A Quantile-Based Framework for Uncovering Nonlinear Gene-Trait Associations in Transcriptome-Wide Studies

Music City Center 

Transcriptome-wide association studies (TWAS) are valuable tools for identifying gene-level associations by integrating genome-wide association studies with gene expression data. However, most TWAS methods focus solely on linear associations between genes and traits, overlooking the complex nonlinear relationships that often exist in biological systems. To address this limitation, we propose a novel framework called QTWAS, which incorporates a quantile-based gene expression model into the TWAS framework. This approach allows for the detection of both nonlinear and heterogeneous gene-trait associations. Through extensive simulations and applications to both continuous and binary traits, we demonstrate that QTWAS is more powerful than conventional TWAS methods in identifying gene-trait associations.

Quantile regression

TWAS

nonlinear genetic associations