B. C. As per 1.2.1. C. D. Headache that does not fulfil criteria B-D for 1.1. Migraine without aura begins during the aura or follows aura within 60 minutes E. Not attributed to another disorder 1.2.3. Typical aura without headache A. At least 2 attacks fulfilling criteria B–D B. Aura consisting of at least one of the following, with or without speech disturbance but no motor weakness: 1. fully reversible visual symptoms including positive features (e.g., flickering lights, spots or lines) and/or negative features (i.e., loss of vision) 2. fully reversible sensory symptoms including positive features (i.e., pins and needles) and/or negative features (i.e. numbness) C. At least two of the following: 1. homonymous visual symptoms and/or unilateral sensory symptoms 2. at least 1 aura symptom develops gradually over >5 minutes and/or different aura symptoms occur in succession over >5 minutes 3. each symptom lasts >5 and <60 minutes D. Headache that does not occur during aura nor follow aura within 60 minutes E. Not attributed to another disorder
Page 38 2.4.3 Probable Migraine
A third category of migraine also of potential interest to this study is the probable migraines
(1.6.), particularly probable migraine without aura (1.6.1.) and probable migraine with aura
(1.6.2.). Patients satisfying these criteria were diagnosed as migrainous disorder according to
ICHD-I (ICHD-I, 1988). A study of 382 headache sufferers reported over a third of subjects
(37%) failed to satisfy all criteria for migraine were diagnosis of ‘migrainous disorder not fulfilling above criteria’ or `atypical migraine’ according to ICHD-I (Martínez-Martín et al.,
2001). The authors suggested revision of the strict diagnostic criteria for migraine to minimise ‘atypical’ diagnosis and possible misdiagnosis in clinical settings. Revised ICHD-II criteria expanded to include an additional migraine sub-class, probable migraine. Individuals in this category possess all but a single criterion and do not satisfy criteria outlined for any other ICHD-II listed disorder (Table 2.3).
Table 2.3. ICHD-II diagnostic criteria for the probable migraines (1.6.) (ICHD-II, 2004).
Code Classification/Description 1.6.1. Probable migraine without aura A. Attacks fulfilling all but one of criteria A-D for 1.1. Migraine without aura B. Not attributed to another disorder 1.6.2. Probable migraine with aura A. Attacks fulfilling all but one of criteria A-D for 1.2. Migraine with aura or any of its sub forms B. Not attributed to another disorder
Page 39 2.4.4 A Severe Subtype of MA: Familial Hemiplegic Migraine
FHM is a rare, severe sub-form of MA, following strict autosomal dominant inheritance. This is the first primary headache disorder connected to genetic polymorphisms (De Fusco et al.,
2003; Dichgans et al., 2005; Ophoff et al., 1996). Patients experience typical aura symptoms in addition to motor weakness (hemiparesis) (Table 2.4). The duration of the aura is prolonged, lasting up to 24 hours. Similar attacks must be observed in at least 1 first- or second-degree relative for familial classification. Patients who satisfy the criteria in the absence of affected first- and second-degree relatives are classed as sporadic hemiplegic migraine (SHM) (1.2.5.). Genetic polymorphisms have also been described for sporadic hemiplegic migraine (Thomsen and Olesen, 2004). Presently, 3 types of FHM are recognised primarily due to the associated locus; FHM type 1 (FHM1; MIM141500) linked mutations in the CACNA1A gene on chromosome 19p13, FHM type 2 (FHM2; MIM609634) linked to mutations in the ATP1A2 gene on chromosome 1q21-23, and FHM type 3 (FHM3;
MIM602481) linked to mutations in the SCN1A gene on chromosome 2q24.
Table 2.4. ICHD-II diagnostic criteria for familial hemiplegic migraine (1.2.4) (ICHD-II, 2004).
Code Classification/Description A. At least 2 attacks fulfilling criteria B and C B. Aura consisting of fully reversible motor weakness and at least one of the following: 1. fully reversible visual symptoms including positive features (e.g., flickering lights, spots or lines) and/or negative features (i.e., loss of vision) 2. fully reversible sensory symptoms including positive features (i.e., pins and needles) and/or negative features (i.e., numbness) 3. fully reversible dysphasic speech disturbance C. At least two of the following: 1. at least one aura symptom develops gradually over >5 minutes and/or different aura symptoms occur in succession over >5 minutes 2. each aura symptom lasts >5 minutes and <24 hours 3. headache fulfilling criteria B-D for 1.1. Migraine without aura begins during the aura or follows onset of aura within 60 minutes D. At least one first- or second-degree relative has had attacks fulfilling these criteria A-E E. Not attributed to another disorder
Page 40 2.4.5 The Stages of a Typical Migraine Attack
A typical migraine attack, that is MA or MO, has distinct stages: the prodrome (premotionary phase), aura (for MA attacks only), the headache phase (may be absent in MA), and the postdrome (resolution) (Figure 2.1). The premotionary phase may occur hours or days before the aura and headache phase of an attack. This period is usually characterised by hyperactivity, hypoactivity, depression and food cravings (Blau, 1992). Some individuals may also experience both subtypes, MA and MO throughout their life and should be diagnosed accordingly. Variations in the clinical symptoms (intensity, frequency and duration) experienced by an individual frequently occur in an age-dependent manner.
Figure 2.1. The phases of a typical migraine attack.
Page 41 2.5 Migraine Epidemiology
Epidemiological studies aim to characterise the incidence and prevalence of a disorder in a target population. Incidence is the rate of onset of new cases in a given population over a defined period of time. Prevalence is the total number of cases in a given population at a specific time. Understanding the burden of disease, prevalence and distribution may improve disease management (Lipton and Bigal, 2006). Headache is among the most prevalent, burdensome, and costly disorders reported globally (Jensen and Stovner, 2008). Headache may be symptomatic of an underlying disorder such as infection or brain tumour (secondary), or it may be a distinct clinical entity such as migraine or TTH (primary). This section focuses on current knowledge of the epidemiology of migraine.
2.5.1 Incidence
The incidence of migraine is high in general Western populations and displays substantial age- and sex-specific variation. Recent data from the American Migraine Prevalence and
Prevention (AMPP) study estimated migraine age and gender specific incidence rates using a cross-sectional study approach (Roy and Stewart, 2010). The study included participants from across the US aged between 12 to 100 years of age, with 4386 males and 14 604 females satisfying criteria for migraine. In general, incidence was lower for males (6 per
1,000 person-years in males and 18 per 1,000 person-years in females at peak incidence) and occurred about 4-6 years earlier than in females. For both genders, incidence increased until late teens and early 20s, and then decreased to 0 by 70 years of age. Similar estimates for sex and age-specific migraine incidence were reported using a longitudinal approach of AMPP study data (Stewart et al., 2008). Peak incidence occurred between 20 and 24 years of age in
Page 42 women (18.2/1,000 person-years) and 15 and 19 years of age in men (6.2/1,000 person- years).
Previous incidence studies of migraine subtypes report a higher incidence of MO and later onset compared to MA (Stewart et al., 1991). Peak incidence for MA occurs between 12-13 years of age in females (14.1 per 1,000 person-years) while MO peaks between 14 and 17 years of age (18.9 per 1,000 person-years). In contrast, the incidence of MA peaks at 5 years of age (6.6 per 1,000 person-years) and between 10 and 11 years of age for MO (10 per 1000 person-years). Incidence is higher in males prior to puberty. After puberty incidence is higher in females for the duration of life. Migraine does begin earlier in males, but peaks in both genders by late teens to early 20s. A steady decline in incidence is then observed. Of the 2 primary sub-types, MA begins earlier than MO in both sexes.
Longitudinal studies in the Dutch population between 1989–2001 of participants aged between 25-64 years reported an incidence of 8.1 per 1,000 person-years for migraine
(Lyngberg et al., 2005). Incidence is highest in the 25-34 years of age bracket for both genders. Interestingly, incidence declines between 35-44 years of age, but displays a slight increase between 45-54 years of age, the age when menopause usually occurs (Gold et al.,
2001). This study did not assess incidence relating to age of onset as children and adolescents were not included in the study. While this study provides a general overview for the Dutch population, the sample size (N=549) is considerably smaller than that of AMPP studies (Roy and Stewart, 2010; Stewart et al., 1991; Stewart et al., 2008).
Page 43 2.5.2 Prevalence
The prevalence of migraine displays substantial variation. This may be partially attributable to differences in methodology between studies. Early epidemiological data provide strong evidence that age and sex account for a substantial amount of the variation in prevalence estimates (Stewart et al., 1995). Socioeconomic status, ethnicity and geographical location are also reported to impact prevalence estimates (Lipton et al., 2007). There is a reporting bias in epidemiological studies of migraine and headache disorders as migraine is predominantly investigated in high-income countries. Currently there is a lack of data available for low and middle income countries (Mateen et al., 2008). Of particular note to the
Norfolk Island Health Study, minimal data is available for Australia and Oceania.
2.5.3 Migraine in The United States of America
Large-scale epidemiological studies conducted over a 15-year period in the US indicate migraine prevalence has remained relatively stable. In brief, the American Migraine Study I
(AMS-I) estimated the total prevalence of migraine to be 12.1% (males 5.7%; females
17.6%) using data from 20,468 participants (Stewart et al., 1992). The American Migraine
Study II (AMS-II) estimated a total prevalence of 12.6% (males 6.5%; females 18.2%) using data from 29,727 participants (Lipton et al., 2001). Most recently, the AMPP study estimated total migraine prevalence to be slightly lower, at 11.7% (males 5.6%; females 17.1%) using
162,576 participants (Diamond et al., 2007; Lipton et al., 2007). Based on these estimates, the prevalence of migraine in Western Caucasian populations is approximately 12%.
Migraine prevalence varied substantially across all studies with age. Focusing on AMPP data, prevalence was estimated at 4.0% in males and 6.4% in females during adolescence (12-17 years of age), peaked in both genders between the ages of 30 to 39 years (7.4% of males;
Page 44 24.4% of females), and was lowest for those aged 60 years or older (1.6% of males; 5.0% in women) (Lipton et al., 2007). Interestingly, migraine prevalence is highest during adult years of peak workforce productivity.
Prevalence varies substantially in each study with other demographic factors, notably gender, ethnicity and socioeconomic status (Lipton et al., 2007; Lipton and Scher, 2001; Stewart et al., 1992). In terms of gender, prevalence is highest in females. Ancestry specific differences are also evident as prevalence is higher in those of Caucasian ancestry compared to those of
African-American ancestry. Socioeconomic investigations reveal prevalence to be highest in lower income households. Subtle regional variation in prevalence was also noted (Lipton et al., 2007).
2.5.4 Migraine in Europe
Migraine is highly prevalent in many European nations. In 2004 the total number of individuals with a brain disorder in Europe amounted to 127 million (Andlin-Sobocki et al.,
2005). This rate corresponds to 27% of the inhabitants of the 25 countries European countries included in the study. Migraine was the most prevalent neurological disorder, affecting some
41 million individuals. The 1-year and sex-specific prevalence rates for migraine are similar to 1-year estimates in the US, with a total of 13.7% of individuals affected (7.5% of males and 16.6% of females) (Lipton et al., 2007; Stovner et al., 2006). Like studies in the US, sex- specific prevalence rates support a substantially higher burden of female sufferers across many European nations (Stovner et al., 2006). These estimates are based on the results of 8 epidemiological studies of European adults and it is worthwhile noting that substantial variation in prevalence rates were observed depending on the European nation of origin
(Stovner et al., 2006).
Page 45 2.5.5 Global Estimates
More recent epidemiological investigations have attempted to estimate global prevalence rates for migraine and other headache disorders. One such study assessed epidemiological data from 107 published studies from Africa, Asia, Australia/Oceania, Europe, North
America and Central/South America. Globally, 46% of adults had an active headache disorder, 42% had TTH, 11% had migraine and 3% had chronic daily headache (Stovner et al., 2007). Lifetime prevalence estimates in adults were higher for headache (66%), TTH
(46%), and migraine (14%). Estimates for chronic daily headache (2.9%) were slightly lower
(Stovner et al., 2007). Migraine was found to be most prevalent in Europe (15%) and least prevalent in Africa (5%). Prevalence was estimated at 11% in Asia, 10% in North America, and 9% in Central/South America (Stovner et al., 2007). No estimate was provided for
Australia/Oceania. These estimates should be treated with caution due to the loose criteria for data inclusion, which does not account for variation in sample size, methodology and quality.
In spite of these limitations, results provide a good summary of the global trend for migraine and other headaches. Results indicate headache is a very common ailment, and interestingly the global burden for TTH is substantially higher than migraine. Data also supports prevalence of migraine and other headache disorders to vary with ethnicity and geographical location.
2.5.6 Burden of migraine
Given the high prevalence it is not surprising the burden of migraine is also substantial. Data from the ASM-II found that 91% of migraineurs reported a decrease in normal function due to severe headache and 53% reported severe impairment of daily activities or required bed rest (Lipton et al., 2001). Migraine strongly impacted upon family and social activities
(Lipton et al., 2001). Negative impacts on schooling and employment were also evident with Page 46 51% of subjects reporting a decline in school and work productivity by 50% because of migraine and 31% reporting 1 day absenteeism from work or school in the 3 months prior to the survey (Lipton et al., 2001). Recent studies have estimated that lost productivity due to headache costs the US $18 billion per year (Stewart et al., 2003). Similar trends are also evident in Europe. A substantial portion of the European population have reduced ability to work or be absent due to headache (Stovner et al., 2006). In 2004, migraine cost European society a total of €27 billion, €1.5 billion in healthcare and €25.5 billion in indirect cost through absenteeism and lost productivity at work (Andlin-Sobocki et al., 2005).
2.6 Pathophysiology
The underlying pathophyisology of a disorder provides criteria for candidate gene selection and investigation, thus facilitating genetic research. For migraine, current pathophysiological models suggest symptoms are the result of a cascade of biochemical, vascular and neurological events in the cranial meninges. Hence, this disorder is often described to be of
‘neurovascular’ origin. Cortical spreading depression (CSD), disruption of the meningeal vasculature, dural inflammation and sensitization and activation of the trigeminal nucleus caudas and its connecting efferent and afferent ganglion are all implicated in migraine pathophysiology.
2.6.1 Cortical Spreading Depression
CSD is a slow propagating wave (2-6 mm/min) of neuronal and glial depolarization that spreads across the cortex. Leao first described and characterised cortical spreading depression
(CSD) in 1944 (Leao, 1944). The slowly propagating wave of depression was triggered in rabbit, pigeon and cat cortex by tetanic electrical or mechanical stimulation. Results from feline models using magnetic resonance imaging (MRI) demonstrate that primary waves of
Page 47 CSD can be initiated by potassium application to the cortex and move at a rate of 3.8mm/min
(James et al., 1999). Electrocorticographic (ECoG) depression identical to CSD in animal models has been recorded in humans (Fabricius et al., 2006). Studies using MRI and blood oxygenation level dependent (BOLD) signal changes in humans report CSD to be initiated in the visual cortex and progresses across the occipital cortex at a rate of 3.5mm/min
(Hadjikhani et al., 2001). Studies such as these prove CSD can occur in the human brain.
Unfortunately these studies are limited to patients with brain injury and have not yet been demonstrated in a migraine patient. However, functional imaging studies have demonstrated changes in blood flow and brain activity in migraineurs that is indicative of CSD (Hadjikhani et al., 2001).
CSD can be evoked by a wide range of stimuli including local mechanical stimulation, local injury, high frequency electrical pulses (‘titanic’ stimulation), potassium chloride, potassium ions, hypo-osmotic medium, metabolic inhibitors, ouabain, glutamate receptor agonists, glutamate, acetylcholine and endothelin (Charles and Brennan, 2009; Somjen, 2001). The initiation of CSD causes abrupt shifts in ions across neuronal and glial cell membranes that may increase extracellular concentrations of excitatory neurotransmitters including potassium, sodium, and calcium ions, nitric oxide, arachidonic acid and prostaglandin concentrations (Wei et al. 1992; Strassman et al. 1996). This depolarises adjacent tissues, mediates the release of more neurotransmitter and propagates a wave of depolarisation across the cerebral cortex. CSD activates the trigeminal nerve, initiating a series of neural, vascular and inflammatory events that result in pain (Bolay et al., 2002).
2.6.2 The Trigeminovascular System
Migraine most likely results from a dysfunction in the brainstem nuclei involved in sensory input. Activation of the dorsal rostral brainstem has been demonstrated in humans using Page 48 positron emission tomography (Bahra et al., 2001). The key pathway experienced during the headache phase in both MA and MO involves the trigeminocervical complex whose nuclei reside in the brainstem and also the meningeal vasculature. Sensitisation or activation of perivascular nerve afferents initiates impulses that travel to the trigeminal ganglion.
Experiments have shown that this mediates both vasodilation and protein extravasations in the dura, resulting in local inflammation (Bolay et al., 2002). Information is simultaneously conducted to the trigeminal nucleus, travelling in a reflex arc through the superior salivatory nucleus and pterygopalatine ganglion. Parasympathetic efferents in the dura stimulate vasodilation and pain. Animal models have demonstrated that CSD can activate trigemino- vascular events by mediating the release of ions, neurotransmitters and metabolite molecules in sufficient quantities to sensitise or activate perivascular nerve afferents (Bolay et al.,
2002). The exact trigger of cortical hyper-excitability and sensitization that initiates CSD in
MA and the mechanisms that activate perivascular nerve afferents in MO are still controversial.
2.6.3 A Typical Migraine Gene
The first causal typical migraine gene was reported in 2010 in a multigenerational family with dominant, fully penetrant typical MA (Lafreniere et al., 2010). The TWIK-related spinal cord potassium channel (KCNK18; MIM613655) on chromosome 10q25.3 is a two-pore domain potassium (K2P) channel. Such channel are expressed throughout the CNS and control neuronal resting membrane potential and neuron excitability (Enyedi and Czirjak,
2010). In situ hybridization in the mouse embryo detected expression in the trigeminal ganglion, dorsal root ganglia and autonomic nervous system ganglia (Lafreniere et al., 2010).
The functional consequences of the frame shift mutation F139WfsX24 identified in KCNK18 were further characterised, resulting in a complete cessation of outwardly rectifying whole-
Page 49 cell potassium currents (Lafreniere et al., 2010). The variant caused complete loss of
KCNK18 function through a dominant-negative effect. Interestingly, previous functional studies of the mouse KCNK18 knockout reveal down regulation of KCNK18 channel activity in vivo resulting in altered neuronal excitability (Dobler et al., 2007). The results of these studies indicate functional variants in the KCNK18 gene may be involved in MA pathogenesis by lowering the threshold for CSD.
2.6.4 Pathological insights from Hemiplegic Migraine
The molecular genetics of FHM, a rare Mendelian form of migraine is well characterised and may provide insight into the pathophysiology of MA. Heterozygous missense mutations in three genes located in the central nervous system and encoding either ion channels or ion transport proteins are known to cause this disorder (De Fusco et al., 2003; Dichgans et al.,
2005; Ophoff et al., 1996).
The functional consequences of at least 14 mutations in the alpha-1A subunit of the voltage- dependent P/Q type calcium channel gene (CACNA1A; MIM601011), 24 mutations in the alpha-2 isoform of the sodium-potassium ATPase gene (ATP1A2; MIM182340) and 3 mutations in the alpha subunit of the brain sodium channel gene (SCN1A; MIM182389) have been studied in animal and cellular models and have been reviewed extensively (de Vries et al., 2009; Pietrobon, 2007; Ramagopalan et al., 2007). The results of these studies reveal the following; FHM1 mutations result in gain-of-function of CACNA1A, increased neurotransmitter release from cortical neurons and facilitates propagation of CSD, FHM2 mutations result in sodium-potassium pumps with partial activity with decreased or increased affinity for potassium, and FHM3 mutations result in either a gain- or loss-of-function of
SCN1A (de Vries et al., 2009; Pietrobon, 2007). Studies of FHM1 R192Q mutant mice reveal a lowered threshold for CSD and increased CSD propagation velocity when initiated (van den Page 50 Maagdenberg et al., 2004). Overall, FHM findings implicate a model of lowered threshold for
CSD caused by excessive glutamate release or decreased potassium and glutamate in the brain (Pietrobon, 2007).
2.6.5 Pathophysiology and pharmacology
Ergots were the first documented anti-migraine drug. Ergots act as potent vasoconstrictors, but have low receptor specificity (affinities for serotonin, dopamine and noradrenalin receptors) (Tfelt-Hansen et al., 2000). The ergots were soon followed by non-steroidal anti- inflammatory agents (NSAIDs), which target inflammation (Pfaffenrath and Scherzer, 1995).
The anti-hypertensive calcium channel blockers and β-adrenergic-receptor agonists were the next generation anti-migraine treatments (Limmroth and Michel, 2001). The newer generation serotonin-related drugs such as selective serotonin receptor agonists and serotonin reuptake inhibitors (Adly et al., 1992; Ozyalcin et al., 2005) all act by inhibiting the trigeminovascular system.
The most common preventative therapies for migraine are the triptans, which act as selective serotonin receptor agonists (5-HT1B/1D/1F). These drugs are well characterised, have high receptor-specificity and high efficacy for acute migraine attacks (Ferrari et al., 2001;
Goldstein and Weale, 2001). Tripans prevent attack onset by inhibiting the trigeminovascular system, supporting a neurological model of disease pathology (Goldstein and Weale, 2001;
Potrebic et al., 2003). The high efficacy of triptan-based therapy and knowledge of their effects on the trigeminovascular system has resulted the investigation of serotonin-related genes in numerous migraine association studies (Juhasz et al., 2003; Kusumi et al., 2004;
Marziniak et al., 2005; Racchi et al., 2004).
Page 51 2.6.6 Comorbidity
Comorbidity is the simultaneous presence of disorders in one individual. Migraine has been reported to be comorbid with a number of medical conditions including stroke (Merikangas et al., 1997), psychiatric disorders (Sheftell and Atlas, 2002) and epilepsy (Bigal et al., 2003).
Despite the co-occurrence with other conditions, ICHD-I and ICHD-II criteria specifies that both MA and MO must not be attributable to another disorder. Specifically, physical and neurological examination must rule out headache attributed to: -
1. Head and neck trauma
2. Cranial or cervical vascular disorder
3. Non-vascular intracranial disorder
4. A substance or its withdrawal
5. Infection
6. Disturbance of homeostasis
7. Psychiatric disorder or
8. Headache or facial pain attributed to disorders of the cranium, neck, eyes, ears, nose,
sinuses, teeth, mouth or other facial or cranial structures.
Migraine attributed to any of the above disorders is classified as a secondary headache.
Page 52 2.7 Evidence of Genetic Susceptibility
2.7.1 Twin Concordance and Familial Aggregation
Studies of twin concordance rates and familial aggregation yield important insight into the genetic aetiology of complex disorders. Greater concordance for monozygotic (MZ) twin pairs than dizygotic (DZ) twin pairs suggest a genetic component and increased concordance rate for twins reared-together than those reared-apart implicate shared environmental effects influencing predisposition (Ziegler et al., 1998). Evidence of familial aggregation, the increased occurrence of cases in close relatives of an affected individual and increased relative risk (RR) suggests a heritable component underlying the trait of interest.
An early population-based Danish study found first degree relatives of MO probands to have
1.9 times the risk of MO and 1.4 times the risk of MA (Russell and Olesen, 1995). First- degree relatives of probands with MA had nearly 4 times the risk of MA and no increased risk of MO. First-degree relatives of individuals with no history of migraine had no increased risk of either subtype. Slightly higher estimates for MO RR were reported in a recent study of familial aggregation in a Portuguese population (Lemos et al., 2009). The study found significant increased RR for first-degree relatives of MO probands (RR=3.7) and of MA probands (RR=3.6) compared to the general population. Similar findings for MA have also been reported in a southern Italian town (Cologno et al., 2003). First-degree relatives of MA probands have 3.68 times risk of MA. The results of these studies suggest a genetic basis to both migraine subtypes and indicate that MA has a slightly larger genetic component than
MO.
Evaluation of a cohort from the greater London area (UK) recorded an early age of onset in the proband as well as increased severity with higher levels of family aggregation for general
Page 53 migraine (Stewart et al., 2006). A near 2-fold increase in RR of migraine was found for first- degree relatives of migraine probands (RR=1.88). RR in first-degree relatives of probands reporting onset of migraine before age 16 (RR=2.50) was significantly higher than those with onset at age 16 or older (RR=1.44). For probands reporting severe pain scores, RR of migraine was significantly higher in family members (RR=2.38) compared to family member of probands with less severe pain (RR=1.52).
Twin studies support a significant genetic component and provide evidence for a role for unshared environmental factors (Gervil et al., 1999b; Honkasalo et al., 1995). A Swedish study of twins raised apart and twins raised together found no significant role for shared environmental influence on migraine predisposition (Svensson et al., 2003). Another study of
MA found pair wise concordance rates to be significantly higher in MZ (34%) than DZ twin pairs (12%), providing strong evidence of a genetic component to MA (Ulrich et al., 1999a).
As the pair wise concordance rate is less than 100% in MZ twin pairs, environmental factors are also important in MA aetiology. Like MA, MO is influenced by environmental and genetic factors. A study of MO reported concordance rates of 40% in MZ and 28% in DZ twin pairs (Gervil et al., 1999b). The author noted the genetic component was ‘modest’ and predicted finding susceptibility genes to be ‘laborious’ and ‘difficult’. Interestingly, over 10 years later, causal genetic variant for primary headache disorders are still limited to FHM (De
Fusco et al., 2003; Dichgans et al., 2005; Ophoff et al., 1996) and one gene for MA
(Lafreniere et al., 2010).
Overall these studies provide strong evidence of a genetic component for typical migraine and for the MA and MO subtypes. Results also highlight the complex aetiology of migraine, implicating both genetic and non-shared environmental factors.
Page 54 2.7.2 Heritability
Heritability estimates provide a measure of the genetic component underlying a trait of interest. Heritability is measured on a scale ranging from 0 to 1. A heritability of 0 indicates the phenotype is controlled exclusively by environmental factors and a value of 1 indicates complete regulation by genetic factors. For complex disorders like migraine, estimates generally fall within these 2 extremes, indicating the trait is influenced by a combination of genetic and non-genetic factors. Heritability estimates provide statistical evidence of a genetic component and support deeper investigations by linkage or association mapping.
Results of 2 recent population-based twin studies confirm the existence of genetic and non- genetic components underlying migraine. The GenomeEUtwin project assessed migraine prevalence and heritability in 29,717 twin pairs across 6 countries (Mulder et al., 2003).
Prevalence varied substantially, but was highest in Danish (32%) and Dutch females (34%).
Heritability estimates were significant and ranged from 0.34 for Australian twins to 0.57 for
Dutch twins. No sex specific differences in the genetic variance (heritability) were detected.
In contrast, a Swedish study of twins raised apart (N=314) and twins raised together (N=364) detected a gender effect (Svensson et al., 2003). Heritability estimates for migraine were 0.38 in males and 0.48 in females. The Svensson study also assessed the importance of the shared rearing environment in twins for lifetime migraine. The authors concluded there were no significant shared rearing influences and that environmental influences make family members different, not similar. These current estimates are similar to earlier population-based twin studies, which report a genetic component of 0.40 to 0.52 for migraine (Honkasalo et al.,
1995; Larsson et al., 1995; Ziegler et al., 1998).
Subtype analysis reports heritability estimates of 0.65 for MA (Ulrich et al., 1999b) and 0.61 for MO (Gervil et al., 1999a), which are slightly higher than those estimated for typical Page 55 migraine. Early studies reported MA and MO to be genetically distinct disorders (Russell and
Olesen, 1995; Russell et al., 1996; Russell et al., 2002). There is however, mounting evidence that indicates otherwise. Heritability estimates for typical migraine are generally lower than those observed for MA and MO, suggesting that the subtypes share some, but not all genetic factors (Gervil et al., 1999b; Mulder et al., 2003; Russell and Olesen, 1995; Stam et al., 2010;
Ulrich et al., 1999b). Evidence for shared genetic factors underlying migraine subtypes is also supported by the results of latent class analysis (LCA), a statistical method of grouping similar cases into distinct categories (Ligthart et al., 2006; Nyholt et al., 2004). The extent of unique and common genetic factors underlying migraine phenotypes will likely only be resolved by the identification and characterisation of susceptibility genes.
Pedigree analysis has also provided evidence for a genetic liability. Heritability estimates as high as 0.96 for MA, 0.77 for MO and 0.56 for all migraine have been reported in a Dutch genetic isolate (Stam et al., 2010). Heritability estimates were substantially higher for MA than MO, supporting earlier claims of a larger genetic loading for MA (Gervil et al., 1999b;
Russell and Olesen, 1995; Ulrich et al., 1999b). The high genetic loadings for both subtypes are likely a reflection of the unique genetic architecture characteristic of population isolates
(Arcos-Burgos and Muenke, 2002). These results should be treated with caution, as they are family specific and not calculated at the population level like previous twin studies.
Nonetheless these findings are interesting and provide guidance for future studies in cohorts of similar genetic background.
All of these studies confirm a role for both genetic and environmental factors in predisposition to typical migraine and the subtypes. At the population level, heritability is estimated to range from 0.40 to 0.60 with the residual heritability reflecting the environmental component influencing predisposition. Of the 2 subtypes, MA appears to have
Page 56 a larger genetic loading. In combination with evidence of increased relative risk and increased concordance rates in MZ twin pairs compared to DZ twin pairs there is strong justification for undertaking linkage and association studies in an effort to localise susceptibility genes.
2.7.3 FHM and Common Migraine
FHM is a rare sub-form of MA that follows strict autosomal dominant inheritance. Although distinguishable by the presence of reversible motor weakness and prolonged aura symptoms, there is considerable phenotypic overlap with MA (ICHD-II, 2004). Compared to the general population, FHM probands have a 7.1 times increased risk of MA and virtually no increased risk of MO (Thomsen et al., 2003). FHM-affected first-degree relatives have a 7.6 times increased risk of MA and no increased risk of MO (Thomsen et al., 2003). Non-FHM- affected first-degree relatives have a 2.4 increased risk of MA and no increased risk of MO
(Thomsen et al., 2003). From these findings it is clear that shared genetic factors underlie
FHM and MA, but not FHM and MO, thus suggesting FHM and MA are part of the same spectrum. This shared genetic basis is further supported by family studies that report co- occurrence of MA and/or MO in FHM pedigrees and even within FHM-affected individuals
(De Fusco et al., 2003; Ophoff et al., 1994; Thomsen et al., 2007). Knowledge of FHM may provide insight into common forms of migraine.
Page 57 2.8 Molecular Genetics
2.8.1 Familial Hemiplegic Migraine
Migraine accompanied by recurrent hemiplegia, was first described in a multigenerational
UK family in the year 1910 (Clarke, 1910). Affected family members experienced recurrent migraine attacks accompanied by transient visual disturbances, speech disturbances and hemiplegia. This is believed to be the first documented case of familial hemiplegic migraine
(FHM). FHM is a severe sub-type of MA, which follows simple Mendelian inheritance.
Transient motor weakness accompanies typical aura symptoms (visual, sensory and aphasic) during an attack (ICHD-II, 2004). For familial diagnosis, at least 1 first- or second-degree relative must also be affected. In cases of identical symptomatology in the absence of affected first- or second-degree relatives, the disorder is classed as sporadic hemiplegic migraine (SHM) (ICHD-II, 2004). Hemiplegic migraine is rare in the general population. In combination with SHM, FHM is estimated to affect 1 in 10,000 (0.01%) individuals
(Thomsen and Olesen, 2004).
Some patients experience atypical attacks (Table 2.5). Such attacks may include symptoms of ataxia, confusion, coma, epilepsy, fever, nystagmus, prolonged aura, seizures and vertigo (De
Fusco et al., 2003; Ducros et al., 2001; Ducros et al., 1999; Gargus and Tournay, 2007; Joutel et al., 1993; Joutel et al., 1994). Nonpulsatile tinnitus and mild mental retardation has also been observed in affected individuals (Dichgans et al., 2005; Gargus and Tournay, 2007;
Vahedi et al., 2000). Attacks may be triggered by head trauma (Cevoli et al., 2002; Thomsen et al., 2002). FHM, MA and MO may be present in the same family and even the same individual individuals (De Fusco et al., 2003; Ophoff et al., 1994; Thomsen et al., 2007).
Page 58 FHM affected individuals and their relatives are known to have a significantly increased risk of MA, but not MO (Thomsen et al., 2003).
FHM does display a level of phenotypic and genetic complexity. Aside from the presence of atypical symptoms, penetrance is not always complete. A Danish study of 44 FHM families estimated the rate of autosomal dominant inheritance as 77% with some of these families displaying reduced penetrance (Thomsen et al., 2002). The remaining 23% of families displayed complex inheritance. Heterogeneity is also evident at the locus and allelic level.
Presently, 5 FHM loci are known, 3 causative genes have been identified and multiple mutations characterised. Current knowledge of the molecular genetics of FHM are detailed in
Table 2.5 and will be described in the following sections.
Page 59 Table 2.5. A summary of FHM linkage and gene studies.
Phenotype Locus Gene Sample Other Phenotypes/Symptoms Variant(s) Reference FHM1 19p13 CACNA1A 2 French Pedigrees Cerebellar Ataxia, Nystagmus NA (Joutel et al., 1993) 4 French Pedigrees Confusion, Fever, Coma, Progressive NA (Joutel et al., 1994) Cerebellar Ataxia 1 British, I American, 1 MA, MO, Cerebellar Ataxia Present, NA (Ophoff et al., 1994) Dutch Pedigrees Heteroanamnesis 1 UK, 1 Dutch, 1 Italian and NA R192Q, T666M, V714A, I1811L (Soragna et al., 2003) 2 US Pedigrees 1 Danish, 15 French Nystagmus and/or Mild to Moderate D715E (Ducros et al., 1999) pedigrees; 3 Sporadic Cases Statokinetic Cerebellar Ataxia 1 Sporadic Case of French Mental Retardation, Permanent Cerebellar W1385C (Vahedi et al., 2000) Caucasian Ancestry Ataxia With Cerebellar Atrophy, Right- Sided Brain Atrophy 19 FHM Probands; 2 Cerebellar Signs R195K, K1336E, R1668W, (Ducros et al., 2001) Sporadic Cases W1684R, V1696I 1 Spanish Pedigree MA, MO, BM, Paroxysmal Vertigo, V581M, W1245C (Cuenca-León et al., 2008) Instability, Infantile and Generalised Epilepsy FHM2 1q21-23 ATP1A2 3 French Pedigrees Epileptic Seizures NA (Ducros et al., 1997) 1 Italian Pedigree MO, Cerebellar Signs, Triggering by Mild NA (Cevoli et al., 2002) Head Trauma 2 Italian Pedigrees Migraine, Seizures L764P, W887R (De Fusco et al., 2003) 1 Finnish Pedigree MO, Non-Migrainous Headache T345A (Kaunisto et al., 2004) 1 Irish Pedigree MA, MO D999H (Fernandez et al., 2008) 4 Italian Pedigrees; 1 MA, MO R65W, Y9N (Tonelli et al., 2007) Sporadic Case 1 British Caucasian Pedigree MA, MO I286T, T415M (Vanmolkot et al., 2007) 1 Sporadic Case Epileptic Seizures Y1009X (Gallanti et al., 2008) FHM3 2q24 SCN1A 3 German Pedigrees Seizures, Mild Mental Retardation, MA Q1489K (Dichgans et al., 2005) Mother-Daughter of Mixed Vertigo, Ataxia, Nonpulsatile Tinnitus T1174S (Gargus and Tournay, 2007) Ancestry 1 North American Caucasian NA L1649Q (Vanmolkot et al., 2007) Pedigree FHM4 1q31 NA 1 German-Native American MO, Headache NA (Gardner et al., 1997) Pedigree FHM 14q32.12–32.13 NA 1 Spanish Pedigree MA, MO NA (Cuenca-León et al., 2009) ATP1A2= Alpha-2 or the ATPase, Na+/K+ Transporter ; CACNA1A=Alpha-1A subunit of the voltage-dependent P/Q type calcium channel; SCN1A=Alpha subunit of the neuronal type I sodium channel, NA=Not Available
Page 60 2.8.2 Familial Hemiplegic Migraine Type 1: The CACNA1A Gene
Identification of the first FHM locus resulted from knowledge of another rare Mendelian disorder, cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL; MIM125310). While investigating families segregating with
CADASIL phenotypic overlap was observed with MA (Joutel et al. 1993). Although FHM and
CADASIL are clinically distinct disorders it was theorised they may share a common susceptibility gene. At the time a susceptibility locus on chromosome 19 had been linked to
CADASIL (Tournier-Lasserve et al., 1993). Investigators proceeded to test linkage of 2 multigenerational FHM pedigrees to chromosome 19 (Joutel et al., 1993). Strong evidence of linkage (LOD>8.0) was detected on chromosome 19p. CADASIL and FHM shared a 30cM interval susceptibility region on chromosome 19p. The 19p regions became known as the familial hemiplegic migraine type 1 locus (FHM1; MIM141500).
Ophoff et al. (1996) identified mutations in the CACNA1A gene (MIM601011) on chromosome
19p13 in multigenerational FHM families (Ophoff et al., 1996). Ophoff et al. (1996) characterised the CACNA1A gene. The gene covered 300kb and contained 47 exons (Ophoff et al., 1996). The CACNA1A gene (MIM601011) encodes a voltage-dependent P/Q-type calcium channel protein, containing an alpha-1A pore-forming subunit. Abundantly expressed in neuronal tissue, the channel is a multi-subunit complex involved in a variety of calcium dependent processes, including muscle contraction, gene expression and hormone and neurotransmitter release (Dunlap et al., 1995). The channel mediates the influx of calcium ions
(Ca2+) into excitable cells (Diriong et al., 1995).
Page 61 At least 26 missense mutations in the CACNA1A transcript are reported. The functional consequences of these mutations have been characterised in many studies and are supported by animal models such as the CACNA1A knockout, the tottering mouse (Plomp et al., 2000). It is also interesting to note mutations in the CACNA1A gene cause 2 other neurological disorders with autosomal dominant inheritance, episodic ataxia type 2 (EA2; MIM108500), and spinocerebellar ataxia type 6 (SCA6; MIM183086). There is also emerging evidence implicating
CACNA1A involvement in idiopathic generalised epilepsy (IGE; MIM600669).
2.8.3 Familial Hemiplegic Migraine Type 2: The ATP1A2 Gene
Familial hemiplegic migraine type 2 (FHM2; MIM602481) is caused by variants in the alpha-2 isoform of the sodium-potassium ATPase gene (ATP1A2; MIM182340) on chromosome 1q21-
23. This gene is involved in establishing and maintaining electrochemical gradients of sodium and potassium ions across the plasma membrane (Shull and Lingrel, 1987). At least 46 missense mutations, 1 frame-shift and 1 insertion/deletion in the ATP1A2 transcript have been identified.
Functional supporting evidence is available for many of these variants.
2.8.4 Familial Hemiplegic Migraine Type 3: The SCN1A Gene
Familial hemiplegic migraine type 3 (FHM3; MIM609634) is caused by variants in the alpha-2 isoform of the alpha subunit of the brain sodium channel gene (SCN1A; MIM182389) on chromosome 2q24. SCN1A is a transmembrane-spanning, voltage-gated ion channel. At least 5 missense mutations in the SCN1A transcript have been identified. Supporting functional evidence is available for 3 of these variants. SCN1A knockout mice develop severe ataxia and seizures, reduced sodium current density in GABAergic neurons and variation in gene expression levels
Page 62 (Ogiwara et al., 2007; Yu et al., 2006). Mutations in SCN1A are also linked to generalised epilepsy with febrile seizures (GEFS+; MIM604233) and dravet syndrome (MIM607208).
2.8.5 Additional FHM loci
Despite the success of gene-mapping, variants in these 3 genes account for only 50-70% of FHM cases (Thomsen et al., 2007). This indicates that undiscovered causative genes may exist.
Presently, 2 additional FHM regions are known, the Familial Hemiplegic Migraine Type 4
(FHM4; MIM607516) locus on chromosome 1q31 (Gardner et al., 1997) and a recently discovered region on 14q32 (Cuenca-León et al., 2009). Genes are yet to be localised for these regions.
2.8.6 Migraine: Positional Cloning
Interest in migraine gene-mapping commenced soon after identification of the first FHM susceptibility locus on chromosome 19p13 in 1994 (Hovatta et al., 1994; Joutel et al., 1993). It was hypothesised that this locus may represent a shared susceptibility region for both FHM and common forms of migraine (particularly MA) as the disorders display phenotypic similarities and may share some underlying genetic component. This hypothesis was tested in migraine pedigrees from a Finnish isolate, however initial results were disappointing. This first migraine linkage study provided no evidence of linkage to the FHM-linked chromosome 19p13 locus
(Hovatta et al., 1994). Since these early linkage studies of FHM and migraine, the chromosome
19p13 locus has been pursued relentlessly in hope of detecting genetic variants involved in common forms of migraine. There is compelling justification for such investigations. Clinically,
FHM is defined a rare sub-type of MA and displays strong phenotypic overlap (ICHD-II, 2004).
Page 63 FHM and the common migraines are known to segregate in the same families, and even the same individual, suggesting a common genetic aetiology (Cuenca-León et al., 2009; Gardner et al.,
1997). Results of such family-based investigations are conflicting. Some studies exclude the
19p13 locus as a common migraine susceptibility region (Hovatta et al., 1994; Kaunisto et al.,
2005; Kirchmann et al., 2006; Lea et al., 2001; Noble-Topham et al., 2002), while others provide compelling evidence for involvement (Jones et al., 2001; May et al., 1995; Nyholt et al., 1998b;
Terwindt et al., 2001). This region has been replicated in multiple independent studies, providing evidence of involvement in at least some, but not all cases of familial migraine. Susceptibility genes residing in this region are yet to be confirmed.
Three years after the discovery of the first FHM locus, a second locus was identified on the long arm of chromosome 1 (Ducros et al., 1997; Gardner et al., 1997). Like FHM1, knowledge of the molecular aetiology of FHM2 inspired targeted linkage investigations of common migraine. The first chromosome 1 migraine investigation, a study by Lea et al. (2002) provided evidence of linkage to the 1q31 locus with MA (Lea et al., 2002). Results were successfully replicated in a sample of 82 independent families. Whilst this study provided compelling evidence for the involvement of the 1q31 in Australian MA families, targeted investigations in families segregating with common migraine excluded this locus (Kirchmann et al., 2006). The extent of involvement of the FHM2 locus in common forms of migraine is currently unknown.
Chromosomes 1 and 19 have been targeted in linkage studies of migraine on the premise of common pathophysiology and shared genetic background with FHM. Aside from these 2 regions, targeted linkage studies of regions in the genome unlinked to FHM have also been undertaken. The first, an X-chromosomal scan on the premise of possible sex-linked
Page 64 transmission in Australian pedigrees and to address the unequal sex-specific prevalence rates and higher relative risks for first degree female relatives of male probands (Nyholt et al., 1998a).
This study provided compelling evidence for the presence of an X-linked component to familial migraine, which was later refined to Xq24-28 (Nyholt et al., 2000; Nyholt et al., 1998a). No susceptibility genes were elucidated. The second targeted study was of a region on chromosome
15 known to harbour GABAergic receptor genes (Russo et al., 2005). This study provided strong evidence of linkage to chromosome 15q11-13, but failed to identify a susceptibility gene.
The first genome-wide migraine linkage scan was not until 8 years after the original Hovatta study assessing the FHM1 locus (Hovatta et al., 1994; Wessman et al., 2002). Like the Hovatta study, this investigation screened pedigrees from the population isolate, Finland. Strong evidence of linkage was reported for a region on chromosome 4q24. Since this first genome-wide linkage screen, many studies have undertaken genome-wide linkage analysis (with or without X- chromosomal markers) in an attempt to identify migraine susceptibility loci. There are at least
12 regions in the genome displaying significant linkage with migraine and migraine-related phenotypes and are likely to harbour susceptibility genes. Many of these studies also report suggestive evidence of linkage to additional genomic regions where susceptibility genes of small effect size may reside. Aside from recent studies by Anttila et al. (2010) and Lafreniere et al.
(2010) these migraine linkage studies are yet to identify genes and many loci await independent replication.
There is difficulty in directly comparing the results of migraine linkage studies due to methodological differences, particularly sample design, ascertainment, phenotyping and analysis.
These differences may account for the lack of reproducibility of some loci. A summary of
Page 65 migraine linkage study design and methodology is detailed in Table 2.6. Sample designs include twins, sib-pairs, trios, nuclear families and multigenerational pedigrees. Individuals have been recruited from clinics, hospitals, registries and via public announcements. Diagnosis has been established by a range of means including neurological examinations, telephone and/or personal interviews conducted by ‘trained’ staff through to neurologists, questionnaires, or a combination there-of. In all studies phenotyping is derived from either ICHD-I or ICHD-II criterion, however there is considerable variation in linkage phenotypes (Table 2.6). Migraine linkage phenotype can be classed into 3 categories; 1) ICHD-derived clinical end diagnosis, 2) LCA and Bayesian derived methods, and 3) TCA.
Linkage analysis methods use various combinations of parametric or non-parametric analysis, single-point or multipoint analysis, and allele-sharing or variance component-based analysis, all undertaken using a variety of analysis programs. Further difficulty in comparison arises from differences in the number and location of markers genotyped. Some studies have integrated data from multiple sources and attempted to address methodological differences (Anttila et al., 2008;
Chen et al., 2009a; Chen et al., 2009b), but this as yet has not been performed for all genome- wide linkage studies of migraine. One unifying point for all these studies is ethnicity. Although studies have been undertaken in pedigrees from Australia, Canada, Denmark, Finland, Iceland,
Italy, Sweden, and the Netherlands, all subjects are primarily of Caucasian descent.
Page 66 Table 2.6. A comparison of migraine positional study designs.
Study Phenotype(s) Population Cases Diagnosis No. STR Region Program(s) Method Marker(s) (Hovatta et al., 1994) ICHD-I MA/MO Finnish 10 Questionnaire 4 19p13 MLINK Parametric Two-point and Multipoint (Griffiths et al., 1997) ICHD-I MA/MO Australian Caucasian 52 Questionnaire 1 NOS3 FASTLINK Parametric and NP Two-point (Nyholt et al., 1998b) ICHD-I MA/MO Australian Caucasian 51 Clinical Interview 16 19 FASTLINK, VITESSE, Parametric Two-point GENEHUNTER and Multipoint; NP Multipoint (Nyholt et al., 1998a) ICHD-I MA/MO Australian Caucasian 41 Clinical Interview 28 X GENEHUNTER(XGH), Parametric and NP HOMOG multipoint (Nyholt et al., 2000) ICHD-I MA/MO Australian Caucasian 21 Clinical Interview 16 Xq21-qter GENEHUNTER-PLUS Parametric and NP Multipoint (Jones et al., 2001) ICHD-I MA North American 82 Telephone 6 19p GENEHUNTER NP Two-point and Caucasian Questionnaire Multipoint Clinical Interview (Lea et al., 2001) ICHD-I MA/MO Australian Caucasian Questionnaire 4 19p13 GENEHUNTER-PLUS, NP Two-point Clinical Interview SimIBD (Terwindt et al., 2001) ICHD-I MA/MO Dutch Questionnaire 6 19p MAPMAKER/SIBS Sib-pair Analysis Interview (Carlsson et al., 2002) ICHD-I MA/MO Swedish 30 Questionnaire 400 GWS Not Indicated Parametric Two-point (ClassI/ClassII) and Multipoint (Lea et al., 2002) ICHD-I MA/MO Australian Caucasian 60 Questionnaire 8 1q31 Allegro NP Multipoint Clinical Interview (Wessman et al., 2002) ICHD-I MA Finnish 246 Questionnaire 350 GWS LINKAGE, HOMOG, Parametric Two-point SIBPAIR, and NP Affected Sib- GENEHUNTER pair (Björnsson et al., 2003) ICHD-I MO (MO2) Icelandic 289 Questionnaire 500 GWS Allegro NP multipoint, Affected-only Allele Sharing (Cader et al., 2003) ICHD-I MA Canadian 248 Telephone 395 GWS FASTLINK, Simwalk, Parametric Two-point Neurologist GENEHUNTER-PLUS and multipoint (Soragna et al., 2003) ICHD-I MO Italian 22 Clinical examination 482 Autosomal MLINK, FASTMAP Parametric Two-point GWS and Multipoint (Kaunisto et al., 2005) ICHD-I MA Finnish 417 Questionnaire; 8 19p13 MLINK, HOMOG, Parametric and NP Neurologist diagnosis Simwalk2 Two-point (Lea et al., 2005a) ICHD-I MA/MO Australian Caucasian 380 Questionnaire; 400 GWS MERLIN NP multipoint (LCA-severe) Clinical Interview (Nyholt et al., 2005) LCA (TCA) Australian Twins Interview 458 Autosomal SOLAR, MERLIN- Multipoint Variance GWS regress Component Analysis, NP Multipoint (Russo et al., 2005) ICHD-II MA Italian 25 Not specified 14 15q11-q13 LINKAGE, Simwalk Parametric Two-point and Multipoint
Page 67 Table 2.6. A comparison of migraine positional study design (continued).
