Beyreli, İlaydaKarakahya, OğuzhanÇiçek, A. Ercüment2023-02-282023-02-282022-07-0826663899http://hdl.handle.net/11693/111946Autism spectrum disorder and intellectual disability are comorbid neurodevelopmental disorders with complex genetic architectures. Despite large-scale sequencing studies, only a fraction of the risk genes was identified for both. We present a network-based gene risk prioritization algorithm, DeepND, that performs cross-disorder analysis to improve prediction by exploiting the comorbidity of autism spectrum disorder (ASD) and intellectual disability (ID) via multitask learning. Our model leverages information from human brain gene co-expression networks using graph convolutional networks, learning which spatiotemporal neurodevelopmental windows are important for disorder etiologies and improving the state-of-the-art prediction in single- and cross-disorder settings. DeepND identifies the prefrontal and motor-somatosensory cortex (PFC-MFC) brain region and periods from early- to mid-fetal and from early childhood to young adulthood as the highest neurodevelopmental risk windows for ASD and ID. We investigate ASD- and ID-associated copy-number variation (CNV) regions and report our findings for several susceptibility gene candidates. DeepND can be generalized to analyze any combinations of comorbid disorders. © 2022 The Author(s)EnglishAutismComorbidityDeep learningDevelopment/pre-productionDSML3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problemsGenome-wide associationGraph convolutionIntellectual disabilityNode classificationSemisupervised learningArticle10.1016/j.patter.2022.100524