Prevalence and architecture of de novo mutations in developmental disorders

By J. McRae, Stephen Clayton, Tomas W. Fitzgerald, J. Kaplanis, E. Prigmore, D. Rajan, A. Sifrim, Stuart Aitken, N. Akawi, M. Alvi and 291 more. Nature, 2017

1,392 citations recorded by the source corpus.

The genomes of individuals with severe, undiagnosed developmental disorders are enriched in damaging de novo mutations (DNMs) in developmentally important genes. Here we have sequenced the exomes of 4,293 families containing individuals with developmental disorders, and meta-analysed these data with data from another 3,287 individuals with similar disorders. We show that the most important factors influencing the diagnostic yield of DNMs are the sex of the affected individual, the relatedness of their parents, whether close relatives are affected and the parental ages. We identified 94 genes enriched in damaging DNMs, including 14 that previously lacked compelling evidence of involvement in developmental disorders. We have also characterized the phenotypic diversity among these disorders. We estimate that 42% of our cohort carry pathogenic DNMs in coding sequences; approximately half of these DNMs disrupt gene function and the remainder result in altered protein function. We estimate that developmental disorders caused by DNMs have an average prevalence of 1 in 213 to 1 in 448 births, depending on parental age. Given current global demographics, this equates to almost 400,000 children born per year.

Subfields: Cancer Research, Genetics

View Paper: https://www.semanticscholar.org/paper/9044d286c6b26739945bc495bfa86e573c9014e7

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Citations in the corpus

• Predicting Splicing from Primary Sequence with Deep Learning. (2019, positive): “To assess the clinical impact of noncoding mutations that act through altered splicing, we applied the neural network to predict the effects of de novomutations in 4,293 individuals with intellectual disability from the Deciphering Developmental Disorders (DDD) cohort (McRae et al., 2017), 3,953 ind”
• Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism (2019, result): “Consistent with these observations, exome studies identify a higher frequency of disruptive de novo variants in severe NDD than in ASD (Deciphering Developmental Disorders Study, 2017).”
• Accurate proteome-wide missense variant effect prediction with AlphaMissense (2023, methodology): “…evaluated AlphaMissense on a diverse set of benchmark datasets, including annotated missense variants in ClinVar ( 30 ), de novo disease variants ( 54 ), and MAVE data collected in ProteinGym ( 19 ). disorders and controls ( 12 ), MAVE benchmarks in ProteinGym ( 19 ), and additional MAVE…”