A U-Statistic-based random forest approach for genetic interaction study

TL;DR

This paper introduces a novel U-Statistic-based random forest method called Forest U-Test for detecting complex genetic interactions influencing traits, demonstrating superior performance through simulations and real data applications.

Contribution

The paper presents a new Forest U-Test approach that enhances detection of gene-gene and gene-environment interactions in high-dimensional genetic data.

Findings

Outperforms existing methods in simulation studies

Detects significant associations in Cannabis Dependence datasets

Replicates findings across independent datasets

Abstract

Variations in complex traits are influenced by multiple genetic variants, environmental risk factors, and their interactions. Though substantial progress has been made in identifying single genetic variants associated with complex traits, detecting the gene-gene and gene-environment interactions remains a great challenge. When a large number of genetic variants and environmental risk factors are involved, searching for interactions is limited to pair-wise interactions due to the exponentially increased feature space and computational intensity. Alternatively, recursive partitioning approaches, such as random forests, have gained popularity in high-dimensional genetic association studies. In this article, we propose a U-Statistic-based random forest approach, referred to as Forest U-Test, for genetic association studies with quantitative traits. Through simulation studies, we showed that the Forest U-Test outperformed existing methods. The proposed method was also applied to study Cannabis Dependence CD, using three independent datasets from the Study of Addiction: Genetics and Environment. A significant joint association was detected with an empirical p-value less than 0.001. The finding was also replicated in two independent datasets with p-values of 5.93e-19 and 4.70e-17, respectively.