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Cardiomyopathy has variable penetrance. We analyzed age and sex-related genetic differences in 1,397 cardiomyopathy patients (Ontario, UK) with whole genome sequencing. Pediatric cases (n = 471) harbored more deleterious protein-coding variants in Tier 1 cardiomyopathy genes compared to adults (n = 926) (34.6% vs 25.9% respectively, p = 0.0015), with variant enrichment in constrained coding regions. Pediatric patients had a higher burden of sarcomere and lower burden of channelopathy gene variants compared to adults. Specifically, pediatric patients had more MYH7 and MYL3 variants in hypertrophic cardiomyopathy, and fewer TTN truncating variants in dilated cardiomyopathy. MYH7 variants clustered in the myosin head and neck domains in children. OBSCN was a top mutated gene in adults, enriched for protein-truncating variants. In dilated cardiomyopathy, female patients had a higher burden of z-disc gene variants compared to males. Genetic differences may explain age and sex-related variability in cardiomyopathy penetrance. Genotype-guided predictions of age of onset can inform pre-test genetic counseling. Pediatric cardiomyopathy patients were more likely to be genotype-positive than adults with a higher burden of variants in MYH7, MYL3, TNNT2, VCL. Adults had a higher burden of OBSCN and TTN variants. Females with dilated cardiomyopathy (DCM) had a higher burden of z-disc gene variants compared to males. [ABSTRACT FROM AUTHOR]

Background: The majority of cases of Dent's disease are caused by pathogenic variants in the CLCN5 gene, which encodes a voltage-gated chloride ion channel (ClC-5), resulting in proximal tubular dysfunction. We present three members of the same family and one unrelated paediatric patient with the same insertion-deletion CLCN5 variant. The identification of these patients and positive familial segregation led to the re-classification of this variant from one of unknown significance to one of likely pathogenicity. Case presentation: A 41 year old male presented with end stage kidney failure, proteinuria and haematuria. Whole genome sequencing identified an insertion-deletion variant in CLCN5, resulting in a missense change (c.1744_1745delinsAA p.(Ala582Lys)). His brother and nephew, who both exhibited renal impairment, haematuria, proteinuria, glycosuria and nephrocalcinosis, were found to have the same variant. In addition, genetic testing of an unrelated paediatric patient who presented with proteinuria and hypercalciuria, demonstrated the same variant. Conclusions: The identification of this novel variant in four individuals with features of Dent's disease, has led to the re-classification of the variant to one of likely pathogenicity. As a result, our patients and any future patients with the same variant can be offered a likely diagnosis, without the need for kidney biopsy, and their family members can be offered genetic screening. [ABSTRACT FROM AUTHOR]

Background: Cancer genome sequencing enables accurate classification of tumours and tumour subtypes. However, prediction performance is still limited using exome-only sequencing and for tumour types with low somatic mutation burden such as many paediatric tumours. Moreover, the ability to leverage deep representation learning in discovery of tumour entities remains unknown. Methods: We introduce here Mutation-Attention (MuAt), a deep neural network to learn representations of simple and complex somatic alterations for prediction of tumour types and subtypes. In contrast to many previous methods, MuAt utilizes the attention mechanism on individual mutations instead of aggregated mutation counts. Results: We trained MuAt models on 2587 whole cancer genomes (24 tumour types) from the Pan-Cancer Analysis of Whole Genomes (PCAWG) and 7352 cancer exomes (20 types) from the Cancer Genome Atlas (TCGA). MuAt achieved prediction accuracy of 89% for whole genomes and 64% for whole exomes, and a top-5 accuracy of 97% and 90%, respectively. MuAt models were found to be well-calibrated and perform well in three independent whole cancer genome cohorts with 10,361 tumours in total. We show MuAt to be able to learn clinically and biologically relevant tumour entities including acral melanoma, SHH-activated medulloblastoma, SPOP-associated prostate cancer, microsatellite instability, POLE proofreading deficiency, and MUTYH-associated pancreatic endocrine tumours without these tumour subtypes and subgroups being provided as training labels. Finally, scrunity of MuAt attention matrices revealed both ubiquitous and tumour-type specific patterns of simple and complex somatic mutations. Conclusions: Integrated representations of somatic alterations learnt by MuAt were able to accurately identify histological tumour types and identify tumour entities, with potential to impact precision cancer medicine. [ABSTRACT FROM AUTHOR]

Diagnostic whole genome sequencing (WGS) is increasingly used in rare diseases. However, standard, semi-automated WGS analysis may overlook diagnoses in complex disorders. Here, we show that specialist multidisciplinary analysis of WGS, following an initial 'no primary findings' (NPF) report, improves diagnostic rates and alters management. We undertook WGS in 102 adults with diagnostically challenging primary mitochondrial disease phenotypes. NPF cases were reviewed by a genomic medicine team, thus enabling bespoke informatic approaches, co-ordinated phenotypic validation, and functional work. We enhanced the diagnostic rate from 16.7% to 31.4%, with management implications for all new diagnoses, and detected strong candidate disease-causing variants in a further 3.9% of patients. This approach presents a standardised model of care that supports mainstream clinicians and enhances diagnostic equity for complex disorders, thereby facilitating access to the potential benefits of genomic healthcare. This research was made possible through access to the data and findings generated by the 100,000 Genomes Project: http://www.genomicsengland.co.uk. Whole genome sequencing is emerging as a first-line test for rare genetic diseases. In this study, authors maximise diagnoses by supplementing existing semiautomated analyses with clinically driven reevaluation of genomic data by a specialist multidisciplinary team. [ABSTRACT FROM AUTHOR]