Open AccessJoint eQTL mapping and inference of gene regulatory network improves power of detecting bothcis- andtrans-eQTLs
Author(s) -
Xin Zhou,
Xiaodong Cai
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab609
Subject(s) - expression quantitative trait loci , inference , computational biology , quantitative trait locus , multiple comparisons problem , computer science , gene regulatory network , biology , gene , genetics , artificial intelligence , gene expression , mathematics , statistics , single nucleotide polymorphism , genotype
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.