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Elevated polygenic burden for autism is associated with differential DNA methylation at birth.

By Eilis Hannon, Diana Schendel, Christine Ladd-Acosta, Jakob Grove, iPSYCH-Broad ASD Group, Christine Søholm Hansen, Shan V. Andrews, David Michael Hougaard, Michaeline Bresnahan, Ole Mors, Mads Vilhelm Hollegaard, Marie Baekvad-Hansen, Mady Hornig, Preben B Mortensen, Anders D Børglum, Thomas Werge, Marianne Gioertz Pedersen, Merete Nordentoft, Joseph Buxbaum, M. Daniele Fallin, Jonas Byberg-Grauholm, Abraham Reichenberg, Jonathan Mill

Posted 26 Nov 2017
bioRxiv DOI: 10.1101/225193 (published DOI: 10.1186/s13073-018-0527-4)

Background: Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder characterized by deficits in social communication and restricted, repetitive behaviors, interests, or activities. The etiology of ASD involves both inherited and environmental risk factors, with epigenetic processes hypothesized as one mechanism by which both genetic and non-genetic variation influence gene regulation and pathogenesis. Methods: We quantified neonatal methylomic variation in 1,263 infants - of whom ~50% went on to subsequently develop ASD - using DNA isolated from a unique collection of archived blood spots taken shortly after birth. We used matched genetic data from the same individuals to examine the molecular consequences of ASD genetic risk variants, identifying methylomic variation associated with elevated polygenic burden for ASD. In addition, we performed DNA methylation quantitative trait loci (mQTL) mapping to prioritize target genes from ASD GWAS findings. Results: Although we did not identify specific loci showing consistent changes in neonatal DNA methylation associated with later ASD, we found a significant association between increased polygenic burden for autism and methylomic variation at two CpG sites located proximal to a robust GWAS signal for ASD on chromosome 8. Conclusions: This study is the largest analysis of DNA methylation in ASD yet undertaken and the first to integrate both genetic and epigenetic variation at birth in ASD. We demonstrate the utility of using a polygenic risk score to identify molecular variation associated with disease, and of using mQTL to refine the functional and regulatory variation associated with ASD risk variants.

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