Summit-Fa: A New Resource for Improved Transcriptome Imputation Using Functional Annotations

Authors

Hunter J Melton
Zichen Zhang
Chong Wu

Publication Date

3-20-2024

Journal

Human Molecular Genetics

Abstract

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), which improves gene expression prediction accuracy by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models in whole blood using SUMMIT-FA with the comprehensive functional database MACIE and eQTL summary-level data from the eQTLGen consortium. We apply these models to GWAS for 24 complex traits and show that SUMMIT-FA identifies significantly more gene-trait associations and improves predictive power for identifying "silver standard" genes compared to several benchmark methods. We further conduct a simulation study to demonstrate the effectiveness of SUMMIT-FA.

Keywords

Humans, Transcriptome, Genome-Wide Association Study, Computer Simulation, Quantitative Trait Loci, Phenotype, Polymorphism, Single Nucleotide, Genetic Predisposition to Disease

DOI

10.1093/hmg/ddad205

PMID

38129112

PMCID

PMC10954367

PubMedCentral® Posted Date

12-21-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes