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Adjustment for covariates using summary statistics of genome‐wide association studies
Author(s) -
Wang Tao,
Xue Xiaonan,
Xie Xianhong,
Ye Kenny,
Zhu Xiaofeng,
Elston Robert C.
Publication year - 2018
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.22148
Subject(s) - covariate , statistics , genome wide association study , statistical power , genetic association , linear regression , regression analysis , linear model , regression , residual , standard error , type i and type ii errors , summary statistics , econometrics , computer science , mathematics , biology , genetics , single nucleotide polymorphism , genotype , algorithm , gene
Linear regression is a standard approach to identify genetic variants associated with continuous traits in genome‐wide association studies (GWAS). In a standard epidemiology study, linear regression is often performed with adjustment for covariates to estimate the independent effect of a predictor variable or to improve statistical power by reducing residual variability. However, it is problematic to adjust for heritable covariates in genetic association analysis. Here, we propose a new method that utilizes summary statistics of the covariate from additional samples for reducing the residual variability and hence improves statistical power. Our simulation study showed that the proposed methodology can maintain a good control of Type I error and can achieve much higher power than a simple linear regression. The method is illustrated by an application to the GWAS results from the Genetic Investigation of Anthropometric Traits consortium.