A UNIFIED QUANTILE FRAMEWORK FOR NONLINEAR HETEROGENEOUS TRANSCRIPTOME-WIDE ASSOCIATIONS

• 成果类型: Article
• 署名作者: Wang, Tianying; Ionita-Laza, Iuliana; Wei, Ying
• 署名单位: Colorado State University System; Colorado State University Fort Collins; Columbia University; Lund University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS1999
• 发表日期: 2025
• 页码: 967-985
• 关键词: gene-expression; LEVEL

摘要

Transcriptome-wide association studies (TWAS) are powerful tools for identifying gene-level associations by integrating genome-wide association studies and gene expression data. However, most TWAS methods focus on linear associations between genes and traits, ignoring the complex nonlinear relationships that may be present in biological systems. To address this limitation, we propose a novel framework, QTWAS, which integrates a quantilebased gene expression model into the TWAS model, allowing for the discovery of nonlinear and heterogeneous gene-trait associations. Via comprehensive simulations and applications to both continuous and binary traits, we demonstrate that the proposed model is more powerful than conventional TWAS in identifying gene-trait associations.