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      Concept, Design and Implementation of a Cardiovascular Gene-Centric 50 K SNP Array for Large-Scale Genomic Association Studies

      1 , 2 , 3 , 4 , 5 , 6 , 7 , 10 , 8 , 7 , 2 , 9 , 10 , 9 , 11 , 12 , 9 , 9 , 13 , 2 , 32 , 36 , 9 , 14 , 7 , 15 , 16 , 7 , 17 , 18 , 19 , 20 , 7 , 2 , 2 , 21 , 7 , 11 , 23 , 33 , 22 , 24 , 24 , 15 , 25 , 21 , 2 , 1 , 9 , 26 , 35 , 15 , 27 , 28 , 29 , 8 , 34 , 2 , 30 , 2 , 7 , 31 , 1 , 9 , 32 , 5 , 1 , 9 , 2 , 3 , 21 , 1 , *

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a “cosmopolitan” tagging approach to capture the genetic diversity across ∼2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.

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          Most cited references 27

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          Genomewide association analysis of coronary artery disease.

          Modern genotyping platforms permit a systematic search for inherited components of complex diseases. We performed a joint analysis of two genomewide association studies of coronary artery disease. We first identified chromosomal loci that were strongly associated with coronary artery disease in the Wellcome Trust Case Control Consortium (WTCCC) study (which involved 1926 case subjects with coronary artery disease and 2938 controls) and looked for replication in the German MI [Myocardial Infarction] Family Study (which involved 875 case subjects with myocardial infarction and 1644 controls). Data on other single-nucleotide polymorphisms (SNPs) that were significantly associated with coronary artery disease in either study (P 80%) of a true association: chromosomes 1p13.3 (rs599839), 1q41 (rs17465637), 10q11.21 (rs501120), and 15q22.33 (rs17228212). We identified several genetic loci that, individually and in aggregate, substantially affect the risk of development of coronary artery disease. Copyright 2007 Massachusetts Medical Society.
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            Newly identified loci that influence lipid concentrations and risk of coronary artery disease.

            To identify genetic variants influencing plasma lipid concentrations, we first used genotype imputation and meta-analysis to combine three genome-wide scans totaling 8,816 individuals and comprising 6,068 individuals specific to our study (1,874 individuals from the FUSION study of type 2 diabetes and 4,184 individuals from the SardiNIA study of aging-associated variables) and 2,758 individuals from the Diabetes Genetics Initiative, reported in a companion study in this issue. We subsequently examined promising signals in 11,569 additional individuals. Overall, we identify strongly associated variants in eleven loci previously implicated in lipid metabolism (ABCA1, the APOA5-APOA4-APOC3-APOA1 and APOE-APOC clusters, APOB, CETP, GCKR, LDLR, LPL, LIPC, LIPG and PCSK9) and also in several newly identified loci (near MVK-MMAB and GALNT2, with variants primarily associated with high-density lipoprotein (HDL) cholesterol; near SORT1, with variants primarily associated with low-density lipoprotein (LDL) cholesterol; near TRIB1, MLXIPL and ANGPTL3, with variants primarily associated with triglycerides; and a locus encompassing several genes near NCAN, with variants strongly associated with both triglycerides and LDL cholesterol). Notably, the 11 independent variants associated with increased LDL cholesterol concentrations in our study also showed increased frequency in a sample of coronary artery disease cases versus controls.
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              Efficiency and power in genetic association studies.

