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Reference: Pasaniuc, B, Zaitlen, N, Lettre, G et al., (2011). Enhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium. PLoS genetics, 7 (4), e1001371.Citable link to this page:

 

Enhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium.

Abstract: While genome-wide association studies (GWAS) have primarily examined populations of European ancestry, more recent studies often involve additional populations, including admixed populations such as African Americans and Latinos. In admixed populations, linkage disequilibrium (LD) exists both at a fine scale in ancestral populations and at a coarse scale (admixture-LD) due to chromosomal segments of distinct ancestry. Disease association statistics in admixed populations have previously considered SNP association (LD mapping) or admixture association (mapping by admixture-LD), but not both. Here, we introduce a new statistical framework for combining SNP and admixture association in case-control studies, as well as methods for local ancestry-aware imputation. We illustrate the gain in statistical power achieved by these methods by analyzing data of 6,209 unrelated African Americans from the CARe project genotyped on the Affymetrix 6.0 chip, in conjunction with both simulated and real phenotypes, as well as by analyzing the FGFR2 locus using breast cancer GWAS data from 5,761 African-American women. We show that, at typed SNPs, our method yields an 8% increase in statistical power for finding disease risk loci compared to the power achieved by standard methods in case-control studies. At imputed SNPs, we observe an 11% increase in statistical power for mapping disease loci when our local ancestry-aware imputation framework and the new scoring statistic are jointly employed. Finally, we show that our method increases statistical power in regions harboring the causal SNP in the case when the causal SNP is untyped and cannot be imputed. Our methods and our publicly available software are broadly applicable to GWAS in admixed populations.

Peer Review status:Peer reviewedPublication status:PublishedVersion:Publisher's version Funder: National Institutes of Health   Funder: National Heart, Lung, and Blood Institute   Funder: Norris Foundation   Funder: Broad Institute   Funder: National Institute for Child Health and Development   Funder: U.S. Army Medical Research and Material Command   Funder: Breast Cancer Research Foundation   Funder: United States Army Medical Research Program   Funder: Center for Environmental Health and Susceptibility   Funder: National Institute of Environmental Health Sciences   Funder: National Cancer Institute   Funder: Breast Cancer Family Registry   Notes: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.

Bibliographic Details

Publisher: Public Library of Science

Publisher Website: http://www.plos.org

Journal: PLoS geneticssee more from them

Publication Website: http://www.plosgenetics.org

Issue Date: 2011-4-21

pages:Article: e1001371

pages:e1001371Identifiers

Urn: uuid:f5f68b9b-a43a-471a-bad4-28407dc8d227

Source identifier: 138819

Eissn: 1553-7404

Doi: https://doi.org/10.1371/journal.pgen.1001371

Issn: 1553-7390 Item Description

Type: Journal article;

Language: eng

Version: Publisher's versionKeywords: Humans Breast Neoplasms Coronary Disease Diabetes Mellitus, Type 2 Odds Ratio Chromosome Mapping Gene Frequency Genotype Linkage Disequilibrium Phenotype Genetics, Population Software Genome, Human African Americans Genetic Variation Genome-Wide Association Study Algorithms Principal Component Analysis Polymorphism, Single Nucleotide Female Male Receptor, Fibroblast Growth Factor, Type 2 Tiny URL: pubs:138819

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Author: Pasaniuc, B - - - Zaitlen, N - - - Lettre, G - - - Chen, GK - - - Tandon, A - - - Kao, WH - - - Ruczinski, I - - - Fornage, M - -

Source: https://ora.ox.ac.uk/objects/uuid:f5f68b9b-a43a-471a-bad4-28407dc8d227



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