Genetic Epidemiology of Health Disparities in Allergy and Clinical Immunology

Total Page:16

File Type:pdf, Size:1020Kb

Genetic Epidemiology of Health Disparities in Allergy and Clinical Immunology Molecular mechanisms in allergy and clinical immunology (Supported by an unrestricted educational grant from Genentech, Inc. and Novartis Pharmaceuticals Corporation) Series editors: William T. Shearer, MD, PhD, Lanny J. Rosenwasser, MD, and Bruce S. Bochner, MD Reviews and feature articles Genetic epidemiology of health disparities in allergy and clinical immunology Kathleen C. Barnes, PhD Baltimore, Md This activity is available for CME credit. See page 45A for important information. The striking racial and ethnic disparities in disease prevalence for common disorders, such as allergic asthma, cannot be Abbreviations used explained entirely by environmental, social, cultural, or CF: Cystic fibrosis economic factors, and genetic factors should not be ignored. CFTR: Cystic fibrosis transmembrane conductance Unfortunately, genetic studies in underserved minorities are regulator hampered by disagreements over the biologic construct of race CTLA4: Cytotoxic T lymphocyte–associated 4 and logistic issues, including admixture of different races and LD: Linkage disequilibrium ethnicities. Current observations suggest that the frequency NOS: Nitric oxide synthase of high-risk variants in candidate genes can differ between SNP: Single nucleotide polymorphism African Americans, Puerto Ricans, and Mexican Americans, and this might contribute to the differences in disease prevalence. Maintenance of certain allelic variants in the population over time might reflect selective pressures in influences (ie, gene-environment interactions). Con- previous generations. For example, significant associations versely, the quest for causal variants has been met with between markers in certain candidate genes (eg, STAT6, ADRB2, and IFNGR1) for traits such as high total IgE levels considerable success for a number of the monogenic observed in resistance to extracellular parasitic disease in one disorders, such as cystic fibrosis (CF) and sickle cell population and atopic asthma in another supports the common anemia, wherein a major gene is known and confounding disease/common variant model for disease. Herein is a factors, although not entirely absent, have much less discussion of how genetic variants might explain, at least in effect. For example, the DF508 variant in the CF trans- part, the marked disparities observed in risk to allergic asthma. membrane conductance regulator (CFTR) gene is the most (J Allergy Clin Immunol 2006;117:243-54.) common cause of CF in most populations,1 the most common fatal recessive disorder among populations of Key words: Asthma, allergy, genetics, disparities, ethnicity European descent. This variant is believed to have emerged more than 52,000 years ago during the Paleo- Despite the explosion of data on the role of genetics in lithic period from a single origin followed by diffusion the pathophysiology of both common and uncommon along a southeast-northwest gradient across Europe, re- diseases, identifying the precise genes that confer suscep- sulting in higher frequencies of the mutation in northern tibility to many complex disorders, including allergic Europe and lower frequencies toward the south.2 The asthma, remains a daunting task. Identification of these heterozygote state is believed to confer some protection genes has been hampered by variability in the clinical against cholera-induced secretory diarrhea because altered phenotype, genetic heterogeneity among human popula- CFTR expression results in modified Cl2 secretion; CFTR tions, and a failure to consider important environmental knockout murine models support this.3 The exceptionally high CF heterozygosity frequency (1 in 20) of the CFTR From The Johns Hopkins Asthma and Allergy Center. mutation supports the notion of heterozygote advantage Supported by the Allergy and Asthma Foundation of America Young and could reflect selective pressure from cholera epi- Investigator Award and National Institutes of Health grants A150024 and demics in early European history. HL066583. KCB was supported in part by the Mary Beryl Patch Turnbull In contrast, sickle cell disease, a common autosomal Scholar Program. Received for publication November 16, 2005; revised November 29, 2005; recessive disorder, caused a variant in the b-globin gene accepted for publication November 29, 2005. (HbS) believed to have spread as a protective mechanism Reprint requests: Kathleen C. Barnes, PhD, The Johns Hopkins Asthma and against malaria, which would explain the high frequency Allergy Center, 5501 Hopkins Bayview Circle, Room 3A.62, Baltimore, of this variant in African and Mediterranean populations.4 MD 21224. E-mail: [email protected]. 