Multi-ancestry colocalization approaches

Area of Science:

• Genetics
• Statistical Genetics
• Genomics

Background:

• Genome-wide association studies (GWAS) identify numerous variants for complex traits, but distinguishing causal variants is challenging.
• Multi-ancestry fine-mapping methods enhance resolution by utilizing cross-ancestry differences in linkage disequilibrium (LD) and minor allele frequencies (MAFs).
• Current colocalization methods lack multi-ancestry capabilities, limiting the biological interpretation of genetic associations in diverse populations.

Purpose of the Study:

• To develop and evaluate novel multi-ancestry colocalization methods.
• To integrate multi-ancestry fine-mapping tools (SuSiEx, MsCAVIAR) with single-ancestry colocalization methods (coloc, eCAVIAR).
• To assess the performance of proposed methods, including coloc_SuSiEx, eCAVIAR_SuSiEx, eMsCAVIAR, and coloc_MsCAVIAR, via simulations and real data application.

Main Methods:

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

Main Results:

• For single causal variant loci, all four methods showed comparable performance in credible set size and causal variant prioritization.
• MsCAVIAR-based methods incurred higher computational costs than SuSiEx-based methods.
• eCAVIAR-based methods reported lower locus-level colocalization posterior probabilities compared to coloc-based methods.
• The coloc_SuSiEx approach was identified as the preferred method for analyzing loci with multiple causal variants.

Conclusions:

• The developed multi-ancestry colocalization methods address a critical gap in genetic analysis.
• coloc_SuSiEx is recommended for multi-ancestry colocalization analyses, particularly in regions with multiple causal variants.
• This work facilitates more robust genetic discovery by integrating diverse genomic datasets across ancestries.