  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, we
propose a novel quantile transcriptomics framework, QTWAS, that takes into
account the nonlinear and heterogeneous nature of gene-trait associations. Our
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, we demonstrate 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. We further show applications to 100 continuous
traits from the UK Biobank and 10 binary traits related to brain disorders.
