DetectGxT: detecting gene-by-treatment interactions on molecular count phenotypes accounting for allelic additivity
TL;DR
An R software package is developed that uses nonlinear regression to more accurately model the relationship between the genotype and the transformed molecular count phenotypes and suggests an advantage of nonlinear regression over linear regression in detecting G×T interactions on molecular phenotypes.
Abstract
Motivation Identifying the mechanisms by which genetic variants affect the molecular response to an applied treatment is important across multiple biological fields, and an effective approach to this end is interaction molecular QTL mapping. However, the statistical models commonly used to detect such gene-by-treatment interactions (G×T) are non-trivially misspecified, and this can lead to decreased power. Results We developed an R software package, DetectGxT, that uses nonlinear regression to more accurately model the relationship between the genotype and the transformed molecular count phenotypes. It also optionally models donor or polygenic random effects. Simulations show that nonlinear regression can increase the power to detect interactions. In existing interaction expression QTL mapping data from primary human neural progenitor cells, nonlinear and linear regression approaches identified overlapping but distinct sets of gene-SNP pairs with significant G×T interactions. Overall, our results suggest an advantage of nonlinear regression over linear regression in detecting G×T interactions on molecular phenotypes. Availability The DetectGxT software is available at https://github.com/yharigaya/detectgxt. Contact milove@email.unc.edu, william.valdar@unc.edu