A0593
Title: A unified quantile framework reveals nonlinear heterogeneous transcriptome-wide associations
Authors: Tianying Wang - Colorado State University (United States) [presenting]
Iuliana Ionita-Laza - Columbia University (United States)
Ying Wei - Columbia University (United States)
Abstract: Transcriptome-wide association studies (TWAS) are powerful tools for identifying putative causal genes 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 exist in biological systems. To address this limitation, a novel quantile transcriptomics framework, QTWAS, is proposed that takes into account the nonlinear and heterogeneous nature of gene-trait associations. The approach integrates a quantile-based gene expression model into the TWAS model, which allows for the discovery of nonlinear and heterogeneous gene-trait associations. By conducting comprehensive simulations and examining various psychiatric and neurodegenerative traits, it is demonstrated that the proposed model outperforms traditional techniques in identifying gene-trait associations. Additionally, QTWAS can uncover important insights into nonlinear relationships between gene expression levels and phenotypes, complementing traditional TWAS approaches. Applications are further shown to 100 continuous traits from the UK Biobank and ten binary traits related to brain disorders.
