GMI: GROUP-LEVEL MAIN EFFECTS AND INTERACTIONS IN HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO PATHWAY AND INTERACTION DISCOVERY IN GENE EXPRESSION ANALYSIS

• 成果类型: Article
• 署名作者: Nie, Jinyu; Liu, Li; Hu, Taobo; Liu, Wei; Lin, Huazhen
• 署名单位: Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; Wuhan University; Stockholm University; Sichuan University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/26-AOAS2179
• 发表日期: 2026-06
• 页码: 1078-1098
• 关键词: high dimension; higher-level interaction analysis; variable selection; bayesian variable-selection; breast-cancer; designed experiments; regression; PURSUIT; biology; MODEL

摘要

Genetic interactions are essential for understanding the risk and progression of complex diseases. However, signals from individual genes and their pairwise interactions are often weak; most phenotypes are driven by alterations in a limited number of pathways and interactions between them. Identifying such pathways and their interactions is critical in biomedical research. Although traditional analyses have extended beyond main effects to include gene-gene interactions, most existing methods remain at the gene level and fail to capture higher-level pathway interactions. In this paper we propose a novel group-level model that jointly identifies key pathways and their interactions associated with clinical outcomes such as disease status or survival. The model involves estimating a high-dimensional binary matrix, which presents significant computational challenges. To overcome this, we reformulate the problem as a standard high-dimensional estimation task with hierarchical and exclusivity constraints and develop a two-stage estimation procedure. Theoretical analysis, simulation studies, and applications to TCGA breast cancer and Michigan lung cancer datasets demonstrate the superior performance of our method. In particular, our approach yields biologically meaningful insights, reveals novel gene-pathway mechanisms, and achieves substantially improved prediction accuracy and sensitivity, with comparable specificity to competing methods, including those modeling gene-gene interactions or employing two-step procedures that separately estimate pathways and their interactions.