Genetic Variants Associated with the Development of Type 2 Diabetes: Approaches to Their Identification

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Abstract

In the development of type 2 diabetes (T2D), an important role is played by a combination of environmental factors (hypodynamia, hypernutrition, etc.) and genetic variants that predispose the development of the disease. The contribution of inherited traits to the development of T2D can reach 80%, which is confirmed by the results of a number of published studies. At the same time, the multifactorial and polygenetic nature of T2D makes it difficult to establish direct cause-effect relations between individual genetic variants and specific metabolic changes. This explains a large number of studies and a long ongoing search for the most convenient and effective strategy for assessing the role of single nucleotide polymorphisms (SNP), the main type of genetic variation in the human genome. Involvement of specialists from various fields and the emergence of many methods for processing and interpreting data have led to the parallel development of scientific approaches. In this review of the main approaches (except mathematical ones) their characteristics will be described and the results obtained with their help will be evaluated, with special focus on new features of modern methods of genome editing, in particular the CRISPR/Cas9 system, and the future prospects in this area.

About the authors

Alexandra V. Stepanova

Lomonosov Moscow State University

Email: a-stepforward@yandex.ru
ORCID iD: 0000-0002-5290-0874

MD, PhD-student

SPIN- cod: 3651-4770

Russian Federation

Konstantin Y. Kulebyakin

Lomonosov Moscow State University

Author for correspondence.
Email: konstantin-kuleb@mail.ru
ORCID iD: 0000-0001-6954-5787
PhD, Faculty of Basic Medicine.

27-1, Lomonosovsky av., Moscow 119991

SPIN- cod: 7573-8527 Russian Federation

Tatyana N. Kochegura

Moscow State University Lomonosov M.V.

Email: t_kochegur@mail.ru
ORCID iD: 0000-0002-4869-4051

MD, PhD, Medical scientific and educational center

Russian Federation

Marina V. Shestakova

Endocrinology Research Centre

Email: nephro@endocrincentr.ru
ORCID iD: 0000-0002-5057-127X

MD, Phd, Professor

SPIN-cod: 7584-7015

 

Russian Federation

Vsevolod A. Tkachuk

Lomonosov Moscow State University

Email: tkachuk@fbm.msu.ru
ORCID iD: 0000-0002-7492-747X

Phd, Professor

SPIN-cod: 5515-4266

 