0091-6749/$32.00 A life-threatening complication of sickle cell disease is Ó 2006 American Academy of Allergy, Asthma and Immunology acute chest syndrome, affecting some 1 in 1085 patients 5 doi:10.1016/j.jaci.2005.11.030 with sickle cell disease. Both a promoter variant 243 244 Barnes J ALLERGY CLIN IMMUNOL FEBRUARY 2006 feature articles Reviews and (T-786C) in the endothelial nitric oxide synthase (NOS) Europe, East Asia, Oceania-Pacific, and the Americas. gene,6 or NOS3, and a polymorphism in the NOS1 gene7 There is typically good agreement between assignment have been shown to be associated with acute chest syn- to these 5 populations on the basis of genetic marker data drome. Interestingly, it has also been shown that heterozy- and self-reported ancestry.23 However, on the basis of the gous carriers of a point mutation in the promoter of the biogeographic distribution of our genetic variation, there NOS2 are protected against severe malaria,8 suggesting are actually few sound arguments for lumping individuals an overall selective advantage for variants in these genes into these specific racial categories. In the past several de- as well. Striking differences in frequencies of variants in cades, the long-standing ‘‘Multiregional Origin model’’ to nitric oxide genes have been observed when comparing explain evolution of modern human origins, which alleged populations originating in regions endemic for malaria that parallel evolution of Homo sapiens from Homo erec- (eg, African descent) with European populations (NOS1 tus took place within geographically dispersed populations CA repeat in exon 29 and intronic AAT repeat,9 NOS3 as H erectus migrated out of Africa more than 1,000,000 Glu298Asp polymorphism,10 NOS3(G894T) polymor- years ago,24 has been replaced by the Recent African phism,11 and 393 endothelial cell NOS allele12). Collec- Origin model (Out of Africa hypothesis25,26). The Recent tively, genetic studies of monogenic lung disorders African Origin model argues that H sapiens evolved in suggest that (1) the presence of high-risk variants can dif- Africa some 100,000 to 200,000 years ago (probably from fer substantially among ethnic groups, contributing to the an East African gene pool), after which its anatomically marked differences in disease prevalence among ethnic modern members migrated and replaced archaic human groups, and (2) the maintenance of these variants in the groups throughout Europe and Asia, and only within the population over time might be due to a selective advantage past 100,000 years has racial differentiation occurred. for the variant in previous generations. Historical evidence for recent expansions across Europe, Asia, the Americas, and the Pacific (dating back from 6023 thousand years ago) correlates well with pat- ETHNICITY AND RACE AS CONSTRUCTS: terns of allele frequency variation,27 with higher levels IS THERE BIOLOGIC RELEVANCE? of sustained genetic diversity in the more ancient African populations and less diversity in the younger, non-African Given the negative and in many cases devastating use of populations.28 The end result of this process is a gradation the social construct of race in our recent past,13 the use of of genetic differentiation across groups of H sapiens,29 the term in biomedical research has been met with a wide with no clear-cut boundaries defining races. In light of a range of consternation and has generated a plethora of more accurate time frame for evolution of our species, editorials variously calling for the judicious use of the con- the amount of genetic diversity across the human genome cept as a legitimate tool for identifying disease-associated is, perhaps not surprisingly, rather low. H sapiens as a spe- genes14,15 to the stance that race is biologically meaning- cies share approximately 99.6% to 99.8% of genetic mate- less.16 At the very least, most experts in the field agree that rial with each other, and only a fraction of the total genome race as a social construct represents a continuum rather (eg, approximately 10 million of 3 billion nucleotides) than a term with clear-cut boundaries, a point that has accounts for variability among populations.22 Put another been illustrated by large-scale genotyping among diverse way, human genomes vary roughly one every thousand biogeographic populations.17 (For in-depth reviews on bases (kilobase). Additionally, genetic diversity is highest this topic, see supplement in Nature Genetics.18) How- among individuals within populations (85%), and varia- ever, whether we adopt the constructs of race, ethnicity, tion between groups or populations only accounts for or self-defined group identity according to our ancestors’ approximately 15%.30 geographic origins (eg, ‘‘biogeography’’), there is no denying that health disparities, including asthma, exist among certain racial-ethnic groups in the United States. GENOMIC AND POPULATION STRUCTURE Consider,
Recommended publications
  • Estimation of DNA Contamination and Its Sources in Genotyped Samples
