Joint eQTL mapping and inference of gene regulatory network improves power of detecting both cis- and trans-eQTLs.

Abstract

Genetic variations of expression quantitative trait loci (eQTLs) play a critical role in influencing complex traits and diseases development. Two main factors that affect the statistical power of detecting eQTLs are: (i) relatively small size of samples available, and (ii) heavy burden of multiple testing due to a very large number of variants to be tested. The later issue is particularly severe when one tries to identify trans-eQTLs that are far away from the genes they influence. If one can exploit co-expressed genes jointly in eQTL-mapping, effective sample size can be increased. Furthermore, using the structure of the gene regulatory network (GRN) may help to identify trans-eQTLs without increasing multiple testing burden. In this article, we use the structure equation model (SEM) to model both GRN and effect of eQTLs on gene expression, and then develop a novel algorithm, named sparse SEM for eQTL mapping (SSEMQ), to conduct joint eQTL mapping and GRN inference. The SEM can exploit co-expressed genes jointly in eQTL mapping and also use GRN to determine trans-eQTLs. Computer simulations demonstrate that our SSEMQ significantly outperforms nine existing eQTL mapping methods. SSEMQ is further used to analyze two real datasets of human breast and whole blood tissues, yielding a number of cis- and trans-eQTLs. R package ssemQr is available at https://github.com/Ivis4ml/ssemQr.git. Supplementary data are available at Bioinformatics online.