Skip to main content

Advertisement

Genetics of Type 2 Diabetes: the Power of Isolated Populations

  • Genetics (AP Morris, Section Editor)
  • Published:
Current Diabetes Reports Aims and scope Submit manuscript

Abstract

Type 2 diabetes (T2D) affects millions of people worldwide. Improving the understanding of the underlying mechanisms and ultimately improving the treatment strategies are, thus, of great interest. To achieve this, identification of genetic variation predisposing to T2D is important. A large number of variants have been identified in large outbred populations, mainly from Europe and Asia. However, to elucidate additional variation, isolated populations have a number of advantageous properties, including increased amounts of linkage disequilibrium, and increased probability for presence of high frequency disease-associated variants due to genetic drift. Collectively, this increases the statistical power to detect association signals in isolated populations compared to large outbred populations. In this review, we elaborate on why isolated populations are a powerful resource for the identification of complex disease variants and describe their contributions to the understanding of the genetics of T2D.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Canada)

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. IDF diabetes atlas, 7th edition. 2015. Available from: http://www.diabetesatlas.org/

  2. Medici F, Hawa M, Ianari A, et al. Concordance rate for type II diabetes mellitus in monozygotic twins: actuarial analysis. Diabetologia. 1999;42:146–50.

    Article  CAS  PubMed  Google Scholar 

  3. Morris AP, Voight BF, Teslovich TM, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44:981–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Mahajan A, Go MJ, Zhang W, et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet. 2014;46:234–44. The first major meta-analysis of type 2 diabetes genetics across multiple ancestries.

    Article  CAS  PubMed  Google Scholar 

  5. Altshuler D, Hirschhorn JN, Klannemark M, et al. The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet. 2000;26:76–80.

    Article  CAS  PubMed  Google Scholar 

  6. Nielsen E-MD, Hansen L, Carstensen B, et al. The E23K variant of Kir6.2 associates with impaired post-OGTT serum insulin response and increased risk of type 2 diabetes. Diabetes. 2003;52:573–7.

    Article  CAS  PubMed  Google Scholar 

  7. Lim ET, Würtz P, Havulinna AS, et al. Distribution and medical impact of loss-of-function variants in the Finnish founder population. PLoS Genet. 2014;10:e1004494.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lohmueller KE. The impact of population demography and selection on the genetic architecture of complex traits. PLoS Genet. 2014;10:e1004379. Carefully demonstrates the effect of recent population growth on the genetic architecture of complex traits.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Masel J. Genetic drift. Curr Biol. 2011;21:R837–8.

    Article  CAS  PubMed  Google Scholar 

  10. Kruglyak L. Prospects for whole-genome linkage disequilibrium mapping of common disease genes. Nat Genet. 1999;22:139–44.

    Article  CAS  PubMed  Google Scholar 

  11. Zuk O, Schaffner SF, Samocha K, et al. Searching for missing heritability: designing rare variant association studies. Proc Natl Acad Sci. 2014;111:E455–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Moltke I, Fumagalli M, Korneliussen TS, et al. Uncovering the genetic history of the present-day Greenlandic population. Am J Hum Genet. 2015;96:54–69. Careful studies of the genetic history of the Greenlandic population.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Service S, DeYoung J, Karayiorgou M, et al. Magnitude and distribution of linkage disequilibrium in population isolates and implications for genome-wide association studies. Nat Genet. 2006;38:556–60.

    Article  CAS  PubMed  Google Scholar 

  14. Weiss KM, Terwilliger JD. How many diseases does it take to map a gene with SNPs? Nat Genet. 2000;26:151–7.

    Article  CAS  PubMed  Google Scholar 

  15. Lonjou C, Collins A, Morton NE. Allelic association between marker loci. Proc Natl Acad Sci. 1999;96:1621–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Skotte L, Korneliussen TSS, Moltke I, et al. Ancestry specific association mapping in admixed populations. bioRxiv; 2015. Available from: http://biorxiv.org/content/early/2015/01/22/014001

  17. Newman DL, Abney M, McPeek MS, et al. The importance of genealogy in determining genetic associations with complex traits. Am J Hum Genet. 2001;69:1146–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Norio R. Finnish disease heritage I: characteristics, causes, background. Hum Genet. 2003;112:441–56.

    PubMed  Google Scholar 

  19. Lohmueller KE. The distribution of deleterious genetic variation in human populations. Curr Opin Genet Dev. 2014;29:139–46.