    Gregory Zajac ORCID iD: 0000-0001-6411-9666 Estimation of DNA contamination and its sources in genotyped samples Gregory J. M. Zajac1; Lars G. Fritsche1; Joshua S. Weinstock1; Susan L. Dagenais2; Robert H. Lyons2; Chad M. Brummett3; Gonçalo. R. Abecasis1 1) Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI; MI; 2) Department of Biological Chemistry and DNA Sequencing Core, University of Michigan, Ann Arbor, MI; 3) Department of Anesthesiology, Division of Pain Medicine, University of Michigan Medical School, Ann Arbor, MI. Corresponding author: Gregory JM Zajac 734-763-5315 [email protected] University of Michigan School of Public Health - Department of Biostatistics 1415 Washington Heights Ann Arbor, MI 48109-2029 Grant Numbers: HG007022 Author Manuscript This is the author manuscript accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/gepi.22257. This article is protected by copyright. All rights reserved. Abstract Array genotyping is a cost-effective and widely used tool that enables assessment of up to millions of genetic markers in hundreds of thousands of individuals. Genotyping array data are typically highly accurate but sensitive to mixing of DNA samples from multiple individuals prior to or during genotyping. Contaminated samples can lead to genotyping errors and consequently cause false positive signals or reduce power of association analyses. Here, we propose a new method to identify contaminated samples and the sources of contamination within a genotyping batch.
    [Show full text]
  • Fifty Years of Genetic Epidemiology, with Special Reference to Japan
    J Hum Genet (2006) 51:269–277 DOI 10.1007/s10038-006-0366-9 MINIREVIEW Newton E. Morton Fifty years of genetic epidemiology, with special reference to Japan Received: 13 December 2005 / Accepted: 18 December 2005 / Published online: 15 February 2006 Ó The Japan Society of Human Genetics and Springer-Verlag 2006 Abstract Genetic epidemiology deals with etiology, dis- Introduction tribution, and control of disease in groups of relatives and with inherited causes of disease in populations. It The Japan Society of Human Genetics (JSHG) was took its first steps before its recognition as a discipline, established at a time when the science it represented was and did not reach its present scope until the Human growing so explosively that Carlson (2004) signalised the Genome Project succeeded. The intimate relationship death of classical genetics. Compared to their predeces- between genetics and epidemiology was discussed by sors, geneticists in 1956 were more distrustful of eugenics Neel and Schull (1954), just a year after Watson and and more committed to cytogenetics, biochemistry, and Crick reported the DNA double helix, and 2 years be- medical genetics, but not yet prepared for the genomic fore human cytogenetics and the Japan Society of Hu- revolution that the double helix would ultimately bring man Genetics were founded. It is convenient to divide (Yanase 1997). Population genetics was beginning to the next half-century into three phases. The first of these split into two major branches, one concerned with events (1956–1979) was before DNA polymorphisms were that took place in the past under poorly known forces of typed, and so the focus was on segregation and linkage systematic pressure and chance (evolutionary genetics), of major genes, cytogenetics, population studies, and the other dealing with etiology, distribution, and control biochemical genetics.
    [Show full text]
  • Manual for Conducting a Large-Scale GWAS and Whole Genome
    (COVNET) NIH Lead Investigator: Stephen Chanock, M.D. ([email protected]) Director, Division of Cancer Epidemiology and Genetics, NCI NIH Investigators: Sharon Savage, M.D. ([email protected]) Lisa Mirabello, Ph.D. ([email protected]) Meredith Yeager, Ph.D. ([email protected]) Mitchell Machiela, Sc.D. ([email protected]) Joshua Sampson, Ph.D. ([email protected]) Amy Hutchinson, M.S. ([email protected]) Belynda Hicks, M.S. ([email protected]) Division of Cancer Epidemiology and Genetics, NCI Mary Carrington, Ph.D. ([email protected]) Center for Cancer Research, NCI Leslie Biesecker, M.D. ([email protected]) Teri Manolio, M.D., Ph.D. ([email protected]) National Human Genome Research Institute Steven Holland, M.D. ([email protected]) National Institute of Allergy and Infectious Diseases Project Manager Vibha Vij, M.S., MPH ([email protected]) Division of Cancer Epidemiology and Genetics, NCI Website: https://dceg.cancer.gov/research/how-we-study/genomic-studies/covnet Table of Contents Sections 1.0 Background and Rationale for a Large Crowdsourced GWAS of COVID19 Infection ...... 2 2.0 Biospecimens: genomic DNA, blood/blood products, buccal cells/saliva .......................... 6 3.0 Patient Phenotypes ............................................................................................................. 10 4.0 Genotyping and Imputation ............................................................................................... 10 5.0 Analysis Plan ....................................................................................................................
