Convolutional neural network model to predict causal risk factors that share complex regulatory features

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Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional features, we developed a convolutional neural network framework for combinatorial, nonlinear modeling of complex patterns shared by risk variants scattered among multiple associated loci. When applied for major psychiatric disorders and autoimmune diseases, neural and immune features, respectively, exhibited high explanatory power while reflecting the pathophysiology of the relevant disease. The predicted causal variants were concentrated in active regulatory regions of relevant cell types and tended to be in physical contact with transcription factors while residing in evolutionarily conserved regions and resulting in expression changes of genes related to the given disease. We demonstrate some examples of novel candidate causal variants and associated genes. Our method is expected to contribute to the identification and functional interpretation of potential causal noncoding variants in post-GWAS analyses.
Publisher
OXFORD UNIV PRESS
Issue Date
2019-12
Language
English
Article Type
Article
Citation

NUCLEIC ACIDS RESEARCH, v.47, no.22, pp.e146 - e146

ISSN
0305-1048
DOI
10.1093/nar/gkz868
URI
http://hdl.handle.net/10203/271903
Appears in Collection
BiS-Journal Papers(저널논문)
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