Abstract
Essentials Identifying genetic variants in platelet disorders is challenging due to its heterogenous nature. We combine WES, RNAseq, and python-based bioinformatics to identify novel gene variants. We find novel candidates in patient data by cross-referencing against a murine RNAseq model of thrombopoiesis. This innovative combined bioinformatic approach provides novel data for future research in the field. ABSTRACT: Background The UK Genotyping and Phenotyping of Platelets study has recruited and analyzed 129 patients with suspected heritable bleeding. Previously, 55 individuals had a definitive genetic diagnosis based on whole exome sequencing (WES) and platelet morphological and functional testing. A significant challenge in this field is defining filtering criteria to identify the most likely candidate mutations for diagnosis and further study. Objective Identify candidate gene mutations for the remaining 74 patients with platelet-based bleeding with unknown genetic cause, forming the basis of future re-recruitment and further functional testing and assessment. Methods Using python-based data frame indexing, we first identify and filter all novel and rare variants using a panel of 116 genes known to cause bleeding across the full cohort of WES data. This identified new variants not previously reported in this cohort. We then index the remaining patients, with rare or novel variants in known bleeding genes against a murine RNA sequencing dataset that models proplatelet-forming megakaryocytes. Results Filtering against known genes identified candidate variants in 59 individuals, including novel variants in several known genes. In the remaining cohort of "unknown" patients, indexing against differentially expressed genes revealed candidate gene variants in several novel unreported genes, focusing on 14 patients with a severe clinical presentation. Conclusions We identified candidate mutations in a cohort of patients with no previous genetic diagnosis. This work involves innovative coupling of RNA sequencing and WES to identify candidate variants forming the basis of future study in a significant number of undiagnosed patients.