    [Show full text]
  • M21-515 Fundamentals of Genetic Epidemiology Summer 2018
    M21-515 Fundamentals of Genetic Epidemiology Summer 2018 Revised: 7/13/2018 Course Masters Treva Rice, Ph.D. ([email protected]) Yun Ju Sung, Ph.D. ([email protected]) Lab Instructor Oyomoare Osazuwa-Peters ([email protected]) Grading Computer Lab Assignments 30% Daily Quiz 30% Midterm Exam (covers lectures / homework 1-5) 20% Final Project 20% Final Grade (+/– letter grades) Software Software packages include: 1. R (http://www.r-project.org/) 2. PEDSTATS, Merlin, QTDT (http://www.sph.umich.edu/csg/abecasis) 3. PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/) Textbook Austin, MA. Genetic Epidemiology: Methods & Applications 2013, CABI: Oxfordshire, UK. Format 2-week INTENSIVE course Morning lecture: 9:00 am – 12:00 noon Afternoon computer lab: 1:30 pm – 4:00 pm 10-minute break ~ every hour 1st Homework Due first day of class, Textbook (Chapters 1-2) Assignment On-line genetics tutorial, Chapters 3, 4, and 6 (http://anthro.palomar.edu/tutorials/biological.htm) Prerequisite 1) Knowledge of R programming, either having taken the MSIBS summer R-course or experience in R-programming; 2) experience in Linux/Unix operating system. The core competency for the Fundamentals of Genetic Epidemiology course (M21-515) is for students to understand basic concepts, methods and analytical approaches in genetic epidemiology. Learning objectives are to • Understand familial resemblance, heritability and family study designs • Appreciate maximum likelihood methods and hypothesis testing • Be aware of selected molecular and population genetics principles, including Hardy-Weinberg Equilibrium • Grasp the basic concepts and principles underlying genetic linkage and association • Be able to perform analysis in heritability, linkage and association using selected software and critically evaluate and interpret the corresponding results Page 1 of 10 Additional Information 1.
    [Show full text]
  • The Views of Participants in DNA Biobanks
    The Views of Participants in DNA Biobanks Kelly E. Ormond,I Maureen E. Smith,II & Wendy A. WolfIII Abstract Biobanks are generally created with the long-term goal of establishing genotype-phenotype correlations. These resources collect and link DNA with health information for use in future genetic research studies. The biobanking process can vary with regard to specific characteristics in study design. Biobanks may extract DNA by using DNA from leftover samples or obtaining new DNA samples specifically for the biobank. Biobanks also vary in whether they use an opt- in or opt-out informed consent process. Some biobanks collect health information at a single point in time, while others “re-access” such information at designated points in the future to categorize subjects accurately into affected/unaffected status. These study design differences can influence the perception of participants and affect their willingness to participate. This paper will address the following three key issues: (1) why people decide to participate in DNA biobanks, (2) what enrollees understand about their participation in DNA biobanks, and (3) how participants feel about the possibility that biobanks will re-contact them, either to obtain new information for future studies or to share potential study results. Finally, we will suggest how information related to these three key issues ought to inform future study design for biobanks. I. INTRODUCTION.................................................................................................................81 II. WHY DO PEOPLE DECIDE TO PARTICIPATE IN DNA BIOBANKS?...........................82 III. WHAT DO ENROLLEES UNDERSTAND ABOUT THEIR PARTICIPATION IN A DNA BIOBANK? ..............................................................................................................................83 IV. HOW DO PARTICIPANTS FEEL ABOUT BIOBANK RE-CONTACT, AND HOW SHOULD THIS INFORMATION INFORM STUDY DESIGN?..............................................84 V.
