FIRE: Functional inference of genetic variants that regulate gene expression

Nilah M. Ioannidis, Joe R. Davis, Marianne K. DeGorter, Nicholas B. Larson, Shannon K. McDonnell, Amy J. French, Alexis J. Battle, Trevor J. Hastie, Stephen N. Thibodeau, Stephen B. Montgomery, Carlos D. Bustamante, Weiva Sieh, Alice S. Whittemore

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Motivation: Interpreting genetic variation in noncoding regions of the genome is an important challenge for personal genome analysis. One mechanism by which noncoding single nucleotide variants (SNVs) influence downstream phenotypes is through the regulation of gene expression. Methods to predict whether or not individual SNVs are likely to regulate gene expression would aid interpretation of variants of unknown significance identified in whole-genome sequencing studies. Results: We developed FIRE (Functional Inference of Regulators of Expression), a tool to score both noncoding and coding SNVs based on their potential to regulate the expression levels of nearby genes. FIRE consists of 23 random forests trained to recognize SNVs in cis-expression quantitative trait loci (cis-eQTLs) using a set of 92 genomic annotations as predictive features. FIRE scores discriminate cis-eQTL SNVs from non-eQTL SNVs in the training set with a cross-validated area under the receiver operating characteristic curve (AUC) of 0.807, and discriminate cis-eQTL SNVs shared across six populations of different ancestry from non-eQTL SNVs with an AUC of 0.939. FIRE scores are also predictive of cis-eQTL SNVs across a variety of tissue types.

Original languageEnglish (US)
Pages (from-to)3895-3901
Number of pages7
Issue number24
StatePublished - Dec 15 2017

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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