Study Phenotype(s) Population Cases Diagnosis No. STR Region Program(s) Method Marker(s) (Anttila et al., 2006) TCA (ICHD-II Finnish (225) Questionnaire 350 GWS LINKAGE, HOMOG, Parametric and NP MA/MO) GENEHUNTER Affected-only (Kirchmann et al., 2006) ICHD-I MA Danish 161 Telephone 66 1 & 19 Allegro Parametric and NP Clinical Interview Multipoint (Anttila et al., 2008) ICHD-II MA/MO Finnish 248 Questionnaire 387 GWS MERLIN NP Multipoint (ICHD/LCA/TCA) Clinical examination ICHD-II MA/MO Australian Caucasian 269 Telephone (ICHD/LCA/TCA) Questionnaire ICHD-II MA/MO Finnish Replication 79 Questionnaire (ICHD/LCA/TCA) Clinical examination (Ligthart et al., 2008) ICHD-II MA/MO Dutch 1494 Questionnaire 345 Autosomal MERLIN NP Multipoint (ICHD/LCA/TCA) GWS (Chen et al., 2009b) Australian Twins 4148 Telephone 1770 Autosomal MERLIN-qtl NP Multipoint QTL LCA/GoM/Fanny Questionnaire GWS (Chen et al., 2009a) Australian Twins 4148 Telephone 1770 Autosomal MERLIN-qtl NP Multipoint QTL Bayesian Modelling Questionnaire GWS
Fanny = grade of membership 'fuzzy' clustering; GoM = grade of membership; GWS=Genome-Wide Scan; ; ICHD=International Classification of Headache Disorders; LCA = latent class analysis; NOS3 = Nitric oxide synthase gene; NP = non-parametric; MA = migraine with aura; MO = migraine without aura; MO2 = ICHD-I diagnosis relaxing criteria C or D, but not both; QTL = quantitative trait linkage; STR = short tandem repeat; TCA = trait component analysis (analysis of individual and/or grouped migraine symptom phenotypes)
Page 68 2.8.7 Migraine: Association-Based Approaches
Variants in at least 38 genes have been positively associated with migraine phenotypes (Table 2.7). These putative migraine genes have diverse functions including vascular tone and function (ACE, EDNRA, NOS3, MMP3), homocysteine metabolism (MTHFR, MTHFD1, TYMS), serotonin transport and metabolism (MAOA,
SLC6A4, STX1A, DDC, HTR2C, HTR2A, HTR1A, HTR2B, HTR1B, ESR1), dopamine transport and metabolism (DBH, DDC, DRD4, DRD2, SLC6A3), or function as hormone receptors (ESR2, PGR). Some of the variants in these genes have known functional consequences like the MTHFR C677T polymorphism that alters enzymatic activity, and insertion-deletions in the DBH and SLC6A4 genes that alter gene transcription (Fernandez et al., 2006; Marziniak et al., 2005; Ueland et al., 2001).
Very few of these studies include independent replication cohorts in study design to confirm initial positive associations (Colson et al., 2004; Colson et al., 2005;
Corominas et al., 2009; Oterino et al., 2008). Sample design and size may also be an issue, especially if the genetic component of migraine includes individual genetic variants of small effect size (Ioannidis et al., 2006). Despite limitations, associations within theses putative susceptibility genes may provide molecular insight into future migraine genetic studies, particularly as they are selected on the premise of biological plausibility.
Page 69 Table 2.7. Summary of positive gene associations with migraine.
Locus Gene Associated P-Value OR (95%CI) Reference Phenotype
1p36.3 MTHFR MA 0.029 4.069 (1.153-14.359) (Joshi et al., 2009) MA 0.0001 14.105 (2.417-82.320) (Kara et al., 2003) MA <0.01 - (Kowa et al., 2000) MA 0.002 2.89 (1.47-5.72) (Lea et al., 2005a) MA 0.017 2.54 (1.37-4.71) (Lea et al., 2004) MA 0.039 1.88 (1.01-3.25) MO 0.018 3.25 (1.20-8.70) (Oterino et al., 2005) MA <0.006 2.05 (1.2-3.4) (Scher et al., 2006) 2q36.3-q37.1 HTR2B MO 0.0017 1.43 (1.16-1.76) (Corominas et al., 2009b) 4q31.2 EDNRA MA-MO <0.001 0.50 (0.34-0.74) (Tzourio et al., 2001) 5p15.3 SLC6A3 MA 0.0082 0.81 (0.69-0.95) (Todt et al., 2009) 5q11.2-q13 HTR1A TC 0.008 - (Marziniak et al., 2007) 5q33 GRIA1 MA 0.0005 1.9 (1.4-2.5) (Formicola et al., 2010) 6p21.3 HLA-DRB1 MO 0.02 1.97 (1.10-3.54) (Rainero et al., 2005) 6p21.3 LTA MO 0.018 1.46 (1.066-2.023) (Asuni et al., 2009) MO 0.004 - (Trabace et al., 2002) 6p21.3 TNF MA 0.015 2.293 (1.172-4.487) (Ghosh et al., 2010) MO <0.0001 3.73 (2.40-5.82) (Mazaheri et al., 2006) MO <0.001 3.3 (2.09-5.24) (Rainero et al., 2004) 6q13 HTR1B TC < 0.05 - (Marziniak et al., 2007) 6q14-q15 CNR1 MA-MO 0.017 - (Juhasz et al., 2009) 6q25.1 ESR1 MA-MO 0.003 - (Colson et al., 2004) MA-MO 0.000008 - MA-MO 0.00003 3.2 (1.9-5.3) (Colson et al., 2005) MA-MO 0.0001 1.729 (1.309-2.284) (Joshi et al., 2010) MA-MO 0.005 - (Oterino et al., 2008) MA-MO 0.009 - MA-MO (Oterino et al., 2006)
(FEMALE) 0.008 1.6 (1.1-2.4) 7p11 DDC MA 0.0019 2.31 (1.48-3.59) (Corominas et al., 2009b) 7q11.2 STX1A MA-MO–HM 0.008 1.71 (1.16-2.52) (Corominas et al., 2009a) 7q11.2 STX1A MA-MO 0.008 3.27 (1.35-7.88) (Lemos et al., 2010) 7q36 NOS3 MA 0.05 2.21(1.0-5.04) (Borroni et al., 2006) 8q22-q23 MTDH (Anttila et al., 2010) 9q34 DBH MA-MO 0.004 - (Fernandez et al., 2009) MA-MO 0.013 - MA 0.003 2.03 (1.26-3.28) (Fernandez et al., 2006) MA-MO 0.006 - (Lea et al., 2000) MA 0.0012 0.77 (0.65-0.90) (Todt et al., 2009) 10q25.3 KCNK18 (Lafreniere et al., 2010) 11p15.3-p14 TPH1 MA_MO 0.01 - (Erdal et al., 2007) 11p15.5 DRD4 MA 0.0009 - (Mochi et al., 2003) 11q22 PGR MA-MO 0.02 - (Colson et al., 2005) MA-MO 3.00E-03 - MA-MO 0.0001 0.292 (0.155-0.549) (Joshi et al., 2010) 11q23 MMP3 MA-MO-BM-CM <0.001 - (Kara et al., 2007) 11q23.1 DRD2 MA 0.007 - (Peroutka et al., 1997) 13q14-q21 HTR2A MA-MO 0.049 1.45 (1.00-2.12) (Juhasz et al., 2003) MA 0.02 - (Erdal et al., 2001) 14q ESR2 MA-MO 0.005 - (Oterino et al., 2008) MA-MO 0.009 - 14q24 MTHFD1 MO 0.018 3.25 (1.2-8.7) (Oterino et al., 2005) 17q11.1-q12 SLC6A4 MA 0.02 2.82 (1.61-6.84) (Bayerer et al., 2010) MA <0.05 2.60 (1.75-3.85) (Borroni et al., 2005) MA <0.001 4.38 (1.94-9.87) (Marziniak et al., 2005) MO 0.017 1.68 (1.05-2.69) (Ogilvie et al., 1998) MA 0.03 5.3 (1.3-21.7) (Racchi et al., 2004) MA 0.0396 2.028 (Szilagyi et al., 2006) MA-MO 0.01 - (Yilmaz et al., 2001)
Page 70 Table 2.7. Summary of positive gene associations with migraine (continued).
Locus Gene Gene Associated P-Value OR (95%CI) Reference ID Phenotype 17q23 ACE 1636 MA-MO <0.001 - (Kara et al., 2007) MA <0.01 5.26(1.69-16.34) (Kowa et al., 2005) MA 0.002 2.89 (1.47-5.72) (Lea et al., 2005b) MA-MO 0.045 0.331 (0.108-1.014) (Lin et al., 2005) MO 0.044 - (Paterna et al., 2000) MA 0.04 2.830 (1.045-7.662) (Joshi et al., 2009) 18p11.32 TYMS 7298 MO 0.02 0.28 (0.1-0.8) (Oterino et al., 2005) 19p13 CACNA1A 773 MA-MO-BM- 0.002 0.26 (D'Onofrio et al., 2009) SHM-FHM 19p13.2 INSR 3643 MA 0.002 - (McCarthy et al., 2001) MO 0.016 - 3643 MA 0.005 1.59 (1.15-2.21 (Netzer et al., 2008) 19p13.2- (Menon et al., 2010) p13.1 NOTCH3 4854 MA 0.001 - MA 0.003 - MO 0.006 4.5 (1.6-12.9) (Schwaag et al., 2006) 19q13.2 APOE 348 MA-MO <0.001 4.85 (1.92-12.72) (Gupta et al., 2009) 20q13.2 GNAS 2778 MA 0.001 1.79 (1.27-2.53) (Oterino et al., 2007) MA-MO 0.019 2.20 (1.14-4.40) 22q11.2 ADORA2A 135 MA 0.0091 - (Hohoff et al., 2007)
(Corominas et al., Xp11.23 MAOA 4128 MA 0.0061 1.41 (1.10-1.80) 2009b) Xq24 HTR2C 3358 MA 0.03 5.12 (1.13-23.17) (Kusumi et al., 2004) Xq25-q26 GRIA3 2892 MA 0.003 2.0 (1.4-2.8) (Formicola et al., 2010)
BM=Basilar Migraine; CI=Confidence Interval; CM=Complicated Migraine; FHM=Familial Hemiplegic Migraine; MA=Migraine with Aura; MO=Migraine without Aura; NMH=Non-Migrainous Headache; OR=Odds Ratio; SHM=Sporadic Hemiplegic Migraine; TC=Trait Component; TTH=Tension Type Headache ACE=Angiotensin I Converting Enzyme; ADORA2A=Adenosine A2a Receptor; APOE=Apolipoprotein E; CACNA1A=Voltage-Dependent, P/Q type, Alpha 1A Subunit, Calcium Channel; CNR1= Cannabinoid Receptor 1; DBH=Dopamine Beta-Hydroxylase; DDC=Dopa Decarboxylase; DRD2=Dopamine Receptor D2; DRD4=Dopamine Receptor D4; EDNRA=Endothelin Receptor Type A; ESR1=Estrogen Receptor 1; ESR2=Estrogen Receptor 2; GNAS=GNAS Complex Locus; GRIA1=Ionotropic AMPA Glutamate Receptor 1; GRIA3=Ionotrophic AMPA Glutamate Receptor 3; HLA- DRB1=Major Histocompatibility Complex, Class II, DR Beta 1; HTR1A=5-Hydroxytryptamine (Serotonin) Receptor 1A; HTR1B=5-Hydroxytryptamine (Serotonin) Receptor 1B; HTR2A=5-Hydroxytryptamine (Serotonin) Receptor 2A; HTR2B=5- Hydroxytryptamine (Serotonin) Receptor 2B; HTR2C=5-Hydroxytryptamine (Serotonin) Receptor 2C; INSR=Insulin Receptor; KCNK18=Potassium Channel, Subfamily K, Member 18; LTA=Lymphotoxin Alpha (TNF Superfamily, Member 1); MAOA=Monoamine Oxidase A; MMP3=Matrix Metallopeptidase 3 (Stromelysin 1, Progelatinase); MTDH=Metadherin; MTHFD1=Methylenetetrahydrofolate Dehydrogenase (NADP+ Dependent) 1, Methenyltetrahydrofolate Cyclohydrolase, Formyltetrahydrofolate Synthetase; MTHFR=Methylenetetrahydrofolate Reductase (NAD(P)H); NOS3=Nitric Oxide Synthase 3; NOTCH3=NOTCH3; PGR= Progesterone Receptor; SLC6A3=Solute Carrier Family 6 (Neurotransmitter Transporter, Dopamine), Member 3; SLC6A4=Solute Carrier Family 6 (Neurotransmitter Transporter, Serotonin), Member; STX1A=Syntaxin 1A; TNF=Tumor Necrosis Factor; TPH1=Tryptophan Hydroxylase 1; TYMS=Thymidylate Synthetase
Page 71 2.8.8 Migraine: GWAS
Recently, a genome wide assessment of migraine in six population-based cohorts
(N=10,980) provided marginal evidence of association to a SNP (rs9908234) located in a nerve growth factor (NGFR) gene on chromosome 17q21-22 (Ligthart et al.,
2011). This study also evaluated previously identified migraine candidate genes, migraine GWAS genes and the known FHM genes. Nominally significant SNPs were identified in the migraine genes LTA, ESR1, INSR, PGCP, SLC1A2 and MTHD and the FHM genes CACNA1A and ATP1A2. Although the study did not provide genome wide significant association of any SNP to migraine, it did provide nominal evidence of association with the MTDH gene. Interestingly, even though a large case-control cohort was used the study was underpowered.
In comparison, the first published migraine GWAS by Anttila et al. (2008) had a discovery cohort which included 2,731 cases and 10,747 population-matched controls from 3 European populations and a replication cohort of 3,202 cases and 40,062 controls from 4 European populations. Despite these cohort sizes, the authors concluded migraine sub-grouping was likely underpowered. Even with a total of
56,742 participants, the meta-analysis revealed the effect size was small (OR=1.18) for rs1835740 (P=1.69x10-11). The minor allele frequency in controls (MAF=0.206) indicated the variant is common.
Collectively, the Ligthart and Anttila studies provide a benchmark in migraine genetics for sample sizes required to reliably detect common risk variants at the population level. These studies highlight the need for large, well-ascertained cohorts and the importance of replication and functional analysis. Assessment of the correlation between a candidate intergenic SNP and patterns of gene expression was Page 72 required to identify a novel gene involved in glutamate homeostasis in the Anttila et al. (2010) study.
2.8.9 Migraine: A Familial Gene
Until recently, knowledge of genes causally involved in headache disorders was restricted to FHM. In 2010 a frame shift mutation in a potassium channel gene,
KCNK18 was identified in a multigenerational family with dominant, fully penetrant typical MA (Lafreniere et al., 2010). Study design was undertaken in multiple stages; genotyping 5,744 autosomal SNPs in family members, parametric linkage analysis of
SNPs on chromosome 10 to refine the critical region, sequencing of family members, replication in a migraine case-control cohort, and lastly functional characterisation.
The frame shift mutation F139WfsX24 resulted in complete cessation of outwardly rectifying whole-cell potassium currents. This study supports a model of CNS dysfunction in migraine pathophysiology.
Page 73 2.9 Cardiovascular Disease Risk
Cardiovascular disease (CVD) is a rising problem in Westernised nations and like migraine, is a complex multifactorial disorder. The term cardiovascular disease is broad and encompasses rheumatic fever/rheumatic heart disease, hypertensive diseases, ischemic (coronary) heart disease, pulmonary heart disease and diseases of pulmonary circulation, other forms of heart disease, cerebrovascular disease (stroke), atherosclerosis, and other diseases of arteries, arterioles, and capillaries, diseases of veins, lymphatics, and lymph nodes. In the USA alone, an estimated 81,100,000
American adults (more than 1 in 3) have 1 or more types of CVD (Lloyd-Jones et al.,
2010). Age-adjusted prevalence rates for different ethnic groups in the USA are detailed in Table 2.8. The burden CVD places on society is significant, with direct and indirect cost in the USA in 2010 estimated at $503.2 billion (Lloyd-Jones et al.,
2010).
Table 2.8 American Heart Association age adjusted prevalence rates from National
Health Interview Survey of adults aged 18 or over in 2008. Data is adapted from the
American Heart Association report (Lloyd-Jones et al., 2010).
Ethnic Group Heart Coronary Hyper- Stroke Disease Heart tension Disease Whites 12.1% 6.5% 23.3% 2.7% Black or African Americans 10.2% 5.6% 31.8% 3.6% Hispanics or Latinos 8.1% 5.7% 21.0% 2.6% Asians 5.2% 2.9% 21.0% 1.8% American Indians/ Alaska Natives 12.1% 6.6% 25.3% 3.9%
Page 74 As human populations transition from a traditional hunter-gatherer to a Western way of life, they experience major lifestyle changes. These changes typically include an increasingly sedentary routine, high calorie intake (particularly increased salt and saturated fat consumption), as well as exposure to substances such as alcohol and cigarettes. These changes can drastically alter the health demographics of the population, leading to increases in the incidence of obesity, hypertension, diabetes mellitus, and CVD. The impact of recent Westernisation on CVD incidence and prevalence is exemplified in isolated populations such as Kosrae (Shmulewitz et al.,
2001) and the American Indians of Central Arizona, South-western Oklahoma and
North and South Dakota (Howard et al., 1999) whose traditional patterns of activity have shifted dramatically changed with recent Westernisation. The onset of
Westernisation has led to a dramatic increase in the incidence and prevalence of obesity, type II diabetes mellitus, hypertension and cardiovascular disease in both of these populations.
Like many worldwide isolates, the inhabitants of Pitcairn and later Norfolk Island sustained a period of isolation after founding. This south pacific population has a sedentary lifestyle and until recently has survived primarily on the success of the local agricultural and fishing industries. Initial analysis of several important environmental and lifestyle factors known to confer CVD risk in the Norfolk cohort revealed similar estimates to the Australian mainland population (Table 2.9) (Bellis et al., 2005).
Univariate genome wide linkage analysis confirmed the positive family history of heart disease by assessing various biochemical and anthropometric risk predictors and defined several susceptibility loci in the Norfolk pedigree. The present findings in the
Norfolk pedigree await further evaluation by multivariate analysis methods to discern
Page 75 the pattern of CVD risk trait clustering and to identify shared genomic susceptibility regions underlying risk (Table 2.10).
Table 2.9. Comparison of the prevalence of CVD risk factors in the Norfolk population (N=600) and mainland Australia (Bellis et al., 2005).
Risk Factor Factors increasing Proportion of Proportion of CVD risk Norfolk with Australia with Elevated Risk elevated risk BMI >25 57% 60% High Blood Pressure Diagnosed hypertension 17% 30% Smoker Current Smoker 22% 24% Sedentary Exercise < once/week 20% 54% Heart Disease Existing or experienced 7% 4% Genetic Predisposition Family history of CVD 61% 52% TC/ HDLc Ratio >4 49% 50%
Table 2.10. Preliminary univariate genome wide linkage scan results for CVD risk traits in the Norfolk pedigree (Bellis et al., 2008).
Risk Trait h2* Loci Marker LOD Score BMI 0.295 TC 0.405 2q37.1 D2S206 1.80 TG 0.236 HDLc 0.449 18q22.3 D18S1161 1.83 20p12.3 D20S117 1.69 LDLc 0.419 SBP 0.314 1p36.22 D1S2667 2.01 8p12 D8S505 1.82 DBP 0.169^ *P<0.05, ^P>0.05
CVD risk is determined by a complex interplay of genetic and non-genetic factors, many of which represent modifiable lifestyle choices (Table 2.11). Major risk factors include a positive family history, increasing age, tobacco use, physical inactivity, unhealthy diet, obesity, dyslipidemia, hypertension, diabetes mellitus and metabolic syndrome (syndrome X). As risk factors aggregate in an individual, the risk of CVD onset and progression dramatically increases. Studies such as the Framingham Heart
Page 76 Study have used this knowledge to develop a range of predictive risk scores, including a 30-year CVD risk calculator that assesses the following predictors; gender, age,
SBP, antihypertensive treatment, smoking, diabetes mellitus, TC, HDLc and BMI
(Pencina et al., 2009). Additional factors such as elevated TG are recognised as an independent risk factor for CVD, but is also correlated with other lipid risk factors
(high LDLc and low HDLc), non-lipid risk factors (i.e. hypertension) and emerging risk factors (NCEP and ATP-III, 2002).
Table 2.11. Risk factors for risk of CVD development and progression (NCEP and
ATP-III, 2002).
Modifiable Risk Factors Non-Modifiable Risk Factors Hypertension Age Cigarette Smoking Male Gender Thrombogenic/Hemostatic State Family History of Premature CHD Diabetes Obesity Physical Inactivity Atherogenic Diet
2.9.1 The Correlations Between CVD Risk Traits
Metabolic syndrome exemplifies the degree of correlation between individual CVD risk factors. This syndrome is characterised by the co-occurrence of visceral obesity, dyslipidemia, hyperglycaemia, and hypertension in an individual (Alberti et al.,
2005). The high degree of correlation between these phenotypes and their component traits suggests a portion of the genetic component underlying CVD risk factors may be due to shared genetic factors. Genomic research has attempted to define the shared genetic component by extending traditional univaritate analysis methods to multivariate strategies for metabolic syndrome (Austin et al., 2004; Cai et al., 2004;
Shmulewitz et al., 2001; Tang et al., 2003). Other studies have applied this method to
Page 77 dissect the relationships between multiple phenotypes underlying risk of obesity (He et al., 2008), CVD (Goodman et al., 2005) and insulin resistance (Arya et al., 2002).
The results of these investigations are summarised in Table 2.12. These studies demonstrate that multiple, highly related phenotypic variables can be reduced to anywhere between 2 and 4 independent risk factors that can explain up to 97% of the total phenotypic variance of the original variables. These independent risk factors are heritable and can be assessed in terms of genome wide linkage data to identify novel loci that simultaneously influence multiple traits.
The 2000 Norfolk Island Health Study ascertained a wide range of CVD risk predictors that can be broadly classified as markers for hypertension, obesity and dislipidemia. These traits will be defined in the following section.
2.9.2 Hypertension
Hypertension is a chronic condition in which the blood pressure remains elevated. As blood pressure in an individual increases with age and varies under emotional or other stresses, there is no universally accepted value for raised blood pressure. Normal adult diastolic blood pressure is in the range of 70-90mmHg (Black et al., 2003). Normal adult systolic blood pressure is in the range of 110-140mmHg (Black et al., 2003). An individual is considered hypertensive if they sustain diastolic pressure greater than
90mmHg or systolic pressure in excess of 140mmHg (WHO-ISH, 1999).
Page 78 Table 2.12. Summary of multivariate analysis studies of CVD risk, obesity, insulin resistance and metabolic syndrome.
Study Original Extracted Interpretation Proportion of h2 (SE) Loci Reference Phenotype Traits Factors/ Total Variance Components Explained (%) CVD Risk 9 F1 Adiposity 27.38 NA NA (Goodman et al., 2005) F2 Cholesterol 22.28 NA NA F3 Carbohydrate-Metabolic 17.31 NA NA Insulin 8 F1 Adiposity-Insulin 35.00 0.51 (0.13) 6q24.1-24.2, 6q25.1-26 (Arya et al., 2002) Resistance F2 Blood Pressure 21.00 0.58 (0.16) NA Syndrome F3 Lipid Profile 12.00 0.49 (0.13) 7q21.3-7q31.1 Obesity 4 PC1 BMI, Fat Mass, PFM 61.84 0.44 (0.03) 20p12 (He et al., 2008) PC2 Lean Mass, PFM 35.03 0.61 (0.03) 5q35.2, 7p22.3 Metabolic 11 F1 Obesity 23.60 NA NA (Shmulewitz et al., 2001) Syndrome F2 Hyperlipidemia 18.90 NA NA F3 Hypertension 18.50 NA NA F4 Increased HDLc, Low TG 12.10 NA NA Metabolic 13 F1* MMS Factor 81.08 0.41 (0.03) 2q36 (Tang et al., 2003) Syndrome Metabolic 9 F1 Lipids 24.30 0.52 (0.13) NA (Austin et al., 2004) Syndrome F2 Body Fat, Insulin, Glucose, CRP 21.60 0.27 (0.11) NA F3 Blood Pressure 18.70 0.24 (0.11) NA Metabolic 14 F1 Body Size-Adiposity 41.00 0.49 (0.01) 1p36 (Cai et al., 2004) Syndrome F2 Insulin Sensitivity 12.00 0.42 (0.01) 3q12 F3 Blood Pressure 11.00 0.26 (0.08) 6q14 F4 Lipid Levels 10.00 0.64 (0.09) 4p15
BMI=Body Mass Index, CRP=C-Reactive Protein, HDLc=High Density Lipoprotein Cholesterol, PC=Principal Component, PFM=Percentage of Fat Mass, NA=Not Available, MMS=Multiple Metabolic Syndrome, TG=Total Triglycerides, SE=Standard Error, *Only first extracted factor analysed
Page 79 2.9.3 Obesity
Obesity is defined as the abnormal or excessive accumulation of adipose tissue, to the point of impairing an individual’s health (WHO, 1996). One measure of obesity is body mass index (BMI), an index of weight-for-height used to classify underweight, overweight or obesity in adults (Table 2.13) (WHO, 2000). It is defined as the weight in kilograms divided by the square height in meters (kg/m2). This criterion does not account for the variation according to body build and proportion, as such other measures such as waist circumference and waist-to-hip ratio may also be used as obesity indicators.
Table 2.13. Classification of adults according to BMI (WHO, 2000).
Classification BMI Risk of Comorbidities Underweight <18.50 Low* Normal Range 18.50-24.99 Average Overweight >25.00 Preobese 25.00-29.99 Increased Obese Class I 30.00-34.99 Moderate Obese Class II 35.00-39.99 Severe Obese Class III >40.00 Very Severe *Risk of other clinical problems increased
Abdominal fat accumulation is also a useful measure for identifying individuals at risk of obesity-related illness. A high waist-to-hip ratio (WHR; waist circumference/hip circumference) greater than 1.00 in males and 0.85 in females indicates abdominal obesity (WHO, 2000). Individually, waist circumference is also a good measure of obesity risk. WHO guidelines associate an increased risk of obesity in Caucasian males and females with waist circumferences equal or greater than 94cm and 80cm, respectively (WHO, 2000). Risk is substantially increased in Caucasian males and females with a waist circumference equal or greater than 102cm and 88cm, respectively (WHO, 2000). Page 80 As of October 2005, single gene mutations in 11 different genes and 50 loci were known to influence Mendelian obesity and related Mendelian syndromes in humans
(Rankinen et al., 2006). For non-Mendelian obesity phenotypes a total of 52 QTLs and positive associations in 127 candidate genes were reported (Rankinen et al.,
2006). In the mouse, 244 genes resulted in phenotypes affecting body weight and adiposity when mutated or expressed as transgenes and a total of 408 QTLs were identified in animal models (Rankinen et al., 2006).
2.9.4 Dyslipidemia
Dyslipidemia is a disorder of lipoprotein metabolism. Artherogenic dyslipidemia occurs in individuals with premature chronic heart disease (CHD) and is typically characterised by general obesity, abdominal obesity, insulin resistance, and physical inactivity. Individuals also experience altered serum cholesterol levels notably an elevation in total triglycerides and small LDLc particles and reduction in HDLc
(NCEP and ATP-III, 2002). Consequently, total cholesterol, total triglycerides, LDLc and HDLc levels are all used as biochemical markers of cardiovascular and circulatory system health (Table 2.14).
Table 2.14. Adult Treatment Panel III (ATP III) classification of serum cholesterol levels (NCEP and ATP-III, 2002).
Levels TC LDLc TG HDLc (mg/dL) (mg/dL) (mg/dL) (mg/dL) Optimal <200 <100 <150 <40 Near /Above Optimal 100-129 Borderline High 200-239 130-159 150-199 High >240 160-189 200-499 >60 Very High >190 >500 TC=Total Cholesterol, LDLc=Low Density Lipoprotein Cholesterol, TG=Total Triglycerides, HDLc=High Density Lipoprotein Cholesterol
Page 81 LDLc comprises 60-70% of total serum cholesterol and contains a single apolioprotein (apo B-100), which is a major atherogenic lipoprotein and a primary target for lipid lowering therapy (NCEP and ATP-III, 2002). Elevated serum LDLc represents a major risk factor for CHD, artherosclerosis, and myocardial ischemia
(NCEP and ATP-III, 2002). Comparatively, HDLc comprises 20-30% of total serum cholesterol and contains two major apolioproteins, apo A-I and apo A-II, whose levels are inversely correlated with CVD risk (NCEP and ATP-III, 2002). Total serum triglyceride levels are correlated with TC, HDLc and LDLc level and is an important lipid risk factor for CHD and atherosclerosis (NCEP and ATP-III, 2002).
2.10 Summary
The research described in this thesis centres on two complex multifactorial disorders, migraine and CVD risk. Although both these phenotypes are heritable in nature, disease onset and predisposition is also determined by non-genetic factors. Some causative genes and risk variants are known for both migraine and CVD risk, but they only partially account for the entire genetic component. Previous assessment of cardiovascular phenotypes in the Norfolk pedigree confirmed and replicated known susceptibility loci while also identifying several novel loci (Bellis et al., 2008). The success of these preliminary genome wide screens o suggests that the analysis of alternate phenotypes such as migraine may prove promising using the Norfolk pedigree. Furthermore, considering the tendency of CVD risk traits to cluster together, initial findings may benefit from further evaluation by multivariate analysis strategies.
The following chapter will address the ascertainment of the Norfolk cohort and the methodologies employed to screen genomic markers in terms of migraine and multiple correlated CVD risk traits.
Page 82 Chapter 3: Materials and Methods
3.1 Overview
The research described in this thesis utilises both phenotypic and genetic data (DNA) collected during the Norfolk Island Health Study, undertaken in the year 2000 by the
Genomics Research Centre, Griffith University Gold Coast, Australia. The population residing on Norfolk Island was purposefully selected for this study because of its unique background and population structure. This included the qualities of geographical isolation, population admixture, a limited number of founding individuals, strict immigration laws, the presence of an extended genealogy and the availability of detailed genealogical records. The presence of all of these characteristics in one population is unusual, even for an isolate. This chapter describes the general materials and methods undertaken to investigate the genetic basis of complex disease in subjects from the Norfolk isolate. The following results chapters present more thorough detail of the application of specific techniques.
The primary objective of this study was to characterise two complex genetic disorders, migraine and CVD risk in an extended pedigree from the Norfolk isolate.
This involved demographic characterisation, heritability estimation and lastly, gene- mapping strategies to potentially identify genomic regions harbouring susceptibility genes. To achieve these goals, DNA, phenotypic data and genealogical information for each participant was required. Clinical diagnostic criterion and empirically derived statistical methods were employed to define phenotypes. Lastly, a genome wide scan of STR markers and a genome wide scan of single nucleotide polymorphisms (SNPs)
Page 83 were undertaken and analysed by linkage and association methods especially suited to large, complex genealogies, respectively.
3.2 Materials
Autoclaved or filter sterilised deionised water from a Milli-Q Water Purification
System was utilised in all aqueous solutions, dilutions and reactions. DNA was extracted from venous blood using a variety of reagents. All reagents were of analytical grade and were supplied by Sigma-Aldrich (St Louis, CA), Astral Scientific
(Gymea, Australia), and Gibco (Rockville, MD) unless otherwise stated. Proteinase K was obtained from Astral Scientific (Gymea, Australia). Quantum Scientific Pty Ltd
(Milton, QLD) supplied all disposable laboratory equipment. Chemicals required to make the buffers required for DNA extraction were obtained from Sigma Chemical
Company (St. Louis, MO), Astral Scientific (Gymea, Australia), and Gibco
(Rockville, MD). A NanoDrop ND-1000 Spectrophotometer (NanoDrop
Technologies, Inc.) was used to quantitate all genomic DNA and oligonucleotides.
Statistical analyses conducted at Griffith University were performed on a Sun Solaris
High Performance Computing (HPC) cluster. Griffith's Sun Solaris HPC cluster consisted of 1 head node and 8 computational nodes. Each computational node was a
SunFire v880 with 8x Ultra SPARC-III 900MHz CPUs, 8GB of memory and 144GB of internal disk.
3.3 Sample Ascertainment
Subjects were the participants of the Norfolk Island Health Study, a family-based study of a genetically isolated island community in the South Pacific Ocean. Subjects were ascertained based on permanent resident status (not selected on phenotypes of Page 84 interest). As a large portion of the permanent residents are descended from Bounty
Mutineer and Tahitian (Polynesian) founders, this ensured sampling of individuals from the same genealogical background. Therefore, the majority of participants were members of a single, large, extended pedigree. Subjects were recruited through a range of local media announcements including radio and newspaper.
Phenotypic data and biological specimens were collected from 600 individuals. All biological samples were from venous blood and were not taken after fasting.
Phenotypic data was obtained via a comprehensive medical questionnaire that included family history. Subjects also provided information on their genealogical history. Blood specimens and questionnaires for each subject were labelled with a unique barcode. Blood samples and questionnaires were shipped to Australia.
Queensland Medical Laboratories (QML), Brisbane, Australia, conducted a full blood biochemistry analysis. Remaining blood was stored at -80°C until DNA extraction at the Genomics Research Centre, Griffith University (Gold Coast), Australia. All phenotypic data, including blood biochemistry was entered into a Microsoft ExcelTM spreadsheet and ordered by barcode.
3.4 Demographics and Phenotyping
In the year 2000, approximately 1200 permanent residents resided on Norfolk Island.
Of these individuals, approximately 900 were aged 18 years old or older at the time of recruitment. The Norfolk Island Health Study Clinic successfully recruited 600 (261 males, 339 females) permanent residents aged 18 years or older (approximately two- thirds of the permanent adult population).
Page 85 Initial screening of important CVD risk factors indicated that the Norfolk Islanders were a high risk population for CVD development (Bellis et al., 2005). The present study aimed to further dissect the genetic and environmental component underlying
CVD risk in Norfolk Islanders by assessing traits that were markers of obesity, hypertension, and dislipidemia. Obesity markers were select arthropometric measures that included BMI (calculated as kg/m2), hip circumference (cm), waist circumference
(cm), percentage body fat (%) and weight (kg). A trained member of the Norfolk
Island Health Study Clinic recorded all measurements. Systolic and diastolic blood pressure measurements, markers of hypertension were recorded using an automatic electronic blood pressure monitor operated by a trained member of the Norfolk Island
Health Study Clinic. Lastly, a standard blood screen was performed by QML,
Brisbane and measures of serum cholesterol (dislipidemia indicators) were obtained from this screen. These included HDLc (mmol/L), LDLc (mmol/L), TC (mmol/L), and TG (mmol/L).
In addition to CVD risk, this study aimed to characterise migraine in the Norfolk
Island cohort. Migraine phenotype information was obtained via interviews using a comprehensive migraine questionnaire, which was followed up by a qualified migraine diagnostician. Information obtained related to age of onset, symptoms, frequency, severity, treatment and treatment response. Diagnosis was established in accordance with the current ICHD-II diagnostic guidelines.
Page 86 3.5 Standard Protocol Approvals
Prior to the commencement of this study, the Griffith University Human Ethics
Committee approved all research protocols. All patients gave informed written consent prior to participation.
3.6 DNA Extraction
While the majority of samples were collected and prepared in 2000 and 2001, the continuation of the Norfolk Island Health Study has required DNA stocks to be replenished from original blood samples stored at the Genomics Research Centre during the course of this experimentation. DNA was extracted from whole venous blood using a modified version of a standard salting-out procedure (Miller et al.,
1988). This method involved DNA isolation followed by a precipitation reaction.
3.6.1 DNA Isolation
The DNA isolation stage required the removal of red blood cells, followed by overnight incubation to ensure complete lymphocyte lysis and the release of DNA into solution. Blood samples were removed from -80°C storage, completely thawed and transferred to a 50mL centrifuge tube. The original storage vile was rinsed with sodium-potassium-magnesium (NKM) buffer (0.14M NaCl, 30mM KCl, 3mM
MgCl2) to ensure the entire blood specimen was removed and transferred to the centrifuge tube. Each sample was brought to a final volume of 25mL with NKM buffer and shaken vigorously. Samples were centrifuged at 4°C for 25min at 4,800 revolutions per minute (rpm), equivalent to 4,894 relative centrifugal force (rcf) using a Sigma 4K15 centrifuge. The supernatant was discarded and each sample was bought
Page 87 to a final volume of 25mL with RSB buffer and centrifuged at 4°C for 15mins at
4000rpm (equivalent to 3,399rcf using a Sigma 4K15 centrifuge). The supernatant was discarded. The pellet was resuspended in 1mL of RSB buffer, prior to the addition of 4mL of lympholysis solution and 250μL of Proteinase K. Samples were placed in a 37°C shaking water bath overnight.
Once complete lymphocyte lysis was ensured, a salting out procedure was employed to precipitate DNA from the solution. Samples were removed from the water bath.
2mL of saturated sodium chloride (NaCl) solution was added to each specimen.
Samples were mixed for 15s by inversion, prior to centrifugation at 4°C for 15min at
2,500rpm (equivalent to 1,328rcf using a Sigma 4K15 centrifuge). This step ensures the removal of proteins from the lymphocytes. The DNA-containing supernatant was collected and transferred to a 15mL centrifuge tube. This centrifugation step was repeated to ensure maximum removal of proteins from solution. The supernatant was collected and transferred to a new 50mL centrifuge tube.
The volume of each sample was approximated and 2 volumes of room temperature absolute ethanol were added to each sample. Tubes were gently swirled to precipitate the DNA strands. DNA was removed using an inoculation loop and transferred into a new tube containing 2mL of Tris-EDTA (TE) (10mM Tris-Cl-EDTA) buffer at pH 8.
DNA was dissolved in TE by incubation at 37°C for 2h, mixing at regular intervals.
DNA can be stored for infinite amounts of time at 4°C in TE buffer.
3.6.2 Ethanol Precipitation of DNA Stocks
Suspension of DNA in TE buffer permits long term storage of DNA stocks.
Unfortunately, the presence of ethylenediaminetetraacetic acid (EDTA) is known to
Page 88 inhibit PCR reactions. Therefore, prior to experimentation, especially those involving
PCR the dissolved DNA requires precipitation with ethanol to remove the EDTA and/or any residual tri-phosphates and suspension in sterile water.
To perform an ethanol precipitation a 100μL aliquot of DNA suspended in TE buffer is added to a 1.5mL centrifuge tube containing 200μL of chilled absolute ethanol and
10μL of sodium acetate solution (pH 5.5). The solution is mixed, frozen with liquid nitrogen and centrifuged at 4°C for 15 minutes at 10,000rpm (equivalent to 9,660rcf using a Scientifix TechComp CT15RT centrifuge). The supernatant was discarded and the DNA pellet dried using a savant speed vacuum. The pellet was resuspended in
100μL of sterile Milli-Q water and incubated in a 37°C oven for 24 hours.
Double-stranded DNA was quantitated using a NanoDrop ND-1000
Spectrophotometer (NanoDrop Technologies, Inc.), which uses fiber optics and surface tension to accurately measure DNA concentration and quality (purity). Only
0.5 µl to 2.0 µl of sample, without the need for containment devices such as cuvettes or capillaries was required for quantitation.
All DNA was diluted to a working concentration of μg/mL. The purity of the DNA was measured by the ratio of absorbance at 260nm and 280nm. Pure double-stranded
DNA has a purity of 1.8. Deviation from this value indicates contamination by proteins, phenols or other nucleic acids.
3.7 Genome Wide Linkage Scan
This study undertook a genome wide scan of STR markers to identify genomic regions involved in the predisposition of 2 common disorders; migraine and CVD risk
Page 89 in an extended pedigree from Norfolk Island. DNA from all subjects (N=600) was genotyped at the Australian Genome Research Facility (AGRF), Melbourne, Australia using the Applied Biosystems PRISM Human Linkage Mapping Set version 2.5
(medium density) [MD10-LMSV2.5]. The markers included in this mapping set were selected according to chromosomal location and heterozygosity from the Généthon human linkage map (Dib et al., 1996; Gyapay et al., 1994; Weissenbach et al., 1992).
In total, the linkage map comprised of 400 highly polymorphic STR markers with an expected average heterozygosity of 0.79 and an average distance between adjacent markers of 9.2 centimorgans (cM) (maximum gap 26.11cM).
Markers were individually amplified by PCR. All primers were labelled with the fluorescent dyes FAM, HEX and NED (Applied Biosystems LMSV2.5). PCR reactions were performed in 386-well plates in a final volume of 6uL. Each reaction contained 30ng of genomic DNA, 1 x PCR buffer, 0.5 pmol of each primer, 0.5 units
(U) of AmpliTaq Gold, 2.5mM MgCl2, and 0.25mM of dATP, dGTP, dCTP and dTTP. PCR amplification was performed in a PTC-225 DNA Engine Tetra (MJ
Research inc, Waltham, MA, USA) using the following cycling parameters: initial denaturation at 94°C for 10min; 30 cycles of denaturation, annealing and extension at
94°C for 15s, 55°C for 15s and 72°C for 30s, respectively; and a final extension at
72°C for 5min.
After PCR, amplicons were denatured using a formamide loading buffer. Where possible products from the same DNA sample were multiplexed (10-20 products per lane) with an internal size standard (GeneScanTM-500 LIZ® size standard). For every run, size standards were evaluated using a standard curve generated using the
Local Southern Method (Genescan 3.1 software). Samples were resolved by capillary
Page 90 electrophoresis at 80-100V on a 6% polyacridamide gel using an Applied Biosystems
3730 Genetic Analyser. Microsatellite markers were genotyped in 28 multiplex panels that each included between 10 and 20 pooled products. Quality control within and between plates was ensured by the inclusion of internal positive controls. Raw electrophoresis data was transferred to an off-line computer. Tracking for each sample lane was assigned using Genescan version 3.1 (Applied Biosystems) software. Each batch (gel) was manually screened prior to analysis. Minor variations were corrected using a standard curve generated using the Local Southern Method (Genescan version
3.1 software) and size standard patterns for each sample lane of any one gel were manually corrected.
Raw electrophoretogram data was imported into Genotyper version 2.1 software
(Applied Biosystems) for interpretation and genotype assignment. For each STR marker, peaks in the electrophoretogram were sized against the standard curve and designated as alleles. All peaks were greater than 10 fluorescent units in height.
Stutter peaks, which can arise as a result of phenomena such as Taq polymerase slippage and low signals in relation to main peaks, were filtered out. Data was initially screened for typing errors using PedManager version 0.9 (http://www- genome.wi.mit.edu/).
3.8 Genome Wide Association Scan
This study also undertook a genome wide scan of autosomal SNPs markers to identify genomic regions involved in the predisposition of migraine in the extended Norfolk
Island. All genotyping for this genome wide association study (GWAS) was undertaken at the Department of Genetics, Texas Biomedical Research Institute, San
Page 91 Antonio, Texas 78245-0549, USA. DNA samples were genotyped according to the manufacturer’s instructions on Illumina Infinium High Density (HD) Human610-
Quad DNA analysis BeadChip version 1. A total of 620,901 genome wide markers were genotyped in a sub-sample of 285 related individuals (135 males; 150 females).
Markers had a median spacing of 2.7kb (mean = 4.7kb) throughout the genome.
Each Human610-Quad DNA analysis BeadChip employed a four-sample format requiring 200ng of DNA per sample. Samples were scanned on the Illumina
BeadArray 500GX Reader. The infinium HD assay protocol utilises single-tube sample preparation and whole-genome amplification without PCR or ligation steps.
The whole-genome genotyping method using the single-base extension assay has 4 steps: a single-tube whole-genome amplification, an array-based hybridization capture, an ‘on array’ enzymatic allele-specific primer extension and lastly, an amplified-signal detection (Gunderson et al., 2005; Steemers et al., 2006). The unlabelled DNA is fragmented and hybridised to 50-mer oligo probes on the
BeadChip. Second is the allele detection, which involves an enzymatic single base extension with labelled nucleotide. The samples are then washed, stained and scanned. Using a 4-sample format, each sample can be scanned in 9 minutes.
Raw data was obtained using Illumina BeadScan image data acquisition software
(version 2.3.0.13). Preliminary analysis of raw data was undertaken in Illumina
BeadStudio software (version 1.5.0.34) with the recommended parameters for the
Infinium assay and using genotype cluster files provided by Illumina. Individuals with a call rate below 95% and SNPs with a call rate below 99%, deviating from Hardy-
-7 Weinberg equilibrium (PHWE<1x10 ) or with a minor allele frequency of less than
1% were excluded from analysis.
Page 92 Genotypic data was analysed for discrepancies, including Mendelian inheritance violations using the PEDSYS program INFER (Dyke, 1996) and Simwalk2 (Sobel et al., 2002). The Pedigree RElationship Statistical Test (PREST) was used to verify the pedigree structure and detect relationship misspecification (McPeek and Sun, 2000).
Discrepant genotypes were blanked prior to analysis. SNPs were annotated using information available from the National Centre for Biotechnology Information
(NCBI) Build 36.3.
3.9 The Norfolk Genealogy
The history of the Norfolk Islanders and their forefathers the Pitcairn Islanders, who originated from the mutiny on the Bounty, is extremely well documented.
Genealogists from Norfolk Island have maintained exhaustive records of all the individuals who have contributed to the present population. This information has been entered into the genealogy program Brother’s Keeper 6.0 (Rockford, MI USA). At the time of this study the complete Norfolk genealogy encompassed 6,379 individuals and is highly complex, containing multiple inbreeding loops particularly in the early generations (Bellis et al., 2005). This initial version of the pedigree spanned 11 generations and comprised 2,185 families. 62.8% (N=377) of the subjects were revealed to possess ancestral lineages either directly or via marriage to the Norfolk genealogy and fall within the lower 5 generations of the pedigree. The remaining
37.2% (N=223) of subjects likely represent new immigrants settling Norfolk Island as they hold permanent resident status. These individuals did not possess ancestral heritage to the founders or links via marriage at the time of sampling. Given their permanent resident status they may be considered potential ‘new founders’ for future investigations, particularly longitudinal studies.
Page 93 The complete 6,379 member pedigree compiled from genealogical and participant questionnaires was validated using the Pedigree RElationship Statistical Test
(PREST) (McPeek and Sun, 2000) prior to all genetic analyses (Bellis et al., 2008).
Using maker data, the program PREST detects mis-specified relationships in a pedigree by determining whether the observed pattern of identity-by-descent (IBD) allele sharing fits the expected pattern of IBD allele sharing given the degree of relationship between relative pairs. Pedigree errors such as mis-specified paternity were resolved by reconfiguring the pedigree structure. Re-assignments were confirmed by re-running the pedigree through PREST. This produced a final inferred pedigree structure of 6,537 (Bellis et al., 2008). The number of pedigree members is inflated in comparison to the original structure. The additional individuals were imputed into the pedigree in cases where only one parent was specified to ensure the pedigree conformed to standard linkage format (Lathrop et al., 1984).
The complete pedigree structure is highly complex and computationally demanding for IBD and quantitative trait locus (QTL) estimation, therefore pedigree trimming was required. The program PEDTRIM, included in the PEDSYS database suite
(Dyke, 1996) was employed to partition the genealogy and remove uninformative individuals. This program ‘trims’ individuals if their phenotypic or genotypic data is missing or incomplete, or if they have no offspring or no parents and a single offspring (Dyke, 1996). PEDTRIM produced a pedigree structure of 1,078 individuals, which included 377 ascertained subjects and one inbreeding loop. The
1,078-member pedigree structure was employed in all linkage investigations described in this thesis.
