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Nat Commun. 2018 Feb 16;9(1):702. doi: 10.1038/s41467-018-03133-y.

Identifying noncoding risk variants using disease-relevant gene regulatory networks.

Gao L1, Uzun Y2,3, Gao P2,3, He B2,3, Ma X4, Wang J5, Han S6, Tan K7,8,9,10,11.

Author information

1
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
2
Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
3
Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
4
School of Computer Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China.
5
The Jackson Laboratory, Farmington, CT, 06032, USA.
6
Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
7
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. tank1@email.chop.edu.
8
Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. tank1@email.chop.edu.
9
Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. tank1@email.chop.edu.
10
Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. tank1@email.chop.edu.
11
Department of Cell & Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. tank1@email.chop.edu.

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.

PMID:
29453388
PMCID:
PMC5816022
DOI:
10.1038/s41467-018-03133-y
[Indexed for MEDLINE]
Free PMC Article

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