Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

Family studies of type 1 diabetes reveal additive and epistatic effects between MGAT1 and three other polymorphisms

Genes & Immunity volume 15, pages 218–223 (2014)Cite this article

Subjects

Abstract

In a recent study on multiple sclerosis (MS), we observed additive effects and epistatic interactions between variants of four genes that converge to induce T-cell hyperactivity by altering Asn-(N)-linked protein glycosylation: namely, the Golgi enzyme MGAT1, cytotoxic T-lymphocyte antigen 4 (CTLA-4), interleukin-2 receptor-α (IL2RA) and interleukin-7 receptor-α (IL7RA). As the CTLA-4, IL2RA and IL7RA variants are associated with type 1 diabetes (T1D), we examined for joint effects in T1D. Employing a novel conditional logistic regression for family-based data sets, epistatic and additive effects were observed using 1423 multiplex families from the Type 1 Diabetes Genetic Consortium data set. The IL2RA and IL7RA variants had univariate association in MS and T1D, whereas the MGAT1 and CTLA-4 variants associated with only MS or T1D, respectively. However, similar to MS, the MGAT1 variant haplotype interacted with CTLA4 (P=0.03), and a combination of IL2RA and IL7RA (P=0.01). The joint effects of MGAT1, CTLA4, IL2RA, IL7RA and the two interactions using a multiple conditional logistic regression were statistically highly significant (P<5 × 10−10). The MGAT1–CTLA-4 interaction was replicated (P=0.01) in 179 trio families from the Genetics of Kidneys in Diabetes study. These data are consistent with defective N-glycosylation of T cells contributing to T1D pathogenesis.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1

Similar content being viewed by others

References

  1. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ et al. Finding the missing heritability of complex diseases. Nature 2009; 461: 747–753.

    Article  CAS  Google Scholar 

  2. Mkhikian H, Grigorian A, Li CF, Chen HL, Newton B, Zhou RW et al. Genetics and the environment converge to dysregulate N-glycosylation in multiple sclerosis. Nat Commun 2011; 2: 334.

    Article  Google Scholar 

  3. Li CF, Zhou RW, Mkhikian H, Newton BL, Yu Z, Demetriou M . Hypomorphic MGAT5 polymorphisms promote multiple sclerosis cooperatively with MGAT1 and interleukin-2 and 7 receptor variants. J Neuroimmunol 2013; 256: 71–76.

    Article  CAS  Google Scholar 

  4. Grigorian A, Mkhikian H, Li CF, Newton BL, Zhou RW, Demetriou M . Pathogenesis of multiple sclerosis via environmental and genetic dysregulation of N-glycosylation. Semin Immunopathol 2012; 34: 415–424.

    Article  CAS  Google Scholar 

  5. Demetriou M, Granovsky M, Quaggin S, Dennis JW . Negative regulation of T-cell activation and autoimmunity by Mgat5 N-glycosylation. Nature 2001; 409: 733–739.

    Article  CAS  Google Scholar 

  6. Lee SU, Grigorian A, Pawling J, Chen IJ, Gao G, Mozaffar T et al. N-glycan processing deficiency promotes spontaneous inflammatory demyelination and neurodegeneration. J Biol Chem 2007; 282: 33725–33734.

    Article  CAS  Google Scholar 

  7. Grigorian A, Lee SU, Tian W, Chen IJ, Gao G, Mendelsohn R et al. Control of T Cell-mediated autoimmunity by metabolite flux to N-glycan biosynthesis. J Biol Chem 2007; 282: 20027–20035.

    Article  CAS  Google Scholar 

  8. Grigorian A, Araujo L, Naidu NN, Place D, Choudhury B, Demetriou M . N-acetylglucosamine inhibits T-helper 1 (Th1) / T-helper 17 (Th17) responses and treats experimental autoimmune encephalomyelitis. J Biol Chem 2011; 286: 40133–40141.

    Article  CAS  Google Scholar 

  9. Grigorian A, Mkhikian H, Demetriou M . Interleukin-2, Interleukin-7, T cell-mediated autoimmunity, and N-glycosylation. Ann NY Acad Sci 2012; 1253: 49–57.

    Article  CAS  Google Scholar 

  10. Zhernakova A, van Diemen CC, Wijmenga C . Detecting shared pathogenesis from the shared genetics of immune-related diseases. Nat Rev Genet 2009; 10: 43–55.

    Article  CAS  Google Scholar 

  11. Cotsapas C, Voight BF, Rossin E, Lage K, Neale BM, Wallace C et al. Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet 2011; 7: e1002254.

    Article  CAS  Google Scholar 

  12. Smyth DJ, Plagnol V, Walker NM, Cooper JD, Downes K, Yang JHM et al. Shared and distinct genetic variants in type 1 diabetes and celiac disease. New Engl J Med 2008; 359: 2767–2777.

    Article  CAS  Google Scholar 

  13. Sirota M, Schaub MA, Batzoglou S, Robinson WH, Butte AJ . Autoimmune disease classification by inverse association with snp alleles. PLoS Genet 2009; 5: e1000792.

    Article  Google Scholar 

  14. Todd JA . Etiology of type 1 diabetes. Immunity 2010; 32: 457–467.

    Article  CAS  Google Scholar 

  15. Maier LM, Lowe CE, Cooper J, Downes K, Anderson DE, Severson C et al. IL2RA genetic heterogeneity in multiple sclerosis and type 1 diabetes susceptibility and soluble interleukin-2 receptor production. PLoS Genet 2009; 5: e1000322.

    Article  Google Scholar 

  16. Self SG, Longton G, Kopecky KJ, Liang KY . On estimating HLA/disease association with application to a study of aplastic anemia. Biometrics 1991; 47: 53–61.

