Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Rakitsch, Barbara; Stegle, Oliver
• Is Part Of: Genome biology. Volume 17:Issue 1(2016)
• Page Start: 1
• Page End: 13
• Publication Date: 2016-12
• Subjects: eQTL mapping -- Causal reasoning -- Genome-wide association studies -- Confounder correction -- Competing exposures; Genomes -- Periodicals; Biology -- Periodicals; Molecular biology -- Periodicals; 572.8633
• DOI: 10.1186/s13059-016-0895-2
• Languages: English
• ISSNs: 1474-760X

Abstract

Abstract Expression quantitative trait loci (eQTL) mapping is a widely used tool to study the genetics of gene expression. Confounding factors and the burden of multiple testing limit the ability to map distaltrans eQTLs, which is important to understand downstream genetic effects on genes and pathways. We propose a two-stage linear mixed model that first learns local directed gene-regulatory networks to then condition on the expression levels of selected genes. We show that this covariate selection approach controls for confounding factors and regulatory context, thereby increasing eQTL detection power and improving the consistency between studies. GNet-LMM is available at:https://github.com/PMBio/GNetLMM .