Leveraging phenotypic variability to identify genetic interactions in human phenotypes

Abstract

Although thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly because of the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to when assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.

Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers

“…S 3 ). This quantitative relationship confirms a similar result found for BMI in the UKB, and may be due to the fact that interactions are more likely to be present at loci with established biological connections to the phenotype of interest 16 , 20 . vQTL and ME loci did not show significant differential enrichment for epigenomic features using the Locus Overlap Analysis (LOLA) method 21 (see Methods).…”

“…GEIs may induce differences in the variance of continuous phenotypes across genotypes. Thus, tests for genetic markers associated with this variance, termed variance-quantitative trait loci (vQTLs), represent an alternate strategy to identify loci harboring underlying GEIs for quantitative traits 10 – 16 . vQTLs can be identified in genome-wide scans analogous to those testing for phenotypic mean differences in typical genome-wide association studies (GWAS).…”

“…Such scenarios are quite common in practice due to the high dimensionality, dense correlation structure, and poor measurement of typical environmental exposures 17 . Recent studies in the UK Biobank (UKB) have demonstrated that a genome-wide vQTL discovery approach for anthropometric and lung function-related traits prioritizes variants enriched for GEI effects 15 , 16 . However, it is unclear whether vQTL effects are observed more broadly for cardiometabolic traits and to what degree the specific underlying GEI relationships can be uncovered using a more comprehensive array of environmental exposures.…”

Compositional correlation analysis of gene expression time series

“…Understanding the temporal relationships between the expression profiles of genes is crucial in determining the causes, functions and consequences of the biological processes like the cell cycle [16]; identifying the roles of genes in the stages of developmental processes of organisms [17,18]; determination of genetic relatedness among various species [19]; investigating the functions of individual genes by exploring genetic interactions [20], and developing drugs to cure diseases by identifying genes that act in response to a certain disease [21]. Consequently, as the presented results also suggest, the compositional correlation method seems to be a very appropriate method for finding the associated genes by a complete comparison of the expression series, a task which is impossible to be made manually because of thousands of genes to be compared.…”

PRICKLE1 × FOCAD Interaction Revealed by Genome-Wide vQTL Analysis of Human Facial Traits

“…Although these two non-additive genetic inheritance patterns are not always related, their relationship suggests an enrichment of the interaction effects among variance-controlling SNPs at vQTLs, indicating that these SNPs can be prioritized for interaction tests. Indeed, this variance prioritization strategy has been shown to be useful in studying other quantitative human phenotypes ( Paré et al, 2010 ; Hulse and Cai, 2013 ; Rask-Andersen et al, 2017 ; Sarkar et al, 2019 ; Wang et al, 2019 ; Young et al, 2018 ; Johnson et al, 2020 ; Marderstein et al, 2021 ).…”