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The Multiplex Genotyping Method for Single-Nucleotide Polymorphisms of Genes Associated with Obesity and Body Mass Index

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Abstract

Obesity is one of the global health problems resulting in significant economic and social damage in both developed and developing countries. Overweight and obesity are key risk factors of diabetes and cardiovascular and oncological diseases that cause high morbidity and mortality. In the present paper, the method of multiplex genotyping of polymorphic variants of genes associated with obesity and variability of body mass index (BMI) was developed on the basis of multilocus PCR and MALDI-TOF mass spectrometry of DNA molecules. The frequencies of 51 single-nucleotide polymorphisms of obesity candidate genes in a population sample of Russians in Kemerovo were characterized. The results obtained were compared with the data for populations from the 1000 Genomes project. The association of markers rs12446632 of the LOC105371116 locus and rs16851483 of the RASA2 gene with BMI variability in the Russian population of Kemerovo was shown.

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REFERENCES

  1. Dedov, I.I., Mel’nichenko G.A., and Romantsova, T.I., Pathogenetic aspects of obesity, Ozhirenie Metabol., 2004, no. 1, pp. 3–9.

  2. Dedov, I.I., Mel’nichenko G.A., Shestakova, M.V., et al., Russian national clinical recommendations for morbid obesity treatment in adults: 3rd revision (morbid obesity treatment in adults), Ozhirenie Metabol., 2018, vol. 15, no. 1, pp. 53–70. https://doi.org/10.14341/OMET2018153-70

    Article  Google Scholar 

  3. Romieu, I., Dossus, L., Barquera, S., et al., Energy balance and obesity: what are the main drivers?, Cancer Causes Control, 2017, vol. 28, no. 3, pp. 247–258. https://doi.org/10.1007/s10552-017-0869-z

    Article  PubMed  PubMed Central  Google Scholar 

  4. Huvenne, H., Dubern, B., Clément, K., and Poitou, C., Rare genetic forms of obesity: clinical approach and current treatments in 2016, Obes. Facts, 2016, vol. 9, no. 3, pp. 158–173. https://doi.org/10.1159/000445061

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. GWAS Catalog. https://www.ebi.ac.uk/gwas/. Accessed December, 2018.

  6. Rohde, K., Keller, M., la Cour Poulsen, L., et al., Genetics and epigenetics in obesity, Metabolism, 2019, vol. 92, pp. 37–50. https://doi.org/10.1016/j.metabol.2018.10.007

    Article  CAS  PubMed  Google Scholar 

  7. Srivastava, A., Srivastava, N., and Mittal, B., Genetics of obesity, Indian J. Clin. Biochem., 2016, vol. 31, no. 4, pp. 361–371. https://doi.org/10.1007/s12291-015-0541-x

    Article  PubMed  Google Scholar 

  8. Day, F.R. and Loos, R.J., Developments in obesity genetics in the era of genome-wide association studies, J. Nutrigenet. Nutrigenomics, 2011, vol. 4, no. 4, pp. 222–238. https://doi.org/10.1159/000332158

    Article  PubMed  Google Scholar 

  9. Gabriel, S., Ziaugra, L., and Tabbaa, D., SNP genotyping using the Sequenom MassARRAY iPLEX platform, Curr. Protoc. Hum. Genet., 2009, vol. 60, pp. 2.12.1–2.12.18. https://doi.org/10.1002/0471142905.hg0212s60

  10. Stepanov, V.A. and Trifonova, E.A., Multiplex SNP genotyping by MALDI-TOF mass spectrometry: Frequencies of 56 immune response gene SNPs in human populations, Mol. Biol. (Moscow), 2013, vol. 47, no. 6, pp. 852—862. https://doi.org/10.1134/S0026893313060149

    Article  CAS  Google Scholar 

  11. AgenaCx customer portal. https://www.agenacx.com. Accessed January, 2019.

  12. Weir, B.S., Genetic Data Analysis: Method for Discrete Population Genetic Data, Sunderland: Sinauer Associates, 1990.

    Google Scholar 

  13. Glantz, S.A., Primer of Biostatistics, New York: McGraw-Hill, 1997, 4th ed.

    Google Scholar 

  14. Triska, P., Chekanov, N., Stepanov, V., et al., Between lake Baikal and the Baltic sea: genomic history of the gateway to Europe, BMC Genet., 2017, vol. 18, suppl. 1. https://doi.org/10.1186/s12863-017-0578-3

  15. Stepanov, V.A., Evolution of genetic diversity and human diseases, Russ. J. Genet., 2016, vol. 52, no. 7, pp. 746—756. https://doi.org/10.1134/S1022795416070103

    Article  CAS  Google Scholar 

  16. Stepanov, V.A., Etnogenomika naseleniya Severnoi Evrazii (Ethnogenomics of the Northern Eurasia Population), Tomsk: Pechatnaya Manufaktura, 2002.

