| Autor: |
Pshennikova VG; Department of Molecular Genetics, Federal State Budgetary Scientific Institution 'Yakut Science Centre of Complex Medical Problems', Yakutsk, Russia.; Laboratory of Molecular Biology, Institute of Natural Sciences, M.K. Ammosov North-Eastern Federal University, Yakutsk, Russia., Barashkov NA; Department of Molecular Genetics, Federal State Budgetary Scientific Institution 'Yakut Science Centre of Complex Medical Problems', Yakutsk, Russia.; Laboratory of Molecular Biology, Institute of Natural Sciences, M.K. Ammosov North-Eastern Federal University, Yakutsk, Russia., Romanov GP; Department of Molecular Genetics, Federal State Budgetary Scientific Institution 'Yakut Science Centre of Complex Medical Problems', Yakutsk, Russia.; Laboratory of Molecular Biology, Institute of Natural Sciences, M.K. Ammosov North-Eastern Federal University, Yakutsk, Russia., Teryutin FM; Department of Molecular Genetics, Federal State Budgetary Scientific Institution 'Yakut Science Centre of Complex Medical Problems', Yakutsk, Russia.; Laboratory of Molecular Biology, Institute of Natural Sciences, M.K. Ammosov North-Eastern Federal University, Yakutsk, Russia., Solov'ev AV; Department of Molecular Genetics, Federal State Budgetary Scientific Institution 'Yakut Science Centre of Complex Medical Problems', Yakutsk, Russia.; Laboratory of Molecular Biology, Institute of Natural Sciences, M.K. Ammosov North-Eastern Federal University, Yakutsk, Russia., Gotovtsev NN; Department of Molecular Genetics, Federal State Budgetary Scientific Institution 'Yakut Science Centre of Complex Medical Problems', Yakutsk, Russia.; Laboratory of Molecular Biology, Institute of Natural Sciences, M.K. Ammosov North-Eastern Federal University, Yakutsk, Russia., Nikanorova AA; Department of Molecular Genetics, Federal State Budgetary Scientific Institution 'Yakut Science Centre of Complex Medical Problems', Yakutsk, Russia.; Laboratory of Molecular Biology, Institute of Natural Sciences, M.K. Ammosov North-Eastern Federal University, Yakutsk, Russia., Nakhodkin SS; Laboratory of Molecular Biology, Institute of Natural Sciences, M.K. Ammosov North-Eastern Federal University, Yakutsk, Russia., Sazonov NN; Laboratory of Molecular Biology, Institute of Natural Sciences, M.K. Ammosov North-Eastern Federal University, Yakutsk, Russia., Morozov IV; Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.; Novosibirsk State University, Novosibirsk, Russia., Bondar AA; Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia., Dzhemileva LU; Laboratory of Human Molecular Genetics, Institute of Biochemistry and Genetics, Ufa Scientific Centre, Russian Academy of Sciences, Ufa, Russia.; Department of Immunology and Human Reproductive Health, Bashkir State Medical University, Ufa, Russia., Khusnutdinova EK; Laboratory of Human Molecular Genetics, Institute of Biochemistry and Genetics, Ufa Scientific Centre, Russian Academy of Sciences, Ufa, Russia.; Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, Russia., Posukh OL; Novosibirsk State University, Novosibirsk, Russia.; Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia., Fedorova SA; Department of Molecular Genetics, Federal State Budgetary Scientific Institution 'Yakut Science Centre of Complex Medical Problems', Yakutsk, Russia.; Laboratory of Molecular Biology, Institute of Natural Sciences, M.K. Ammosov North-Eastern Federal University, Yakutsk, Russia. |
| Abstrakt: |
In silico predictive software allows assessing the effect of amino acid substitutions on the structure or function of a protein without conducting functional studies. The accuracy of in silico pathogenicity prediction tools has not been previously assessed for variants associated with autosomal recessive deafness 1A (DFNB1A). Here, we identify in silico tools with the most accurate clinical significance predictions for missense variants of the GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) connexin genes associated with DFNB1A. To evaluate accuracy of selected in silico tools (SIFT, FATHMM, MutationAssessor, PolyPhen-2, CONDEL, MutationTaster, MutPred, Align GVGD, and PROVEAN), we tested nine missense variants with previously confirmed clinical significance in a large cohort of deaf patients and control groups from the Sakha Republic (Eastern Siberia, Russia): Сх26: p.Val27Ile, p.Met34Thr, p.Val37Ile, p.Leu90Pro, p.Glu114Gly, p.Thr123Asn, and p.Val153Ile; Cx30: p.Glu101Lys; Cx31: p.Ala194Thr. We compared the performance of the in silico tools (accuracy, sensitivity, and specificity) by using the missense variants in GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) genes associated with DFNB1A. The correlation coefficient ( r ) and coefficient of the area under the Receiver Operating Characteristic (ROC) curve as alternative quality indicators of the tested programs were used. The resulting ROC curves demonstrated that the largest coefficient of the area under the curve was provided by three programs: SIFT (AUC = 0.833, p = 0.046), PROVEAN (AUC = 0.833, p = 0.046), and MutationAssessor (AUC = 0.833, p = 0.002). The most accurate predictions were given by two tested programs: SIFT and PROVEAN (Ac = 89%, Se = 67%, Sp = 100%, r = 0.75, AUC = 0.833). The results of this study may be applicable for analysis of novel missense variants of the GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) connexin genes. |
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