Multi-ancestry colocalization approaches

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) identify numerous variants for complex traits, but distinguishing causal variants is challenging.
• Existing fine-mapping methods primarily focus on single ancestries, limiting their application to diverse populations.
• Current colocalization methods lack multi-ancestry capabilities, hindering the integration of increasingly available multi-ancestry genetic data.

Purpose of the Study:

• To develop and evaluate novel multi-ancestry colocalization approaches.
• To integrate multi-ancestry fine-mapping tools (SuSiEx, MsCAVIAR) with single-ancestry colocalization methods (coloc, eCAVIAR).
• To assess the performance of new methods (coloc_SuSiEx, eCAVIAR_SuSiEx, eMsCAVIAR, coloc_MsCAVIAR) in identifying shared causal variants across ancestries.

Main Methods:

• Proposed four novel multi-ancestry colocalization methods: coloc_SuSiEx, eCAVIAR_SuSiEx, eMsCAVIAR, and coloc_MsCAVIAR.
• Evaluated method performance using simulation studies for loci with single and multiple causal variants.
• Applied the developed approaches to multi-ancestry Type 2 Diabetes (T2D) GWAS data and European protein quantitative trait loci (pQTL) data.

Main Results:

• For single causal variants, all four methods showed comparable performance in prioritizing true causal variants and similar credible set sizes.
• MsCAVIAR-based methods were computationally more intensive than SuSiEx-based methods.
• eCAVIAR-based methods reported lower locus-level colocalization posterior probabilities compared to coloc-based methods.
• coloc_SuSiEx was identified as the preferred approach for analyzing loci with multiple causal variants.

Conclusions:

• The developed multi-ancestry colocalization methods effectively address the need for analyzing diverse genetic datasets.
• coloc_SuSiEx is recommended for loci with multiple causal variants due to its balance of performance and computational efficiency.
• This work facilitates more accurate and biologically relevant interpretation of genetic associations across different ancestries.