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.

  • Review Article
  • Published:

Common and rare forms of diabetes mellitus: towards a continuum of diabetes subtypes

Nature Reviews Endocrinology volume 12, pages 394–406 (2016)Cite this article

Subjects

Key Points

  • The common disease of type 2 diabetes mellitus (T2DM) is genetically distinct from, but clinically similar to, rarer forms of diabetes mellitus

  • Today, understanding of the pathophysiology of rare forms of diabetes mellitus is greater than that of the common form of T2DM

  • As genetic studies examine rarer variants in large populations, higher penetrance variants have been found for T2DM even as previously 'Mendelian' variants have been found to have lower penetrances

  • The increasing overlap between regions associated with T2DM and genes or variants relevant to monogenic diseases suggests an overlapping disease aetiology, possibly including distinct T2DM subtypes

  • We propose that a unified diabetes mellitus risk model, spanning variants of all frequencies and penetrances, might provide future insights into understanding or treatment of all forms of diabetes mellitus

Abstract

Insights into the genetic basis of type 2 diabetes mellitus (T2DM) have been difficult to discern, despite substantial research. More is known about rare forms of diabetes mellitus, several of which share clinical and genetic features with the common form of T2DM. In this Review, we discuss the extent to which the study of rare and low-frequency mutations in large populations has begun to bridge the gap between rare and common forms of diabetes mellitus. We hypothesize that the perceived division between these diseases might be due, in part, to the historical ascertainment bias of genetic studies, rather than a clear distinction between disease pathophysiologies. We also discuss possible implications of a new model for the genetic basis of diabetes mellitus subtypes, where the boundary between subtypes becomes blurred.

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: Monogenic forms of diabetes mellitus.
Figure 2: Variants associated with monogenic diabetes mellitus, T2DM or levels of glucose or insulin.
Figure 3: An allelic series in HNF1A.
Figure 4: A unified model of diabetes mellitus risk.

Similar content being viewed by others

References

  1. World Health Organization. Global status report on noncommunicable diseases. [online], (2014).

  2. Anderson, M. S. & Bluestone, J. A. The NOD mouse: a model of immune dysregulation. Annu. Rev. Immunol. 23, 447–485 (2005).

    Article  CAS  PubMed  Google Scholar 

  3. Fajans, S. S. & Bell, G. I. MODY: history, genetics, pathophysiology, and clinical decision making. Diabetes Care 34, 1878–1884 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Polak, M. & Cave, H. Neonatal diabetes mellitus: a disease linked to multiple mechanisms. Orphanet J. Rare Dis. 2, 12 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Maassen, J. A. et al. Mitochondrial diabetes: molecular mechanisms and clinical presentation. Diabetes 53, S103–S109 (2004).

    Article  CAS  PubMed  Google Scholar 

  6. Hattersley, A., Bruining, J., Shield, J., Njolstad, P. & Donaghue, K. C. The diagnosis and management of monogenic diabetes in children and adolescents. Pediatr. Diabetes 10 (Suppl. 12), 33–42 (2009).

    Article  PubMed  Google Scholar 

  7. Molven, A. & Njolstad, P. R. Role of molecular genetics in transforming diagnosis of diabetes mellitus. Expert Rev. Mol. Diagn. 11, 313–320 (2011).

    Article  CAS  PubMed  Google Scholar 

  8. McCarthy, M. I. & Hattersley, A. T. Learning from molecular genetics: novel insights arising from the definition of genes for monogenic and type 2 diabetes. Diabetes 57, 2889–2898 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Klupa, T., Skupien, J. & Malecki, M. T. Monogenic models: what have the single gene disorders taught us? Curr. Diab. Rep. 12, 659–666 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tallapragada, D. S., Bhaskar, S. & Chandak, G. R. New insights from monogenic diabetes for 'common' type 2 diabetes. Front. Genet. 6, 251 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Kahn, S. E., Cooper, M. E. & Del Prato, S. Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. Lancet 383, 1068–1083 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. DeFronzo, R. A., Bonadonna, R. C. & Ferrannini, E. Pathogenesis of NIDDM. A balanced overview. Diabetes Care 15, 318–368 (1992).

    Article  CAS  PubMed  Google Scholar 

  13. Srinivasan, S. & Florez, J. C. Therapeutic challenges in diabetes prevention: we have not found the 'exercise pill'. Clin. Pharmacol. Ther. 98, 162–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  14. Shields, B. M. et al. Maturity-onset diabetes of the young (MODY): how many cases are we missing? Diabetologia 53, 2504–2508 (2010).

