17177
Early Intervention in Autism: Wide-Locus GWAS Leading to Novel Treatment Options
The prevalence of autism spectrum disorders (ASD) has increased 20-fold over the past 50 years to >1% of U.S. children. Although twin studies attest to a high degree of heritability, the genetic risk factors are still poorly understood.
Objectives:
From recent results in a comorbid disease, childhood absence epilepsy, we had hypothesized that axonal guidance and calcium signaling are involved in autism as well. Our study aimed at identifying overlapping genetic risk factors for both neurodevelopmental diseases as well as indications for differences in the etiology to guide with developing novel autism-specific pharmacological interventions.
Methods:
We analyzed data from the two stages of the Autism Genome Projects as independent populations using u-statistics for genetically structuredwide-locus data and added data from unrelated controls to explore epistasis. To account for systematic, but disease-unrelated differences in (non-randomized) genome-wide association studies (GWAS) and for conducting multiple tests in overlapping genetic regions, we present a novel study-specific criterion for ‘genome-wide significance’.
Results:
Enrichment of the results in both studies with related genes confirms this hypothesis. Additional ASD-specific variations identified in this study suggest protracted growth factor signaling as causing more severe forms of ASD. Another cluster of related genes suggests a novel class of ion channels as additional, ASD-specific drug targets. The involvement of growth factors suggests the time of accelerated neuronal growth and pruning at 9–24 months of age as the period where treatment with a novel class of ion channel modulators would be most effective in preventing progression to more severe forms of autism.
Conclusions:
These results are the first to suggest a pharmacological intervation in autism based directly on observations in patients with autism. The major difference for this novel approach to identify ASD-specific risk factors and drug targets and previous statistical approaches is that genetic risk factors are assumed to be epistatic (within several neighboring SNPs) and in linkage disequilibrium with more than a single SNP. By extension, the same computational biostatistics approach could yield profound insights into the etiology of many common diseases from the genetic data collected over the last decade.