Antonis C. Antoniou

This paper introduces S4-Oligo, an optimized polygenic score method for clinical use, which matches or exceeds existing models' accuracy while using fewer SNPs.

This study identifies two cell-of-origin-driven subtypes of BRCA1-associated TNBC, showing that tumor subtype depends on the cell type initiating the cancer.

The paper uses case-control data from over 400,000 women to classify BRCA1 and BRCA2 variants, finding high consistency with ClinVar annotations for 785 previously unclassified variants.

The paper develops a pipeline for integrated breast cancer risk assessment using polygenic risk scores and other factors in the eMERGE study, showing high concordance between API and UI results.

The paper updates breast cancer risk estimates for BRCA2 carriers aged 60 and older using data from two large studies.

This study identifies genetic signals associated with age at menarche, highlights genes involved in pubertal development, and links reproductive timing to later life disease risk.

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 study analyzes 17,425 breast cancer families to assess genetic factors contributing to familial risk, finding a recessive risk component and polygenic factors explain residual familial variance.

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 develops a multifactorial risk model for epithelial tubo-ovarian cancer incorporating genetic and epidemiological factors to stratify risk and guide screening.

ClinGen and the PGS Catalog expand the GRIPS guideline into a 22-item Polygenic Risk Score Reporting Statement covering study populations, model construction, evaluation, and clinical interpretation.

The paper maps chromatin interactions at 139 breast cancer risk signals using Capture Hi-C and identifies candidate target genes and functional mechanisms.

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