Gene-Gene and Gene-Environment Interactions in Case-Control Studies Based on Hierarchies of Dirichlet Processes

Article Type

Research Article

Publication Title

Statistics and Applications

Abstract

It is becoming increasingly clear that complex interactions among genes and environmental factors play crucial roles in triggering complex diseases. Thus, understanding such interactions is vital, which is possible only through statistical models that adequately account for such intricate, albeit unknown, dependence structures. In this article, we propose and develop a novel nonparametric Bayesian model for case-control genotype data using hierarchies of Dirichlet processes that offers a more realistic and nonparametric dependence structure among the genes, induced by the environmental variables. In this regard, we propose a novel and highly parallelisable MCMC algorithm that is rendered quite efficient by the combination of modern parallel computing technology, effective Gibbs sampling steps, retrospective sampling and Transformation based Markov Chain Monte Carlo (TMCMC). We devise appropriate Bayesian hypothesis testing procedures to detect the roles of genes and environment in case-control studies. Applying our ideas to 5 biologically realistic case-control genotype datasets simulated under distinct set-ups, we obtain encouraging results in each case. We finally apply our ideas to a real, myocardial infarction dataset, and obtain interesting results on gene-gene and gene environment interaction, that broadly agree with the results reported in the literature, but provide further important insights.

First Page

327

Last Page

360

Publication Date

1-1-2024

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