Inferred expression regulator activities suggest genes mediating cardiometabolic genetic signals

Master regulator activity QTL protocol to implicate regulatory pathways potentially mediating GWAS signals using eQTL data

Here, we present a protocol to identify transcriptional regulators potentially mediating downstream biological effects of germline variants associated with complex traits of interest, which enables functional hypothesis generation independent of colocalizing expression quantitative trait loci (eQTLs). We describe steps for tissue-/cell-type-specific co-expression network modeling, expression regulator activity inference, and identification of representative phenotypic master regulators. Finally, we detail activity QTL and eQTL analyses. This protocol requires genotype, expression, and relevant covariables and phenotype data from existing eQTL datasets. For complete details on the use and execution of this protocol, please refer to Hoskins et al.1.

Identifying genetic regulatory variants that affect transcription factor activity

Genetic variants affecting gene expression levels in humans have been mapped in the Genotype-Tissue Expression (GTEx) project. Trans-acting variants impacting many genes simultaneously through a shared transcription factor (TF) are of particular interest. Here, we developed a generalized linear model (GLM) to estimate protein-level TF activity levels in an individual-specific manner from GTEx RNA sequencing (RNA-seq) profiles. It uses observed differential gene expression after TF perturbation as a predictor and, by analyzing differential expression within pairs of neighboring genes, controls for the confounding effect of variation in chromatin state along the genome. We inferred genotype-specific activities for 55 TFs across 49 tissues. Subsequently performing genome-wide association analysis on this virtual trait revealed TF activity quantitative trait loci (aQTLs) that, as a set, are enriched for functional features. Altogether, the set of tools we introduce here highlights the potential of genetic association studies for cellular endophenotypes based on a network-based multi-omics approach. The transparent peer review record is available.