A scalable hierarchical lasso for gene-environment interactions

TL;DR

This paper introduces a scalable hierarchical lasso model for selecting gene-environment interactions, emphasizing efficiency and accuracy in high-dimensional genetic data analysis.

Contribution

It proposes a novel regularized regression approach with an efficient algorithm and screening rules, improving selection performance and scalability over existing methods.

Findings

Outperforms existing methods in simulation studies

Offers high accuracy in predictor screening

Demonstrates effectiveness on real data

Abstract

We describe a regularized regression model for the selection of gene-environment (GxE) interactions. The model focuses on a single environmental exposure and induces a main-effect-before-interaction hierarchical structure. We propose an efficient fitting algorithm and screening rules that can discard large numbers of irrelevant predictors with high accuracy. We present simulation results showing that the model outperforms existing joint selection methods for (GxE) interactions in terms of selection performance, scalability and speed, and provide a real data application. Our implementation is available in the gesso R package.