    [Show full text]
  • Genetic Epidemiology Network of Arteriopathy (GENOA)
    Genetic Epidemiology Network of Arteriopathy (GENOA) Study Description The Genetic Epidemiology Network of Arteriopathy (GENOA) From its inception in 1995, GENOA's long-term objective was to elucidate the genetics of hypertension and its arteriosclerotic target-organ damage, including both atherosclerotic (macrovascular) and arteriolosclerotic (microvascular) complications involving the heart, brain, kidneys, and peripheral arteries. Two GENOA cohorts were originally ascertained (1995-2000) through sibships in which at least 2 siblings had essential hypertension diagnosed prior to 60 years of age. All siblings in the sibship were invited to participate, both normotensive and hypertensive. These include non-Hispanic White Americans from Rochester, MN (n =1583 at the 1st exam) and African Americans from Jackson, MS (N=1854 at the 1st exam). A third Hispanic American cohort from Starr County, TX, was ascertained through diabetic rather than hypertensive sibships (N=1812). The Hispanic American cohort is not included in this genetic analysis because no genome-wide genotype data is currently available for this cohort. Exclusion criteria for the white and African American cohorts were secondary hypertension, alcoholism or drug abuse, pregnancy, insulin-dependent diabetes mellitus, or active malignancy. The GENOA data consists of biological samples (DNA, serum, urine) as well as demographic, anthropometric, environmental, clinical, biochemical, physiological, and genetic data for understanding the genetic predictors of diseases of the heart, brain, kidney, and peripheral arteries. Data were collected during two phases (Phase I from 1995-2000, Phase II from 2001-2004) and multiple ancillary studies (eg., chronic kidney disease assessments, brain MRIs, 24-hour urine and blood pressure assessments, etc). Written informed consent was obtained from all subjects and approval was granted by participating institutional review boards.
    [Show full text]
  • Pharmacogenomics, How to Deal with Different Types of Variants in Next Generation Sequencing Data in the Personalized Medicine Area
    Journal of Clinical Medicine Review Pharmacogenomics, How to Deal with Different Types of Variants in Next Generation Sequencing Data in the Personalized Medicine Area Alireza Tafazoli 1,2 , Natalia Wawrusiewicz-Kurylonek 3, Renata Posmyk 3 and Wojciech Miltyk 1,* 1 Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Białystok, 15-089 Białystok, Poland; [email protected] 2 Clinical Research Centre, Medical University of Białystok, 15-276 Bialystok, Poland 3 Department of Clinical Genetics, Medical University of Białystok, 15-089 Białystok, Poland; [email protected] (N.W.-K.); [email protected] (R.P.) * Correspondence: [email protected]; Tel.: +48-857485845 Abstract: Pharmacogenomics (PGx) is the knowledge of diverse drug responses and effects in people, based on their genomic profiles. Such information is considered as one of the main directions to reach personalized medicine in future clinical practices. Since the start of applying next generation sequencing (NGS) methods in drug related clinical investigations, many common medicines found their genetic data for the related metabolizing/shipping proteins in the human body. Yet, the employing of technology is accompanied by big obtained data, which most of them have no clear guidelines for consideration in routine treatment decisions for patients. This review article talks about different types of NGS derived PGx variants in clinical studies and try to display the current and newly developed approaches to deal with pharmacogenetic data with/without clear guidelines for considering in clinical settings. Citation: Tafazoli, A.; Keywords: pharmacogenomics; NGS variants; personalized medicine Wawrusiewicz-Kurylonek, N.; Posmyk, R.; Miltyk, W.
