Fergus J. Couch

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

The paper identifies reversion mutations in homologous recombination genes across cancers, showing they restore DNA repair and confer PARP inhibitor resistance through specific structural alterations.

The paper combines enriched case data with population-based case-control analyses using a likelihood-ratio methodology to classify variants in breast cancer susceptibility genes.

This paper presents a single-cell RNA sequencing atlas of the adult human male breast, revealing distinct transcriptional properties and composition compared to female breast tissue.

The paper identifies a rare multipotent epithelial cell in the fallopian tube as a potential cell-of-origin for high-grade serous ovarian cancer using multi-omic profiling and organoid models.

The paper refines ACMG/AMP variant classification criteria for BRCA1 and BRCA2 by calibrating evidence strength and improving classification consistency through expert panel review.

The paper specifies gene-specific rules for interpreting ATM variants using ACMG/AMP guidelines, improving classification accuracy in a pilot study.

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

The paper identifies how germline and somatic mutations interact to cause resistance to CDK4/6 inhibitors in breast cancer and proposes a sequential treatment strategy using PARP inhibitors.

The paper functionally characterizes BRCA2 variants using saturation genome editing and classifies them clinically, finding functionally abnormal variants are associated with increased cancer risk.

The paper analyzes rare copy number variants (CNVs) in breast cancer cases and controls, identifying associations with known and novel susceptibility loci.

The paper develops a tool to estimate mutation prevalence in hereditary cancer genes based on clinical and demographic factors using data from 150,000 multi-gene panel tests.

This study identifies 32 new breast cancer susceptibility loci through GWAS, with associations varying by tumor subtype and genetic correlations among subtypes.

BRCA-ML is a machine learning model trained to predict the functional impact of missense variants in BRCA1 and BRCA2 genes, outperforming existing methods.

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

The paper provides structured recommendations for applying the PS3/BS3 functional evidence criteria in variant interpretation using the ACMG/AMP framework, including a four-step framework and minimum control requirements.

This study quantifies genetic correlations between six solid cancers and other diseases using GWAS data, finding shared heritability and functional enrichment in regulatory regions.