Improved methods for multi-trait fine mapping of pleiotropic risk loci

Area of Science:

• Genetics
• Bioinformatics
• Statistical Genetics

Background:

• Genome-wide association studies (GWAS) identify genetic variants associated with complex traits and diseases.
• Variants identified by GWAS are often in linkage disequilibrium (LD) with the true causal variant, necessitating fine-mapping.
• Statistical fine-mapping methods are crucial for prioritizing variants for functional validation.

Purpose of the Study:

• To introduce fastPAINTOR, a novel approach for improving genetic fine-mapping accuracy.
• To enhance fine-mapping resolution at pleiotropic risk loci by leveraging correlated trait information and functional annotation data.
• To improve computational efficiency through a new importance sampling scheme for model inference.

Main Methods:

• Developed fastPAINTOR, a new statistical fine-mapping method.
• Incorporated functional annotation data and evidence from correlated traits.
• Implemented an importance sampling scheme for efficient model inference.
• Evaluated performance using simulations and real-world GWAS data for lipids.

Main Results:

• fastPAINTOR demonstrated increased fine-mapping resolution compared to existing methods when using functional annotation data in simulations.
• Jointly modeling pleiotropic risk regions significantly improved fine-mapping resolution over single-trait or standard pleiotropic strategies.
• Reduced the number of single nucleotide polymorphisms (SNPs) needed to capture 90% of causal variants from 23 to 12 per locus when fine-mapping two traits simultaneously.
• Observed largely sustained improvements in real lipid GWAS data.

Conclusions:

• fastPAINTOR offers a more accurate and efficient approach to genetic fine-mapping.
• Leveraging multi-trait and functional data enhances the ability to pinpoint causal variants at complex trait loci.
• The method has practical implications for reducing the cost and effort of functional validation in genetic studies.