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

Authors

Long Gao
Yasin Uzun
Peng Gao
Bing He
Xiaoke Ma
Jiahui Wang, The Jackson LaboratoryFollow
Shizhong Han
Kai Tan

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).

Recommended Citation

Gao L, Uzun Y, Gao P, He B, Ma X, Wang J, Han S, Tan K. Identifying noncoding risk variants using disease-relevant gene regulatory networks. Nat Commun 2018 Feb 16; 9(1):702