Molecular AutismGenetic & Genomic MedicinePeer-reviewed journal article
Characterizing Genetic Pathways Unique to Autism Spectrum Disorder at Multiple Levels of Biological Analysis
Lukas S. Schaffer, Sophie BreunigiD, Jeremy M LawrenceiD, Isabelle Francesca FooteiD, Andrew David GrotzingeriD
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by atypical patterns of social functioning and repetitive/restricted behaviors. ASD commonly co-occurs with ADHD and, despite their clinical distinctiveness, the two share considerable genetic overlap. Given their shared genetic liability, it is unclear which genetic pathways confer unique risk for ASD independent of ADHD. We applied Genomic Structural Equation Modeling (SEM) to GWAS summary statistics for ASD and ADHD, decomposing the genetic signal for ASD into that which is unique to ASD (uASD) and that which is shared with ADHD. We computed genetic correlations between uASD and 75 external traits to estimate genetic overlap between uASD and other clinically relevant phenotypes. We went on to apply Stratified Genomic SEM to identify classes of genes enriched for uASD. Finally, we implemented Transcriptome-Wide SEM (T-SEM) to explore patterns of gene-expression associated with uASD. We observed positive genetic correlations between uASD and several external traits, most notably those relating to cognitive/educational outcomes and internalizing psychiatric traits. Stratified Genomic SEM showed that heritability for uASD was significantly enriched in genes involved in evolutionarily conserved processes, as well as for a histone mark in the germinal matrix. T-SEM revealed 83 unique genes with expression associated with uASD, many of which were novel. These findings delineate the unique biological underpinnings of ASD which exist independent of ADHD and demonstrate the utility of Genomic SEM and its extensions for disambiguating shared and unique risk pathways for genetically overlapping traits.
- organism: human
- assay: GWAS summary statistics
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 6
- FWCI
- 1.46
- Field percentile
- top 20%
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