Acta Diabetologica

, Volume 55, Issue 9, pp 909–916 | Cite as

Association analysis of copy number variations in type 2 diabetes-related susceptible genes in a Chinese population

  • Yu-Xiang YanEmail author
  • Jia-Jiang-Hui Li
  • Huan-Bo Xiao
  • Shuo Wang
  • Yan He
  • Li-Juan WuEmail author
Original Article



Copy number variations (CNVs) have been implicated as an important genetic marker of common disease. In this study, we explored genetic effects of common CNVs in Type 2 diabetes (T2D) related susceptible genes in Chinese population.


Seven common CNV loci were selected from genes enclosing the susceptible single nucleotide polymorphisms (SNPs) of T2D confirmed by genome-wide association studies (GWAS) and replication studies conducted in east Asia population. The CNVs and SNPs were genotyped in 504 T2D patients and 494 non-T2D controls. Cumulative effect of the positive CNV loci was measured using genetic risk score (GRS). Multiplicative and additive interaction between candidate CNV loci and SNPs were assessed.


Compared with the common two copies, the deletion of nsv6360 (adjusted OR = 2.28, 95% CI 1.37–3.78, P = 0.001), nsv8414 (adjusted OR = 1.89, 95% CI 1.16–3.08, P = 0.006) and nsv1898 (adjusted OR = 1.84, 95% CI 1.19–2.84, P = 0.005) were significantly associated with increased risk of T2D (P < 0.007). Significant dose–response relationship was observed between GRS and the risk of T2D (χ2 for trend = 19.51, P < 0.001). In addition, significant additive interactions between nsv8414 and rs17584499 in PTPRD (AP = 0.60, 95% CI 0.12–1.07) and nsv1898 and rs16955379 in CMIP (AP = 0.46, 95% CI 0.01–0.91) were observed.


There were three CNV loci (nsv6360, nsv8414 and nsv1898) associated with T2D, and a significant cumulative effect of these loci on the risk of T2D. The comprehensive effects of both CNVs and SNPs may provide a more useful tool for the identification of genetic susceptibility for T2D.


Type 2 diabetes Copy number variations Association Interaction 



This study was supported by the National Natural Science Foundation (81573214, 81773511), the Beijing Municipal Natural Science Foundation (7162020).

Compliance with ethical standards

Conflict of interest

The authors declares that they have no conflict of interest.

Ethical standard

This experiment was approved by the Ethics Committee of Capital Medical University. This study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki. All subjects signed informed consent forms.

Human and animal rights

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008.

Informed consent

Informed consent was obtained from all patients for being included in the study.

Supplementary material

592_2018_1168_MOESM1_ESM.docx (25 kb)
Supplementary material 1 (DOCX 24 KB)


