High Body Mass Polygenic Risk in Mothers Enhances De Novo Functional Mutations in Epigenetic and Microtubule Gene Pathways in Their Offspring With Autism Spectrum Disorder

Abstract Background. Autism Spectrum Disorder (ASD) is a neurodevelopmental diagnosis that encompasses deficits in social communication in addition to repetitive and restrictive behaviors and interests. Accumulated evidence implicates over 100 risk genes and suggests possible genetic subtypes. We tested one previously characterized subtype relating to high maternal body mass index (BMI) as an enhancing risk factor in genetically vulnerable offspring. Methods. Using 1,300 families from the Simons Simplex Collection (SSC), we created an objectively defined subgroup of mothers in the highest quartile of the distribution of derived BMI polygenic risk scores. Polygenic risk for BMI reflects background genetic risk independent of the many environmental modifiers of BMI.Results. In the ASD offspring of mothers in this highest quartile, we found significant associations with de novo, putatively functional variants in genes in pathways related to chromatin state, chromatin structure, histone activity, and microtubule function. These gene pathways represent potential epigenetic vulnerability to alterations in the metabolic prenatal environment and/or alterations in microtubule-related brain development processes. The observed pathway enrichments were maternal-specific, and were not observed in neurotypical offspring. Two-thirds of the 36 genes in the significant epigenetic pathways and over half of the 33 genes in the significant microtubule pathways had existing ASD or neurodevelopmental risk evidence. Limitations. Though tests and simulations were done to ensure robustness of results, these findings have not been replicated in an external cohort.Conclusions. Our results suggest that epigenetic modification and/or microtubule deficits may be unique to a subset of ASD probands of mothers at increased genetic metabolic risk, pending external replication. Beyond the current application of these methods, our approach presents a strategy to reveal genetic subsets through polygenic risk stratification across phenotypic domains.