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

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 studi

SUBMITTER: Parrish RL 

PROVIDER: S-EPMC11300466 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature