A U-Statistic-based random forest approach for genetic interaction study
This addresses the problem of identifying genetic interactions for researchers in genetics and bioinformatics, representing an incremental improvement over existing random forest approaches.
The authors tackled the challenge of detecting gene-gene and gene-environment interactions in complex traits by proposing a U-Statistic-based random forest method, which outperformed existing methods in simulations and detected significant joint associations with empirical p-values less than 0.001 in real datasets.
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.