Page 94 The number and type of pair wise relationships in the 1,078 member pedigree were calculated using SOLAR (Sequential Oligogenic Linkage Analysis Routines) version
4.0.1 and are detailed in Table 3.1 (Bellis et al., 2007). Using the program SOLAR version 4.0.1, the mean inbreeding coefficient was determined to be 0.0044, with a maximum observed inbreeding coefficient of 0.0684, reflecting the presence of consanguineous unions in the pedigree (Bellis et al., 2007). Interestingly, the level of kinship (the pair wise coefficient of relationship) revealed the majority of sampled individuals were related by less than third-degree on average (=0.125) (Bellis et al.,
2007). This is likely a reflection of the number of newly married-in individuals
(founders) (N=124) amongst the ascertained subjects and also the sampling of individuals in the genealogy.
Table 3.1. Number of relative pairs within the Norfolk Island linkage pedigree (Bellis et al., 2008).
Number of relative pairs Pair wise Relationship(s) Trimmed Pedigree (N) Ascertained Subjects (N) Pedigree members 1,078 377 Founders 587 124 Non-founders 491 253 Relative pairs Parent-offspring 982 142 Siblings 270 80 Grandparent-grandchild 908 19 Avuncular 612 120 Half Siblings 50 20 3rd degree 1,677 283 4th degree 1,317 315 5th degree 952 404 6th degree 648 372 7th degree 286 239 8th degree 50 50 Unrelated 225,349 27,670
Page 95 3.10 Pedigree Cleaning
The confirmation of relationships in a pedigree and the elimination of typing errors is vital step to obtain accurate estimates of association or linkage. Although the AGRF employs pedigree-checking procedures to detect typing errors, the Norfolk pedigree required more comprehensive data cleaning strategies due to its size and complexity and as the structure had not been validated at the time of the GWS. While pedigree verification and trimming was specific to the GWS, the methods used to eliminate typing errors and construct of IBD matrices are applicable to both genome wide and fine mapping of STR markers.
Typing errors were resolved using the program Simwalk2 (Sobel et al., 2002) and the
PEDSYS (Dyke, 1996) program INFER. The program PREST (McPeek and Sun,
2000) was used to detect mis-specified relationships and confirm relationship re- assignments according to the pattern of IBD allele sharing in the complete pedigree structure. The program Loki (Heath, 1997) was used to compute IBD and multipoint identity-by-descent (MIBD) matrices for each relative pair, which are required for two-point and multipoint linkage analysis, respectively. The programs PREST,
Simwalk2 and Loki require the order and position of each marker along a chromosome to be specified. Positional information for all autosomal markers was obtained from the Marshfield Centre for Medical Genetics
(http://research.marshfieldclinic.org/genetics). All analyses utilised sex-averaged marker maps in cMs.
The validated 6,537-member pedigree was computationally demanding due to size, pedigree sampling and the presence of multiple inbreeding loops, hence pedigree
Page 96 partitioning was required. The program PEDTRIM was used to produce a trimmed
1,078-member pedigree, which included 377 ascertained subjects and one inbreeding loop. Calculations of the inherent of this trimmed pedigree indicate that a QTL accounting for approximately 30% of the variation would be detected with 50% power (Bellis et al., 2008).
3.11 Data Screening
To provide an overview of the population demographics, descriptive statistics for the entire sample (N=600) and pedigree subset (N=377) were generated using SPSS
(versions 14.0 and 17.0). For continuous variables mean values were calculated and the results presented followed by standard deviation and number of individuals included in the analysis. Continuous measurements were screened for the presence of outliers and conformance to normality. Measurements greater than or equal to 4 standard deviations from the mean were considered extreme outliers and were either blanked or in the case of data entry errors, corrected prior to analysis. Where possible, traits whose distribution deviated from normality were transformed prior to analysis.
Parametric and non-parametric correlations, chi-square analysis, student’s t-tests, logistic regression and one-way analysis of variance (ANOVA) were all undertaken in
SPSS (versions 14.0 and 17.0).
3.12 Principal Component Analysis
Principal component analysis (PCA) was used to examine the relationship between
CVD risk phenotypes in the Norfolk pedigree. PCA is a multivariate data reduction technique for variables measured on an interval or continuous scale. This technique is used to reduce the dimensionality of multiple inter-correlated variables, while Page 97 maximising the amount of variation in the original set of variables (Jolliffe, 2002).
PCA transforms the original variables into a new set of uncorrelated (independent) variables referred to as principal components (PCs). For the PCA to group variables, they must be correlated. If all original variables are independent the derived PCs will be the same as the original variables.
The original variables are grouped according to the PC they display the strongest correlation. Component loading value greater than or equal to 0.4 indicate meaningful correlations between the PC and the variable. The derived PCs are ordered so the first few retain the maximum amount of variation in all the original variables. Successive components explained progressively smaller portions of the total variance. The variances of the PCs are called Eigen values. As one analysis may produce many PCs, the user must determine which PCs should be retained. There are a number of methods to determine how many PCs should be retained to maximize the overall variation in the dataset (Jolliffe, 2002). One method commonly employed is Kaiser’s rule (Kaiser 1960, 1960). Using this rule, PCs with Eigen values greater or equal to 1 are retained.
A valid PCA has 4 statistical assumptions: random, independent sampling, at least interval level measurements, linearity, and normality. PCA is well suited for analysing quantitative cardiovascular risk traits, such as those available in the Norfolk genealogy. As using the Norfolk pedigree violates the assumption of random, independent sampling, the robustness of the PCA must be assessed. One method of addressing this statistical violation is to randomly sample a subset (i.e. 20%) of the entire population, perform PCA and compare the columns (vectors) of the component matrix of the random sample with the columns of the component matrix for the entire
Page 98 sample. The columns of the matrix represent each derived principal component satisfying the Eigen value ≥ 1 criterion. The degree of similarity can be directly assessed by the congruence coefficient (Lorenzo-Seva and ten Berge, 2006). If a consistently high degree of similarity is observed between the random samples and entire sample, the PCA can be considered robust to the statistical violation. This method has been previously employed in the assessment of CVD phenotypic data using PCA in the presence of population substructure (Chen et al., 1999).
The congruence coefficient is defined as the cosine of the angle between 2 vectors, give by (Korth and Tucker, 1975);
Where x and y represent the 2 vectors, which are columns of a pattern or structure matrix. In the case of PCA the vectors are the derived principal components. The terms xi and yi are the component loadings of variable i on PCs x and y, respectively, i
= 1, . . ., n. The congruence ranges from 0 to 1, with 0 indicating no similarity and 1 indicating perfect agreement. Interpretation of the congruence varies substantially
(Lorenzo-Seva and ten Berge, 2006).
3.13 Quantitative Genetic Analysis
Quantitative genetics is concerned with complex (multifactorial) traits that display continuous variation. The variation in the trait is the result of multiple genetic and non-genetic (environmental) factors and their interactions. Although quantitative
Page 99 genetics is primarily concerned with continuous or metric traits (also known as quantitative traits), the analysis of categorical and dichotomous traits is also possible by this method. Categorical traits must be either re-coded as a dichotomous variable or possess enough categories for the trait to be considered quantitative. Dichotomous traits can be analysed by assuming an underlying genetic liability (Duggirala et al.,
1997; Hartl and Clark, 2007). Assuming this model, the presence of a dichotomous trait is determined by an underlying risk (liability), which follows a continuous distribution. An individual expresses the trait if the liability exceeds a certain threshold.
Quantitative genetic theory is concerned with partitioning the total phenotypic variance of a trait of interest into components (Amos, 1994). In the simplest variance
2 component (VC) model, the total phenotypic variance ( P) is the sum of the genetic
2 2 (genotypic) variance ( G) and the environmental variance ( e) (Hartl and Clark,
2007);
These variance components may be further partitioned, depending on the model. For instance, the genetic variance can be partitioned into components surmising the additive genetic variance attributable to individual alleles and dominance variance, the combination of alleles into genotypes (Hartl and Clark, 2007). It is theorised that partitioning out the environmental components should facilitate the characterisation of the genetic component influencing the trait.
Page 100 VC methods have been used extensively in the analysis of complex polygenic phenotypes and have successfully localised QTLs influencing a diverse range of phenotypes. VC based programs such as SOLAR, enable powerful penetrance model- free linkage analysis of quantitative (and qualitative) traits in pedigrees of arbitrary size and complexity (Almasy and Blangero, 1998; Blangero et al., 2000; Williams-
Blangero et al., 1999). SOLAR is particularly versatile, as it enables modelling of genotype by environment interactions, epistasis and pleiotropy, as well as single point and multipoint linkage, association, multivariate, mitochondrial and oligogenic analyses (Almasy et al., 1997; Blangero et al., 2001; Comuzzie et al., 1997; Duggirala et al., 1997; Williams et al., 1997). Due to the size and complexity of the Norfolk genealogy, VC linkage methods are well suited to approximating the genomic location of susceptibility loci influencing selected phenotypes. The following sections describe the application of VC methods in heritability estimation and linkage analysis with particular reference to the SOLAR software package.
3.13.1 Heritability Estimation
Heritability is a measure of the genetic component influencing a trait of interest.
Heritability is measured on a scale ranging from 0 to 1. A value of 0 indicates the phenotype is completely controlled by non-genetic (environmental) factors. A value of 1 indicates the trait is completely controlled by genetic factors. Generally heritability estimates fall within these 2 extremes, indicating the trait is influenced by a combination of genetic and non-genetic factors. For example, a heritability value of
0.62 indicates 62% of the phenotypic variation is attributable to genetic factors and
38% to non-genetic factors. Heritability estimates provide statistical evidence of the involvement of a genetic component to the phenotype, and thus support deeper
Page 101 investigations by linkage analysis. Gene mapping of a trait purely controlled by non- genetic factors would be unproductive.
There are various methods of estimating inheritance. Quantitative genetics distinguishes two measures of the heritable component of a phenotype: the narrow- sense heritability (h2) and the broad-sense heritability (H2). Both measures are expressed as ratios of the total phenotypic variance. While both measures incorporate the additive genetic variance, the broad sense heritability incorporates 2 additional variance components, dominance variance and epistatic variance (Hartl and Clark,
2007). This section will specifically focus on modelling inheritance using generalised
VC methods in the absence of dominance and/or epistatic effects.
In its simplest form, the narrow sense heritability for a quantitative trait is defined as
2 2 the ratio of the additive genetic variance ( g) to the total phenotypic variance ( P)
(Hartl and Clark, 2007):
The heritability model therefore reflects the proportion of the phenotypic variance explained by genetic factors.
For a polygenic model, the estimated additive heritability is expressed as the sum of
2 the genetic effects attributable to a QTL ( q) and residual (additive) polygenic effects
2 2 ( p) to the total phenotypic variance ( P).
Page 102 The additive heritability (h2) of a polygenic model expressed as a function of these variance components given by (Göring et al., 2001):
This model assumes a major trait locus (QTL) and an infinite number of genetic factors with small additive effects contributing to the overall phenotypic variance. The residual heritability is attributable to the effects of non-genetic factors.
The genetic component of the phenotype of interest is estimated using samples of related individuals, which may include sibling-pairs (sib pairs), monozygotic (MZ) and dizygotic (DZ) twins, nuclear families or extended pedigrees (Williams-Blangero and Blangero, 2006). Heritability is estimated by assessing how the phenotype co- varies in relative pairs and comparing it to that which is expected given the degree of relationship between the 2 individuals. This is the genetic covariance (or correlation).
The degree to which the observed covariance fits that which is expected assuming the phenotype is purely attributable to genetic factors provides an overall estimate of the genetic component (Hartl and Clark, 2007). In addition to estimating the additive heritability, programs such as SOLAR provide P-values calculated by likelihood ratio tests, where the likelihood of the model is estimated and compared to the likelihood of the model where the heritability is constrained to zero.
Valid estimates of trait heritability are dependent on accurate phenotyping, adequate sampling of individuals and for families, accurate knowledge of the pedigree structure. In the case of continuous phenotypes, the trait is assumed to have an underlying multivariate normal distribution. Deviations from normality require
Page 103 correction by transformation and/or the removal of outliers. Where traits cannot be normalised, some statistical programs offer alternative functions (i.e. ‘tdist’ and ‘lod adjustment’ functions in SOLAR) to correct the test statistic for the effects of the statistical violation (Blangero and Almasy, 1996).
In addition to the variance components, the covariate effects of dichotomous and/or continuous variables of interest (and their interactions) can be included in the polygenic model (Duggirala et al., 1997). This allows for the effects of the variance components and covariates on the additive heritability to be simultaneously estimated by maximum likelihood techniques. This is a mixed polygenic model. The most commonly screened covariates in genetic models are age, age-squared, sex and age- by-sex interaction terms and/or environmental factors. The inclusion or exclusion of covariates in the heritability model is determined by a statistical threshold (P-value) for significance, typically P ≤ 0.05.
Heritability screening of dichotomous and continuous phenotypes in the Norfolk
Island pedigree utilised maximum likelihood VC methods implemented by the statistical program SOLAR version 4.0.1.
3.13.2 Variance Component Linkage Methods
Once the additive heritability is estimated and statistical evidence supporting a genetic component for the trait of interest is provided, linkage may be tested at defined chromosomal locations throughout the genome to approximate a QTL. In the simplest
VC linkage model, the total phenotypic variance of the quantitative trait of interest is expressed as the sum of the QTL specific effects, residual additive polygenic effects and individual-specific random environmental effects (Williams and Blangero, 1999).
Page 104 This model is extended to related individuals by observing how the phenotypes of relative pairs co-vary given the degree of relationship between the individuals and the proportions of genes they share IBD at a specific marker locus (Amos, 1994). This is the genetic covariance between the relative pairs, or in the case of a pedigree, the covariance matrix. By observing the trait covariances between different classes of relatives at specific locations throughout the genome, evidence for a QTL can be obtained.
The covariance matrix for a pedigree (Ω) is estimated as (Williams and Blangero,
1999):
Where Π is a matrix with elements (πmij) providing the estimated proportion of genes
2 individuals i and j share IBD at a given chromosomal position (q); q is the variance component corresponding to the additive genetic effects due to the locus (the QTL
2 effect size); Φ is the kinship coefficient matrix; p is the variance due to additive
2 effects of genes elsewhere in the genome; Ι is the identity matrix; and e is the variance due to environmental effects specific to the individual (Williams and
Blangero, 1999). The IBD matrix is used to conduct two-point (single-point) linkage analysis, where linkage is tested between each marker and the phenotype independently. For multipoint linkage analysis the IBD matrix is a multipoint IBD
(MIBD) matrix, which utilises marker information across a single chromosome, generally at 1cM increments.
Therefore, the VC method can be used in pedigrees of arbitrary size and complexity by specifying the covariance matrix for relative pairs. The covariance matrix can be Page 105 surmised as a function of the relationship between the genes shared IBD at a defined chromosomal position and the QTL effect size, kinship and the additive genetic variance, and the identity matrix and the environmental variance. If the variance component is significantly greater than zero, there is evidence for a QTL at a given chromosomal location influencing the phenotype.
3.13.3 The LOD Score
To estimate linkage, the VC method measures how the phenotype co-varies in relative pairs, given the degree of relationship between the 2 individuals and observed IBD sharing at a defined chromosomal location (Göring et al., 2001). The presence of a
QTL at a particular chromosomal location (linkage) is tested by comparing the likelihood of a linkage model to a basic polygenic model with no linkage at each marker.
This maximum likelihood-ratio test is given in the form of a logarithm of the odds
(LOD) score (Z), which is given by (Göring et al., 2001);
Where, L ( ) represents the likelihood. Under the null hypothesis of no linkage the
2 QTL effect size is constrained to zero ( q = 0). Under the alternate hypothesis of linkage the difference of the log10 likelihoods produces a LOD score. If the variance component is significantly greater than zero at a tested chromosomal location, there is evidence for the presence of a QTL.
Page 106 This LOD score is equivalent to the LOD scores of parametric linkage analysis
(Blangero et al., 2001). Like parametric LOD scores, under the null hypothesis of no linkage, the VC LOD score is distributed as an equal mixture of a chi-square ( 2) random variable a point mass of 0 and a degree of freedom of 1 (Göring et al., 2001).
As a result, point-wise p-values can be estimated for each LOD score value (Nyholt,
2000). Alternatively, P-values can be estimated for each LOD score by performing simulations in SOLAR for continuous phenotypes. Linkage analysis of dichotomous and continuous quantitative (continuous) phenotypes in the Norfolk Island pedigree utilised maximum likelihood VC methods implemented by the statistical program
SOLAR version 4.0.1.
3.13.4 Variance Component Association Methods
The program SOLAR can also be used to undertake association testing in large pedigrees. The test is performed using measured genotype analysis (Boerwinkle et al.,
1986), embedded in a VC-based linkage model (Blangero et al., 2005). The test assumes an additive model of allelic effect, where SNP genotypes AA (homozygote),
AB (heterozygote) and BB (other homozygote) are coded as -1, 0 and 1, respectively and then the variation of the trait mean by genotype is assessed using a general linear regression (Blangero et al., 2005). This method of analysis has been employed to analyse re-sequencing data in related individuals to identify a quantitative trait nucleotide (QTN) influencing the quantitative phenotype after localisation of the
QTL. Using this method, researchers have successfully identified sequence variants in the cis-regulated vanin 1 (VNN1; MIM603570) gene influencing high density lipoprotein cholesterol concentrations in Mexican American families (Goring et al.,
2007). This method has also been employed in the Framingham Heart study and also
Page 107 in the Collaborative Study on the Genetics of Alcoholism to analyse SNP data in extended pedigrees with success (Zlojutro et al., 2010). In the current family-based association analysis of dichotomous and continuous (quantitative) phenotypes in the
Norfolk Island pedigree utilised measured genotype analysis embedded in a maximum likelihood VC linkage approach, implemented by the statistical program SOLAR version 4.0.1.
Page 108 Chapter 4: Principal Component and Linkage Analysis of Cardiovascular Risk Traits in the Norfolk Isolate
4.1 Overview
This chapter has been published as an original investigation using variance components linkage analysis.
An individual’s risk of developing CVD is influenced by genetic and environmental factors. This study aimed to map genetic loci for CVD-risk traits in a unique population isolate derived from Norfolk Island. The investigation focused on 377 individuals descended from the population founders. Principal components analysis was used to extract orthogonal components from 11 cardiovascular risk traits.
Multipoint variance component methods were used to assess genome-wide linkage using SOLAR to the derived factors. A total of 4 principal components accounting for
83% of the total variance were derived. Principal component 1 was loaded with body size indicators; principal component 2 with body size, cholesterol and triglyceride levels; principal component 3 with the blood pressures; and principal component 4
LDLc and total cholesterol levels. Suggestive evidence of linkage for principal component 2 (h2=0.35) was observed on chromosome 5q35 (LOD=1.85; P=0.0008).
While peak regions on chromosome 10p11.2 (LOD=1.27; P=0.005) and 12q13
(LOD=1.63; P=0.0054) were observed to segregate with principal components 1
(h2=0.33) and 4 (h2=0.42), respectively. This study investigated a number of CVD risk traits in a unique isolated population. Findings support the clustering of CVD risk
Page 109 traits and provide interesting evidence of a region on chromosome 5q35 segregating with weight, waist circumference, HDLc and total triglyceride levels.
4.2 Background
Cardiovascular disease is a leading cause of morbidity and mortality worldwide. The
World Health Organisation reported a total of 16.7 million deaths globally in 2002 to be a direct result of CVD (Mackay and Mensah, 2004). An individual’s risk of developing CVD is influenced by multiple environmental and genetic factors. Major risk factors include tobacco use, physical inactivity, unhealthy diet, obesity, dyslipidemia, hypertension, diabetes mellitus and the metabolic syndrome.
Combinations of these risk factors along with a positive family history significantly increase the likelihood of disease and its related effects on morbidity and mortality
(Expert Panel on Detection, 2001).
Genome-wide studies of cardiovascular-related phenotypes report linkage to various chromosomal regions, indicating that this disorder is genetically heterogeneous. To simplify dimensions of CVD risk, multivariate data reduction techniques such as factor analysis and PCA have been employed to extract uncorrelated components from numerous inter-correlated phenotypes (Bellis et al., 2005; Goodman et al.,
2005). This method has been used to identify loci linked to the clustering of CVD indicators, particularly those comprising the metabolic syndrome (Cai et al., 2004; He et al., 2008; Shmulewitz et al., 2001; Tang et al., 2003). Of particular relevance is the use of a genetic isolate derived from Kosrae (Shmulewitz et al., 2001). Isolated populations, like that of Kosrae, offer several advantages in gene mapping studies compared to outbred populations, with extreme geographical and cultural isolation
Page 110 reducing the effects of non-genetic variables by promoting a uniform lifestyle (Varilo and Peltonen, 2004). Genetic heterogeneity may also be reduced if the isolate is derived from a limited number of ancestors and has undergone population bottlenecks and endogamy during population expansion.
In the present study we tested the descendents of HMAS Bounty mutineers and
Tahitian population founders derived from the Norfolk genetic isolate (Hoare, 1999).
Prior demographic analysis indicates this population possesses unique characteristics which may facilitate gene mapping studies of complex multifactorial diseases such as
CVD (Bellis et al., 2005). Quantitative epidemiological data for related individuals included measures of body size, blood pressure and lipid levels, all factors that contribute to risk of CVD. To examine the relationship of these indicators, principal component analysis was performed to extract orthogonal components. Linear scores were calculated for each individual and used to determine the heritability of each principal component in the cohort. Quantitative trait loci segregating with individual factors were then identified by multipoint variance components linkage analysis using a genome-wide panel of STRs.
4.3 Materials and Methods
4.3.1 Sample Ascertainment
Norfolk is a small, isolated volcanic island situated in the South Pacific Ocean, approximately 1,700 kilometres northeast of Sydney. In 2001, the Island’s permanent population totalled 1574 individuals of whom 756 claimed to be of Pitcairn decent
(Matthews, 2001). The population supports itself from local produce, however as a result of both isolation and small land mass the population is highly dependent on
Page 111 imports of primary produce and manufactured goods (Matthews, 2001). The Islanders live a relatively homogeneous lifestyle due to their isolation, strict quarantine and immigration laws and community-centred culture. Furthermore, a large proportion of the present adult population are descended from 9 Isle of Man HMAS Bounty
Mutineer and 6 Tahitian lineages, including acting lieutenant Fletcher Christian
(Macgregor et al., 2010; McEvoy et al., 2010).
4.3.2 Phenotyping
Collection and phenotypic characterisation of the Norfolk Island cohort has been previously described in detail (Bellis et al., 2008; Bellis et al., 2005). In brief, ethical clearance was granted by the Griffith University Human Research Ethics Committee prior to the commencement of the study. Study participants over 18 years of age were recruited via local media announcements on the Island. All participants signed informed consent statements prior to inclusion in the study. A detailed questionnaire was used to obtain specific information from study participants including ancestry, lifestyle habits and extensive medical history. Participants were extensively phenotyped for anthropometric measures, blood pressure, lipids, lipoproteins and blood chemistry. Blood samples were not taken after fasting at the time of collection.
Responses to medical questionnaires indicated that 40 individuals reported anti- hypertensive use and 22 individuals reported use of lipid lowering therapy at the time of recruitment. Systolic and diastolic blood pressure values were corrected for anti- hypertensive use in accordance with the methods described by Tobin et al. (2005).
Page 112 4.3.3 Pedigree Structure
A total of 377 individuals were determined to have familial links to the Tahitian
(Polynesian) and HMAS Bounty mutineer founders and were thus the focus of this investigation. These related individuals comprise the current Norfolk pedigree
(N=6,537) that extends 11 generations to the original population founders (Bellis et al., 2005). To alleviate analysis burden imparted by the presence of multiple inbreeding and marriage loops in early generations and the large volume of missing data, the pedigree was trimmed (N=1,078) using a peeling algorithm in the pedigree database management system PEDSYS (Dyke, 1995).
Norfolk Island represents an admixed population of Caucasian (Isle of Mann) and
Polynesian (Tahitian) ancestry, which had expanded 11 generations at the time of collection. Recent admixture is an important consideration in genetic studies.
Depending on the ethnic origin, trait alleles can display markedly different frequencies and may confer varied risks in the case of disease. The haplotype surrounding the trait (or disease) allele may extend longer distances in admixed populations, leading to extended regions of LD between loci. The presence of long range LD means that lower density STR maps should be sufficient to identify regions segregating with traits of interest. Due to these facts, admixture mapping has been used to localise loci for numerous disorders, including multiple sclerosis, prostate cancer and hypertension in populations with African American ancestry (Freedman et al., 2006; Reich et al., 2005; Zhu et al., 2005). Though the extent of admixture is yet to be characterised in the Norfolk population, the extent of LD across a well- characterised region of the X chromosome and also between variants in the NOS2A gene on chromosome 17q11.2-q12 has been previously assessed (Bellis et al., 2007).
Page 113 This study revealed LD to extend approximately 9.5–11.5Mb across the Xq13.3 region in the Norfolk Island population, a result comparable to those reported in other known population isolates (Angius et al., 2002).
4.3.4 Genome Wide Scan
DNA was isolated from lymphocytes using a standard salting out procedure (Miller et al., 1988). Samples were genotyped at the Australian Genome Research Facility
(AGRF) using the Applied Biosystems PRISM Human Linkage Mapping Set version
2.5. The linkage mapping set comprised of 382 highly polymorphic microsatellite markers, spaced at an average distance of 10cM throughout the human genome.
Markers were individually amplified by PCR using fluorescently labelled primer pairs. Markers were then pooled into panels for capillary separation on the Applied
Biosystems 3730 DNA Analyser. Genotyping results were analysed using Applied
Biosystems GeneMapper software version 4.0.
4.3.5 Statistical Analysis
Data was screened using SPSS version 14.0. Measurements greater than or equal to 4 standard deviations from the mean were assessed and any data entry errors or extreme outliers were excluded. Factors with a high kurtosis were log transformed. Only subjects with measurements for all phenotypes were included in the PCA. Differences in the sex-specific means of the quantitative phenotypes were investigated by one- way analysis of variance (ANOVA).
Principal components analysis was used to extract orthogonal components from cardiovascular and obesity related measurements. Obesity related traits included BMI calculated as kg/m2, hip circumference, waist circumference, percentage body fat and Page 114 weight. Cardiovascular related traits included SBP, DBP, TC, TG and HDLc and
LDLc levels. The initial solution, principal component 1 explained the maximum variance, while successive components explained progressively smaller portions of the total variance. Principal components were simplified by orthogonal rotation
(varimax). This minimised the number of variables with high loadings on each factor.
Principal components with Eigen values greater or equal to 1 were retained (principal components with variances less than 1 contain less information than one of the original variables and hence are not worth retaining). Relationships between components are explained by component loadings, values greater than or equal to 0.4 were used to indicate meaningful correlations between the component and the variable. PCA has 4 statistical assumptions; 1) random, independent sampling; 2) interval level measurements; 3) linearity; and 4) normality. In order to assess the violation of random, independent sampling, PCA component matrices were calculated
100 times for N=75 randomly selected individuals (20% of the total cohort).
Coefficients of congruence were computed for the entire dataset and each random sample (Korth and Tucker, 1975).
A regression method was used to estimate factor score coefficients for the retained principal components and formed the basis of linkage phenotypes for each individual.
Component scores had a mean of 0 and a standard deviation of 1, skewness and kurtosis were less than 0.6, satisfying the assumption of normality. The 4 component scores were screened for the covariate effects of age, sex, age2 and their interactions, prior to calculating heritability estimates using SOLAR.
Genotypic data was analysed for discrepancies, including Mendelian inheritance violations using the PEDSYS program INFER and Simwalk2 (Dyke, 1995; Sobel et
Page 115 al., 2002). The Pedigree RElationship Statistical Test (PREST) was used to verify the pedigree structure and detect relationship misspecification (McPeek and Sun, 2000).
Discrepant genotypes for were blanked prior to analysis. IBD matrices were computed using the program Loki at points throughout the genome for every relative pair
(Heath, 1997). Sex-averaged chromosomal marker maps were obtained from the
Marshfield Centre for Medical Genetics (http://research.marshfieldclinic
.org/genetics). Multipoint variance component linkage methods were used to assess linkage between the 382 autosomal markers and quantitative phenotypes using the statistical program SOLAR 4.0.6 (Almasy and Blangero, 1998). Additionally, each quantitative phenotype was simulated under the null hypothesis of no linkage. In this process, a fully informative marker, unlinked to the trait was simulated and trait linkage was tested at that marker 10,000 times for each quantitative linkage phenotype. This information was used to calculate empirical P-values for LOD scores.
For LOD-score analysis in human pedigrees it has been proposed that a point-wise P- value of 4.9x10-5 (LOD ~3.3) is indicative of significant linkage, while a point-wise
P-value of 1.7x10-3 (LOD ~1.9) is suggestive at the genome-wide level (Lander and
Kruglyak, 1995). As these values do not take into account the effects of multiple independent tests, a Bonferroni correction was applied to adjust the significance thresholds for this study. Adjusted P-values of 4.3x10-4 for suggestive linkage and
1.2x10-5 for significant linkage were used to interpret results.
4.4 Results
Table 4.1 displays the population and sex-specific means and standard deviations of the original measurements used in the PCA for related Norfolk Island Individuals.
The cohort used in this study consisted of 171 men and 206 women. The mean age of
Page 116 both male and female were 48.9 and 49.5 years, respectively, with little deviation in the variance between genders (P>0.05). The remaining 11 quantitative phenotypes indicated significant (P<0.05) sex-specific differences in trait variance. Males were observed to have significantly higher values pertaining to hip and waist circumference, BMI, weight, total triglycerides, total cholesterol, LDLc and diastolic and systolic blood pressures compared to females. Females had significantly higher values of percentage body fat and HDLc levels than men.
Analysis of BMI revealed Norfolk Islanders, particularly males were on average slightly overweight. The accepted ranges for BMI were as follows; values of 25-
29kg/m2 were classified as overweight, 30-34kg/m2 as obese and 35 or above morbidly obese. Of particular interest to CVD, trait break down of BMI indicated that
39.3 percent of adults were overweight, 16.1 percent were obese and 3.2 percent were morbidly obese. Women had a higher percentage of body fat than men, however this difference is expected.
Waist circumference is an indicator of abdominal obesity, which according to the
World Health Organisation (WHO) increases the risk of CVD in males and females with measurements exceeding 101cm and 89cm respectively (Mackay and Mensah,
2004). The average waist circumference of males (95.1cm) and females (81.2cm) was within the recommended range. According to World Health Organisation guidelines
24.6 percent of males and 22.6 of females had values of waist circumference exceeding the recommended range and therefore at an increased risk of developing
CVD.
The average systolic (127.4) and diastolic (75.9) blood pressure, a strong indicator of hypertension and CVD, was within expected normal limits. Evaluation of blood Page 117 pressure revealed 27.7% of related individuals to be hypertensive based on a systolic value > 140mmHg and/or a diastolic reading > 90 mmHg. A total of 40 individuals reported using anti-hypertensive medications at the time of collection. As medication affects blood pressure measurements and may impact upon the reliability of the resulting test statistics, both diastolic blood pressure (DBP) and systolic blood pressure (SBP) measurements were adjusted using a correction devised by Tobin et al.
(2005). This required adding 15mmHg and 10mmHg from SBP and DBP scores for those individuals using antihypertensive medications, respectively (Tobin et al.,
2005). The original and adjusted SBP and DBP are reported in Table 4.1. A slight increase (29.6%) in the number of hypertensive individuals was observed when this correction was taken into account.
Lipid analysis was compared to published WHO European guidelines (Mackay and
Mensah, 2004). Mean total cholesterol (5.6mmol/L) exceeded the WHO European recommendation of less than 5.0mmol/L. Mean total triglycerides (2.0mmol/L) exceeded the recommended less than 1.7mmol/L. Mean LDLc levels (2.8mmol/L) were in the healthy range of less than 3.0mmol/L. While HDLc levels in men
(1.3mmol/L) and women (1.5mmol/L) were within the recommended limits (equal or greater than 1.0mmol/L in men and 1.2mmol/L in females). Bivariate Pearson’s correlation coefficients and significance levels between the 11 variables are shown in
Table 4.2. Examination of the component matrix indicated that there was a high degree of correlation among the variables. In total, 41 pairs of variables were significant below the P=0.001 level. The high level of correlations between the 11 variables supported the use of PCA.
Page 118 Table 4.1. Phenotypic characteristics of participants (mean +/- standard deviation).
Both Sexes Males Females Trait (N = 377) (N = 171) (N = 206) Age (years) 49.2 ± 16.6 48.9 ± 16.9 49.5 ± 16.4 BMI (kg/m2) 26.3 ± 4.6 27.5 ± 4.1 25.4 ± 4.7 DBP (mmHg) 75.9 ± 13.3 79.3 ± 12.6 73.0 ± 13.2 DBP with Adjustment (mmHg) 76.8 ± 13.6 80.5 ± 13.9 73.6 ± 12.6 HDLc (mmol/L) 1.4 ± 0.4 1.3 ± 0.3 1.5 ± 0.4 Hip Circumference (cm) 101.9 ± 9.6 103.3 ± 7.3 100.9 ± 11.0 LDLc (mmol/L) 2.8 ± 1.0 3.0 ± 0.9 2.7 ± 1.0 Percent Body Fat (%) 30.3 ± 8.7 24.4 ± 6.6 35.3 ± 6.9 SBP (mmHg) 127.4 ± 22.3 134.0 ± 19.6 121.8 ± 22.9 SBP with Adjustment (mmHg) 129.2 ± 24.0 135.7 ± 21.2 123.7 ± 24.9 TC (mmol/L) 5.6 ± 1.1 5.8 ± 1.1 5.5 ± 1.1 TG (mmol/L) 2.0 ± 1.2 2.3 ± 1.3 1.7 ± 1.1 Waist Circumference (cm) 87.3 ± 13.7 95.1 ± 11.1 81.2 ± 12.4 Weight (kg) 76.0 ± 16.2 85.5 ± 14.6 68.3 ± 13.0 BMI = Body Mass Index, DBP = Diastolic Blood Pressure, HDLc = High Density Lipoprotein cholesterol, LDLc = Low Density Lipoprotein Cholesterol, SBP = Systolic Blood Pressure, TC = Total Cholesterol and TG = Total Triglycerides
Page 119
Table 4.2. Pearson’s correlation coefficient matrix of the traits after normalisation and blood pressure adjustments.
DBP adjusted DBP HDLc Hip Circumference LDLc Body Percent Fat SBPadjusted TC TG* Waist Circumference Weight BMI BMI 1.00 DBP adjusted 0.43‡ 1.00 HDLc -0.36‡ -0.11 1.00 Hip Circumference 0.86‡ 0.36‡ -0.23‡ 1.00 LDLc 0.20‡ 0.29‡ -0.06 0.18‡‡ 1.00 Percent Body Fat 0.48‡ 0.13† 0.06 0.50‡ 0.03 1.00 SBP adjusted 0.44‡ 0.76‡ -0.14† 0.37‡ 0.26‡ 0.06 1.00 TC 0.26‡ 0.35‡ 0.04 0.23‡ 0.92‡ 0.11†† 0.35‡ 1.00 TG* 0.44‡ 0.34‡ -0.48‡ 0.33‡ 0.25‡ 0.12†† 0.38‡ 0.41‡ 1.00 Waist Circumference 0.81‡ 0.43‡ -0.39‡ 0.77‡ 0.26‡ 0.17† 0.48‡ 0.31‡ 0.46‡ 1.00 Weight 0.84‡ 0.39‡ -0.42‡ 0.75‡ 0.15† 0.14† 0.36‡ 0.18† 0.38‡ 0.82‡ 1.00 BMI = Body Mass Index, DBP = Diastolic Blood Pressure, HDLc = High Density Lipoprotein cholesterol, LDLc = Low Density Lipoprotein Cholesterol, SBP = Systolic Blood Pressure, TC = Total Cholesterol and TG = Total Triglycerides *Log transformed; ††P<0.05; †P<0.01; ‡‡P<0.001; ‡P<0.0001
Page 120 PCA extracted 4 components with Eigen values greater than or equal to, which explained nearly 83% of the total variation of the 11 original quantitative traits (Table
4.3). Principal component 1 has high loadings of traits that reflect body size, particularly adiposity (hip circumference, BMI, Percentage body fat, weight and waist circumference) and explained the largest portion of the total variance (44%). Principal component 1 is a strong indicator of atherosclerosis. Principal component 2 was loaded predominantly with HDLc, TG, weight and waist circumference, all of which are associated with obesity and atherogenic dyslipidemia. Comparably, principal component 3 contained high loadings of blood pressure, reflecting the risk of essential hypertension and lastly, principal component 4 was loaded with LDLc and TC, both a strong indicator of ischemic stroke and heart attack risk. A graphical depiction of trait clustering for principal components 1, 2 and 3 is detailed in Figure 4.1.
Table 4.3. Coefficients and variances of principal components satisfying the Eigen value > 1 criterion.
Variable Principal Component 1 2 3 4 BMI 0.83 0.39 0.28 0.09 DBP adjusted 0.18 0.07 0.89 0.17 HDLc -0.05 -0.88 0.03 0.05 Hip Circumference 0.87 0.26 0.22 0.08 LDLc 0.05 0.08 0.11 0.95 Percent Body Fat 0.78 -0.31 -0.08 0.07 SBP adjusted 0.14 0.14 0.90 0.15 TC 0.11 0.05 0.20 0.96 TG* 0.15 0.63 0.23 0.33 Waist Circumference 0.61 0.54 0.35 0.14 Weight 0.63 0.57 0.27 0.01
Eigen value 4.88 1.86 1.29 1.09 Total Variance (%) 44.35 16.87 11.73 9.92 Accumulative Variance (%) 44.35 61.21 72.94 82.86
*Log transformed. Component loadings in bold type are >0.4.
Page 121
Figure 4.1. Plot of rotated principal components.
To address the violation of independent random sampling, 20% (N=75) of the cohort was randomly sampled 100 times. The first 4 principal components were assessed using the coefficient of congruence (Korth and Tucker, 1975). This coefficient is used to compare the component loadings of two separate groups by measuring the cosine of the angle between the two vectors. As the coefficient approaches 1, vector similarity increases. For the current study, the mean coefficient of congruence between the entire sample and the 100 random samples were as follows; 0.99 (95%
CI=0.99-1.00) for principal component 1; 0.91 (95% CI=0.89-0.94) for principal component 2; 0.72 (95% CI=0.66-0.77) for principal component 3; and 0.75 (95%
CI=0.70-0.79) for principal component 4. These results indicate that the structures of principal component 1 and 2 were very close to the structure of the whole data, while principal component 3 and 4 were a poorer fit. The first 4 derived components for the
100 random samples accounted for 83.82% (95% CI=83.50-84.14) of the total trait
Page 122 variance. The total variance of each individual component were; 45.07% (95%
CI=44.32-45.82) for principal component 1; 17.08% (95% CI=16.68-17.48) for principal component 2; 12.19% (95% CI=11.95-12.42) for principal component 3; and 9.48% (95% CI=9.26-9.70) for principal component 4.
After assessing the coefficient of congruence we performed heritability screening for the 4 components. Three of the four components were significantly heritable after covariate correction. The heritability of principal components 1, 2 and 4 were 0.33 ±
0.14 (P=0.007), 0.35 ± 0.17 (P=0.02) and 0.42 ± 0.12 (P=0.0002). Principal component 3 was not significantly heritable (h2=0.12 ± 0.19, p=0.14). Table 4.4 details the PCA phenotypes and markers that produced maximum LOD scores in regions throughout the genome. The highest LOD score detected in this study was linked to principal component 2. Suggestive linkage for this phenotype was observed on chromosome 5q35 with a maximum LOD score of 1.85 for marker D5S400
(P=0.0008) (Figure 4.2 and Table 4.4). The 1-LOD-support interval extended from
174 to 193cM. Principal component 2 was observed to segregate with additional regions on chromosomes 1, 7, 14, 15, 17, 18 and 20 (p<0.01, LOD >1) (Figure 4.2 and Table 4.4). Analysis of principal components 1 and 4 identified peak linkage signals on chromosomes 10 (LOD=1.27; p=0.005) and 12 (LOD=1.63; p=0.003), respectively (Table 4.4). Additional linkage peaks for principal component 1 were localised to chromosomes 1, 7, 12, 17 and 20, while only one other peak was observed on chromosome 21 for principal component 3 (Table 4.4).
Page 123
D5S1981 D5S406 D5S2095 D5S416 D5S419 D5S426 D5S418 D5S407 D5S647 D5S424 D5S641 D5S428 D5S644 D5S433 D5S2027 D5S471 D5S2115 D5S436 D5S410 D5S422 D5S400 D5S408
3
2 LOD
1
0 0 25 50 75 100 125 150 175 200 cM
Figure 4.2. Genome wide multipoint linkage results for principal components 2 on chromosome 5
Table 4.4. Summary of PCA genome scan results.
Principal Chromo- cM Nearest Marker LOD* Empirical Component some (cM) P-value 1 1 237 D1S213 (232.69) 1.10 0.008 7 30 D7S507 (32.20) 1.22 0.006 10 67 D10S208 (61.08) 1.27 0.005 12 74 D12S83 (75.06) 1.02 0.01 17 56 D17S798 (56.73) 1.00 0.01 20 24 D20S115 (25.13) 1.19 0.006 2 1 46 D1S234 (45.44) 1.11 0.01 5 181 D5S400 (180.40) 1.85 0.0008 7 56 D7S484 (55.58) 1.01 0.01 14 113 D14S985 (118.30) 1.11 0.01 15 15 D15S1002 (15.51 1.09 0.01 17 76 D17S1868 (75.89) 1.20 0.008 18 79 D18S474 (74.33) 1.02 0.01 20 3 D20S117 (2.94) 1.03 0.01 4 12 65 D12S368 (67.52) 1.63 0.003 21 17 D21S1256 (13.20) 1.10 0.01 *Only chromosomal regions producing LOD Scores >1 are displayed
Page 124 4.5 Discussion
Isolates, such as Norfolk are unique populations to study complex multi-factorial disorders. The combination of geographical and cultural isolation leads to individuals sharing a common environment, minimising differences in lifestyle factors such as diet, exercise and sanitation compared to outbred populations. This has been observed particularly in Amish and Hutterite populations (Hsueh et al., 2001; Ober et al.,
1998a). The homogeneous environment shared by individuals is of great significance in studies of complex disorders, particularly those of cardiovascular origin where there appears to be a threshold effect influenced by lifestyle factors. Norfolk is also of interest in genetic studies as a large number of individuals in this population can trace their heritage back to a small number of families derived from the original Bounty mutineer and Polynesian founders. The limited number of ancestors minimises genetic heterogeneity. This can reduce the number of susceptibility genes underlying disease. As a result of the expected reduction in genetic and non-genetic variables, population isolates with known founder effect have been exploited in numerous gene mapping studies of complex disorders (Hovatta et al., 1999; Hsueh et al., 2001; Hsueh et al., 2000; Laitinen et al., 2001; Ober et al., 1998a; Pajukanta et al., 1999;
Shmulewitz et al., 2006).
This current study focused on a large complex family from the Norfolk Isolate to dissect the genetic and environmental component underlying CVD risk. We performed PCA with orthogonal rotation to reduce 11 inter-correlated variables into groups of independent (un-correlated) components. This data reduction method identified principal components that explained 83% of the variation in the original quantitative traits. PCA identified four distinct components underlying CVD risk in
Page 125 this family. The component accounting for the largest portion of variation was strongly loaded by variables relating to obesity, which has long been known to be an independent predictor of CVD-related morbidity and mortality (Hubert et al., 1983).
The second largest component, reflected traits of obesity loaded with HDLc and triglyceride levels. Increased waist circumference, elevated TG and reduced HDLc are
3 indicators of metabolic syndrome and a strong predictor of CVD risk (NCEP, 2002).
Principal component 3 reflected the risk of essential hypertension. Lastly, principal component 4 was composed of serum LDLc and TC levels, which when elevated promote arteriosclerosis, increasing the risk of CHD, ischemic stroke and heart attack
(Stamler et al., 2000).
Previous PCA studies have focused primarily on the metabolic syndrome, a major risk factor for CVD characterised by the clustering of traits of insulin resistance, hypertension, dyslipidemia and obesity (Arya et al., 2002; Cai et al., 2004; He et al.,
2008; Shmulewitz et al., 2001; Shmulewitz et al., 2006; Tang et al., 2003).
Unfortunately, as values of serum levels of fasting glucose and fasting insulin were not available for our study participants, insulin resistance could not be directly assessed. However, our findings are consistent with other studies as obesity and lipid levels displayed high loadings in the derived components (Austin et al., 2004; Bellis et al., 2005; Cai et al., 2004; Goodman et al., 2005; Shmulewitz et al., 2001; Tang et al., 2003). In particular, the loading patterns of weight and waist circumference in both the first and second components suggest a close relationship between traits of obesity and CVD risk in Norfolk Islanders. This is supported by demographic studies, which report a higher prevalence of obesity and dyslipidemia in individuals of
Polynesian ancestry (Hodge et al., 1997; Simmons et al., 2001).
Page 126 This study is an extension of an epidemiological cardiovascular study of Norfolk
Islanders, which reported heritability estimates for eight of the eleven phenotypes presented in the current study (Bellis et al., 2005). Heritability estimates in the original paper ranged from 0.19 for DBP to 0.63 to LDL-cholesterol. In the current study we detected a substantial genetic contribution for 3 of the 4 components, with estimates ranging from 0.27 to 0.43. The third component, which reflected blood pressure, was not significantly heritable. Heritability estimates for the 4 principal components in this study are within the range of those reported by Bellis et al (2005).
In the previous study, DBP was not significantly heritable (h2=0.19, P=0.06).
Likewise in this study, the blood pressure component was not significantly heritable.
The genetic predisposition of these phenotypes was investigated by means of variance component linkage analysis. The highest LOD peak in this study occurred on chromosome 5q35. This locus was linked to the second component comprising weight, waist circumference, HDLc and TG levels. A LOD score of 1.85 (P=0.0008) was detected at marker D5S400. This locus was suggestive of linkage at the genome- wide threshold (P=1.7x10-3). However, after adjustment for the effects of multiple testing across the four principle components, the 5q35 locus was no longer suggestive of linkage. However, this linkage peak is of potential interest as it has been reported with a range of traits related to CVD risk.
Linkage analysis in founder isolates of Kosraean and Amish origin has reported the
5q35 locus to segregate with serum leptin levels and BMI, respectively (Platte et al.,
2003; Shmulewitz et al., 2006). Additional family studies have reported segregation with traits of BMI, HDLc levels, LDLc levels, serum leptin levels, lean mass, body fat measures and type II diabetes mellitus within a 10cM interval of D5S400 (Almasy et
Page 127 al., 1999; Elbein and Hasstedt, 2002; Feitosa et al., 2002; Hager et al., 1998; He et al.,
2008; Pe´russe et al., 2001; Reynisdottir et al., 2003; Zhao et al., 2007). Linkage results indicate the chromosome 5 locus is strongly influenced by traits of obesity.
Interestingly, the 5q34-qter chromosomal region contains numerous obesity gene candidates. One such gene, the Beta-2-Adrenergic Receptor (ADRB2; OMIM 109690) spaning 5q32-34, a member of the G protein-coupled receptor super family, functions as a major lypolytic receptor in human adipocytes (Barbe et al., 1996). Variations of this gene have been reportedly associated with nocturnal asthma, arteriosclerosis, hypertension, obesity, metabolic syndrome and type 2 diabetes mellitus (Barbato et al., 2007; Contopoulos-Ioannidis et al., 2005; Dallongeville et al., 2003; Jocken et al.,
2006; Krushkal et al., 1998; Large et al., 1997). However, despite the number of positive associations there appears to be inconsistencies between studies, for instance a candidate gene study conducted in 7,808 middle-aged white subjects was unable to demonstrate any consistent associations between ADRB2 variants and obesity, hypertension or type 2 diabetes (Gjesing et al., 2007).
In addition to the 5q35 locus, we identified a peak LOD score linked to principal components 1 and 4 on chromosome 10p11.2 near marker D10S208 and 12q13 near marker D12S368, respectively. Though neither suggestive nor significant at the genome-wide threshold for linkage, these two regions have been reported to segregate with CVD-related risk traits. The chromosome 10 marker, D10S208 has been reported to be linked with obesity in 2 large studies in European and African Americans (Dong et al., 2003; Price et al., 2001). The second strongest signal in this study (LOD=1.63;
P=0.003) was observed on chromosome 12q13. The component segregating with this locus was loaded with LDLc and TC and is in the same vicinity of a region reported to segregate with LDLc and LDL-apoB levels (Feitosa et al., 2005). In addition to
Page 128 these peak regions for the 3 heritable principal components, linkage peaks (LOD>1;
P<0.01) were identified on chromosomes 1, 7, 12, 14, 15, 17, 18, 20 and 21. Though these genetic loci were neither suggestive nor significant at the genome-wide threshold for linkage, their presence supports the underlying heterogeneous nature of cardiovascular-related phenotypes.
