Epistasis and destabilizing mutations shape gene expression variability in humans via distinct modes of action

Abstract

Increasing evidence shows that, like phenotypic mean, phenotypic variance is also genetically determined, but the underlying mechanisms of genetic control over the variance remain obscure. Here, we conducted variance-association mapping analyses to identify expression variability QTLs (evQTLs), i.e., genomic loci associated with gene expression variance, in humans. We discovered that common genetic variations may contribute to increasing gene expression variability via two distinct modes of action—epistasis and destabilization. Specifically, the epistasis model explains a quarter of the identified evQTLs, of which the formation is attributed to the presence of “third-party” eQTLs that influence the level of gene expression in a fraction, rather than the entire set, of sampled individuals. The destabilization model explains the other three-quarters of evQTLs, which tend to be associated with mutations that disrupt the stability of the transcription process of genes. To show the destabilizing effect, we measured discordant gene expression between monozygotic twins, and time-course stability of gene expression in single samples using repetitive qRT-PCR assays. The destabilizing evQTL SNPs were found to be associated with more pronounced expression discordance between twin pairs and less stable gene expression in single samples. Together, our results suggest that common SNPs may work interactively or independently to shape the variability of gene expression in humans. These findings contribute to the understanding of the mechanisms of genetic control over phenotypic variance and may have implications for the development of variability-centered analytic methods for quantitative trait mapping.

Transcriptomic analysis of fetal membranes reveals pathways involved in preterm birth

“…Specifically, the Uruguayan population is admixed, with a predominance of European contribution, and a Native American and African contribution of 10.4% and 5.6%, respectively [68,69]. Common genetic variation affects expression variability [70], as well as the admixtured genome structure [71,72], stressing the necessity of population-specific transcriptome analyses or alternatively including diversity in high throughput population analyses.…”

Aging Shapes the Population-Mean and -Dispersion of Gene Expression in Human Brains

“…Previously, we have shown that both common and rare genetic variants may confer regulatory function to contribute to gene expression dispersion (Hulse and Cai, 2013 ; Wang et al, 2014 ; Zeng et al, 2015 ). In particular, common genetic variants contribute to gene expression variability via distinct modes of action—e.g., epistasis and destabilizing mutations (Wang et al, 2015 ). Rare and private regulatory variants have been found to be responsible for extreme gene expression in outlier samples (Montgomery et al, 2011 ; Zeng et al, 2015 ; Zhao et al, 2016 ).…”

Aging shapes the population-mean and ‐dispersion of gene expression in human brains

“…Previously, we have shown that both common and rare genetic variants may confer regulatory function to contribute to gene expression dispersion [50][51][52] . In particular, common genetic variants contribute to gene expression variability via distinct modes of action-e.g., epistasis and decanalization 53 . Rare and private regulatory variants have been found to be responsible for extreme gene expression in outlier samples 52,54,55 .…”