A unified quantile framework for nonlinear heterogeneous transcriptome-wide associations

TL;DR

This paper introduces QTWAS, a novel framework that incorporates a quantile-based gene expression model into TWAS to effectively detect nonlinear and heterogeneous gene-trait associations, outperforming traditional methods.

Contribution

The paper presents a new nonlinear TWAS framework, QTWAS, that captures complex gene-trait relationships overlooked by existing linear models.

Findings

QTWAS outperforms traditional TWAS in simulations.

QTWAS effectively identifies nonlinear gene-trait associations.

Application to real data demonstrates improved detection power.

Abstract

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 quantile-based 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.