4.6 Conclusion
In conclusion, principal component analysis reduced 11 interrelated CVD risk traits to
4 newly defined components. As these components are uncorrelated, each one can be interpreted to represent a distinct phenotype underlying CVD risk in Norfolk
Islanders. Heritability screening indicated that these components are influenced by a strong genetic element in this family. Linkage analysis identified a suggestive locus underlying CVD risk on chromosome 5, which has been reported to segregate with similar phenotypes. Our findings support the clustering of CVD risk factors in
Norfolk Islanders and report a potentially interesting latent cardiovascular risk variable loaded with obesity and lipid levels on chromosome 5q35. Further investigations are warranted in this population to characterise the nature and involvement of this locus in CVD risk.
Page 129 Chapter 5: Genome Wide Linkage Analysis of Migraine in the Norfolk Genetic Isolate
5.1 Overview
Migraine is a complex neurological disorder with a strong genetic component. In an effort to identify migraine susceptibility genes, we initiated a study of the isolated population of Norfolk Island, Australia. Over 80% of the permanent inhabitants of
Norfolk Island are descended from 18th Century English sailors involved in the infamous mutiny on the Bounty and their Polynesian consorts. In total, 600 subjects were phenotyped using a comprehensive medical questionnaire incorporating ICHD-
II criterion. A total of 154 migraine cases were identified. All subjects were genotyped for a medium-density microsatellite genome scan. Genotype data was analysed using multipoint variance components linkage methods in the program
SOLAR. A total of 377 individuals related through the extended 6,537-member
Norfolk genealogy were informative for linkage analysis, including 96 migraine- affected subjects. Heritability estimation supported a significant genetic component for migraine (h2=0.53, P<0.05). Linkage peaks were detected for a region on chromosome 13q33.1 (LOD=1.6; P=0.003) and chromosome 9q22.32 (LOD=1.26;
P=0.008). Assessment of microsatellites in the vicinity of these peaks in the remaining 223 unrelated Norfolk Island Health Study participants provided some evidence of association to chromosome 13 marker D13S173. On-going recruitment of pedigree members, increasing current marker density and further replication in independent populations may strengthen initial migraine findings on chromosome
9q22.22 and 13q33.1 in the Norfolk Island pedigree.
Page 130 5.2 Background
Migraine is a common episodic neurological disorder with an annual prevalence of
5.6% in males and 17.1% in females in the United States of America (Lipton et al.,
2007). Clinical diagnosis is established using ICHD-II criterion, which recognises 2 forms of migraine: MA and MO (ICHD-II, 2004). These two types of migraine are differentiated by the presence or absence of aura, a reversible focal neurological symptom preceding or accompanying the headache phase of an attack. Individuals may experience MA, MO or a combination of both forms of migraine with varying frequency throughout life. The headache is though to be caused by activation of the trigeminovascular system and the aura by spreading depression, a slow propagating wave of neuronal and glial depolarization that spreads across the cortex (Goadsby et al., 2009; Hadjikhani et al., 2001).
Knowledge of the underlying molecular genetic mechanisms has recently advanced with the discovery of a autosomal dominant MA gene on chromosome 10q25.3 encoding a potassium channel (KCNK18) (Lafreniere et al., 2010). This gene is predominantly expressed in trigeminal ganglion and dorsal root ganglia (Lafreniere et al., 2010). Functional analysis indicates a frame shift mutation in KCNK18 results in complete loss of channel function. This discovery supports the theory migraine is a genetically determined channelopathy like FHM, a rare subtype of MA that is also caused by ion channel and ion transporter disruption (De Fusco et al., 2003; Dichgans et al., 2005; Ophoff et al., 1996).
In addition to the discovery of a gene for a strictly Mendelian form of MA, the first published migraine genome-wide association study has emerged. The study
Page 131 convincingly identified a risk variant, rs1835740 associated with typical migraine
(MA and/or MO). This variant was initially identified in a cohort of 2,731 migraine cases ascertained from three European headache clinics and 10,747 population- matched controls and replicated in 3,202 cases and 40,062 controls (Anttila et al.,
2010). Functional analysis detected correlations of the sequence variant rs1835740 with metadherin (MTDH) transcript levels, a gene involved in glutamate homeostasis.
This study established the first common genetic risk factor for migraine.
Given the complex aetiology of migraine, it is likely the underlying genetic component is yet to be fully elucidated despite these recent advances. This study undertook an autosomal genome wide screen in an extended pedigree from the geographically isolated Norfolk Island in an effort to identify migraine susceptibility loci.
5.3 Materials and Methods
5.3.1 Sample Ascertainment
Data collection procedures have been described by Bellis et al. (2005) in a demographic investigation of CVD risk phenotypes (Bellis et al., 2005). Briefly, the study protocol was approved by the Griffith University Human Research Ethics
Committee prior to commencement. All subjects provided signed, informed consent prior to participation. In brief, subjects were ascertained based on permanent resident status (not selected on phenotypes of interest), to ensure sampling of individuals from the same genealogical background. Phenotypic data and biological specimens were obtained from 600 subjects (261 males, 339 females) with a mean age of 50.8 years
(standard deviation of 16.4 years). All biological samples were from venous blood.
Page 132 Genealogical data was obtained via questionnaire, and municipal and historical records. Migraine was diagnosed in accordance with current ICHD-II using interviews with a migraine questionnaire and followed up by qualified migraine diagnostician (ICHD-II, 2004). In total, 154 (25.7%) migraine cases were identified and are detailed in Table 5.1.
Norfolk Island census data indicates a large portion of the permanent residents are of
Pitcairn descent (Matthews, 2001). These individuals are related through a 6,379- member, 10-generation genealogy founded by Isle of Man Bounty Mutineers and their
Tahitian consorts in 1790. At the commencement of the Norfolk Island Health Study in 2000, it was hypothesised the ascertained subjects (n = 600) resided within the last five generations of the genealogy. All 600 participants were genotyped for 400 genome wide microsatellite markers to validate relationship status of all individuals in the sample population using identity-by-descent matrices (Bellis et al., 2008). PREST analysis produced an inferred pedigree structure of 6,537, which was inflated from
6,379 because of the coding of missing parents. Of the 600 subjects, 377 (171 males,
206 females) possessed direct lineage to initial population founders or possessed links via marriage. A total of 96 (25.5%) migraine cases were identified in these 377 related individuals (Table 5.1). These individuals were integrated into all subsequent heritability and linkage analyses.
The size and complexity of the original genealogical structure (N=6,537) and large volume of missing data prohibited direct use in variance component linkage analysis
(Bellis et al., 2008). Hence, pedigree was split (N=1,078) using a peeling algorithm in the pedigree database management system PEDSYS (Southwest Foundation for
Biomedical Research, San Antonio, Texas, USA) to facilitate analysis (Dyke, 1996).
Page 133 This 1,078 member pedigree has been previously employed in genome-wide screens of cardiovascular risk traits (Chapter 4) (Bellis et al., 2008).
Table 5.1. Migraine Demographics in the Norfolk Island cohort and pedigree.
Entire Cohort (N=600) Pedigree (N=377)
Total Migraine 154 96 Average Age in Years (SD) 49.01 (16.31) 46.41 (16.48)
Female Migraine 113 71 Average Age in Years (SD) 49.90 (15.68) 49.74 (16.76)
Male Migraine 41 25 Average Age in Years (SD) 46.56 (17.92) 42.14 (15.71)
MA* 105 64 MO 49 32
* Individuals experiencing both types of migraine were classified as MA
5.3.2 Genotyping
Genome wide STR data detailed in Chapter 3 and in individual assessments of cardiovascular risk traits (Bellis et al., 2008) was available for preliminary assessment of the migraine phenotype. Briefly, the genome screen included 400 highly polymorphic autosomal microsatellites markers genotyped across all 600 participants.
Markers had an average spacing of 10cM throughout the human genome. All PCRs were performed under standard conditions using fluorescently labelled primer pairs.
Markers were organised into multiplex panels and electrophoresed on a 3730 DNA
Analyzer (Applied Biosystems). Data was analysed using Applied Biosystems
Genescan version 3.1 and Genotyper version 2.1 software. Sex-averaged chromosomal maps were obtained from the Marshfield Centre for Medical Genetics
(http://research.marshfieldclinic.org/genetics). Pedigree structure validation,
Page 134 elimination of typing errors and estimation of multipoint identity-by-descent allele sharing matrices have been described (Bellis et al., 2008).
5.3.3 Statistical Analyses
Heritability (h2) was estimated as the ratio of the trait variance that is explained by additive polygenic effects to total phenotypic variance of the trait (Göring et al., 2001) using the Sequential Oligonucleotide Linkage Analysis Routines (SOLAR) v4.0.6 software package (Texas Biomedical Research Institute, San Antonio, Texas, USA).
The polygenic model applied assumes an infinite number of genetic factors with a small additive effect contributing to the trait variance. Estimates were screened for the covariate effects of age, age-squared, sex and their interactions to allow for differential symptom prevalence in males and females and adjust for the variable age of onset. Covariates with p-values less than or equal to 0.05 were retained in the final model. Heritability was measured on a scale ranging from 0 to 1. A value of 0 indicates the phenotype is completely controlled by non-genetic (environmental) factors. As the score approaches 1 the genetic component increases.
Linkage was tested throughout the genome using multipoint variance component analysis. This method localises QTLs for dichotomous traits by assuming the trait has a latent liability threshold with an underlying multivariate normal distribution
(Duggirala et al., 1997). All heritability estimations and linkage analyses utilise maximum likelihood, variance component methods implemented in the SOLAR v4.0.6 software package (Almasy and Blangero, 1998). Like parametric LOD scores, under the null hypothesis of no linkage, the variance component LOD score is distributed as an equal mixture of a chi-square random variable at point mass of 0 and a degree of freedom of 1 (Blangero et al., 2001). As a result, point-wise P-values can
Page 135 be estimated for each LOD score value using the method described by Nyholt et al.
(2000). A SOLAR LOD score of 3.0, 2.1, 1.2 and 0.59 equate to point wise P values of 1x10-4, 1x10-3, 1x10-2, and 0.05, respectively.
5.3.4 Replication Cohort
Implicated variants were further assessed in 223 unrelated Norfolk Island Health
Study participants (90 males; 133 females) included in the initial genome wide scan.
This unrelated subpopulation included 58 migraineurs (41MA; 17MO). Conformance with Hardy Weinberg Equilibrium (HWE) was tested in controls using GENEPOP version 4.0 Hardy Weinberg Exact Test (http://genepop.curtin.edu.au/) (Raymond and
Rousset, 1995; Rousset, 2008). This test uses a Markov chain algorithm to estimate without bias the exact P-value of this test for multiallelic markers. Allelic association was tested at polymorphic loci implicated in the pedigree genome wide linkage scan using the program CLUMP (Sham and Curtis, 1995). CLUMP performs chi-squared tests for allelic association employing an empirical Monte Carlo test of significance that does not require correction for multiple alleles at a highly polymorphic locus. A total of four ‘chi-squared’ test statistics are generated for each marker analysed. Three of these tests combine rare alleles present in a marker data set by collapsing adjacent columns before performing the chi-squared test.
5.4 Results
5.4.1 Pedigree
This study aimed to define the genetic basis of migraine within an extended pedigree whose origins date to the infamous ‘mutiny on the Bounty’. Previous studies of CVD
Page 136 risk determined the current trimmed pedigree structure has good power to estimate the heritability of continuous (quantitative) phenotypes whose variation is partially attributable to additive genetics and to detect QTLs for such phenotypes (Bellis et al.,
2007). The present study sought to characterise the genetics of a dichotomous trait, migraine.
Calculations were performed using the program SOLAR to determine the power to obtain a significantly (P<0.05) non-zero estimate of the heritability for a discrete phenotype given that a specified fraction of the phenotypic variance is due to additive genetics using the 1,078-member Norfolk Island pedigree. Assuming a general discrete trait prevalence of approximately 24% and 377 phenotyped individuals, the study achieves 80% power to detect an observed heritability of 61% at a significance level of 0.05 and 60% power to detect an observed heritability of 45% at a significance level of 0.05 (Figure 5.1). The 1,078-member Norfolk Island pedigree possesses good power to estimate discrete trait heritability. Upon calculation, a heritability of 0.53 was estimated for migraine (SE=0.302; P=0.016). Covariates age, age-squared and sex were retained in the final model (P<0.05).
Page 137 Figure 5.1. Power to detect a significantly (P<0.05) non-zero estimate of the heritability for a discrete phenotype in the 1,078-member Norfolk pedigree.
Previous estimates of the inherent power of the 1,078-member Norfolk Island pedigree for quantitative trait analysis (assuming 377 ascertained individuals) calculated 50% power to detect a quantitative trait loci (QTL) accounting for approximately 30% of trait variation (Bellis et al., 2008). As a significant non-zero heritability estimate was detected for migraine, further simulations were performed in
SOLAR to estimate the LOD score one would expect to obtain for a QTL having an effect size for a discrete phenotype (i.e. QTL heritability). Calculations were undertaken assuming the 1,078-member pedigree with 377 ascertained individuals and the assumed allele frequency set to the default value of 0.2113. Estimates for a
LOD score of 3 (significant linkage), a LOD score of 2 (suggestive linkage), a LOD score of 1.18 (point wise P=0.002), and a LOD score of 0.59 (point wise P=0.05)
Page 138 were generated. Data derived from the Norfolk pedigree suggests 60% power to detect a LOD score or 3 when considering a QTL that accounts for 100% of the variation in the trait; 80% power to detect a LOD score or 2 when considering a QTL that accounts for 90% of the variation in the trait; 80% power to detect a LOD score of 1.18 when considering a QTL that accounts for 70% of the variation in the trait;
80% power to detect a LOD score of 0.059 when considering a QTL that accounts for
55% of the variation in the trait. For comparison, 50% power is achieved to detect
LOD scores of 3.0, 2.0, 1.12, and 0.59 when considering a QTL that accounts for 90,
70, 52, and 36% of the trait variation. The Norfolk pedigree has inadequate power to detect genes of small to moderate effect sizes influencing the variation in discrete phenotypes.
The maximum LOD score obtained in our study was 1.60 (point-wise P=0.003) on chromosome 13q33.1 nearest marker D13S158 (102cM) (Figure 5.2). The 1-LOD- unit support interval around the linkage peak on chromosome 13q was approximately
26cM long, extending between markers D13S265 and D13S1265. Potential evidence of linkage (LOD>1.2) was also detected for migraine on chromosome 9q22.32
(100cM) nearest marker D9S287 with a LOD score of 1.26 (point-wise P=0.008)
(Figure 5.2). Additional linkage peaks were identified on chromosome 2, 4, 10 and 12 exceeding the threshold for nominal significance (LOD>0.59; point wise P<0.05).
Results are detailed in Table 5.2.
Page 139 Chromosome 9 3.0
2.5
2.0
1.5
LOD Score LOD 1.0
0.5
0.0 0 50 100 150 200 Genetic Position (cM)
Chromosome 13 3.0
2.5
2.0 1.5
1.0 LOD Score LOD 0.5 0.0 0 20 40 60 80 100 120 140 Genetic Position (cM)
Figure 5.2. Multipoint variance component linkage results for chromosome 9 and 13 for migraine.
Table 5.2. Multipoint genome wide results exceeding the nominal threshold
(LOD>0.59; P<0.05) for linkage.
Chromo- LOD Point wise Position Nearest Position some Score P-value (cM) Marker (cM) 2 0.9188 0.02 69 D2S2259 68.38 4 1.0068 0.016 173 D4S1539 172.85 9 1.2603 0.008 100 D9S287 100.82 10 0.9674 0.017 20 D10S189 20.36 12 0.7655 0.03 146 D12S324 151.13 13 1.6001 0.003 102 D13S158 100.73
Page 140 5.4.2 Replication Cohort
The initial genome wide screen included all 600 Norfolk Island participants to establish pedigree membership. Of these individuals, 223 individuals were unrelated, were not used in the linkage analyses but were available for replication testing. From the Norfolk Island pedigree genome wide scan, 2 microsatellites D9S287 and
D13S158 provided some evidence for linkage with migraine. These markers along with flanking microsatellites (D9S283 and D9S1690 on chromosome 9; and D13S159 and D13S173 on chromosome 13) were tested in the unrelated subpopulation from the
Norfolk Island Health Study using the programs GENEPOP and CLUMP. Due to the small sample size, migraine subtypes MA and MO were analysed as a single phenotype (migraine). The HWE exact test for multiallelic markers was performed using GENEPOP with the following parameters: dememorisation number of 1,000, a total of 200 batches, with 10,000 iterations performed per batch. All 6 microsatellites conformed to HWE in the control group (P>0.05) (Table 5.3).
Table 5.3. GENEPOP HWE Exact Test for microsatellites on chromosome 9 and 13.
Chromosome Marker Location (cM) HWE P* HWE SE
9 D9S283 93.61 0.879 0.005 D9S287 100.73 0.261 0.008 D9S1690 111.41 0.101 0.003
13 D13S159 95.00 0.175 0.010 D13S158 100.82 0.190 0.006 D13S173 106.26 0.467 0.006
*Exact P-values; SE=Standard Error
Page 141 Allelic association was then tested using the program CLUMP, which performs 4 chi- squared tests for multi-allelic markers using the following format; ‘T1’, a ‘raw’ contingency table (2-by-n, where n is the number of alleles); ‘T2’, a table with rare alleles (allele count < 5) grouped together to eliminate small cell counts; ‘T3’, a 2-by-
2 contingency table obtained by comparing one column of the original table against the total of all the other columns with the largest obtained chi-squared statistic reported; and ‘T4’, a 2-by-2 contingency table obtained by grouping columns of the original table to maximise the chi-squared statistic (Sham and Curtis, 1995).
The normal chi-square (T1) and the ‘clumped’ chi-square (T4) statistic perform similarly well and are recommended for result interpretation (Sham and Curtis, 1995).
The power to detect association diminishes with the remaining two statistics, T2 and
T3, (Sham and Curtis, 1995). For the Norfolk study, a T1 chi-square statistic producing an empirical p-value less than or equal to 0.05 was selected to indicate a meaningful association. As the CLUMP software performs all four tests simultaneously, the results of each of test in the Norfolk case-control cohort are reported in Table 5.4 for comparative purposes. Using the T1 statistic, a borderline association (empirical P=0.051) was detected for the chromosome 13 microsatellite,
D13S173. This microsatellite was significant for the CLUMP T2 and T3 tests with empirical P-values of 0.015 and 0.046, respectively. No evidence of association was detected for the chromosome 9 microsatellites for the T1 statistic (P>0.05), however association (empirical P=0.037) of the microsatellite D9S287 with migraine was detected using the CLUMP T3 test.
Page 142 Table 5.4. CLUMP tests of allelic association for microsatellites on chromosome 9 and 13.
Marker Migraine Control T1 P* T2 P* T3 P* T4 P* Allele Allele 2 2 2 2 Count Count
D9S283 114 328 9.55 0.751 4.00 0.687 1.36 0.728 3.77 0.852 D9S287 116 330 8.71 0.480 8.31 0.037 4.78 0.081 4.78 0.410 D9S1690 112 326 4.37 0.898 3.29 0.776 1.72 0.701 2.63 0.863
D13S159 114 326 27.23 0.191 14.97 0.245 4.09 0.340 17.02 0.143 D13S158 116 330 9.32 0.518 4.92 0.428 1.61 0.674 5.00 0.482 D13S173 114 328 15.32 0.051 13.90 0.015 7.07 0.046 7.86 0.134
*Empirical P-values using 10,000 Monte Carlo simulations; Empirical P-values < 0.05 are in bold
5.5 Discussion
The Norfolk population isolate is a highly unique island community. The current population structure includes a large multigenerational pedigree derived from 17 founding individuals, admixture, as well as cultural and geographical isolation. This chapter investigation assessed migraine using autosomal genome wide STR data in an effort to identify susceptibility loci. Implementing variance components methods, a migraine heritability estimate of 0.53 was estimated using 377 individuals in a trimmed 1,078-member version of the Norfolk pedigree. By comparison, population based twin studies estimate heritability for common migraine to vary from 0.34 to
0.57 (Honkasalo et al., 1995; Larsson et al., 1995; Mulder et al., 2003; Svensson et al.,
2003; Ziegler et al., 1998). Although the present study did not analyse migraine subtypes, heritability estimates of 0.65 are reported for MA (Ulrich et al., 1999a) and
0.61 for MO (Gervil et al., 1999a) are reported for population-based twin studies.
Heritability estimates for common migraine in the extended Norfolk pedigree are modest and reflect current estimates in population based twin studies.
Page 143 After detecting a significant genetic component, linkage was tested across the autosomes. A region of potential interest was detected on chromosome 9q22.32, residing near a familial occipitotemporal lobe epilepsy and combined MA locus on chromosome 9q21-q22 (MIM 611631) (Deprez et al., 2007). Near the linkage peak detected in the Norfolk pedigree resides the gabba-aminobutyric acid-B receptor type
2 (GABBR2; MIM607340) gene on chromosome 9q22.1. This gene is a receptor for the major inhibitory neurotransmitter in the brain, gabba-amino-butyric acid (GABA).
GABA type B receptors are a family of g-protein coupled receptors widely expressed in the peripheral and central nervous system, that inhibit or depress synaptic transmission via second messenger coupling (Kornau, 2006). Common variants in the
GABBR2 have been positively associated with mesial temporal lobe epilepsy (Wanga et al., 2008). The 9q22.32 linkage peak supports the findings of the Deprez et al.
(2007) study and may be of further interest considering the potential gene candidate,
GABBR2 located in close proximity to the 9q22.32 locus.
In addition to the chromosome 9q22.32 locus, a linkage peak of potential interest also occurred on chromosome 13q (LOD=1.60; point-wise p=0.003). The 1-LOD-support interval spanned 13q33.1 to 13q33.3. The peak marker D13S158, is within 10cM of a signal linked to migraine symptom phenotype, pulsation (LOD=3.31; p=0.00005) in a large Dutch cohort (Ligthart et al., 2008). Interestingly, other neurological disorders display linkage and association to chromosome 13q32-34. A bipolar locus is reported at 13q32-q33 (Badner and Gershon, 2002) and the 13q-related schizophrenia susceptibility locus (SCZD7; MIM603176) at 13q34 (Chumakov et al., 2002). Both bipolar disorder and schizophrenia display association with variants in the D-amino acid oxidase activator gene located at 13q34 (DAOA; MIM607408) (Chumakov et al.,
2002; Hattori et al., 2003). DAOA encodes a protein that is expressed in the human
Page 144 brain and is involved in degrading D-serine, a potent activator of N-methyl-D- aspartate-type glutamate receptor (NMDAR2D; MIM602717) (Chumakov et al.,
2002). Glutamate is a major excitatory neurotransmitter in the mammalian central nervous system. Disruption of normal glutamate homeostasis is hypothesised to contribute to the pathogenesis of a range of neurological disorders, including migraine
(Vikelis and Mitsikostas, 2007). Given current knowledge of the chromosome 13q32-
34 locus in bipolar disorder and schizophrenia, as well as the close proximity of the potential gene candidate DAOA, further assessment of this region in terms of migraine is warranted.
Nominal evidence (P<0.05) of replication was detected for the peak STR marker on chromosome 9 and for an STR marker within the 1-LOD-support interval on chromosome 13 in a subpopulation of 223 unrelated individuals recruited during the
Norfolk Island Health Study using the T2 and T3 CLUMP chi-square tests.
Unfortunately, these positive findings were not evident using the T1 CLUMP test
(P>0.05). However there was a trend for association (P<0.051) with marker D13S173 across the T1, T2 and T3 tests that supported the modest chromosome 13 linkage signal.
Aside from the overlap of chromosome 9 and 13 regions with some neurological phenotypes and the presence of a gene candidate under each peak, no replication
(LOD>0.59; point wise P<0.05) of known migraine loci was observed (Table 5.5). In particular, no evidence of replication was detected in the present study of the common migraine susceptibility variant on chromosome 8q22.1 (Anttila et al., 2010) or MA gene, KCNK18 on chromosome 10q25.3 (Lafreniere et al., 2010).
Page 145 Table 5.5. A comparison of the Norfolk Island genome wide linkage scan with known migraine loci.
Locus Phenotype Peak Closest LOD Reference Marker Norfolk Score Marker 1q31 MA and MO D1S2782 D1S249 0.000 (Lea et al., 2002) 4q21 Relaxed MO D4S2409 D4S1534 0.000 (Björnsson et al., 2003) 4q24 MO D4S1534 D4S1534 0.000 (Björnsson et al., 2003) 4q24 MA D4S1647 D4S414 0.000 (Wessman et al., 2002) 5q21 Pulsating Trait D5S2501 D5S2027 0.016 (Nyholt et al., 2005) 6p21.1-p12.2 MA and MO D6S452 D6S1610 0.385 (Carlsson et al., 2002) 8q22.1 MA and MO rs1835740 D8S270 0.150 (Anttila et al., 2010) 10q22-q23 MA and MO 103cM D10S1686 0.063 (Anttila et al., 2008) 10q25.3 MA KCNK18 D10S1693 0.000 (Lafreniere et al., 2010) 11q24 MA D11S4464 D11S4151 0.000 (Cader et al., 2003) 14q22.1 MO D14S978 D14S276 0.045 (Soragna et al., 2003) 15q11.2-q13 MA D15S97 D15S131 0.000 (Russo et al., 2005) 17p13 Pulsating Trait D17S945 D17S1852 0.216 (Anttila et al., 2006) 18q12.1 TCA D18S877 D18S478 0.000 (Anttila et al., 2006) 19p13 MA and MO D19S226 D19S226 0.000 (Nyholt et al., 1998b) 19p13 MA D19S592 D19S884 0.000 (Jones et al., 2001)
In the present study, 96 (25.5%) pedigree members suffered from migraine. By subtype, the cases included 64 (17%) MA sufferers and 32 (8.5%) MO sufferers.
Phenotyping included an interview with a migraine questionnaire, which was followed up by qualified migraine diagnostician, which may be less accurate in a diagnostic setting than a direct interview with a qualified neurologist (Rasmussen et al., 1991). Migraine was analysed as a single phenotype as the study hypothesis was for a common variant(s) underlying migraine. In the past, the theory of a common genetic basis has been highly contentious (Nyholt et al., 2004; Russell et al., 1996).
Strong evidence for a shared genetic basis has emerged with the recent discovery of a common migraine risk variant on chromosome 8 (Anttila et al., 2010). Results were not further stratified by subtype due to the power constraints in related and unrelated cohorts.
Page 146 Analysis of quantitative and dichotomous traits by the variance components approach has superior power to analyse extended pedigrees compared to nuclear families with the same number of cases (Duggirala et al., 1997). Even with use of the 1,078- member extended Norfolk pedigree, calculations revealed a reduction in power to detect a QTL explaining discrete trait variation compared to quantitative trait variation. This observation is not unique to the Norfolk pedigree. The variance component approach is a powerful method to detect linkage of quantitative phenotypes (Almasy and Blangero, 1998), however the precision of heritability estimates and power to detect linkage are diminished with trait dichotomization
(Duggirala et al., 1997). These limitations may be addressed in future studies of migraine in the Norfolk Island Health Study through on-going recruitment of pedigree members, increasing marker density, and further replication studies.
5.6 Conclusion
This study performed dichotomous trait analysis in an extended pedigree from the
Norfolk Island isolate. Results revealed good power to estimate heritability for discrete phenotypes, but reduced power to detect QTLs for discrete traits compared to quantitative traits. Despite this limitation, a significant, yet modest heritability estimate was detected for migraine. Peak linkage signals occurred on chromosomes
9q22.22 and 13q33.1. Focussing on these regions, some evidence of replication was detected in an unrelated sub-population also from Norfolk Island for chromosome marker D13S173. These linkage peaks overlap with regions reported for familial occipitotemporal lobe epilepsy and combined MA, migraine trait symptom ‘pulsation’ and bipolar disorder. Furthermore, gene candidates, GABBR2 and DAOA, lie within the vicinity of the chromosome 9 and 13 signals. Although interesting, current
Page 147 findings in the Norfolk pedigree are below the threshold for genome wide suggestive linkage. On-going recruitment of pedigree members, analysis of additional markers, and replication in independent populations may potentially strengthen and confirm present linkage evidence.
Page 148
Chapter 6: Genomic Analysis of ‘Bounty’ Descendents Implicates a Novel Neurotransmitter Pathway in Migraine Susceptibility
6.1 Overview
Migraine is a common neurological disease with a complex genetic aetiology. The disease affects ~12% of the Caucasian population and females are 3 times more likely than males to be diagnosed. In an effort to identify loci involved in migraine susceptibility we performed a pedigree-based genome-wide association study
(pGWAS) of the isolated population of Norfolk Island. This unique population originates from a small number of British and Polynesian founders who are descendents of the Bounty mutiny and forms a very large multigenerational pedigree.
In this study we identified a high prevalence (25.5%) and heritability of migraine in the Norfolk Island population. This study undertook a pedigree-based GWAS and utilised a statistical and pathological prioritisation approach (candidate gene analysis) to implicate a number of neurotransmitter-related gene variants in migraine susceptibility.
The results of the pGWAS identified 7 SNPs with marginal evidence for association at a P-value less than 1x10-5. The strongest association (P=1.96x10-6) was detected for a SNP in an intronic region of the ADAMTS-like protein 1 (ADAMTSL1) gene on chromosome 9p22.2-p22.1. In contrast, the candidate gene analysis identified variants in or near 8 novel genes. Of particular interest the RNA-editing gene ADARB2 gene, the glutamate receptor gene GRM7, the glutamate transporter gene SLC17A8, and the
Page 149
serotonin receptor gene HTR7. The most significantly associated candidate gene,
ADARB2 encodes an enzyme involved in RNA editing and downstream regulation of neurotransmitters. Disruption of this post-transcriptional mechanism might partly explain the long hypothesised role of serotoninergic and glutamatergic systems in migraine pathology.
6.2 Background
Migraine is a chronic and debilitating neurological disorder characterised by recurrent attacks of severe headache usually accompanied by nausea, vomiting, photophobia and phonophobia. Clinical diagnosis is established by fulfilment of symptom-based criteria described in the ICHD-II, which recognises 2 forms of migraine, MA and MO
(ICHD-II, 2004). The prevalence of migraine is high in Westernised nations where
17.1% of adult females and 5.6% of adult males are affected annually (Lipton et al.,
2007). Ion channel and ion transport genes are implicated in the rare, autosomal dominant MA subtype, FHM (De Fusco et al., 2003; Dichgans et al., 2005; Ophoff et al., 1996) and more recently in an extended pedigree with autosomal dominant MA
(Lafreniere et al., 2010). Functional analyses indicate mutations in these genes alter normal neural activity and promote cortical hyperexcitability (Lafreniere et al., 2010;
Pietrobon, 2007). In addition to these rare, familial genetic variants, a population level migraine risk variant has recently been discovered on chromosome 8 (Anttila et al.,
2010). The minor allele frequency (MAF=0.206) by meta-analysis indicates the variant is extremely common and conveys mild genetic risk (OR=1.18). Migraine is phenotypically and genetically heterogeneous as no single variant explains the entire underlying genetic component across different families and populations.
Page 150
In an effort to further elucidate the genetic component underlying migraine, we initiated a study of the Norfolk Island genetic isolate whose population structure includes an extended pedigree founded by 18th Century Isle of Man Bounty mutineers and their Tahitian consorts. Heritability screening of the Norfolk Island pedigree provided evidence of a significant genetic component for migraine (Chapter 5).
Analysis of a 10cM density microsatellite scan revealed moderate evidence of linkage to regions on chromosome 9 and 13 (Chapter 5). These loci were nominally replicated in unrelated Norfolk Island Cohort members and also provided support and replication of known migraine and epilepsy loci (Deprez et al., 2007; Ligthart et al.,
2008). These findings may benefit from re-evaluation with high-density SNP panels as traditional genome-wide microsatellite maps leave broad areas of the genome uncovered (Evans and Cardon, 2004; John et al., 2004). On this premise, genome wide SNP genotyping at a mean density of 4.7kb was undertaken in core-members of the Norfolk Island pedigree.
The aim of the current study was to map genes associated with migraine risk in the
Norfolk Island isolate by undertaking a pedigree-based genome-wide association study (pGWAS) of the core Norfolk pedigree using the Illumina 610-quad genotyping
BeadChip and a linkage-based association testing algorithm implemented in the
SOLAR program (Almasy and Blangero, 1998).
Page 151
6.3 Methods
6.3.1 Sample Ascertainment
The study protocol was approved by the Griffith University Human Research Ethics
Committee. All subjects provided signed, informed consent prior to participation.
Data collection procedures have been described in detail elsewhere (Bellis et al.,
2005). In brief, subjects were ascertained according to permanent resident status (not selected on phenotypes of interest), to ensure sampling of individuals from the same genealogical background. In the first instance phenotypic data and biological specimens were obtained from 600 subjects (261 males, 339 females) with a mean age of 50.8 years (standard deviation of 16.4 years). Venous blood specimens were available for 600 individuals from their visit to a temporary research clinic on Norfolk
Island, carried out during 2000. Blood samples were collected in EDTA tubes. DNA was isolated from a 10-20ml sample using a standard salting-out procedure (Miller et al., 1988). DNA concentration (ng/µl) and purity (260nm:280nm) were determined spectrophometrically using the NanoDrop ND-1000 (NanoDrop Technologies, Inc.).
Phenotypic data were obtained via a comprehensive medical questionnaire that included a section specific to migraine. Detailed questions regarding family history, symptoms, triggers, and medication were obtained. Migraine diagnosis was established in accordance with current IHS guidelines (ICHD-II, 2004).
6.3.2 Genealogical Structure
Genealogical data was obtained via questionnaire, and municipal and historical records. These records indicate Pitcairn Island was settled by 9 Isle of man ‘Bounty’ mutineers, 12 Tahitian women and 6 Tahitian men in 1790 (Hoare, 1999). Pedigree
Page 152
reconstruction and validation has confirmed current descendents possess lineages to all 9 ‘Bounty’ mutineers, 6 of the Tahitian women and 2 additional Caucasian sailors who joined the small colony during the early 19th century (Bellis et al., 2005;
Macgregor et al., 2010; McEvoy et al., 2009). A total of 377 individuals were determined to have familial links to these 17 founders and were integrated into heritability analyses. The size and complexity of the genealogical structure (N=6,537) and large volume of missing data prohibited direct use in variance component linkage analysis (Bellis et al., 2008). To facilitate analysis, the pedigree was split (N=1,078) using a peeling algorithm in the pedigree database management system PEDSYS
(Dyke, 1996). This 1,078 member pedigree has been previously employed in genome- wide screens of cardiovascular risk traits (Bellis et al., 2008).
6.3.3 SNP genotyping
DNA samples were genotyped according to the manufacturer’s instructions on
Illumina Infinium High Density (HD) Human610-Quad DNA analysis BeadChip version 1. A total of 620,901 genome wide markers were genotyped in a sub-sample of 285 related individuals (135 males; 150 females). Of these related individuals include 76 migraine cases (22 males; 54 females). Markers had a median spacing of
2.7kb (mean = 4.7kb) throughout the genome. Each Human610-Quad DNA analysis
BeadChip employed a four-sample format requiring 200ng of DNA per sample.
Samples were scanned on the Illumina BeadArray 500GX Reader. Raw data was obtained using Illumina BeadScan image data acquisition software (version 2.3.0.13).
Preliminary analysis of raw data was undertaken in Illumina BeadStudio software
(version 1.5.0.34) with the recommended parameters for the Infinium assay and using genotype cluster files provided by Illumina. Individuals with a call rate below 95%
Page 153
and SNPs with a call rate below 99%, deviating from Hardy-Weinberg equilibrium
-7 (PHWE<1x10 ) or with a minor allele frequency of less than 1% were excluded from analysis. Genotypic data was analysed for discrepancies, including Mendelian inheritance violations using the PEDSYS program INFER (Dyke, 1996) and
Simwalk2 (Sobel et al., 2002). The Pedigree RElationship Statistical Test (PREST) was used to verify the pedigree structure and detect relationship misspecification
(McPeek and Sun, 2000). Discrepant genotypes were blanked prior to analysis. SNPs were annotated using information available from the National Centre for
Biotechnology Information (NCBI) Build 36.3.
6.3.4 Statistical analysis: Heritability and Pedigree-Based Association
General characteristics of the subjects in each group were assessed using SPSS version 14.0 for windows (SPSS, Chicago, IL). All statistical analyses on related individuals were conducted using variance components-based methodology implemented in the Sequential Oligonucleotide Linkage Analysis Routines (SOLAR) version 4.0.6 software package. Heritability (h2) estimates were calculated as the ratio of the trait variance that is explained by additive polygenic effects to total phenotypic variance of the trait (Göring et al., 2001). The applied polygenic model assumes an infinite number of genetic factors, each with a small additive effect contributing to the trait variance (‘narrow sense’ heritability). Estimates were screened for the covariate effects of age, age-squared, sex and their interactions to allow for differential symptom prevalence in males and females and adjusted for the variable age of onset.
Covariates with P-values less than or equal to 0.05 were retained in the final model.
Dichotomous trait analysis was enabled by assuming a liability threshold model, with an underlying multivariate normal distribution (Duggirala et al., 1997).
Page 154
Genome-wide association testing was performed using measured genotype analysis
(Boerwinkle et al., 1986), embedded in a variance components-based linkage model
(Blangero et al., 2005). This assumed an additive model of allelic effect, where SNP genotypes AA, AB and BB were coded as -1, 0 and 1, respectively and used as a linear predictor of phenotype (Blangero et al., 2005). A total of 544,590 SNPs across chromosomes 1 to 22 were available for analysis. Genome-wide significance of a genetic loci was based on a local type I error of α equals 0.05/544,590 SNPs, which equals 9.2x10-8 by Bonferroni adjustment. SNP results were annotated using the
Whole Genome Association Study Viewer (WGA Viewer) program (Ge et al., 2008) and NCBI Build 37.1.
6.3.5 Candidate Gene Analysis
A candidate gene association analysis was also undertaken as part of this study. The same statistical approach was applied for association analysis of candidate loci, however a local type I error of α = 0.05 was applied. A Bonferroni adjustment was not required to protect against type I error inflation as the application of selection criteria for candidate genes negates the global null hypothesis. This approach has been successfully implemented in a GWAS to identify novel loci influencing serum cholesterol levels (Igl et al., 2010). This approach was implemented to identify novel gene candidates for future evaluation in the Norfolk pedigree.
Page 155
6.4 Results
6.4.1 Genome Wide Association Analysis
Migraine phenotype information was analysed from a 377-member pedigree previously described (Bellis et al., 2008; Macgregor et al., 2010). Of this pedigree, 96 individuals screened positive for migraine according to the ICHD-II criteria. The remaining 281 individuals were not affected with migraine at the time of recruitment.
Heritability of the migraine phenotype was estimated by SOLAR using an age and sex adjusted model assuming additive genetic factors. This analysis produced an h2 of
0.53 (P=0.016), which is consistent with other studies and warrants a pGWAS to map susceptibility genes.
Illumina 610-quad genotype data was collected for n=285 individuals who were selected from the core 377-member pedigree and were highly informative for linkage.
A high proportion of affected females were observed (74%), which is consistent with the female-male ratio of approximately 3 to 1 (P=0.0012). Migraineurs were slightly younger (46 yrs) on average compared to non-migraineurs (50yrs) (P=0.035). A pGWAS was performed by testing SNPs for association by measured genotype analysis within a linkage-based probit regression model adjusted for covariates age, sex, sex-squared and their interactions. A Manhattan plot of P-values is depicted in
Figure 6.1. The beta coefficient is a measure of risk. A negative beta indicates the minor allele increases migraine risk: a positive beta indicates a decreased risk. The most strongly associated SNP occurred in the intronic region of the ADAMTSL1 gene
(MIM 609198) on chromosome 9p22.2-p22.1 (rs4977338; P=1.96x10-6). The beta
Page 156
coefficient supported an increased risk of migraine (Beta=-0.823). A total of 204
SNPs were typed across this gene.
Figure 6.1. Manhattan Plot of autosomal genome-wide associations for migraine in the Norfolk Island pedigree.
Page 157
Table 6.1. Summary of the top 0.05% of SNPs (n=172) detected in the Norfolk study.
RANK SNP P-VALUE BETA MAF CHR POSITION SNP TYPE MINOR/ GENE DIST. TO (BP) MAJOR GENE ALLELE 1 rs4977338 1.96E-6 -0.823 0.140 9 18718086 INTRONIC T/G ADAMTSL1 0 2 rs11930554 2.84E-6 1.068 0.138 4 131787382 INTERGENIC C/T AC092540.1 -359452 3 rs11936003 2.84E-6 1.068 0.138 4 131788092 INTERGENIC G/A AC092540.1 -360162 4 rs7079024 3.03E-6 0.630 0.470 10 3445668 INTERGENIC C/T RP11-482E14.1 83418 5 rs7690766 3.35E-6 1.064 0.135 4 131774208 INTERGENIC G/A AC092540.1 -346278 6 rs883248 3.83E-6 0.666 0.439 10 1250184 INTRONIC G/A ADARB2 0 7 rs4807347 9.56E-6 0.941 0.144 19 2857287 3PRIME UTR A/C ZNF555 0 8 rs2525570 1.15E-5 0.603 0.469 17 29681245 INTRONIC G/A NF1 0 9 rs10512405 1.21E-5 -0.542 0.403 9 113236797 INTRONIC C/T SVEP1 0 10 rs10795033 1.72E-5 0.578 0.470 10 3447072 INTERGENIC C/T RP11-482E14.1 82014 11 rs2646179 1.80E-5 0.570 0.429 2 175598518 WITHIN NON-CODING GENE G/A AC018890.2 0 12 rs4280415 1.97E-5 -0.531 0.450 2 29783021 INTRONIC C/T ALK 0 13 rs7039314 2.05E-5 -0.542 0.426 9 3781983 INTERGENIC C/T GLIS3 42144 14 rs10817026 2.32E-5 -0.519 0.407 9 113239268 INTRONIC T/C SVEP1 0 15 rs16882131 2.56E-5 -0.601 0.216 6 52008933 UPSTREAM T/C MIR206 -214 16 rs2271275 2.67E-5 0.650 0.368 10 1230968 NON-SYNONYMOUS G/A ADARB2 0 17 rs1391950 2.70E-5 0.546 0.490 3 7058417 INTRONIC G/A GRM7 0 18 rs17576051 2.84E-5 -0.978 0.069 3 45449980 INTRONIC G/A LARS2 0 19 rs9505597 2.93E-5 -0.536 0.326 6 9044593 INTRONIC C/T RP11-354I10.1 79861 20 rs6730459 3.13E-5 0.719 0.212 2 171078637 INTRONIC A/G MYO3B 0 21 rs1046914 3.43E-5 0.673 0.328 10 1228206 3PRIME UTR G/A ADARB2 0 22 rs7575145 3.59E-5 -0.535 0.450 2 175599106 WITHIN NON-CODING GENE C/T AC018890.2 0 23 rs11615115 4.02E-5 3.790 0.045 12 100802452 INTRONIC G/A SLC17A8 0 24 rs6708544 4.49E-5 -0.529 0.451 2 175604194 WITHIN NON-CODING GENE A/G AC018890.2 0 25 rs1374315 4.69E-5 -1.866 0.018 2 174883399 INTERGENIC G/A AC016737.1 -24337 26 rs6023232 5.03E-5 0.712 0.210 20 53043792 INTERGENIC T/G DOK5 -48344 27 rs12498616 5.10E-5 0.888 0.132 4 131866685 INTERGENIC A/G AC021203.1 -327712 28 rs887500 5.14E-5 0.801 0.151 19 2862785 UPSTREAM G/T ZNF556 -4548 29 rs2600685 5.19E-5 0.521 0.492 2 175627048 INTRONIC A/G CHRNA1 0 30 rs6680365 5.38E-5 0.561 0.347 1 7236810 INTRONIC A/G AL596210.1 0 Page 158
Table 6.1. Summary of the top 0.05% of SNPs (n=172) detected in the Norfolk study (continued).
RANK SNP P-VALUE BETA MAF CHR POSITION SNP TYPE MINOR/ GENE DIST. TO (BP) MAJOR GENE ALLELE 31 rs1886066 5.56E-5 -0.515 0.334 6 9057578 INTRONIC G/A RP11-354I10.1 66876 32 rs7744460 5.56E-5 -0.515 0.334 6 9065215 INTRONIC T/G RP11-354I10.1 59239 33 rs9558976 5.60E-5 -0.533 0.345 13 107878591 INTRONIC A/G FAM155A 0 34 rs9301200 5.60E-5 -0.533 0.342 13 107882985 INTRONIC G/A FAM155A 0 35 rs11979133 6.12E-5 -0.702 0.198 7 78215346 INTRONIC C/T MAGI2 -64382 36 rs10418996 6.48E-5 0.793 0.147 19 2860166 DOWNSTREAM T/G ZNF555 2484 37 rs12223849 6.66E-5 -0.542 0.263 11 62717297 INTERGENIC T/C AP000438.1 -13596 38 rs779331 6.80E-5 0.539 0.325 3 191570663 INTERGENIC A/C RP11-655G22.1 99473 39 rs2271741 6.84E-5 -0.529 0.310 2 169078534 INTRONIC C/A STK39 0 40 rs11713183 7.26E-5 -0.511 0.427 3 7078179 INTRONIC T/C GRM7 0 41 rs10811278 7.60E-5 0.540 0.383 9 19898792 INTERGENIC A/G SLC24A2 -111866 42 rs10903399 7.68E-5 0.638 0.331 10 1227868 DOWNSTREAM C/T ADARB2 205 43 rs7031120 7.80E-5 -0.537 0.247 9 88669475 INTRONIC G/T GOLM1 0 44 rs7232459 8.02E-5 0.907 0.119 18 60703005 INTERGENIC A/G PHLPP1 55339 45 rs16937677 8.30E-5 1.249 0.080 9 19642563 INTRONIC A/G SLC24A2 0 46 rs7734335 8.40E-5 -0.497 0.398 5 75967232 INTRONIC G/A IQGAP2 0 47 rs10969478 8.50E-5 -0.510 0.356 9 29954934 INTERGENIC A/G RP11-460C6.1 -128225 48 rs12451822 8.78E-5 0.737 0.172 17 14584316 INTERGENIC T/C AC013248.1 24077 49 rs10825819 8.92E-5 -0.643 0.203 10 58296254 INTERGENIC A/G AC025039.1 59469 50 rs10892178 9.02E-5 0.542 0.380 11 117685343 INTERGENIC T/C FXYD2 5447 51 rs4737206 9.19E-5 -0.496 0.346 8 66129990 INTERGENIC C/T RP11-822K17.1 61977 52 rs12421579 9.55E-5 -0.621 0.301 11 71291490 INTRONIC T/G KRTAP5-11 0 53 rs2827129 9.60E-5 -0.725 0.133 21 23291775 INTERGENIC T/C AP000472.2 13860 54 rs7562354 9.66E-5 -0.484 0.432 2 241228985 DOWNSTREAM T/C AC124861.2 126 55 rs11519679 9.83E-5 11.258 0.050 12 93886589 INTRONIC C/T MRPL42 0 56 rs12552329 9.97E-5 -0.500 0.348 9 29958616 INTERGENIC T/C RP11-460C6.1 -131907 57 rs11985941 0.0001 0.884 0.125 8 38419633 INTERGENIC G/T C8orf86 -33453 58 rs3900537 0.0001 -2.244 0.015 8 19784574 INTERGENIC G/A LPL -11710 59 rs17369819 0.0001 -1.001 0.058 8 75997034 INTERGENIC T/C CRISPLD1 50241 60 rs7657203 0.0001 -1.386 0.027 4 174660965 INTERGENIC G/A AC079789.1 -105442 Page 159
Table 6.1. Summary of the top 0.05% of SNPs (n=172) detected in the Norfolk study (continued).
