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.
Recommended Citation
Gao, Long; Uzun, Yasin; Gao, Peng; He, Bing; Ma, Xiaoke; Wang, Jiahui; Han, Shizhong; and Tan, Kai, "Identifying noncoding risk variants using disease-relevant gene regulatory networks." (2018). Faculty Research 2018. 47.
https://mouseion.jax.org/stfb2018/47
Comments
Open access under Creative Commons Attribution (CC BY) license (Creative Commons Attribution 4.0 International License).