Inherited thrombocytopenia (IT) is comprised of a group of hereditary disorders characterized by a reduced platelet count as the main feature, and often with abnormal platelet function, which can subsequently lead to impaired haemostasis. Inherited thrombocytopenia results from genetic mutations in genes implicated in megakaryocyte differentiation and/or platelet formation and clearance. The identification of the underlying causative gene of IT is challenging given the high degree of heterogeneity, but important due to the presence of various clinical presentations and prognosis, where some defects can lead to hematological malignancies. Traditional platelet function tests, clinical manifestations, and hematological parameters allow for an initial diagnosis. However, employing Next-Generation Sequencing (NGS), such as Whole Genome and Whole Exome Sequencing (WES) can be an efficient method for discovering causal genetic variants in both known and novel genes not previously implicated in IT. To date, 40 genes and their mutations have been implicated to cause many different forms of inherited thrombocytopenia. Nevertheless, despite this advancement in the diagnosis of IT, the molecular mechanism underlying IT in some patients remains unexplained. In this review, we will discuss the genetics of thrombocytopenia summarizing the recent advancement in investigation and diagnosis of IT using phenotypic approaches, high-throughput sequencing, targeted gene panels, and bioinformatics tools.

Inherited bleeding disorders (IBDs) comprise an extremely heterogeneous group of diseases that reflect abnormalities of blood vessels, coagulation proteins, and platelets. Previously the UK-GAPP study has used whole-exome sequencing in combination with deep platelet phenotyping to identify pathogenic genetic variants in both known and novel genes in approximately 40% of the patients. To interrogate the remaining "unknown" cohort and improve this detection rate, we employed an IBD-specific gene panel of 119 genes using the Congenica Clinical Interpretation Platform to detect both single-nucleotide variants and copy number variants in 126 patients. In total, 135 different heterozygous variants in genes implicated in bleeding disorders were identified. Of which, 22 were classified pathogenic, 26 likely pathogenic, and the remaining were of uncertain significance. There were marked differences in the number of reported variants in individuals between the four patient groups: platelet count (35), platelet function (43), combined platelet count and function (59), and normal count (17). Additionally, we report three novel copy number variations (CNVs) not previously detected. We show that a combined single-nucleotide variation (SNV)/CNV analysis using the Congenica platform not only improves detection rates for IBDs, suggesting that such an approach can be applied to other genetic disorders where there is a high degree of heterogeneity.

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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 BackgroundThe 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. ObjectiveIdentify 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. MethodsUsing 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. ResultsFiltering 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. ConclusionsWe 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.