Alison M. Dunning

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 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.

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

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.

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 paper develops polygenic risk scores for breast cancer prediction in women of African ancestry by combining African and European ancestry datasets, improving prediction accuracy.

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

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

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 identifies 15 common and 9 low-frequency protein-coding variants associated with body fat distribution, implicating novel genes related to lipid homeostasis and type 2 diabetes.

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

The paper identifies 14 genes, including NTN4, associated with breast cancer risk variants through allelic expression imbalance analysis.