Transcription factor regulation of eQTL activity across individuals and tissues

Abstract

Tens of thousands of genetic variants associated with gene expression ( cis -eQTLs) have been discovered in the human population. These eQTLs are active in various tissues and contexts, but the molecular mechanisms of eQTL variability are poorly understood, hindering our understanding of genetic regulation across biological contexts. Since many eQTLs are believed to act by altering transcription factor (TF) binding affinity, we hypothesized that analyzing eQTL effect size as a function of TF level may allow discovery of mechanisms of eQTL variability. Using GTEx Consortium eQTL data from 49 tissues, we analyzed the interaction between eQTL effect size and TF level across tissues and across individuals within specific tissues and generated a list of 6,262 TF-eQTL interactions across 1,598 genes that are supported by at least two lines of evidence. These TF-eQTLs were enriched for various TF binding measures, supporting with orthogonal evidence that these eQTLs are regulated by the implicated TFs. We also found that our TF-eQTLs tend to overlap genes with gene-by-environment regulatory effects and to colocalize with GWAS loci, implying that our approach can help to elucidate mechanisms of context-specificity and trait associations. Finally, we highlight an interesting example of IKZF1 TF regulation of an APBB1IP gene eQTL that colocalizes with a GWAS signal for blood cell traits. Together, our findings provide candidate TF mechanisms for a large number of eQTLs and offer a generalizable approach for researchers to discover TF regulators of genetic variant effects in additional QTL datasets.

EMO: Predicting Non-coding Mutation-induced Up- and Down-regulation of Risk Gene Expression using Deep Learning

“…Regulatory features like transcription factors and chromatin markers play vital roles in gene expression regulation and have been reported to be effective in associating eQTLs with target genes[25, 26]. In other words, in the sequence between non-coding mutations and the TSS, regulatory feature regions will provide more information for eQTLs sign prediction compared to other regions.…”

Unbiased identification of unknown cellular and environmental factors that mediate eQTLs using principal interaction component analysis

“…For example, blood-based gene expression levels of other genes were used previously to identify context dependent effects 18 , some of which were related to type 1 interferon signaling. A recent study using the GTEx dataset also revealed context dependent eQTLs that could be attributed to transcript factor levels 19 . The limitations of these methods are that not all confounding contexts might be known or easily measurable, and that individual (gene expression) measurements might not be perfect proxies for specific contexts, and thus can be noisy: for instance, cell type quantifications can differ, depending on the used technology and gate settings whereas measured expression levels of specific genes are unlikely to perfectly reflect environmental stimuli or transcription factor activity.…”