Neurodevelopmental disorders, at-large, are genetically complex with hundreds of independent risk loci. Disruption of this diverse set of factors ultimately leads to the behaviorally defined clinical phenotypes that we have today, such as autism spectrum disorder (ASD). We still have little understanding of: (1) the core biology (pathophysiology) behind these conditions; (2) whether our clinically defined groups are single conditions or collections of hundreds of similar phenotypic presentations; and (3) how many roads may lead to the same underlying condition.

Sofie Salama and David Haussler will test the hypothesis that changes in NOTCH2NL gene dosage contribute to the neurological phenotypes observed in individuals with autism who carry 1q21.1 distal deletions and duplications. This will be done by re-analyzing existing genome sequencing data from over 4,000 autism families and by developing new long DNA molecule sequencing methods that enable assembly of this complex genomic region in many individuals.

Computational gene risk prediction methods and network-based analyses are major tools in analyzing large-scale autism genomic studies for (i) imputing the insufficient statistical signal and providing a genome-wide risk ranking and (ii) finding out the affected cellular circuitries such as pathways and networks of genes. Here, Ercüment Çiçek and his team plan to develop a novel cross-disorder gene discovery algorithm that can analyze related disorders simultaneously and explicitly learn shared and disorder-specific genetic components.