SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning

Randy L. Parrish, Aron S. Buchman, Shinya Tasaki, Yanling Wang, Denis Avey, Jishu Xu, Philip L. De Jager, David A. Bennett, Michael P. Epstein and Jingjing Yang ()
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Randy L. Parrish: Emory University School of Medicine
Aron S. Buchman: Rush University Medical Center
Shinya Tasaki: Rush University Medical Center
Yanling Wang: Rush University Medical Center
Denis Avey: Rush University Medical Center
Jishu Xu: Rush University Medical Center
Philip L. De Jager: Columbia University Irving Medical Center
David A. Bennett: Rush University Medical Center
Michael P. Epstein: Emory University School of Medicine
Jingjing Yang: Emory University School of Medicine

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies show that SR-TWAS improves power, due to increased training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real studies identify 6 independent significant risk genes for Alzheimer’s disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson’s disease (PD) for substantia nigra tissue. Relevant biological interpretations are found for these significant risk genes.

Date: 2024
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DOI: 10.1038/s41467-024-50983-w

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