Volume 15, Issue 2 (6-2023)                   IJDO 2023, 15(2): 73-80 | Back to browse issues page


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Sefid F, Azamirad G, Asadollahi S, Kalantar S M, Khalilzade S H, Vahidi Mehrjardi M Y. Common Polymorphisms Identified In Patients with Type 2 Diabetes Mellitus Revealed From Next-Generation Sequencing Analysis. IJDO 2023; 15 (2) :73-80
URL: http://ijdo.ssu.ac.ir/article-1-789-en.html
Diabetes Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Abstract:   (420 Views)
Objective: Type 2 diabetes mellitus (T2DM) is a multifactorial genetic condition caused by the combination of genes and environmental factors. Several variations linked to T2DM have been discovered in recent genetic investigations, particularly genome-wide association studies (GWAS). This study aimed to investigate genes involved in T2DM, focusing on the NGS analysis and studying the genetic basis of T2DM to improve diagnosis, prevention, and treatment.
Materials and Methods: We selected 5 families based on the diagnosis of diabetes at the age of 30 years or earlier in at least 3 consecutive generations for NGS analyses.
Results: For each of the 5 participants tested thus far, a mean of 11 to 21 variants of clinical significance were detected. These variants were located in different genes, which indicate the association of these genes with susceptibility to diabetes. WFS1 and INS gene mutations were present in all five diabetic patients analyzed. Specifically, mutations in WFS1, KCNJ11, ABCC8, HNF1B, INS, GCKR, HNF1A and PCSK1N account for 25%, 13%, 8%, 7%, 7%, 6%, 6% and 6% of patients, respectively.
Conclusion: WFS1 is the most often altered gene in our participants with putative alterations, according to our findings (25%). WFS1 mutations were discovered in all of the probands.
 
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Type of Study: Research | Subject: Special
Received: 2023/01/5 | Accepted: 2023/03/25 | Published: 2023/06/20