RANK SNP P-VALUE BETA MAF CHR POSITION SNP TYPE MINOR/ GENE DIST. TO (BP) MAJOR GENE ALLELE 61 rs1298145 0.0001 0.511 0.368 4 8220400 INTRONIC G/A SH3TC1 0 62 rs17051078 0.0001 1.079 0.085 4 131652629 INTERGENIC C/T AC092540.1 -224699 63 rs4738648 0.0001 -0.483 0.404 8 59057413 INTRONIC C/T FAM110B 0 64 rs6941112 0.0001 0.629 0.243 6 31946614 INTRONIC A/G STK19 0 65 rs10898347 0.0001 -0.612 0.291 11 71294802 INTRONIC T/C AP000867.1 0 66 rs2525574 0.0001 0.517 0.490 17 29705947 STOP LOST ;SPLICE SITE C/T AC135724.1 0 67 rs709086 0.0001 0.528 0.296 3 191456220 INTERGENIC C/T AC072023.2 -97007 68 rs709087 0.0001 0.528 0.296 3 191456299 INTERGENIC C/T AC072023.2 -97086 69 rs709094 0.0001 0.528 0.296 3 191463845 INTERGENIC C/A AC072023.2 -104632 70 rs2039331 0.0001 0.558 0.321 9 8988375 INTRONIC G/A PTPRD 0 71 rs10254840 0.0001 -0.775 0.117 7 92333408 INTRONIC G/A CDK6 0 72 rs7815122 0.0001 -0.698 0.137 8 120869022 UPSTREAM T/G DSCC1 -852 73 rs10847807 0.0001 -0.582 0.199 12 129722628 INTRONIC T/G TMEM132D 0 74 rs4759808 0.0001 -0.582 0.199 12 129722961 INTRONIC T/C TMEM132D 0 75 rs7579874 0.0001 -0.645 0.135 2 62190001 INTRONIC C/A COMMD1 0 76 rs17139483 0.0001 -0.612 0.158 16 6213749 INTERGENIC T/C AC006112.1 -214155 77 rs1349335 0.0001 0.490 0.481 16 57628827 DOWNSTREAM G/A GPR114 3234 78 rs9267803 0.0001 0.772 0.167 6 32101762 UPSTREAM T/C FKBPL -3694 79 rs4590119 0.0001 0.559 0.316 4 116507286 INTERGENIC C/T NDST4 -472254 80 rs10930865 0.0001 0.869 0.097 2 180201455 INTERGENIC A/C AC093911.1 51021 81 rs6084878 0.0001 0.686 0.193 20 4744291 INTERGENIC T/C RASSF2 16378 82 rs4643089 0.0001 -0.510 0.260 11 120941583 INTRONIC G/A TBCEL 0 83 rs10958805 0.0001 0.677 0.183 8 38540944 INTERGENIC T/C TACC1 -45158 84 rs9889382 0.0001 -0.525 0.222 17 35018201 INTERGENIC A/G AC015938.1 -20448 85 rs11674035 0.0001 -0.651 0.102 2 16921112 WITHIN NON-CODING GENE C/T AC008069.1 0 86 rs1862059 0.0001 -0.651 0.102 2 16926228 WITHIN NON-CODING GENE A/G AC008069.1 0 87 rs2388129 0.0001 0.523 0.367 5 20166988 INTERGENIC C/T AC094103.1 137166 88 rs6589849 0.0001 0.662 0.219 11 98886331 INTERGENIC A/C AP000923.1 -104575 89 rs9640606 0.0001 0.626 0.236 7 92301040 INTRONIC A/G CDK6 0 90 rs2237573 0.0001 -0.751 0.135 7 92313733 INTRONIC T/G CDK6 0 Page 160
Table 6.1. Summary of the top 0.05% of SNPs (n=172) detected in the Norfolk study (continued).
RANK SNP P-VALUE BETA MAF CHR POSITION SNP TYPE MINOR/ GENE DIST. TO (BP) MAJOR GENE ALLELE 91 rs9947451 0.0001 -0.751 0.122 18 60743100 INTERGENIC G/A BCL2 47479 92 rs916162 0.0001 -0.480 0.462 12 52661714 INTERGENIC A/G AC021066.1 -9377 93 rs1561836 0.0002 0.846 0.128 5 22794657 INTRONIC C/T CDH12 0 94 rs4238497 0.0002 -0.847 0.071 15 28280835 INTRONIC A/G OCA2 0 95 rs10799615 0.0002 0.570 0.268 1 20541370 INTERGENIC A/G UBXN10 18829 96 rs11010965 0.0002 1.393 0.072 10 37258105 INTERGENIC T/C RP11-322I2.2 -24439 97 rs10817025 0.0002 -0.459 0.387 9 113234507 NON-SYNONYMOUS C/T SVEP1 0 98 rs528431 0.0002 -0.472 0.387 11 117662909 INTRONIC T/G DSCAML1 0 99 rs2295283 0.0002 -0.508 0.327 1 12082926 NON-SYNONYMOUS A/G MIIP 0 100 rs1032474 0.0002 -0.768 0.097 9 14775853 SYNONYMOUS A/G FREM1 0 101 rs4415414 0.0002 -0.768 0.100 9 14778244 INTRONIC T/G FREM1 0 102 rs6557416 0.0002 0.528 0.457 6 155605487 INTRONIC A/C TFB1M 0 103 rs814836 0.0002 5.237 0.066 6 41389328 INTERGENIC A/G RP1-149M18.2 15029 104 rs12957678 0.0002 -0.755 0.121 18 55218139 INTRONIC T/C FECH 0 105 rs500044 0.0002 -0.628 0.137 9 4002823 INTRONIC C/T GLIS3 0 106 rs2800143 0.0002 -0.654 0.128 10 92463214 INTERGENIC A/G HTR7 37366 107 rs11220594 0.0002 0.609 0.273 11 126601132 INTERGENIC T/C KIRREL3 -168320 108 rs9293054 0.0002 -1.425 0.027 5 23123829 INTERGENIC T/C AC010460.1 -175435 109 rs1539000 0.0002 -0.493 0.368 9 18602403 INTRONIC C/T ADAMTSL1 0 110 rs10813208 0.0002 -0.485 0.355 9 29954848 INTERGENIC T/C RP11-460C6.1 -128139 111 rs10511835 0.0002 -0.485 0.354 9 29958595 INTERGENIC A/G RP11-460C6.1 -131886 112 rs12097284 0.0002 5.282 0.049 1 18159342 INTERGENIC T/C ACTL8 5784 113 rs7849848 0.0002 4.788 0.049 9 105259717 INTERGENIC T/C RP11-342F21.1 -17847 114 rs12090000 0.0002 -1.290 0.027 1 177152360 INTRONIC G/A FAM5B 0 115 rs85425 0.0002 -0.863 0.083 14 59384953 INTERGENIC T/C RP11-112J1.1 123206 116 rs7606532 0.0002 -0.580 0.156 2 133505873 INTRONIC C/A NCKAP5 0 117 rs12795310 0.0002 -0.496 0.299 11 120931054 INTRONIC C/T TBCEL 0 118 rs17123151 0.0002 -1.229 0.033 1 62623177 INTRONIC A/G INADL 0 119 rs6425412 0.0002 11.251 0.034 1 177073727 INTRONIC G/A ASTN1 0 120 rs6936346 0.0002 0.532 0.285 6 32200754 INTERGENIC T/C NOTCH4 -8910 Page 161
Table 6.1. Summary of the top 0.05% of SNPs (n=172) detected in the Norfolk study (continued).
RANK SNP P-VALUE BETA MAF CHR POSITION SNP TYPE MINOR/ GENE DIST. TO (BP) MAJOR GENE ALLELE 121 rs711076 0.0002 -0.471 0.348 12 77905207 INTERGENIC G/A AC073528.1 -60714 122 rs779279 0.0002 0.462 0.428 3 191543927 INTERGENIC A/C RP11-655G22.1 126209 123 rs1421713 0.0002 -0.458 0.374 5 164583556 INTERGENIC A/G AC091907.1 -221282 124 rs13378764 0.0002 -0.539 0.200 13 100853824 INTRONIC A/G PCCA 0 125 rs2829160 0.0002 0.705 0.152 21 25918589 WITHIN NON-CODING GENE G/T AP000476.1 0 126 rs651984 0.0002 5.210 0.052 6 71599887 INTRONIC C/T B3GAT2 0 127 rs10842390 0.0002 4.559 0.035 12 24814274 INTERGENIC A/G RP11-615I16.1 18174 128 rs13019329 0.0002 0.473 0.429 2 225015584 INTERGENIC C/T AC019109.1 -18797 129 rs12266938 0.0002 0.662 0.173 10 3862940 INTERGENIC C/T RP11-464C19.1 -13202 130 rs9582378 0.0002 -0.537 0.200 13 100878484 INTRONIC G/A PCCA 0 131 rs12939076 0.0002 -0.525 0.183 17 78634810 INTRONIC A/C RPTOR 0 132 rs210993 0.0002 0.503 0.344 5 161619504 INTERGENIC A/G GABRG2 36959 133 rs7857674 0.0002 -0.502 0.276 9 75123453 INTERGENIC G/T TMC1 -13264 134 rs10490849 0.0002 -1.461 0.027 3 144079222 INTERGENIC G/A AC022495.1 -173360 135 rs1728369 0.0002 0.813 0.127 16 86386213 INTERGENIC G/T AC092327.1 -6928 136 rs12818967 0.0002 1.253 0.067 12 131797608 INTERGENIC G/T AC092850.2 -13312 137 rs11217806 0.0002 4.511 0.042 11 120169962 INTRONIC C/T POU2F3 0 138 rs7028191 0.0002 -0.530 0.313 9 137117496 INTERGENIC T/C RP11-145E17.1 68281 139 rs12150753 0.0002 0.721 0.126 18 60089644 INTERGENIC T/C RP11-640A1.1 6181 140 rs11642698 0.0002 -0.474 0.342 16 74073632 INTERGENIC C/T RP11-133M24.1 -98209 141 rs8071435 0.0002 0.769 0.131 17 1365747 DOWNSTREAM A/G MYO1C 1733 142 rs7086377 0.0002 0.482 0.396 10 3596526 INTERGENIC T/G RP11-482E14.2 -17303 143 rs11001775 0.0002 0.956 0.081 10 78184711 INTRONIC A/C C10orf11 0 144 rs10414890 0.0002 0.497 0.456 19 40106388 INTERGENIC G/A LGALS13 8275 145 rs12333888 0.0002 0.479 0.396 7 104117988 INTRONIC A/C LHFPL3 0 146 rs4348565 0.0002 -0.460 0.383 9 103412325 INTERGENIC A/G MURC 62137 147 rs1388214 0.0002 -0.467 0.341 12 77901810 INTERGENIC A/G AC073528.1 -64111 148 rs9384738 0.0002 0.947 0.097 6 110669277 INTRONIC T/C C6orf186 0 149 rs7245501 0.0002 0.585 0.223 19 57663794 3PRIME UTR A/G DUXA 0 150 rs4596408 0.0002 0.474 0.385 5 39447059 INTERGENIC G/T DAB2 -21724 Page 162
Table 6.1. Summary of the top 0.05% of SNPs (n=172) detected in the Norfolk study (continued).
RANK SNP P-VALUE BETA MAF CHR POSITION SNP TYPE MINOR/ GENE DIST. TO (BP) MAJOR GENE ALLELE 151 rs749467 0.0002 0.551 0.318 16 6167496 INTERGENIC A/G AC006112.1 -260408 152 rs9897203 0.0002 -1.425 0.027 17 62530143 INTRONIC A/G CCDC45 0 153 rs8074368 0.0002 -1.425 0.027 17 62596796 INTRONIC C/T SMURF2 0 154 rs10964389 0.0002 0.882 0.113 9 20022425 INTERGENIC C/T SLC24A2 -235499 155 rs10964390 0.0002 0.882 0.113 9 20022916 INTERGENIC C/T SLC24A2 -235990 156 rs2286531 0.0002 0.794 0.141 17 9808571 5PRIME_UTR G/T RCVRN 0 157 rs6710206 0.0002 -0.871 0.063 2 130491622 INTERGENIC G/A AC079776.2 135144 158 rs4233595 0.0002 -0.870 0.063 2 130483910 INTERGENIC G/A AC079776.2 142856 159 rs10484882 0.0002 -0.702 0.104 6 52011469 UPSTREAM T/C RP11-771D21.1 143 160 rs2451948 0.0002 -0.604 0.158 9 109518208 INTERGENIC G/A RP11-308N19.4 32997 161 rs2383928 0.0002 -0.620 0.132 8 75910104 INTRONIC T/C CRISPLD1 0 162 rs17785871 0.0002 1.006 0.099 9 109860088 WITHIN NON-CODING GENE T/C RP11-508N12.2 0 163 rs13412991 0.0002 -0.473 0.362 2 134323037 INTRONIC C/T NCKAP5 0 164 rs2356215 0.0002 1.035 0.085 10 17073900 INTRONIC T/C CUBN 0 165 rs11636500 0.0002 -0.699 0.118 15 82451027 INTRONIC A/C EFTUD1 0 166 rs6729271 0.0003 -0.451 0.435 2 238825809 INTERGENIC C/T RAMP1 5053 167 rs4926853 0.0003 -0.528 0.273 1 50709222 INTERGENIC G/A RP11-567C20.1 -20423 168 rs9314061 0.0003 -0.493 0.245 5 164760466 INTERGENIC T/C AC008415.2 -275980 169 rs12515866 0.0003 -0.493 0.245 5 164774447 INTERGENIC T/C AC008415.2 -261999 170 rs11714003 0.0003 -0.783 0.089 3 54234467 INTRONIC G/A CACNA2D3 0 171 rs8094228 0.0003 -0.548 0.210 18 24453731 WITHIN NON-CODING GENE T/C C18orf16 0 172 rs4672473 0.0003 -0.605 0.145 2 62240786 INTRONIC G/T COMMD1 0
Page 163
6.4.2 Candidate Gene Association Analysis
We used a functional prioritisation approach to assess candidate gene association. Focussing on the top 0.05% of SNPs yielding the lowest P-value from the pGWAS, we prioritised SNPs based on their functional plausibility in terms of disease pathology. To do this we implemented a similar approach to Igl et al (2010), prioritising SNPs based on P-value as well as plausibility for a functional role in disease pathology. Specifically, the study focused on the top 0.05% of SNPs yielding the lowest P-value from the pGWAS. These SNPs were assessed according to whether they were physically near genes with known annotation placing more value on genes with a putative role in migraine neuropathology i.e. genes that are known to a) be expressed in the brain or central nervous system (CNS) b) regulate neurological pathways (e.g. neurotransmitters) c) be plausibly related to migraine neuropathology (e.g. cellular hyperexcitability, ion channel disruption).
Using this strategy to assess we prioritised 12 SNPs in 8 genes (Table 6.2). These were as follows; the astrotactin 1 gene (ASTN1) facilitates glial-guided neuronal movement in cortical regions during brain development (Fink et al., 1997); a gene encoding the alpha 2/ delta 3 subunit of the voltage-dependent calcium channel (CACNA2D3) has been shown to have wide expression in fetal tissue (including brain) (Hanke et al., 2001) and is involved in synaptogenesis (Bauer et al., 2010); the glutamate receptor 7 gene (GRM7) is a member of the metabotropic glutamate receptor family of genes that are widely expressed in neural cells and are important modulators of glutamate transmission (Okamoto et al., 1994); the catherin
12 gene (CDH12) is specifically expressed in the brain and is involved in the development and function of the mammalian CNS (Mayera et al., 2010); the GABA-A receptor gene
(GABRG2) regulates neurotransmission in the mammalian CNS and is implicated in epilepsy phenotypes (Benarroch, 2007); the RNA-specific adenosine deaminase gene (ADARB2) gene
Page 164
is expressed in the CNS and is involved in RNA editing and downstream regulation of neurotransmitters (Maas et al., 2003); the gene encoding serotonin receptor 7 (HTR7) is predominantly expressed throughout the brain and functions by positively activating adenylate cyclase via g-protein coupling (Bard et al., 1993); the vesicular glutamate transporter 3 gene (SLC17A8) is expressed in regions of the brain and is involved in the transports glutamate into synaptic vesicles at glutamergic synapses (Kanai and Hediger,
2004).
Page 165
Table 6.2. Candidate SNPs (n=12) selected from the Norfolk Island cohort.
DIST. SNP REF. P- POSITION MINOR/ GENE BETA* FUNCTION MAF TO GENE MIM LOCATION NO. VALUE (BP) MAJOR ID GENE SYMBOL ALLELE (BP) rs6425412 2.00E-4 11.25 177073727 INTRONIC G/A 0.034 0 ASTN1 460 600904 1q25.2 rs11714003 3.00E-4 -0.78 54234467 INTRONIC G/A 0.089 0 CACNA2D3 55799 606399 3p21.1 rs1391950 2.70E-5 0.55 7058417 INTRONIC G/A 0.490 0 GRM7 2917 604101 3p26.1-p25.1 rs11713183 7.26E-5 -0.51 7078179 INTRONIC T/C 0.427 0 GRM7 2917 604101 3p26.1-p25.1 rs1561836 2.00E-4 0.85 22794657 INTRONIC C/T 0.128 0 CDH12 1010 600562 5p14-p13 rs210993 2.00E-4 0.50 161619504 INTERGENIC A/G 0.344 36959 GABRG2 2566 137164 5q31.1-q33.1 rs10903399 7.68E-5 0.64 1227868 DOWNSTREAM C/T 0.330 205 ADARB2 105 602065 10p15.3 rs1046914 3.43E-5 0.67 1228206 3PRIME UTR G/A 0.328 0 ADARB2 105 602065 10p15.3 rs2271275 2.67E-5 0.65 1230968 NON-SYNON G/A 0.368 0 ADARB2 105 602065 10p15.3 rs883248 3.83E-6 0.67 1250184 INTRONIC G/A 0.439 0 ADARB2 105 602065 10p15.3 rs2800143 2.00E-4 -0.65 92463214 INTERGENIC A/G 0.128 37366 HTR7 3363 182137 10q21-q24 rs11615115 4.02E-5 3.79 100802452 INTRONIC G/A 0.045 0 SLC17A8 246213 607557 12q23.1 ADARB2=adenosine deaminase, RNA-specific, B2 (RED2 homolog rat); ASTN1=astrotactin 1; CACNA2D3=calcium channel, voltage-dependent, alpha 2/delta subunit 3; CDH12=cadherin 12, type 2 (N-cadherin 2); GABRG2=GABA A receptor, gamma 2; GRM7=glutamate receptor, metabotropic 7; HTR7=5-hydroxytryptamine (serotonin) receptor 7 (adenylate cyclase-coupled); SLC24A2=solute carrier family 24 (sodium/potassium/calcium exchanger), member 2; SLC17A8=solute carrier family 17 (sodium- dependent inorganic phosphate cotransporter), member 8; BP=base pairs; MAF=Minor Allele Frequency. * The beta coefficient is a measure of risk. A negative beta indicates the minor allele increases migraine risk; a positive beta indicates a decreased risk.
Page 166
6.5 Discussion
6.5.1 pGWAS Summary
This study undertook a GWAS using the Norfolk Island pedigree in an effort to localise susceptibility gene(s) underlying migraine. By typing a high density SNP panel (average density ~4.7kb) in core Norfolk pedigree members, the study aimed to enhance the detection of potential susceptibility loci compared to the initial linkage approach (average density
~10cM). A total of 7 SNPs showed marginal evidence for association at a P-value less than
1x10-5. The most significantly associated SNP (P=1.96x10-6) occurred in an intronic region of the ADAMTS-like protein 1 (ADAMTSL1) gene on 9p22.2-p22.1. ADAMTSL1 encodes a secreted glycoprotein that localises to the extracellular matrix (Hirohata et al., 2002). This gene is related to the ADAMTS (a disintegrin-like and metalloproteinase with thrombospondin type 1 motifs) family of proteases, some of which are implicated in inflammatory vascular disease (Salter et al., 2010). The exact biological functions of the ADAMTSL1 gene are yet to be fully characterised. However, given the roles of members of the ADAMTS family, this extracellular matrix gene could potentially mediate neurovascular inflammatory processes, a speculative role that will require confirmation by functional analysis.
6.5.2 Candidate Gene Analysis
To ensure no important findings were missed, we performed an exploratory analysis by applying statistical and biological prioritisation to identify potential gene candidates. We screened the top 0.05% of SNPs ranked by P-value for variants within or flanking genes expressed in the brain or CNS, regulate neurological pathways and/or plausibly related to migraine neuropathology. Using this approach, 12 statistically significant SNPs were identified across 8 genes (P = 3.83x10-6 – 2.00x10-4) involved in CNS regulation and
Page 167
development, neurotransmission, and neurotransmitter transport and metabolism. The occurrence of these genes in the top 0.05% of the probability distribution for the high-density genome scan and selection following current knowledge of migraine pathology provides strong support for involvement in the Norfolk pedigree. These initial findings will require verification in independent replication populations and characterisation by functional analyses.
A novel candidate among these genes, was the CNS expressed the ADARB2 gene that mediates RNA editing and downstream regulation of neurotransmitters (Maas et al., 2003).
The human homologue of ADARB2 in the rat, RED2, displays brain-specific expression, with high transcript levels occurring in the olfactory bulb and thalamus (Melcher et al., 1996).
Members of the double-stranded RNA- (dsRNA) specific adenosine deaminase gene family of RNA-editing enzymes, which include ADARB2, are known to modify glutamate receptor B
(GluR-) B pre mRNA (Melcher et al., 1996). Adenosine deaminase RNA editing has been demonstrated to modify serotonin receptors resulting in altered G protein-coupling efficacy
(Maas et al., 2003; Wang et al., 2000). The role of adenosine deaminase RNA editing of glutamate and serotonin receptor transcripts is further exemplified by the disorder, amyotrophic lateral sclerosis (ALS) (MIM 105400). In affected individuals editing of the messenger RNA encoding the GluR2 subunit of glutamate AMPA receptors in the spinal motor neurons is defective (Kawahara et al., 2004). RNA editing in ALS affected individuals fails to substitute an arginine for a glutamine residue at a crucial site in the GluR2 subunit.
This interferes with normal functioning of the glutamate receptors and may be a contributory cause of neuronal death in ALS patients. These findings provide compelling support for a role of ADARB2 in migraine susceptibility, through modification of serotonin and glutamate receptor pre mRNA in the CNS.
Page 168
In addition to ADARB2, 2 SNPs in a glutamate receptor gene GRM7, a SNP in the glutamate transporter gene SLC17A8, and a SNP in close proximity to a serotonin receptor gene, HTR7.
Both GRM7 and HTR7 display high expression levels in the brain, and their ligands, glutamate and serotonin are major excitatory neurotransmitters in the mammalian CNS (Bard et al., 1993; Makoff et al., 1996). The role of glutamate in migraine pathology has gained momentum with the recent discovery of a plausible genetic risk variant implicated in a large- scale genome wide association of migraine (Anttila et al., 2010). Quantitative expression analysis linked the variant to a nearby gene, MTDH that regulates glutamate homeostasis.
MTDH down regulates SLC1A2, the gene encoding the major glutamate transporter in the brain and fits well with the theory of altered glutamate release or glutamate uptake underlying migraine attack risk (Andreou and Goadsby, 2009a; Goadsby and Classey, 2000).
Many biological activities of the neurotransmitter Five-hydroxytryptamine (5-HT; serotonin) are mediated by serotonin receptors. With the exception of serotonin receptor 3 (HTR3A;
MIM182139) and its subunits, which function as a ligand-gated ion channel, the serotonin receptor family (including HTR7) are transmembrane-spanning g-protein-coupled receptors
(GRCRs) (Hoyer et al., 2002). HTR7 is widely and predominantly expressed throughout the brain (Bard et al., 1993). HTR7 functions by positively activating adenylate cyclase via g- protein coupling. So far roles have been described in circadian rhythm function, neuroendocrine function and affective behaviour disorders (Vanhoenacker et al., 2000). This gene is a strong biologically plausible candidate, especially given the amounting evidence of altered serotonergic neurotransmission during and between migraine attacks (Hamel, 2007).
A role for serotonergic system disruption during migraine attacks is further supported by the triptans, a class of serotonin receptor agonist used to treat migraine. Triptans modulate trigeminovascular responses in neurons in the ventroposteromedial (VPM) nucleus, which are likely involved in the transmission of pain (Shields and Goadsby, 2006).
Page 169
6.5.3 Linkage versus Association
At the completion of this study, the results of both linkage and association approaches were evaluated. Figure 6.2 depicts the chromosome 9 and 13 pGWAS results, highlighting the peak linkage regions on chromosomes 9 and 13 for reader evaluation. Little overlap was evident across the 2 methods. This result is not unexpected, due to the different genotyping platforms, marker densities, statistical methods and cohort sizes.
Page 170
a)
b)
Figure 6.2. Comparison of linkage and association findings in the Norfolk pedigree for chromosome 9 (a) and chromosome 13 (b). The y-axis is the –log(P-value), the x-axis is marker position in base pairs. The blue region highlights the peak linkage signal and 1-LOD support interval for that chromosome. The red dots do not map to the y-axis, they are merely positional indicators. For chromosome 9 the red markers in the highlighted region from left to right represent the location of D9S283, D9S287 and D9S1690.
For chromosome 13 the red markers in the highlighted region from left to right represent the location D13S159, D13S158 and D13S173.
Page 171
6.6 Conclusion
In summary, although this study does not provide genome wide significant association of a
SNP with migraine risk in the Norfolk pedigree, it provides suggestive evidence for variants in genes regulating serotonin and glutamate pathways in the CNS, particularly the brain. The study still lacks power and would benefit from the continual recruitment and genotyping of pedigree-members, replication in independent case-control cohorts and functional analyses to support initial findings.
RNA-editing genes have been suggested as candidates for complex neurological disorders such as epilepsy, depression and schizophrenia (Maas et al., 2006). The ADARB2 SNP rs2271275 has previously been associated with early-onset obsessive-compulsive disorder in some American families (Hanna et al., 2007). The ADARB2 locus on chromosome 10p15.3 has not previously been implicated in migraine susceptibility. However, a recent migraine
GWAS conducted in European populations did provide evidence supporting a link between a locus on 8q22.1 (rs1835740) and glutamate regulation (Anttila et al., 2010). We did not find any trend toward a statistical association of rs1835740 (P = 0.54), which is more likely to be explained by differences in the unique Norfolk Island isolate.
The inclusion of brain expressed glutamate and serotonin receptor genes in our top 0.05% of
SNPs, considered in combination with ADARB2 and the results of Antilla et al. (2010), may help explain the long hypothesised involvement of serotoninergic and glutamatergic system disruption in migraine pathophysiology, perhaps via post-transcriptional modification. In future studies of the Norfolk pedigree it may be worth focusing specifically on the glutamate and serotonin pathways to assess whether genetic variants affecting neurotransmitter homeostasis are associated with migraine.
Page 172
Chapter 7: A Systematic Analysis of Putative Migraine Susceptibility Genes in the Norfolk ‘Mutiny on the Bounty’ Pedigree Implicates the Estrogen Receptor Gene (ESR1)
7.1 Overview
Migraine is a highly prevalent, complex neurovascular disease in Westernised nations.
Despite recent identification of a familial gene for MA and GWAS evidence for a population-level risk variant, the genetic component remains largely undefined.
Positive associations are reported for variants in numerous candidate genes, some of which occur across multiple independent studies. Individually, variants in putative migraine genes may contribute small effects to the overall genetic profile.
Collectively, they could then explain sizeable portions of the underlying genetic component.
This study identified these putative migraine susceptibility genes and assessed these genes in the context of a recent pedigree-based genome wide association study
(pGWAS) to determine if common findings are present in the Norfolk Island population. SNPs within the coding region of putative genes implicated in MA, MO, typical migraine (MA and MO) and FHM were assessed. At least 36 genes are positively associated with ICHD defined migraine and an additional 5 genes involved in FHM, MA and recent GWAS findings were evaluated in the Norfolk pedigree.
These genes have diverse biological functions, but are largely related through their
Page 173
influences on serotonergic, dopaminergic, and in particular, glutamergic pathways, but also via vascular and hormonal influences.
The strongest associated SNP, rs2813554 (P=0.0011), occurred in an intronic region of the estrogen receptor ESR1 gene and was significant at the gene level. A total of 9 other SNPs across ESR1 were significant at the nominal level (P<0.05) and formed 2 distinct haplotypes blocks extending 41kb and 39kb across the hormone receptor transcript. Gene candidates, MTHD and NGFR, identified in 2 recently published migraine GWA studies provided no evidence of association in the Norfolk pedigree
(P>0.05). However, association was detected with rs3858331 (p=0.0012) in the MA gene KCNK18 and rs8104676 (P=0.016) in the FHM1 gene CACNA1A.
In summary, assessment of 38 migraine genes in the Norfolk Island cohort provided compelling evidence implicating the estrogen receptor gene, ESR1. Results of this study provide further evidence that ESR1 is a contributory susceptibility factor for common migraine.
7.2 Background
Knowledge of migraine genetics has rapidly advanced with the discovery of a familial
MA gene encoding a potassium channel, KCNK18, and identification of a common migraine risk variant rs1835740 correlating with MTDH transcript levels (Anttila et al., 2010; Lafreniere et al., 2010). However, given the complex aetiology of migraine it is likely the underlying genetic component is yet to be fully elucidated. Biologically meaningful associations of small effect size (i.e. risk ratios) are expected to contribute to the underlying genetic component of common complex disease phenotypes (Barton and Keightley, 2002; Wright et al., 2003). It is estimated that 20 genes can explain
Page 174
50% of the burden of a disease in the population if predisposing genotypes are common (25%), even if the individual effect size for each gene is weak-to-moderate
(RR = 1.2–1.5) (Yang et al., 2005). In the advent of genome wide scans it is difficult to discern these meaningful weak associations from spurious associations (Khoury et al., 2007).
Past migraine association studies report many positively associated genetic variants in biologically plausible genes. These include variants such as the C677T polymorphism in the homocystine metabolism gene, MTHFR (Kara et al., 2003; Lea et al., 2004;
Oterino et al., 2004; Scher et al., 2006) or the G594A polymorphism in the hormone receptor gene, ESR1 (Colson et al., 2004), as well as other variants such as VNTRs
(DBH, SLC6A4) (Fernandez et al., 2006; Marziniak et al., 2005) and insertion/deletions (PGR, ACE) (Colson et al., 2005; Kowa et al., 2005). Interestingly, variants in several candidate genes i.e. MTHFR, ESR1 and SLC6A4 are consistently detected across cohorts, suggesting that they may contribute small effects to the overall genetic profile of migraine and collectively explain sizeable portions of the underlying genetic component. As candidate genes are selected on a prior hypothesis relating to positional, pharmacological, and pathophysiological knowledge of a disease (or a combination thereof) they may converge on distinct biological pathways and signalling cascades (Carter, 2006). Knowledge of the relationships between such genes may improve understanding of underlying molecular genetics, provide new gene targets and highlight the potential for gene-gene interactions in disease aetiology
(Carter, 2006).
Genetic studies have identified numerous putative migraine susceptibility genes conferring genetic risk in some populations. Such genes may be considered a priori in
Page 175
future genetic studies. This investigation analyses the potential role of these genes in the ‘Mutiny on the Bounty’ pedigree in the context of recent genome-wide SNP data obtained using Illumina HumanHap BeadChips.
7.3 Methodology
7.3.1 Norfolk Island Cohort
Norfolk Island is a self-governing Australian territory located in the South Pacific
Ocean between New Caledonia, New Zealand, and Australia along the Norfolk Ridge.
The majority of current, permanent residents are descended from 9 Isle of Man
(Caucasian), ‘Bounty’ Mutineers and 6 Tahitian (Polynesian) women, and 2 European
Whalers (Male) who joined the small colony in the early 19th century (Macgregor et al., 2010). Population structure, pedigree verification and CVD-risk trait molecular genetics have been well characterised (Bellis et al., 2008; Bellis et al., 2007; Bellis et al., 2005; Macgregor et al., 2010; McEvoy, 2010). The molecular genetics of migraine have also been investigated (refer to Chapters 5 and 6). This study aimed to screen previously positively associated, biologically plausible migraine genes in the context of the recent Norfolk Island pGWAS. We hypothesised consistent nominal association (P<0.05) across populations support a role in genetic risk and therefore previously associated genes may be considered a priori and thus treated with more weight than SNPs in other genes and in non-genic regions (Need et al., 2009).
Data collection procedures are described in detail by Bellis et al. (2005) (Bellis et al.,
2005). Genome-wide SNP genotyping, data screening procedures and statistical analysis are described in Chapter 6. Briefly, a sub-sample of 285 related individuals
(135 males; 150 females) descended from the population founders were genotyped for
Page 176
620,901 genome wide markers (mean spacing 4.7kb) in accordance to the manufacturer’s instructions on Illumina Infinium High Density (HD) Human610-
Quad DNA analysis BeadChip version 1. Of these related individuals include 76 migraine cases (22 males; 54 females). Association between SNPs and migraine was tested using measured genotype analysis (Boerwinkle et al., 1986), embedded in a variance components-based linkage model (Blangero et al., 2005) and annotated using the Whole Genome Association Study Viewer (WGA Viewer) program
(http://people.genome.duke.edu/~dg48/WGAViewer/) (Ge et al., 2008) and NCBI
Build 37.1. Variants within the genomic sequence of positive migraine candidate genes identified through literature review were selected and annotated using WGA
Viewer. Locality and statistical information were compiled. Haplotypic assessment of potential migraine susceptibility genes was undertaken in the Norfolk pedigree using the program Haploview. Like, the candidate gene association analysis described in the original pGWAS (Chapter 6) a local type I error of α = 0.05 was applied. Genome- wide Bonferroni adjustment was not required to protect against type I error inflation as the application of selection criteria for candidate genes negates the global null hypothesis (Igl et al., 2010).
7.3.3 Ethics Statement
All individuals from the pedigree and case-control cohort provided informed consent prior to participation. The study was approved by the Griffith University Human
Research Ethics Committee.
Page 177
7.3.4 Gene Selection
Genes associated with migraine were identified by literature surveys and from the
Genetic Association Database (http://geneticassociationdb.nih.gov) (Becker et al.
2004). Literature surveys were primarily undertaken using the advance search option in the NCBI PubMed database. Search strings included the words ‘migraine’,
‘association’ and ‘genetic’. References listed in relevant publications were also examined. For the purpose of this investigation the inclusion criteria was as follows; any gene for which at least one family or case-control association study including single variant, interaction analyses and haplotypic associations. Statistical significance was accepted as that defined by the original authors. Migraine classification was restricted to MA, MO or combined MA and/or MO defined by ICHD-I and ICHD-II diagnostic criterion to minimise phenotypic heterogeneity (ICHD-I, 1988; ICHD-II,
2004). We have included these 3 migraine phenotypes as there is evidence that MA and MO are not distinct clinical entities and in fact share a common genetic basis
(Nyholt et al., 2004). Articles assessing co-morbid disorders were excluded from the present study so as not to confound findings.
In addition to the above criteria, we chose to assess the 3 FHM genes CACNA1A,
ATP1A2 and SCN1A, as there is evidence to suggest MA, MO and FHM share some, but not all genetic factors, as demonstrated by Todt et al. (2005). This report found rare variants in the ATP1A2 gene, E174K and C515Y, are associated with common migraine in a multigenerational FHM affected pedigree (Todt et al., 2005). To facilitate study comparison the results of 2 recent GWAS were also evaluated in term of the Norfolk Island cohort (Anttila et al., 2010; Ligthart et al., 2011), as well as the recently identified MA gene, KCNK18 (Lafreniere et al., 2010).
Page 178
Although negative associations are reported for many of the genes examined in this study, they were not included in the present investigation. The broad inclusion criteria aimed to provide a comprehensive list of genes whose contribution to migraine genetic risk may be confirmed or contested by future research and account for the assumption of genetic heterogeneity in different populations. Gene function and class information are derived primarily from NCBI Entrez Gene database
(http://www.ncbi.nlm.nih.gov/gene/?term). For consistency, only Hugo gene nomenclature committee approved gene symbols are cited (Wain et al., 2002).
7.4 Results
7.4.1 Putative Migraine Genes
A total of 56 studies were identified that followed ICHD-I and/or ICHD-II diagnostic criteria and reported statistically significant variant, interaction analyses and haplotypic associations for migraine phenotypes in family or case-control study design. Positive associations with migraine were reported for variants across 36 genes in candidate gene studies, including the FHM1 gene CACNA1A. The results of these studies are surmised in Table 7.1.
In addition to the genes identified by the study selection criteria GWAS, familial MA and FHM studies were also evaluated for screening in the Norfolk cohort. From these a number of additional genes of interest were identified; the MTDH gene from the first published migraine GWAS (Anttila et al., 2010), NGFR from the second published migraine GWAS (Ligthart et al., 2011), the familial MA gene KCNK18
(Lafreniere et al., 2010), and FHM2 and FHM3 genes ATP1A2 and SCN1A, respectively. Although the variant detected in NGFR did not satisfy genome-wide
Page 179
thresholds it is included to facilitate comparison of the Norfolk Island study with emerging GWAS data.
Genes associated with migraine can be grouped into broad families with similar functions. ACE, ENDRA, NOS3 and MMP3 regulate vascular tone and function.
ESR1, ESR2, and PGR function as hormone receptors in the central nervous system
(CNS). DDC, HTR1A, HTR1B, HTR2A, HTR2B, HTR2C, MAOA, SLC6A4, STX1A, and TPH1 regulate serotonin transport and metabolism. DBH, DDC, DRD2, DRD4, and SLC6A3 regulate dopamine transport and metabolism. Many of these genes are also related through their effects on glutamate homeostasis, glutamate being a major excitatory neurotransmitter in the mammalian CNS. Dopamine has many actions in the CNS, including modulation of glutamate transmission (Tseng and O'Donnell,
2004). DRD2 and CNR1 receptor agonists inhibit glutamate release (Carter, 2006).
Like dopamine, serotonin-related genes are also known to influence glutamate availability. HTR1A receptor antagonists potentiate N-methyl-o-aspartate (NMDA)- induced glutamate release from rat cortical pyramidal neurons (Dijk et al., 1995).
There is also evidence suggesting activation of HTR2A receptors increases the release of glutamate (Aghajanian and Merek, 2000). STX1A is associated with the SNARE complex that regulates glutamate storage and release (Calakos and Scheller, 1996).
MTHFR, MTHFD1, TYMS are involved in homocysteine metabolism. However, accumulation of homocysteine can led to neurotoxicity through its affects on NMDA receptor agonists and consequently, glutamate availability (Kruman et al., 2000). The hormone estrogen influences neuronal growth, differentiation and survival and may have a role in protecting primary cortical neurons against glutamate toxicity (Singera et al., 1996). Specifically, estrogen, via ESR1 is demonstrated to inhibit l-glutamate uptake activity by astrocytes (Sato et al., 2003). Like estrogen, progesterone is
Page 180
neuroprotective, studies of rat cerebral cortex suggest it protects against glutamate- induced toxicity (Kaur et al., 2007). TNF inhibits glutamate uptake (Zou and Crews,
2005). GRIA1, GRIA3 and MTDH are directly involved in glutamate transport, metabolism and homeostasis (Anttila et al., 2010). Lastly ADORA2A and GNAS regulate adenylate cyclase activity, which is linked to glutamate receptors (Schoepp,
1994).
Of the remaining putative migraine genes, HLA-DRB1 is expressed in antigen presenting cells and plays a key role in the immune system; INSR receptor binding stimulates glucose uptake into cells; LTA is involved in lipid metabolism; and
CACNA1A is a subunit of a pore forming voltage-gated calcium channel that regulates calcium ion entry into excitable cells (Kordasiewicz et al., 2006). Lastly, NOTCH3 signalling pathways are involved in development and maturation of organs and mutations in NOTCH3 cause cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL; MIM125310). There is evidence in rat models that NOTCH3 promotes astroglia differentiation from multipotent progenitors (Tanigaki et al., 2001).
Page 181
Table 7.1. Migraine genes identified through candidate gene studies.
Chr Gene Reference Criteria Associated Case Control Population P-Value OR (95%CI) Phenotype 1p36.3 MTHFR (Joshi et al., 2009) ICHD-II MA 67MA; 83MO; 220NMH 150 India 0.029 4.069 (1.153-14.359) (Kara et al., 2003) ICHD-I MA 23MA; 20MO; 9TTH 136 Turkey 0.0001 14.105 (2.417-82.320) (Kowa et al., 2000) ICHD-I MA 22MA; 52MO; 47TTH 261 Japan <0.01 - (Lea et al., 2005a) ICHD-II MA 170MA; 100MO 270 Australia (Caucasian) 0.002 2.89 (1.47-5.72) (Lea et al., 2004) ICHD-I MA 168MA; 100MO 269 Australia (Caucasian) 0.017 2.54 (1.37-4.71) MA 247MA; 72MO 269 Australia (Caucasian) 0.039 1.88 (1.01-3.25) (Oterino et al., 2005) ICHD-II MO 138MA; 191MO 237 Spain (Caucasian) 0.018 3.25 (1.20-8.70) (Scher et al., 2006) ICHD-I MA 187MA; 226MO 1212 Netherlands <0.006 2.05 (1.2-3.4) 2q36.3-q37.1 HTR2B (Corominas et al., 2009b) ICHD-II MO 220MA; 308MO 528 Spanish 0.0017 1.43 (1.16-1.76) 4q31.2 EDNRA (Tzourio et al., 2001) ICHD-I MA-MO 140MA and/or MO 1039 France <0.001 0.50 (0.34-0.74) 5p15.3 SLC6A3 (Todt et al., 2009) ICHD-II MA 650MA 650 Germany 0.0082 0.81 (0.69-0.95) 5q11.2-q13 HTR1A (Marziniak et al., 2007) ICHD-II TC 98MA; 99MO 117 Germany 0.008 - 5q33 GRIA1 (Formicola et al., 2010) ICHD-II MA 135MA; 109MO 204 Italy (Caucasian) 0.0005 1.9 (1.4-2.5) 6p21.3 HLA-DRB1 (Rainero et al., 2005) ICHD-II MO 41MA; 214MO 325 Italy 0.02 1.97 (1.10-3.54) 6p21.3 LTA (Asuni et al., 2009) ICHD-II MO 299MO 278 Sardinia (Italy) 0.018 1.46 (1.066-2.023) (Trabace et al., 2002) ICHD-I MO 32MA; 47MO 101 Italy 0.004 - 6p21.3 TNF (Ghosh et al., 2010) ICHD-II MA 63MA ; 89MO; 160TTH 216 India 0.015 2.293 (1.172-4.487) (Mazaheri et al., 2006) ICHD-II MO 221MO 183 Iran <0.0001 3.73 (2.40-5.82) (Rainero et al., 2004) ICHD-I MO 38MA; 261MO 306 Northern Italy <0.001 3.3 (2.09-5.24) 6q13 HTR1B (Marziniak et al., 2007) ICHD-II TC 98MA; 99MO 117 Germany < 0.05 - 6q14-q15 CNR1 (Juhasz et al., 2009) ICHD MA-MO 195 684 European (Caucasian) 0.017 - 6q25.1 ESR1 (Colson et al., 2004) ICHD-I MA-MO 139MA; 85MO 224 Australia (Caucasian) 0.003 - MA-MO 221MA; 39MO 260 Australia (Caucasian) 0.000008 - (Colson et al., 2005) ICHD-I MA-MO 189MA-MO 217 Australia (Caucasian) 0.00003 3.2 (1.9-5.3) (Joshi et al., 2010) ICHD-II MA-MO 84MA; 133MO; 179TTH 217 North India 0.0001 1.729 (1.309-2.284) (Oterino et al., 2008) ICHD MA-MO 198MA; 158MO 374 Northern Spain 0.005 - MA-MO 407MA-MO 187 Northern Spain 0.009 - (Oterino et al., 2006) ICHD MA-MO (Female) 367MA-MO 232 Northern Spain 0.008 1.6 (1.1-2.4) 7p11 DDC (Corominas et al., 2009b) ICHD-II MA 220MA; 308MO 528 Spain 0.0019 2.31 (1.48-3.59) 7q11.2 STX1A (Corominas et al., 2009a) ICHD-II MA-MO–HM 86MA; 102MO; 22HM 210 Spain (Caucasian) 0.008 1.71 (1.16-2.52) (Lemos et al., 2010) ICHD-II MA-MO 77MA; 111MO 287 Portugal 0.008 3.27 (1.35-7.88) 7q36 NOS3 (Borroni et al., 2006) ICHD-II MA 53MA; 103MO 125 Italy 0.05 2.21(1.0-5.04) 9q34 DBH (Anttila et al., 2010) ICHD-II MA-MO 115MA; 85MO 200 Australia (Caucasian) 0.004 - (Fernandez et al., 2009) MA-MO 204MA; 41MO 300 Australia (Caucasian) 0.013 ICHD-II MA 166MA; 103MO 275 Australia (Caucasian) 0.003 2.03 (1.26-3.28) (Fernandez et al., 2006) ICHD-I MA-MO 98MA; 79MO 182 Australia (Caucasian) 0.006 - (Lea et al., 2000) ICHD-II MA 650MA 650 Germany 0.0012 0.77 (0.65-0.90)
Page 182
Table 7.1. Migraine genes identified through candidate gene studies (continued).
Chr Gene Reference Criteria Associated Case Control Population P-Value OR (95%CI) Phenotype 11p15.3-p14 TPH1 (Erdal et al., 2007) ICHD-II MA-MO 59 MA and/or MO 62 Turkey 0.01 - 11p15.5 DRD4 (Mochi et al., 2003) ICHD-II MO 45MA: 145MO trios Austria 0.01 0.47 11q22 PGR (Colson et al., 2005) ICHD-I MA-MO 144MA; 88MO 216 Australia (Caucasian) 0.02 - MA-MO 227MA; 50MO 263 Australia (Caucasian) 3.00E-03 - (Joshi et al., 2010) ICHD-II MA-MO 84MA; 133MO; 179TTH 217 North India 0.0001 0.292 (0.155-0.549) (Kara et al., 2007) 59MA; 109MO; 10BM; 11q23 MMP3 ICHD-II MA-MO-BM-CM 2compilcated migraine 210 Western Turkey <0.001 - 11q23.1 DRD2 (Peroutka et al., 1997) ICHD-I MA 52MA; 77MO 121 USA 0.007 - 13q14-q21 HTR2A (Juhasz et al., 2003) ICHD-I MA-MO 10MA; 116MO 110 Hungary (female only) 0.049 1.45 (1.00-2.12) (Erdal et al., 2001) ICHD-I MA 24MA; 37MO 44 Turkey 0.02 - 14q23.2 ESR2 (Oterino et al., 2008) ICHD MA-MO 198MA; 158MO 374 Northern Spain 0.005 - MA-MO 407MA-MO 187 Northern Spain 0.009 - 14q24 MTHFD1 (Oterino et al., 2005) ICHD-II MO 138MA; 191MO 237 Spain (Caucasian) 0.018 3.25 (1.2-8.7) (Bayerer et al., 2010) 32MA; 178MO; 41MA- 17q11.1-q12 SLC6A4 ICHD-II MA MO 192 Germany (Caucasian) 0.02 2.82 (1.61-6.84) (Borroni et al., 2005) ICHD-I MA 52MA; 92MO 105 Italy <0.05 2.60 (1.75-3.85) (Marziniak et al., 2005) ICHD-I MA 96MA; 101MO 115 Germany <0.001 4.38 (1.94-9.87) (Ogilvie et al., 1998) 94MA; 173MO; 18MA-
ICHD-I MO MO 133 Denmark 0.017 1.68 (1.05-2.69) (Racchi et al., 2004) ICHD-I MA 44MA 48 Italy 0.03 5.3 (1.3-21.7) (Szilagyi et al., 2006) ICHD-II MA 38MA; 49MO 464 Hungary 0.0396 2.028 (Yilmaz et al., 2001) ICHD-I MA-MO 23MA; 29MO 80 Turkey 0.01 - (Kara et al., 2007) 59MA; 109MO; 10BM; 17q23 ACE ICHD-II MA-MO 2compilcated migraine 210 Western Turkey <0.001 - (Kowa et al., 2005) ICHD-I MA 54MA; 122MO; 78TTH 248 Japan <0.01 5.26(1.69-16.34) (Lea et al., 2005b) ICHD-II MA 170MA; 100MO 270 Australia (Caucasian) 0.002 2.89 (1.47-5.72) (Lin et al., 2005) ICHD-II MA-MO 24 migraine 200 China ( Han origin) 0.045 0.331 (0.108-1.014) (Paterna et al., 2000) ICHD-I MO 302MO 201 Sicily (Italy) 0.044 - (Joshi et al., 2009) ICHD-II MA 67MA; 83MO; 220NMH 150 India 0.04 2.830 (1.045-7.662) 18p11.32 TYMS (Oterino et al., 2005) ICHD-II MO 138MA; 191MO 237 Spain (Caucasian) 0.02 0.28 (0.1-0.8) 19p13 CACNA1A (D'Onofrio et al., 2009) ICHD-II MA-MO-BM- 32MA; 57MO; 10BM; 11 107 Italy and Belgium 0.002 0.26 SHM-FHM SHM; 7FHM (Caucasian) 19p13.2 INSR (McCarthy et al., 2001) ICHD-I MA 167MA; 164MO 765 USA (Caucasian) 0.002 - MO 145MA; 110MO 237 Australia (Caucasian) 0.016 - (Netzer et al., 2008) ICHD-II MA 1278MA 446 German 0.005 1.59 (1.15-2.21 19p13.2-p13.1 NOTCH3 (Menon et al., 2010) ICHD-II MA 160MA; 98MO 494 Australia (Caucasian) 0.001 - MA 215MA;39MO 229 Australia (Caucasian) 0.003 - (Schwaag et al., 2006) ICHD-I MO 27MA; 70MO 97 Germany (Caucasian) 0.006 4.5 (1.6-12.9)
Page 183
Table 7.1. Migraine genes identified through candidate gene studies (continued).
