Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits

Abstract

Identifying causal genetic variants and understanding their mechanisms of effect on traits remains a challenge in genome-wide association studies (GWASs). In particular, how genetic variants (i.e., trans-eQTLs) affect expression of remote genes (i.e., trans-eGenes) remains unknown. We hypothesized that some trans-eQTLs regulate expression of distant genes by altering the expression of nearby genes (cis-eGenes). Using published GWAS datasets with 39,165 single-nucleotide polymorphisms (SNPs) associated with 1,960 traits, we explored whole blood gene expression associations of trait-associated SNPs in 5,257 individuals from the Framingham Heart Study. We identified 2,350 trans-eQTLs (at p < 10 À7 ); more than 80% of them were found to have cis-associated eGenes. Mediation testing suggested that for 35% of trans-eQTL-transeGene pairs in different chromosomes and 90% pairs in the same chromosome, the disease-associated SNP may alter expression of the transeGene via cis-eGene expression. In addition, we identified 13 trans-eQTL hotspots, affecting from ten to hundreds of genes, suggesting the existence of master genetic regulators. Using causal inference testing, we searched causal variants across eight cardiometabolic traits (BMI, systolic and diastolic blood pressure, LDL cholesterol, HDL cholesterol, total cholesterol, triglycerides, and fasting blood glucose) and identified several cis-eGenes (ALDH2 for systolic and diastolic blood pressure, MCM6 and DARS for total cholesterol, and TRIB1 for triglycerides) that were causal mediators for the corresponding traits, as well as examples of trans-mediators (TAGAP for LDL cholesterol). The finding of extensive evidence of genome-wide mediation effects suggests a critical role of cryptic gene regulation underlying many disease traits.

Identification of putative regulatory regions and transcription factors associated with intramuscular fat content traits

“…In this study, although we used a panel of SNPs, in which most of the mutations are in introns and intergenic regions, we observed a enrichment of the intergenic regions for the identified trans-eQTLs. These findings corroborate the expected biologic function of the trans-eQTLs as potential distant regulators of gene expression [ 37 ].…”

A trans-eQTL network regulates osteoclast multinucleation and bone mass

“…These approaches identified trans -eQTLs, which regulate gene expression that are often observed in clusters, also known as trans -eQTL hotspots ( Albert et al, 2018 ; Brem et al, 2002 ; Morley et al, 2004 ; Schadt et al, 2003 ). Many of the trans -eQTLs discovered to date in different model organisms tend to influence the expression of multiple genes ( Albert et al, 2018 ; Bagnati et al, 2019 ; Grundberg et al, 2012 ; Johnson et al, 2015 ; Kang et al, 2014 ; Small et al, 2011 ; Yao et al, 2017 ), which suggests coordinated (network-based) regulatory mechanisms might be perturbed during disease pathogenesis. Although identification of such networks has informed our understanding of gene-gene interactions, their functional validation remains challenging.…”

Power, false discovery rate and Winner’s Curse in eQTL studies

“…Typically, eQTL studies have sample sizes of 50 to 1000, with the accessibility of the tissue or condition a major determining factor (Supplementary Table S1). A recent trans- eQTL study performed in whole blood had a size of 5257 samples (6) and a study combined data for 2116 whole blood samples to identify context-specific eQTLs (14). Perhaps the exemplar multiple human tissue resource, the Genotype-Tissue Expression (GTEx) project (15), comprises 44 tissues with a sample size range of 70–361 in its V6p data release (16).…”