Genetic Variants Associated with Complex Human Diseases Show Wide Variation across Multiple Populations
Genetic Variants Associated with Complex Human Diseases Show Wide Variation across Multiple Populations
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DOI：
10.1159/000218711
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发表时间：
2010-01-01
影响因子：
1.7
通讯作者：
Rotimi, C.
中科院分区：
文献类型：
--
作者：
Adeyemo, A.;Rotimi, C.
背景资料：广泛使用的全基因组关联研究（GWAS）已导致成功地识别多种复杂的人类疾病的遗传易感性变异。鉴于非欧洲血统人群中这些基因座遗传变异的数据有限，我们调查了11个人群组中的基因座变异，这些基因座显示出GWAS与几种复杂的人类疾病的强烈和一致的关联。研究方法：来自国际人类基因组单体型图项目第3阶段的数据，包括11个人群，被用来估计基因座的等位基因频率，这些基因座显示出GWAS与26种复杂人类疾病和性状中的任何一种的强烈和一致的关联。等位基因频率汇总统计和F（ST）在每个位点被用来估计群体分化。结果如下：这些复杂的人类疾病和性状的易感基因座在11个人群中的等位基因频率和F（ST）存在很大差异。等位基因频率在人群中差异很大，通常高达20至40倍。F（ST）作为群体分化的指标，在研究的基因座之间也有很大差异（例如，2型糖尿病为0.019至0.201，前列腺癌为0.022至0.520，血脂水平为0.006至0.520）。结论：这些风险等位基因中的任何一个所造成的公共卫生风险都可能在人群中表现出广泛的差异，这仅仅是其频率的函数，并且这种风险差异可能会被基因-基因和基因-环境相互作用放大。这些分析提供了令人信服的理由，包括来自世界不同地区的多个人群，在国际努力使用基因组工具，以了解疾病的病因和不同种族群体之间的疾病分布差异。版权所有（C）2009 S. Karger AG，巴塞尔
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