Chr Gene Reference Criteria Associated Case Control Population P-Value OR (95%CI) Phenotype 19q13.2 APOE (Gupta et al., 2009) ICHD-II MA-MO 50 migraine; 50TTH 50 India <0.001 4.85 (1.92-12.72) 20q13.2 GNAS (Oterino et al., 2007) ICHD MA 194MA; 171MO 347 Northern Spain 0.001 1.79 (1.27-2.53) MA-MO 384MA-MO 174 Northern Spain 0.019 2.20 (1.14-4.40) 22q11.2 ADORA2A (Hohoff et al., 2007) ICHD-II MA 122MA; 143MO 154 Germany 0.0091 -
Xp11.23 MAOA (Corominas et al., 2009b) ICHD-II MA 220MA; 308MO 528 Spanish 0.0061 1.41 (1.10-1.80) Xq24 HTR2C (Kusumi et al., 2004) ICHD-I MA 37MA; 80MO; 43TTH 360 Japan 0.03 5.12 (1.13-23.17) Xq25-q26 GRIA3 (Formicola et al., 2010) ICHD-II MA 135MA; 109MO 204 Italy (Caucasian) 0.003 2.0 (1.4-2.8) ACE=Angiotensin I-Converting Enzyme; ADORA2A=Adenosine A2 Receptor; APOE= Apolipoprotein E; CACNA1A=Calcium Channel, Voltage-Dependent, P/Q Type, Alpha-1a Subunit; CNR1=Cannabinoid Receptor 1; DBH=Dopamine Beta-Hydroxylase, Plasma; DDC=Dopa Decarboxylase; DRD2=Dopamine Receptor D2; DRD4=Dopamine Receptor D4; ENDRA=Endothelin Receptor, Type A; ESR1=Estrogen Receptor 1; ESR2=Estrogen Receptor 2; GNAS=GNAS Complex Locus; GRIA1=Glutamate Receptor 1; GRIA3=Glutamate Receptor, Iopnotropic AMPA 3; HLA-DRB1=Major Histocompatibility Complex, Class II, Dr Beta-1; HTR1A=5-Hydroxytryptamine Receptor 1A; HTR1B=5-Hydroxytryptamine Receptor 1B; HTR2A=5-Hydroxytryptamine Receptor 2A; HTR2B=5-Hydroxytryptamine Receptor 2B; HTR2C=5-Hydroxytryptamine Receptor 2C; INSR=Insulin Receptor; LTA=Lymphotoxin-Alpha (Tumor Necrosis Factor Beta); MAOA=Monoamine Oxidase A; MMP3=Matrix Metalloproteinase 3; MTDH=Metadherin; MTHFD1=Methylenetetrahydrofolate Dehydrogenase; MTHFR=5,10-Methylenetetrahydrofolate Reductase; NOS3=Nitric Oxide Synthase 3; NOTCH3=Notch, Drosophila, Homolog Of, 3; PGR=Progesterone Receptor; SLC6A3=Solute Carrier Family 6 (Neurotransmitter Transporter, Dopamine), Member 3; SLC6A4=Solute Carrier Family 6 (Neurotransmitter Transporter, Serotonin), Member 4; STX1A=Syntaxin 1A; TNF=Tumor Necrosis Factor; TPH1=Tryptophan Hydroxylase; TYMS=Thymidylate Synthetase
MA=Migraine With Aura; MO=Migraine Without Aura; BH= Basalar Migraine; CM=Complicated Migraine; FHM=Familial Hemiplegic Migraine; HM=Hemiplegic Migraine; NMH=Non-Migrainous Headache; SHM=Sporadic Hemiplegic Migraine; TC=Trait Component; TTH=Tension Type Headache
Page 184
7.4.2 Norfolk Pedigree
Variants in genes of positional and functional relevance and displaying previous positive association should be treated with more weight than variants in other genes or non-coding regions. This is particularly important when studying common, complex traits, as each causal gene may only make a small contribution to the overall genetic component (Hirschhorn and
Daly, 2005). To determine if common findings are present between the Norfolk Island study and previously reported candidate gene associations we assessed all SNPs in the coding region of candidate genes implicated in MA, MO, typical migraine (MA and MO) and FHM.
We report the SNP with the lowest p-value in the coding region of each candidate gene. A total of 32 genes identified through candidate genes studies, 2 genes identified from recent
GWAS, 1 familial MA gene and 3 FHM genes were available for assessment in the Norfolk pedigree. Results are detailed in Table 7.2. In total, 38 genes were evaluated. To interpret findings a similar approach to Ligthart et al. (2011) was implemented using a gene-based test result at an alpha level of 0.05. With Bonferroni correction, this corresponded to a gene-based
P-value of 0.05/38 = 0.0013.
Nominally significant associations (P<0.05) were detected for variants in MTHFR, SLC6A3,
GRIA1, LTA, HLA-DRB1, TNF, CNR1, ESR1, KCNK18, PGR, MMP3, CACNA1A, and INSR genes. Of these, multiple positive associations were detected for MTHFR, SLC6A3, LTA,
TNF, ESR1, PGR and LTA. The most strongly associated SNP, rs2813554 (P=0.0011) occurred in the intronic region of the ESR1 gene and was significant at the gene-level. P- values less than 0.05 were detected for a total of 10 SNPs within the coding region of ESR1 gene. Variants were also detected in another hormone receptor gene, PGR (P=0.0069).
Page 185
Table 7.2. Migraine gene findings in the Norfolk Island pedigree.
Locus Gene No. NCBI dbSNP NCBI Build Function Minor/ MAF Beta* P-Value SNPs in Ref No. 37.1 Position Major Gene (BP) Allele Genes identified through candidate gene studies
1p36.3 MTHFR 14 rs6696752 11835935 Intronic T/C 0.301 -0.301 0.021 rs4846048 11846252 3 Prime UTR G/A 0.309 -0.282 0.030 rs2274976 11850927 Non-Synonymous A/G 0.084 0.633 0.018 2q36.3-q37.1 HTR2B 4 rs4973377 231981992 Intronic A/G 0.105 0.309 0.126 4q31.2 ENDRA 15 rs6537485 148428527 Intronic A/T 0.081 -0.278 0.187 5p15.3 SLC6A3 21 rs27072 1394522 3prime UTR T/C 0.202 0.342 0.038 rs6869645 1404548 Intronic T/C 0.026 -0.743 0.044 rs6350 1443199 Synonymous Coding T/C 0.049 -0.674 0.016 5q11.2-q13 HTR1A 1 rs1423691 63251662 Downstream T/C 0.414 0.070 0.587 5q33 GRIA1 106 rs778821 152972201 Intronic A/G 0.433 0.322 0.0096 6p21.3 HLA-DRB1 3 rs35265698 32561334 Upstream G/C 0.209 -0.341 0.022 6p21.3 LTA 14 rs2009658 31538244 Upstream G/C 0.211 0.409 0.0093 rs2844482 31539767 Upstream A/G 0.214 0.410 0.0088 rs1800683 31540071 5prime UTR A/G 0.418 -0271 0.027 rs2229094 31540556 Non-Synonymous Coding C/T 0.270 0.302 0.033 rs1041981 31540784 Non-Synonymous Coding A/C 0.417 -0.271 0.027 6p21.3 TNF 5 rs1800683 31540071 5 Prime UTR G/A 0.418 -0.271 0.027 rs2229094 31540556 Non-Synonymous C/T 0.270 0.302 0.033 rs1041981 31540784 Non-Synonymous A/C 0.417 -0.271 0.027 6q13 HTR1B 3 rs9361235 78175842 Upstream T/C 0.366 -0.100 0.4525 6q14-q15 CNR1 11 rs6454676 88877455 Upstream A/G 0.093 -0.623 0.0030 6q25.1 ESR1 113 rs3020343 152054363 Intronic T/C 0.473 0.239 0.046 rs3020348 152057914 Intronic A/C 0.473 0.239 0.045 rs1336981 152082369 Intronic G/A 0.389 -0.278 0.023 rs2485209 152089768 Intronic C/A 0.407 -0.274 0.024 rs7767143 152095694 Intronic G/A 0.241 -0.373 0.0071 rs9341066 152419526 Intronic A/G 0.048 -0.681 0.023 rs2813544 152425582 Intronic G/A 0.181 -0.444 0.0034 rs2813550 152441587 Intronic C/A 0.240 0.334 0.045 rs2813554 152442338 Intronic A/G 0.170 -0.498 0.0011 rs9322361 152459143 Intronic G/A 0.101 -0.421 0.027 Page 186
Table 7.2. Migraine gene findings in the Norfolk Island pedigree (continued).
Locus Gene No. NCBI dbSNP NCBI Build Function Minor/ MAF Beta* P-Value SNPs in Ref No. 37.1 Position Major Gene (BP) Allele Genes identified through candidate gene studies
7p11 DDC 36 rs17133877 50608698 Intronic A/G 0.067 0.477 0.078 7q11.2 STX1A 3 rs3793243 73121347 Intronic T/C 0.392 -0.160 0.201 7q36 NOS3 5 rs2373961 150681210 Intronic A/G 0.320 0.153 0.273 9q34 DBH 19 rs2519155 136504598 Intronic G/A 0.250 0.156 0.278 11p15.3-p14 TPH1 5 rs17794760 18055920 Intronic A/G 0.169 -0.209 0.185 11p15.5 DRD4 1 rs3758653 636399 Intronic C/T 0.208 0.169 0.270 11q22 PGR 40 rs493220 100960366 Intronic G/A 0.239 0.402 0.0069 11q23 MMP3 6 rs566125 102710471 Intronic T/C 0.147 0.344 0.043 11q23.1 DRD2 20 rs4648317 113331532 Intronic T/C 0.254 -0.174 0.219 13q14-q21 HTR2A 41 rs1923885 47423086 Intronic C/T 0.351 -0.249 0.053 14q ESR2 48 rs1887994 64760611 Intonic T/G 0.089 0.352 0.095 14q24 MTHFD1 15 rs2230491 64935411 Non-Synonymous C/T 0.122 -0.379 0.062 17q11.1-q12 SLC6A4 10 rs2020936 28550814 Intronic C/T 0.192 0.298 0.055 17q23 ACE 9 rs4311 61560763 Intronic T/C 0.408 -0.054 0.672 18p11.32 TYMS 2 rs2244500 661005 Intronic T/C 0.403 -0.166 0.215 19p13.2 INSR 58 rs4804366 7185328 Intronic C/T 0.132 -0.418 0.019 rs4499341 7200990 Intronic C/T 0.413 -0.245 0.049 19p13.2-p13.1 NOTCH3 7 rs1044009 15271771 Intronic C/T 0.182 -0.201 0.164 19q13.2 APOE 3 rs8106922 45401666 Intronic G/A 0.379 0.121 0.357 20q13.2 GNAS 13 rs919197 57480933 Intronic T/C 0.483 -0.161 0.208 22q11 ADORA2A 11 rs5996696 24827622 Intronic C/A 0.069 -0.210 0.349
Genes identified through GWAS
8q22-q23 MTDH 5 rs1311 98738361 3Prime UTR C/T 0.078 -0.145 0.532 17q21-q22 NGFR 7 rs734194 47591609 Intronic T/G 0.106 0.381 0.104
Page 187
Table 7.2. Migraine gene findings in the Norfolk Island pedigree (continued).
Locus Gene No. NCBI dbSNP NCBI Build Function Minor/ MAF Beta* P-Value SNPs in Ref No. 37.1 Position Major Gene (BP) Allele Genes for MA
10q25.3 KCNK18 8 rs3858331 118963970 Intronic C/T 0.027 -1.286 0.0012
Genes for Familial Hemiplegic Migraine
1q21-23 ATP1A2 10 rs1023420 160094343 Intronic C/T 0.219 -0.204 0.133 2q24 SCN1A 19 rs6731591 166898249 Intronic C/T 0.113 -0.309 0.099 19p13 CACNA1A 82 rs8104676 13585915 Intronic A/G 0.091 0.629 0.016
P-values<0.05 are highlighted in bold, NA=not available, BP=base pairs; MAF=Minor Allele Frequency *The beta coefficient is a measure of risk. A negative beta indicates the minor allele increases migraine risk, a positive beta indicates a decreased risk.
Page 188
To further investigate the large number of variants displaying nominal association in
ESR1, haplotype analysis was undertaken. As the current Norfolk pedigree structure contains marriage loops, the pedigree was split using the program Jenti to facilitate analysis. Parameters were set to select sub-pedigrees with more than 3 sampled individuals and a kinship coefficient of 0.125 to account for any sparse relationships in the extended pedigree. The 10 significant ESR1 SNPs formed two distinct haplotypes blocks extending 41kb and 39kb (Figure 7.1).
Figure 7.1. Migraine ESR1 haplotypes in the Norfolk pedigree.
Page 189
7.5 Discussion
This study highlights positive association for variants within 38 putative migraine genes, including FHM1 gene CACNA1A, and MA and typical migraine genes,
KCNK18 and MTDH, respectively. Putative migraine susceptibility genes can be grouped into several broad categories. These include those regulating glutamate, serotonin and dopamine metabolism, availability and transport. The remaining genes were hormone receptors, modulators of vascular tone and function, CNS ion transporters or other functions. Interestingly, many of these genes converge on biological pathways directly or indirectly affecting glutamate homeostasis and availability (see results). The role of the excitatory neurotransmitter glutamate, along with the pain modulators serotonin and dopamine in migraine pathophysiology and therapeutics has been reviewed in detail elsewhere (Andreou and Goadsby, 2009b;
D’Andrea and Leon, 2010; Goadsby et al., 2009).
Association, linkage, independent replication, and functional studies provide strong support for gene involvement. Many studies of multigenerational migraine pedigrees using microsatellite markers report positive findings. To date, regions of significant linkage have been reported on chromosomes 1q31 (MIM607516), 4q21
(MIM157300), 4q24 (MIM157300), 4q28 (MIM157300), 5q21 (MIM609570,
6p12.2-p21.1 (MIM607498), 10q22-23 (MIM611706), 10q25.3 (MIM613656), 11q24
(MIM609670), 14q21.2-q22.3 (MIM607501), 15q11-q13 (MIM609179), 17p13
(MIM610208), 18q12 (MIM610209) and 19p13 (MIM141500) and Xq24-28
(MIM300125). Some associated migraine genes are located within or near these
Page 190
chromosomal regions, in some instances positional information was a factor in original gene selection.
In terms of association studies, the genes ACE, DBH, ESR1, INSR, MTHFR,
NOTCH3, PGR, SLC6A4, and TNF and have all been implicated by 3 or more independent cohorts. However, of these genes only TNF, PGR, INSR and NOTCH3 are in close proximity or within chromosomal regions directly implicated by linkage analysis. To date, linkage and association studies have not provided conclusive expression or other functional data in support of the involvement of these genes.
Presently, positive association and/or linkage, independent replication and functional data implicating a MA gene and migraine risk variant are limited to KCNK18 and rs1835740, respectively (Anttila et al., 2010; Lafreniere et al., 2010). It is interesting to note the rs183570 population-level variant on chromosome 8 does not overlap with reported regions of significant linkage, whilst the family-specific KCNK18 locus,
10q25.3 is distal to the previously implicated 10q22-23 locus.
We attempted to standardise study inclusion and minimise variation by restricting migraine phenotyping to ICHD-I and ICHD-II criterion, however studies vary by cohort size, number of markers typed, availability of independent replication cohorts and significance thresholds. Many of these studies select single gene or genes in related pathways for analysis according to current theories of disease mechanisms and therefore may be subject to selection bias. Also, there is considerable lack of replication, functional and/or expression data to confirm significant findings.
Putative migraine susceptibility genes were assessed a priori to determine whether nominal evidence of association (P<0.05) and significant (gene-level) association
(P<0.0013) was present in the Norfolk genealogy. In total, 793 SNPS were available Page 191
across 38 genes, including FHM genes, CACNA1A, ATP1A2 and SCN1A, the recently implicated MA gene KCNK18 and GWAS-implicated MTHD and NGFR. Thirteen of the 38 genes displayed nominal-level significance (P-values 0.0011–0.046), including
MA gene KCNK18 and FHM gene CACNA1A. No evidence of association was detected for genes regulating serotonin transport and metabolism, which is interesting considering pGWAS results in the Norfolk Island pedigree provided evidence of association for serotonin receptor gene HTR7. Strong associations were detected for multiple variants in 2 hormone genes, ESR1 and PGR. These 2 genes encode receptors with affinity for the female sex hormones, estrogen and progesterone, respectively. Haplotypic assessment identified 2 risk haplotypes extending 41kb and
39kb across the coding region of ESR1.
Estrogen has widespread effects throughout the brain, which includes regulation of serotoninergic pathways and protection against glutamate-induced toxicity in neurons
(McEwen, 2002; Sato et al., 2003; Singera et al., 1996). Estrogen also acts as a vasodilator through direct action on vascular smooth muscle or by stimulating release of endothelium-derived nitric oxide or other substances (White, 2002). Disruptions of serotonin and/or glutamate homeostasis, and cerebral vasodilatation, are all reported during migraine attacks. There is strong evidence to suggest these hormones can act as triggers for migraine attacks (Gupta et al., 2007; MacGregor, 2004). As estrogen is a female sex hormone, its disruption may account for the higher prevalence of female migraineurs (Lipton et al., 2007) and the observed changes in incidence with female hormonal milestones. The increase in estrogen levels throughout each trimester of pregnancy correlate with a decrease in migraine incidence (Scharff et al., 1997).
Page 192
7.6 Conclusion
At least 41 genes are reportedly associated with ICHD defined migraine and FHM.
Whilst these genes have diverse biological functions, they are largely related through serotonergic, dopaminergic, and in particular, glutamergic pathways, but also have roles in hormonal and vascular function. Of the 38 genes tested, only the ESR1 SNP rs2813554 (P=0.0011) was significant at the gene-level. Nine other SNPs were implicated across the ESR1 gene that defined 2 distinct risk haplotypes. Gene analysis in the Norfolk Island pedigree provided compelling evidence for a role of ESR1 in migraine risk. Results suggest ESR1 is a susceptibility factor for common migraine in the Norfolk pedigree.
Page 193
Chapter 8: Conclusions and Future Directions
8.1 Overview
Norfolk Island is a small volcanic island in the South Pacific located almost 1,600 kilometres northeast of Sydney, Australia. The Norfolk Island population is descendent from 11 British ‘Bounty’ Mutineers and 6 Tahitian women, who colonised nearby Pitcairn Island in 1790 (Macgregor et al., 2010). In 1856, the small community of 193 relocated to Norfolk Island (then uninhabited) when population growth became unsustainable on Pitcairn (Hoare, 1999). The present day Norfolk Islanders have maintained a relatively homogeneous lifestyle due to geographic isolation, strict quarantine and immigration laws, and community centred culture. However, in the history of the world, this population is a very recent isolate, having been founded only
150 years ago. Given the unique population history and presence of an extended genealogy with unbroken lineage to the original Bounty mutineer founders, this population is of particular interest for studies of complex disease. In an effort to localise susceptibility loci the research described in this thesis involved genome wide molecular genetic analysis of two complex phenotypes, CVD risk and migraine in the
Norfolk Island Bounty mutineer pedigree.
8.2 CVD Risk
Medical and epidemiological data has been previously analysed to determine the prevalence of CVD risk-traits in the Norfolk Island Health Study cohort (N=600).
Results indicated the Norfolk Island cohort and pedigree subset to have an increased
Page 194
prevalence of hypertension, obesity and other CVD risk factors compared to outbred
Caucasian populations (Bellis et al., 2008; Bellis et al., 2005). Power calculations using a pedigree built from historical genealogical records and questionnaire data estimated 80% power to detect suggestive linkage (LOD>2) for a quantitative trait with a significant (P=0.05) observed heritability of 16% (Bellis et al., 2005).
The initial pedigree structure was validated using GWS data (400 STR markers at an average density of 10cM), which involved computation of IBD allele-sharing probabilities to determine exact relationships (Bellis et al., 2008). A total of 377 of the
600 participants were genetically related and possessed either direct lineage to population founders or were recently married-in to the approximate 6,500-member pedigree. Uninformative individuals (N=223) were excluded from all pedigree analyses to reduce the computational burden. This pedigree was trimmed (N=1,078) to remove all but 1 breeding loop to alleviate computational burden. This 1,078- member Norfolk Island pedigree with 377 ascertained members had as estimated 50% power to detect of a QTL accounting for approximately 30% of trait variation (Bellis et al., 2008).
CVD-risk traits SBP, DBP, total cholesterol, triglycerides, HDLc, LDLc, and BMI were analysed in the initial autosomal GWS (Bellis et al., 2008). A maximum LOD score of 2.01 on chromosome 1p36 was detected for SBP. Additional QTLs were detected on chromosome 2q37.1 for total triglycerides, 18q22.3 and 20p12.3 for
HDLc and chromosomes 8p12 for SBP. As CVD-risk phenotypes have tendency to aggregate, multivariate analysis approaches have the potential locate QTLs undetectable by univariate analysis (Cai et al., 2004; He et al., 2008; Shmulewitz et al., 2001; Tang et al., 2003). With this considered, a central aim of the present study
Page 195
was to further explore the demographic and molecular aetiology of CVD-risk traits in the Norfolk pedigree by multivariate analysis.
This study included original CVD-risk traits SBP, DBP, total cholesterol, triglycerides, HDL-C, LDL-C, and BMI and was extended to include additional obesity related traits hip circumference, waist circumference, percentage body fat and weight. Glucose levels were not included as measurements were non-fasting and no suitable corrective measure was identified. Data screening indicated the 11 CVD risk variables included in this study were highly correlated, supporting use of multivariate methods. Using the multivariate analysis method, principal component analysis, a total of 4 principal components were extracted.
The extracted principal components accounted for 83% of the variation in the original
11 variables. Principal component 1 explained 44.35% of the overall variance, was loaded with body size indicators and interpreted as ‘obesity’ risk. The remaining principal components explained between 9.92-16.87% of the overall variance and were interpreted as ‘obesity and Syndrome X’ risk (principal component 1), ‘essential hypertension’ risk (principal component 3) and ‘stroke and heart attack’ risk
(principal component 4). A significant genetic component was detected for principal components 1, 2 and 4.
The highest detected LOD score, 1.85 resulted from principal component 2 on chromosome 5q35. The highest LOD scores detected for heritable principal components 1 and 4 occurred on chromosomes 10p11.2 (LOD=1.27) and 12q13
(LOD=1.63), respectively. These loci were undetected using univariate analysis methods.
Page 196
The 5q34-ter chromosomal region is often implicated as harbouring a gene or genes responsible for lipid level regulation, body mass determination and even type II diabetes mellitus (Almasy et al., 1999; Elbein and Hasstedt, 2002; Feitosa et al., 2002;
Hager et al., 1998; He et al., 2008; Pe´russe et al., 2001; Platte et al., 2003;
Reynisdottir et al., 2003; Shmulewitz et al., 2006; Zhao et al., 2007). One possible candidate in the 5q35 region in the ADRB2 gene, a major lypolytic receptor in human adipocytes (Barbe et al., 1996). Allelic variants in ADRB2 have been shown to be associated with arteriosclerosis, hypertension, obesity, metabolic syndrome and type
II diabetes mellitus (Barbato et al., 2007; Dallongeville et al., 2003; Jocken et al.,
2006; Krushkal et al., 1998; Large et al., 1997).
Overall results support the clustering of CVD risk traits and highlight a region on chromosome 5q35 segregating with weight, waist circumference, HDLc and total triglyceride levels. Findings demonstrate that multivariate analysis strategies are warranted for highly inter-related variables and can identify genomic regions previously undetected by univariate analysis strategies.
8.3 Migraine: Genome Wide Linkage Scan
Another central aim of this study was to assess a discrete phenotype for the first time in the Norfolk Island Health Study Cohort, migraine. Specific aims of this investigation were to determine whether a discrete trait like migraine had a detectable genetic component using variance components methodology and to analyse genome wide STR markers to identify genomic regions harbouring a susceptibility gene or genes.
Page 197
As per the original quantitative trait CVD studies, power estimates were generated for a discrete trait. The study possessed good power to detect a significantly (P<0.05) non-zero estimate of the heritability for a discrete with a high prevalence in ascertained pedigree-members (~24%) (Refer to Chapter 5). Subsequent simulations were performed to estimate the LOD score one would expect to obtain for a QTL of varying effect sizes for discrete trait migraine. Results revealed reduced power to detect QTLs accounting for discrete trait variation, especially compared with previous estimates for quantitative traits (Bellis et al., 2008; Bellis et al., 2005). This finding is expected, as the power to detect linkage is known to diminish with trait dichotomization (Duggirala et al., 1997).
In total 25.5% of pedigree members (N=377) were migraine affected and 26.0% of non-pedigree members (N=223) were migraine affected. All participants were genotyped for a medium-density genome wide panel of microsatellite markers. The highest LOD scores occurred on chromosomes 9q22.32 (LOD=1.26) and 13q33.1
(LOD=1.6) in the Norfolk pedigree. Although short of suggestive linkage, results supported and replicated known loci for familial occipitotemporal lobe epilepsy with combined MA and for migraine symptom phenotype pulsation (Deprez et al., 2007;
Ligthart et al., 2008). Furthermore, evaluation of the chromosome 9 and 13 1-LOD- support interval provided some evidence of replication for the chromosome 13 marker
D13S173 in unrelated members of the Norfolk Island Health Study cohort.
Although a significant genetic component was evident for migraine and moderate linkage signals were detected, discrete trait power calculations indicated the recruitment of additional individuals and analysis of higher density marker panels should improve the ability to detect susceptibility loci.
Page 198
8.4 Migraine: Genome Wide Association Scan
Genome wide linkage scans and candidate gene studies have given way to genome wide association studies that use thousands of cases and controls and hundreds-of- thousands of genome wide single nucleotide polymorphisms (SNPs). Due to rapidly emerging high-throughput genotyping technologies and availability of the
International HapMap data, which has identified over 3.1 million human SNPs, map density is ever increasing (Ding and Jin, 2009; The International HapMap
Consortium, 2005, 2007). Simulations of genome-wide microsatellite and SNP scans varying marker density, heterozygosity (microsatellite markers) and minor allele frequency (SNP) in sib pairs indicate the greatest amount of inheritance information to be extracted using the highest density marker map possible (Evans and Cardon,
2004). Simulations also revealed traditional 10cM-density microsatellite panel to have significantly less information content than high density SNP and microsatellite panels.
The authors concluded that low information content from linkage studies using sparse panels of STRs would benefit from reanalysis using high density microsatellite markers (Evans and Cardon, 2004).
The initial findings of genome wide linkage analysis using microsatellites for individual CVD risk traits, clusters of CVD-risk traits and migraine indicate the
Norfolk Health Study Cohort, particularly the Bounty mutineer genealogy would benefit from recruitment of additional volunteers and/or significantly increasing marker density. Since the commencement of the 2000 Norfolk Island Health Study, the Genomics Research Centre has continued to work closely with the Norfolk Island community and in 2009 commenced a second phase of recruitment. However, genotyping data from new participants was not available for the present study.
Page 199
The final component of the work described in this thesis centred on a genome wide scan of SNP markers aimed at identifying genomic variants associated with migraine.
This involved high density SNP genotyping using the Illumina BeadChip platform in core pedigree members. A total of 7 SNPs showed marginal evidence for association
(P-value<1x10-5) with the most significantly associated SNP (P=1.96x10-6) occurring in an intronic region of the ADAMTSL1 gene on 9p22.2-p22.1. The function of this gene is currently unknown.
A candidate gene analysis was undertaken, with SNPs prioritised according to P-value and plausibility for a functional role in disease pathology. Using this approach the study focused on the top 0.05% of SNPs yielding the lowest P-value from the pGWAS. This yielded 172 SNPs, which were then assessed according to whether they were physically near or within the coding region of genes that are known to be expressed in the brain or CNS, regulate neurological pathways and plausibly related to migraine neuropathology. Using this approach, a number of neurotransmitter related variants were implicated in the pGWAS, including the genes ASTN1,
CACNA2D3, GRM7, CDH12, GABRG2, ADARB2, HTR7 and SLC17A8.
Of these genes, ADARB2 is a novel migraine candidate that mediates adenosine deaminase RNA editing and downstream regulation of neurotransmitters (Maas et al.,
2006). Specifically, this gene may be involved in the modification of serotonin and glutamate receptor pre mRNA in the CNS. Considering this role, it was interesting that the candidate gene analysis simultaneously identified a brain-expressed serotonin receptor gene (HTR7), glutamate receptor gene (GRM7) and a gene that mediates glutamate transport into synaptic vesicles (SLC17A8). Collectively, association of variants in these genes provides compelling evidence for serotonin and glutamate
Page 200
pathway involvement in migraine in the Norfolk pedigree and supports current theories of migraine neuropathy. Altered serotonergic neurotransmission occurs during and between migraine attacks (Hamel, 2007) and drugs such as triptans, a class of serotonin receptor agonists, are often used to treat migraine. The disruption of the neurotransmitter serotonin via g-protein coupled receptors such as HTR7 might partly explain the long hypothesised role of serotoninergic system disruption in migraine pathology. Likewise, GRM7 and SLC17A8 support the theory that altered glutamate release or uptake confers migraine attack risk (Andreou and Goadsby, 2009a;
Goadsby and Classey, 2000).
Overall, the Norfolk Island pGWAS candidate gene analysis identified several novel migraine genes, of which 4 were related through their effects on neurotransmitter pathways regulating serotonin and glutamate. Additional research will be required to confirm the involvement of serotonin and glutamate in migraine pathology in the
Norfolk pedigree. Despite these positive findings, overall the study had limited power which could be aided by on-going recruitment of pedigree members and possibly implementing strategies to minimise phenotypic and genetic heterogeneity (e.g. stratifying participants by comorbid disorders or migraine trait components) (Anttila et al., 2006; Stam et al., 2010). This may include identifying migraine probands on the
Island and building complete, individual sub-pedigrees. In addition, it may be worth focusing specifically on the glutamate pathway and serotonin pathway to systematically assess whether genetic variants influencing glutamate and serotonin levels are associated with migraine in the Norfolk pedigree.
Page 201
8.5 Systematic Analysis of Putative Migraine Susceptibility
Genes
Complex traits and disease result from interplay of multiple genetic variants and environmental risk factors. The number and effect sizes of individual genetic variants and their role in common disease is often unclear. A study of the population attributable risk for common disease determined a large number of rare genotypes
(frequency < 1/5000) are required to explain 50% of a common disease in a population even if risk rations are large (RR=10-20) (Yang et al., 2005). In comparison, approximately 20 genes can explain 50% of the burden of a disease in the population if predisposing genotypes are common (25%), even if the individual effect size for each gene is weak-to-moderate (RR = 1.2–1.5) (Yang et al., 2005). In the search for variants underlying complex disease, biologically meaningful associations of small effect size (i.e. risk ratios) can be expected (Barton and
Keightley, 2002; Wright et al., 2003).
No evidence supporting the presence of a single QTL was evident in the results of genome wide linkage and association approaches in the Norfolk pedigree. This may be due to insufficient power, but also may be due to the presence of genetic heterogeneity. In an effort to identify variants of potential weak-to-moderate effect size, a systematic analysis of putative migraine susceptibility genes, FHM genes, pGWAS-implicated genes and the recently discovered MA gene, KCNK18 were screened in the Norfolk pGWAS. The study hypothesised that consistent nominal association (P<0.05) supports genetic risk and therefore previously associated genes may be considered a priori and thus treated with more weight than SNPs in other genes and non-genic regions (Need et al., 2009). A local type I error of α = 0.05 was Page 202
applied adjusted for the final 38 genes tested (p=0.0013) (Ligthart et al., 2011).
Genome-wide Bonferroni adjustment was not required to protect against type I error as candidate gene selection negates the global null hypothesis.
A total of 56 candidate gene studies reported statistically significant association(s) with ICHD defined migraine and symptom phenotypes. Positive associations were reported for variants in across 36 genes. Additional genes were also selected based on
FHM and MA pathology and previous migraine GWAS results. Collectively, 40 genes were assessed in the Norfolk pedigree. These genes were predominantly functionally related through serotonin, dopamine and particularly glutamate pathways in the CNS, however regulatory roles in hormonal, vascular, and inflammatory pathways were also identified. No association to migraine GWAS genes MTDH and
NGFR was detected. However, findings did provide support for familial MA and
FHM genes KCNK18 and CACNA1A in the Norfolk core pedigree members. The study also provided compelling evidence for involvement of the female hormone receptor ESR1 in migraine risk. Given estrogens distinct effect on serotonin and glutamate pathways (McEwen, 2002; Sato et al., 2003; Singera et al., 1996) this finding supports the results of the pGWAS candidate gene analysis and provide impetus to focus on neurotransmitter pathway dysfunction in the Norfolk pedigree.
Page 203
8.6 Norfolk Island Health Study: Future Directions
8.6.1 Quantitative Traits: pGWAS
The work described in this thesis includes an autosomal genome wide linkage scan with an approximate 10cM density analysed for quantitative and qualitative phenotypes present in the Norfolk genealogy (described in Chapters 4 and 5). These studies detected moderate linkage signals requiring further verification by increasing sample size and/or increasing marker density. The scope of work described in this thesis included the re-analysis of migraine using a high density SNP panel. However, on-going progression of the Norfolk Island project will require re-analysis of uni- and multivariate quantitative CVD-risk phenotypes using the wealth of available genotypic data.
8.6.2 Expression Analysis
Normal variation in gene expression levels in complex organisms, including humans, is likely to account for sizeable portions of phenotypic variation (Morley et al., 2004).
Studies have demonstrated that differences in gene expression levels in humans are influenced by heritable factors (Dixon et al., 2007; Morley et al., 2004). The abundance of a gene transcript may be regulated by variation (polymorphisms) in regulatory elements that may be mapped with considerable power (Cookson et al.,
2009). Such polymorphisms are referred to as expression quantitative trait loci
(eQTLs).
Page 204
The future of the Norfolk Island Health Study project will be directed towards eQTL mapping to investigate the genetic basis of CVD phenotypes and migraine. This approach involves measurement of transcriptome wide gene expression by microarray analysis of transcript levels in human tissue of interest and the subsequent association of variation in expression to "expression quantitative trait loci" using genome wide microsatellite markers, SNPs and other variants to identify novel susceptibility genes
(Cookson et al., 2009).
Even in the absence of strong linkage evidence, combination of the existing linkage results and the new transcript data to be gathered should improve the ability to identify potential causal genes that lie within the linkage regions, because the two sources of information (linkage and correlation with transcription) are independent and additive. More importantly, the transcript-based eQTL approach is sufficiently powerful to identify potentially causal genes in areas where there is no current linkage evidence (Goring et al., 2007). Using this method sequence variants influencing
HDLc concentrations have been identified in the cis-regulated vanin 1 (VNN1) gene in the San Antonio Heart Study (Goring et al., 2007). Similar strategies have also been used to identify genetic variants correlated with levels of gene expression associated with Asthma, Celiac Disease, and Alzheimer Disease (Hunt et al., 2008;
Moffatt et al., 2007; Webster et al., 2009).
In 2009 the Genomics Research Centre initiated a second wave of participant recruitment on Norfolk Island. As with the 2000 recruitment, signed informed consent, medical and genealogical questionnaire responses, EDTA venous blood specimens for biochemical measures and DNA extraction were obtained from volunteers. In addition, venous blood samples were collected using Qiagen PAXgene
Page 205
blood RNA tubes, so that variation in leukocyte gene expression may be utilised for eQTL mapping. Characterisation of functional variants, their effect on gene expression, and the resulting influence on complex phenotypes will be essential for understanding the genetic basis of complex disease in the Norfolk Island pedigree.
Page 206
References
Adly, C., Straumanis, J., and Chesson, A. (1992). Fluoxetine prophylaxis of migraine. Headache 32, 101-104.
Aghajanian, G.K., and Merek, G.J. (2000). Serotonin model of schizophrenia: emerging role of glutamate mechanisms. Brain Res Rev 31, 302-312.
AHA (2003). Heart Disease and Stroke Statistics-2004 Update. (Dallas, Texas, American Heart Association).
AIHW, Moon, L., and Waters, A.-M. (2006). Socioeconomic inequalities in cardiovascular disease in Australia: current picture and trends since the 1990's. (AIHW Cat. No. AUS 74. Canberra: AIHW).
Alberti, K.G.M.M., Zimmet, P., and Shaw, J. (2005). The metabolic syndrome—a new worldwide definition. The Lancet 366, 1059-1062.
Almasy, L., and Blangero, J. (1998). Multipoint Quantitative-Trait Linkage Analysis in General Pedigrees. The American Journal of Human Genetics 62, 1198-1211.
Almasy, L., Hixson, J.E., Rainwater, D.L., Cole, S., Williams, J.T., Mahaney, M.C., VandeBerg, J.L., Stern, M.P., MacCluer, J.W., and Blangero, J. (1999). Human Pedigree-Based Quantitative-Trait–Locus Mapping: Localization of Two Genes Influencing HDL-Cholesterol Metabolism. American Journals Human Genetics 64, 1686–1693.
Alvarez, A., del Castillo, I., Villamar, M., Aguirre, L.A., Gonzalez-Neira, A., Lopez- Nevot, A., Moreno-Pelayo, M.A., and Moreno, F. (2005). High prevalence of the W24X mutation in the gene encoding connexin-26 (GJB2) in Spanish Romani (gypsies) with autosomal recessive non-syndromic hearing loss. Am J Med Genet 137A, 255-258.
Amos, C.I. (1994). Robust variance-components approach for assessing genetic linkage in pedigrees. Am J Hum Genet 54, 535-543.
Andlin-Sobocki, P., Jönsson, B., Wittchen, H.-U., and Olesen, J. (2005). Cost of disorders of the brain in Europe. European Journal of Neurology 12, 1-27.
Andreou, A.P., and Goadsby, P.J. (2009a). Therapeutic potential of novel glutamate receptor antagonists in migraine. Expert Opin Investig Drugs 2009 18, 789-803.
Andreou, A.P., and Goadsby, P.J. (2009b). Therapeutic potential of novel glutamate receptor antagonists in migraine. Expert Opin Investig Drugs 18, 789-803.
Angius, A., Bebbere, D., Petretto, E., Falchi, M., Forabosco, P., Maestrale, G., Casu, G., Persico, I., Melis, P., and Pirastu, M. (2002). Not all isolates are equal: linkage disequilibrium analysis on Xq13.3 reveals different patterns in Sardinian sub- populations. Human Genetics 111, 9-15.
Page 207
Anttila, V., Kallela, M., Oswell, G., Kaunisto, M.A., Nyholt, D.R., Hämäläinen, E., Havanka, H., Ilmavirta, M., Terwilliger, J., Sobel, E., et al. (2006). Trait Components Provide Tools to Dissect the Genetic Susceptibility of Migraine. The American Journal of Human Genetics 79, 85-99.
Anttila, V., Nyholt, D.R., Kallela, M., Artto, V., Vepsäläinen, S., Jakkula, E., Wennerström, A., Tikka-Kleemola, P., Kaunisto, M.A., Hämäläinen, E., et al. (2008). Consistently Replicating Locus Linked to Migraine on 10q22-q23. The American Journal of Human Genetics 82, 1051-1063.
Anttila, V., Stefansson, H., Kallela, M., Todt, U., Terwindt, G.M., Calafato, M.S., Nyholt, D.R., Dimas, A.S., Freilinger, T., ller-Myhsok, B., et al. (2010). Genome-wide association study of migraine implicates a common susceptibility variant on 8q22.1. Nat Genet [Epub ahead of print].
Arcos-Burgos, M., and Muenke, M. (2002). Genetics of population isolates. Clin Genet 61, 233-247.
Arya, R., Blangero, J., Williams, K., Almasy, L., Dyer, T.D., Leach, R.J., O'Connell, P., Stern, M.P., and Duggirala, R. (2002). Factors of Insulin Resistance Syndrome- Related Phenotypes Are Linked to Genetic Locations on Chromosomes 6 and 7 in Nondiabetic Mexican-Americans. Diabetes 51, 841-847.
Asuni, C., Stochino, M., Cherchi, A., Manchia, M., Congiu, D., Manconi, F., Squassina, A., Piccardi, M., and Del Zompo, M. (2009). Migraine and tumour necrosis factor gene polymorphism. Journal of Neurology 256, 194-197.
Aulchenko, Y.S., Vaessen, N., Heutink, P., Pullen, J., Snijders, P.J., Hofman, A., Sandkuijl, L.A., Houwing-Duistermaat, J.J., Edwards, M., Bennett, S., et al. (2003). A genome-wide search for genes involved in type 2 diabetes in a recently genetically isolated population from the Netherlands. Diabetes 52, 3001-3004.
Austin, M.A., Edwards, K.L., McNeely, M.J., Chandler, W.L., Leonetti, D.L., Talmud, P.J., Humphries, S.E., and Fujimoto, W.Y. (2004). Heritability of Multivariate Factors of the Metabolic Syndrome in Nondiabetic Japanese Americans. Diabetes 53, 1166-1169.
Badner, J.A., and Gershon, E.S. (2002). Meta-analysis of whole-genome linkage scans of bipolar disorder and schizophrenia. Mol Psychiatry 7, 405-411.
Bahra, A., Matharu, M.S., Buchel, C., Frackowiak, R.S., and Goadsby, P.J. (2001). Brainstem activation specific to migraine headache. The Lancet 357, 1016-1017.
Balaci, L., Spada, M.C., Olla, N., Sole, G., Loddo, L., Anedda, F., Naitza, S., Zuncheddu, M.A., Maschio, A., Altea, D., et al. (2007). IRAK-M is involved in the pathogenesis of early-onset persistent asthma. Am J Hum Genet 80, 1103-1114.
Barbato, E., Berger, A., Delrue, L., Van Durme, F., Manoharan, G., Boussy, T., Heyndrickx, G.R., De Bruyne, B., Ciampi, Q., Vanderheyden, M., et al. (2007). GLU- 27 variant of beta2-adrenergic receptor polymorphisms is an independent risk factor for coronary atherosclerotic disease. Atherosclerosis 194, e80-e86.
Page 208
Barbe, P., Millet, L., Galitzky, J., Lafontan, M., and Berlan, M. (1996). In situ assessment of the role of the beta 1-, beta 2- and beta 3-adrenoceptors in the control of lipolysis and nutritive blood flow in human subcutaneous adipose tissue. British Journal of Pharmacology 117, 907-913.
Bard, J.A., Zgombick, J., Adham, N., Vaysse, P., Branchek, T.A., and Weinshank, R.L. (1993). Cloning of a Novel Human Serotonin Recepto(r5-HT7)Positively Linked to Adenylate Cycla. Journal of Biological Chemistry 268, 23422-23426.
Barton, N.H., and Keightley, P.D. (2002). Understanding Quantitative Genetic Variation. Nature Reviews Genetics 3, 11-21.
Bauer, C.S., Tran-Van-Minh, A., Kadurin, I., and Dolphin, A.C. (2010). A new look at calcium channel a2d subunits. Current Opinion in Neurobiology 20, 563-571.
Bayerer, B., Engelbergs, J., Savidoy, I., Boes, T., Kuper, M., Schorn, C.F., Wissmann, A., Knop, D., Diener, H.C., and Limmroth, V. (2010). Single nucleotide polymorphisms of the serotonin transporter gene in migraine--an association study. Headache 50, 319-322.
Behar, D.M., Hammer, M.F., Garrigan, D., Villems, R., Bonne-Tamir, B., Richards, M., Gurwitz, D., Rosengarten, D., Kaplan, M., Della Pergola, S., et al. (2004). MtDNA evidence for a genetic bottleneck in the early history of the Ashkenazi Jewish population. Eur J HUm Genet 12, 355-364.
Bellis, C., Cox, H., Dyer, T., Charlesworth, J., Begley, K., Quinlan, S., Lea, R., Heath, S., Blangero, J., and Griffiths, L. (2008). Linkage mapping of CVD risk traits in the isolated Norfolk Island population. Human Genetics 124, 543-552.
Bellis, C., Cox, H.C., Ovcaric, M., Begley, K.N., Lea, R.A., Quinlan, S., Burgner, D., Heath, S.C., Blangero, J., and Griffiths, L.R. (2007). Linkage disequilibrium analysis in the genetically isolated Norfolk Island population. Heredity 100, 366-373.
Bellis, C., Hughes, R.M., Begley, K.N., Quinlan, S., Lea, R.A., Heath, S.C., Blangero, J., and Griffiths, L.R. (2005). Phenotypical characterisation of the isolated norfolk island population focusing on epidemiological indicators of cardiovascular disease. Hum Hered 60, 211-219.
Benarroch, E.E. (2007). GABAA receptor heterogeneity, function, and implications for epilepsy. Neurology 68, 612-614.
Bigal, M.E., Lipton, R.B., Chohen, J., and Silberstein, S.D. (2003). Epilepsy and migraine. Epilepsy & Behaviour 4, 13-24.
Bittles A. H. (2001). Consanguinity and its relevance to clinical genetics. Clin Genet 60, 89-98.
Björnsson, Á., Gudmundsson, G., Gudfinnsson, E., Hrafnsdóttir, M., Benedikz, J., Skúladóttir, S., Kristjánsson, K., Frigge, M.L., Kong, A., Stefánsson, K., et al. (2003). Localization of a Gene for Migraine without Aura to Chromosome 4q21. The American Journal of Human Genetics 73, 986-993.
Page 209
Black, H.R., Goodfriend, T.L., Izzo, J.L., and Pressure, A.H.A.C.f.H.B. (2003). Hypertension primer : the essentials of high blood pressure (Sydney, Lippincott Williams & Wilkins).
Blangero, J., and Almasy, L. (1997). Multipoint oligogenic linkage analysis of quantitative traits. Genet Epidemiol 14, 959-964.
Blangero, J., and Almasy, L.A. (1996). SOLAR: Sequential Oligogenic Linkage Analysis Routines. Technical Notes. Population Genetics Laboratory. Southwest Foundation for Biomedical Research, San Antonio, TX.
Blangero, J., Goring, H.H.H., Kent, J.W., Williams, J.T., Peterson, C.P., Almasy, L., and Dyer, T.D. (2005). Quantitative Trait Nucleotide Analysis Using Bayesian Model Selection. Human Biology 77, 541-559.
Blangero, J., Williams, J.T., and Almasy, L. (2001). Variance component methods for detecting complex trait loci. Adv Genet 42, 151-181.
Blau, J.N. (1992). Classical migraine: symptoms between visual aura and headache onset. The Lancet 340, 355-356.
Boerwinkle, E., Chakraborty, R., and Sing, C.F. (1986). The use of measured genotype information in the analysis of quantitative phenotypes in man. Annals of Human Genetics 50, 181-194.
Bolay, H., Reuter, U., Dunn, A.K., Huang, Z., Boas, D.A., and Moskowitz, M.A. (2002). Intrinsic brain activity triggers trigeminal meningeal afferents in a migraine model. Nat Med 8, 136-142.
Bonnen, P.E., Lowe, J.K., Altshuler, D.M., Breslow, J.L., Stoffel, M., Friedman, J.M., and Pe’er, I. (2010). European admixture on the Micronesian island of Kosrae: lessons from complete genetic information. European Journal of Human Genetics 18, 309-316.
Bonnen, P.E., Pe'er, I., Plenge, R.M., Salit, J., Lowe, J.K., Shapero, M.H., Lifton, R.P., Breslow, J.L., Daly, M.J., Reich, D.E., et al. (2006a). Evaluating potential for whole-genome studies in Kosrae, an isolated population in Micronesia. Nat Genet 38, 214-217.
