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. 2012 Apr 4;485(7397):237-41.
doi: 10.1038/nature10945.

De Novo Mutations Revealed by Whole-Exome Sequencing Are Strongly Associated With Autism

Free PMC article

De Novo Mutations Revealed by Whole-Exome Sequencing Are Strongly Associated With Autism

Stephan J Sanders et al. Nature. .
Free PMC article


Multiple studies have confirmed the contribution of rare de novo copy number variations to the risk for autism spectrum disorders. But whereas de novo single nucleotide variants have been identified in affected individuals, their contribution to risk has yet to be clarified. Specifically, the frequency and distribution of these mutations have not been well characterized in matched unaffected controls, and such data are vital to the interpretation of de novo coding mutations observed in probands. Here we show, using whole-exome sequencing of 928 individuals, including 200 phenotypically discordant sibling pairs, that highly disruptive (nonsense and splice-site) de novo mutations in brain-expressed genes are associated with autism spectrum disorders and carry large effects. On the basis of mutation rates in unaffected individuals, we demonstrate that multiple independent de novo single nucleotide variants in the same gene among unrelated probands reliably identifies risk alleles, providing a clear path forward for gene discovery. Among a total of 279 identified de novo coding mutations, there is a single instance in probands, and none in siblings, in which two independent nonsense variants disrupt the same gene, SCN2A (sodium channel, voltage-gated, type II, α subunit), a result that is highly unlikely by chance.

Conflict of interest statement

The authors have no competing financial interests to declare.


Figure 1
Figure 1. Enrichment of non-synonymous de novo variants in probands compared with sibling controls
a) The rate of de novo variants is shown for 200 probands (red) and matched unaffected siblings (blue). ‘All’ refers to all RefSeq genes in hg18, ‘Brain’ refers to the subset of genes that are brain-expressed and ‘Non-syn’ to non-synonymous SNVs (including missense, nonsense and splice site SNVs). Error bars represent the 95% CI and p-values are calculated with a two-tailed binomial exact test. b) The proportion of transmitted variants in brain-expressed genes is equal between 200 probands (red) and matched unaffected siblings (blue) for all mutation types and allele frequencies, including common (≥1%); rare (<1%), and novel (single allele in one of the 400 parents); in contrast both non-synonymous and nonsense de novo variants show significant enrichment in probands compared to unaffected siblings (73.7% vs. 66.7%, p=0.01, asymptotic test and 9.5% vs. 3.1%, p=0.01 respectively). c) The frequency distribution of brain-expressed non-synonymous de novo SNVs is shown per sample for probands (red) and siblings (blue). Neither distribution differs from the Poisson distribution (black line) suggesting that multiple de novo SNVs within a single individual do not confirm ASD risk. ‘Nonsense’ represents the combination of nonsense and splice site SNVs.
Figure 2
Figure 2. Identification of multiple de novo mutations in the same gene reliably distinguishes risk-associated mutations
a) This plot shows the results of a simulation experiment modeling the likelihood, measured in −log(P) values, of observing two independent nonsense/splice site de novo mutations in the same brain-expressed gene among unrelated probands. We modeled the observed rate of de novo brain-expressed mutations in probands and siblings and evaluated models of locus heterogeneity, including 100, 333, 667, or 1,000 contributing genes, as well as using the top 1% of genes derived from a model of exponential distribution of risk. A total of 150,000 iterations were run. The identification of two or more independent nonsense/splice site de novo variants in a brain-expressed gene provides significant evidence for ASD association (p<0.05) for all models irrespective of increasing sample size. This observation remained statistically significant when the simulation was repeated using the lower bound of the 95% confidence interval for the estimate of de novo mutation rate (Fig. S7). b) The simulation described in ‘a’ was used to predict the number of genes that will be found to carry two or more nonsense/splice site de novo mutations for a sample of a given size (specified on the x-axis). c) The simulation was repeated for non-synonymous de novo mutations. The identification of three or more independent non-synonymous de novo mutations in a brain-expressed gene provides significant evidence for ASD association (p<0.05) in the sample reported here, however this threshold is sensitive both to sample size and heterogeneity models.

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