Leveraging information between multiple population groups and traits improves fine-mapping resolution.

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Statistical fine-mapping is crucial for identifying causal variants in genetic association studies.
• Improving fine-mapping resolution requires integrating multi-trait information and population-specific linkage disequilibrium patterns.

Purpose of the Study:

• To introduce MGflashfm and MGfm, novel statistical fine-mapping methods.
• To evaluate the performance of these methods using simulation studies and real-world genetic data.
• To provide practical tools for fine-mapping using summary statistics, even with out-of-sample reference panels.

Main Methods:

• MGflashfm: Jointly fine-maps multiple traits and population groups using summary statistics.
• MGfm: Analyzes each trait separately using a similar framework.
• Developed a practical approach for fine-mapping with out-of-sample reference panels.

Main Results:

• Both MGflashfm and MGfm demonstrated good calibration in simulations.
• Mean proportion of causal variants with posterior probability > 0.80 exceeded 0.75 for MGflashfm and 0.70 for MGfm.
• MGflashfm achieved a median 10.5% reduction in 99% credible set size compared to MGfm for lipid traits across five populations.

Conclusions:

• MGflashfm and MGfm are effective and well-calibrated statistical fine-mapping tools.
• These methods significantly improve the resolution of causal variant identification.
• The reliance on summary statistics makes MGflashfm and MGfm highly valuable for large-scale genetic consortia.