<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Hyperparathyroidism-Jaw Tumor (HPT-JT) is an autosomal dominant disorder with variable expression, with an estimated prevalence of 6.7 per 1,000 population. Genetic testing for predisposing <italic>CDC73</italic> (HRPT2) mutations has been an important clinical advance, aimed at early detection and/or treatment to prevent advanced disease. The aim of this study is to assess the most deleterious SNPs mutations on <italic>CDC73</italic> gene and to predict their influence on the functional and structural levels using different bioinformatics tools. Method: Computational analysis using twelve different in-silico tools including SIFT, PROVEAN, PolyPhen-2, SNAP2, PhD-SNP, SNPs&amp;GO, P-Mut, I-Mutant ,Project Hope, Chimera, COSMIC and dbSNP Short Genetic Variations were used to identify the impact of mutations in <italic>CDC73</italic> gene that might be causing jaw tumor. Results: From (733) SNPs identified in the <italic>CDC73</italic> gene we found that only Eleven SNPs (G49C, L63P, L64P, D90H, R222G, W231R, P360S, R441C, R441H, R504S and R504H) has deleterious effect on the function and structure of protein and expected to cause the syndrome. Conclusion: Eleven substantial genetic/molecular aberrations in <italic>CDC73</italic> gene identified that could serve as diagnostic markers for hyperparathyroidism-jaw tumor (HPT-JT).</p></abstract>

Hyperparathyroidism-Jaw Tumor (HPT-JT) is an autosomal dominant disorder with variable expression, with an estimated prevalence of 6.7 per 1,000 population. Genetic testing for predisposing CDC73 (HRPT2) mutations has been an important clinical advance, aimed at early detection and/or treatment to prevent advanced disease. The aim of this study is to assess the most deleterious SNPs mutations on CDC73 gene and to predict their influence on the functional and structural levels using different bioinformatics tools. Method: Computational analysis using twelve different in-silico tools including SIFT, PROVEAN, PolyPhen-2, SNAP2, PhD-SNP, SNPs&GO, P-Mut, I-Mutant ,Project Hope, Chimera, COSMIC and dbSNP Short Genetic Variations were used to identify the impact of mutations in CDC73 gene that might be causing jaw tumor. Results: From (733) SNPs identified in the CDC73 gene we found that only Eleven SNPs (G49C, L63P, L64P, D90H, R222G, W231R, P360S, R441C, R441H, R504S and R504H) has deleterious effect on the function and structure of protein and expected to cause the syndrome. Conclusion: Eleven substantial genetic/molecular aberrations in CDC73 gene identified that could serve as diagnostic markers for hyperparathyroidism-jaw tumor (HPT-JT).

<italic>In silico</italic> analysis of <italic>CDC73</italic> gene revealing 11 novel SNPs with possible association to Hyperparathyroidism-Jaw Tumor syndrome

The EuroBiotech Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

 analysis of  gene revealing 11 novel SNPs with possible association to Hyperparathyroidism-Jaw Tumor syndrome

Hyperparathyroidism-Jaw Tumor (HPT-JT) is an autosomal dominant disorder with variable expression, with an estimated prevalence of 6.7 per 1,000 population....

In silico analysis of CDC73 gene revealing 11 novel SNPs with possible association to Hyperparathyroidism-Jaw Tumor syndrome

Article Category: Research Article

Pubblicato online: 30 apr 2020

Pagine: 67 - 81

DOI: https://doi.org/10.2478/ebtj-2020-0008

Parole chiave
SNPs, PHPT, gene, HPT-JT, in silico analysis

© 2020 Abdelrahman H. Abdelmoneim, Alaa I. Mohammed, Esraa O. Gadim, Mayada Alhibir Mohammed, Sara H. Hamza, Sara A. Mirghani, Thwayba A. Mahmoud and Mohamed A. Hassan, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

In silico analysis of CDC73 gene revealing 11 novel SNPs with possible association to Hyperparathyroidism-Jaw Tumor syndrome

Article Category: Research Article

Pubblicato online: 30 apr 2020

Pagine: 67 - 81

DOI: https://doi.org/10.2478/ebtj-2020-0008

Parole chiaveSNPs, PHPT, gene, HPT-JT, in silico analysis

© 2020 Abdelrahman H. Abdelmoneim, Alaa I. Mohammed, Esraa O. Gadim, Mayada Alhibir Mohammed, Sara H. Hamza, Sara A. Mirghani, Thwayba A. Mahmoud and Mohamed A. Hassan, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
SNPs, PHPT, gene, HPT-JT, in silico analysis