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• W2555602226 abstract "Recently, there is increasing interest to detect associations between rare variants and complex traits. Rare variant association studies usually need large sample sizes due to the rarity of the variants, and large sample sizes typically require combining information from different geographic locations within and across countries. Although several statistical methods have been developed to control for population stratification in common variant association studies, these methods are not necessarily controlling for population stratification in rare variant association studies. Thus, new statistical methods that can control for population stratification in rare variant association studies are needed. In this article, we propose a principal component based nonparametric regression (PC-nonp) approach to control for population stratification in rare variant association studies. Our simulations show that the proposed PC-nonp can control for population stratification well in all scenarios, while existing methods cannot control for population stratification at least in some scenarios. Simulations also show that PC-nonp's robustness to population stratification will not reduce power. Furthermore, we illustrate our proposed method by using whole genome sequencing data from genetic analysis workshop 18 (GAW18)." @default.
• W2555602226 created "2016-11-30" @default.
• W2555602226 creator A5056056728 @default.
• W2555602226 creator A5061115409 @default.
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• W2555602226 date "2016-11-18" @default.
• W2555602226 modified "2023-09-25" @default.
• W2555602226 title "A Nonparametric Regression Approach to Control for Population Stratification in Rare Variant Association Studies" @default.
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• W2555602226 doi "https://doi.org/10.1038/srep37444" @default.
• W2555602226 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5114546" @default.
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• W2555602226 hasPublicationYear "2016" @default.
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