High-grade serous ovarian carcinoma (HGSOC) has a significant hereditary component, only half of which is explained. Previously, we performed germline exome sequencing on BRCA1 and BRCA2-negative HGSOC patients, revealing three proposed and 43 novel candidate genes enriched with rare loss-of-function variants. For validation, we undertook case-control analyses using genomic data from disease-free controls.
View Article and Find Full Text PDFBackground: TP53 variant classification benefits from the availability of large-scale functional data for missense variants generated using cDNA-based assays. However, absence of comprehensive splicing assay data for TP53 confounds the classification of the subset of predicted missense and synonymous variants that are also predicted to alter splicing. Our study aimed to generate and apply splicing assay data for a prioritised group of 59 TP53 predicted missense or synonymous variants that are also predicted to affect splicing by either SpliceAI or MaxEntScan.
View Article and Find Full Text PDFThe effectiveness and cost-effectiveness of genetic testing for hereditary breast and ovarian cancer largely rely on the identification and clinical management of individuals with a pathogenic variant prior to developing cancer. Simulation modelling is commonly utilised to evaluate genetic testing strategies due to its ability to synthesise collections of data and extrapolate over long time periods and large populations. Existing genetic testing simulation models use simplifying assumptions for predictive genetic testing and risk management uptake, which could impact the reliability of their estimates.
View Article and Find Full Text PDFAims: Myxobacteria are non-pathogenic, saprophytic, soil-dwelling predatory bacteria known for their antimicrobial potential. Many pathogenic bacteria form biofilms to protect themselves from antimicrobial agents and the immune system. This study has investigated the predatory activities of myxobacteria against pathogenic bacteria in biofilms.
View Article and Find Full Text PDFBackground: There is considerable heterogeneity in fine particulate matter (PM)-mortality associations between studies, potentially due to differences in exposure assessment methods. Our aim was to evaluate associations of PM predicted from different models with nonaccidental and cause-specific mortality.
Methods: We followed 107,906 participants of the Nurses' Health Study cohort from 2001 to 2016.