DOCSLIB.ORG

Genetic Epidemiology Network of Arteriopathy (GENOA)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read 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. Given that our informed consent documentation limits data sharing to GENOA investigators and our collaborators, we are unable to make individual-level GENOA phenotype and genotype data available on dbGAP at this time. We are working on this consent issue. However, we have made our analysis results available and we fully welcome collaboration with researchers that would like to include the GENOA sample in their analyses. We can easily allow transfer of the individual-level data with an appropriate Data Transfer Agreement. If you would like to collaborate with GENOA, please contact: Sharon L.R. Kardia, Ph.D. Professor and Chair, Department of Epidemiology School of Public Health, University of Michigan 1415 Washington Heights, Room 4659 Ann Arbor, MI 48109 (734) 647-1029 [email protected] Family Blood Pressure Program (FBPP) GENOA is one of four research networks that form the NHLBI Family Blood Pressure Program (FBPP). GENOA’s parent program, the FBPP, is an unprecedented collaboration to identify genes influencing blood pressure (BP) levels, hypertension, and its target-organ damage. This program has conducted over 21,000 physical examinations, assembled a shared database of several hundred BP and hypertension-related phenotypic measurements, completed genome-wide linkage analyses for BP, hypertension, and hypertension associated risk factors and complications, and published over 130 manuscripts on program findings. The FBPP emerged from what was initially funded as four independent networks of investigators (HyperGEN, GenNet, SAPPHIRe and GENOA) competing to identify genetic determinants of hypertension in multiple ethnic groups. Realizing the greater likelihood of success through collaboration, the investigators began working together during the first funding cycle (1995-2000) and formalized this arrangement in the second cycle (2000-2005), creating a single confederation with program-wide and network-specific goals. Hypertension case definition and exclusion criteria for GENOA Individuals in GENOA belong to sibships identified in which at least two siblings had essential hypertension diagnosed prior to 60 years of age. After identification of the initial pairs of hypertensive siblings, all siblings in the sibship were invited to participate regardless of hypertension status. Hypertension case definition: Essential hypertension diagnosed prior to age 60 years of age, defined as: 1) average of the last 2 out of 3 systolic BP readings ≥ 140mmHg, or 2) an average of the last 2 out of 3 diastolic BP readings ≥ 90 mmHg, or 3) previous diagnosis of hypertension and antihypertensive medication prescribed by a physician to be taken daily during the last month. Exclusion criteria: Pregnancy or breast feeding, Type I diabetes mellitus (juvenile onset, insulin dependent), diagnosis of hypertension ≥ 60 yrs of age, or secondary causes of hypertension including but not limited to prior knowledge of renal parenchymal disease or serum creatinine ≥ 2.5 mg/dL, renal vascular disease, primary aldosteronism, pheochromocytoma, coarctation of aorta, hypertension associated with current use of oral contraceptive agents, prescription or non-prescription drugs, or active alcohol abuse. Sample size Non-Hispanic whites from Rochester, MN Phase I: N=1,583 Phase II: N=1,241 African Americans from Jackson, MS Phase I: N=1,854 Phase II: N=1,482 Key GENOA Publications Daniels PR, Kardia SL, Hanis CL, Brown CA, Hutchinson R, Boerwinkle E, Turner ST, Genetic Epidemiology Network of Arteriopathy study. Familial Aggregation of Hypertension Treatment and Control in the Genetic Epidemiology Network of Arteriopathy (GENOA) Study. Am J Med 2004; 116(10): 676-681. PMID: 15121494. FBPP Investigators. Multi-Center Genetic Study of Hypertension: The Family Blood Pressure Program (FBPP). Hypertension 