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.

  • Letter
  • Published:

Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion

Nature Genetics volume 45, pages 197–201 (2013)Cite this article

Subjects

Abstract

Insulin secretion has a crucial role in glucose homeostasis, and failure to secrete sufficient insulin is a hallmark of type 2 diabetes. Genome-wide association studies (GWAS) have identified loci contributing to insulin processing and secretion1,2; however, a substantial fraction of the genetic contribution remains undefined. To examine low-frequency (minor allele frequency (MAF) 0.5–5%) and rare (MAF < 0.5%) nonsynonymous variants, we analyzed exome array data in 8,229 nondiabetic Finnish males using the Illumina HumanExome Beadchip. We identified low-frequency coding variants associated with fasting proinsulin concentrations at the SGSM2 and MADD GWAS loci and three new genes with low-frequency variants associated with fasting proinsulin or insulinogenic index: TBC1D30, KANK1 and PAM. We also show that the interpretation of single-variant and gene-based tests needs to consider the effects of noncoding SNPs both nearby and megabases away. This study demonstrates that exome array genotyping is a valuable approach to identify low-frequency variants that contribute to complex traits.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Figure 1: Manhattan plot for the fasting proinsulin analysis.
Figure 2: MADD is located in a region of unusually high LD on chromosome 11 at 46–57 Mb.

Similar content being viewed by others

References

  1. Strawbridge, R.J. et al. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes 60, 2624–2634 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Scott, R.A. et al. Large-scale association study using the Metabochip array reveals new loci influencing glycemic traits and provides insight into the underlying biological pathways. Nat. Genet. 44, 991–1005 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Kiezun, A. et al. Exome sequencing and the genetic basis of complex traits. Nat. Genet. 44, 623–630 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Stančáková, A. et al. Changes in insulin sensitivity and insulin release in relation to glycemia and glucose tolerance in 6,414 Finnish men. Diabetes 58, 1212–1221 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kang, H.M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42, 105–116 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ingelsson, E. et al. Detailed physiologic characterization reveals diverse mechanisms for novel genetic loci regulating glucose and insulin metabolism in humans. Diabetes 59, 1266–1275 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Yang, H., Sasaki, T., Minoshima, S. & Shimizu, N. Identification of three novel proteins (SGSM1, 2, 3) which modulate small G protein (RAP and RAB)-mediated signaling pathway. Genomics 90, 249–260 (2007).

    Article  CAS  PubMed  Google Scholar 

  9. Nottingham, R.M., Ganley, I.G., Barr, F.A., Lambright, D.G. & Pfeffer, S.R. RUTBC1 protein, a Rab9A effector that activates GTP hydrolysis by Rab32 and Rab33B proteins. J. Biol. Chem. 286, 33213–33222 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Rutter, G.A. & Hill, E.V. Insulin vesicle release: walk, kiss, pause.then run. Physiology (Bethesda) 21, 189–196 (2006).

    CAS  Google Scholar 

  11. Isken, O. & Maquat, L.E. The multiple lives of NMD factors: balancing roles in gene and genome regulation. Nat. Rev. Genet. 9, 699–712 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Coppola, T. et al. The death domain of Rab3 guanine nucleotide exchange protein in GDP/GTP exchange activity in living cells. Biochem. J. 362, 273–279 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Regazzi, R. et al. Expression, localization and functional role of small GTPases of the Rab3 family in insulin-secreting cells. J. Cell Sci. 109, 2265–2273 (1996).

    CAS  PubMed  Google Scholar 

  14. Piper Hanley, K. et al. In vitro expression of NGN3 identifies RAB3B as the predominant Ras-associated GTP-binding protein 3 family member in human islets. J. Endocrinol. 207, 151–161 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Price, A.L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132–135 author reply 135–139 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Manning, A.K. et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Amundadottir, L. et al. Genome-wide association study identifies variants in the ABO locus associated with susceptibility to pancreatic cancer. Nat. Genet. 41, 986–990 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Tanikawa, C. et al. A genome-wide association study identifies two susceptibility loci for duodenal ulcer in the Japanese population. Nat. Genet. 44, 430–4 S1–S2 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Kathiresan, S. et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 41, 56–65 (2009).

    Article  CAS  PubMed  Google Scholar 

  20. Sabatti, C. et al. Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat. Genet. 41, 35–46 (2009).

    Article  CAS  PubMed  Google Scholar 

  21. Voight, B.F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ishibashi, K., Kanno, E., Itoh, T. & Fukuda, M. Identification and characterization of a novel Tre-2/Bub2/Cdc16 (TBC) protein that possesses Rab3A-GAP activity. Genes Cells 14, 41–52 (2009).