    Article  CAS  PubMed  Google Scholar 

  15. Steele, A. M. et al. Prevalence of vascular complications among patients with glucokinase mutations and prolonged, mild hyperglycemia. JAMA 311, 279–286 (2014).

    Article  CAS  PubMed  Google Scholar 

  16. Isomaa, B. et al. Chronic diabetic complications in patients with MODY3 diabetes. Diabetologia 41, 467–473 (1998).

    Article  CAS  PubMed  Google Scholar 

  17. Gardner, D. S. & Tai, E. S. Clinical features and treatment of maturity onset diabetes of the young (MODY). Diabetes Metab. Syndr. Obes. 5, 101–108 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Barrett, T. G. & Bundey, S. E. Wolfram (DIDMOAD) syndrome. J. Med. Genet. 34, 838–841 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Semple, R. K., Savage, D. B., Cochran, E. K., Gorden, P. & O'Rahilly, S. Genetic syndromes of severe insulin resistance. Endocr. Rev. 32, 498–514 (2011).

    Article  CAS  PubMed  Google Scholar 

  20. Heidet, L. et al. Spectrum of HNF1B mutations in a large cohort of patients who harbor renal diseases. Clin. J. Am. Soc. Nephrol. 5, 1079–1090 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Raeder, H. et al. Mutations in the CEL VNTR cause a syndrome of diabetes and pancreatic exocrine dysfunction. Nat. Genet. 38, 54–62 (2006).

    Article  CAS  PubMed  Google Scholar 

  22. Murphy, R., Ellard, S. & Hattersley, A. T. Clinical implications of a molecular genetic classification of monogenic β-cell diabetes. Nat. Clin. Pract. Endocrinol. Metab. 4, 200–213 (2008).

    Article  CAS  PubMed  Google Scholar 

  23. Vaxillaire, M. & Froguel, P. Monogenic diabetes in the young, pharmacogenetics and relevance to multifactorial forms of type 2 diabetes. Endocr. Rev. 29, 254–264 (2008).

    Article  CAS  PubMed  Google Scholar 

  24. Gao, R. et al. Evaluation of a target region capture sequencing platform using monogenic diabetes as a study-model. BMC Genet. 15, 13 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Ellard, S. et al. Improved genetic testing for monogenic diabetes using targeted next-generation sequencing. Diabetologia 56, 1958–1963 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Bonnefond, A. et al. Highly sensitive diagnosis of 43 monogenic forms of diabetes or obesity through one-step PCR-based enrichment in combination with next-generation sequencing. Diabetes Care 37, 460–467 (2014).

    Article  CAS  PubMed  Google Scholar 

  27. Alkorta-Aranburu, G. et al. Phenotypic heterogeneity in monogenic diabetes: the clinical and diagnostic utility of a gene panel-based next-generation sequencing approach. Mol. Genet. Metab. 113, 315–320 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Bamshad, M. J. et al. Exome sequencing as a tool for Mendelian disease gene discovery. Nat. Rev. Genet. 12, 745–755 (2011).

    Article  CAS  PubMed  Google Scholar 

  29. Bonnefond, A. et al. Molecular diagnosis of neonatal diabetes mellitus using next-generation sequencing of the whole exome. PLoS ONE 5, e13630 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Johansson, S. et al. Exome sequencing and genetic testing for MODY. PLoS ONE 7, e38050 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lango Allen, H. et al. GATA6 haploinsufficiency causes pancreatic agenesis in humans. Nat. Genet. 44, 20–22 (2012).

    Article  CAS  Google Scholar 

  32. Bonnefond, A. et al. Whole-exome sequencing and high throughput genotyping identified KCNJ11 as the thirteenth MODY gene. PLoS ONE 7, e37423 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Prudente, S. et al. Loss-of-function mutations in APPL1 in familial diabetes mellitus. Am. J. Hum. Genet. 97, 177–185 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kerns, S. L. et al. A novel variant in CDKN1C is associated with intrauterine growth restriction, short stature, and early-adulthood-onset diabetes. J. Clin. Endocrinol. Metab. 99, E2117–E2122 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Al-Maawali, A. et al. Prenatal growth restriction, retinal dystrophy, diabetes insipidus and white matter disease: expanding the spectrum of PRPS1-related disorders. Eur. J. Hum. Genet. 23, 310–316 (2015).

    Article  CAS  PubMed  Google Scholar 

  36. Thomas, P. M. et al. Mutations in the sulfonylurea receptor gene in familial persistent hyperinsulinemic hypoglycemia of infancy. Science 268, 426–429 (1995).

