Exhaustive non-synonymous variants functionality prediction enables high resolution characterization of the neurofibromin architecture

Ofer Isakov, Deeann Wallis, D. Gareth Evans, Shay Ben-Shachar

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Neurofibromatosis type I (NF1) is caused by heterozygous loss-of-function variants in the NF1 gene encoding neurofibromin which serves as a tumor suppressor that inhibits RAS signaling and regulates cell proliferation and differentiation. While, the only well-established functional domain in the NF1 protein is the GAP-related domain (GRD), most of the identified non-truncating disease-causing variants are located outside of this domain, supporting the existence of other important disease-associated domains. Identifying these domains may reveal novel functions of NF1. Methods: By implementing inferential statistics combined with machine-learning methods, we developed a novel NF1-specific functional prediction model that focuses on nonsynonymous single nucleotide variants (SNVs). The model enables annotating all possible NF1 nonsynonymous variants, thus mapping the range of pathogenic non-truncating variants at the codon level across the NF1 gene. Findings: The generated model demonstrates high absolute prediction value for missense and splice-site variations (area under the ROC curve of 0.96) outperforming 14 other established models. By reviewing the entire dataset of nonsynonymous variants, two novel domains (Armadillo type fold 1 and 2) were identified as being associated with pathogenicity (OR 1.86; CI 1.04 to 3.34 and OR 2.08; CI 1.08 to 4.04, respectively; P <.05). Specific exons and codons associated with increased pathogenicity were also detected along the gene inside and outside the GRD domain. Interpretation: The developed model, enabled better prediction of pathogenicity for variants in NF1 gene, as well as elucidation of novel NF1-associated domains in addition to the GRD. Fund: This work was partially supported by the Kahn foundation. DGE is supported by the all Manchester NIHR Biomedical Research Centre (IS-brC-1215-20007).

Original languageEnglish
Pages (from-to)508-516
Number of pages9
JournalEBioMedicine
Volume36
DOIs
StatePublished - Oct 2018

Keywords

  • Functional annotation
  • Genetic variant
  • Machine learning
  • Neurofibromatosis 1
  • Variant prioritization

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