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      Population Structure of Hispanics in the United States: The Multi-Ethnic Study of Atherosclerosis

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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

          Using ∼60,000 SNPs selected for minimal linkage disequilibrium, we perform population structure analysis of 1,374 unrelated Hispanic individuals from the Multi-Ethnic Study of Atherosclerosis (MESA), with self-identification corresponding to Central America (n = 93), Cuba (n = 50), the Dominican Republic (n = 203), Mexico (n = 708), Puerto Rico (n = 192), and South America (n = 111). By projection of principal components (PCs) of ancestry to samples from the HapMap phase III and the Human Genome Diversity Panel (HGDP), we show the first two PCs quantify the Caucasian, African, and Native American origins, while the third and fourth PCs bring out an axis that aligns with known South-to-North geographic location of HGDP Native American samples and further separates MESA Mexican versus Central/South American samples along the same axis. Using k-means clustering computed from the first four PCs, we define four subgroups of the MESA Hispanic cohort that show close agreement with self-identification, labeling the clusters as primarily Dominican/Cuban, Mexican, Central/South American, and Puerto Rican. To demonstrate our recommendations for genetic analysis in the MESA Hispanic cohort, we present pooled and stratified association analysis of triglycerides for selected SNPs in the LPL and TRIB1 gene regions, previously reported in GWAS of triglycerides in Caucasians but as yet unconfirmed in Hispanic populations. We report statistically significant evidence for genetic association in both genes, and we further demonstrate the importance of considering population substructure and genetic heterogeneity in genetic association studies performed in the United States Hispanic population.

          Author Summary

          Using genotype data from about 60,000 distinct genetic markers, we examined population structure in 1,374 unrelated Hispanic individuals from the Multi-Ethnic Study of Atherosclerosis (MESA), with self-identification corresponding to Central America (n = 93), Cuba (n = 50), the Dominican Republic (n = 203), Mexico (n = 708), Puerto Rico (n = 192), and South America (n = 111). By comparing genetic ancestry of MESA Hispanic participants to reference samples representing worldwide diversity, we show major differences in ancestry of MESA Hispanics reflecting their Caucasian, African, and Native American origins, with finer differences corresponding to North-South geographic origins that separate MESA Mexican versus Central/South American samples. Based on our analysis, we define four subgroups of the MESA Hispanic cohort that show close agreement with the following self-identified regions of origin: Dominican/Cuban, Mexican, Central/South American, and Puerto Rican. We examine association of triglycerides with selected genetic markers, and we further demonstrate the importance of considering differences in genetic ancestry (or factors associated with genetic ancestry) when performing genetic studies of the United States Hispanic population.

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          Most cited references 12

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          Family-based association tests for genomewide association scans.

          With millions of single-nucleotide polymorphisms (SNPs) identified and characterized, genomewide association studies have begun to identify susceptibility genes for complex traits and diseases. These studies involve the characterization and analysis of very-high-resolution SNP genotype data for hundreds or thousands of individuals. We describe a computationally efficient approach to testing association between SNPs and quantitative phenotypes, which can be applied to whole-genome association scans. In addition to observed genotypes, our approach allows estimation of missing genotypes, resulting in substantial increases in power when genotyping resources are limited. We estimate missing genotypes probabilistically using the Lander-Green or Elston-Stewart algorithms and combine high-resolution SNP genotypes for a subset of individuals in each pedigree with sparser marker data for the remaining individuals. We show that power is increased whenever phenotype information for ungenotyped individuals is included in analyses and that high-density genotyping of just three carefully selected individuals in a nuclear family can recover >90% of the information available if every individual were genotyped, for a fraction of the cost and experimental effort. To aid in study design, we evaluate the power of strategies that genotype different subsets of individuals in each pedigree and make recommendations about which individuals should be genotyped at a high density. To illustrate our method, we performed genomewide association analysis for 27 gene-expression phenotypes in 3-generation families (Centre d'Etude du Polymorphisme Humain pedigrees), in which genotypes for ~860,000 SNPs in 90 grandparents and parents are complemented by genotypes for ~6,700 SNPs in a total of 168 individuals. In addition to increasing the evidence of association at 15 previously identified cis-acting associated alleles, our genotype-inference algorithm allowed us to identify associated alleles at 4 cis-acting loci that were missed when analysis was restricted to individuals with the high-density SNP data. Our genotype-inference algorithm and the proposed association tests are implemented in software that is available for free.
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            PLINK: a tool set for whole-genome association and population-based linkage analyses

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              A generalized family-based association test for dichotomous traits.

              Recent advances in genotyping technology make it possible to utilize large-scale association analysis for disease-gene mapping. Powerful and robust family-based association methods are crucial for successful gene mapping. We propose a family-based association method, the generalized disequilibrium test (GDT), in which the genotype differences of all discordant relative pairs are utilized in assessing association within a family. The improvement of the GDT over existing methods is threefold: (1) information beyond first-degree relatives is incorporated efficiently, yielding substantial gains in power in comparison to existing tests; (2) the GDT statistic is implemented via a robust technique that does not rely on large sample theory, resulting in further power gains, especially at high levels of significance; and (3) covariates and weights based on family size are incorporated. Advantages of the GDT over existing methods are demonstrated by extensive computer simulations and by application to recently published large-scale genome-wide linkage data from the Type 1 Diabetes Genetics Consortium (T1DGC). In our simulations, the GDT consistently outperforms other tests for a common disease and frequently outperforms other tests for a rare disease; the power improvement is > 13% in 6 out of 8 extended pedigree scenarios. All of the six strongest associations identified by the GDT have been reported by other studies, whereas only three or four of these associations can be identified by existing methods. For the T1D association at gene UBASH3A, the GDT resulted in a genome-wide significance (p = 4.3 x 10(-6)), much stronger than the published significance (p = 10(-4)).
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                April 2012
                April 2012
                12 April 2012
                : 8
                : 4
                Affiliations
                [1 ]Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
                [2 ]Department of Public Health Sciences, Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, Virginia, United States of America
                [3 ]Department of Medicine, Columbia University, New York, New York, United States of America
                [4 ]Department of Medicine and Department of Epidemiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America
                [5 ]Department of Medicine, Division of Nephrology, University of California San Francisco, San Francisco, California, United States of America
                [6 ]Division of General Internal Medicine, San Francisco VA Medical Center, San Francisco, California, United States of America
                [7 ]Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America
                [8 ]Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
                [9 ]Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington, United States of America
                [10 ]Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States of America
                [11 ]Department of Medicine and Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
                [12 ]Department of Epidemiology, Center for Social Epidemiology and Population Health, University of Michigan, Ann Arbor, Michigan, United States of America
                Vanderbilt University School of Medicine, United States of America
                Author notes

                Conceived and designed the experiments: AM SSR JIR JCM. Performed the experiments: KW KDT MYT MMS SSR JIR JCM. Analyzed the data: AM JD XG W-MC QW. Wrote the paper: AM WP CJR CAP KFK MOG AVD-R SSR JCM.

                Article
                PGENETICS-D-11-02017
                10.1371/journal.pgen.1002640
                3325201
                22511882
                Manichaikul et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Page count
                Pages: 14
                Categories
                Research Article
                Biology
                Genetics
                Genome-Wide Association Studies
                Human Genetics

                Genetics

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