提出基于U统计量的随机森林方法，提升复杂性状基因互作检测能力。
A U-Statistic-based random forest approach for genetic interaction study
- 用U统计量改进随机森林，增强对基因-基因/基因-环境互作的检测力
- 在模拟和真实数据中均显著优于现有方法，发现大麻依赖显著联合效应
- 适合高维遗传关联分析，尤其适用于复杂性状的交互作用研究
复杂性状受多个遗传变异、环境风险因素及其相互作用影响。尽管单个遗传变异的关联已取得进展，但基因间与基因-环境互作的检测仍具挑战。当涉及大量遗传变异和环境因素时，因特征空间指数级增长和计算强度，互作搜索通常仅限于成对互作。相比之下，递归分割方法如随机森林在高维遗传关联研究中日益流行。本文提出一种基于U统计量的随机森林方法（Forest U-Test），用于定量性状的遗传关联研究。模拟研究表明，Forest U-Test优于现有方法。该方法应用于大麻依赖（Cannabis Dependence, CD）研究，使用来自《成瘾：遗传与环境研究》（Study of Addiction: Genetics and Environment）的三个独立数据集，检测到显著联合关联，经验p值小于0.001；并在两个独立数据集中成功复现，p值分别为5.93e-19和4.70e-17。
原文摘要 · Abstract (English)
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.
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