Russian Federation

References

  1. Dedov II, Smirnova OM, Kononenko IV. Significance of the results of genome-wide association studies for primary prevention of type 2 diabetes mellitus and its complications. Personalised approach. Diabetes mellitus. 2014;17(2):10–19. (In Russ). doi: 10.14341/DM2014210-19.
  2. Kim S, Misra A. SNP genotyping: technologies and biomedical applications. Annu Rev Biomed Eng. 2007;9:289–320. doi: 10.1146/annurev.bioeng.9.060906.152037.
  3. Maurano MT, Humbert R, Rynes E, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337(6099):1190–1195. doi: 10.1126/science.1222794.
  4. Sachidanandam R, Weissman D, Schmidt SC, et al. A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature. 2001;409(6822):928–933. doi: 10.1038/35057149.
  5. Goodarzi MO. Genetics of obesity: what genetic association studies have taught us about the biology of obesity and its complications. Lancet Diabetes Endocrinol. 2018;6(3):223–236. doi: 10.1016/s2213-8587(17)30200-0.
  6. Sandholm N, Groop PH. Genetic basis of diabetic kidney disease and other diabetic complications. Curr Opin Genet Dev. 2018;50:17–24. doi: 10.1016/j.gde.2018.01.002.
  7. Kononenko IV, Mayorov AY, Koksharova EO, Shestakova MV. Pharmacogenetics of hypoglycemic agents. Diabetes mellitus. 2015;18(4):28–34. (In Russ). doi: 10.14341/DM7681.
  8. Roses AD. Pharmacogenetics and the practice of medicine. Nature. 2000;405(6788):857–865. doi: 10.1038/35015728.
  9. Willemsen G, Ward KJ, Bell CG, et al. The concordance and heritability of type 2 diabetes in 34,166 twin pairs from international twin registers: the Discordant Twin (DISCOTWIN) Consortium. Twin Res Hum Genet. 2015;18(6):762–771. doi: 10.1017/thg.2015.83.
  10. cdc.gov [Internet]. National Diabetes Statistics Report [cited 2019 Feb 12]. Available from: https://www.cdc.gov/diabetes/data/statistics/statistics-report.html.
  11. Fajans SS, Bell GI. MODY: history, genetics, pathophysiology, and clinical decision making. Diabetes Care. 2011;34(8):1878–1884. doi: 10.2337/dc11-0035.
  12. Polak M, Cavé H. Neonatal diabetes mellitus: a disease linked to multiple mechanisms. Orphanet J Rare Dis. 2007;2:12. doi: 10.1186/1750-1172-2-12.
  13. Naylor RN, Greeley SA, Bell GI, Philipson LH. Genetics and pathophysiology of neonatal diabetes mellitus. J Diabetes Investig. 2011;2(3):158–169. doi: 10.1111/j.2040-1124.2011.00106.x.
  14. Vaxillaire M, Bonnefond A, Froguel P. The lessons of early-onset monogenic diabetes for the understanding of diabetes pathogenesis. Best Pract Res Clin Endocrinol Metab. 2012;26(2):171–187. doi: 10.1016/j.beem.2011.12.001.
  15. Dorak MT. Genetic association studies. New York, USA: Garland Science; 2016. 240 p. doi: 10.4324/9781315209364.
  16. Tabor HK, Risch NJ, Myers RM. Candidate-gene approaches for studying complex genetic traits: practical considerations. Nat Rev Genet. 2002;3(5):391–397. doi: 10.1038/nrg796.
  17. Patnala R, Clements J, Batra J. Candidate gene association studies: a comprehensive guide to useful in silico tools. BMC Genet. 2013;14:39. doi: 10.1186/1471-2156-14-39.
  18. Altshuler D, Hirschhorn JN, Klannemark M, et al. The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet. 2000;26(1):76–80. doi: 10.1038/79216.
  19. Kong Y, Sharma RB, Ly S, et al. CDKN2A/BT2D genome-wide association study risk SNPs impact locus gene expression and proliferation in human islets. Diabetes. 2018;67(5):872–884. doi: 10.2337/db17-1055.
  20. Bush WS, Moore JH. Chapter 11: genome-wide association studies. PLoS Comput Biol. 2012;8(12):e1002822. doi: 10.1371/journal.pcbi.1002822.
  21. Cirillo E, Kutmon M, Gonzalez Hernandez M, et al. From SNPs to pathways: biological interpretation of type 2 diabetes (T2DM) genome wide association study (GWAS) results. PLoS One. 2018;13(4):e0193515. doi: 10.1371/journal.pone.0193515.
  22. Gaj T, Gersbach CA, Barbas CF 3rd. ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering. Trends Biotechnol. 2013;31(7):397–405. doi: 10.1016/j.tibtech.2013.04.004.
  23. Zeng H, Guo M, Zhou T, et al. An isogenic human ESC platform for functional evaluation of genome-wide-association-study-identified diabetes genes and drug discovery. Cell Stem Cell. 2016;19(3):326–340. doi: 10.1016/j.stem.2016.07.002.
  24. Claussnitzer M, Dankel SN, Klocke B, et al. Leveraging cross-species transcription factor binding site patterns: from diabetes risk loci to disease mechanisms. Cell. 2014;156(1–2):343–358. doi: 10.1016/j.cell.2013.10.058.
  25. Fuchsberger C, Flannick J, Teslovich TM, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536(7614):41–47. doi: 10.1038/nature18642.
