Gene-Based Association Tests Using GWAS Summary Statistics and Incorporating eQTL

Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases, 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 the 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 two powerful and computationally efficient gene-based association tests, Overall and Copula. These two tests aggregate information from three traditional types of gene-based association tests and also incorporate expression quantitative trait locus (eQTL) data into GWAS using GWAS summary statistics. Overall utilizes the extended Simes procedure and Copula utilizes the Gaussian copula approximation-based method. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the P values of these two methods can be calculated analytically. Simulation studies show that these two tests can control type I error rate very well and have higher power than the tests that we compared. We also apply these two methods to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that these two newly developed methods can identify more significant genes than other methods we compared with.