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 10,098 TF-eQTL interactions across 2,136 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.

Genome-wide imputed differential expression enrichment analysis identifies trait-relevant tissues

“…Also, although the training datasets had their traitrelevant tissues ranked in the top 3, for the test datasets, we highlight the top 5 tissues (top 10%) of the 49 tissues. Highlighting the top 5 tissues aligns well with gene expression profiling in GTEx, which showed that approximately a third of eQTL effects were estimated to be active in all or almost all tissues, while a fifth of eQTL effects were active in five or fewer tissues (Flynn et al, 2022). Therefore, of the genes imputed from GTEx eQTL data, approximately a third may be imputed in all tissues-and thus provide minimal insight into tissue ranking/ prioritisation, while a fifth will be imputed in five or fewer tissues.…”

Detecting isoform-level allelic imbalance accounting for inferential uncertainty

“…The list of candidate regulatory SNPs can be further refined by integrating allelic signal at the epigenomic level, including allelic binding of proteins [40, 70–72], allelic accessibility [73, 74] or allelic methylation [75]. Alternatively, search for altered transcription factor binding motifs can be combined with RNA or protein abundance of potential regulators to winnow down the list of candidate causal regulatory variants [11, 76, 77]. It may also be of interest to detect in which cell types the allelic signal may be strongest or exclusively present, as has been investigated in recent methods for single cell allelic expression or accessibility datasets [78–80].…”

Human Genes Involved in the Interaction between Host and Gut Microbiome: Regulation and Pathogenic Mechanisms

“…The latter are important because the nucleotide variation is associated with the differential expression of a target gene [ 62 ]. Environmental factors can affect gene regulation through eQTLs according to their rate of variation [ 63 ]. Moreover, both animal and human studies suggest a significant association between similar microbiome and gene regulation mechanisms.…”