Evaluating the performance of a clinical genome sequencing program for diagnosis of rare genetic disease, seen through the lens of craniosynostosis.

PURPOSE: Genome sequencing (GS) for diagnosis of rare genetic disease is being introduced into the clinic, but the complexity of the data poses challenges for developing pipelines with high diagnostic sensitivity. We evaluated the performance of the Genomics England 100,000 Genomes Project (100kGP) panel-based pipelines, using craniosynostosis as a test disease. METHODS: GS data from 114 probands with craniosynostosis and their relatives (314 samples), negative on routine genetic testing, were scrutinized by a specialized research team, and diagnoses compared with those made by 100kGP. RESULTS: Sixteen likely pathogenic/pathogenic variants were identified by 100kGP. Eighteen additional likely pathogenic/pathogenic variants were identified by the research team, indicating that for craniosynostosis, 100kGP panels had a diagnostic sensitivity of only 47%. Measures that could have augmented diagnoses were improved calling of existing panel genes (+18% sensitivity), review of updated panels (+12%), comprehensive analysis of de novo small variants (+29%), and copy-number/structural variants (+9%). Recent NHS England recommendations that partially incorporate these measures should achieve 85% overall sensitivity (+38%). CONCLUSION: GS identified likely pathogenic/pathogenic variants in 29.8% of previously undiagnosed patients with craniosynostosis. This demonstrates the value of research analysis and the importance of continually improving algorithms to maximize the potential of clinical GS.

Adding evidence to the role of NEUROG1 in congenital cranial dysinnervation disorders.

Congenital cranial dysinnervation disorders (CCDDs) are a heterogeneous group of neurodevelopmental phenotypes caused by a primary disturbance of innervation due to deficient, absent, or misguided cranial nerves. Although some CCDDs genes are known, several clinical phenotypes and their aetiologies remain to be elucidated. We describe a 12-year-old boy with hypotonia, developmental delay, sensorineural hearing loss, and keratoconjunctivitis due to lack of corneal reflex. He had a long expressionless face, severe oromotor dysfunction, bilateral agenesis/severe hypoplasia of the VIII nerve with marked atresia of the internal auditory canals and cochlear labyrinth malformation. Trio-exome sequencing identified a homozygous loss of function variant in the NEUROG1 gene (NM_006161.2: c.202G > T, p.Glu68*). NEUROG1 is considered a causal candidate for CCDDs based on (i) the previous report of a patient with a homozygous gene deletion and developmental delay, deafness due to absent bilateral VIII nerves, and severe oromotor dysfunction; (ii) a second patient with a homozygous NEUROG1 missense variant and corneal opacity, absent corneal reflex and intellectual disability; and (iii) the knockout mouse model phenotype which highly resembles the disorder observed in humans. Our findings support the growing compelling evidence that loss of NEUROG1 leads to a very distinctive disorder of cranial nerves development.

The contribution of X-linked coding variation to severe developmental disorders.

Over 130 X-linked genes have been robustly associated with developmental disorders, and X-linked causes have been hypothesised to underlie the higher developmental disorder rates in males. Here, we evaluate the burden of X-linked coding variation in 11,044 developmental disorder patients, and find a similar rate of X-linked causes in males and females (6.0% and 6.9%, respectively), indicating that such variants do not account for the 1.4-fold male bias. We develop an improved strategy to detect X-linked developmental disorders and identify 23 significant genes, all of which were previously known, consistent with our inference that the vast majority of the X-linked burden is in known developmental disorder-associated genes. Importantly, we estimate that, in male probands, only 13% of inherited rare missense variants in known developmental disorder-associated genes are likely to be pathogenic. Our results demonstrate that statistical analysis of large datasets can refine our understanding of modes of inheritance for individual X-linked disorders.