Somatic Mosaicism in Simplex Autism Spectrum Disorder

Background: Autism spectrum disorder (ASD) has a complex genetic architecture, with many potential contributing risk factors. Somatic mosaicism has previously been implicated as a risk factor in other neurodevelopmental disorders, including lissencephaly, epilepsy, and overgrowth syndromes such as hemimegalencephaly (reviewed in Poduri et al. 2013). Genetic pathways involved in these syndromes have also been implicated in autism etiology, suggesting a potential role for somatic variants in ASD risk as well. However, little is known about how frequent somatic mosaic events may be during development and how their occurrence relates to the pathogenesis of complex disorders.

Objectives: We are systematically characterizing somatic mosaic mutations in 400 families from the Simons Simplex Collection, consisting of both parents, an ASD proband, and an unaffected sibling. Our goals are to determine the rates of mosaic mutations in affected and unaffected children, evaluate transgenerational risk, and to infer when and where in early development these mutations might be taking place. We are also evaluating the incidence of mosaic mutations in genes and pathways previously implicated in ASD risk.

Methods: Exome libraries were generated using the NimbleGen SeqCap EZ Human Exome v2.0 in-solution capture and sequenced on the Illumina platform. We developed a Somatic Mosaic Caller (SMOC) that builds an error model specific to the capture/sequencing method and then identifies significant outliers from this model, distinguishing potential mosaic events from sequencing errors. With this approach, we have been able to detect somatic mosaicism with 2-27% allele frequency in exome and targeted resequencing data. Variants of interest are being validated using a modified molecular inversion probe (MIP) protocol, enabling massive multiplexing of validation sites and samples. These MIPs are synthesized so that each probe molecule, and thus each capture event, incorporates a unique molecular tag. This allows us to achieve statistical accuracy and correct sequencing errors by sampling each tagged molecule multiple times.

Results: Using our SMOC approach, we anticipate identifying most mosaic mutations present at >10% MAF in these samples. To date, we have observed that ~5% of apparent de novo mutations in ASD probands show characteristics consistent with somatic mosaic events. We have so far validated somatic mosaic mutations in several genes that also harbor germline de novo mutations, including AKR1C2, FBN1, NAV2, NTNG1, and TSNARE1. These are likely candidates for contributing to disease risk, particularly NAV2 and NTNG1. NAV2 functions in neurite outgrowth and cerebellar development, while NTNG1 has been implicated in Rett’s syndrome and schizophrenia. We have also identified a maternal somatic mosaic variant in ADNP that was transmitted through germline to the proband, which is heterozygous. This indicates that parental somatic mosaicism can also contribute to disease risk.

Conclusions: Somatic mosaic events are an understudied class of genetic variation that may help explain several phenomena, including multiplex families with subclinical phenotypes and strongly discordant monozygotic twins. This systematic evaluation of somatic mosaicism in ASD families will help elucidate how frequently somatic mutations occur during development and their contributions to the pathogenesis of neurodevelopmental disorders.