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

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.

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.

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.

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).