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A panel of ancestry informative markers to estimate and correct potential effects of population stratification in Han
Pengfei Qin1, Zhiqiang Li2, Wenfei Jin1
11] Max Planck Independent Research Group on Population Genomics, Chinese Academy of Sciences and Max Planck Society Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Shanghai, China [2] Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences and Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
Abstract:
Population stratification acts as a confounding factor in genetic association studies and may lead to false-positive or false-negative results. Previous studies have analyzed the genetic substructures in Han Chinese population, the largest ethnic group in the world comprising ∼20% of the global human population. In this study, we examined 5540 Han Chinese individuals with about 1 million single-nucleotide polymorphisms (SNPs) and screened a panel of ancestry informative markers (AIMs) to facilitate the discerning and controlling of population structure in future association studies on Han Chinese. Based on genome-wide data, we first confirmed our previous observation of the north-south differentiation in Han Chinese population. Second, we developed a panel of 150 validated SNP AIMs to determine the northern or southern origin of each Han Chinese individual. We further evaluated the performance of our AIMs panel in association studies in simulation analysis. Our results showed that this AIMs panel had sufficient power to discern and control population stratification in Han Chinese, which could significantly reduce false-positive rates in both genome-wide association studies (GWAS) and candidate gene association studies (CGAS). We suggest this AIMs panel be genotyped and used to control and correct population stratification in the study design or data analysis of future association studies, especially in CGAS which is the most popular approach to validate previous reports on genetic associations of diseases in post-GWAS era.
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