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Open AccessArticle

In-silico Analysis of NF1 Missense Variants in ClinVar: Translating Variant Predictions into Variant Interpretation and Classification

1
R&D Department, BioTechnology Services srl, 71122 Foggia, Italy
2
Sezione di Igiene, Dipartimento di Scienze Biomediche e Oncologia Umana, Università degli Studi di Bari Aldo Moro, 70124 Bari, Italy
3
Laboratorio di Genetica Medica, Dipartimento di Scienze Biomediche e Oncologia Umana, Università degli Studi di Bari Aldo Moro, 70124 Bari, Italy
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(3), 721; https://doi.org/10.3390/ijms21030721
Received: 25 September 2019 / Revised: 9 January 2020 / Accepted: 20 January 2020 / Published: 22 January 2020
(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data)
Background: With the advent of next-generation sequencing in genetic testing, predicting the pathogenicity of missense variants represents a major challenge potentially leading to misdiagnoses in the clinical setting. In neurofibromatosis type 1 (NF1), where clinical criteria for diagnosis may not be fully present until late infancy, correct assessment of variant pathogenicity is fundamental for appropriate patients’ management. Methods: Here, we analyzed three different computational methods, VEST3, REVEL and ClinPred, and after extracting predictions scores for 1585 NF1 missense variants listed in ClinVar, evaluated their performances and the score distribution throughout the neurofibromin protein. Results: For all the three methods, no significant differences were present between the scores of “likely benign”, “benign”, and “likely pathogenic”, “pathogenic” variants that were consequently collapsed into a single category. The cutoff values for pathogenicity were significantly different for the three methods and among benign and pathogenic variants for all methods. After training five different models with a subset of benign and pathogenic variants, we could reclassify variants in three sharply separated categories. Conclusions: The recently developed metapredictors, which integrate information from multiple components, after gene-specific fine-tuning, could represent useful tools for variant interpretation, particularly in genetic diseases where a clinical diagnosis can be difficult. View Full-Text
Keywords: variant interpretation; missense variants; NF1; VEST3; REVEL; ClinPred variant interpretation; missense variants; NF1; VEST3; REVEL; ClinPred
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MDPI and ACS Style

Accetturo, M.; Bartolomeo, N.; Stella, A. In-silico Analysis of NF1 Missense Variants in ClinVar: Translating Variant Predictions into Variant Interpretation and Classification. Int. J. Mol. Sci. 2020, 21, 721. https://doi.org/10.3390/ijms21030721

AMA Style

Accetturo M, Bartolomeo N, Stella A. In-silico Analysis of NF1 Missense Variants in ClinVar: Translating Variant Predictions into Variant Interpretation and Classification. International Journal of Molecular Sciences. 2020; 21(3):721. https://doi.org/10.3390/ijms21030721

Chicago/Turabian Style

Accetturo, Matteo; Bartolomeo, Nicola; Stella, Alessandro. 2020. "In-silico Analysis of NF1 Missense Variants in ClinVar: Translating Variant Predictions into Variant Interpretation and Classification" Int. J. Mol. Sci. 21, no. 3: 721. https://doi.org/10.3390/ijms21030721

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