shaPRS

shaPRS refines polygenic risk scores by integrating pleiotropic effects from two genome-wide summary statistic datasets (two distinct diseases or ancestral populations) to improve SNP effect estimates and their standard errors.

Key Features:

• Pleiotropy and ancestry integration: Leverages shared genetic influences across traits or ancestries to inform PRS estimation.
• Dual-dataset summary statistics: Operates using genome-wide summary statistics from two distinct diseases or ancestral populations.
• SNP-level effect and standard error refinement: Produces improved genetic effect estimates and associated standard errors at single nucleotide polymorphisms (SNPs).
• Homogeneity detection: Identifies SNPs exhibiting homogeneity of effect between the two datasets.
• Heterogeneity handling: For SNPs with significant heterogeneity, retains the genetic effect estimate from the disease or population most closely related to the target population.
• Pre-processing for PRS pipelines: Functions as an agnostic pre-processing step to integrate with existing PRS generation pipelines.
• Validation: Demonstrated improvements in PRS accuracy via simulation studies and real-world applications, including across diverse ancestries.

Scientific Applications:

• PRS accuracy for complex diseases: Improves polygenic risk score accuracy for complex disease phenotypes.
• Cross-ancestry PRS transferability: Enhances PRS performance and applicability across diverse ancestral populations.
• Pleiotropic signal exploitation: Utilizes pleiotropy between traits or populations to strengthen genetic effect estimation.

Methodology:

Uses genome-wide summary statistics from two diseases or ancestral populations; performs per-SNP assessment of effect homogeneity between datasets; for homogeneous SNPs, combines information to update effect estimates and standard errors; for heterogeneous SNPs, retains the effect estimate from the dataset closest to the target population.