    Article  CAS  PubMed  Google Scholar 

  20. Casals F, Hodgkinson A, Hussin J, et al. Whole-exome sequencing reveals a rapid change in the frequency of rare functional variants in a founding population of humans. PLoS Genet. 2013;9:e1003815.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Szpiech ZA, Xu J, Pemberton TJ, et al. Long runs of homozygosity are enriched for deleterious variation. Am J Hum Genet. 2013;93:90–102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Joshi PK, Esko T, Mattsson H, et al. Directional dominance on stature and cognition in diverse human populations. Nature. 2015;523:459–62.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Lohmueller KE, Indap AR, Schmidt S, et al. Proportionally more deleterious genetic variation in European than in African populations. Nature. 2008;451:994–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Simons YB, Turchin MC, Pritchard JK, et al. The deleterious mutation load is insensitive to recent population history. Nat Genet. 2014;46:220–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Fu W, Gittelman RM, Bamshad MJ, et al. Characteristics of neutral and deleterious protein-coding variation among individuals and populations. Am J Hum Genet. 2014;95:421–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Henn BM, Botigué LR, Bustamante CD, et al. Estimating the mutation load in human genomes. Nat Rev Genet. 2015;16:333–43. Highlights how deleterious mutations can evolve as if they were neutral, and shows why this leads to a correlation between burden of mutations and distance to Africa.

    Article  CAS  PubMed  Google Scholar 

  27. Do R, Balick D, Li H, et al. No evidence that selection has been less effective at removing deleterious mutations in Europeans than in Africans. Nat Genet. 2015;47:126–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Henn BM, Botigué LR, Peischl S, et al. Distance from sub-Saharan Africa predicts mutational load in diverse human genomes. Proc Natl Acad Sci. 2016;113:E440–9.

    Article  CAS  PubMed  Google Scholar 

  29. Wright AF, Carothers AD, Pirastu M. Population choice in mapping genes for complex diseases. Nat Genet. 1999;23:397–404.

    Article  CAS  PubMed  Google Scholar 

  30. Jørgensen ME, Borch-Johnsen K, Stolk R, et al. Fat distribution and glucose intolerance among Greenland Inuit. Diabetes Care. 2013;36:2988–94.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Schulz LO, Chaudhari LS. High-risk populations: the Pimas of Arizona and Mexico. Curr Obes Rep. 2015;4:92–8.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Cross HE. Population studies and the Old Order Amish. Nature. 1976;262:17–20.

    Article  CAS  PubMed  Google Scholar 

  33. Hsueh WC, Mitchell BD, Aburomia R, et al. Diabetes in the Old Order Amish: characterization and heritability analysis of the Amish family diabetes study. Diabetes Care. 2000;23:595–601.

    Article  CAS  PubMed  Google Scholar 

  34. Amish Studies. 2016. Available from: http://groups.etown.edu/amishstudies.

  35. Scott LJ, Mohlke KL, Bonnycastle LL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007;316:1341–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT and Novartis Institutes of BioMedical Research LU, Saxena R, Voight BF, Lyssenko V, et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316:1331–6.

    Article  Google Scholar 

  37. Grant SF, Thorleifsson G, Reynisdottir I, et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet. 2006;38:320–3.

    Article  CAS  PubMed  Google Scholar 

  38. Steinthorsdottir V, Thorleifsson G, Sulem P, et al. Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat Genet. 2014;46:294–8. A well-designed study demonstrating the power of combining cutting-edge genotyping technology and extensive genealogical information to identify rare and low-frequency variants associated with type 2 diabetes.

    Article  CAS  PubMed  Google Scholar 

  39. Steinthorsdottir V, Thorleifsson G, Reynisdottir I, et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet. 2007;39:770–5.

    Article  CAS  PubMed  Google Scholar 

  40. Flannick J, Thorleifsson G, Beer NL, et al. Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat Genet. 2014;46:357–63. Major study identifying type 2 diabetes protective rare loss-of-function variants in beta cell expressed SLC30A8, and pinpoints this protein as a potential treatment target.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Moltke I, Grarup N, Jørgensen ME, et al. A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature. 2014;512:190–3. Elegant study identifying a type 2 diabetes risk variant in TBC1D4, and determining its functional role in relation to diabetes, and thereby demonstrating the power of the Greenlandic population in genetic association studies.

    Article  CAS  PubMed  Google Scholar 

  42. Hanson RL, Muller YL, Kobes S, et al. A genome-wide association study in American Indians implicates DNER as a susceptibility locus for type 2 diabetes. Diabetes. 2014;63:369–76.

    Article  CAS  PubMed  Google Scholar 

  43. Baier LJ, Muller YL, Remedi MS, et al. ABCC8 R1420H loss-of-function variant in a Southwest American Indian community: association with increased birth weight and doubled risk of type 2 diabetes. Diabetes. 2015;64:4322–32. Example of successful identification of a type 2 diabetes associated variant by targeted sequencing in an isolated population.

