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Joint eQTL mapping and Inference of Gene Regulatory Network Improves Power of Detecting both cis- and trans-eQTLs

By Xin Zhou, Xiaodong Cai

Posted 25 Apr 2020
bioRxiv DOI: 10.1101/2020.04.23.058735

Motivation: 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: 1) relatively small size of samples available, and 2) 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. Results: In this paper, we employ 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 eight existing eQTL mapping methods. SSEMQ is further employed to analyze a real dataset of human breast tissues, yielding a number of cis- and trans-eQTLs. Availability: R package ssemQr is available on https://github.com/Ivis4ml/ssemQr.git. ### Competing Interest Statement The authors have declared no competing interest.

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