METRO: Multi-ancestry transcriptome-wide association studies for powerful gene-trait association detection

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Integrative analysis of genome-wide association studies (GWAS) and gene expression data can elucidate complex disease mechanisms.
• Transcriptome-wide association studies (TWAS) offer a powerful approach for this integration.

Purpose of the Study:

• To introduce METRO, a novel method for enhancing TWAS by leveraging multi-ancestry gene expression data.
• To improve the power and accuracy of TWAS, particularly for underrepresented ancestries.

Main Methods:

• METRO incorporates expression prediction models from diverse genetic ancestries.
• A likelihood-based inference framework is used to combine ancestry-specific models.
• Calibrated p-values are generated to enhance statistical rigor.

Main Results:

• Simulations and real-data applications demonstrate METRO's improved TWAS power.
• METRO identified significantly more gene-trait associations compared to existing methods, especially for African ancestry GWAS.
• Key genes associated with high-density lipoprotein and type II diabetes were identified.

Conclusions:

• METRO provides a powerful and robust framework for multi-ancestry TWAS.
• The method is crucial for advancing genetic discovery in diverse populations with smaller GWAS sample sizes.
• METRO facilitates a deeper understanding of the genetic architecture of complex diseases across ancestries.