SUMMIT-FA: A new resource for improved transcriptome imputation using functional annotations

Transcriptome-wide association studies (TWAS) integrate gene expression prediction models and genome-wide association studies (GWAS) to identify gene–trait associations. The power of TWAS is determined by the sample size of GWAS and the accuracy of the expression prediction model. Here, we present a new method, the Summary-level Unified Method for Modeling Integrated Transcriptome using Functional Annotations (SUMMIT-FA), that improves the accuracy of gene expression prediction by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models using SUMMIT-FA with a comprehensive functional database MACIE and the eQTL summary-level data from the eQTLGen consortium. By applying the resulting models to GWASs for 24 complex traits and exploring it through a simulation study, we show that SUMMIT-FA improves the accuracy of gene expression prediction models in whole blood, identifies significantly more gene-trait associations, and improves predictive power for identifying “silver standard” genes compared to several benchmark methods.