Journal
PUBLIC HEALTH GENOMICS
Volume 13, Issue 2, Pages 72-79Publisher
KARGER
DOI: 10.1159/000218711
Keywords
Complex disease; Genome wide association studies; Population genetics
Funding
- NIH
- National Human Genome Research Institute
- NATIONAL HUMAN GENOME RESEARCH INSTITUTE [ZIAHG200362, Z01HG200362] Funding Source: NIH RePORTER
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Background: The wide use of genome wide association studies (GWAS) has led to the successful identification of multiple genetic susceptibility variants to several complex human diseases. Given the limited amount of data on genetic variation at these loci in populations of non-European origin, we investigated population variation among 11 population groups for loci showing strong and consistent association from GWAS with several complex human diseases. Methods: Data from the International HapMap Project Phase 3, comprising 11 population groups, were used to estimate allele frequencies at loci showing strong and consistent association from GWAS with any of 26 complex human diseases and traits. Allele frequency summary statistics and F(ST) at each locus were used to estimate population differentiation. Results: There is wide variation in allele frequencies and F(ST) across the 11 population groups for susceptibility loci to these complex human diseases and traits. Allele frequencies varied widely across populations, often by as much as 20- to 40-fold. F(ST), as a measure of population differentiation, also varied widely across the loci studied (for example, 0.019 to 0.201 for type 2 diabetes, 0.022 to 0.520 for prostate cancer loci, and 0.006 to 0.520 for serum lipid levels). Conclusions: The public health risk posed by any of these risk alleles is likely to show wide variation across populations simply as a function of its frequency, and this risk difference may be amplified by gene-gene and gene-environment interactions. These analyses offer compelling reasons for including multiple human populations from different parts of the world in the international effort to use genomic tools to understand disease etiology and differential distribution of diseases across ethnic groups. Copyright (C) 2009 S. Karger AG, Basel
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