    Article  CAS  PubMed  Google Scholar 

  44. Rampersaud E, Damcott CM, Fu M, et al. Identification of novel candidate genes for type 2 diabetes from a genome-wide association scan in the Old Order Amish: evidence for replication from diabetes-related quantitative traits and from independent populations. Diabetes. 2007;56:3053–62.

    Article  CAS  PubMed  Google Scholar 

  45. Wang SR, Agarwala V, Flannick J, et al. Simulation of Finnish population history, guided by empirical genetic data, to assess power of rare-variant tests in Finland. Am J Hum Genet. 2014;94:710–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Arcos-Burgos M, Muenke M. Genetics of population isolates. Clin Genet. 2002;61:233–47.

    Article  CAS  PubMed  Google Scholar 

  47. Kittles RA, Bergen AW, Urbanek M, et al. Autosomal, mitochondrial, and Y chromosome DNA variation in Finland: evidence for a male-specific bottleneck. Am J Phys Anthropol. 1999;108:381–99.

    Article  CAS  PubMed  Google Scholar 

  48. Helgason A, Nicholson G, Stefánsson K, et al. A reassessment of genetic diversity in Icelanders: strong evidence from multiple loci for relative homogeneity caused by genetic drift. Ann Hum Genet. 2003;67:281–97.

    Article  CAS  PubMed  Google Scholar 

  49. Kushner JA, Ciemerych MA, Sicinska E, et al. Cyclins D2 and D1 are essential for postnatal pancreatic beta-cell growth. Mol Cell Biol. 2005;25:3752–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Huyghe JR, Jackson AU, Fogarty MP, et al. Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat Genet. 2013;45:197–201.

    Article  CAS  PubMed  Google Scholar 

  51. Yaghootkar H, Stancáková A, Freathy RM, et al. Association analysis of 29,956 individuals confirms that a low-frequency variant at CCND2 halves the risk of type 2 diabetes by enhancing insulin secretion. Diabetes. 2015;64:2279–85.

    Article  CAS  PubMed  Google Scholar 

  52. Thanabalasingham G, Owen KR. Diagnosis and management of maturity onset diabetes of the young (MODY). BMJ. 2011;343:d6044.

    Article  PubMed  Google Scholar 

  53. Edghill EL, Khamis A, Weedon MN, et al. Sequencing PDX1 (insulin promoter factor 1) in 1788 UK individuals found 5% had a low frequency coding variant, but these variants are not associated with type 2 diabetes. Diabet Med. 2011;28:681–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Raghavan M, DeGiorgio M, Albrechtsen A, et al. The genetic prehistory of the New World Arctic. Science. 2014;345:1255832.

    Article  PubMed  Google Scholar 

  55. Jeppesen C, Jørgensen ME, Bjerregaard P. Assessment of consumption of marine food in Greenland by a food frequency questionnaire and biomarkers. Int J Circumpolar Health. 2012;71:18361.

    Article  PubMed  Google Scholar 

  56. Fumagalli M, Moltke I, Grarup N, et al. Greenlandic Inuit show genetic signatures of diet and climate adaptation. Science. 2015;349:1343–7. This study demonstrates the existence of several genetic regions influenced by adaptation in the Greenlandic population.

    Article  CAS  PubMed  Google Scholar 

  57. Sano H, Kane S, Sano E, et al. Insulin-stimulated phosphorylation of a Rab GTPase-activating protein regulates GLUT4 translocation. J Biol Chem. 2003;278:14599–602.

    Article  CAS  PubMed  Google Scholar 

  58. Williams RC, Long JC, Hanson RL, et al. Individual estimates of European genetic admixture associated with lower body-mass index, plasma glucose, and prevalence of type 2 diabetes in Pima Indians. Am J Hum Genet. 2000;66:527–38.

    Article  CAS  PubMed  Google Scholar 

  59. Rong R, Hanson RL, Ortiz D, et al. Association analysis of variation in/near FTO, CDKAL1, SLC30A8, HHEX, EXT2, IGF2BP2, LOC387761, and CDKN2B with type 2 diabetes and related quantitative traits in Pima Indians. Diabetes. 2009;58:478–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Haiman CA, Fesinmeyer MD, Spencer KL, et al. Consistent directions of effect for established type 2 diabetes risk variants across populations: the population architecture using Genomics and Epidemiology (PAGE) Consortium. Diabetes. 2012;61:1642–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Guo T, Hanson RL, Traurig M, et al. TCF7L2 is not a major susceptibility gene for type 2 diabetes in Pima Indians: analysis of 3,501 individuals. Diabetes. 2007;56:3082–8.

    Article  CAS  PubMed  Google Scholar 

  62. Hanson RL, Bogardus C, Duggan D, et al. A search for variants associated with young-onset type 2 diabetes in American Indians in a 100K genotyping array. Diabetes. 2007;56:3045–52.

