Though the genetic etiology of autism is complex, our understanding can be improved by identifying genes and gene-gene interactions that contribute to the development of specific autism subtypes. Identifying such gene groupings will allow individuals to be diagnosed and treated according to their precise characteristics. To this end, we developed a method to associate gene combinations with groups with shared autism traits, targeting genetic elements that distinguish patient populations with opposing phenotypes. Our computational method prioritizes genetic variants for genome-wide association, then utilizes Frequent Pattern Mining to highlight potential interactions between variants. We introduce a novel genotype assessment metric, the Unique Inherited Combination support, which accounts for inheritance patterns observed in the nuclear family while estimating the impact of genetic variation on phenotype manifestation at the individual level. High-contrast variant combinations are tested for significant subgroup associations. We apply this method by contrasting autism subgroups defined by severe or mild manifestations of a phenotype. Significant associations connected 286 genes to the subgroups, including 193 novel autism candidates. 71 pairs of genes have joint associations with subgroups, presenting opportunities to investigate interacting functions. This study analyzed 12 autism subgroups, but our informatics method can explore other meaningful divisions of autism patients, and can further be applied to reveal precise genetic associations within other phenotypically heterogeneous disorders, such as Alzheimer's disease.

Propranolol, a nonselective beta-adrenergic antagonist, has shown potential for improving anxiety in autistic individuals. Heart rate variability (HRV), a noninvasive cardiac marker of autonomic nervous system functioning, may help identify individuals most likely to benefit from propranolol. Objectives: Determine if baseline resting HRV and other cardiac measures predict the response to propranolol for anxiety and core autism symptomology in autistic children and young adults. Methods: Sixty-two autistic individuals (ages 7–24) participated in a two-phase (i.e., a 12-week randomized controlled trial and a 12-week open-label extension) trial of propranolol. Baseline (i.e., resting state, prior to treatment) HRV and other cardiac measures were obtained from an electrocardiogram. Clinical global impression for anxiety symptoms and overall behavioral treatment impact were assessed after the 12-week trial period. Group-level (i.e., all participants) and responder groups (i.e., strong, minimal, and non-responders to propranolol) were analyzed for treatment effects. Results: HRV variables predicted group-level anxiety response to propranolol, particularly for strong responders. Also, lower baseline values of parasympathetic HRV indices were significantly correlated with greater behavioral improvement after treatment with propranolol. Last, several baseline cardiac variables were associated with improvement in multiple behavioral domains after treatment with propranolol. Conclusions: HRV may be a potential biomarker for predicting reduced anxiety and behavioral symptoms in response to propranolol in autistic children and young adults. Identifying autonomic profiles associated with positive treatment outcomes could guide future personalized interventions in autism. The results presented herein should be regarded as preliminary until the findings are replicated in future clinical trials.

While genetic mechanisms in neurodevelopmental disorders are well studied, the mechanisms of environmental factors such as prenatal stress are less understood. Our lab previously characterized miRNA changes associated with stress during pregnancy in mouse brains and in maternal blood from mothers of children with ASD and indicated that prenatal stress can be linked to epigenetic markers. These miRNAs could be used as discovery biomarkers for stress exposure, as well as predictors of neurodevelopmental outcomes. In this pilot study, we gathered saliva samples and stress survey questionnaires from 83 pregnant African American women (ages 18–40) at the time of their ultrasound performed at 20 weeks. miRNA analysis was performed on the 10 highest- and 10 lowest-stress subjects. Out of 6631 miRNAs examined, 34 had significant differential expression, with 5 being upregulated and 29 downregulated in the high-stress group. Predicted targets of differentially expressed miRNAs revealed significant enrichment in neurodevelopmental pathways, including forebrain development, sensory system development, and neuronal growth regulation. This may suggest the potential developmental salience of these miRNA profiles. Future research will examine the neurodevelopmental outcomes of these pregnancies to determine the predictive potential of these miRNAs. This may help identify individuals at greatest risk after stress exposure during pregnancy.