Use of machine learning to shorten observation-based screening and diagnosis of autism.

The Autism Diagnostic Observation Schedule-Generic (ADOS) is one of the most widely used instruments for behavioral evaluation of autism spectrum disorders. It is composed of four modules, each tailored for a specific group of individuals based on their language and developmental level. On average, a module takes between 30 and 60 min to deliver. We used a series of machine-learning algorithms to study the complete set of scores from Module 1 of the ADOS available at the Autism Genetic Resource Exchange (AGRE) for 612 individuals with a classification of autism and 15 non-spectrum individuals from both AGRE and the Boston Autism Consortium (AC). Our analysis indicated that 8 of the 29 items contained in Module 1 of the ADOS were sufficient to classify autism with 100% accuracy. We further validated the accuracy of this eight-item classifier against complete sets of scores from two independent sources, a collection of 110 individuals with autism from AC and a collection of 336 individuals with autism from the Simons Foundation. In both cases, our classifier performed with nearly 100% sensitivity, correctly classifying all but two of the individuals from these two resources with a diagnosis of autism, and with 94% specificity on a collection of observed and simulated non-spectrum controls. The classifier contained several elements found in the ADOS algorithm, demonstrating high test validity, and also resulted in a quantitative score that measures classification confidence and extremeness of the phenotype. With incidence rates rising, the ability to classify autism effectively and quickly requires careful design of assessment and diagnostic tools. Given the brevity, accuracy and quantitative nature of the classifier, results from this study may prove valuable in the development of mobile tools for preliminary evaluation and clinical prioritization-in particular those focused on assessment of short home videos of children--that speed the pace of initial evaluation and broaden the reach to a significantly larger percentage of the population at risk.

Comparative analysis of neurological disorders focuses genome-wide search for autism genes.

The behaviors of autism overlap with a diverse array of other neurological disorders, suggesting common molecular mechanisms. We conducted a large comparative analysis of the network of genes linked to autism with those of 432 other neurological diseases to circumscribe a multi-disorder subcomponent of autism. We leveraged the biological process and interaction properties of these multi-disorder autism genes to overcome the across-the-board multiple hypothesis corrections that a purely data-driven approach requires. Using prior knowledge of biological process, we identified 154 genes not previously linked to autism of which 42% were significantly differentially expressed in autistic individuals. Then, using prior knowledge from interaction networks of disorders related to autism, we uncovered 334 new genes that interact with published autism genes, of which 87% were significantly differentially regulated in autistic individuals. Our analysis provided a novel picture of autism from the perspective of related neurological disorders and suggested a model by which prior knowledge of interaction networks can inform and focus genome-scale studies of complex neurological disorders.