An Improved Version of Logistic Bayesian LASSO for Detecting Rare Haplotype-Environment Interactions with Application to Lung Cancer.
The importance of haplotype association and gene-environment interactions (GxE) in the context of rare variants has been underlined in voluminous literature. Recently, a software based on logistic Bayesian LASSO (LBL) was proposed for detecting GxE, where G is a rare (or common) haplotype variant (rHTV)-it is called LBL-GxE. However, it required relatively long computation time and could handle only one environmental covariate with two levels. Here we propose an improved version of LBL-GxE, which is not only computationally faster but can also handle multiple covariates, each with multiple levels. We also discuss details of the software, including input, output, and some options. We apply LBL-GxE to a lung cancer dataset and find a rare haplotype with protective effect for current smokers. Our results indicate that LBL-GxE, especially with the improvements proposed here, is a useful and computationally viable tool for investigating rare haplotype interactions.
Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA.
Yuan
Y
Zhang
Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA.
Swati
S
Biswas
GWAS; GxE; MCMC; logistic Bayesian LASSO; rHTV; rare variants; retrospective likelihood