  17. The Human Gene Database GeneCards. https://www.genecards.org/. Accessed December, 2018.

  18. Arafeh, R., Qutob, N., Emmanuel, R., et al., Recurrent inactivating RASA2 mutations in melanoma, Nat. Genet., 2015, vol. 47, no. 12, pp. 1408–1410. https://doi.org/10.1038/ng.3427

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Tidyman, W.E. and Rauen, K.A., Expansion of the RASopathies, Curr. Genet. Med. Rep., 2016, vol. 4, no. 3, pp. 57–64.

    Article  PubMed  PubMed Central  Google Scholar 

  20. HaploReg v4.1. https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php. Accessed December, 2018.

  21. Atanes, P., Ruz-Maldonado, I., Hawkes, R., et al., Identifying signalling pathways regulated by GPRC5B in β-Cells by CRISPR-Cas9-mediated genome editing, Cell Physiol. Biochem., 2018, vol. 45, no. 2, pp. 656–666. https://doi.org/10.1159/000487159

    Article  CAS  PubMed  Google Scholar 

  22. Kim, Y.J., Greimel, P., and Hirabayashi, Y., GPRC5B-mediated sphingomyelin synthase 2 phosphorylation plays a critical role in insulin resistance, Science, 2018, vol. 8, pp. 250–266. https://doi.org/10.1016/j.isci.2018.10.001

    Article  CAS  Google Scholar 

  23. Kim, Y.J., Sano, T., Nabetani, T., et al., GPRC5B activates obesity-associated inflammatory signaling in adipocytes, Sci. Signal., 2012, vol. 5, no. 251. https://doi.org/10.1126/scisignal.2003149

  24. Willer, C.J., Speliotes, E.K., Loos, R.J., et al., Six new loci associated with body mass index highlight a neuronal influence on body weight regulation, Nat. Genet., 2009, vol. 41, no. 1, pp. 25–34. https://doi.org/10.1038/ng.287

    Article  CAS  PubMed  Google Scholar 

  25. Speliotes, E.K., Willer, C.J., Berndt, S.I., et al., Association analyses of 249 796 individuals reveal 18 new loci associated with body mass index, Nat. Genet., 2010, vol. 42, no. 11, pp. 937–948. https://doi.org/10.1038/ng.686

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Berndt, S.I., Gustafsson, S., Mägi, R., et al., Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture, Nat. Genet., 2013, vol. 45, no. 5, pp. 501–512. https://doi.org/10.1038/ng.2606

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Locke, A.E., Kahali, B., Berndt, S.I., et al., Genetic studies of body mass index yield new insights for obesity biology, Nature, 2015, vol. 518, no. 7538, pp. 197–206. https://doi.org/10.1038/nature14177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Felix, J.F., Bradfield, J.P., Monnereau, C., et al., Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index, Hum. Mol. Genet., 2016, vol. 25, no. 2, pp. 389–403. https://doi.org/10.1093/hmg/ddv472

    Article  CAS  PubMed  Google Scholar 

  29. Justice, A.E., Winkler, T.W., Feitosa, M.F., et al., Genome-wide meta-analysis of 241 258 adults accounting for smoking behaviour identifies novel loci for obesity traits, Nat. Commun., 2017, vol. 8. https://doi.org/10.1038/ncomms14977

  30. Akiyama, M., Okada, Y., Kanai, M., et al., Genome-wide association study identifies 112 new loci for body mass index in the Japanese population, Nat. Genet., 2017, vol. 49, no. 10, pp. 1458–1467. https://doi.org/10.1038/ng.3951

    Article  CAS  PubMed  Google Scholar 

  31. Graff, M., Scott, R.A., Justice, A.E., et al., Genome-wide physical activity interactions in adiposity—a meta-analysis of 200 452 adults, PLoS Genet., 2017, vol. 13, no. 4. e1006528. https://doi.org/10.1371/journal.pgen.1006528

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ng, M.C.Y., Graff, M., Lu, Y., et al., Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium, PLoS Genet., 2017, vol. 13, no. 4. e1006719. https://doi.org/10.1371/journal.pgen.1006719

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Hinney, A., Nguyen, T.T., Scherag, A., et al., Genome wide association (GWA) study for early onset extreme obesity supports the role of fat mass and obesity associated gene (FTO) variants, PLoS One, 2007, vol. 2, no. 12. e1361. https://doi.org/10.1371/journal.pone.0001361

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Loos, R.J., Lindgren, C.M., Li, S., et al., Common variants near MC4R are associated with fat mass, weight and risk of obesity, Nat. Genet., 2008, vol. 40, no. 6, pp. 768–775. https://doi.org/10.1038/ng.140

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Graff, M., Ngwa, J.S., Workalemahu, T., et al., Genome-wide analysis of BMI in adolescents and young adults reveals additional insight into the effects of genetic loci over the life course, Hum. Mol. Genet., 2013, vol. 22, no. 17, pp. 3597–3607. https://doi.org/10.1093/hmg/ddt205

