BTOB: Extending the Biased GWAS to Bivariate GWAS

Junxian Zhu; Qiao Fan; Wenying Deng; Yimeng Wang; Xiaobo Guo

doi:10.3389/fgene.2021.654821

BTOB: Extending the Biased GWAS to Bivariate GWAS

Front Genet. 2021 May 6:12:654821. doi: 10.3389/fgene.2021.654821. eCollection 2021.

Authors

Junxian Zhu¹, Qiao Fan², Wenying Deng³, Yimeng Wang¹, Xiaobo Guo¹

Affiliations

¹ Department of Statistical Science, School of Mathematics, Sun Yat-sen University, Guangzhou, China.
² Center for Quantitative Medicine, Duke-National University of Singapore Medical School, Singapore, Singapore.
³ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States.

Abstract

In recent years, a number of literatures published large-scale genome-wide association studies (GWASs) for human diseases or traits while adjusting for other heritable covariate. However, it is known that these GWASs are biased, which may lead to biased genetic estimates or even false positives. In this study, we provide a method called "BTOB" which extends the biased GWAS to bivariate GWAS by integrating the summary association statistics from the biased GWAS and the GWAS for the adjusted heritable covariate. We employ the proposed BTOB method to analyze the summary association statistics from the large scale meta-GWASs for waist-to-hip ratio (WHR) and body mass index (BMI), and show that the proposed approach can help identify more susceptible genes compared with the corresponding univariate GWASs. Theoretical results and simulations also confirm the validity and efficiency of the proposed BTOB method.

Keywords: GWAS; biased; bivariate GWAS; heritable covariate; summary association statistics.