SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Colocalization analysis is crucial for identifying shared genetic signals across traits from genome-wide association studies (GWAS).
• Existing methods may struggle with multiple causal variants within a single locus, limiting target prioritization for functional studies.

Purpose of the Study:

• To introduce SharePro, a novel method extending the COLOC framework for improved colocalization analysis.
• To address limitations of current methods when dealing with multiple causal variants in genetic loci.

Main Methods:

• SharePro integrates linkage disequilibrium (LD) modeling with colocalization assessment by grouping correlated variants.
• An efficient variational inference algorithm is employed for accurate estimation of posterior colocalization probabilities.

Main Results:

• SharePro demonstrated increased statistical power and controlled false positive rates in simulations at low computational cost.
• The method provided stronger and more consistent evidence for known drug-target-trait relationships compared to existing approaches.
• SharePro successfully identified biologically plausible colocalized signals in a challenging case study involving GWAS for R-spondin 3 abundance and bone mineral density.

Conclusions:

• SharePro offers a powerful and accurate approach for colocalization analysis, particularly in complex genetic loci.
• The method enhances the ability to prioritize genetic targets for functional follow-up in GWAS.
• SharePro is implemented in Python and readily available for use in genetic research.