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

Date
2022-07-08
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Source Title
Patterns
Print ISSN
26663899
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Publisher
Cell Press
Volume
3
Issue
7
Pages
1 - 16
Language
English
Type
Article
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Abstract

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. © 2022 The Author(s)

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Keywords
Autism, Comorbidity, Deep learning, Development/pre-production, DSML3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems, Genome-wide association, Graph convolution, Intellectual disability, Node classification, Semisupervised learning
Citation
Published Version (Please cite this version)