Michael J. Lush

This study identifies seven novel genetic loci associated with high-grade serous ovarian cancer risk using a large-scale GWAS and develops polygenic scores for improved risk prediction.

This study examines differences in polygenic risk score distributions across European populations and shows how country-specific calibration can improve breast cancer risk prediction.

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.

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.

The paper identifies breast cancer risk variants associated with tumor subtypes and shows their differential effects on luminal and non-luminal subtypes.

This study fine-maps 150 breast cancer risk regions to identify 178 high confidence target genes using genomic and functional annotations.