DeepND: Deep multitask learning of gene risk for comorbid neurodevelopmental disorders

ABSTRACT: Summary Autism 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. Graphical abstract Highlights • DeepND can co-analyze comorbid neurodevelopmental disorders to discover risk genes• The approach employs multitask learning to learn shared and disorder-specific weights• DeepND uses graph convolution to process gene interactions in multiple networks• The model includes a mixture-of-experts model to detect informative networks The bigger picture While risk-gene-discovery algorithms have complemented exome/genome-sequencing studies of neurodevelopmental disorders, they are not capable of co-analyzing multiple comorbid conditions like autism and intellectual disability. A common approach is analyzing disorders one by one and comparing the outcomes. With this approach, the method does not utilize cross-disorder interactions and is bound by limited evidence per disorder. We address this gap with a technique, Deep Neurodevelopmental Disorders (DeepND), that uses multitask learning to co-analyze data from multiple disorders to learn shared and disorder-specific patterns. DeepND includes graph convolutional neural networks that process gene-interaction information from multiple networks. DeepND also learns which networks are important for disorder etiologies. Based on this, we propose an interpretable risk-gene-discovery algorithm for neuropsychiatric disorders. We propose a neural-network-based model that can simultaneously analyze multiple comorbid neurodevelopmental disorders to discover risk genes. DeepND learns the shared and disorder-specific interaction patterns of a risk gene in multiple disorders. It can also extract which gene-interaction networks are the most informative for the gene risk assessment and the etiology of each disorder. Our method can also work with any combination of comorbid conditions and is not limited to neurodevelopmental disorders.

SUBMITTER: Beyreli I 

PROVIDER: S-EPMC9278518 | biostudies-literature |

REPOSITORIES: biostudies-literature