Jacques Simard

PRANA adapts polygenic risk scores to non-European populations, improving predictive accuracy by 5%-20% in South Asian, East Asian, and Ashkenazi Jewish populations.

This paper evaluates methods to incorporate alternative polygenic risk scores into the BOADICEA breast cancer risk prediction model, finding that retrospective likelihood approaches provide more accurate estimates.

This paper updates the BOADICEA cancer risk prediction model to include new genetic variants and continuous risk factors, improving risk reclassification for specific gene carriers.

The paper uses whole-exome sequencing data to identify novel breast cancer susceptibility genes and assess the contribution of coding variants to breast cancer risk.

This study improves transcriptome-wide association studies by integrating pleiotropy-guided methods with GWAS data to identify new cancer susceptibility genes.

This study assesses polygenicity and risk prediction using common variants across 14 cancers, finding varying numbers of susceptibility variants and risk score effectiveness.

DSNetwork aggregates variant deleteriousness predictions from multiple tools to prioritize functional variants in GWAS loci, using a web interface for visualization and analysis.