Semiparametric Mixed Model for Evaluating Pathway-Environment Interaction

TL;DR

This paper introduces a semiparametric mixed model combining kernel machines and splines to evaluate pathway-environment interactions, successfully identifying significant pathways and interactions in Type II diabetes data.

Contribution

It develops a novel semiparametric approach for pathway-environment interaction analysis, integrating kernel methods with mixed effects models for improved detection.

Findings

Identified pathways with significant main effects and interactions.

Detected novel pathway-environment interactions in diabetes data.

Outperformed existing methods in identifying interaction effects.

Abstract

A biological pathway represents a set of genes that serves a particular cellular or a physiological function. The genes within the same pathway are expected to function together and hence may interact with each other. It is also known that many genes, and so pathways, interact with other environmental variables. However, no formal procedure has yet been developed to evaluate the pathway-environment interaction. In this article, we propose a semiparametric method to model the pathway-environment interaction. The method connects a least square kernel machine and a semiparametric mixed effects model. We model nonparametrically the environmental effect via a natural cubic spline. Both a pathway effect and an interaction between a pathway and an environmental effect are modeled nonparametrically via a kernel machine, and we estimate variance component representing an interaction effect under a semiparametric mixed effects model. We then employ a restricted likelihood ratio test and a score test to evaluate the main pathway effect and the pathway-environment interaction. The approach was applied to a genetic pathway data of Type II diabetes, and pathways with either a significant main pathway effect, an interaction effect or both were identified. Other methods previously developed determined many as having a significant main pathway effect only. Furthermore, among those significant pathways, we discovered some pathways having a significant pathway-environment interaction effect, a result that other methods would not be able to detect.