Identifying noncoding risk variants using disease-relevant gene regulatory networks

Long Gao, Yasin Uzun, Peng Gao, Bing He, Xiaoke Ma, Jiahui Wang, Shizhong Han and Kai Tan ()
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Long Gao: University of Pennsylvania
Yasin Uzun: Children’s Hospital of Philadelphia
Peng Gao: Children’s Hospital of Philadelphia
Bing He: Children’s Hospital of Philadelphia
Xiaoke Ma: Xidian University
Jiahui Wang: The Jackson Laboratory
Shizhong Han: Johns Hopkins University School of Medicine
Kai Tan: University of Pennsylvania

Nature Communications, 2018, vol. 9, issue 1, 1-12

Abstract: Abstract Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.

Date: 2018
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DOI: 10.1038/s41467-018-03133-y

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