Jonathan P. Tyrer

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

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

The paper estimates the contribution of rare coding variants to cancer heritability using UK Biobank whole-exome sequencing data and finds ovarian cancer has the highest proportion of heritability from protein-truncating 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 identifies seven novel genetic loci associated with high-grade serous ovarian cancer risk using a large-scale GWAS and develops polygenic scores for improved risk prediction.

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

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.

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

CamCNV improves detection of rare CNVs from Illumina arrays by using z-score segmentation, achieving 85% sensitivity for deletions and 65% for duplications with reliable false discovery rate control.

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

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

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