PattRec: An easy-to-use CNV detection tool optimized for targeted NGS assays with diagnostic purposes.

Genetic laboratories use custom-commercial targeted next-generation sequencing (tg-NGS) assays to identify disease-causing variants. Although the high coverage achieved with these tests allows for the detection of copy number variants (CNVs), which account for an important proportion of the genetic burden in human diseases, an easy-to-use tool for automatic CNV detection is still lacking. This article presents a new CNV detection tool optimized for tg-NGS data: PattRec. PattRec was evaluated using a wide range of data, and its performance compared with those of other CNV detection tools. The software includes features for selecting optimal controls, discarding polymorphic CNVs prior to analysis, and filtering out deletions based on SNV zygosity, and automatically creates an in-house CNV database. There is no need for high level bioinformatic expertise and users can choose color-coded xlsx output that helps to prioritize potentially pathogenic CNVs. PattRec is presented as a Java based GUI, freely available online: https://github.com/irotero/PattRec.

A novel missense mutation in GRIN2A causes a nonepileptic neurodevelopmental disorder.

BACKGROUND: Mutations in the GRIN2A gene, which encodes the GluN2A (glutamate [NMDA] receptor subunit epsilon-1) subunit of the N-methyl-d-aspartate receptor, have been identified in patients with epilepsy-aphasia spectrum disorders, idiopathic focal epilepsies with centrotemporal spikes, and epileptic encephalopathies with severe developmental delay. However, thus far, mutations in this gene have not been associated with a nonepileptic neurodevelopmental disorder with dystonia. OBJECTIVES: The objective of this study was to identify the disease-causing gene in 2 siblings with neurodevelopmental and movement disorders with no epileptiform abnormalities. METHODS: The study method was targeted next-generation sequencing panel for neuropediatric disorders and subsequent electrophysiological studies. RESULTS: The 2 siblings carry a novel missense mutation in the GRIN2A gene (p.Ala643Asp) that was not detected in genomic DNA isolated from blood cells of their parents, suggesting that the mutation is the consequence of germinal mosaicism in 1 progenitor. In functional studies, the GluN2A-A643D mutation increased the potency of the agonists L-glutamate and glycine and decreased the potency of endogenous negative modulators, including protons, magnesium and zinc but reduced agonist-evoked peak current response in mammalian cells, suggesting that this mutation has a mixed effect on N-methyl-d-aspartate receptor function. CONCLUSION: De novo GRIN2A mutations can give rise to a neurodevelopmental and movement disorder without epilepsy. © 2018 International Parkinson and Movement Disorder Society.

Free-access copy-number variant detection tools for targeted next-generation sequencing data.

Copy number variants (CNVs) are intermediate-scale structural variants containing copy number changes involving DNA fragments of between 1 kb and 5 Mb. Although known to account for a significant proportion of the genetic burden in human disease, the role of CNVs (especially small CNVs) is often underestimated, as they are undetectable by traditional Sanger sequencing. Since the development of next-generation sequencing (NGS) technologies, several research groups have compared depth of coverage (DoC) patterns between samples, an approach that may facilitate effective CNV detection. Most CNV detection tools based on DoC comparisons are designed to work with whole-genome sequencing (WGS) or whole-exome sequencing (WES) data. However, few methods developed to date are designed for custom/commercial targeted NGS (tg-NGS) panels, the assays most commonly used for diagnostic purposes. Moreover, the development and evaluation of these tools is hindered by (i) the scarcity of thoroughly annotated data containing CNVs and (ii) a dearth of simulation tools for WES and tg-NGS that mimic the errors and biases encountered in these data. Here, we review DoC-based CNV detection methods described in the current literature, assess their performance with simulated tg-NGS data, and discuss their strengths and weaknesses when integrated into the daily laboratory workflow. Our findings suggest that the best methods for CNV detection in tg-NGS panels are DECoN, ExomeDepth, and ExomeCNV. Regardless of the method used, there is a need to make these programs more user-friendly to enable their use by diagnostic laboratory staff who lack bioinformatics training.

Rare Variants in 48 Genes Account for 42% of Cases of Epilepsy With or Without Neurodevelopmental Delay in 246 Pediatric Patients.

In order to characterize the genetic architecture of epilepsy in a pediatric population from the Iberian Peninsula (including the Canary Islands), we conducted targeted exome sequencing of 246 patients with infantile-onset seizures with or without neurodevelopmental delay. We detected 107 variants in 48 different genes, which were implicated in neuronal excitability, neurodevelopment, synaptic transmission, and metabolic pathways. In 104 cases (42%) we detected variant(s) that we classified as pathogenic or likely pathogenic. Of the 48 mutated genes, 32 were dominant, 8 recessive and 8 X-linked. Of the patients for whom family studies could be performed and in whom pathogenic variants were identified in dominant or X-linked genes, 82% carried de novo mutations. The involvement of small copy number variations (CNVs) is 9%. The use of progressively updated custom panels with high mean vertical coverage enabled establishment of a definitive diagnosis in a large proportion of cases (42%) and detection of CNVs (even duplications) with high fidelity. In 10.5% of patients we detected associations that are pending confirmation via functional and/or familial studies. Our findings had important consequences for the clinical management of the probands, since a large proportion of the cohort had been clinically misdiagnosed, and their families were subsequently able to avail of genetic counseling. In some cases, a more appropriate treatment was selected for the patient in question, or an inappropriate treatment discontinued. Our findings suggest the existence of modifier genes that may explain the incomplete penetrance of some epilepsy-related genes. We discuss possible reasons for non-diagnosis and future research directions. Further studies will be required to uncover the roles of structural variants, epimutations, and oligogenic inheritance in epilepsy, thereby providing a more complete molecular picture of this disease. In summary, given the broad phenotypic spectrum of most epilepsy-related genes, efficient genomic tools like the targeted exome sequencing panel described here are essential for early diagnosis and treatment, and should be implemented as first-tier diagnostic tools for children with epilepsy without a clear etiologic basis.