A unified quantile framework reveals nonlinear heterogeneous transcriptome-wide associations
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Transcriptome-wide association studies (TWAS) are powerful tools for identifying putative causal genes by integrating genome-wide association studies and gene expression data. Most existing methods are based on linear models and therefore may miss or underestimate nonlinear associations. In this article, we propose a robust, quantile-based, unified framework to investigate nonlinear transcriptome-wide associations in a quantile process manner. Through extensive simulations and the analysis of multiple psychiatric and neurodegenerative disorders, we showed that the proposed framework gains substantial power over conventional approaches and leads to insightful discoveries on nonlinear associations between gene expression levels and traits, thereby providing a complementary approach to existing literature. In doing so, we applied the proposed method for 797 continuous traits from the UK Biobank, and the results are available in a public repository.
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