BTOB: Extending the Biased GWAS to Bivariate GWAS.

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

doi:10.3389/fgene.2021.654821

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.

Highlights

Genome-wide association studies (GWASs) have been greatly successful in identifying tens of thousands susceptible genes for complex diseases or traits, revealing the genetic architectures of complex diseases or traits in question (Visscher et al, 2012, 2017)
We develop a simple integration method called BTOB which extends the Biased GWAS TO Bivariate GWAS
There are several concerns that should be noted about multivariate approaches in GWAS

Summary

Introduction

Genome-wide association studies (GWASs) have been greatly successful in identifying tens of thousands susceptible genes for complex diseases or traits, revealing the genetic architectures of complex diseases or traits in question (Visscher et al, 2012, 2017). These large scale studies produce extremely valuable resource for further studies. Recent efforts have indicated that the multivariate GWAS can be conducted

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Conclusion