DeepND: Deep multitask learning of gene risk for comorbid neurodevelopmental disorders
Date
2022-07-08Source Title
Patterns
Print ISSN
26663899
Publisher
Cell Press
Volume
3
Issue
7
Pages
1 - 16
Language
English
Type
ArticleItem Usage Stats
4
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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)
Keywords
AutismComorbidity
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