PurposeGenetic testing is an integral diagnostic component of pediatric medicine. Standard of care is often a time-consuming stepwise approach involving chromosomal microarray analysis and targeted gene sequencing panels, which can be costly and inconclusive. Whole-genome sequencing (WGS) provides a comprehensive testing platform that has the potential to streamline genetic assessments, but there are limited comparative data to guide its clinical use.MethodsWe prospectively recruited 103 patients from pediatric non-genetic subspecialty clinics, each with a clinical phenotype suggestive of an underlying genetic disorder, and compared the diagnostic yield and coverage of WGS with those of conventional genetic testing.ResultsWGS identified diagnostic variants in 41% of individuals, representing a significant increase over conventional testing results (24% P = 0.01). Genes clinically sequenced in the cohort (n = 1,226) were well covered by WGS, with a median exonic coverage of 40 × ±8 × (mean ±SD). All the molecular diagnoses made by conventional methods were captured by WGS. The 18 new diagnoses made with WGS included structural and non-exonic sequence variants not detectable with whole-exome sequencing, and confirmed recent disease associations with the genes PIGG, RNU4ATAC, TRIO, and UNC13A.ConclusionWGS as a primary clinical test provided a higher diagnostic yield than conventional genetic testing in a clinically heterogeneous cohort.

Developmental and epileptic encephalopathy (DEE) is a group of conditions characterized by the co-occurrence of epilepsy and intellectual disability (ID), typically with developmental plateauing or regression associated with frequent epileptiform activity. The cause of DEE remains unknown in the majority of cases. We performed whole-genome sequencing (WGS) in 197 individuals with unexplained DEE and pharmaco-resistant seizures and in their unaffected parents. We focused our attention on de novo mutations (DNMs) and identified candidate genes containing such variants. We sought to identify additional subjects with DNMs in these genes by performing targeted sequencing in another series of individuals with DEE and by mining various sequencing datasets. We also performed meta-analyses to document enrichment of DNMs in candidate genes by leveraging our WGS dataset with those of several DEE and ID series. By combining these strategies, we were able to provide a causal link between DEE and the following genes: NTRK2, GABRB2, CLTC, DHDDS, NUS1, RAB11A, GABBR2, and SNAP25. Overall, we established a molecular diagnosis in 63/197 (32%) individuals in our WGS series. The main cause of DEE in these individuals was de novo point mutations (53/63 solved cases), followed by inherited mutations (6/63 solved cases) and de novo CNVs (4/63 solved cases). De novo missense variants explained a larger proportion of individuals in our series than in other series that were primarily ascertained because of ID. Moreover, these DNMs were more frequently recurrent than those identified in ID series. These observations indicate that the genetic landscape of DEE might be different from that of ID without epilepsy.

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Massively parallel sequencing has revealed many de novo mutations in the etiology of developmental and epileptic encephalopathies (EEs), highlighting their genetic heterogeneity. Additional candidate genes have been prioritized in silico by their co-expression in the brain. Here, we evaluate rare coding variability in 20 candidates nominated with the use of a reference gene set of 51 established EE-associated genes. Variants within the 20 candidate genes were extracted from exome-sequencing data of 42 subjects with EE and no previous genetic diagnosis. We identified 7 rare non-synonymous variants in 7 of 20 genes and performed Sanger sequence validation in affected probands and parental samples. De novo variants were found only in SLC1A2 (aka EAAT2 or GLT1) (c.244G>A [p.Gly82Arg]) and YWHAG (aka 14-3-3g) (c.394C>T [p.Arg132Cys]), highlighting the potential cause of EE in 5% (2/42) of subjects. Seven additional subjects with de novo variants in SLC1A2 (n ¼ 1) and YWHAG (n ¼ 6) were subsequently identified through online tools. We identified a highly significant enrichment of de novo variants in YWHAG, establishing their role in early-onset epilepsy, and we provide additional support for the prior assignment of SLC1A2. Hence, in silico modeling of brain co-expression is an efficient method for nominating EE-associated genes to further elucidate the disorder's etiology and genotype-phenotype correlations.