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PLoS Biol. 2013 Sep;11(9):e1001661. doi: 10.1371/journal.pbio.1001661. Epub 2013 Sep 17.

Generalization and dilution of association results from European GWAS in populations of non-European ancestry: the PAGE study.

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1
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.

Abstract

The vast majority of genome-wide association study (GWAS) findings reported to date are from populations with European Ancestry (EA), and it is not yet clear how broadly the genetic associations described will generalize to populations of diverse ancestry. The Population Architecture Using Genomics and Epidemiology (PAGE) study is a consortium of multi-ancestry, population-based studies formed with the objective of refining our understanding of the genetic architecture of common traits emerging from GWAS. In the present analysis of five common diseases and traits, including body mass index, type 2 diabetes, and lipid levels, we compare direction and magnitude of effects for GWAS-identified variants in multiple non-EA populations against EA findings. We demonstrate that, in all populations analyzed, a significant majority of GWAS-identified variants have allelic associations in the same direction as in EA, with none showing a statistically significant effect in the opposite direction, after adjustment for multiple testing. However, 25% of tagSNPs identified in EA GWAS have significantly different effect sizes in at least one non-EA population, and these differential effects were most frequent in African Americans where all differential effects were diluted toward the null. We demonstrate that differential LD between tagSNPs and functional variants within populations contributes significantly to dilute effect sizes in this population. Although most variants identified from GWAS in EA populations generalize to all non-EA populations assessed, genetic models derived from GWAS findings in EA may generate spurious results in non-EA populations due to differential effect sizes. Regardless of the origin of the differential effects, caution should be exercised in applying any genetic risk prediction model based on tagSNPs outside of the ancestry group in which it was derived. Models based directly on functional variation may generalize more robustly, but the identification of functional variants remains challenging.

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PMID:
24068893
PMCID:
PMC3775722
DOI:
10.1371/journal.pbio.1001661
[Indexed for MEDLINE]
Free PMC Article

Conflict of interest statement

The authors have declared that no competing interests exist. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the NIH, the Centers for Disease Control, the Indian Health Service, or any other funding agency.

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