    Article  CAS  PubMed  Google Scholar 

  37. Gloyn, A. L. et al. Activating mutations in the gene encoding the ATP-sensitive potassium-channel subunit Kir6.2 and permanent neonatal diabetes. N. Engl. J. Med. 350, 1838–1849 (2004).

    Article  CAS  PubMed  Google Scholar 

  38. Njolstad, P. R. et al. Neonatal diabetes mellitus due to complete glucokinase deficiency. N. Engl. J. Med. 344, 1588–1592 (2001).

    Article  CAS  PubMed  Google Scholar 

  39. Lindner, T. H. et al. A novel syndrome of diabetes mellitus, renal dysfunction and genital malformation associated with a partial deletion of the pseudo-POU domain of hepatocyte nuclear factor-1β. Hum. Mol. Genet. 8, 2001–2008 (1999).

    Article  CAS  PubMed  Google Scholar 

  40. Haldorsen, I. S. et al. Lack of pancreatic body and tail in HNF1B mutation carriers. Diabet. Med. 25, 782–787 (2008).

    Article  CAS  PubMed  Google Scholar 

  41. Rubio-Cabezas, O. et al. Homozygous mutations in NEUROD1 are responsible for a novel syndrome of permanent neonatal diabetes and neurological abnormalities. Diabetes 59, 2326–2331 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Hattersley, A. T. Beyond the beta cell in diabetes. Nat. Genet. 38, 12–13 (2006).

    Article  CAS  PubMed  Google Scholar 

  43. Irgens, H. U. et al. Prevalence of monogenic diabetes in the population-based Norwegian Childhood Diabetes Registry. Diabetologia 56, 1512–1519 (2013).

    Article  CAS  PubMed  Google Scholar 

  44. Sagen, J. V. et al. Permanent neonatal diabetes due to mutations in KCNJ11 encoding Kir6.2: patient characteristics and initial response to sulfonylurea therapy. Diabetes 53, 2713–2718 (2004). Reference 44 is a proof of concept study that described the first patient with permanent neonatal diabetes mellitus (PNDM) who successfully switched from insulin to sulfonylurea therapy based on the molecular diagnosis of KCNJ11 -induced PNDM.

    Article  CAS  PubMed  Google Scholar 

  45. Pearson, E. R. et al. Switching from insulin to oral sulfonylureas in patients with diabetes due to Kir6.2 mutations. N. Engl. J. Med. 355, 467–477 (2006). Reference 45 changed clinical practice. In a large series of patients, Pearson et al . showed that most patients with KCNJ11 mutations can safely switch from insulin to oral sulfonylurea therapy.

    Article  CAS  PubMed  Google Scholar 

  46. Pearson, E. R. et al. Genetic cause of hyperglycaemia and response to treatment in diabetes. Lancet 362, 1275–1281 (2003). Reference 46 established that patients with HNF1A-MODY (MODY3) have marked sulfonylurea sensitivity and should be treated accordingly.

    Article  CAS  PubMed  Google Scholar 

  47. Chakera, A. J. et al. Recognition and management of individuals with hyperglycemia because of a heterozygous glucokinase mutation. Diabetes Care 38, 1383–1392 (2015).

    Article  CAS  PubMed  Google Scholar 

  48. Hemminki, K., Li, X., Sundquist, K. & Sundquist, J. Familial risks for type 2 diabetes in Sweden. Diabetes Care 33, 293–297 (2010).

    Article  PubMed  Google Scholar 

  49. Almgren, P. et al. Heritability and familiality of type 2 diabetes and related quantitative traits in the Botnia Study. Diabetologia 54, 2811–2819 (2011).

    Article  CAS  PubMed  Google Scholar 

  50. Guan, W., Pluzhnikov, A., Cox, N. J. & Boehnke, M. Meta-analysis of 23 type 2 diabetes linkage studies from the International Type 2 Diabetes Linkage Analysis Consortium. Hum. Hered. 66, 35–49 (2008).

    Article  PubMed  Google Scholar 

  51. Bonnefond, A. & Froguel, P. Rare and common genetic events in type 2 diabetes: what should biologists know? Cell Metab. 21, 357–368 (2015).

    Article  CAS  PubMed  Google Scholar 

  52. Mohlke, K. L. & Boehnke, M. Recent advances in understanding the genetic architecture of type 2 diabetes. Hum. Mol. Genet. 24, R85–R92 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012). Reference 53, a large meta-analysis, provides evidence supporting a major role for common low-effect variants in type 2 diabetes mellitus susceptibility.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. DIAbetes Genetics Replication Meta-analysis Consortium et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat. Genet. 46, 234–244 (2014).

