The combined dimension of genome and exposome is statistically and computationally challenging for the detection of gene by environment interactions (GxEs). Variance quantitative trait loci (vQTL) can suggest candidate single nucleotide polymorphisms (SNPs) without exposome profiling, by capturing non-uniformity in residual variance of a quantitative trait caused by unaccounted for GxEs.
We propose variance loci analysis (VLA) to improve vQTL with curve upwardness test, which maintains power to detect GxE candidates with weak direct environmental effects, and more importantly, over-come the inability of vQTL to analyze case/control phenotypes.
We ranked the potential of each SNP in GxEs for 3,273 phenotypes from the UK Biobank and hosted the VLA Catalog (genelist.niehs.nih.gov) as an open resource. Combining the rich exposome from the Personalized Environmental Genetic Study (PEGS) and high-ranking SNPs from VLA, we detected sig-nificant GxE between gene ABCB7 and inkjet toners on type 2 diabetes.


CTNND2
TMEM132D
DMD
ABCB7
BUD31P2*
NXF3
RPSAP59*
PTPRK
RPL12P43*
ZNF157
CYCS
C7orf31
CNBD2
RAPGEF5
TRNT1
IL5RA