    [Show full text]
  • The Molecular and Genetic Epidemiology of Cancer
    Vol. 12, 803–805, August 2003 Cancer Epidemiology, Biomarkers & Prevention 803 Meeting Report AACR Special Conference: The Molecular and Genetic Epidemiology of Cancer John D. Potter,1 Ellen Goode, and Libby Morimoto candidates genes for specific cancers. Daehee Kang’s presen- ϳ Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, tation on a large breast cancer study ( 1400 cases and in Washington 98109 ϳ1400 controls) and candidate genes in estrogen metabolism and DNA repair suggested that the use of larger sample sizes Introduction would also benefit the field, particularly when gene-environ- ment and gene-gene interactions are examined. The AACR Special Conference on The Molecular and Genetic Duncan Thomas discussed sample size estimates for stud- Epidemiology of Cancer was held January 18–23, 2003 at ies involving gene-environment interaction (work with Jim Waikoloa, Hawaii. Approximately 400 attendees from around Gauderman) and the impact of, variously, the way in which the world heard state-of-the-art talks, many with new data on penetrance, dominance, and allele frequency are modeled, the methods and applications. A range of sessions covered epide- nature of the exposure distribution, the magnitude of relative miological, statistical, and laboratory methods; genetic suscep- risks, and the complexity of the interaction demonstrated pro- tibility and human biomonitoring; intermediate endpoints; can- grams available on-line.2 In the same session, Nathaniel Roth- cer interventions; biorepositories; ethics; and the impact of man presented work he has been undertaking with Sholom molecular epidemiology on other fields. In addition, 129 post- Wacholder trying to quantify the problem of a large excess of ers were presented, and participants benefited from both scien- false positives and the issue of multiple comparisons in molec- tific sessions and the widespread, often animated informal ular epidemiology; he recommended the use of a new Bayesian discussions.
    [Show full text]
  • Genetic Epidemiology, ⎪ Σ ⎪ 34 – ⎪ ⎛ ⎞ (8), 816 834
    Received: 6 May 2019 | Revised: 11 July 2019 | Accepted: 9 August 2019 DOI: 10.1002/gepi.22257 RESEARCH ARTICLE Estimation of DNA contamination and its sources in genotyped samples Gregory J. M. Zajac1 | Lars G. Fritsche1 | Joshua S. Weinstock1 | Susan L. Dagenais2 | Robert H. Lyons2 | Chad M. Brummett3 | Gonçalo R. Abecasis1 1Department of Biostatistics, Center for Statistical Genetics, University of Abstract Michigan School of Public Health, Ann Array genotyping is a cost‐effective and widely used tool that enables assessment of Arbor, Michigan up to millions of genetic markers in hundreds of thousands of individuals. 2 Department of Biological Chemistry and Genotyping array data are typically highly accurate but sensitive to mixing of DNA DNA Sequencing Core, University of Michigan, Ann Arbor, Michigan samples from multiple individuals before or during genotyping. Contaminated 3Department of Anesthesiology, Division samples can lead to genotyping errors and consequently cause false positive signals of Pain Medicine, University of Michigan or reduce power of association analyses. Here, we propose a new method to Medical School, Ann Arbor, Michigan identify contaminated samples and the sources of contamination within a Correspondence genotyping batch. Through analysis of array intensity and genotype data from Gregory JM Zajac, Department of intentionally mixed samples and 22,366 samples of the Michigan Genomics Biostatistics, School of Public Health, ‐ University of Michigan, 1415 Washington Initiative, an ongoing biobank based study, we show that our method can reliably Heights, Ann Arbor, MI 48109‐2029. estimate contamination. We also show that identifying sources of contamination Email: [email protected] can implicate problematic sample processing steps and guide process improve- Funding information ments.
    [Show full text]
  • Basic Principles and Laboratory Analysis of Genetic Variation Jesus Gonzalez-Bosquet and Stephen J
    UNIT 2. BIOMARKERS: PRACTICAL ASPECTS CHAPTER 6. Basic principles and laboratory analysis of genetic variation Jesus Gonzalez-Bosquet and Stephen J. Chanock UNIT 2 CHAPTER 6 CHAPTER Summary With the draft of the human genome of their functional significance. agnostically using dense data sets and advances in technology, the Understanding the true effect of with billions of data points. These approach toward mapping complex genetic variability on the risk of developments have transformed diseases and traits has changed. complex diseases is paramount. the field, moving it away from the Human genetics has evolved into The importance of designing pursuit of hypothesis-driven, limited the study of the genome as a high-quality studies to assess candidate studies to large-scale complex structure harbouring clues environmental contributions, as well scans across the genome. Together for multifaceted disease risk with as the interactions between genes these developments have spurred a the majority still unknown. The and exposures, cannot be stressed dramatic increase in the discovery discovery of new candidate regions enough. This chapter will address of genetic variants associated with by genome-wide association studies the basic issues of genetic variation, or linked to human diseases and (GWAS) has changed strategies for including population genetics, as traits, many through genome-wide the study of genetic predisposition. well as analytical platforms and association studies (GWAS) (1). More genome-wide, “agnostic” tools needed to investigate the Already over 7400 novel regions of approaches, with increasing contribution of genetics to human the genome have been associated numbers of participants from high- diseases and traits. with more than 75 human diseases quality epidemiological studies are or traits in large-scale GWAS (2).