              We investigated selection and analysis of tag SNPs for genome-wide association studies by specifically examining the relationship between investment in genotyping and statistical power. Do pairwise or multimarker methods maximize efficiency and power? To what extent is power compromised when tags are selected from an incomplete resource such as HapMap? We addressed these questions using genotype data from the HapMap ENCODE project, association studies simulated under a realistic disease model, and empirical correction for multiple hypothesis testing. We demonstrate a haplotype-based tagging method that uniformly outperforms single-marker tests and methods for prioritization that markedly increase tagging efficiency. Examining all observed haplotypes for association, rather than just those that are proxies for known SNPs, increases power to detect rare causal alleles, at the cost of reduced power to detect common causal alleles. Power is robust to the completeness of the reference panel from which tags are selected. These findings have implications for prioritizing tag SNPs and interpreting association studies.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2008
                31 October 2008
                : 3
                : 10
                Affiliations
                [1 ]The Institute for Translational Medicine and Therapeutics, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvannia, United States of America
                [2 ]Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
                [3 ]Divisions of Genetics, Endocrinology & Program in Genomics, Children's Hospital, Boston, Massachusetts, United States of America
                [4 ]Scripps Genomic Medicine, La Jolla, California, United States of America
                [5 ]Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
                [6 ]MRC SGDP Centre, Institute of Psychiatry, London, United Kingdom
                [7 ]The Center for Applied Genomics, Division of Human Genetics, Department of Pediatrics, The Childrens' Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
                [8 ]The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
                [9 ]The Cardiovascular Institute, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
                [10 ]Illumina Incorporated, San Diego, California, United States of America
                [11 ]Division of Prevention and Population Sciences, National Heart, Lung, and Blood Institute, Bethesda, Maryland, United States of America
                [12 ]Beijing Genomics Institute at Shenzhen, Shenzhen, China
                [13 ]Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
                [14 ]Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
                [15 ]Department of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
                [16 ]Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
                [17 ]Department of Clinical Science Malmoe, Diabetes and Endocrinology, Lund University, Sweden and Department of Medicine, Helsinki University Hospital, Helsinki, Finland
                [18 ]Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
                [19 ]Leeds Institute of Genetics, Health & Therapeutics, University of Leeds, Leeds, United Kingdom
                [20 ]Diabetes Genetics Group, Institute of Biomedical and Clinical Science, Peninsula Medical School, Exeter, United Kingdom
                [21 ]Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
                [22 ]Department of Clinical Pharmacology, Barts and The London, Charterhouse Square, London, United Kingdom
                [23 ]Department of Haematology and NHS Blood and Transplant, University of Cambridge, Cambridge, United Kingdom
                [24 ]William Harvey Research Institute, Barts and The London, Charterhouse Square, London, United Kingdom
                [25 ]Human Genetics Center, University of Texas Health Science Center, Houston, Texas, United States of America
                [26 ]Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
                [27 ]Rosetta Inpharmatics, LLC, Merck & Co. Inc., Seattle, Washington, United States of America
                [28 ]V.A. Medical Center and the University of Mississippi Medical Center, Jackson, Mississippi, United States of America
                [29 ]Department of Internal Medicine II - Cardiology, University of Ulm Medical Center, Ulm, Germany
                [30 ]Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
                [31 ]Department of Medicine and Clinical Epidemiology and Biostatistics, Population Genomics Program, McMaster University, Hamilton Health Sciences, Hamilton General Hospital, Hamilton, Ontario, Canada
                [32 ]Departments of Medicine and Human Genetics, McGill University, Montréal, Québec, Canada
                [33 ]Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
                [34 ]Oxford Center for Diabetes, Endocrinology and Metabolism and Oxford NIHR Biomedical Research Center, Churchill Hospital, University of Oxford, Oxford, United Kingdom
                [35 ]Department of Microbiology, Immunology and Molecular Genetics, University of California Los Angeles School of Medicine, Los Angeles, California, United States of America
                [36 ]Genome Québec Innovation Centre, Montréal , Québec, Canada
                Leiden University Medical Center, Netherlands
                Author notes

                Conceived and designed the experiments: BJK ST SSM TB TSP LG JCB DNF GJP PdB MF CWKC RRF EB JGW MIM SG MR JCE DAN JH. Performed the experiments: BJK MH SDB AM EF CEK WK HH SSA JCE. Analyzed the data: BJK ST SSM TB JTG LG JCB SFAG HC MH WK HH JCE DAN JH. Contributed reagents/materials/analysis tools: BJK ST SSM TB LG HC MH GJP SA YG ML SD PdB SDB AM ACE KDT XG SSW TS LCG MB ASH AH NP CWKC WHO ALP PM MC TAD DR ASW TC NJS AJL EES WK MIM SK HH SSA MR JCE DAN DJR JH. Wrote the paper: BJK ST TSP SFAG DNF GJP YG EB JGW MIM HH JCE DAN DJR JH GAF.

                Article
                08-PONE-RA-05100R1
                10.1371/journal.pone.0003583
                2571995
                18974833
                This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
                Page count
                Pages: 9
                Categories
                Research Article
                Biotechnology
                Cardiovascular Disorders
                Genetics and Genomics

                Uncategorized

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