The diagnostic dyad defining autism spectrum disorder (ASD) belies a tremendous phenotypic heterogeneity that remains a key challenge to diagnosis and treatment. The strong genetic component of ASD suggests that heterogeneity in underlying genetic mutations may contribute to phenotypic heterogeneity. However, the germline genetics of ASD has thus far proven insufficient to explain the clinical heterogeneity of the disorder.

Genetic changes can also arise post fertilization, outside of the germline. Such somatic mutations are now known to be widely prevalent within the brain. During mid-fetal corticogenesis, neural progenitor cells undergo rapid and extensive divisions to give rise to ~20 billion neurons. DNA synthesis in these progenitors is susceptible to replication errors, which, if uncorrected, may lead to somatic mutations that are inherited by the neuronal descendants of the progenitor cell in which the initial mutation occurred. Supporting this possibility, clonally derived somatic mutations have been found in the human cortex¹, ², ³.

Around 8–40 percent of neurons in the cerebral cortex, a key nexus in ASD pathogenesis, have been shown to harbor one or more large (>1Mb) copy number variants (CNVs)⁴, and CNVs are a frequently identified pathogenic genetic mutation in ASD. This raises the possibility that at least some of the unexplained clinical heterogeneity in ASD may reflect somatic genetic changes, including somatic CNVs.

Kenneth Kwan and his colleagues at the University of Michigan will explore the possibility that somatic mutations in the brain contribute to phenotypic heterogeneity. Leveraging genome editing and genomics techniques in the mouse developing cortex, Kwan and his team will address the hypothesis that certain regions of the genome, including genetic loci that have been associated with ASD, are more susceptible to brain somatic mutations. The successful completion of the proposed work will increase our understanding of a previously underappreciated genetic mechanism of ASD and ASD phenotypic heterogeneity.