Identifying noncoding risk variants using disease-relevant gene regulatory networks

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


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Validation Study

MeSH terms

  • Autoimmune Diseases / genetics*
  • Gene Regulatory Networks*
  • Genetic Predisposition to Disease*
  • Genetic Techniques*
  • Humans
  • Polymorphism, Single Nucleotide
  • Untranslated Regions*


  • Untranslated Regions