Estimating the Contribution of Coding Mutations to Autism

De novo mutations in protein-coding regions are strongly associated with autism, and family-based sequencing studies have identified numerous genes that harbor excess mutations in probands. However, the aggregate contribution of this class of variation to autism remains unclear. Here, we model the distribution of de novo autosomal coding variant effect sizes in 38,680 autism trios to estimate fundamental features of de novo genetic architecture. We find that damaging de novo single-nucleotide variants and frameshift indels explain 3.4% (95% CI: 2.1% - 4.7%) of autism variance on the observed scale. Approximately 7.0% (95% CI: 5.6% - 8.4%) of cases carry a large-effect mutation (rate ratio > 5), and most such mutations are incompletely penetrant. Although hundreds of genes make some nonzero contribution, 50% of mutational variance on the autosomes is explained by just 15 genes. De novo enrichments vary across cohorts with different ascertainment strategies; making projections for future trio studies, we show that many large-effect genes remain to be found. ### Competing Interest Statement K.J.K. is a member of the scientific advisory board of Nurture Genomics. M.E.T. has received research and/or financial support from Illumina Inc, Microsoft Inc, Pacific Biosciences, Ionis Pharmaceuticals, Levo Therapeutics, BridgeBio, and First Genomic Insights. Z.Z., M.M.M., and P.K. are or were employees of and may be shareholders of GeneDx, LLC. The other authors declare no conflicts of interest. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All samples analyzed in this manuscript are described in detail in the flagship consortium preprint: Satterstrom, F.K. et al. (2026) Rare variation illuminates the distinct and pleiotropic genetic architecture of autism across neuropsychiatric traits, medRxiv [Preprint]. The data had been de-identified before use in the study. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Code for running burdenMLE-DN is available at https://github.com/ajaynadig/burdenMLE-DN, including a wiki and tutorial.