  55. Agarwala, V., Flannick, J., Sunyaev, S., GoT2D Consortium & Altshuler, D. Evaluating empirical bounds on complex disease genetic architecture. Nat. Genet. 45, 1418–1427 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Stahl, E. A. et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat. Genet. 44, 483–489 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012). Reference 57 demonstrated the first systematic enrichment of regulatory DNA for common variant GWAS associations.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat. Genet. 46, 136–143 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Stitzel, M. L. et al. Global epigenomic analysis of primary human pancreatic islets provides insights into type 2 diabetes susceptibility loci. Cell Metab. 12, 443–455 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. The Wellcome Trust Case Control Consortium et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).

  61. Ragvin, A. et al. Long-range gene regulation links genomic type 2 diabetes and obesity risk regions to HHEX, SOX4, and IRX3. Proc. Natl Acad. Sci. USA 107, 775–780 (2010).

    Article  CAS  PubMed  Google Scholar 

  62. Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015). Reference 62 describes a comprehensive and successful strategy for investigating non-coding complex trait associations by combining bioinformatics and genomics methods.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Small, K. S. et al. Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes. Nat. Genet. 43, 561–564 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Travers, M. E. et al. Insights into the molecular mechanism for type 2 diabetes susceptibility at the KCNQ1 locus from temporal changes in imprinting status in human islets. Diabetes 62, 987–992 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Prokopenko, I. et al. A central role for GRB10 in regulation of islet function in man. PLoS Genet. 10, e1004235 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Claussnitzer, M. et al. Leveraging cross-species transcription factor binding site patterns: from diabetes risk loci to disease mechanisms. Cell 156, 343–358 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Gaulton, K. J. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 1415–1425 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. van de Bunt, M. et al. Transcript expression data from human islets links regulatory signals from genome-wide association studies for type 2 diabetes and glycemic traits to their downstream effectors. PLoS Genet. 11, e1005694 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Hegele, R. A., Cao, H., Harris, S. B., Hanley, A. J. & Zinman, B. The hepatic nuclear factor-1α G319S variant is associated with early-onset type 2 diabetes in Canadian Oji-Cree. J. Clin. Endocrinol. Metab. 84, 1077–1082 (1999).

    CAS  PubMed  Google Scholar 

  71. Altshuler, D. et al. The common PPARγ Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat. Genet. 26, 76–80 (2000). Reference 71 describes the first association between a common variant and type 2 diabetes mellitus.

    Article  CAS  PubMed  Google Scholar 

  72. Gloyn, A. L. et al. Large-scale association studies of variants in genes encoding the pancreatic β-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes 52, 568–572 (2003).

    Article  CAS  PubMed  Google Scholar 

  73. Voight, B. F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589 (2010). Reference 73 extended the number of genes known to harbour variants for both common type 2 diabetes mellitus and monogenic forms of diabetes mellitus.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Fuchsberger, C. et al. Genetic architecture of type 2 diabetes from sequence based analyses. Nature (in the press). Reference 74, a comprehensive series of sequencing studies, investigates the contribution of rare and low-frequency variants to the genetic architecture of type 2 diabetes mellitus.

  75. Huyghe, J. R. et al. Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat. Genet. 45, 197–201 (2013). Reference 75 describes the first systematic screen of lower-frequency coding variants in large cohorts of patients with type 2 diabetes mellitus.

    Article  CAS  PubMed  Google Scholar 

  76. Wessel, J. et al. Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat. Commun. 6, 5897 (2015).

    Article  CAS  PubMed  Google Scholar 

  77. Mahajan, A. et al. Identification and functional characterization of G6PC2 coding variants influencing glycemic traits define an effector transcript at the G6PC2–ABCB11 locus. PLoS Genet. 11, e1004876 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Manning, A. K. et al. A low frequency AKT2 coding variant enriched in the Finnish population is associated with fasting insulin levels. Presented at the 60th Annual Meeting of The American Society of Human Genetics (2014).

  79. Voight, B. F. et al. The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 8, e1002793 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Moltke, I. et al. A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature 512, 190–193 (2014). Reference 80 demonstrated the power of studying isolated populations in complex trait analysis, identifying a population-specific variant that confers very high risk for type 2 diabetes mellitus.