References
1. Park KS. The search for genetic risk factors of type 2 diabetes mellitus. Diabetes & Metabolism Journal. 2011; 35(1): 12-22.
2. Whiting DR, Guariguata L, Weil C, Shaw J. IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Research and Clinical Practice. 2011; 94(3): 311-21.
3. Kahn SE, Cooper ME, Del Prato S. Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. The Lancet. 2014; 383(9922): 1068-83.
4. Fowler MJ. Microvascular and macrovascular complications of diabetes. Clinical Diabetes. 2008; 26(2): 77-82.
5. Rich SS. Mapping genes in diabetes: Genetic epidemiological perspective. Diabetes. 1990; 39(11): 1315-19. [DOI:10.2337/diab.39.11.1315]
6. Lindstrom J. Sustained reduction in the incidence of type 2 diabetes by lifestyle intervention: follow-up of the Finnish Diabetes Prevention Study. The Lancet. 2006; 368: 1673-9.
7. Nasykhova YA, Barbitoff YA, Serebryakova EA, Katserov DS, Glotov AS. Recent advances and perspectives in next generation sequencing application to the genetic research of type 2 diabetes. World Journal of Diabetes. 2019; 10(7): 376-395.
8. Chatterjee S, Khunti K, Davies MJ. Type 2 diabetes. The lancet. 2017; 389 (10085): 2239-51.
9. DeFronzo RA, Ferrannini E, Groop L, Henry RR, Herman WH, Holst JJ. Type 2 diabetes mellitus. Nature Reviews Disease Primers. 2015; 1 (1):1-22.
10. Consortium IH. A haplotype map of the human genome. Nature. 2005; 437 (7063): 1299-320.
11. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy‐Moonshine A, et al. From FastQ data to high‐confidence variant calls: the genome analysis toolkit best practices pipeline. Current Protocols in Bioinformatics. 2013; 43(1): 11-0.
12. Kooner JS, Saleheen D, Sim X, Sehmi J, Zhang W, Frossard P, et al. Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nature Genetics. 2011; 43 (10): 984-9.
13. Ellard S, Lango Allen H, De Franco E, Flanagan SE, Hysenaj G, Colclough K, et al. Improved genetic testing for monogenic diabetes using targeted next-generation sequencing. Diabetologia. 2013;56:1958-63.
14. Szopa M, Ludwig-Słomczyńska A, Radkowski P, Skupień J, Zapała B, Płatek T, et al. Genetic testing for monogenic diabetes using targeted next-generation sequencing in patients with maturity-onset diabetes of the young. Polish Archives Of Internal Medicine. 2015;125(11). 845-51.
15. Fareed M, Chauhan W, Fatma R, Din I, Afzal M. Next-generation sequencing technologies in diabetes research. Diabetes Epidemiology and Management. 2022:100097.
16. Ang SF, Lim SC, Tan CS, Fong JC, Kon WY, Lian JX, et al. A preliminary study to evaluate the strategy of combining clinical criteria and next generation sequencing (NGS) for the identification of monogenic diabetes among multi-ethnic Asians. Diabetes Research and Clinical Practice. 2016; 119: 13-22.
17. Khan IA. Do second generation sequencing techniques identify documented genetic markers for neonatal diabetes mellitus?. Heliyon. 2021;7(9): e07903.
18. Cryns K, Sivakumaran TA, Van den Ouweland JM, Pennings RJ, Cremers CW, Flothmann K, et al. Mutational spectrum of the WFS1 gene in Wolfram syndrome, nonsyndromic hearing impairment, diabetes mellitus, and psychiatric disease. Human Mutation. 2003; 22(4): 275-87.
19. Rigoli L, Lombardo F, Di Bella C. Wolfram syndrome and WFS1 gene. Clinical Genetics. 2011; 79(2): 103-17.
20. Minton JA, Hattersley AT, Owen K, McCarthy MI, Walker M, Latif F, et al. Association studies of genetic variation in the WFS1 gene and type 2 diabetes in UK populations. Diabetes. 2002; 51(4): 1287-90.
21. Sandhu MS, Weedon MN, Fawcett KA, Wasson J, Debenham SL, Daly A, et al. Common variants in WFS1 confer risk of type 2 diabetes. Nature Genetics. 2007; 39(8): 951-3.
22. Franks PW, Rolandsson O, Debenham SL, Fawcett KA, Payne F, Dina C, et al. Replication of the association between variants in WFS1 and risk of type 2 diabetes in European populations. Diabetologia. 2008; 51: 458-63. [DOI:10.1007/s00125-007-0887-6]
23. Haghvirdizadeh P, Mohamed Z, Abdullah NA, Haghvirdizadeh P, Haerian MS, Haerian BS. KCNJ11: genetic polymorphisms and risk of diabetes mellitus. Journal of Diabetes Research. 2015; ID 908152.
24. Phani NM, Guddattu V, Bellampalli R, Seenappa V, Adhikari P, Nagri SK, et al. Population specific impact of genetic variants in KCNJ11 gene to type 2 diabetes: A case-control and meta-analysis study. PLoS One. 2014; 9(9): e107021.
25. Qiu L, Na R, Xu R, Wang S, Sheng H, Wu W, et al. Quantitative assessment of the effect of KCNJ11 gene polymorphism on the risk of type 2 diabetes. PloS one. 2014; 9(4): e93961.
26. Babenko AP, Polak M, Cavé H, Busiah K, Czernichow P, Scharfmann R, et al. Activating mutations in the ABCC8 gene in neonatal diabetes mellitus. New England Journal of Medicine. 2006; 355(5): 456-66.
27. Haghverdizadeh P, Haerian MS, Haghverdizadeh P, Haerian BS. ABCC8 genetic variants and risk of diabetes mellitus. Gene. 2014;545(2):198-204.
28. Dorajoo R, Liu J, Boehm BO. Genetics of type 2 diabetes and clinical utility. Genes. 2015; 6(2): 372-84.
29. Floyd JS, Psaty BM. The application of genomics in diabetes: barriers to discovery and implementation. Diabetes Care. 2016; 39(11): 1858-69.

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