Predicting informative spatio-temporal neurodevelopmental windows and gene risk for autism spectrum disorder

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Due to its intricate nature, only a fraction of the risk genes were identified despite the effort spent on large-scale sequencing studies. To perceive underlying mechanisms of ASD and predict new risk genes, a deep learning architecture is designed which processes mutational burden of genes and gene co-expression networks using graph convolutional networks. In addition, a mixture of experts model is employed to detect specific neurodevelopmental periods that are of particular importance for the etiology of the disorder. This end-to-end trainable model produces a posterior ASD risk probability for each gene and learns the importance of each network for this prediction. The results of our approach show that the ASD gene risk prediction power is improved compared to the state-of-the-art models. We identify mediodorsal nucleus of thalamus and cerebellum brain region and neonatal & early infancy to middle & late childhood period (0 month - 12 years) as the most informative neurodevelopmental window for prediction. Top predicted risk genes are found to be highly enriched in ASDassociated pathways and transcription factor targets. We pinpoint several new candidate risk genes in CNV regions associated with ASD. We also investigate confident false-positives and false negatives of the method and point to studies which support the predictions of our method.