    [Show full text]
  • Genetic Epidemiology of Essential Hypertension
    Journal of Human Hypertension (1999) 13, 225–229 1999 Stockton Press. All rights reserved 0950-9240/99 $12.00 http://www.stockton-press.co.uk/jhh ORIGINAL ARTICLE Genetic epidemiology of essential hypertension I Gavras1, A Manolis1,2 and H Gavras1 1Hypertension and Atherosclerosis Section of the Department of Medicine, Boston University School of Medicine, Boston, MA, USA; 2Tzanio Hospital, Piraeus, Greece This review article is intended to introduce the unini- heritable anthropometric, clinical or biochemical tiated clinician to the basic concepts, aims and early characteristics; and some applications of genetic epi- findings of the genetic epidemiology of hypertension. It demiologic techniques, such as linkage and association separates the rare monogenic ‘Mendelian’ hypertensive studies of certain gene polymorphisms with hyperten- disorders from the vast majority of patients with essen- sion using affected sibling pairs and large sibships or tial hypertension, which is a complex, polygenic, multi- wide genomic screens comparing affected and unaffec- factorial disorder resulting from interaction of several ted populations. Although so far there is no genotypic genes with each other and with the environment. It high- variation proven to be causally related to essential lights some clinical strategies used to enhance hypertension, its intermediate phenotypes or any of its searches for ‘candidates genes’, such as subgrouping complications, it is hoped that new, more efficient of populations into relatively homogenous groups or methods of genetic analysis will yield clinically mean- ‘intermediate phenotypes’ according to presumably ingful information. Keywords: gene polymorphisms; candidate genes; monogenic disorders; intermediate phenotypes; polygenic multifactorial traits Hypertensive phenotypes the interaction of several genes with each other and with the environment result in different phenotypes.
    [Show full text]
  • Genetic Epidemiology in Latin America: Identifying Strong Genetic Proxies for Complex Disease Risk Factors
    G C A T T A C G G C A T genes Article Genetic Epidemiology in Latin America: Identifying Strong Genetic Proxies for Complex Disease Risk Factors Carolina Bonilla 1,* and Lara Novaes Baccarini 2 1 Departamento de Medicina Preventiva, Faculdade de Medicina, Universidade de São Paulo, São Paulo 01246, Brazil 2 Faculdade de Saúde Pública, Universidade de São Paulo, São Paulo 01246, Brazil; [email protected] * Correspondence: [email protected]; Tel.: +55-11-3061-8610 Received: 5 March 2020; Accepted: 28 April 2020; Published: 4 May 2020 Abstract: Epidemiology seeks to determine the causal effects of exposures on outcomes related to the health and wellbeing of populations. Observational studies, one of the most commonly used designs in epidemiology, can be biased due to confounding and reverse causation, which makes it difficult to establish causal relationships. In recent times, genetically informed methods, like Mendelian randomization (MR), have been developed in an attempt to overcome these disadvantages. MR relies on the association of genetic variants with outcomes of interest, where the genetic variants are proxies or instruments for modifiable exposures. Because genotypes are sorted independently and at random at the time of conception, they are less prone to confounding and reverse causation. Implementation of MR depends on, among other things, a strong association of the genetic variants with the exposure, which has usually been defined via genome-wide association studies (GWAS). Because GWAS have been most often carried out in European populations, the limited identification of strong instruments in other populations poses a major problem for the application of MR in Latin America.
    [Show full text]