De novo mutations revealed by whole-exome sequencing are strongly associated with autism

Nature

 

485,

 

237–241

 

(10 May 2012)

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 (SNVs) 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.

Conclusions: 

Overall, the results substantially clarify the genomic architecture of ASD, demonstrate significant association of the three genes - SCN2A, KATNAL2 and CHD8 - and predict that approximately 25 to 50 additional ASD-risk genes will be identified as sequencing continues.  

Rare non-synonymous de novo SNVs are associated with risk, with odds ratios for nonsense and splice-site mutations in the range previously described for large multigenetic de novo CNVs.

This data provides on estimates and the true effect size for specific SNVs and mutation classes will be further clarified as more data accumulate.