    Article  CAS  PubMed  Google Scholar 

  23. Yoshimura, S., Egerer, J., Fuchs, E., Haas, A.K. & Barr, F.A. Functional dissection of Rab GTPases involved in primary cilium formation. J. Cell Biol. 178, 363–369 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yaekura, K. et al. Insulin secretory deficiency and glucose intolerance in Rab3A null mice. J. Biol. Chem. 278, 9715–9721 (2003).

    Article  CAS  PubMed  Google Scholar 

  25. Adzhubei, I.A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kakinuma, N., Zhu, Y., Wang, Y., Roy, B.C. & Kiyama, R. Kank proteins: structure, functions and diseases. Cell. Mol. Life Sci. 66, 2651–2659 (2009).

    Article  CAS  PubMed  Google Scholar 

  27. Kowluru, A. Friendly, and not so friendly, roles of Rac1 in islet β-cell function: lessons learnt from pharmacological and molecular biological approaches. Biochem. Pharmacol. 81, 965–975 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hammar, E., Tomas, A., Bosco, D. & Halban, P.A. Role of the Rho-ROCK (Rho-associated kinase) signaling pathway in the regulation of pancreatic β-cell function. Endocrinology 150, 2072–2079 (2009).

    Article  CAS  PubMed  Google Scholar 

  29. Rajagopal, C., Mains, R.E. & Eipper, B.A. Signaling from the secretory granule to the nucleus. Crit. Rev. Biochem. Mol. Biol. 47, 391–406 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Czyzyk, T.A. et al. Deletion of peptide amidation enzymatic activity leads to edema and embryonic lethality in the mouse. Dev. Biol. 287, 301–313 (2005).

    Article  CAS  PubMed  Google Scholar 

  31. Lee, S., Wu, M.C. & Lin, X. Optimal tests for rare variant effects in sequencing association studies. Biostatistics 13, 762–775 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Stumvoll, M., Van Haeften, T., Fritsche, A. & Gerich, J. Oral glucose tolerance test indexes for insulin sensitivity and secretion based on various availabilities of sampling times. Diabetes Care 24, 796–797 (2001).

    Article  CAS  PubMed  Google Scholar 

  33. Retnakaran, R. et al. Hyperbolic relationship between insulin secretion and sensitivity on oral glucose tolerance test. Obesity (Silver Spring) 16, 1901–1907 (2008).

    Article  CAS  Google Scholar 

  34. Matthews, D.R. et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28, 412–419 (1985).

    Article  CAS  PubMed  Google Scholar 

  35. Stančáková, A. et al. Association of 18 confirmed susceptibility loci for type 2 diabetes with indices of insulin release, proinsulin conversion, and insulin sensitivity in 5,327 nondiabetic Finnish men. Diabetes 58, 2129–2136 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Matsuda, M. & DeFronzo, R.A. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 22, 1462–1470 (1999).

    Article  CAS  PubMed  Google Scholar 

  37. Patterson, N., Price, A.L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Weale, M.E. Quality control for genome-wide association studies. Methods Mol. Biol. 628, 341–372 (2010).

    Article  CAS  PubMed  Google Scholar 

  40. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).

    Article  CAS  PubMed  Google Scholar 

  41. Wu, M.C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Aulchenko, Y.S., de Koning, D.J. & Haley, C. Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis. Genetics 177, 577–585 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Aulchenko, Y.S., Ripke, S., Isaacs, A. & van Duijn, C.M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007).

    Article  CAS  PubMed  Google Scholar 

  44. Harrow, J. et al. GENCODE: producing a reference annotation for ENCODE. Genome Biol. 7 (suppl. 1), S4.1–9 (2006).

    Google Scholar 

  45. The UniProt Consortium. Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res. 40, D71–D75 (2012).

Download references

Acknowledgements

This study was supported by the Academy of Finland (contract 124243) (to M.L.), the Finnish Heart Foundation (to M.L.), the Finnish Diabetes Foundation (to M.L.), Tekes (contract 1510/31/06) (to M.L.), the Commission of the European Community (HEALTH-F2-2007-201681) (to M.L.) and US National Institutes of Health grants DK093757 (to K.L.M.), DK072193 (to K.L.M.), DK062370 (to M.B.) and 1Z01 HG000024 (to F.S.C.). Genotyping was conducted at the Genetic Resources Core Facility (GRCF) at the Johns Hopkins Institute of Genetic Medicine.

