Identifying genetic regulatory variants that affect transcription factor activity

Area of Science:

• Genomics and Bioinformatics
• Systems Biology
• Human Genetics

Background:

• The Genotype-Tissue Expression (GTEx) project has mapped genetic variants influencing human gene expression.
• Identifying trans-acting variants that affect multiple genes via shared transcription factors (TFs) is crucial for understanding gene regulation.

Purpose of the Study:

• To develop a generalized linear model (GLM) for estimating individual-specific, protein-level TF activity from GTEx RNA sequencing (RNA-seq) data.
• To identify genotype-specific TF activities and associated quantitative trait loci (aQTLs) across diverse human tissues.

Main Methods:

• A GLM was employed to infer TF activity using differential gene expression after TF perturbation as a predictor.
• Analysis of differential expression in neighboring genes controlled for confounding effects of genomic chromatin state variations.
• Genome-wide association analysis was performed on inferred TF activities to discover aQTLs.

Main Results:

• Genotype-specific activities were inferred for 55 TFs across 49 human tissues.
• Genome-wide association analysis revealed TF activity quantitative trait loci (aQTLs).
• The identified aQTLs were enriched for functional genomic features.

Conclusions:

• The developed methodology enables the estimation of individual-specific TF activity, providing a novel cellular endophenotype.
• This approach facilitates genetic association studies for cellular phenotypes using a network-based, multi-omics strategy.
• The findings underscore the potential of integrating genetic data with gene expression profiles to dissect complex biological networks.