Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies

Area of Science:

• Genetics and Genomics
• Statistical Genetics
• Population Genetics

Background:

• Transcriptome-wide association studies (TWASs) identify genes linked to complex disease risk.
• Non-causal genes and confounding factors like linkage disequilibrium (LD) and eQTL pleiotropy complicate TWAS signal interpretation.
• Fine-mapping is crucial for accurately identifying causal genes within TWAS risk regions.

Purpose of the Study:

• To introduce MA-FOCUS, a novel multi-ancestry framework designed to enhance the identification of genes associated with complex traits.
• To demonstrate the superiority of MA-FOCUS over single-ancestry fine-mapping methods by utilizing ancestry-specific LD and eQTL patterns.
• To investigate the shared genetic architecture of gene expression and blood traits across European and African ancestries.

Main Methods:

• Developed MA-FOCUS, a multi-ancestry fine-mapping framework.
• Performed TWASs for 15 blood traits using genome-wide summary statistics from European (EA) and African (AA) ancestries.
• Utilized lymphoblastoid cell line eQTL data from EA and AA cohorts.

Main Results:

• MA-FOCUS consistently outperformed single-ancestry methods across multiple metrics.
• Gene-level effects showed a 20% stronger correlation across ancestries compared to SNP-level effects.
• Fine-mapping revealed that TWAS risk region genes are more likely to be shared across ancestries than ancestry-specific.
• MA-FOCUS-derived gene sets were significantly enriched in hematopoietic categories (p = 2.36 × 10^-15).

Conclusions:

• Incorporating genetic diversity and accounting for ancestry-specific patterns significantly improves the identification of trait-associated genes.
• MA-FOCUS provides a robust framework for dissecting the genetic architecture of complex traits by leveraging multi-ancestry data.
• The findings highlight the importance of diverse populations in genetic studies to uncover shared and specific genetic influences on complex diseases.