    Article  CAS  Google Scholar 

  17. Schaid DJ, Sommer SS . Genotype relative risks: methods for design and analysis of candidate-gene association studies. Am J Hum Genet 1993; 53: 1114–1126.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Cordell HJ, Clayton DG . A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: application to HLA in type 1 diabetes. Am J Hum Genet 2002; 70: 124–141.

    Article  CAS  Google Scholar 

  19. Kotti S, Bickeboller H, Clerget-Darpoux F . Strategy for detecting susceptibility genes with weak or no marginal effect. Hum Hered 2007; 63: 85–92.

    Article  CAS  Google Scholar 

  20. Yu ZX, Deng L . Pseudosibship methods in the case-parents design. Stat Med 2011; 30: 3236–3251.

    Article  Google Scholar 

  21. Pociot F, Akolkar B, Concannon P, Erlich HA, Julier C, Morahan G et al. Genetics of type 1 diabetes: what's next? Diabetes 2010; 59: 1561–1571.

    Article  CAS  Google Scholar 

  22. Santiago JL, Alizadeh BZ, Martinez A, Espino L, de la Calle H, Fernandez-Arquero M et al. Study of the association between the CAPSL-IL7R locus and type 1 diabetes. Diabetologia 2008; 51: 1653–1658.

    Article  CAS  Google Scholar 

  23. Mueller PW, Rogus JJ, Cleary PA, Zhao Y, Smiles AM, Steffes MW et al. Genetics of Kidneys in Diabetes (GoKinD) study: a genetics collection available for identifying genetic susceptibility factors for diabetic nephropathy in type 1 diabetes. J Am Soc Nephrol 2006; 17: 1782–1790.

    Article  CAS  Google Scholar 

  24. Zhao H, Zhang S, Merikangas KR, Trixler M, Wildenauer DB, Sun F et al. Transmission/disequilibrium tests using multiple tightly linked markers. Am J Hum Genet 2000; 67: 936–946.

    Article  CAS  Google Scholar 

  25. Allen AS, Satten GA . Statistical models for haplotype sharing in case-parent trio data. Hum Hered 2007; 64: 35–44.

    Article  Google Scholar 

  26. Zhang S, Sha Q, Chen HS, Dong J, Jiang R . Transmission/disequilibrium test based on haplotype sharing for tightly linked markers. Am J Hum Genet 2003; 73: 566–579.

    Article  CAS  Google Scholar 

  27. Van der Meulen MA, te Meerman GJ . Haplotype sharing analysis in affected individuals from nuclear families with at least one affected offspring. Genet Epidemiol 1997; 14: 915–920.

    Article  CAS  Google Scholar 

  28. Dudbridge F, Koeleman BP, Todd JA, Clayton DG . Unbiased application of the transmission/disequilibrium test to multilocus haplotypes. Am J Hum Genet 2000; 66: 2009–2012.

    Article  CAS  Google Scholar 

  29. Knapp M, Becker T . Family-based association analysis with tightly linked markers. Hum Hered 2003; 56: 2–9.

    Article  Google Scholar 

  30. Fan R, Knapp M, Wjst M, Zhao C, Xiong M . High resolution T2 association tests of complex diseases based on family data. Ann Hum Genet 2005; 69: 187–208.

    Article  CAS  Google Scholar 

  31. Chapman JM, Cooper JD, Todd JA, Clayton DG . Detecting disease associations due to linkage disequilibrium using haplotype tags: A class of tests and the determinants of statistical power. Hum Hered 2003; 56: 18–31.

    Article  Google Scholar 

  32. Zhang Z, Zhang S, Sha Q . A multi-marker test based on family data in genome-wide association study. BMC Genet 2007; 8: 65.

    Article  Google Scholar 

  33. Shi M, Umbach DM, Weinberg CR . Identification of risk-related haplotypes with the use of multiple SNPs from nuclear families. Am J Hum Genet 2007; 81: 53–66.

    Article  CAS  Google Scholar 

  34. Lee WC . Testing for candidate gene linkage disequilibrium using a dense array of single nucleotide polymorphisms in case-parents studies. Epidemiology 2002; 13: 545–551.

    Article  CAS  Google Scholar 

  35. Cattaert T, Urrea V, Naj AC, De Lobel L, De Wit V, Fu M et al. FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals. PLoS One 2010; 5: e10304.

    Article  Google Scholar 

  36. Yu Z, Wang S . Contrasting linkage disequilibrium as a multilocus family-based association test. Genet Epidemiol 2011; 35: 487–498.

    PubMed  Google Scholar 

Download references

Acknowledgements

We thank the National Institute of Diabetes and Digestive and Kidney Diseases for providing access to the T1DGC and GoKinD DNA samples. Research was supported in part by grant R01HG004960 from the National Human Genome Research Institute to ZY and grant R01AI082266 from the National Institute of Allergy and Infectious Diseases to MD.

Author information

Authors and Affiliations

  1. Department of Statistics, University of California, Irvine, CA, USA

    Z Yu

  2. Department of Neurology, University of California, Irvine, CA, USA

    C F Li, B L Newton & M Demetriou

  3. Institute for Immunology, University of California, Irvine, CA, USA

    C F Li & M Demetriou

  4. Department of Microbiology & Molecular Genetics, University of California, Irvine, CA, USA

    H Mkhikian, R W Zhou & M Demetriou

Authors
  1. Z Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C F Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H Mkhikian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. R W Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. B L Newton
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M Demetriou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M Demetriou.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yu, Z., Li, C., Mkhikian, H. et al. Family studies of type 1 diabetes reveal additive and epistatic effects between MGAT1 and three other polymorphisms. Genes Immun 15, 218–223 (2014). https://doi.org/10.1038/gene.2014.7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/gene.2014.7

Keywords

This article is cited by

Search

Advanced search

Quick links