Simon A. Gayther

This study identifies DNA binding and RAD51 interaction interfaces in BRCA1's disordered region and shows their distinct roles in homologous recombination and DNA repair.

S4-Multi improves polygenic score prediction across diverse ancestries by reducing genetic variants while maintaining accuracy in non-European populations.

This study profiles the proteome of ovarian cancer tumors before and after chemotherapy, revealing how HRD status affects immune pathways and identifies potential therapeutic targets.

This study shows that mutational and clonality profiles in HGSOC are established early and remain stable through therapy resistance.

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 performs cis- and trans-eQTL TWAS on breast and ovarian cancer data to identify 101 and 8 risk-associated genes, respectively, with several driven by trans-eQTLs.

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

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.

This study uses single-cell transcriptomics to identify gene expression networks in the human fallopian tube, revealing epithelial subtypes and their role in cancer progression.

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 shows that DNA methylation and gene expression patterns are preserved in recurrent high grade serous ovarian cancers, with BRCA1/2 carriers showing more shared methylation changes and improved survival.

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

This study combines genome-wide association data from four cancers to identify shared and novel genetic associations and pleiotropic effects.

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

This study integrates a large prostate cancer GWAS with gene expression data to identify 235 genes associated with risk, prioritizing causal genes through Bayesian credible sets.

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 uses transcriptomic and epigenomic profiling to show that high-grade serous ovarian cancers predominantly originate from fallopian tube secretory epithelial cells, with distinct transcription factors driving tumorigenesis based on cell-of-origin.