title: Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression.
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 distal trans 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.
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. rakitsch@ebi.ac.uk.
authors.forename: Barbara
authors.initials: B
authors.lastname: Rakitsch
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. stegle@ebi.ac.uk.
authors.forename: Oliver
authors.initials: O
authors.lastname: Stegle
mesh_terms: D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D008962:Models, Theoretical; D040641:Quantitative Trait Loci / Q000235:genetics*