Detecting genetic effects on phenotype variability to capture gene-by-environment interactions: a systematic method comparison

Abstract

Phenotypic variability has been widely observed across organisms and traits, including in humans. Both gene-gene and gene-environment interactions can lead to an increase in phenotypic variability. Therefore, detecting the underlying genetic variants, or variance Quantitative Trait Loci (vQTLs), can provide novel insights into complex traits. Established approaches to detect vQTLs apply different methodologies from variance-only approaches to mean-variance joint tests, but a comprehensive comparison of these methods is lacking. Here, we review available methods to detect vQTLs in humans, carry out a simulation study to assess their performance under different biological scenarios of gene-environment interactions, and apply the optimal approaches for vQTL identification to gene expression data. We find that the squared residual value linear model (SVLM) method is optimal when the interacting exposure is discrete, and both SVLM and the deviation regression model (DRM) perform well when the interacting exposure is continuous. Additionally, a larger sample size, smaller minor allele frequency, and more balanced sample distribution in different exposure categories increase power of SVLM and DRM. Our results highlight vQTL detection methods that perform optimally under realistic simulation settings and show that their relative performance depends on the type of exposure in the interaction model underlying the vQTL. Author SummaryGenetic background can influence organismal response to changes in the environment, including through effects of gene-environment (GxE) interactions. GxE interactions form a fundamental component of complex phenotypes and disease. The presence of GxE interactions leads to increases in phenotypic variability, where individuals of a certain genotype can display more divergent phenotypes. Therefore, identifying genetic variation that can alter phenotypic variance is an alternative approach to identifying genetic variants that underlie GxE effects. Here, we conduct a systematic assessment of methods that detect genetic variants linked to phenotypic variance under several GxE scenarios. We identify the most optimal approaches for different environmental exposure settings and apply these to validate previously detected GxE signals. We also estimate power of these methods to detect GxE effects. Our results help guide experimental design for future studies aiming to identify genetic variant impacts on phenotypic variance, and their result interpretation in context of gene-environment interactions in complex traits.