  1. 1.
    Wu Y, Ding Y, Tanaka Y, Zhang W (2014) Risk factors contributing to type 2 diabetes and recent advances in the treatment and prevention. Int J Med Sci 11: 1185–1200CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Xu Y, Wang L, He J et al (2013) Prevalence and control of diabetes in Chinese adults. JAMA 310: 948–959CrossRefPubMedGoogle Scholar
  3. 3.
    Gan W, Walters RG, Holmes MV et al (2016) Evaluation of type 2 diabetes genetic risk variants in Chinese adults: findings from 93,000 individuals from the China Kadoorie Biobank. Diabetologia 59:1446–1457CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Morris AP, Voight BF, Teslovich TM et al (2012) Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 44: 981–990CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Eichler EE, Flint J, Gibson G et al (2010) Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 11: 446–450CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Redon R, Ishikawa S, Fitch KR et al (2006). Global variation in copy number in the human genome. Nature 444: 444–454CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Dajani R, Li J, Wei Z et al (2015) CNV Analysis Associates AKNAD1 with Type-2 Diabetes in Jordan Subpopulations. Sci Rep 5:13391CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Jeon JP, Shim SM, Nam HY et al (2010) Copy number variation at leptin receptor gene locus associated with metabolic traits and the risk of type 2 diabetes mellitus. BMC Genom 11:426CrossRefGoogle Scholar
  9. 9.
    Irvin MR, Wineinger NE, Rice TK et al (2011) Genome-wide detection of allele specific copy number variation associated with insulin resistance in African Americans from the HyperGEN study. PLoS One 6:e24052CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Kudo H, Emi M, Ishigaki Y et al (2011) Frequent loss of genome gap region in 4p16.3 subtelomere in early-onset type 2 diabetes mellitus. Exp Diabetes Res 2011:498460CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Lee HS, Moon S, Yun JH et al (2014) Genome-wide copy number variation study reveals KCNIP1 as a modulator of insulin secretion. Genomics 104(2):113–120CrossRefPubMedGoogle Scholar
  12. 12.
    Stranger BE, Forrest MS, Dunning M et al (2007) Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315: 848–853CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Plengvidhya N, Chanprasert K, Tangjittipokin W, Thongnoppakhun W, Yenchitsomanus PT (2012) Identification of copy number variation of CAPN10 in Thais with type 2 diabetes by multiplex PCR and denaturing high performance liquid chromatography (DHPLC). Gene 506:383–386CrossRefPubMedGoogle Scholar
  14. 14.
    Girirajan S, Campbell CD, Eichler EE (2011) Human copy number variation and complex genetic disease. Annu Rev Genet 45: 203–226CrossRefPubMedGoogle Scholar
  15. 15.
    Department of Noncommunicable Disease Surveillance (1999) Definition, diagnosis and classification of diabetes mellitus and its complications: report of a WHO consultation. Part 1. Diagnosis and classification of diabetes mellitus. World Health Organization, GenevaGoogle Scholar
  16. 16.
    Dong J, Liang YZ, Zhang J et al (2017) Potential Role of lipometabolism-related microRNAs in peripheral blood mononuclear cells as biomarkers for coronary artery disease. J Atheroscler Thromb 24: 430–441CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Yan YX, Xiao HB, Wang SS et al (2016) Investigation of the relationship between chronic stress and insulin resistance in a Chinese population. J Epidemiol 26:355–360CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Wang SS, Li YQ, Liang YZ et al (2017) Expression of miR-18a and miR-34c in circulating monocytes associated with vulnerability to type 2 diabetes mellitus and insulin resistance. J Cell Mol Med 21:3372–3380CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Cho YS, Chen CH, Hu C et al (2011) Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat Genet 44:67–72CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Xiang K, Wang Y, Zheng T et al (2004) Genome-wide search for type 2 diabetes impaired glucose homeostasis susceptibility genes in the Chinese significant linkage to chromosome 6q21-q23 and chromosome 1q21-q24. Diabetes 53: 228–234CrossRefPubMedGoogle Scholar
  21. 21.
    Barroso I, Luan J, Sandhu MS et al (2006) Meta-analysis of the Gly482Ser variant in PPARGC1A in type 2 diabetes and related phenotypes. Diabetologia 49:501–505CrossRefPubMedGoogle Scholar
  22. 22.
    Du R, Lu C, Jiang Z et al (2012) Efficient typing of copy number variations in a segmental duplication-mediated rearrangement hotspot using multiplex competitive amplification. J Hum Genet 57: 545–551CrossRefPubMedGoogle Scholar
  23. 23.
    Andersson T, Alfredsson L, Källberg H et al (2005) Calculating measures of biological interaction. Eur J Epidemiol 20:575–579CrossRefPubMedGoogle Scholar
  24. 24.
    Källberg H, Ahlbom A, Alfredsson L et al (2006) Calculating measures of biological interaction using R. Eur J Epidemiol 21:571–573CrossRefPubMedGoogle Scholar
  25. 25.
    Chimienti F, Devergnas S, Pattou F et al (2006) In vivo expression and functional characterization of the zinc transporter ZnT8 in glucose-induced insulin secretion. J Cell Sci 119:4199–206CrossRefPubMedGoogle Scholar
  26. 26.
    Cauchi S, Del Guerra S, Choquet H et al (2010) Meta-analysis and functional effects of the SLC30A8 rs13266634 polymorphism on isolated human pancreatic islets. Mol Genet Metab 100:77–82CrossRefPubMedGoogle Scholar
  27. 27.
    Perry JR, Frayling TM (2008) New gene variants alter type 2 diabetes risk predominantly through reduced beta-cell function. Curr Opin Clin Nutr Metab Care 11:371–377CrossRefPubMedGoogle Scholar
  28. 28.
    Nicolson TJ, Bellomo EA, Wijesekara N et al (2009) Insulin storage and glucose homeostasis in mice null for the granule zinc transporter ZnT8 and studies of the type 2 diabetes-associated variants. Diabetes 58:2070–2083CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Flannick J, Thorleifsson G, Beer NL et al (2014). Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat Genet 46: 357–363CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Chang YC, Chiu YF, Liu PH et al (2012) Replication of genome-wide association signals of type 2 diabetes in Han Chinese in a prospective cohort. Clin Endocrinol 76: 365–372CrossRefGoogle Scholar
  31. 31.
    Chen YT, Lin WD, Liao WL et al (2015) PTPRD silencing by DNA hypermethylation decreases insulin receptor signaling and leads to type 2 diabetes. Oncotarget 6: 12997–3005PubMedPubMedCentralGoogle Scholar
  32. 32.
    Dahlman I, Rydén M, Brodin D et al (2016) Numerous genes in loci associated with body fat distribution are linked to adipose function. Diabetes 65:433–437CrossRefPubMedGoogle Scholar
  33. 33.
    Sakai K, Imamura M, Tanaka Y et al (2013) Replication study for the association of 9 East Asian GWAS-derived loci with susceptibility to type 2 diabetes in a Japanese population. PLoS One 8:e76317CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Ahlqvist E, Storm P, Käräjämäki A et al (2018) Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol 6:361–369CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Italia S.r.l., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Epidemiology and Biostatistics, School of Public HealthCapital Medical UniversityBeijingPeople’s Republic of China
  2. 2.Municipal Key Laboratory of Clinical EpidemiologyBeijingPeople’s Republic of China
  3. 3.Department of Preventive Medicine, Yanjing Medical CollegeCapital Medical UniversityBeijingPeople’s Republic of China

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