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Genetic Determinants of Lipid Traits in Diverse Populations from the Population Architecture using Genomics and Epidemiology (PAGE) Study

1 , 2 , 3 , 4 , 5 , 6 , 2 , 7 , 1 , 8 , 2 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 3 , 2 , 16 , 17 , 10 , 18 , 19 , 10 , 5 , 1 , 20 , 21 , 18 , 4 , 18 , 22 , 2 , 3 , 23 , 1 , 24 , *

PLoS Genetics

Public Library of Science

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      Abstract

      For the past five years, genome-wide association studies (GWAS) have identified hundreds of common variants associated with human diseases and traits, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) levels. Approximately 95 loci associated with lipid levels have been identified primarily among populations of European ancestry. The Population Architecture using Genomics and Epidemiology (PAGE) study was established in 2008 to characterize GWAS–identified variants in diverse population-based studies. We genotyped 49 GWAS–identified SNPs associated with one or more lipid traits in at least two PAGE studies and across six racial/ethnic groups. We performed a meta-analysis testing for SNP associations with fasting HDL-C, LDL-C, and ln(TG) levels in self-identified European American (∼20,000), African American (∼9,000), American Indian (∼6,000), Mexican American/Hispanic (∼2,500), Japanese/East Asian (∼690), and Pacific Islander/Native Hawaiian (∼175) adults, regardless of lipid-lowering medication use. We replicated 55 of 60 (92%) SNP associations tested in European Americans at p<0.05. Despite sufficient power, we were unable to replicate ABCA1 rs4149268 and rs1883025, CETP rs1864163, and TTC39B rs471364 previously associated with HDL-C and MAFB rs6102059 previously associated with LDL-C. Based on significance (p<0.05) and consistent direction of effect, a majority of replicated genotype-phentoype associations for HDL-C, LDL-C, and ln(TG) in European Americans generalized to African Americans (48%, 61%, and 57%), American Indians (45%, 64%, and 77%), and Mexican Americans/Hispanics (57%, 56%, and 86%). Overall, 16 associations generalized across all three populations. For the associations that did not generalize, differences in effect sizes, allele frequencies, and linkage disequilibrium offer clues to the next generation of association studies for these traits.

      Author Summary

      Low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels are well known independent risk factors for cardiovascular disease. Lipid-associated genetic variants are being discovered in genome-wide association studies (GWAS) in samples of European descent, but an insufficient amount of data exist in other populations. Therefore, there is a strong need to characterize the effect of these GWAS–identified variants in more diverse cohorts. In this study, we selected over forty genetic loci previously associated with lipid levels and tested for replication in a large European American cohort. We also investigated if the effect of these variants generalizes to non-European descent populations, including African Americans, American Indians, and Mexican Americans/Hispanics. A majority of these GWAS–identified associations replicated in our European American cohort. However, the ability of associations to generalize across other racial/ethnic populations varied greatly, indicating that some of these GWAS–identified variants may not be functional and are more likely to be in linkage disequilibrium with the functional variant(s).

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      Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge.

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        Potential etiologic and functional implications of genome-wide association loci for human diseases and traits.

        We have developed an online catalog of SNP-trait associations from published genome-wide association studies for use in investigating genomic characteristics of trait/disease-associated SNPs (TASs). Reported TASs were common [median risk allele frequency 36%, interquartile range (IQR) 21%-53%] and were associated with modest effect sizes [median odds ratio (OR) 1.33, IQR 1.20-1.61]. Among 20 genomic annotation sets, reported TASs were significantly overrepresented only in nonsynonymous sites [OR = 3.9 (2.2-7.0), p = 3.5 x 10(-7)] and 5kb-promoter regions [OR = 2.3 (1.5-3.6), p = 3 x 10(-4)] compared to SNPs randomly selected from genotyping arrays. Although 88% of TASs were intronic (45%) or intergenic (43%), TASs were not overrepresented in introns and were significantly depleted in intergenic regions [OR = 0.44 (0.34-0.58), p = 2.0 x 10(-9)]. Only slightly more TASs than expected by chance were predicted to be in regions under positive selection [OR = 1.3 (0.8-2.1), p = 0.2]. This new online resource, together with bioinformatic predictions of the underlying functionality at trait/disease-associated loci, is well-suited to guide future investigations of the role of common variants in complex disease etiology.
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          METAL: fast and efficient meta-analysis of genomewide association scans

          Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/ Contact: goncalo@umich.edu
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            Author and article information

            Affiliations
            [1 ]Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, United States of America
            [2 ]Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
            [3 ]Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
            [4 ]Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
            [5 ]Office of Population Genomics, National Human Genome Research Institute, Bethesda, Maryland, United States of America
            [6 ]Information Sciences Institute, University of Southern California, Los Angeles, California, United States of America
            [7 ]Missouri Breaks Industries Research, Timber Lake, South Dakota, United States of America
            [8 ]Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
            [9 ]Sponsored Programs, Baylor College of Medicine, Houston, Texas, United States of America
            [10 ]Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas, United States of America
            [11 ]Department of Medicine, Weill Cornell Medical College, New York, New York, United States of America
            [12 ]The Translational Genomics Research Institute, Phoenix, Arizona, United States of America
            [13 ]Department of Family Medicine and Community Health, Alpert Medical School of Brown University School of Medicine, Providence, Rhode Island, United States of America
            [14 ]Institute of Molecular Medicine, University of Texas Health Sciences Center at Houston, Texas, United States of America
            [15 ]Division of Epidemiology, School of Public Health, University of Texas Health Sciences Center, Houston, Texas, United States of America
            [16 ]Medstar Research Institute, Washington, D.C., United States of America
            [17 ]Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
            [18 ]Epidemiology Program, University of Hawaii Cancer Center, Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, United States of America
            [19 ]University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States of America
            [20 ]School of Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
            [21 ]Center of Cardiovascular Research, Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, United States of America
            [22 ]Department of Statistics and Biostatistics, Rutgers University, Piscataway, New Jersey, United States of America
            [23 ]Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, United States of America
            [24 ]Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
            Georgia Institute of Technology, United States of America
            Author notes

            Conceived and designed the experiments: LD CLC KT FRS LAH PB CSC SAC CBE MF NF TAM SAP MQ SB CK KEN DCC. Performed the experiments: DD BC. Analyzed the data: LD CLC KT FRS KB-G PB MF NF SAP MQ SB CK KEN DCC. Contributed reagents/materials/analysis tools: JLA GA LGB BC SAC RBD CBE JH KCJ SL LNK ETL JM SAP RVS LRW CAH LLM BVH. Wrote the paper: LD DCC.

            Contributors
            Role: Editor
            Journal
            PLoS Genet
            plos
            plosgen
            PLoS Genetics
            Public Library of Science (San Francisco, USA )
            1553-7390
            1553-7404
            June 2011
            June 2011
            30 June 2011
            : 7
            : 6
            3128106
            21738485
            PGENETICS-D-11-00002
            10.1371/journal.pgen.1002138
            (Editor)
            This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
            Counts
            Pages: 15
            Categories
            Research Article
            Biology
            Genetics
            Human Genetics
            Genetic Association Studies
            Genome-Wide Association Studies
            Population Genetics
            Genetic Polymorphism

            Genetics

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