Author information

Author notes

  1. Michael Boehnke, Markku Laakso and Karen L Mohlke: These authors jointly directed this work.

Authors and Affiliations

  1. Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA

    Jeroen R Huyghe, Anne U Jackson, Heather M Stringham, Xueling Sim, Lingyao Yang, Christian Fuchsberger, Tanya M Teslovich, Laura J Scott, Hyun Min Kang, Gonçalo R Abecasis & Michael Boehnke

  2. Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA

    Marie P Fogarty, Martin L Buchkovich & Karen L Mohlke

  3. Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland

    Alena Stančáková, Henna Cederberg, Johanna Kuusisto & Markku Laakso

  4. Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland, USA

    Peter S Chines & Francis S Collins

  5. Center for Inherited Disease Research, Johns Hopkins University, Baltimore, Maryland, USA

    Jane M Romm, Hua Ling, Ivy McMullen, Roxann Ingersoll, Elizabeth W Pugh & Kimberly F Doheny

  6. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Benjamin M Neale & Mark J Daly

  7. Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA

    Benjamin M Neale & Mark J Daly

  8. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA

    Benjamin M Neale & Mark J Daly

  9. Department of Preventive Medicine, University of Southern California (USC) Keck School of Medicine, Los Angeles, California, USA

    Richard M Watanabe

  10. Department of Physiology and Biophysics, Keck School of Medicine of USC, Los Angeles, California, USA

    Richard M Watanabe

Authors
  1. Jeroen R Huyghe
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Anne U Jackson
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Marie P Fogarty
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Martin L Buchkovich
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Alena Stančáková
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Heather M Stringham
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Xueling Sim
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Lingyao Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Christian Fuchsberger
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Henna Cederberg
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Peter S Chines
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Tanya M Teslovich
    View author publications

    You can also search for this author inPubMed Google Scholar

  13. Jane M Romm
    View author publications

    You can also search for this author inPubMed Google Scholar

  14. Hua Ling
    View author publications

    You can also search for this author inPubMed Google Scholar

  15. Ivy McMullen
    View author publications

    You can also search for this author inPubMed Google Scholar

  16. Roxann Ingersoll
    View author publications

    You can also search for this author inPubMed Google Scholar

  17. Elizabeth W Pugh
    View author publications

    You can also search for this author inPubMed Google Scholar

  18. Kimberly F Doheny
    View author publications

    You can also search for this author inPubMed Google Scholar

  19. Benjamin M Neale
    View author publications

    You can also search for this author inPubMed Google Scholar

  20. Mark J Daly
    View author publications

    You can also search for this author inPubMed Google Scholar

  21. Johanna Kuusisto
    View author publications

    You can also search for this author inPubMed Google Scholar

  22. Laura J Scott
    View author publications

    You can also search for this author inPubMed Google Scholar

  23. Hyun Min Kang
    View author publications

    You can also search for this author inPubMed Google Scholar

  24. Francis S Collins
    View author publications

    You can also search for this author inPubMed Google Scholar

  25. Gonçalo R Abecasis
    View author publications

    You can also search for this author inPubMed Google Scholar

  26. Richard M Watanabe
    View author publications

    You can also search for this author inPubMed Google Scholar

  27. Michael Boehnke
    View author publications

    You can also search for this author inPubMed Google Scholar

  28. Markku Laakso
    View author publications

    You can also search for this author inPubMed Google Scholar

  29. Karen L Mohlke
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

J.R.H. led statistical analysis, and J.R.H., A.U.J., H.M.S., X.S., L.Y. and C.F. performed statistical analysis. J.R.H., M.P.F., M.L.B. and P.S.C. performed bioinformatics analysis. A.S., H.C., J.K. and M.L. obtained and analyzed phenotype data. J.M.R., H.L., I.M., R.I., E.W.P. and K.F.D. generated genotype data. B.M.N., M.J.D. and G.R.A. designed the genotyping array. H.M.K. and G.R.A. developed statistical analysis tools. J.K. and M.L. designed and supervised the METSIM study. J.R.H., M.P.F., M.L.B., M.B. and K.L.M. drafted the manuscript, and all authors reviewed the manuscript. J.R.H., A.U.J., M.P.F., M.L.B., A.S., H.M.S., X.S., L.Y., C.F., T.M.T., L.J.S., F.S.C., G.R.A., R.M.W., M.B., M.L. and K.L.M. contributed to discussion and interpreted the data. M.B., M.L. and K.L.M. designed and supervised the study.

Corresponding author

Correspondence to Karen L Mohlke.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1–7 and Supplementary Figures 1–8 (PDF 3289 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Huyghe, J., Jackson, A., Fogarty, M. et al. Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat Genet 45, 197–201 (2013). https://doi.org/10.1038/ng.2507

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.2507

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing