Genomics & Informatics

Korea Genome Organization (한국유전체학회)
권호 표지
DOI QR코드

DOI QR Code

Genetic Association Analysis of Fasting and 1- and 2-Hour Glucose Tolerance Test Data Using a Generalized Index of Dissimilarity Measure for the Korean Population

  • Yee, Jaeyong (Department of Physiology and Biophysics, Eulji University) ;
  • Kim, Yongkang (Department of Statistics, Seoul National University) ;
  • Park, Taesung (Department of Statistics, Seoul National University) ;
  • Park, Mira (Department of Preventive Medicine, Eulji University)
  • Received : 2016.10.21
  • Accepted : 2016.11.16
  • Published : 2016.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Glucose tolerance tests have been devised to determine the speed of blood glucose clearance. Diabetes is often tested with the standard oral glucose tolerance test (OGTT), along with fasting glucose level. However, no single test may be sufficient for the diagnosis, and the World Health Organization (WHO)/International Diabetes Federation (IDF) has suggested composite criteria. Accordingly, a single multi-class trait was constructed with three of the fasting phenotypes and 1- and 2-hour OGTT phenotypes from the Korean Association Resource (KARE) project, and the genetic association was investigated. All of the 18 possible combinations made out of the 3 sets of classification for the individual phenotypes were taken into our analysis. These were possible due to a method that was recently developed by us for estimating genomic associations using a generalized index of dissimilarity. Eight single-nucleotide polymorphisms (SNPs) that were found to have the strongest main effect are reported with the corresponding genes. Four of them conform to previous reports, located in the CDKAL1 gene, while the other 4 SNPs are new findings. Two-order interacting SNP pairs of are also presented. One pair (rs2328549 and rs6486740) has a prominent association, where the two single-nucleotide polymorphism locations are CDKAL1 and GLT1D1. The latter has not been found to have a strong main effect. New findings may result from the proper construction and analysis of a composite trait.

Keywords

References

  1. Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 2010;11:446-450. https://doi.org/10.1038/nrg2809
  2. Evans DM, Marchini J, Morris AP, Cardon LR. Two-stage two-locus models in genome-wide association. PLoS Genet 2006;2:e157. https://doi.org/10.1371/journal.pgen.0020157
  3. Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, et al. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 2001;69:138-147. https://doi.org/10.1086/321276
  4. Kim K, Kwon MS, Oh S, Park T. Identification of multiple gene-gene interactions for ordinal phenotypes. BMC Med Genomics 2013;6 Suppl 2:S9. https://doi.org/10.1186/1755-8794-6-S3-S9
  5. Yee J, Kim Y, Park T, Park M. Using the generalized index of dissimilarity to detect gene-gene interactions in multi-class phenotypes. PLoS One 2016;11:e0158668. https://doi.org/10.1371/journal.pone.0158668
  6. World Health Organization. Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia: Report of a WHO/IDF Consultation. Geneva: World Health Organization, 2006.
  7. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2005;28 Suppl 1:S37-S42. https://doi.org/10.2337/diacare.28.suppl_1.S37
  8. Sakoda JM. A generalized index of dissimilarity. Demography 1981;18:245-250. https://doi.org/10.2307/2061096
  9. Jensen DD, Cohen PR. Multiple comparisons in induction algorithms. Mach Learn 2000;38:309-338. https://doi.org/10.1023/A:1007631014630
  10. Yee J, Kwon MS, Park T, Park M. A modified entropy-based approach for identifying gene-gene interactions in case-control study. PLoS One 2013;8:e69321. https://doi.org/10.1371/journal.pone.0069321
  11. Saxena R, Hivert MF, Langenberg C, Tanaka T, Pankow JS, Vollenweider P, et al. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet 2010;42:142-148. https://doi.org/10.1038/ng.521
  12. Zheng C, Dalla Man C, Cobelli C, Groop L, Zhao H, Bale AE, et al. A common variant in the MTNR1b gene is associated with increased risk of impaired fasting glucose (IFG) in youth with obesity. Obesity (Silver Spring) 2015;23:1022-1029.
  13. Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, et al. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 2009;41:527-534. https://doi.org/10.1038/ng.357
  14. Chen P, Takeuchi F, Lee JY, Li H, Wu JY, Liang J, et al. Multiple nonglycemic genomic loci are newly associated with blood level of glycated hemoglobin in East Asians. Diabetes 2014; 63:2551-2562. https://doi.org/10.2337/db13-1815
  15. Peng G, Luo L, Siu H, Zhu Y, Hu P, Hong S, et al. Gene and pathway-based second-wave analysis of genome-wide association studies. Eur J Hum Genet 2010;18:111-117. https://doi.org/10.1038/ejhg.2009.115
  16. Ryu J, Lee C. Association of glycosylated hemoglobin with the gene encoding CDKAL1 in the Korean Association Resource (KARE) study. Hum Mutat 2012;33:655-659. https://doi.org/10.1002/humu.22040
  17. Quaranta M, Burden AD, Griffiths CE, Worthington J, Barker JN, Trembath RC, et al. Differential contribution of CDKAL1 variants to psoriasis, Crohn's disease and type II diabetes. Genes Immun 2009;10:654-658. https://doi.org/10.1038/gene.2009.51
  18. Ng MC, Saxena R, Li J, Palmer ND, Dimitrov L, Xu J, et al. Transferability and fine mapping of type 2 diabetes loci in African Americans: the Candidate Gene Association Resource Plus Study. Diabetes 2013;62:965-976. https://doi.org/10.2337/db12-0266
  19. Takatsu H, Baba K, Shima T, Umino H, Kato U, Umeda M, et al. ATP9B, a P4-ATPase (a putative aminophospholipid translocase), localizes to the trans-Golgi network in a CDC50 protein- independent manner. J Biol Chem 2011;286:38159-38167. https://doi.org/10.1074/jbc.M111.281006
  20. Miyaki K, Oo T, Song Y, Lwin H, Tomita Y, Hoshino H, et al. Association of a cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like 1 (CDKAL1) polymorphism with elevated hemoglobin A(1)(c) levels and the prevalence of metabolic syndrome in Japanese men: interaction with dietary energy intake. Am J Epidemiol 2010;172:985-991. https://doi.org/10.1093/aje/kwq281
  21. Ryoo H, Woo J, Kim Y, Lee C. Heterogeneity of genetic associations of CDKAL1 and HHEX with susceptibility of type 2 diabetes mellitus by gender. Eur J Hum Genet 2011;19:672-675. https://doi.org/10.1038/ejhg.2011.6
  22. Wu Y, Li H, Loos RJ, Yu Z, Ye X, Chen L, et al. Common variants in CDKAL1, CDKN2A/B, IGF2BP2, SLC30A8, and HHEX/IDE genes are associated with type 2 diabetes and impaired fasting glucose in a Chinese Han population. Diabetes 2008;57: 2834-2842. https://doi.org/10.2337/db08-0047
  23. Foster MC, Yang Q, Hwang SJ, Hoffmann U, Fox CS. Heritability and genome-wide association analysis of renal sinus fat accumulation in the Framingham Heart Study. BMC Med Genet 2011;12:148. https://doi.org/10.1186/1471-2350-12-148
  24. Dahlin A, Litonjua A, Irvin CG, Peters SP, Lima JJ, Kubo M, et al. Genome-wide association study of leukotriene modifier response in asthma. Pharmacogenomics J 2016;16:151-157. https://doi.org/10.1038/tpj.2015.34