  26. Molven A, Njølstad PR. Role of molecular genetics in transforming diagnosis of diabetes mellitus. Expert Rev Mol Diagn. 2011;11(3):313–320. doi: 10.1586/erm.10.123.
  27. Botstein D, Risch N. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat Genet. 2003;33 Suppl:228–237. doi: 10.1038/ng1090.
  28. Hani EH, Boutin P, Durand E, et al. Missense mutations in the pancreatic islet beta cell inwardly rectifying K + channel gene (KIR6.2/BIR ): a meta-analysis suggests a role in the polygenic basis of Type II diabetes mellitus in Caucasians. Diabetologia. 1998;41(12):1511–1515. doi: 10.1007/s001250051098.
  29. Haghverdizadeh P, Sadat Haerian M, Haghverdizadeh P, Sadat Haerian B. ABCC8 genetic variants and risk of diabetes mellitus. Gene. 2014;545(2):198–204. doi: 10.1016/j.gene.2014.04.040.
  30. Daly AK, Day CP. Candidate gene case-control association studies: advantages and potential pitfalls. Br J Clin Pharmacol. 2001;52(5):489–499. doi: 10.1046/j.0306-5251.2001.01510.x.
  31. Brown AE, Walker M. Genetics of insulin resistance and the metabolic syndrome. Curr Cardiol Rep. 2016;18(8):75. doi: 10.1007/s11886-016-0755-4.
  32. Gaulton KJ. Mechanisms of type 2 diabetes risk loci. Curr Diab Rep. 2017;17(9):72. doi: 10.1007/s11892-017-0908-x72.
  33. Billings LK, Florez JC. The genetics of type 2 diabetes: what have we learned from GWAS?. Ann N Y Acad Sci. 2010;1212:59-77. doi: 10.1111/j.1749-6632.2010.05838.x
  34. Sandhu MS, Weedon MN, Fawcett KA, et al. Common variants in WFS1 confer risk of type 2 diabetes. Nat Genet. 2007;39(8):951–953. doi: 10.1038/ng2067.
  35. Dorajoo R, Liu J, Boehm BO. Genetics of type 2 diabetes and clinical utility. Genes (Basel). 2015;6(2):372–384. doi: 10.3390/genes6020372.
  36. Klupa T, Skupien J, Malecki M. Monogenic models: what have the single gene disorders taught us? Curr Diab Rep. 2012;12(6):659–666. doi: 10.1007/s11892-012-0325-0.
  37. Ali O. Genetics of type 2 diabetes. World J Diabetes. 2013;4(4):114–123. doi: 10.4239/wjd.v4.i4.114.
  38. Turner MD, Cassell PG, Hitman GA. Calpain-10: from genome search to function. Diabetes Metab Res Rev. 2005;21(6):505–514. doi: 10.1002/dmrr.578.
  39. Grant SF, Thorleifsson G, Reynisdottir I, et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet. 2006;38(3):320–323. doi: 10.1038/ng1732.
  40. Pritchard JK, Przeworski M. Linkage disequilibrium in humans: models and data. Am J Hum Genet. 2001;69(1):1–14. doi: 10.1086/321275.
  41. Visscher P, Wray N, Zhang Q, et al. 10 Years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101(1):5–22. doi: 10.1016/j.ajhg.2017.06.005.
  42. Weijers RN. Risk loci for type 2 diabetes – quo vadis? Clin Chem Lab Med. 2009;47(4):383–386. doi: 10.1515/cclm.2009.077.
  43. Watanabe RM. Statistical issues in gene association studies. In: DiStefano JK, editor. Disease gene identification: methods and protocols. New York, USA: Humana Press; 2011. pp. 17–36.
  44. Liao HK, Hatanaka F, Araoka T, et al. In vivo target gene activation via CRISPR/Cas9-mediated trans-epigenetic modulation. Cell. 2017;171(7):1495–1507. doi: 10.1016/j.cell.2017.10.025.
  45. Claussnitzer M, Dankel SN, Kim KH, et al. FTO obesity variant circuitry and adipocyte browning in humans. N Engl J Med. 2015;373(10):895–907. doi: 10.1056/nejmoa1502214.
  46. Teo AK, Gupta MK, Doria A, Kulkarni RN. Dissecting diabetes/metabolic disease mechanisms using pluripotent stem cells and genome editing tools. Mol Metab. 2015;4(9):593–604. doi: 10.1016/j.molmet.2015.06.006.
  47. Florez JC. Mining the genome for therapeutic targets. Diabetes. 2017;66(7):1770–1778. doi: 10.2337/dbi16-0069.
  48. Kato N. Insights into the genetic basis of type 2 diabetes. J Diabetes Investig. 2013;4(3):233–244. doi: 10.1111/jdi.12067.
  49. Davidson HW, Wenzlau JM, O’Brien RM. Zinc transporter 8 (ZnT8) and β cell function. Trends Endocrinol Metab. 2014;25(8):415–424. doi: 10.1016/j.tem.2014.03.008.
  50. Carroll D. Genome editing: past, present, and future. Yale J Biol Med. 2017;90(4):653–659.
  51. Maeder M, Gersbach C. Genome-editing technologies for gene and cell therapy. Mol Ther. 2016;24(3):430–446. doi: 10.1038/mt.2016.10.
  52. da Silva Xavier G, Bellomo E, McGinty J, et al. Animal models of GWAS-identified type 2 diabetes genes. J Diabetes Res. 2013;2013:906590. doi: 10.1155/2013/906590.
  53. Flannick J, Johansson S, Njølstad PR. Common and rare forms of diabetes mellitus: towards a continuum of diabetes subtypes. Nat Rev Endocrinol. 2016;12(7):394–406. doi: 10.1038/nrendo.2016.50.

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