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Using Genetic Methods to Get Insight Into Human Complex Traits

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Using Genetic Methods to Get Insight Into Human Complex Traits Using genetic methods to get insight into human complex traits Bochao Danae Lin READING COMMITTEE: Prof. CV Dolan, Vrije Universiteit Amsterdam Prof. NG Martin, QIMR Berghofer Medical Research Institute, Brisbane, Australia Prof. H Snieder, Universitair Medisch Centrum Groningen Prof. M Kayser, Erasmus Universiteit Medisch Centrum Rotterdam Dr. DJA Smit, Academisch Medisch Centrum Amsterdam Dr. R Menezes, Vrije Universiteit Medisch Centrum Amsterdam PARANYMPHS: Jenny van Dongen Junfeng Wang ACKNOWLEDGEMENTS: I would like to acknowledge the financial support I received from the China Scholarship Council (201206180099) for my study. I also acknowledge the financial support from the Faculty of Behavioural and Movement Sciences to visit Notre Dame University, United States. The data collection in the Netherlands Twin Register was supported by multiple grants from the Netherlands Organization for Scientific Research and Development Grant (ZonMW); the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI‐NL) [184.021.007]; the European Research Council, Genetics of Mental Illness [ERC‐230374]; the National Institute for Mental Health (NIMH) [1RC2 MH089951‐01: Integration of Genomics & Transcriptomics in Normal Twins & Major Depression, and 1RC2MH089995‐01: Genomics of Developmental Trajectories in Twins]; and the Avera Institute for Human Genetics , Sioux Falls (USA). I very warmly thank all twins and their family members for taking part in the research projects of the Netherlands Twin Register. ISBN: 978‐94‐6295‐615‐5 Cover design and Layout: Saymand Alerachi Printed by: Proefschriftmaken VRIJE UNIVERSITEIT Using genetic methods to get insight into human complex traits ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus prof.dr. V. Subramaniam, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de Faculteit der Gedrags- en Bewegingswetenschappen op donderdag 20 april 2017 om 11.45 uur in het auditorium van de universiteit, De Boelelaan 1105 door Bochao Lin geboren te Jilin, China promotor: prof.dr. D.I. Boomsma copromotoren: dr. J.J. Hottenga dr. A.H.M. Willemsen Table of Contents Chapter 1 General Introduction ........................................................................... 1 Part I: Visible traits .................................................................................................. 15 Chapter 2 Heritability and Genome‐Wide Association studies for hair color in a Dutch twin family based sample ........................................................................ 17 Appendix I. Details on methodology .................................................................. 34 Chapter 3 The genetic overlap between hair and eye color .............................. 45 Appendix II. GWAS results for eye color ............................................................. 51 Part II: Hematological profiles ................................................................................ 55 Chapter 4 Causes of variation in the neutrophil– lymphocyte and platelet‐ lymphocyte ratios: a twin‐family study .............................................................. 57 Appendix III. Age and BMI interaction effects on NLR and PLR .......................... 73 Appendix IV. Results for NLR and PLR in the total and unhealthy population ... 75 Chapter 5 SNP heritability and effects of genetic variants for neutrophil‐to‐ lymphocyte and platelet‐to‐lymphocyte ratio ................................................... 83 Appendix V. Details on eQTL analysis ............................................................... 107 Chapter 6 Heritability and GWA studies for the monocyte‐lymphocyte ratio . 111 Chapter 7 The interactive effects of age, sex, and lifestyle on the hematological profile. ............................................................................................................... 131 Part III: Epigenome‐wide studies .......................................................................... 145 Chapter 8 Blood hypomethylation is associated with elevated myeloid‐lymphoid ratios in cell‐specific active genomic regions ................................................... 147 Chapter 9 Epigenome‐wide association study for platelet‐lymphocyte ratio (PLR) level ................................................................................................................... 161 Chapter 10 General Summary and Discussion ................................................. 169 Bibliography .......................................................................................................... 182 Chapter 1 General Introduction We are all unique, even if we share certain characteristics with our family members and those around us. The individual differences that can be observed in the population are caused by a combination of genetic and environmental factors. In this PhD thesis I endeavor to add to our understanding of the way genetic factors explain individual differences in human complex traits by applying a variety of methodological tools to different sets of personal characteristics, which in genetics are commonly referred to as ‘phenotypes’. In this first chapter I provide a general background, describe the methods I applied to answer the questions about the etiology of individual differences, and I introduce the traits of interest, in which variation is analyzed in this thesis. 1.1 General background. Genetic studies of human complex traits aim to clarify the contribution of genetic factors to variation in the trait. The phrase ‘complex trait’ or ‘complex phenotype’ refers to traits, which result from variation at multiple genomic sites, and across multiple environmental factors. Complex traits do not follow a Mendelian pattern of inheritance and often show a continuous distribution in the population, either on the scale of measurement or on the underlying liability scale. Family studies, in particular twin studies, have proven to be useful tools to determine to which extent genetic factors influence individual variation in a traits, or stated differently, to provide us with estimates for the heritability of a trait [1]. Such studies, however, do not identify the source of the genetic contribution to individual differences, namely the variation in DNA sequence in human genomes. Until recently, there were two main approaches to gain information on the genetic variants influencing phenotypes of interest: candidate gene association studies and linkage studies. In a candidate gene association study the focus is on associations between the variation in the phenotype and variation in one or a few preselected genes. For example, my first genetic study conducted as part of my Master program investigated the association between rs16970495 (an intro variant on RASGRF1) and myopia in a sample of 557 Chinese adults. In this study I found that the A allele of rs16970495 was associated with an increased risk of myopia (OR=1.21, P=.003). However, such candidate gene studies are generally based on limited prior knowledge, especially in psychology or psychiatry, and often prove difficult to replicate in follow up studies [2]. Linkage studies do not focus on a specific gene but use data from family members to map individual differences in a trait to variation in genetic markers of a known chromosomal location. A location that correlates with the 1 quantitative trait of interest is referred to as a quantitative trait locus (QTL), and is more likely to contain a causal genetic variant. However, linkage studies require family data, such as big pedigrees, or large samples of sibling pairs and have a low resolution mapping, with resulting QTLs thus referring to broad chromosomal regions, rather than to a specific base pair position. In addition, linkage studies are well suited for Mendelian traits with high penetrance but less so for complex traits [3]. Fortunately, advances in genotyping technology have now made it both time‐ and cost‐ wise possible to explore a large part of the variation in the human genome. Assessment of genomic variation in DNA can be done by typing samples on SNP (single nucleotide polymorphisms) arrays, or by sequencing the complete genome, which provides additional information of genetic variants such as insertions and deletions (indels) and copy number variants (CNV). These developments have further been supported by advances in computational support and methodologies for high‐throughput data analysis. Imputation of missing genotypes in subjects with SNP array data, using a large reference panel of sequenced individuals such as the 1000 Genomes project [4], provides the possibilities for GWA studies to identify complex trait loci. The combination of arrays and sequence information results in a nearly complete number of studied genetic markers across the genome to map associations. Furthermore it allows to harmonize and combine datasets or results across research groups for meta‐analysis [5]. As a result, the last decade has seen a large number of identified genes, and more scientific achievements obtained by genome‐wide association studies (GWAS) for human complex traits. The results obtained by GWAS studies have provided the input for new analyses techniques, furthering our understanding of the way genetic influences are involved in individual variation, done by exploring the degree to which genes cluster in pathways, influence multiple traits or are differently expressed during the lifespan.
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