A framework for the interpretation of de novo mutation in human disease

Kaitlin E. Samocha, Elise B. Robinson, Stephan J. Sanders, Christine Stevens, Aniko Sabo, Lauren M. McGrath, Jack A. Kosmicki, Karola Rehnström, Swapan Mallick, Andrew Kirby, Dennis P. Wall, Daniel G. MacArthur, Stacey B. Gabriel, Mark DePristo, Shaun M. Purcell, Aarno Palotie, Eric Boerwinkle, Joseph D. Buxbaum, Edwin H. Cook, Richard A. GibbsGerard D. Schellenberg, James S. Sutcliffe, Bernie Devlin, Kathryn Roeder, Benjamin M. Neale, Mark J. Daly

Research output: Contribution to journalArticlepeer-review

595 Scopus citations


Spontaneously arising (de novo) mutations have an important role in medical genetics. For diseases with extensive locus heterogeneity, such as autism spectrum disorders (ASDs), the signal from de novo mutations is distributed across many genes, making it difficult to distinguish disease-relevant mutations from background variation. Here we provide a statistical framework for the analysis of excesses in de novo mutation per gene and gene set by calibrating a model of de novo mutation. We applied this framework to de novo mutations collected from 1,078 ASD family trios, and, whereas we affirmed a significant role for loss-of-function mutations, we found no excess of de novo loss-of-function mutations in cases with IQ above 100, suggesting that the role of de novo mutations in ASDs might reside in fundamental neurodevelopmental processes. We also used our model to identify ∼1,000 genes that are significantly lacking in functional coding variation in non-ASD samples and are enriched for de novo loss-of-function mutations identified in ASD cases.

Original languageEnglish
Pages (from-to)944-950
Number of pages7
JournalNature Genetics
Issue number9
StatePublished - 2014


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