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.
Original language | English (US) |
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Pages (from-to) | 624-635 |
Number of pages | 12 |
Journal | Human molecular genetics |
Volume | 33 |
Issue number | 7 |
DOIs | |
State | Published - Apr 1 2024 |
Keywords
- eQTL Prediction
- functional annotations
- low-heritability Genes
- TWAS
ASJC Scopus subject areas
- Molecular Biology
- Genetics
- Genetics(clinical)