SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification.

Area of Science:

• Genetics
• Bioinformatics
• Genomic Epidemiology

Background:

• Moderate to low expression heritability genes contribute significantly to complex trait etiology.
• Conventional transcriptome-wide association studies (TWAS) struggle to capture these genes due to limited reference datasets for genetic prediction models.
• Accurate genetic prediction of gene expression is crucial for understanding complex traits.

Purpose of the Study:

• Introduce the Summary-level Unified Method for Modeling Integrated Transcriptome (SUMMIT) to enhance TWAS.
• Improve the accuracy of expression prediction models and the statistical power of TWAS.
• Identify genes associated with complex traits, including COVID-19 severity.

Main Methods:

• Developed SUMMIT, a novel method utilizing large expression quantitative trait loci (eQTL) summary-level data.
• Applied SUMMIT to eQTLGen consortium data for model development.
• Validated SUMMIT through simulation studies and analysis of genome-wide association study (GWAS) summary statistics for 24 complex traits.

Main Results:

• SUMMIT significantly improves the accuracy of gene expression prediction in blood.
• Successfully developed prediction models for genes with low expression heritability.
• Demonstrated higher statistical power compared to benchmark TWAS methods.
• Identified 11 likely causal genes associated with COVID-19 severity in a case study.

Conclusions:

• SUMMIT offers a powerful approach to overcome limitations in current TWAS methods.
• The method enhances the ability to detect genetically influenced complex traits, particularly those involving genes with lower heritability.
• SUMMIT provides valuable insights into the genetic architecture of complex diseases like COVID-19.