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Wheeler, E., Huang, N., Bochukova, E.G., et al., Genome-wide SNP and CNV analysis identifies common and low-frequency variants associated with severe early-onset obesity, Nat. Genet., 2013, vol. 45, no. 5, pp. 513–517. https://doi.org/10.1038/ng.2607

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Wen, W., Zheng, W., Okada, Y., et al., Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index, Hum. Mol. Genet., 2014, vol. 23, no. 20, pp. 5492–5504. https://doi.org/10.1093/hmg/ddu248

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Meyre, D., Delplanque, J., Chevre, J.C., et al., Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations, Nat. Genet., 2009, vol. 41, no. 2, pp. 157–159. https://doi.org/10.1038/ng.301

    Article  CAS  PubMed  Google Scholar 

  39. Scherag, A., Dina, C., Hinney, A., et al., Two new loci for body-weight regulation identified in a joint analysis of genome-wide association studies for early-onset extreme obesity in French and German study groups, PLoS Genet., 2010, vol. 6, no. 4. e1000916. https://doi.org/10.1371/journal.pgen.1000916

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Dorajoo, R., Blakemore, A.I., Sim, X., et al., Replication of 13 obesity loci among Singaporean Chinese, Malay and Asian-Indian populations, Int. J. Obes. (London), 2012, vol. 36, no. 1, pp. 159–163. https://doi.org/10.1038/ijo.2011.86

    Article  CAS  Google Scholar 

  41. Warrington, N.M., Howe, L.D., Paternoster, L., et al., A genome-wide association study of body mass index across early life and childhood, Int. J. Epidemiol., 2015, vol. 44, no. 2, pp. 700–712. https://doi.org/10.1093/ije/dyv077

    Article  PubMed  PubMed Central  Google Scholar 

  42. Okada, Y., Kubo, M., Ohmiya, H., et al., Common variants at CDKAL1 and KLF9 are associated with body mass index in east Asian populations, Nat. Genet., 2012, vol. 44, no. 3, pp. 302–306. https://doi.org/10.1038/ng.1086

    Article  CAS  PubMed  Google Scholar 

  43. Thorleifsson, G., Walters, G.B., Gudbjartsson, D.F., et al., Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity, Nat. Genet., 2009, vol. 41, no. 1, pp. 18–24. https://doi.org/10.1038/ng.274

    Article  CAS  PubMed  Google Scholar 

  44. Comuzzie, A.G., Cole, S.A., Laston, S.L., et al., Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population, PLoS One, 2012, vol. 7, no. 12. e51954. https://doi.org/10.1371/journal.pone.0051954

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Cotsapas, C., Speliotes, E.K., Hatoum, I.J., et al., Common body mass index-associated variants confer risk of extreme obesity, Hum. Mol. Genet., 2009, vol. 18, no. 18, pp. 3502–3507. https://doi.org/10.1093/hmg/ddp292

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wen, W., Cho, Y.S., Zheng, W., et al., Meta-analysis identifies common variants associated with body mass index in east Asians, Nat. Genet., 2012, vol. 44, no. 3, pp. 307–311. https://doi.org/10.1038/ng.1087

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Pei, Y.F., Zhang, L., Liu, Y., et al., Meta-analysis of genome-wide association data identifies novel susceptibility loci for obesity, Hum. Mol. Genet., 2014, vol. 23, no. 3, pp. 820–830. https://doi.org/10.1093/hmg/ddt464

    Article  CAS  PubMed  Google Scholar 

  48. Wan, E.S., Cho, M.H., Boutaoui, N., et al., Genome-wide association analysis of body mass in chronic obstructive pulmonary disease, Am. J. Respir. Cell Mol. Biol., 2011, vol. 45, no. 2, pp. 304–310. https://doi.org/10.1165/rcmb.2010-0294OC

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Namjou, B., Keddache, M., Marsolo, K., et al., EMR-linked GWAS study: investigation of variation landscape of loci for body mass index in children, Front Genet., 2013, vol. 4. https://doi.org/10.3389/fgene.2013.00268

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Funding

This work was financially supported by the Russian Foundation for Basic Research (project no. 18-04-00758).

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Correspondence to E. A. Trifonova.

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Statement of compliance with standards of research involving humans as subjects. All procedures performed in the study involving human participants are in accordance with the ethical standards of the institutional and/or national research ethics committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from each participant involved in the study.

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Translated by D. Novikova

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Trifonova, E.A., Popovich, A.A., Vagaitseva, K.V. et al. The Multiplex Genotyping Method for Single-Nucleotide Polymorphisms of Genes Associated with Obesity and Body Mass Index. Russ J Genet 55, 1282–1293 (2019). https://doi.org/10.1134/S1022795419100144

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