Document Type

Article

Publication Date

2-16-2018

JAX Source

Nat Commun 2018 Feb 16; 9(1):702

Volume

9

Issue

1

First Page

702

Last Page

702

ISSN

2041-1723

PMID

29453388

DOI

https://doi.org/10.1038/s41467-018-03133-y

Grant

GM104369, GM108716

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. Nat Commun 2018 Feb 16; 9(1):702.

Comments

Open access under Creative Commons Attribution (CC BY) license (Creative Commons Attribution 4.0 International License).

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