IMR Press / FBE / Volume 4 / Issue 7 / DOI: 10.2741/E576

Frontiers in Bioscience-Elite (FBE) is published by IMR Press from Volume 13 Issue 2 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on as a courtesy and upon agreement with Frontiers in Bioscience.

Open Access Article

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

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1 Department of Epidemiology, Michigan State University, East Lansing, MI 48824

*Author to whom correspondence should be addressed.

Academic Editor: Rongling Wu

Front. Biosci. (Elite Ed) 2012, 4(7), 2607–2617;
Published: 1 June 2012
(This article belongs to the Special Issue Dynamic genetics and genomics)

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 geneenvironment 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-Statisticbased random forest approach, referred to as Forest U-Test, for genetic association studies with quantitative traits. Through simulation studies, we showed that the Forest UTest outperformed exiting 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 4.70e17, respectively.

Forest u-test
Random forest
Quantitative trait
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