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Background: Individuals with germline pathogenic variants in BRCA1 or BRCA2 are at a high risk of breast and ovarian carcinomas with BRCA1/2 deficiency and homologous recombination deficiency that can be detected by analysis of genome-wide genomic instability features such as large-scale state transitions, telomeric allelic imbalances, and genomic loss of heterozygosity. Malignancies with homologous recombination deficiency are more sensitive to platinum-based therapies and poly(ADP-ribose) polymerase inhibitors. We investigated the fraction of non-breast or ovarian malignancies that have BRCA1/2 deficiency and genomic instability features., Methods: The full tumor history of a large, historical, clinic-based, consecutive cohort of 2965 individuals with germline pathogenic variants in BRCA1/2 was retrieved from the Dutch nationwide pathology databank (Palga). In total, 169 non-breast or ovarian malignancies were collected and analyzed using targeted next-generation sequencing and shallow whole-genome sequencing to determine somatic second-hit alterations and genomic instabilities indicative of homologous recombination deficiency, respectively., Results: BRCA1/2 deficiency was detected in 27% (21/79) and 23% (21/90) of 20 different types of non-breast or ovarian malignancies in individuals with germline pathogenic variants in BRCA1 and BRCA2, respectively. These malignancies had a higher genomic instability score than BRCA1- or BRCA2-proficient malignancies (P < .001 and P < .001, respectively)., Conclusions: BRCA1/2 deficiency and genomic instability features were found in 27% and 23% of a broad spectrum of non-breast or ovarian malignancies in individuals with germline pathogenic variants in BRCA1 and BRCA2, respectively. Evaluation of the effectiveness of poly(ADP-ribose) polymerase inhibitors in these individuals should be focused on tumors with a confirmed absence of a wild-type allele., (© The Author(s) 2024. Published by Oxford University Press.)

Variant interpretation remains a major challenge in medical genetics. We developed Meta-Domain HotSpot (MDHS) to identify mutational hotspots across homologous protein domains. We applied MDHS to a dataset of 45,221 de novo mutations (DNMs) from 31,058 individuals with neurodevelopmental disorders (NDDs) and identified three significantly enriched missense DNM hotspots in the ion transport protein domain family (PF00520). The 37 unique missense DNMs that drive enrichment affect 25 genes, 19 of which were previously associated with NDDs. 3D protein structure modeling supports the hypothesis of function-altering effects of these mutations. Hotspot genes have a unique expression pattern in tissue, and we used this pattern alongside in silico predictors and population constraint information to identify candidate NDD-associated genes. We also propose a lenient version of our method, which identifies 32 hotspot positions across 16 different protein domains. These positions are enriched for likely pathogenic variation in clinical databases and DNMs in other genetic disorders., Competing Interests: Declaration of interests The authors have no competing interests., (Copyright © 2022 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Untargeted metabolomics (UM) allows for the simultaneous measurement of hundreds of metabolites in a single analytical run. The sheer amount of data generated in UM hampers its use in patient diagnostics because manual interpretation of all features is not feasible. Here, we describe the application of a pathway-based metabolite set enrichment analysis method to prioritise relevant biological pathways in UM data. We validate our method on a set of 55 patients with a diagnosed inherited metabolic disorder (IMD) and show that it complements feature-based prioritisation of biomarkers by placing the features in a biological context. In addition, we find that by taking enriched pathways shared across different IMDs, we can identify common drugs and compounds that could otherwise obscure genuine disease biomarkers in an enrichment method. Finally, we demonstrate the potential of this method to identify novel candidate biomarkers for known IMDs. Our results show the added value of pathway-based interpretation of UM data in IMD diagnostics context., (© 2022 The Authors. Journal of Inherited Metabolic Disease published by John Wiley & Sons Ltd on behalf of SSIEM.)

The number of de novo mutations (DNMs) in the human germline is correlated with parental age at conception, but this explains only part of the observed variation. We investigated whether there is a family-specific contribution to the number of DNMs in offspring. The analysis of DNMs in 111 dizygotic twin pairs did not identify a substantial family-specific contribution. This result was corroborated by comparing DNMs of 1669 siblings to those of age-matched unrelated offspring following correction for parental age. In addition, by modeling DNM data from 1714 multi-offspring families, we estimated that the family-specific contribution explains ∼5.2% of the variation in DNM number. Furthermore, we found no substantial difference between the observed number of DNMs and those predicted by a stochastic Poisson process. We conclude that there is a small family-specific contribution to DNM number and that stochasticity explains a large proportion of variation in DNM counts., (© 2021 Goldmann et al.; Published by Cold Spring Harbor Laboratory Press.)