    Article  CAS  PubMed  Google Scholar 

  63. Deng Z, Shen J, Ye J, et al. Association between single nucleotide polymorphisms of delta/notch-like epidermal growth factor (EGF)-related receptor (DNER) and Delta-like 1 Ligand (DLL 1) with the risk of type 2 diabetes mellitus in a Chinese Han population. Cell Biochem Biophys. 2015;71:331–5.

    Article  CAS  PubMed  Google Scholar 

  64. Bar Y, Efrat S. The NOTCH pathway in β-cell growth and differentiation. Vitam Horm. 2014;95:391–405.

    Article  CAS  PubMed  Google Scholar 

  65. Huang K, Nair AK, Muller YL, et al. Whole exome sequencing identifies variation in CYB5A and RNF10 associated with adiposity and type 2 diabetes. Obesity. 2014;22:984–8.

    Article  CAS  PubMed  Google Scholar 

  66. Flanagan SE, Patch A-M, Mackay DJG, et al. Mutations in ATP-sensitive K+ channel genes cause transient neonatal diabetes and permanent diabetes in childhood or adulthood. Diabetes. 2007;56:1930–7.

    Article  CAS  PubMed  Google Scholar 

  67. Gloyn AL, Weedon MN, Owen KR, et al. Large-scale association studies of variants in genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes. 2003;52:568–72.

    Article  CAS  PubMed  Google Scholar 

  68. Florez JC, Burtt N, de Bakker PI, et al. Haplotype structure and genotype-phenotype correlations of the sulfonylurea receptor and the islet ATP-sensitive potassium channel gene region. Diabetes. 2004;53:1360–8.

    Article  CAS  PubMed  Google Scholar 

  69. Pearson ER. Dissecting the etiology of type 2 diabetes in the Pima Indian population. Diabetes. 2015;64:3993–5.

    Article  CAS  PubMed  Google Scholar 

  70. Proks P, Reimann F, Green N, et al. Sulfonylurea stimulation of insulin secretion. Diabetes. 2002;51 Suppl 3:S368–76.

    Article  CAS  PubMed  Google Scholar 

  71. Hegele RA, Cao H, Harris SB, et al. The hepatocyte nuclear factor-1 alpha G319S variant is associated with early-onset type 2 diabetes in Canadian Oji-Cree. JCEM. 1999;84:1077–82.

    CAS  PubMed  Google Scholar 

  72. Estrada K, Aukrust I, Bjørkhaug L, et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA. 2014;311:2305–14.

    Article  PubMed  Google Scholar 

  73. Génin E, Clerget-Darpoux F. Association studies in consanguineous populations. Am J Hum Genet. 1996;58:861–6.

    PubMed  PubMed Central  Google Scholar 

  74. Damcott CM, Pollin TI, Reinhart LJ, et al. Polymorphisms in the transcription factor 7-like 2 (TCF7L2) gene are associated with type 2 diabetes in the Amish: replication and evidence for a role in both insulin secretion and insulin resistance. Diabetes. 2006;55:2654–9.

    Article  CAS  PubMed  Google Scholar 

  75. Mounier C, Lavoie L, Dumas V, et al. Specific inhibition by hGRB10zeta of insulin-induced glycogen synthase activation: evidence for a novel signaling pathway. Mol Cell Endocrinol. 2001;173:15–27.

    Article  CAS  PubMed  Google Scholar 

  76. Deng Y, Bhattacharya S, Swamy OR, et al. Growth factor receptor-binding protein 10 (Grb10) as a partner of phosphatidylinositol 3-kinase in metabolic insulin action. J Biol Chem. 2003;278:39311–22.

    Article  CAS  PubMed  Google Scholar 

  77. Langlais P, Dong LQ, Ramos FJ, et al. Negative regulation of insulin-stimulated mitogen-activated protein kinase signaling by Grb10. Mol Endocrinol. 2004;18:350–8.

    Article  CAS  PubMed  Google Scholar 

  78. Prokopenko I, Poon W, Mägi R, et al. A central role for GRB10 in regulation of islet function in man. PLoS Genet. 2014;10:e1004235.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk). MKA was supported by a research grant from the Danish Diabetes Academy supported by the Novo Nordisk Foundation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Niels Grarup.

Ethics declarations

Conflict of Interest

Mette Korre Andersen, Casper-Emil Tingskov Pedersen, Ida Moltke, Torben Hansen, Anders Albrechtsen, and Niels Grarup declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

This article is part of the Topical Collection on Genetics

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Andersen, M.K., Pedersen, CE.T., Moltke, I. et al. Genetics of Type 2 Diabetes: the Power of Isolated Populations. Curr Diab Rep 16, 65 (2016). https://doi.org/10.1007/s11892-016-0757-z

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s11892-016-0757-z

Keywords