De novo mutation hotspots in homologous protein domains identify function-altering mutations in neurodevelopmental disorders.

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.

Missense variants in ANKRD11 cause KBG syndrome by impairment of stability or transcriptional activity of the encoded protein.

PURPOSE: Although haploinsufficiency of ANKRD11 is among the most common genetic causes of neurodevelopmental disorders, the role of rare ANKRD11 missense variation remains unclear. We characterized clinical, molecular, and functional spectra of ANKRD11 missense variants. METHODS: We collected clinical information of individuals with ANKRD11 missense variants and evaluated phenotypic fit to KBG syndrome. We assessed pathogenicity of variants through in silico analyses and cell-based experiments. RESULTS: We identified 20 unique, mostly de novo, ANKRD11 missense variants in 29 individuals, presenting with syndromic neurodevelopmental disorders similar to KBG syndrome caused by ANKRD11 protein truncating variants or 16q24.3 microdeletions. Missense variants significantly clustered in repression domain 2 at the ANKRD11 C-terminus. Of the 10 functionally studied missense variants, 6 reduced ANKRD11 stability. One variant caused decreased proteasome degradation and loss of ANKRD11 transcriptional activity. CONCLUSION: Our study indicates that pathogenic heterozygous ANKRD11 missense variants cause the clinically recognizable KBG syndrome. Disrupted transrepression capacity and reduced protein stability each independently lead to ANKRD11 loss-of-function, consistent with haploinsufficiency. This highlights the diagnostic relevance of ANKRD11 missense variants, but also poses diagnostic challenges because the KBG-associated phenotype may be mild and inherited pathogenic ANKRD11 (missense) variants are increasingly observed, warranting stringent variant classification and careful phenotyping.

Application of metabolite set enrichment analysis on untargeted metabolomics data prioritises relevant pathways and detects novel biomarkers for inherited metabolic disorders.

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.

Differences in the number of de novo mutations between individuals are due to small family-specific effects and stochasticity.

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.