2002; 39(1): 3-9. PMID: 11799070. Kardia SL, Greene MT, Boerwinkle E, Turner ST, Kullo IJ. Investigating the complex genetic architecture of ankle- brachial index, a measure of peripheral arterial disease, in non-Hispanic whites. BMC Med Genomics 2008; 1:16. PMID: 18482449. Khawaja FJ, Bailey KR, Turner ST, Kardia SL, Mosley TH Jr, Kullo IJ. Association of novel risk factors with the ankle brachial index in African American and non-Hispanic white populations. Mayo Clin Proc. 2007; 82(6): 709-16. PMID: 17550751. Knopman DS, Mosley TH Jr, Bailey KR, Jack CR Jr, Schwartz GL, Turner ST. Associations of microalbuminuria with brain atrophy and white matter hyperintensities in hypertensive sibships. J Neurol Sci 2008; 271(1-2): 53-60. PMID: 18442832. Lange LA, Lange EM, Bielak LF, Langefeld CD, Kardia SL, Royston P, Turner ST, Sheedy PF 2nd, Boerwinkle E, Peyser PA. Autosomal genome-wide scan for coronary artery calcification loci in sibships at high risk for hypertension. Arterioscler Thromb Vasc Biol 2002; 22(3): 418-23. PMID: 11884284. Meyers KJ, Mosley TH, Fox E, Boerwinkle E, Arnett DK, Devereux RB, Kardia SL. Genetic variations associated with echocardiographic left ventricular traits in hypertensive blacks. Hypertension 2007; 49(5): 992-9. PMID: 17339538. Rule AD, de Andrade M, Matsumoto M, Mosley TH, Kardia S, Turner ST. Association between SLC2A9 transporter gene variants and uric acid phenotypes in African American and white families. Rheumatology (Oxford) 2010; Dec 24. [Epub ahead of print] PMID: 21186168. Smith JA, Turner ST, Sun YV, Fornage M, Kelly RJ, Mosley TH, Jack CR, Kullo IJ, Kardia SL. Complexity in the genetic architecture of leukoaraiosis in hypertensive sibships from the GENOA Study. BMC Med Genomics 2009; 2:16. PMID: 19351393. Turner ST, Kardia SL, Mosley TH, Rule AD, Boerwinkle E, de Andrade M. Influence of genomic loci on measures of chronic kidney disease in hypertensive sibships. J Am Soc Nephrol 2006; 17(7): 2048-55. PMID: 16775034. Turner ST, Fornage M, Jack CR Jr, Mosley TH, Knopman DS, Kardia SL, Boerwinkle E, de Andrade M. Genomic susceptibility Loci for brain atrophy, ventricular volume, and leukoaraiosis in hypertensive sibships. Arch Neurol 2009; 66(7): 847-57. PMID: 19597086. Outcome Measures Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Pulse Pressure (PP) Blood pressure measures were obtained at the Phase I examination. Examinations were conducted in the morning after an overnight fast of at least eight hours. Using random zero sphygmomanometers and cuffs appropriate for arm size, three readings of blood pressure were taken in the right arm after the participant rested in the sitting position for at least five minutes; the last two readings were averaged for the analyses. Pulse pressure (PP) was calculated as the difference between the average systolic and diastolic blood pressures. Body Mass Index (BMI) Body mass index (BMI) was calculated using height and weight measures obtained at the Phase I examination. Height was measured by stadiometer, weight by electronic balance, and body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Ankle-Brachial Index (ABI) Ankle-brachial index (ABI) was measured during the Phase II examination. Measurements were taken while participants were in the supine position following a 5-min rest. Appropriately sized BP cuffs were placed on each arm and ankle, and a Doppler ultrasonic instrument (Medisonics, Minneapolis MN) was used to detect each pulse. The cuff was inflated to 10 mm Hg above SBP and deflated at 2 mm Hg/s. The first reappearance of the pulse was taken as the SBP. To calculate ABI, the SBP at each ankle site (posterior tibial and dorsalis pedis) was divided by the higher of the 2 brachial pressures. The lowest
Load more

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]
  • 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.
    [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]
  • 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]