Paul D.P. Pharoah

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

The paper uses saturation genome editing to assess the functional impact of RAD51D and XRCC2 variants, showing high accuracy in distinguishing pathogenic from benign variants.

The paper evaluates EHR-based phenotyping accuracy in the All of Us Research Program to optimize statistical power for genetic association testing, finding disparities in accuracy across racial groups.

S4-Multi improves polygenic score prediction across diverse ancestries by reducing genetic variants while maintaining accuracy in non-European 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 uses exome sequencing to identify HELB as a novel susceptibility gene for non-high grade serous epithelial ovarian cancer, with a strong association found in genetic analyses.

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 introduces an improved polygenic score method, S4, which enhances accuracy across multiple ancestries and phenotypes using summary statistics.

The paper identifies risk alleles at the 17q22 locus associated with breast cancer by analyzing allelic expression ratios in case-control studies.

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 assesses breast cancer risks linked to rare missense variants in five susceptibility genes using logistic and mixture models, finding varying risk contributions across genes.

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.

chromMAGMA identifies candidate risk genes for epithelial ovarian cancer by mapping risk variants to noncoding regulatory elements, revealing new genes and pathways involved in the disease.

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

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

The paper identifies RCCD1 as a susceptibility gene for breast and ovarian cancer by fine-mapping a risk locus and showing its expression and interaction patterns are associated with cancer risk.

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

This paper evaluates how sample size affects polygenic hazard score models for prostate cancer, finding that 20-30k samples are needed for discovery models, but 10k suffice if SNPs are pre-established.

This study uses Mendelian randomization to show that higher physical activity levels are associated with lower risks of breast and colorectal cancer.

The paper shows oncologic therapy increases clonal hematopoiesis with DDR mutations, which predicts risk of therapy-related myeloid neoplasms.

The paper evaluates a polygenic hazard score for prostate cancer screening, showing it improves positive predictive value by adjusting screening age based on genetic risk.

This paper identifies novel pleiotropic risk loci for melanoma and nevus density, implicating multiple biological pathways and gene expression under MITF regulation.

This study identifies 21 novel candidate susceptibility genes for high grade serous ovarian cancer using transcriptome-wide association analysis across multiple tissue types.

The paper develops and validates a new walking pace function using crowd-sourced GPS data, finding that a linear spline function performs best compared to existing methods.

The paper critiques the use of p-values and confidence intervals in statistical analysis.

This paper develops a genetic risk score to personalize prostate cancer screening age based on polygenic hazard scores and validates its predictive power in a separate dataset.