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A Gene Enrichment Approach Applied to Sleep and Autism
Sleep problems/disorders (SD) are common in autism spectrum disorder (ASD), and up to 80% of individuals diagnosed with ASD will experience a SD within their lifetime (Richdale & Schreck, 2009). SDs regularly include increased sleep onset latency, frequent and prolonged night awakenings, insomnia, and/or early morning rise times (Cortesi et al., 2010; Krakowiak et al., 2008; Richdale & Baglin, 2015). Although SD and ASD are highly comorbid, relatively little is known about the mechanistic intersections of these two disorder classes.
Objectives:
Using a gene enrichment approach, the present study (1) identifies genes that contribute to both SD and ASD and (2) discusses common mechanistic/biological pathways.
Methods:
We used the core autism gene set from the Autism KB database library (Xu et al., 2011). The ASD core gene set includes 171 genes, each with a total evidence score of 16 or higher (higher scores indicate greater confidence in their ASD affiliation). Additionally, we hand-curated a sleep gene database using existing literature. Inclusion in the final sleep gene set required a minimum of two peer-reviewed empirical findings (per gene) indicating the association of structural variations in that gene with a sleep phenotype, or at least one peer-reviewed GWAS finding. The final curated sleep gene set contained 154 genes.
First, we determined the number of genes appearing in both the sleep and ASD core gene datasets and calculated whether the ASD gene set was enriched for sleep-related genes (e.g., whether more genes were in both gene sets than expected by chance). Specifically, we compared our final sleep gene set against the published ASD core gene set. We used the Nematode bioinformatics analysis tools and data (Lund, n.d.) to calculate the statistical significance of genetic overlap between the two gene sets. Then, we subjected the list of overlapping genes to an over-representation pathway analysis (Marceau & Abel, 2018). The over-representation analysis was conducted by uploading the overlapping genes into the online over-representation tool maintained by the Consensus Pathway Database (CPDB; http://cpdb.molgen.mpg.de/).
Results:
Overall, we identified 16 common genes across the sleep and ASD core gene sets (Table 1). The expected number of overlapping genes was (154[sleep gene set size]*171[ASD core gene set size])/19,000[total genes in genome]=1.386. The representation factor was therefore 16[identified genes]/1.386[expected genes]=11.54, p<6.645e-13, indicating significantly more overlap than expected by chance (based on the size of each gene set and the total number of genes in the genome).
The identified overlapping genes are involved in circadian entrainment (CACNA1C, GRIA3), melatonin synthesis (ASMT), and are linked with several known genetic syndromes (e.g., MECP2, MAOA, UBE3A). An over-representation analysis identified several enriched pathways (Table 2) that suggest dopamine and other chemical synapses in the neuronal system as potential shared mechanisms of SD and ASD.
Conclusions:
The gene overlap set and the highlighted biological pathways discussed in this study serve as a stepping-stone for new genetic investigations of SD and ASD comorbidity or may be used in existing ASD genome databases to answer critical questions about SD in individuals with ASD across the lifespan.