Gene-based association tests using GWAS summary statistics and incorporating eQTL

Xuewei Cao,Qiuying Sha,Shuanglin Zhang,Xuexia Wang

doi:10.1038/s41598-022-07465-0

Abstract

Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with.

Highlights

Genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by genome-wide association studies (GWAS)
Genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with a wide range of complex human traits[1,2], there is a common limitation in which GWAS focus on only a single genetic variant with a trait at a time
In GWAS summary statistics, the Z-scores of genetic variants in a gene are assumed to asymptotically follow a multivariate normal distribution with a correlation matrix among all genetic variants in a gene under the null h ypothesis[11], where the correlation matrix can be estimated by linkage disequilibrium (LD) among the genetic variants in the g ene[12,13]

Summary

Introduction

Genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. Inspired by the advantage of OT, in this paper, we propose a more powerful and computationally efficient method, called Overall, to aggregate the information from three types of traditional gene-based association tests (BT, SKAT, SKATO) with multiple eQTL—derived weights using GWAS summary statistics.

Results

Conclusion