Abstract
Increasing evidence shows that phenotypic variance is genetically controlled, and the variance itself is a quantitative trait. The precise mechanism of genetic control over the variance, however, remains to be determined. Here, using complex trait analysis of gene expression, we show that common genetic variation contributes to increasing gene expression variability via distinct modes of action—e.g., epistasis and decanalization. We focused on expression variability QTLs (evQTLs), i.e., genetic loci associated with gene express variance, in the human genome. We found that a quarter of evQTLs could be attributed to the presence of “third-party” eQTLs. These SNPs are associated with gene expression in a fraction, rather than the entire set, of samples. Many additional evQTLs do not interact with other SNPs and are thus unexplained by the epistasis model; these are attributable to the decanalizing effect of evQTL variants. Here we present the decanalization model, which predicts that evQTLs influence gene expression variability through modulating the sensitivity of transcriptional machinery to environmental perturbation. To validate the model we measured the discordant gene expression between monozygotic twins, and also estimated the amplitude of stochastic gene expression noise using repeated RT-qPCR assays on single samples. Both measures were found to be associated with genotypes of evQTLs explained by the decanalization model. Together, our results suggest that genetic variants work interactively or independently to influence gene expression variability. We anticipate our analysis to be a starting point for more sophisticated mechanistic analyses and opens a new, variability-centered research avenue for mapping complex traits.
Author Summary It is increasingly appreciated that phenotypic variance is genetically controlled. The effects of genotypes on phenotypic variance represent a critical source of phenotypic differences among individuals. Here we study the mechanisms of genetic control of phenotypic variance through the lens of evQTLs in humans. We show that two distinct modes of action, namely epistasis and decanalization, contribute to the formation of evQTLs. Combining computational and experimental analyses, we show that gene expression variability in populations is correlated with the stochastic noise of gene expression in individuals. Such a pattern is more likely to be detected with evQTLs resulted from decanalizing variants, rather than epistatic interactions between variants. We demonstrate the decanalizing function conferred by evQTL variants by showing the increased discordant gene expression between monozygotic twins, as well as the noisier gene expression in cell lines. Our findings have implications for complex trait studies, calling for a paradigm shift in the methodology of disease risk loci mapping to take into consideration the impact of genetic variants on phenotypic variability.