Adjustment for covariates using summary statistics of genome-wide association studies

Tao Wang, Xiaonan Xue, Xianhong Xie, Kenny Ye, Xiaofeng Zhu, Robert C. Elston

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)812-825
Number of pages14
JournalGenetic Epidemiology
Issue number8
StatePublished - Dec 2018


  • continuous traits
  • genome-wide association studies (GWAS)
  • linear regression, pleiotropy
  • meta-analysis

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)


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