Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression

By Barbara Rakitsch, Oliver Stegle. 2016

26 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

Citations in the corpus

• Deep learning for computational biology (2016, neutral): “Better statistical methods have helped to increase the power to detect regulatory QTL (Kang et al , 2008; Stegle et al , 2010; Parts et al , 2011; Rakitsch & Stegle, 2016); however, any mapping approach is intrinsically limited to variation that is present in the training population.”