Bonnen, P.E., Pe’er, I., Plenge, R.M., Salit, J., Lowe, J.K., Shapero, M.H., Lifton, R.P., Breslow, J.L., Daly, M.J., Reich, D.E., et al. (2006b). Evaluating potential for whole-genome studies in Kosrae, an isolated population in Micronesia. Nature Genet 38, 214-217.
Bork, J.M., Peters, L.M., Riazuddin, S., Bernstein, S.L., Ahmed, Z.M., Ness, S.L., Polomeno, R., Ramesh, A., Schloss, M., Srisailpathy, C.R.S., et al. (2001). Usher syndrome 1D and nonsyndromic autosomal recessive deafness DFNB12 are caused by allelic mutations of the novel cadherin-like gene CDH23. Am J Hum Genet 68, 26- 37.
Page 210
Borroni, B., Brambilla, C., Liberini, P., Rao, R., Archetti, S., Gipponi, S., Dalla Volta, G., and Padovani, A. (2005). Functional serotonin 5–HTTLPR polymorphism is a risk factor for migraine with aura. The Journal of Headache and Pain 6, 182-184.
Borroni, B., Rao, R., Liberini, P., Venturelli, E., Cossandi, M., Archetti, S., Caimi, L., and Padovani, A. (2006). Endothelial nitric oxide synthase (Glu298Asp) polymorphism is an independent risk factor for migraine with aur. Headache 46, 1575-1579.
Bray, S.M., Mulle, J.G., Dodd, A.F., Pulver, A.E., Wooding, S., and Warren, S.T. (2010). Signatures of founder effects, admixture, and selection in the Ashkenazi Jewish population. PNAS 107, 16222-16227.
Burkhardt, R., Kenny, E.E., Lowe, J.K., Birkeland, A., Josowitz, R., Noel, M., Salit, J., Maller, J.B., Pe’er, I., Daly, M.J., et al. (2008). Common SNPs in HMGCR in Micronesians and Caucasians associated with LDL-cholesterol levels affect alternative splicing of exon13. Arterioscler Thromb Vasc Biol 28, 2078–2084.
Cader, Z.M., Noble-Topham, S., Dyment, D.A., Cherny, S.S., Brown, J.D., Rice, G.P.A., and Ebers, G.C. (2003). Significant linkage to migraine with aura on chromosome 11q24. Hum Mol Genet 12, 2511-2517.
Cai, G., Cole, S., Freeland-Graves, J.H., Maccluer, J.W., Blangero, J., and Comuzzie, A.G. (2004). Principal Component for Metabolic Syndrome Risk Maps to Chromosome 4p in Mexican Americans: The San Antonio Family Heart Study. Human Biology 76, 651-655.
Calakos, N., and Scheller, R.H. (1996). Synaptic vesicle biogenesis, docking, and fusion: a molecular description. Physiol Rev 76, 1-29.
Carlsson, A., Forsgren, L., Nylander, P.O., Hellman, U., Forsman-Semb, K., Holmgren, G., Holmberg, D., and Holmberg, M. (2002). Identification of a susceptibility locus for migraine with and without aura on 6p12.2-p21.1. Neurology 59, 1804-1807.
Carrasquillo, M.M., Zlotogora, J., Barges, S., and Chakravarti, A. (1997). Two Different Connexin 26 Mutations in an Inbred Kindred Segregating Non-Syndromic Recessive Deafness: Implications for Genetic Studies in Isolated Populations. Hum Mol Genet 6, 2163-2172.
Carter, C. (2006). Schizophrenia susceptibility genes converge on interlinked pathways related to glutamatergic transmission and long-term potentiation, oxidative stress and oligodendrocyte viability. Schizophrenia Research 86, 1-14.
Casseles, S. (2006). Overweight in the Pacific: links between foreign dependence, global food trade, and obesity in the Federated States of Micronesia. Globalization and Health 2, 1-8.
Cevoli, S., Pierangeli, G., Monari, L., Valentino, M.L., Bernardoni, P., Mochi, M., Cortelli, P., and Montagna, P. (2002). Familial hemiplegic migraine: clinical features and probable linkage to chromosome 1 in an Italian family. Neurological Sciences 23, 7-10. Page 211
Charles, A., and Brennan, K.C. (2009). Cortical spreading depression—new insights and persistent questions. Cephalalgia 29, 1115-1124.
Chen, C., Keith, J., Nyholt, D., Martin, N., and Mengersen, K. (2009a). Bayesian latent trait modeling of migraine symptom data. Human Genetics 126, 277-288.
Chen, C., Mengersen, K., Keith, J., Martin, N., and Nyholt, D. (2009b). Linkage and heritability analysis of migraine symptom groupings: a comparison of three different clustering methods on twin data. Human Genetics 125, 591-604.
Chen, W., Srinivasan, S.R., Elkasabany, A., and Berenson, G.S. (1999). Cardiovascular Risk Factors Clustering Features of Insulin Resistance Syndrome (Syndrome X) In a Biracial (Black-White) Population of Children, Adolescents, and Young Adults: The Bogalusa Heart Study. Am J Epidemiol 150, 667-674.
, M., Grazia Piras, M., et al. (2008). Variations in the G6PC2/ABCB11 genomic region are associated with fasting glucose levels J Clin Inves 118, 2620-2628.
Chishti, M.S., Bhatti, A., Tamim, S., Lee, K., McDonald, M.-L., Leal, S.M., and Ahmad, W. (2008). Splice-site mutations in the TRIC gene underlie autosomal recessive nonsyndromic hearing impairment in Pakistani families. J Hum Genet 53, 101-105.
Ciullo, M., Bellenguez, C., Colonna, V., Nutile, T., Calabria, A., Pacente, R., Iovino, G., Trimarco, B., Bourgain, C., and Persico, M.G. (2006). New susceptibility locus for hypertension on chromosome 8q by efficient pedigree-breaking in an Italian isolate. Human Molecualr Genetics 15, 1735-1743.
Clarke, J.M. (1910). On Recurrent Motor Paralysis in Migraine, with Report of a Family in which Recurrent Hemiplegia Accompanied the Attacks. Br Med J 1, 1534– 1538.
Cologno, D., Pascale, A.D., and Manzoni, G.C. (2003). Familial Occurrence of Migraine With Aura in a Population-Based Study. Headache: The Journal of Head and Face Pain 43, 231-234.
Colson, N., Lea, R., Quinlan, S., MacMillan, J., and Griffiths, L. (2004). The estrogen receptor 1 G594A polymorphism is associated with migraine susceptibility in two independent case/control groups. neurogenetics 5, 129-133.
Colson, N.J., Lea, R.A., Quinlan, S., MacMillan, J., and Griffiths, L.R. (2005). Investigation of hormone receptor genes in migraine. neurogenetics 6, 17-23.
Conrad, D.F., Jakobsson, M., Coop, G., Wen, X., Wall, J.D., Rosenberg, N.A., and Pritchard, J.K. (2006). A worldwide survey of haplotype variation and linkage disequilibrium in the human genome. Nature Genetics 38, 1251-1260.
Contopoulos-Ioannidis, D.G., Manoli, E.N., and Ioannidis, J.P.A. (2005). Meta- analysis of the association of beta2-adrenergic receptor polymorphisms with asthma phenotypes. Journal of Allergy and Clinical Immunology 115, 963-972. Page 212
Cookson, W., Liang, L., Abecasis, G., Moffatt, M., and Lathrop, M. (2009). Mapping complex disease traits with global gene expression. Nature Reviews Genetics 10, 184- 194.
Corominas, R., Ribasés, M., Cuenca-León, E., Narberhaus, B., Serra, S.A., del Toro, M., Roig, M., Fernández-Fernández, J.M., Macaya, A., and Cormand, B. (2009a). Contribution of syntaxin 1A to the genetic susceptibility to migraine: A case-control association study in the Spanish population. Neuroscience Letters 455, 105-109.
Corominas, R., Sobrido, M.J., Ribasés, M., Cuenca-León, E., Blanco-Arias, P., Narberhaus, B., Roig, M., Leira, R., López-González, J., Macaya, A., et al. (2009b). Association study of the serotoninergic system in migraine in the spanish population. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 153B, 177- 184.
Cuenca-León, E., Corominas, R., Fernàndez-Castillo, N., Volpini, V., Toro, M.d., Roig, M., Macaya, A., and Cormand, B. (2008). Genetic analysis of 27 Spanish patients with hemiplegic migraine, basilar-type migraine and childhood periodic syndromes. Cephalalgia 28, 1039-1047.
Cuenca-León, E., Corominas, R., Montfort, M., Artigas, J., Roig, M., Bayés, M., Cormand, B., and Macaya, A. (2009). Familial hemiplegic migraine: linkage to chromosome 14q32 in a Spanish kindred. neurogenetics 10, 191-198.
D'Onofrio, M., Ambrosini, A., Di Mambro, A., Arisi, I., Santorelli, F.M., Grieco, G.S., Nicoletti, F., Nappi, G., Pierelli, F., Schoenen, J., et al. (2009). The interplay of two single nucleotide polymorphisms in the CACNA1A gene may contribute to migraine susceptibility. Neuroscience Letters 453, 12-15.
D’Andrea, G., and Leon, A. (2010). Pathogenesis of migraine: from neurotransmitters to neuromodulators and beyond. Neurol Sci 31, S1–S7.
Dallongeville, J., Helbecque, N., Cottel, D., Amouyel, P., and Meirhaeghe, A. (2003). The Gly16>Arg16 and Gln27>Glu27 Polymorphisms of Beta2-Adrenergic Receptor Are Associated with Metabolic Syndrome in Men. J Clin Endocrinol Metab 88, 4862- 4866.
De Fusco, M., Marconi, R., Silvestri, L., Atorino, L., Rampoldi, L., Morgante, L., Ballabio, A., Aridon, P., and Casari, G. (2003). Haploinsufficiency of ATP1A2 encoding the Na+/K+ pump alpha2 subunit associated with familial hemiplegic migraine type 2. Nat Genet 33, 192-196. de Vries, B., Frants, R.R., Ferrari, M.D., and van den Maagdenberg, A.M.J.M. (2009). Molecular genetics of migraine. Human Genetics 126, 115-132.
DeLisi, L.E., Mesen, A., Rodriguez, C., Bertheau, A., LaPrade, B., Llach, M., Riondet, S., Razi, K., Relja, M., Byerley, W., et al. (2002). Genome-wide scan for linkage to schizophrenia in a Spanish-origin cohort from Costa Rica. Am J Hum Genet 114, 497-508.
Dichgans, M., Freilinger, T., Eckstein, G., Babini, E., Lorenz-Depiereux, B., Biskup, S., Ferrari, M.D., Herzog, J., van den Maagdenberg, A.M.J.M., Pusch, M., et al. Page 213
(2005). Mutation in the neuronal voltage-gated sodium channel SCN1A in familial hemiplegic migraine. The Lancet 366, 371-377.
Dijk, S.N., Francis, P.T., Stratmann, G.C., and Bowen, D.M. (1995). NMDA-induced glutamate and aspartate release from rat cortical pyramidal neurones: evidence for modulation by a 5-HT1A antagonist. Br J Pharmacol 115, 1169–1174.
Diriong, S., Lory, P., Williams, M.E., Ellis, S.B., Harpold, M.M., and Taviaux, S. (1995). Chromosomal Localization of the Human Genes for [alpha]1A, [alpha]1B, and [alpha]1E Voltage-Dependent Ca2+ Channel Subunits. Genomics 30, 605-609.
Dixon, A.L., Liang, L., Moffatt, M., Chen, W., Heath, S., Wong, K.C.C., Taylor, J., Burnett, E., Gut, I., Farrall, M., et al. (2007). A genome-wide association study of global gene expression. Nature Genet 39, 1202-1207.
Dobler, T., Springauf, A., Tovornik, S., Weber, M., Schmitt, A., Sedlmeier, R., Wischmeyer, E., and Döring, F. (2007). TRESK two-pore-domain K+ channels constitute a significant component of background potassium currents in murine dorsal root ganglion neurones. J Physiol 585, 867-879.
Dong, C., Wang, S., Li, W.-D., Li, D., Zhao, H., and Price, R.A. (2003). Interacting Genetic Loci on Chromosomes 20 and 10 Influence Extreme Human Obesity. The American Journal of Human Genetics 72, 115-124.
Doucette, L., Merner, N.D., Cooke, S., Ives, E., Galutira, D., Walsh, V., Walsh, T., MacLaren, L., Cater, T., Fernandez, B., et al. (2009). Profound, prelingual nonsyndromic deafness maps to chromosome 10q21 and is caused by a novel missense mutation in the Usher syndrome type IF gene PCDH15. Europ J Hum Genet 17, 554-564.
Du, X., Schwander, M., Moresco, E.M.Y., Viviani, P., Haller, C., Hildebrand, M.S., Pak, K., Tarantino, L., Roberts, A., Richardson, H., et al. (2008). A catechol-O- methyltransferase that is essential for auditory function in mice and humans. Proc. Nat Acad Sci 105, 14609-14614.
Ducros, A., Denier, C., Joutel, A., Cecillon, M., Lescoat, C., Vahedi, K., Darcel, F., Vicaut, E., Bousser, M.-G., and Tournier-Lasserve, E. (2001). The Clinical Spectrum of Familial Hemiplegic Migraine Associated with Mutations in a Neuronal Calcium Channel. N Engl J Med 345, 17-24.
Ducros, A., Denier, C., Joutel, A., Vahedi, K., Michel, A., Darcel, F., Madigand, M., Guerouaou, D., Tison, F., Julien, J., et al. (1999). Recurrence of the T666M Calcium Channel CACNA1A Gene Mutation in Familial Hemiplegic Migraine with Progressive Cerebellar Ataxia. The American Journal of Human Genetics 64, 89-98.
Ducros, A., Joutel, A., Cecillon, M., Tournier-Lasserve, E., Vahedi, K., Bousser, M.- G., Ferreira, A., Bernard, E., Verier, A., Echenne, B., et al. (1997). Mapping of a second locus for familial hemiplegic migraine to 1q21-q23 and evidence of further heterogeneity. Annals of Neurology 42, 885-890.
Page 214
Duggirala, R., Williams, J.T., Williams-Blangero, S., and Blangero, J. (1997). A variance component approach to dichotomous trait linkage analysis using a threshold model. Genetic Epidemiology 14, 987-992.
Dunlap, K., Luebke, J.I., and Turner, T.J. (1995). Exocytotic Ca2+ channels in mammalian central neurons. Trends in Neurosciences 18, 89-98.
Dyke, B. (1995). PEDSYS: a pedigree data management system. User‘s manual., Tech. Rep. No. 2 edn (San Antonio, Texas, Southwest Foundation for Biomedical Research).
Dyke, B. (1996). PEDSYS: A pedigree data management system, 2.0 edn. (Population Genetics Laboratory, Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio).
Eaves, I.A., Merriman, T.R., Barber, R.A., Nutland, S., Tuomilehto-Wolf, E., Tuomilehto, J., Cucca, F., and Todd, J.A. (2000). The genetically isolated populations of Finland and sardinia may not be a panacea for linkage disequilibrium mapping of common disease genes. Nat Genet 25, 320-323.
Elbein, S.C., and Hasstedt, S.J. (2002). Quantitative Trait Linkage Analysis of Lipid- Related Traits in Familial Type 2 Diabetes: Evidence for Linkage of Triglyceride Levels to Chromosome 19q. Diabetes 51, 528-535.
Enyedi, P., and Czirjak, G. (2010). Molecular background of leak K+ currents: two- pore domain potassium channels. Physiol Rev 90, 559-605.
Erdal, M.E., Herken, H., Yilmaz, M., and BayazIt, Y.A. (2001). Association of the T102C polymorphism of 5-HT2A receptor gene with aura in migraine. Journal of the Neurological Sciences 188, 99-101.
Erdal, N., Herken, H., Yilmaz, M., Erdal, E., and Bayazit, Y.A. (2007). The A218C polymorphism of tryptophan hydroxylase gene and migraine. Journal of Clinical Neuroscience 14, 249-251.
Evans, D.M., and Cardon, L.R. (2004). Guidelines for Genotyping in Genomewide Linkage Studies: Single-Nucleotide-Polymorphism Maps Versus Microsatellite Maps. The American Journal of Human Genetics 75, 687-692.
Expert Panel on Detection, E.a.T.o.H.B.C.i.A. (2001). Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 285, 2486-2497.
Fabricius, M., Fuhr, S., Bhatia, R., Boutelle, M., Hashemi, P., Strong, A.J., and Lauritzen, M. (2006). Cortical spreading depression and peri-infarct depolarization in acutely injured human cerebral cortex. Brain 129, 778-790.
Feitosa, M., Borecki, I., Rankinen, T., Rice, T., Després, J., Chagnon, Y., Gagnon, J., Leon, A., Skinner, J., Bouchard, C., et al. (2005). Evidence of QTLs on chromosomes 1q42 and 8q24 for LDL-cholesterol and apoB levels in the HERITAGE Family Study. The Journal of Lipid Research 46, 281-286. Page 215
Feitosa, M.F., Borecki, I.B., Rich, S.S., Arnett, D.K., Sholinsky, P., Myers, R.H., Leppert, M., and Province, M.A. (2002). Quantitative-Trait Loci Influencing Body- Mass Index Reside on Chromosomes 7 and 13: The National Heart, Lung, and Blood Institute Family Heart Study. American Journal of Human Genetics 70, 72-82.
Fernandez, D.M., Hand, C.K., Sweeney, B.J., and Parfrey, N.A. (2008). A Novel ATP1A2 Gene Mutation in an Irish Familial Hemiplegic Migraine Kindred. Headache: The Journal of Head and Face Pain 48, 101-108.
Fernandez, F., Colson, N., Quinlan, S., MacMillan, J., Lea, R., and Griffiths, L. (2009). Association between migraine and a functional polymorphism at the dopamine β-hydroxylase locus. Neurogenetics 10, 199-208.
Fernandez, F., Lea, R.A., Colson, N.J., Bellis, C., Quinlan, S., and Griffiths, L.R. (2006). Association between a 19 bp deletion polymorphism at the dopamine beta- hydroxylase (DBH) locus and migraine with aura. J Neurol Sci 251, 118-123.
Ferrari, M.D., Roon, K.I., Lipton, R.B., and Goadsby, P.J. (2001). Oral triptans (serotonin 5-HT1B/1D agonists) in acute migraine treatment: a meta-analysis of 53 trials. The Lancet 358, 1668-1675.
Fink, J.M., Hirsch, B.A., Zheng, C., Dietz, G., Hatten, M.E., and Ross, M.E. (1997). Astrotactin (ASTN), a Gene for Glial- Guided Neuronal Migration, Maps to Human Chromosome 1q25.2. Genomics 40, 202-205.
Formicola, D., Aloia, A., Sampaolo, S., Farina, O., Diodato, D., Griffiths, L., Gianfrancesco, F., Di Iorio, G., and Esposito, T. (2010). Common variants in the regulative regions of GRIA1 and GRIA3 receptor genes are associated with migraine susceptibility. BMC Medical Genetics 11, 103.
Fuller, G.N., and Gale, M.V. (1988). British Medical Journal. 297, 1670-1672.
Gallanti, A., Tonelli, A., Cardin, V., Bussone, G., Bresolin, N., and Bassi, M.T. (2008). A novel de novo nonsense mutation in ATP1A2 associated with sporadic hemiplegic migraine and epileptic seizures. Journal of the Neurological Sciences 273, 123-126.
Gan-Or, Z., Giladi, N., Rozovski, U., Shifrin, C., Rosner, S., Gurevich, T., Bar-Shira, A., and Orr-Urtreger, A. (2008). Genotype-phenotype correlations between GBA mutations and Parkinson disease risk and onset. Neurology 70, 2777-2238.
Gardner, K., Barmada, M.M., Ptacek, L.J., and Hoffman, E.P. (1997). A new locus for hemiplegic migraine maps to chromosome 1q31. Neurology 49, 1231-1238.
Gargus, J.J., and Tournay, A. (2007). Novel Mutation Confirms Seizure Locus SCN1A is Also Familial Hemiplegic Migraine Locus FHM3. Pediatric Neurology 37, 407-410.
Ge, D., Zhang, K., Need, A.C., Martin, O., Fellay, J., Urban, T.J., Telenti, A., and Goldstein, D.B. (2008). WGAViewer: Software for genomic annotation of whole genome association studies. Genome Research 18, 640-643.
Page 216
Gervil, M., Ulrich, V., Kaprio, J., Olesen, J., and Russell, M.B. (1999a). The relative role of genetic and environmental factors in migraine without aura. Neurology 53, 995-.
Gervil, M., Ulrich, V., Kyvik, K.O., Olesen, J., and Russell, M.B. (1999b). Migraine without aura: A population-based twin study. Annals of Neurology 46, 606-611.
Ghosh, J., Joshi, G., Pradhan, S., and Mittal, B. (2010). Investigation of TNFA 308G > A and TNFB 252G > A polymorphisms in genetic susceptibility to migraine. Journal of Neurology 257, 898-904.
Ginns, E.I., Ott, J., Egeland, J.A., Allen, C.R., Fann, C.S.J., Pauls, D.L., Weissenbach, J., Carulli, J.P., Falls, K.M., Keith, T.P., et al. (1996). A genome-wide search for chromosomal loci linked to bipolar affective disorder in the Old Order Amish. Nature Genetics 12, 431-435.
Gjesing, A., Andersen, G., Burgdorf, K., Borch-Johnsen, K., Jørgensen, T., Hansen, T., and Pedersen, O. (2007). Studies of the associations between functional beta2- adrenergic receptor variants and obesity, hypertension and type 2 diabetes in 7,808 white subjects. Diabetologia 50, 563-568.
Goadsby, P.J., Charbit, A.R., Andreou, A.P., Akerman, S., and Holland, P.R. (2009). Neurobiology of migraine. Neuroscience 161, 327-341.
Goadsby, P.J., and Classey, J.D. (2000). Glutamatergic transmission in the trigeminal nucleus assessed with local blood flow. . Brain Res 2000 875, 119-124.
Gold, E.B., Bromberger, J., Crawford, S., Samuels, S., Greendale, G.A., Harlow, S.D., and Skurnick, J. (2001). Factors Associated with Age at Natural Menopause in a Multiethnic Sample of Midlife Women. Am J Epidemiol 153, 865-874.
Goldstein, D.B., and Weale, M.E. (2001). Population genomics: linkage disequilibrium holds the key. Curr Biol 11, R576-579.
Goodman, E., Dolan, L.M., Morrison, J.A., and Daniels, S.R. (2005). Factor Analysis of Clustered Cardiovascular Risks in Adolescence: Obesity Is the Predominant Correlate of Risk Among Youth. Circulation 111, 1970-1977.
Goring, H.H.H., Curran, J.E., Johnson, M.P., Dyer, T.D., Charlesworth, J., Cole, S.A., Jowett, J.B.M., Abraham, L.J., Rainwater, D.L., Comuzzie, A.G., et al. (2007). Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes. Nature Genetics 39, 1208-1216.
Göring, H.H.H., Terwilliger, J.D., and Blangero, J. (2001). Large Upward Bias in Estimation of Locus-Specific Effects from Genomewide Scans The American Journal of Human Genetics 69, 1357-1369.
Green, J.S., Parfrey, P.S., Harnett, J.D., Farid, N.R., Cramer, B.C., Johnson, G., Heath, O., McManamon, P.J., O'Leary, E., and Pryse-Phillips (1989). The Cardinal Manifestations of Bardet–Biedl Syndrome, a Form of Laurence–Moon–Biedl Syndrome. N Engl J Med 321, 1002-1009. Page 217
Griffiths, L.R., Nyholt, D.R., Curtain, R.P., Goadsby, P.J., and Brimage, P.J. (1997). Migraine association and linkage studies of an endothelial nitric oxide synthase (NOS3) gene polymorphism. Neurology 49, 614-617.
Gu, S., Pakstis, A.J., Li, H., Speed, W.C., Kidd, J.R., and Kidd, K.K. (2007). Significant varia- tion in haplotype block structure but conservation in tagSNP pat- terns among global populations. . Eur J Hum Genet 15, 302-312.
Guilford, P., Ayadi, H., Blanchard, S., Chaïb, H., Le Paslier, D., Weissenbach, J., Drira, M., and Petit, C. (1994). A human gene responsible for neurosensory non- syndromic deafness is a candidate homologue of the mouse sh-1 gene. Hum Mol Genet 3, 989-993.
Gunderson, K.L., Steemers, F.J., Lee, G., Mendoza, L.G., and Chee, M.S. (2005). A genome-wide scalable SNP genotyping assay using microarray technology. Nature Genetics 37, 549-554.
Gupta, R., Kumar, V., Luthra, K., Banerjee, B., and Bhatia, M.S. (2009). Polymorphism in apolipoprotein E among migraineurs and tension-type headache subjects. Journal of Headache and Pain 10, 115-120.
Gupta, S., Mehrotra, S., Villalón, C.M., Perusquía, M., Saxena, P.R., and MaassenVanDenBrink, A. (2007). Potential role of female sex hormones in the pathophysiology of migraine. Pharmacology & Therapeutics 113, 321-340.
Hadjikhani, N., Sanchez Del Rio, M., Wu, O., Schwartz, D., Bakker, D., Fischl, B., Kwong, K.K., Cutrer, F.M., Rosen, B.R., Tootell, R.B., et al. (2001). Mechanisms of migraine aura revealed by functional MRI in human visual cortex. Proc Natl Acad Sci USA 98, 4687-4692.
Hager, J., Dina, C., Francke, S., Dubois, S., Houari, M., Vatin, V., Vaillant, E., Lorentz, N., Basdevant, A., Clement, K., et al. (1998). A genome-wide scan for human obesity genes reveals a major susceptibility locus on chromosome 10. Nature Genetics 20, 304-308.
Hamel, E. (2007). Serotonin and Migraine: Biology and Clinical Implications. Cephalalgia 27, 1293-1300.
Hanke, S., Bugert, P., Chudek, J., and Kovacs, G. (2001). Cloning a calcium channel alpha2delta-3 subunit gene from a putative tumor suppressor gene region at chromosome 3p21.1 in conventional renal cell carcinoma. Gene 7, 69-75.
Hanna, G.L., Weele, J.V.-V., Cox, N.J., Van Etten, M., Fischer, D.J., Himle, J.A., Bivens, N.C., Wu, X., Roe, C.A., Hennessy, K.A., et al. (2007). Evidence for a Susceptibility Locus on Chromosome 10p15 in Early-Onset Obsessive-Compulsive Disorder. Biological psychiatry 62, 856-862.
Hanson, R.L., Ehm, M.G., Pettitt, D.J., Prochazka, M., Thompson, D.B., Timberlake, D., Foroud, T., Kobes, S., Baier, L., Burns, D.K., et al. (1998). An autosomal genomic scan for loci linked to type II diabetes mellitus and body-mass index in Pima Indians. Am J Hum Genet 63, 1130-1138.
Page 218
Hartl, D.L., and Clark, A.G. (2007). Principles of Population Genetics, Fourth Edition, 3rd edn (Sunderland, MA, Sinauer Associates).
He, L.N., Liu, Y.J., Xiao, P., Zhang, L., Guo, Y., Yang, T.L., Zhao, L.J., Drees, B., Hamilton, J., Deng, H.Y., et al. (2008). Genomewide Linkage Scan for Combined Obesity Phenotypes using Principal Component Analysis. Annals of Human Genetics 0, 1-8.
Heath, S.C. (1997). Markov chain Monte Carlo segregation and linkage analysis for oligogenic models. Am J Hum Genet 61, 748-760.
Helgason, A., Nicholson, G., Stefánsson, K., and Donnelly, P. (2003). A Reassessment of Genetic Diversity in Icelanders: Strong Evidence from Multiple Loci for Relative Homogeneity Caused by Genetic Drift. Annals of Human Genetics 67, 281-297.
Hirohata, S., Wang, L.W., Miyagi, M., Yan, L., Seldin, M.F., Keene, D.R., Crabb, J.W., and Apte, S.S. (2002). Punctin, a Novel ADAMTS-like Molecule, ADAMTSL- 1, in Extracellular Matrix. The Journal of Biological Chemistry 272, 12182-12189.
Hirschhorn, J.N., and Daly, M.J. (2005). Genome-wide association studies for common diseases and complex traits. Nature Reviews Genetics 6, 95-108.
Hoare, M. (1999). Norfolk Island : a revised and enlarged history 1774-1998, 5th Edition edn (Rockhampton, Central Queensland University Press).
Hodge, A.M., Dowse, G.K., Toelupe, P., Collins, V.R., and Zimmet, P.Z. (1997). The association of modernization with dyslipidaemia and changes in lipid levels in the Polynesian population of Western Samoa. Int J Epidemiol 26, 297-306.
Hoggart, C.J., Parra, E.J., Shriver, M.D., Bonilla, C., Kitles, R.A., Clayton, D.G., and McKeigue, P.M. (2003). Control of Confounding of Genetic Associations in Stratified Populations. American Journal of Human Genetics 72, 1492-1504.
Hohoff, C., Marziniak, M., Lesch, K.-P., Deckert, J., Sommer, C., and Mössner, R. (2007). An adenosine A2A receptor gene haplotype is associated with migraine with aura. Cephalalgia 27, 177-181.
Hovatta, I., Kallela, M., Färkkilä, M., and Peltonen, L. (1994). Familial migraine: Exclusion of the susceptibility gene from the reported locus of familial hemiplegic migraine on 19p. Genomics 23, 707-709.
Hovatta, I., Varilo, T., Suvisaari, J., Terwilliger, J.D., Ollikainen, V., Arajärvi, R., Juvonen, H., Kokko-Sahin, M.-L., Väisänen, L., Mannila, H., et al. (1999). A Genomewide Screen for Schizophrenia Genes in an Isolated Finnish Subpopulation, Suggesting Multiple Susceptibility Loci. The American Journal of Human Genetics 65, 1114-1124.
Howard, B.V., Lee, E.T., Cowan, L.D., Devereux, R.B., Galloway, J.M., Go, O.T., Howard, W.J., Rhoades, E.R., Robbins, D.C., Sievers, M.L., et al. (1999). Rising Tide of Cardiovascular Disease in American Indians The Strong Heart Study. Circulation 99, 2389-2395. Page 219
Hoyer, D., Hannon, J.P., and Martin, G.R. (2002). Molecular, pharmacological and functional diversity of 5-HT receptors. Pharmacology Biochemistry and Behavior 71, 533-554.
Hsueh, W.-C., Mitchell, B.D., Schneider, J.L., Wagner, M.J., Bell, C.J., Nanthakumar, E., and Shuldiner, A.R. (2000). QTL Influencing Blood Pressure Maps to the Region of PPH1 on Chromosome 2q31-34 in Old Order Amish. Circulation 101, 2810-2816.
Hubert, H.B., Feinleib, M., McNamara, P.M., and Castelli, W.P. (1983). Obesity as an independent risk factor for cardiovascular disease: a 26- year follow-up of participants in the Framingham Heart Study. Circulation 67, 968-977.
Human Genome Sequencing, C. (2004). Finishing the euchromatic sequence of the human genome. Nature 431, 931-945.
Hussels, I.E., and Morton, N.E. (1972). Pingelap and Mokil Atolls: Achromatopsia. American Journal of Human Genetics 24, 304-309.
ICHD-I (1988). Classification and diagnostic criteria for the headache disorders, cranial neuralgias and facial pain. Cephalalgia 8, 1-96.
ICHD-II (2004). International classification of headache disorders, 2nd edition. Cephalalgia 24(suppl 1), 1–160.
Igl, W., Johansson, Ã.s., Wilson, J.F., Wild, S.H., PolaÅ¡ek, O., Hayward, C., Vitart, V., Hastie, N., Rudan, P., Gnewuch, C., et al. (2010). Modeling of Environmental Effects in Genome-Wide Association Studies Identifies SLC2A2 and HP as Novel Loci Influencing Serum Cholesterol Levels. PLoS Genet 6, e1000798.
Ioannidis, J.P.A., Trikalinos, T.A., and Khoury, M.J. (2006). Implications of Small Effect Sizes of Individual Genetic Variants on the Design and Interpretation of Genetic Association Studies of Complex Diseases. American Journal of Epidemiology 164, 609–614.
James, M.F., Smith, M.I., Bockhorst, K.H.J., Hall, D.L., Houston, G.C., Papadakis, N.G., Smith, J.M., Willliams, E.J., Xing, D., Parsons, A.A., et al. (1999). Cortical spreading depression in the gyrencephalic feline brain studied by magnetic resonance imaging. The Journal of Physiology 519, 415-425.
Jensen, R., and Stovner, L.J. (2008). Epidemiology and comorbidity of headache. The Lancet Neurology 7, 354-361.
Jocken, J.W.E., Blaak, E.E., Schiffelers, S., Arner, P., van Baak, M.A., and Saris, W.H.M. (2006). Association of a beta-2 adrenoceptor (ADRB2) gene variant with a blunted in vivo lipolysis and fat oxidation. Int J Obes 31, 813-819.
Jolliffe, I.T. (2002). Principal Component Analysis Second Edition, 2nd edn (New York, Springer).
Jones, K.W., Ehm, M.G., Pericak-Vance, M.A., Haines, J.L., Boyd, P.R., and Peroutka, S.J. (2001). Migraine with Aura Susceptibility Locus on Chromosome Page 220
19p13 Is Distinct from the Familial Hemiplegic Migraine Locus. Genomics 78, 150- 154.
Joshi, G., Pradhan, S., and Mittal, B. (2009). Role of the ACE ID and MTHFR C677T polymorphisms in genetic susceptibility of migraine in a north Indian population. J Neurol Sci 277, 133-137.
Joshi, G., Pradhan, S., and Mittal, B. (2010). Role of the oestrogen receptor (ESR1 PvuII and ESR1 325 C->G) and progesterone receptor (PROGINS) polymorphisms in genetic susceptibility to migraine in a North Indian population. Cephalalgia 30, 311- 320.
Joutel, A., Bousser, M., Biousse, V., Labauge, P., Chabriat, H., Nibbio, A., Maciazek, J., Meyer, B., Bach, M., Weissenbach, J., et al. (1993). A gene for familial hemiplegic migraine maps to chromosome 19. Nature Genetics 5, 40-45.
Joutel, A., Ducros, A., and Vahedi, K. (1994). Genetic heterogeneity of familial hemiplegic migraine. Journal Name: American Journal of Human Genetics; Journal Volume: 55; Journal Issue: Suppl3; Conference: 44 annual meeting of the American Society of Human Genetics, Montreal (Canada), 18-22 Oct 1994; Other Information: PBD: Sep 1994, Medium: X; Size: pp. A16.73.
Juhasz, G., Lazary, J., Chase, D., Pegg, E., Downey, D., Toth, Z.G., Stones, K., Platt, H., Mekli, K., Payton, A., et al. (2009). Variations in the cannabinoid receptor 1 gene predispose to migraine. Neurosci Lett 461, 116-120.
Juhasz, G., Zsombok, T., Laszik, A., Gonda, X., Sotonyi, P., Faludi, G., and Bagdy, G. (2003). Association analysis of 5-HTTLPR variants, 5-HT2A receptor gene 102T/C polymorphism and migraine. Journal of Neurogenetics 17, 231 - 240.
Kaback, M., Lim-Steele, J., Dabholkar, D., Brown, D., Levy, N., and Zeiger, K. (1993). Tay-Sachs Disease Carrier Screening, Prenatal Diagnosis, and the Molecular Era - An International Perspective, 1970 to 1993. JAMA 270, 2307-2315.
Kaiser 1960 (1960). The application of electronic computers to factor analysis. Educ Psychol Meas 20, 141-151.
Kalay, E., Uzumcu, A., Krieger, E., Caylan, R., Uyguner, O., Ulubil-Emiroglu, M., Erdol, H., Kayserili, H., Hafiz, G., Baserer, N., et al. (2007). MYO15A (DFNB3) mutations in Turkish hearing loss families and functional modeling of a novel motor domain mutation. Am J Med Genet 143A, 2382-2389.
Kalaydjieva, L., Morar, B., Chaix, R., and Tang, H. (2005). A newly discovered founder population: the Roma/Gypsies. BioEssays 27, 1084–1094.
Kanai, Y., and Hediger, M.A. (2004). The glutamate/neutral amino acid transporter family SLC1: molecular, physiological and pharmacological aspects. Eur J Physiol 447, 469-479.
Kara, I., Ozkok, E., Aydin, M., Orhan, N., Cetinkaya, Y., Gencer, M., Kilic, G., and Tireli, H. (2007). Combined effects of ACE and MMP-3 polymorphisms on migraine development. Cephalalgia 27, 235-243. Page 221
Kara, I., Sazci, A., Ergul, E., Kaya, G., and Kilic, G. (2003). Association of the C677T and A1298C polymorphisms in the 5,10 methylenetetrahydrofolate reductase gene in patients with migraine risk. Brain Res Mol Brain Res 111, 84-90.
Kaunisto, M.A., Harno, H., Vanmolkot, K.R.J., Gargus, J.J., Sun, G., Hämäläinen, E., Liukkonen, E., Kallela, M., Maagdenberg, A.M.J.M., Frants, R.R., et al. (2004). A novel missense ATP1A2 mutation in a Finnish family with familial hemiplegic migraine type 2. neurogenetics 5, 141-146.
Kaunisto, M.A., Tikka, P.J., Kallela, M., Leal, S.M., Papp, J.C., Korhonen, A., Hämäläinen, E., Harno, H., Havanka, H., Nissilä, M., et al. (2005). Chromosome 19p13 loci in Finnish migraine with aura families. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 132B, 85-89.
Kaur, P., Jodhka, P.K., Underwood, W.A., Bowles, C.A., de Fiebre, N.C., de Fiebre, C.M., and Singh, M. (2007). Progesterone increases brain-derived neuroptrophic factor expression and protects against glutamate toxicity in a mitogen-activated protein kinase- and phosphoinositide-3 kinase-dependent manner in cerebral cortical explants. J Neurosci Res 85, 2441-2449.
Kawahara, Y., Ito, K., Sun, H., Aizawa, H., Kanazawa, I., and Kwak, S. (2004). Glutamate receptors: RNA editing and death of motor neurons. Nature 427, 801-801.
Kew, J., Wright, A., and Halligan, P.W. (1998). Somesthetic aura: the experience of `Alice in Wonderland'. Lancet 351, 582.
Khan, S.Y., Ahmed, Z.M., Shabbir, M.I., Kitajiri, S., Kalsoom, S., Tasneem, S., Shayiq, S., Ramesh, A., Srisailpathy, S., Khan, S.N., et al. (2007). Mutations of the RDX gene cause nonsyndromic hearing loss at the DFNB24 locus. Hum Mutat 28, 417-423.
Khoury, M.J., Little, J., Gwinn, M., and Ioannidis, J.P.A. (2007). On the synthesis and interpretation of consistent but weak gene–disease associations in the era of genome- wide association studies. International Journal of Epidemiology 36.
Kirchmann, M., Thomsen, L.L., and Olesen, J. (2006). Basilar-type migraine: Clinical, epidemiologic, and genetic features. Neurology 66, 880-886.
Kordasiewicz, H.B., Thompson, R.M., Clark, H.B., and Gomez, C.M. (2006). C- termini of P/Q-type Ca(2+) channel alpha1A subunits translocate to nuclei and promote polyglutamine-mediated toxicity. Human Molecular Genetics 15, 1587-1599.
Kornau, H. (2006). GABAB receptors and synaptic modulation. Cell Tissue Res 326, 517-533.
Korth, B., and Tucker, L.R. (1975). The distribution of chance congruence coefficients from simulated data. Psychometrika 40, 361-372.
Kowa, H., Fusayasu, E., Ijiri, T., Ishizaki, K., Yasui, K., Nakaso, K., Kusumi, M., Takeshima, T., and Nakashima, K. (2005). Association of the insertion/deletion polymorphism of the angiotensin I-converting enzyme gene in patients of migraine with aura. Neuroscience Letters 374, 129-131. Page 222
Kowa, H., Yasui, K., Takeshima, T., Urakami, K., Sakai, F., and Nakashima, K. (2000). The homozygous C677T mutation in the methylenetetrahydrofolate reductase gene is a genetic risk factor for migraine. American Journal of Medical Genetics 96, 762-764.
Kristiansson, K., Naukkarinen, J., and Peltonen, P. (2008). Isolated populations and complex disease gene identification Genome Biology 9, 109.
Kruman, I.I., Culmsee, C., Chan, S.L., Kruman, Y., Guo, Z., Penix, L., and Mattson, M.P. (2000). Homocysteine Elicits a DNA Damage Response in Neurons That Promotes Apoptosis and Hypersensitivity to Excitotoxicity. The Journal of Neuroscience 20, 6920-6926.
Krushkal, J., Xiong, M., Ferrell, R., Sing, C.F., Turner, S.T., and Boerwinkle, E. (1998). Linkage and association of adrenergic and dopamine receptor genes in the distal portion of the long arm of chromosome 5 with systolic blood pressure variation. Hum Mol Genet 7, 1379-1383.
Kusumi, M., Araki, H., Ijiri, T., Kowa, H., Adachi, Y., Takeshima, T., Sakai, F., and Nakashima, K. (2004). Serotonin 2C receptor gene Cys23Ser polymorphism: a candidate genetic risk factor of migraine with aura in Japanese population. Acta Neurologica Scandinavica 109, 407-409.
Lafreniere, R.G., Cader, M.Z., Poulin, J.-F., Andres-Enguix, I., Simoneau, M., Gupta, N., Boisvert, K., Lafreniere, F., McLaughlan, S., Dube, M.-P., et al. (2010). A dominant-negative mutation in the TRESK potassium channel is linked to familial migraine with aura. Nature Medicine.
Laitinen, T., Daly, M.J., Rioux, J.D., Kauppi, P., Laprise, C., Petays, T., Green, T., Cargill, M., Haahtela, T., Lander, E.S., et al. (2001). A susceptibility locus for asthma-related traits on chromosome 7 revealed by genome-wide scan in a founder population. Nat Genet 28, 87-91.
Laivuori, H., Lahermo, P., Ollikainen, V., Widen, E., Halva-Mallinen, L., Sundstrom, H., Laitinen, T., Kaaja, R., Ylikorkala, O., and Kere, J. (2003). Susceptibility Loci for Preeclampsia on Chromosomes 2p25 and 9p13 in Finnish Families. Am J Hum Genet 72, 168-177.
Lander, E., and Kruglyak, L. (1995). Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 11, 241-247.
Lander, E.S., and Schork, N.J. (1994). Genetic dissection of complex traits. Science 265, 2037-2048.
Large, V., Hellström, L., Reynisdottir, S., Lönnqvist, F., Eriksson, P., Lannfelt, L., and Arner, P. (1997). Human beta-2 adrenoceptor gene polymorphisms are highly frequent in obesity and associate with altered adipocyte beta-2 adrenoceptor function. The Journal of Clinical Investigation 100, 3005-3013.
Lathrop, G.M., Lalouel, J.M., Julier, C., and Ott, J. (1984). Strategies for multilocus linkage analysis in humans. Proc Natl Acad Sci U S A 81, 3443-3446.
Page 223
Lea, R., Dohy, A., Jordan, K., Quinlan, S., Brimage, P.J., and Griffiths, L.R. (2000). Evidence for allelic association of the dopamine Beta-Hydroxylase Gene (DBH) with Susceptibility to Typical Migraine. Neurogenetics 3, 35 - 40.
Lea, R., Ovcaric, M., Sundholm, J., MacMillan, J., and Griffiths, L. (2004). The methylenetetrahydrofolate reductase gene variant C677T influences susceptibility to migraine with aura. BMC Medicine 2, 3.
Lea, R.A., Curtain, R.P., Hutchins, C., Brimage, P.J., and Griffiths, L.R. (2001). Investigation of the CACNA1A gene as a candidate for typical migraine susceptibility. Neuropsychiatric Genetics 105, 707-712.
Lea, R.A., Nyholt, D.R., Curtain, R.P., Ovcaric, M., Sciascia, R., Bellis, C., Macmillan, J., Quinlan, S., Gibson, R.A., McCarthy, L.C., et al. (2005a). A genome- wide scan provides evidence for loci influencing a severe heritable form of common migraine. Neurogenetics 6, 67-72.
Lea, R.A., Ovcaric, M., Sundholm, J., Solyom, L., Macmillan, J., and Griffiths, L.R. (2005b). Genetic variants of angiotensin converting enzyme and methylenetetrahydrofolate reductase may act in combination to increase migraine susceptibility. Brain Res Mol Brain Res 136, 112-117.
Lea, R.A., Shepherd, A.G., Curtain, R.P., Nyholt, D.R., Quinlan, S., Brimage, P.J., and Griffiths, L.R. (2002). A typical migraine susceptibility region localizes to chromosome 1q31. Neurogenetics 4, 17-22.
Leao, A.A.P. (1944). Spreading depression of activity in the cerebral cortex. J Neurophysiol 7, 359-390.
Lemos, C., Castro, M.-J., Barros, J., Sequeiros, J., Pereira-Monteiro, J., Mendonça, D., and Sousa, A. (2009). Familial Clustering of Migraine: Further Evidence From a Portuguese Study. Headache: The Journal of Head and Face Pain 49, 404-411.
Lemos, C., Pereira-Monteiro, J., Mendonca, D., Ramos, E.M., Barros, J., Sequeiros, J., Alonso, I., and Sousa, A. (2010). Evidence of syntaxin 1A involvement in migraine susceptibility: a Portuguese study. Arch Neurol 67, 422-427.
Lezirovitz, K., Pardono, E., de Mello Auricchio, M.T.B., de Carvalho e Silva, F.L., Lopes, J.J., Abreu-Silva, R.S., Romanos, J., Batissoco, A.C., and Mingroni-Netto, R.C. (2008). Unexpected genetic heterogeneity in a large consanguineous Brazilian pedigree presenting deafness. Europ J Hum Genet 16, 89-96.
Li, Y., Pohl, E., Boulouiz, R., Schraders, M., Nurnberg, G., Charif, M., Admiraal, R.J.C., von Ameln, S., Baessmann, I., Kandil, M., et al. (2010). Mutations in TPRN cause a progressive form of autosomal recessive nonsyndromic hearing loss. Am J Hum Genet 86, 479-484.
Liburd, N., Ghosh, M., Riazuddin, S., Naz, S., Khan, S., Ahmed, Z., Riazuddin, S., Liang, Y., Menon, P.S.N., Smith, T., et al. (2001). Novel mutations of MYO15A associated with profound deafness in consanguineous families and moderately severe hearing loss in a patient with Smith-Magenis syndrome. Hum Genet 109, 535-541.
Page 224
Ligthart, L., de Vries, B., Smith, A.V., Ikram, M.A., Amin, N., Hottenga, J., Koelewijn, S.C., Kattenberg, V.M., de Moor, M.H.M., Janssens, A.C.J.W., et al. (2011). Meta-analysis of genome-wide association for migraine in six population- based European cohorts. European Journal of Human Genetics (advance online publication), 1-7.
Ligthart, L., Nyholt, D.R., Hottenga, J.-J., Distel, M.A., Willemsen, G., and Boomsma, D.I. (2008). A genome-wide linkage scan provides evidence for both new and previously reported loci influencing common migraine. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 147B, 1186-1195.
Limmroth, V., and Michel, M.C. (2001). The prevention of migraine: a critical review with special emphasis on β-adrenoceptor blockers. Br J Clin Pharmacol 52, 237-243.
Lin, J.J., Wang, P.J., Chen, C.H., Yeuh, K.C., Lin, S.Z., and Harn, H.J. (2005). Homozygous deletion genotype of angiotensin converting enzyme confers protection against migraine in man. Acta Neurol Taiwan 14, 120-125.
Lipton, R.B., and Bigal, M.E. (2005). Migraine: Epidemiology, Impact, and Risk Factors for Progression. Headache: The Journal of Head and Face Pain 45, S3-S13.
Lipton, R.B., and Bigal, M.E. (2006). Migraine and other headache disorders (New York, Taylor & Francis Group).
Lipton, R.B., Bigal, M.E., Diamond, M., Freitag, F., Reed, M.L., Stewart, W.F., and on behalf of the, A.A.G. (2007). Migraine prevalence, disease burden, and the need for preventive therapy. Neurology 68, 343-349.
Lipton, R.B., Stewart, W.F., Diamond, S., Diamond, M.L., and Reed, M. (2001). Prevalence and Burden of Migraine in the United States: Data From the American Migraine Study II. Headache: The Journal of Head and Face Pain 41, 646-657.
