Mining for gene-environment and gene-gene interactions: parametric and nonparametric tests for detecting variance quantitative trait loci

Wan-Yu Lin^*

• Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei, Taiwan, Taipei, Taiwan

Detection of variance quantitative trait loci (vQTL) can facilitate the discovery of gene-environment (GxE) and gene-gene interactions (GxG). Identifying vQTLs before direct GxE and GxG analyses can considerably reduce the number of tests and the multiple-testing penalty. Despite some methods proposed for vQTL detection, few studies have performed a head-to-head comparison simultaneously concerning false positive rates (FPRs), power, and computational time. This work compares three parametric and two non-parametric vQTL tests. Simulation studies show that the deviation regression model (DRM) and Kruskal-Wallis test (KW) are the most recommended parametric and non-parametric tests, respectively. The quantile integral linear model (QUAIL, non-parametric) appropriately preserves the FPR under normally or non-normally distributed traits. However, its power is never among the optimal choices, and its computational time is much longer than that of competitors. The Brown-Forsythe test (BF, parametric) can suffer from severe inflation in FPR when SNP’s minor allele frequencies < 0.2. The double generalized linear model (DGLM, parametric) is not valid for non-normally distributed traits, although it is the most powerful method for normally distributed traits. Considering the robustness (to outliers) and computation time, I chose KW to analyze four lipid traits in the Taiwan Biobank. I further showed that GxE and GxG were enriched among 30 vQTLs identified from the four lipid traits.