Despite the high heritability of autism, the genetic risk factors are still poorly understood. In the absence of reliable and feasible biomarkers, autism spectrum disorders are still diagnosed exclusively according to behavioral criteria. Novel therapeutic approaches are urgently needed, yet genome‐wide association studies have not met the need for a better understanding of the etiology of autism.
Knut Wittkowski and his colleagues at Rockefeller University in New York reanalyzed data from two independent stages of the Autism Genome Project using a novel computational biostatistics approach. This method integrates several neighboring single-nucleotide polymorphisms, or common genetic variants, increasing the power of the analysis. It also reduces the risk for artifacts by avoiding unrealistic assumptions, such as additive effects and independence of genetic risk factors.
Wittkowski’s analysis confirmed the importance of an interplay between axonal guidance and excitatory calcium signaling in autism etiology. It also suggests that protracted growth factor signaling is a crucial risk factor for severe forms of autism. Therefore, the time of major neuronal growth in response to environmental stimuli (around 1 year of age) would be the most effective time for preventing disease progression in children with genetic risk factors for autism.