Lloyd-Jones, D., Adams, R.J., Brown, T.M., Carnethon, M., Dai, S., De Simone, G., Ferguson, T.B., Ford, E., Furie, K., Gillespie, C., et al. (2010). Heart Disease and Stroke Statistics␣ 2010 Update: A Report From the American Heart Association. Circulation 121, e46-e215.
Lorenzo-Seva, U., and ten Berge, J.M.F. (2006). Tucker’s congruence coefficient as a meaningful index of factor similarity. Methodology 2, 57-64.
Lowe, J.K., Maller, J.B., Pe’er, I., Neale, B.M., Salit, J., Kenny, E.E., Shea, J.L., Burkhardt, R., Smith, J.G., Ji, W., et al. (2009). Genome-Wide Association Studies in an Isolated Founder Population from the Pacific Island of Kosrae. PLoS Genet 5, 1- 17.
Lucotte, G. (2001). Distribution of the CCR5 gene 32-basepair deletion in West Europe. A hypothesis about the possible dispersion of the mutation by the vikings in historical times. Human Immunology 62, 933-936.
Luoukakis, A. (1984). Norfolk. An island and its people., first edn (Brisbane, Rigby Publishers).
Page 225
Lyngberg, A.C., Rasmussen, B.K., Jorgensen, T., and Jensen, R. (2005). Incidence of Primary Headache: A Danish Epidemiologic Follow-up Study. Am J Epidemiol 161, 1066-1073.
Maas, S., Kawahara, Y., Tamburro, K.M., and Nishikura, K. (2006). A-to-I RNA Editing and Human Disease. 2006 3, 1-9.
Maas, S., Rich, A., and Nishikura, K. (2003). A-to-I RNA Editing: Recent News and Residual Mysteries. Journal of Biological Chemistry 278, 1391-1394.
MacGregor, A.E. (2004). Oestrogen and attacks of migraine with and without aura. The Lancet Neurology 3, 354-361.
Macgregor, S., Bellis, C., Lea, R.A., Cox, H., Dyer, T., Blangero, J., Visscher, P.M., and Griffiths, L.R. (2010). Legacy of mutiny on the Bounty: founder effect and admixture on Norfolk Island. Eur J Hum Genet 18, 67-72.
Mackay, J., and Mensah, G.A. (2004). The atlas of heart disease and stroke (Geneva, WHO).
Mahtani, M.M., Widen, E., Lehto, M., Thomas, J., McCarthy, M., Brayer, J., Bryant, B., Chan, G., Daly, M., Forsblom, C., et al. (1996). Mapping of a gene for type 2 diabetes associated with an insulin secretion defect by a genome scan in Finnish families. Nat Genet 14, 90-94.
Makoff, A., Pilling, C., Harrington, K., and Emson, P. (1996). Human metabotropic glutamate receptor type 7: molecular cloning and mRNA distribution in the CNS. Brain Res Mol Brain Res 40, 165-170.
Marziniak, M., Mossner, R., Kienzler, C., Riederer, P., Lesch, K.P., and Sommer, C. (2007). Functional polymorphisms of the 5-HT1A and 5-HT1B receptor are associated with clinical symptoms in migraineurs. J Neural Transm 114, 1227-1232.
Marziniak, M., Mossner, R., Schmitt, A., Lesch, K.P., and Sommer, C. (2005). A functional serotonin transporter gene polymorphism is associated with migraine with aura. Neurology 64, 157-159.
Mateen, F.J., Dua, T., Steiner, T., and Saxena, S. (2008). Headache disorders in developing countries: research over the past decade. Cephalalgia 28, 1107-1114.
Matthews, S.P. (2001). Norfolk Island Census of Population and Housing 7 August 2001- Statistical report on characteristics of population and dwellings (Norfolk Island, Photopress International).
Mayera, M., Bercsényia, K., Géczia, K., Szabóa, G., and Lele, Z. (2010). Expression of two type II cadherins, Cdh12 and Cdh22 in the developing and adult mouse brain. Gene Expression Patterns 10, 351-360.
Mazaheri, S., Hajilooi, M., and Rafiei, A. (2006). The G-308A promoter variant of the tumor necrosis factor-alpha gene is associated with migraine without aura. Journal of Neurology 253, 1589-1593.
Page 226
McCarthy, L.C., Hosford, D.A., Riley, J.H., Bird, M.I., White, N.J., Hewett, D.R., Peroutka, S.J., Griffiths, L.R., Boyd, P.R., Lea, R.A., et al. (2001). Single-nucleotide polymorphism alleles in the insulin receptor gene are associated with typical migraine. Genomics 78, 135-149.
McEvoy, B.P., Zhao, Z.Z., Macgregor, S., Bellis, C., Lea, R.A., Cox, H., Montgomery, G.W., Griffiths, L.R., and Visscher, P.M. (2010). European and Polynesian admixture in the Norfolk Island population. Heredity 105, 229-234.
McPeek, M.S., and Sun, L. (2000). Statistical Tests for Detection of Misspecified Relationships by Use of Genome-Screen Data. The American Journal of Human Genetics 66, 1076-1094.
Melcher, T., Maas, S., Herb, A., Sprengel, R., Higuchi, M., and Seeburg, P.H. (1996). RED2, a Brain-specific Member of the RNA-specific Adenosine Deaminase Family. Journal of Biological Chemistry 271, 31795-31798.
Menon, S., COx, H.C., Kuwahata, M., Quinlan, S., MacMillian, J.C., Haupt, L.M., Lea, R.A., and Griffiths, L.R. (2010). Association of a Notch 3 gene polymorphism with migraine susceptibility. Cephalalgia 31, 264-270.
Merikangas, K.R., Fenton, B.T., Cheng, S.H., Stolar, M.J., and Risch, N. (1997). Association between migraine and stroke in a large-scale epidemiological study of the United States. Arch Neurol 54, 362 - 368.
Miller, S.A., Dykes, D.D., and Polesky, H.F. (1988). A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 16, 1215.
Mocci, E., Concas, M.P., Fanciulli, M., Pirastu, N., Adamo, M., Cabras, V., Fraumene, C., Persico, I., Sassu, A., Picciau, A., et al. (2009). Microsatellites and SNPs linkage analysis in a Sardinian genetic isolate confirms several essential hypertension loci previously identified in different populations. BMC Medical Genetics 10, 1-13.
Mochi, M., Cevoli, S., Cortelli, P., Pierangeli, G., Soriani, S., Scapoli, C., and Montagna, P. (2003). A genetic association study of migraine with dopamine receptor 4, dopamine transporter and dopamine-beta-hydroxylase genes. Neurological Sciences 23, 301-305.
Moises, H.W., Yang, L., Kristbjarnarson, H., Wiese, C., Byerley, W., Macciardi, F., Arolt, V., Blackwood, D., Liu, X., and Sjögren, B.e.a. (1995). An international two- stage genome-wide search for schizophrenia susceptibility genes. Nature Genetics 11, 321-324.
Morell, R.J., Kim, H.J., Hood, L.J., Goforth, L., Friderici, K., Fisher, R., Camp, G.V., Berlin, C.I., Oddoux, C., Ostrer, H., et al. (1998). Mutations in the Connexin 26 Gene (GJB2) among Ashkenazi Jews with Nonsyndromic Recessive Deafness. N Engl J Med 339, 1500-1505.
Morley, M., Molony, C.M., Weber, T.M., Devlin, J.L., Ewens, K.G., Spielman, R.S., and Cheung, V.G. (2004). Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743-747. Page 227
Mulder, E.J., van Baal, C., Gaist, D., Kallela, M., Kaprio, J., Svensson, D.A., Nyholt, D.R., Martin, N.G., MacGregor, A.J., Cherkas, L.F., et al. (2003). Genetic and Environmental Influences on Migraine: A Twin Study Across Six Countries. Twin Research 6, 422-431.
Naz, S., Alasti, F., Mowjoodi, A., Riazuddin, S., Sanati, M.H., Friedman, T.B., Griffith, A.J., Wilcox, E.R., and Riazuddin, S. (2003). Distinctive audiometric profile associated with DFNB21 alleles of TECTA. J Med Genet 40, 360-363.
Naz, S., Giguere, C.M., Kohrman, D.C., Mitchem, K.L., Riazuddin, S., Morell, R.J., Ramesh, A., Srisailpathy, S., Deshmukh, D., Riazuddin, S., et al. (2002). Mutations in a novel gene, TMIE, are associated with hearing loss linked to the DFNB6 locus. Am J Hum Genet 71, 632-636.
Naz, S., Griffith, A.J., Riazuddin, S., Hampton, L.L., Battey, J.F., Jr.,, Khan, S.N., Riazuddin, S., Wilcox, E.R., and Friedman, T.B. (2004). Mutations of ESPN cause autosomal recessive deafness and vestibular dysfunction. J Med Genet 41, 591-595.
NCEP (2002). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Final Report. Circulation 106, 3143-.
NCEP, and ATP-III (2002). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Final Report. Circulation 2002;106;3143 106, 3143-3421.
Need, A.C., Ge, D., Weale, M.E., Maia, J., Feng, S., Heinzen, E.L., Shianna, K.V., Yoon, W., Kasperavičiūtė, D., Gennarelli, M., et al. (2009). A Genome-Wide Investigation of SNPs and CNVs in Schizophrenia. PLos Genetics 5, e1000373.
Netzer, C., Freudenberg, J., Heinze, A., Heinze-Kuhn, K., Goebel, I., McCarthy, L.C., Roses, A.D., Göbel, H., Todt, U., and Kubisch, C. (2008). Replication study of the insulin receptor gene in migraine with aura. Genomics 91, 503-507.
Norio, R. (2003). Finnish Disease Heritage II: population prehistory and genetic roots of Finns. Human Genetics 112, 457-469.
Nussbaum, R.L., Willard, H.F., and Mcinnes, R.R. (2007). Thompson & Thompson Genetics in Medicine, 6th Edition (W.B. Saunders Co).
Nyholt, D.R. (2000). All LODs Are Not Created Equal. The American Journal of Human Genetics 67, 282-288.
Nyholt, D.R., Curtain, R.P., and Griffiths, L.R. (2000). Familial typical migraine: significant linkage and localization of a gene to Xq24-28. Hum Genet 107, 18-23.
Nyholt, D.R., Dawkins, J.L., Brimage, P.J., Goadsby, P.J., Nicholson, G.A., and Griffiths, L.R. (1998a). Evidence for an X-linked genetic component in familial typical migraine. Hum Mol Genet 7, 459-463.
Page 228
Nyholt, D.R., Gillespie, N.G., Heath, A.C., Merikangas, K.R., Duffy, D.L., and Martin, N.G. (2004). Latent class and genetic analysis does not support migraine with aura and migraine without aura as separate entities. Genetic Epidemiology 26, 231- 244.
Nyholt, D.R., Lea, R.A., Goadsby, P.J., Brimage, P.J., and Griffiths, L.R. (1998b). Familial typical migraine: linkage to chromosome 19p13 and evidence for genetic heterogeneity. Neurology 50, 1428-1432.
Nyholt, D.R., Morley, K.I., Ferreira, M.A.R., Medland, S.E., Boomsma, D.I., Heath, A.C., Merikangas, K.R., Montgomery, G.W., and Martin, N.G. (2005). Genomewide Significant Linkage to Migrainous Headache on Chromosome 5q21. The American Journal of Human Genetics 77, 500-512.
Ober, C., Abney, M., and McPeek, M.S. (2001). The genetic dissection of complex traits in a founder population. Am J Hum Genet 69, 1068-1079.
Ober, C., Cox, N.J., Abney, M., Di Rienzo, A., Lander, E.S., Changyaleket, B., Gidley, H., Kurtz, B., Lee, J., Nance, M., et al. (1998a). Genome-wide search for asthma susceptibility loci in a founder population. The Collaborative Study on the Genetics of Asthma. Hum Mol Genet 7, 1393-1398.
Ober, C., Hyslop, T., Elias, S., Weitkamp, L.R., and Hauck, W.W. (1998b). Human leukocyte antigen matching and fetal loss: results of a 10 year prospective study. . Hum Reprod 13, 33-38.
Ober, C., Tan, Z., Sun, Y., Possick, J.D., Pan, L., Nicolae, R., Radford, S., Parry, R.R., Heinzmann, A., Deichmann, K.A., et al. (2008). Effect of variation in CHI3L1 on serum YKL-40 level, risk of asthma, and lung function. N Engl J Med 358, 1682- 1691.
Ogilvie, A., Russell, M., Dhall, P., Battersby, S., Ulrich, V., Smith, C.D., Goodwin, G., Harmar, A., and Olesen, J. (1998). Altered allelic distributions of the serotonin transporter gene in migraine without aura and migraine with aura. Cephalalgia 18, 23- 26.
Okamoto, N., Hori, S., Akazawa, C., Hayashi, Y., Shigemoto, R., Mizuno, N., and Nakanishim, S. (1994). Molecular Characterizationof a New Metabotropic Glutamate Receptor mGluR7 Coupled to Inhibitory CyclicAMP Signal Transduction. The Journal of Biological Chemistry 269, 1231-1236.
Ophoff, R.A., Terwindt, G.M., Vergouwe, M.N., van Eijk, R., Oefner, P.J., Hoffman, S.M.G., Lamerdin, J.E., Mohrenweiser, H.W., Bulman, D.E., Ferrari, M., et al. (1996). Familial Hemiplegic Migraine and Episodic Ataxia Type-2 Are Caused by Mutations in the Ca2+ Channel Gene CACNL1A4. Cell 87, 543-552.
Ophoff, R.A., Van Eijk, R., and Sandkuijl, L.A. (1994). Genetic heterogeneity of familial hemiplegic migraine. Journal Name: Genomics; Journal Volume: 22; Journal Issue: 1; Other Information: PBD: 1 Jul 1994, Medium: X; Size: pp. 21-26.
Page 229
Oterino, A., Ruiz-Alegría, C., Castillo, J., Valle, N., Bravo, Y., Cayón, A., Alonso, A., Tejera, P., Ruiz-Lavilla, N., Muñoz, P., et al. (2007). GNAS1 T393C polymorphism is associated with migraine. Cephalalgia 27, 429-434.
Oterino, A., Toriello, M., Cayón, A., Castillo, J., Colas, R., Alonson-Arranz, A., Ruiz- Alegria, C., Quintela, E., Monton, F., Ruiz-Lavilla, N., et al. (2008). Multilocus Analyses Reveal Involvement of the ESR1, ESR2, and FSHR Genes in Migraine. Headache: The Journal of Head and Face Pain 48, 1438-1450.
Oterino, A., Valle, N., Pascual, J., Bravo, Y., Muñoz, P., Castillo, J., Ruiz-Alegría, C., Sánchez-Velasco, P., Leyva-Cobián, F., and Cid, C. (2005). Thymidylate synthase promoter tandem repeat and MTHFD1 R653Q polymorphisms modulate the risk for migraine conferred by the MTHFR T677 allele. Molecular Brain Research 139, 163- 168.
Oterino, A.n., Pascual, J., de AlegrÕa, C.R., Valle, N., Castillo, J.s., Bravo, Y., González, F.l., Sánchez-Velasco, P., Cayón, A., Leyva-Cobián, F., et al. (2006). Association of migraine and ESR1 G325C polymorphism. NeuroReport 17, 61-64.
Ozyalcin, S.N., Talu, G.K., Kiziltan, E., Yucel, B., Ertas, M., and Disci, R. (2005). The efficacy and safety of venlafaxine in the prophylaxis of migraine. Headache 45, 144-152.
Pajukanta, P., Terwilliger, J.D., Perola, M., Hiekkalinna, T., Nuotio, I., Ellonen, P., Parkkonen, M., Hartiala, J., Ylitalo, K., Pihlajamäki, J., et al. (1999a). Genomewide Scan for Familial Combined Hyperlipidemia Genes in Finnish Families, Suggesting Multiple Susceptibility Loci Influencing Triglyceride, Cholesterol, and Apolipoprotein B Levels. The American Journal of Human Genetics 64, 1453-1463.
Pajukanta, P., Terwilliger, J.D., Perola, M., Hiekkalinna, T., Nuotio, I., Ellonen, P., Parkkonen, M., Hartiala, J., Ylitalo, K., Pihlajamaki, J., et al. (1999b). Genomewide scan for familial combined hyperlipidemia genes in finnish families, suggesting multiple susceptibility loci influencing triglyceride, cholesterol, and apolipoprotein B levels. Am J Hum Genet 64, 1453-1463.
Palmer, L.J., and Cardon, L.R. (2005). Shaking the tree: mapping complex disease genes with linkage disequilibrium. The Lancet 366, 1223-1234.
Palo, O.M., Antila, M., Silander, K., Hennah, W., Kilpinen, H., Soronen, P., Tuulio- Henri nnqvist, J., et al. (2007). Association of distinct allelic haplotypes of DISC1 with psychotic and bipolar spectrum disorders and with underlying cogni- tive impairments. Hum Mol Genet 16, 2517-2528.
Parsons, M.J., Mata, I., Beperet, M., Iribarren-Iriso, F., Arroyo, B., Sainz, R., Arranz, M.J., and Kerwin, R. (2007). A dopamine D2 receptor gene-related poly- morphism is associated with schizophrenia in a Spanish population isolate. Psychiatr Genet 17, 159-163.
Paterna, S., Di Pasquale, P., D’Angelo, A., Seidita, G., Tuttolomondo, A., Cardinale, A., Maniscalchi, T., Follone, G., Giubilato, A., Tarantello, M., et al. (2000).
Page 230
Angiotensin-Converting Enzyme Gene Deletion Polymorphism Determines an Increase in Frequency of Migraine Attacks in Patients Suffering from Migraine without Aura. European Neurology 43, 133-136.
Paunio, T., Ekelund, J., Varilo, T., Parker, A., Hovatta, I., Turunen, J.A., Rinard, K., Foti, A., Terwilliger, J.D., Juvonen, H., et al. (2001). Genome-wide scan in a nationwide study sample of schizophrenia families in Finland reveals susceptibility loci on chromosomes 2q and 5q. Hum Mol Genet 10, 3037-3048.
Pe´russe, L., Rice, T., Chagnon, Y.C., Despre´s, J., Lemieux, S., Roy, S., Lacaille, M., Ho-Kim, M., Chagnon, M., Province, M.A., et al. (2001). A Genome-Wide Scan for Abdominal Fat Assessed by Computed Tomography in the Que´bec Family Study. Diabetes 50, 614-621.
Peltonen, L., Palotie, A., and Lange, K. (2000). Use of population isolates for mapping complex traits. Nat Rev Genet 1, 182-190.
Pencina, M.J., D'Agostino, R.B.S., Larson, M.G., Massaro, J.M., and S., V.R. (2009). Predicting the 30-year risk of cardiovascular disease: the framingham heart study. Circulation 119, 3078-3084.
Peroutka, S.J., Wilhoit, T., and Jones, K. (1997). Clinical susceptibility to migraine with aura is modified by dopamine D2 receptor (DRD2) NcoI alleles. Neurology 49, 201 - 206.
Pfaffenrath, V., and Scherzer, S. (1995). Analgesics and NSAIDs in the treatment of the acute migraine attack. Cephalalgia 15, 14-20.
Pichler, I., Fuchsberger, C., Platzer, C., Caliskan, M., Marroni, F., Pramstaller, P.P., and Ober, C. (2010). Drawing the history of the Hutterite population on a genetic landscape: inference from Y-chromosome and mtDNA genotypes. European Journal of Human Genetics 18, 463-470.
Pietrobon, D. (2007). Familial Hemiplegic Migraine. Neurotherapeutics 4, 274-284.
Platte, P., Papanicolaou, G.J., Johnston, J., Klein, C.M., Doheny, K.F., Pugh, E.W., Roy-Gagnon, M.H., Stunkard, A.J.F., C. A., and Wilson, A.F. (2003). A study of linkage and association of body mass index in the old order Amish. American Journal of Medical Genetics Part C: Seminars in Medical Genetics 121C, 71-80.
Plomp, J.J., Vergouwe, M.N., Van den Maagdenberg, A.M., Ferrari, M.D., Frants, R.R., and Molenaar, P.C. (2000). Abnormal transmitter release at neuromuscular junctions of mice carrying the tottering alpha1A Ca2+ channel mutation. Brain 123, 463-471.
Potrebic, S., Ahn, A.H., Skinner, K., Fields, H.L., and Basbaum, A.L. (2003). Peptidergic nociceptors of both trigeminal and dorsal root ganglia express serotonin 1D receptors: implications for the selective antimigraine action of triptans. J Neurosci 23, 10988-10997.
Page 231
Price, R., Li, W., Bernstein, A., Crystal, A., Golding, E., Weisberg, S., and Zuckerman, W. (2001). A locus affecting obesity in human chromosome region 10p12. Diabetologia 44, 363-366.
Racchi, M., Leone, M., Porrello, E., Rigamonti, A., Govoni, S., Sironi, G., Montomoli, C., and Bussone, G. (2004). Familial Migraine With Aura: Association Study With 5-HT1B/1D, 5-HT2C, and hSERT Polymorphisms. Headache: The Journal of Head and Face Pain 44, 311-317.
Rainero, I., Fasano, E., Rubino, E., Rivoiro, C., Valfrè, W., Gallone, S., Savi, L., Gentile, S., Giudice, R., Martino, P., et al. (2005). Association between migraine and HLA–DRB1 gene polymorphisms. The Journal of Headache and Pain 6, 185-187.
Rainero, I., Grimaldi, L.M.E., Salani, G., Valfre, W., Rivoiro, C., Savi, L., and Pinessi, L. (2004). Association between the tumor necrosis factor-{alpha} -308 G/A gene polymorphism and migraine. Neurology 62, 141-143.
Ramagopalan, S.V., Ramscar, N.E., and Cader, M.Z. (2007). Molecular mechanisms of migraine?
Rankinen, T., Zuberi, A., Chagnon, Y.C., Weisnagel, S.J., Argyropoulos, G., Walts, B., Perusse, L., and Bouchard, C. (2006). The Human Obesity Gene Map: The 2005 Update. Obesity 14, 529-644.
Rasmussen, B.K., Jensen, R., and Olesen, J. (1991). Questionnaire versus clinical interview in the diagnosis of headache. Headache 31, 290-295.
Raymond, M., and Rousset, F. (1995). GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Heredity 86, 248-249.
Rehman, A.U., Morell, R.J., Belyantseva, I.A., Khan, S.Y., Boger, E.T., Shahzad, M., Ahmed, Z.M., Riazuddin, S., Khan, S.N., Riazuddin, S., et al. (2010). Targeted capture and next-generation sequencing identifies C9orf75, encoding taperin, as the mutated gene in nonsyndromic deafness DFNB79. Am J Hum Genet 86, 378-388.
Reynisdottir, I., Thorleifsson, G., Benediktsson, R., Sigurdsson, G., Emilsson, V., Einarsdottir, A.S., Hjorleifsdottir, E.E., Orlygsdottir, G.T., Bjornsdottir, G.T., Saemundsdottir, J., et al. (2003). Localization of a Susceptibility Gene for Type 2 Diabetes to Chromosome 5q34-q35.2. The American Journal of Human Genetics 73, 323-335.
Riazuddin, S., Ahmed, Z.M., Fanning, A.S., Lagziel, A., Kitajiri, S., Ramzan, K., Khan, S.N., Chattaraj, P., Friedman, P.L., Anderson, J.M., et al. (2006a). Tricellulin is a tight-junction protein necessary for hearing. Am J Hum Genet 79, 1040-1051.
Riazuddin, S., Khan, S.N., Ahmed, Z.M., Ghosh, M., Caution, K., Nazli, S., Kabra, M., Zafar, A.U., Chen, K., Naz, S., et al. (2006b). Mutations in TRIOBP, which encodes a putative cytoskeletal-organizing protein, are associated with nonsyndromic recessive deafness. Am J Hum Genet 78, 137-142.
Rousset, F. (2008). Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol Ecol Resources 8, 103-106. Page 232
Roy, J., and Stewart, W.F. (2010). Estimation of age-specific incidence rates from cross-sectional survey data. Statistics in Medicine 29, 588-596.
Russell, M.B., and Olesen, J. (1995). Increased familial risk and evidence of genetic factor in migraine. BMJ 311, 541-544.
Russell, M.B., Rasmussen, B.K., Fenger, K., and Olesen, J. (1996). Migraine without aura and migraine with aura are distinct clinical entities: a study of four hundred and eighty-four male and female migraineurs from the general population. Cephalalgia 16, 239-245.
Russo, L., Mariotti, P., Sangiorgi, E., Giordano, T., Ricci, I., Lupi, F., Chiera, R., Guzzetta, F., Neri, G., and Gurrieri, F. (2005). A New Susceptibility Locus for Migraine with Aura in the 15q11-q13 Genomic Region Containing Three GABA-A Receptor Genes. The American Journal of Human Genetics 76, 327-333.
Salter, R.C., Ashlin, T.G., Kwan, A.P.L., and Ramji, D.P. (2010). ADAMTS proteases: key roles in atherosclerosis? J Mol Med 88, 1203-1211.
Sanna, S., Jackson, A.U., Nagaraja, R., Willer, C.J., Chen, W.M., Bonnycastle, L.L., Shen, H., Timpson, N., Lettre, G., Usala, G., et al. (2008). Common variants in the GDF5-UQCC region are associated with variation in human height. Nat Genet 40, 198-203.
Sato, K., Matsuki, N., Ohno, Y., and Nakazawa, K. (2003). Estrogens inhibit l- glutamate uptake activity of astrocytes via membrane estrogen receptor α. Journal of Neurochemistry 86, 1498–1505.
Scharff, L., Marcus, D.A., and C., T.D. (1997). Headache during pregnancy and in the postpartum: a prospective study. Headache 37, 203-210.
Scher, A.I., Terwindt, G.M., Verschuren, W.M.M., Kruit, M.C., Blom, H.J., Kowa, H., Frants, R.R., van den Maagdenberg, A.M.J.M., van Buchem, M., Ferrari, M.D., et al. (2006). Migraine and MTHFR C677T genotype in a population-based sample. Annals of Neurology 59, 372-375.
Schoepp, D.D. (1994). Novel functions for subtypes of metabotropic glutamate receptors. Neurochemistry International 24, 439-449.
Schultz, J.M., Khan, S.N., Ahmed, Z.M., Riazuddin, S., Waryah, A.M., Chhatre, D., Starost, M.F., Ploplis, B., Buckley, S., Velasquez, D., et al. (2009). Noncoding mutations of HGF are associated with nonsyndromic hearing loss, DFNB39. Am J Hum Genet 85, 25-39.
Schulz, L.O., Bennett, P.H., Ravussin, E., Kidd, J.R., Kidd, K.K., Esparza, J., and Valencia, M.E. (2006). Effects of Traditional and Western Environments on Prevalence of Type 2 Diabetes in Pima Indians in Mexico and the U.S. Diabetes Care 29, 1866-1871.
Schwaag, S., Evers, S., Schirmacher, A., Stögbauer, F., Ringelstein, E., and Kuhlenbäumer, G. (2006). Genetic variants of the NOTCH3 gene in migraine - a mutation analysis and association study. Cephalalgia 26, 158-161. Page 233
, M., Usala, G., et al. (2007). Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet 3, e115.
Service, S., Deyoung, J., Karayiorgou, M., Roos, J.L., Pretorious, H., Bedoya, G., Ospina, J., Ruiz-Linares, A., Macedo, A., Palha, J.A., et al. (2006). Magnitude and distribution of linkage disequilibrium in population isolates and implications for genome-wide association studies. Nat Genet 38, 556-560.
Shahin, H., Walsh, T., Sobe, T., Sa'ed, J.A., Rayan, A.A., Lynch, E.D., Lee, M.K., Avraham, K.B., King, M.-C., and Kanaan, M. (2006). Mutations in a novel isoform of TRIOBP that encodes a filamentous-acting binding protein are responsible for DFNB28 recessive nonsyndromic hearing loss. Am J Hum Genet 78, 144-152.
Sham, P.C., and Curtis, D. (1995). Monte Carlo tests for associations between disease and alleles at highly polymorphic loci. Ann Hum Genet 59, 97-105.
Shearer, A.E., Hildebrand, M.S., Bromhead, C.J., Kahrizi, K., Webster, J.A., Azadeh, B., Kimberling, W.J., Anousheh, A., Nazeri, A., Stephan, D., et al. (2009). A novel splice site mutation in the RDX gene causes DFNB24 hearing loss in an Iranian family. Am J Med Genet 149A, 555-558.
Sheftell, F.D., and Atlas, S.J. (2002). Migraine and psychiatric comorbidity: from theory and hypotheses to clinical application. Headache 42, 934-944.
Shields, K.G., and Goadsby, P.J. (2006). Serotonin receptors modulate trigeminovascular responses in ventroposteromedial nucleus of thalamus: A migraine target? Neurobiology of Disease 23, 491-501.
Shmulewitz, D., Auerbach, S.B., Lehner, T., Blundell, M.L., Winick, J.D., Youngman, L.D., Skilling, V., Heath, S.C., Ott, J., Stoffel, M., et al. (2001). Epidemiology and Factor Analysis of Obesity, Type II Diabetes, Hypertension, and Dyslipidemia (Syndrome X) on the Island of Kosrae, Federated States of Micronesia. Human Heredity 51, 8-19.
Shmulewitz, D., Heath, S.C., Blundell, M.L., Han, Z., Sharma, R., Salit, J., Auerbach, S.B., Signorini, S., Breslow, J.L., Stoffel, M., et al. (2006). Linkage analysis of quantitative traits for obesity, diabetes, hypertension, and dyslipidemia on the island of Kosrae, Federated States of Micronesia. PNAS 103, 3502-3509.
Shull, M.M., and Lingrel, J.B. (1987). Multiple genes encode the human Na+,K+- ATPase catalytic subunit. Proc Nat Acad Sci 84, 4039-4043.
Simmons, D., Thompson, C.F., and Volklander, D. (2001). Polynesians: prone to obesity and Type 2 diabetes mellitus but not hyperinsulinaemia. Diabetic Medicine 18, 193-198.
Singera, C.A., Rogers, K.L., Stricklandc, T.M., and Dorsab, D.M. (1996). Estrogen protects primary cortical neurons from glutamate toxicity. Neuroscience Letters 212, 13-16.
Page 234
Sirmaci, A., Erbek, S., Price, J., Huang, M., Duman, D., Cengiz, F.B., Bademci, G., Tokgoz-Yilmaz, S., Hismi, B., Ozdag, H., et al. (2010). A truncating mutation in SERPINB6 is associated with autosomal-recessive nonsyndromic sensorineural hearing loss. Am J Hum Genet 86, 797-804.
Smith, J.G., Lowe, J.K., Kovvali, S., Maller, J.B., Salit, J., Daly, M.J., Stoffel, M., Altshuler, D.M., Friedman, J.M., Breslow, J.L., et al. (2009). Genome-wide association study of electrocardiographic conduction measures in an isolated founder population: Kosrae. Heart Rhythm 6, 634-641.
Sobel, E., Papp, J.C., and Lange, K. (2002). Detection and Integration of Genotyping Errors in Statistical Genetics. The American Journal of Human Genetics 70, 496-508.
Somjen, G.G. (2001). Mechanisms of Spreading Depression and Hypoxic Spreading Depression-Like Depolarization. Physiological Reviews 81, 1065-1095.
Soragna, D., Vettori, A., Carraro, G., Marchioni, E., Vazza, G., Bellini, S., Tupler, R., Savoldi, F., and Mostacciuolo, M.L. (2003). A Locus for Migraine without Aura Maps on Chromosome 14q21.2-q22.3. The American Journal of Human Genetics 72, 161-167.
Squires, N. (2006). Battle for Norfolk Island. (From our own correspondent. British Broadcasting Corporation, London.).
Stam, A.H., de Vries, B., Janssens, A.C.J.W., Vanmolkot, K.R.J., Aulchenko, Y.S., Henneman, P., Oostra, B.A., Frants, R.R., van den Maagdenberg, A.M.J.M., Ferrari, M.D., et al. (2010). Shared genetic factors in migraine and depression: Evidence from a genetic isolate. Neurology 74, 288-294.
Stamler, J., Daviglus, M.L., Garside, D.B., Dyer, A.R., Greenland, P., and Neaton, J.D. (2000). Relationship of Baseline Serum Cholesterol Levels in 3 Large Cohorts of Younger Men to Long-term Coronary, Cardiovascular, and All-Cause Mortality and to Longevity. JAMA 284, 311-318.
Steemers, F.J., Chang, W., Lee, G., Barker, D.L., Shen, R., and Gunderson, K.L. (2006). Whole-genome genotyping with the single-base extension assay. Nature Methods 3, 31-33.
Steinthorsdottir, V., Thorleifsson, G., Reynisdottir, I., Benediktsson, R., Jonsdottir, T., Walters, G.B., Styrkarsdottir, U., Gretarsdottir, S., Emils- son, V., Ghosh, S., et al. (2007). A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet 39, 770-775.
Stewart, W.F., Bigal, M.E., Kolodner, K., Dowson, A., Liberman, J.N., and Lipton, R.B. (2006). Familial risk of migraine: Variation by proband age at onset and headache severity. Neurology 66, 344-348.
Stewart, W.F., Linet, M.S., Celentano, D.D., Natta, M.V., and Ziegler, D. (1991). Age- and Sex-specific Incidence Rates of Migraine with and without Visual Aura. Am J Epidemiol 134, 1111-1120.
Page 235
Stewart, W.F., Lipton, R.B., Celentano, D.D., and Reed, M.L. (1992). Prevalence of Migraine Headache in the United States: Relation to Age, Income, Race, and Other Sociodemographic Factors. JAMA 267, 64-69.
Stewart, W.F., Ricci, J.A., Chee, E., Morganstein, D., and Lipton, R. (2003). Lost Productive Time and Cost Due to Common Pain Conditions in the US Workforce. JAMA 290, 2443-2454.
Stewart, W.F., Simon, D., Shechter, A., and Lipton, R.B. (1995). Population variation in migraine prevalence: A meta-analysis. Journal of Clinical Epidemiology 48, 269- 280.
Stewart, W.F., Wood, C., Reed, M.L., Roy, J., and Lipton, R.B. (2008). Cumulative lifetime migraine incidence in women and men. Cephalalgia 28, 1170-1178.
Stovner, L.J., Hagen, K., Jensen, R., Katsarava, Z., Lipton, R.B., Scher, A.I., Steiner, T.J., and Zwart, J.A. (2007). The global burden of headache: a documentation of headache prevalence and disability worldwide. Cephalalgia 27, 193-210.
Stovner, L.J., Zwart, J.A., Hagen, K., Terwindt, G.M., and Pascual, J. (2006). Epidemiology of headache in Europe. European Journal of Neurology 13, 333-345.
Sundin, O.H., Yang, J., Li, Y., Zhu, D., Hurd, J.N., Mitchell, T.N., Silva, E.D., and Maumenee, I.H. (2000). Genetic basis of total colourblindness among the Pingelapese islanders. Nature Genetics 25, 289-293.
Svensson, D.A., Larsson, B., Waldenlind, E., and Pedersen, N.L. (2003). Shared Rearing Environment in Migraine: Results From Twins Reared Apart and Twins Reared Together. Headache: The Journal of Head and Face Pain 43, 235-244.
Szilagyi, A., Boor, K., Orosz, I., Szantal, E., Szekely, A., Kalasz, H., Sasvari-Szekely, M., and Farkas, V. (2006). Contribution of serotonin transporter gene polymorphisms to pediatric migraine. Headache 46, 478-485.
Tang, W., Miller, M.B., Rich, S.S., North, K.E., Pankow, J.S., Borecki, I.B., Myers, R.H., Hopkins, P.N., Leppert, M., and Arnett, D.K. (2003). Linkage Analysis of a Composite Factor for the Multiple Metabolic Syndrome: The National Heart, Lung, and Blood Institute Family Heart Study. Diabetes 52, 2840-2847.
Tanigaki, K., Nogaki, F., Takahashi, J., Tashiro, K., Kurooka, H., and Honjo, T. (2001). Notch1 and Notch3 instructively restrict bFGF-responsive multipotent neural progenitor cells to an astroglial fate. Neuron 29, 45-55.
Teare, M., and Barrett, J.H. (2005). Genetic linkage studies. The Lancet 366, 1036- 1044.
Terwindt, G.M., Ophoff, R.A., van Eijk, R., Vergouwe, M.N., Haan, J., Frants, R.R., Sandkuijl, L.A., and Ferrari, M.D. (2001). Involvement of the CACNA1A gene containing region on 19p13 in migraine with and without aura. Neurology 56, 1028- 1032.
Page 236
Tfelt-Hansen, P., Saxena, P.R., Dahlof, C., Pascual, J., Lainez, M., Henry, P., Diener, H., Schoenen, J., Ferrari, M.D., and Goadsby, P.J. (2000). Ergotamine in the acute treatment of migraine: a review and European consensus. Brain 123, 9-18.
The International HapMap Consortium (2005). A haplotype map of the human genome. Nature 437, 1299-1320.
Thompson, E.E., Sun, Y., Nicolae, D., and Ober, C. (2010). Shades of Gray: A Comparison of Linkage Disequilibrium Between Hutterites and Europeans. Genetic Epidemiology 34, 133-139.
Thomsen, L., and Olesen, J. (2004). Sporadic hemiplegic migraine. Cephalalgia 24, 1016-1023.
Thomsen, L.L., Eriksen, M.K., Roemer, S.F., Andersen, I., Olesen, J., and Russell, M.B. (2002). A population-based study of familial hemiplegic migraine suggests revised diagnostic criteria. Brain 125, 1379-1391.
Thomsen, L.L., Kirchmann, M., Bjornsson, A., Stefansson, H., Jensen, R.M., Fasquel, A.C., Petursson, H., Stefansson, M., Frigge, M.L., Kong, A., et al. (2007). The genetic spectrum of a population-based sample of familial hemiplegic migraine. Brain 130, 346-356.
Thomsen, L.L., Olesen, J., and Russell, M.B. (2003). Increased risk of migraine with typical aura in probands with familial hemiplegic migraine and their relatives. European Journal of Neurology 10, 421-427.
Thorleifsson, G., Magnusson, K.P., Sulem, P., Walters, G.B., Gudbjartsson, D.F., Stefansson, H., Jonsson, T., Jonasdottir, A., Jonasdottir, A., Stefans- dottir, G., et al. (2007). Common sequence variants in the LOXL1 gene confer susceptibility to exfolia- tion glaucoma. Science 317, 1397-1400.
Tikka-Kleemola, P., Artto, V., Vepsalainen, S., Sobel, E.M., Raty, S., Kaunisto, M.A., Anttila, V., Hamalainen, E., Sumelahti, M.L.M.D., Ilmavirta, M.M.D., et al. (2010). A visual migraine aura locus maps to 9q21-q22. Neurology April 74, 1171- 1177.
Tobin, M.D., Sheehan, N.A., Scurrah, K.J., and Burton, P.R. (2005). Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure. Statistics in Medicine 24, 2911-2935.
Todt, U., Dichgans, M., Jurkat-Rott, K., Heinze, A., Zifarelli, G., Koenderink, J.B., Goebel, I., Zumbroich, V., Stiller, A., Ramirez, A., et al. (2005). Rare missense variants in ATP1A2 in families with clustering of common forms of migraine. Human Mutation 26, 315-321.
Todt, U., Netzer, C., Toliat, M., Heinze, A., Goebel, I., Nürnberg, P., Göbel, H., Freudenberg, J., and Kubisch, C. (2009). New genetic evidence for involvement of the dopamine system in migraine with aura. Human Genetics 125, 265-279.
Tonelli, A., Gallanti, A., Bersano, A., Cardin, V., Ballabio, E., Airoldi, G., Redaelli, F., Candelise, L., Bresolin, N., and Bassi, M.T. (2007). Amino acid changes in the Page 237
amino terminus of the Na,K-adenosine triphosphatase alpha-2 subunit associated to familial and sporadic hemiplegic migraine. Clinical Genetics 72, 517-523.
Tournier-Lasserve, E., Joutel, A., Melki, J., Weissenbach, J., Lathrop, G.M., Chabriat, H., Mas, J.-L., Cabanis, E.-A., Baudrimont, M., Maciazek, J., et al. (1993). Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy maps to chromosome 19q12. Nat Genet 3, 256-259.
Trabace, S., Brioli, G., Lulli, P., Morellini, M., Giacovazzo, M., Cicciarelli, G., and Martelletti, P. (2002). Tumor Necrosis Factor Gene Polymorphism in Migraine. Headache: The Journal of Head and Face Pain 42, 341-345.
Tseng, K.Y., and O'Donnell, P. (2004). Dopamine–Glutamate Interactions Controlling Prefrontal Cortical Pyramidal Cell Excitability Involve Multiple Signaling Mechanisms. The Journal of Neuroscience 24, 5131-5139.
Tzourio, C., El Amrani, M., Poirier, O., Nicaud, V., Bousser, M.-G., and Alperovitch, A. (2001). Association between migraine and endothelin type A receptor (ETA -231 A/G) gene polymorphism. Neurology 56, 1273-1277.
Ueland, P.M., Hustad, S., Schneede, J., Refsum, H., and Vollset, S.E. (2001). Biological and clinical implications of the MTHFR C677T polymorphism. Trends in Pharmacological Sciences 22, 195-201.
Ulrich, V., Gervil, M., Kyvik, K.O., Olesen, J., and Russell, M.B. (1999a). Evidence of a genetic factor in migraine with aura: A population-based Danish twin study. Annals of Neurology 45, 242-246.
Ulrich, V., Gervil, M., Kyvik, K.O., Olesen, J., and Russell, M.B. (1999b). The inheritance of migraine with aura estimated by means of structural equation modelling. Journal of Medical Genetics 36, 225-227.
Vahedi, K., Denier, C., Ducros, A., Bousson, V., Levy, C., Chabriat, H., Haguenau, M., Tournier-Lasserve, E., and Bousser, M.G. (2000). CACNA1A gene de novo mutation causing hemiplegic migraine, coma, and cerebellar atrophy. Neurology 55, 1040-1042. van den Maagdenberg, A.M.J.M., Pietrobon, D., Pizzorusso, P., Kaja, S., Broos, L.A.M., Cesetti, T., van de Ven, R.C.G., Tottene, A., van der Kaa, J., Plomp, J.J., et al. (2004). A Cacna1a Knockin Migraine Mouse Model with Increased Susceptibility to Cortical Spreading Depression. Neuron 41, 701-710.
Vanhoenacker, P., Haegeman, G., and Leysen, J.E. (2000). 5-HT7 receptors: current knowledge and future prospects. Trends in Pharmacological Science 21, 70-77.
Vanmolkot, K.R.J., Babini, E., Vries, E., Stam, A.H., Freilinger, T., Terwindt, G.M., Norris, J., Haan, J., Frants, R.R., Ramadan, N.M., et al. (2007). The novel L1649Q mutation in the SCN1A epilepsy gene is associated with familial hemiplegic migraine: genetic and functional studies. Human Mutation 28, 522.
Varilo, T., and Peltonen, L. (2004). Isolates and their potential use in complex gene mapping efforts. Curr Opin Genet Dev 14, 316-323. Page 238
Vikelis, M., and Mitsikostas, D.D. (2007). The role of glutamate and its receptors in migrain. CNS Neurol Disord Drug Targets 6, 251-257.
, D., Valdivia, L.F., de Vries, P., and Saxena, P.R. (2003). Migraine: Pathophysiology, Pharmacology, Treatment and Future Trends. Current Vascular Pharmacology 1, 71-84.
Wain, H.M., Bruford, E.A., Lovering, R.C., Lush, M.J., Wright, M.W., and Povey, S. (2002). Guidelines for Human Gene Nomenclature. Genomics 79, 464-470.
Waldo, M.C. (1999). Schizophrenia in Kosrae, Micronesia: prevalence, gender ratios, and clinical symptomatology. Schizophrenia Research 3, 175-181.
Walsh, T., Shahin, H., Elkan-Miller, T., Lee, M.K., Thornton, A.M., Roeb, W., Abu Rayyan, A., Loulus, S., Avraham, K.B., King, M.-C., et al. (2010). Whole exome sequencing and homozygosity mapping identify mutation in the cell polarity protein GPSM2 as the cause of nonsyndromic hearing loss DFNB82. Am J Hum Genet 87, 90-94.
Wang, A., Liang, Y., Fridell, R.A., Probst, F.J., Wilcox, E.R., Touchman, J.W., Morton, C.C., Morell, R.J., Noben-Trauth, K., Camper, S.A., et al. (1998a). Association of unconventional myosin MYO15 mutations with human nonsyndromic deafness DFNB3. Science 280, 1447-1451.
Wang, D.G., Fan, J.B., Siao, C.J., Berno, A., Young, P., Sapolsky, R., Ghandour, G., Perkins, N., Winchester, E., Spencer, J., et al. (1998b). Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science 280, 1077-1082.
Wang, L., Fan, C., Topol, S.E., Topol, E.J., and Wang, Q. (2003). Mutation of MEF2A in an Inherited Disorder with Features of Coronary Artery Disease. Science 302, 1578-1581.
Wang, Q. (2005). Molecular genetics of coronary artery disease. Current Opinion in Cardiology 20, 182-188.
Wanga, X., Sunb, W., Zhua, X., Lib, L., Wua, X., Linb, H., Zhua, S., Liub, A., Dua, T., Liua, Y., et al. (2008). Association between the γ-aminobutyric acid type B receptor 1 and 2 gene polymorphisms and mesial temporal lobe epilepsy in a Han Chinese population. 81 2, 198-203.
Wessman, M., Kallela, M., Kaunisto, M.A., Marttila, P., Sobel, E., Hartiala, J., Oswell, G., Leal, S.M., Papp, J.C., Hämäläinen, E., et al. (2002). A Susceptibility Locus for Migraine with Aura, on Chromosome 4q24. The American Journal of Human Genetics 70, 652-662.
White, R.E. (2002). Estrogen and vascular function. Vascular Pharmacology 38, 73- 80.
WHO (1996). World health statistics annual 1995 (Geneva).
Page 239
WHO (2000). Obesity: Preventing and Managing the Global Epidemic. In WHO Technical Report Series 894 (Geneva).
WHO (2001). The World Health Report 2001. (Geneva: WHO 2001. Available at http://www.who.int/whr/2001/en/index.html. Last Accessed 18 May 2009.).
WHO-ISH (1999). 1999 Guidelines for management of hypertension. Cardiovascular Prevention 2, 76-111.
Williams, J.T., and Blangero, J. (1999). Power of variance component linkage analysis to detect quantitative trait loci. Ann Hum Genet 63, 545-563.
Williams-Blangero, S., and Blangero, J. (2006). Collection of pedigree data for genetic analysis in isolate populations. Hum Biol 78, 89-101.
Woolley, N., Holopainen, P., Ollikainen, V., Mustalahti, K., Mäki, M., Kere, J., and Partanen, J. (2002). A new locus for coeliac disease mapped to chromosome 15 in a population isolate. Human Genetics 11, 40-45.
Wright, A., Charlesworth, B., Rudan, I., Carothers, A., and Campbell, H. (2003). A polygenic basis for late-onset disease. Trends in Genetics 19, 97-106.
Yang, Q., Khoury, M.J., Friedman, J.M., Little, J., and Flanders, W.D. (2005). How many genes underlie the occurrence of common complex diseases in the population? International Journal of Epidemiology 34, 1129-1137.
Yilmaz, M., Erdal, M.E., Herken, H., Caataloluk, O., Barlas, O., and Bayazit, Y.A. (2001). Significance of serotonin transporter gene polymorphism in migraine. Journal of the Neurological Sciences 186, 27-30.
Zeegers, M.P., van Poppel, F., Vlietinck, R., Spruijt, L., and Ostrer, H. (2004). Founder mutations among the Dutch. Eur J Hum Genet 12, 591-600.
Zhao, L., Xiao, P., Liu, Y., Xiong, D., Shen, H., Recker, R.R., and Deng, H. (2007). A genome-wide linkage scan for quantitative trait loci underlying obesity related phenotypes in 434 Caucasian families. Human Genetics 121, 145-148.
Ziegler, D.K., Hur, Y.-M., Bouchard, T.J., Hassanein, R.S., and Barter, R. (1998). Migraine in Twins Raised Together and Apart. Headache: The Journal of Head and Face Pain 38, 417-422.
Zou, J.Y., and Crews, F.T. (2005). TNFα potentiates glutamate neurotoxicity by inhibiting glutamate uptake in organotypic brain slice cultures: neuroprotection by NFκB inhibition. Brain Research 1034, 11-24.
Page 240