    Article  CAS  PubMed  Google Scholar 

  81. Steinthorsdottir, V. et al. Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat. Genet. 46, 294–298 (2014).

    Article  CAS  PubMed  Google Scholar 

  82. Lohmueller, K. E. et al. Whole-exome sequencing of 2,000 Danish individuals and the role of rare coding variants in type 2 diabetes. Am. J. Hum. Genet. 93, 1072–1086 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. The SIGMA Type 2 Diabetes Consortium et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA 311, 2305–2314 (2014). Reference 83 provides strong support for the existence of an allelic series of high-to-low effect diabetes variants in HNF1A.

  84. Bonnefond, A. et al. Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes. Nat. Genet. 44, 297–301 (2012). Reference 84 demonstrated the ability of functional assays to discriminate disease alleles from benign alleles.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Majithia, A. R. et al. Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc. Natl Acad. Sci. USA 111, 13127–13132 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Laeya, A. et al. Functional investigations of the monogenic diabetes gene HNF1A identify rare variants as risk factors for type 2 diabetes in the general population. Diabetes 64 (Suppl 1) A460 (2015).

    Google Scholar 

  87. Flannick, J. et al. Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat. Genet. 46, 357–363 (2014). Reference 87 was the first study to pinpoint genetic variants that confer strong protective effects against type 2 diabetes mellitus.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Flannick, J. et al. Assessing the phenotypic effects in the general population of rare variants in genes for a dominant Mendelian form of diabetes. Nat. Genet. 45, 1380–1385 (2013). Reference 88 used population based sequencing to yield unbiased estimates of disease risk associated with rare coding variants in Mendelian disease genes.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Guey, L. T. et al. Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants. Genet. Epidemiol. 35, 236–246 (2011).

    PubMed  Google Scholar 

  90. Antoniou, A. et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case Series unselected for family history: a combined analysis of 22 studies. Am. J. Hum. Genet. 72, 1117–1130 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Terwilliger, J. D. & Weiss, K. M. Confounding, ascertainment bias, and the blind quest for a genetic 'fountain of youth'. Ann. Med. 35, 532–544 (2003).

    Article  PubMed  Google Scholar 

  92. Goring, H. H., Terwilliger, J. D. & Blangero, J. Large upward bias in estimation of locus-specific effects from genomewide scans. Am. J. Hum. Genet. 69, 1357–1369 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Gail, M. & Chatterjee, N. in Proceedings of the Second Seattle Symposium in Biostatistics (Seattle, 2004).

    Google Scholar 

  94. MacArthur, D. G. et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science 335, 823–828 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Kohane, I. S., Hsing, M. & Kong, S. W. Taxonomizing, sizing, and overcoming the incidentalome. Genet. Med. 14, 399–404 (2012).

    Article  CAS  PubMed  Google Scholar 

  96. Sulem, P. et al. Identification of a large set of rare complete human knockouts. Nat. Genet. 47, 448–452 (2015).

    Article  CAS  PubMed  Google Scholar 

  97. Plon, S. E. et al. Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results. Hum. Mutat. 29, 1282–1291 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015). Reference 99 is highly influential and provides recommendations for variant interpretation in rare disorders.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Boj, S. F., Parrizas, M., Maestro, M. A. & Ferrer, J. A transcription factor regulatory circuit in differentiated pancreatic cells. Proc. Natl Acad. Sci. USA 98, 14481–14486 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Odom, D. T. et al. Control of pancreas and liver gene expression by HNF transcription factors. Science 303, 1378–1381 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Baumhueter, S. et al. HNF-1 shares three sequence motifs with the POU domain proteins and is identical to LF-B1 and APF. Genes Dev. 4, 372–379 (1990).

    Article  CAS  PubMed  Google Scholar 

  102. Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Erdmann, J. et al. New susceptibility locus for coronary artery disease on chromosome 3q22.3. Nat. Genet. 41, 280–282 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Groop, L. & Pociot, F. Genetics of diabetes — are we missing the genes or the disease? Mol. Cell. Endocrinol. 382, 726–739 (2014).

    Article  CAS  PubMed  Google Scholar 

  105. Melnikov, A. et al. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotechnol. 30, 271–277 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Melnikov, A., Rogov, P., Wang, L., Gnirke, A. & Mikkelsen, T. S. Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes. Nucleic Acids Res. 42, e112 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  107. Cooper, G. M. Parlez-vous VUS? Genome Res. 25, 1423–1426 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. MacArthur, D. G. et al. Guidelines for investigating causality of sequence variants in human disease. Nature 508, 469–476 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. George, S. et al. A family with severe insulin resistance and diabetes due to a mutation in AKT2. Science 304, 1325–1328 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Tan, K. et al. Analysis of genetic variation in Akt2/PKB-β in severe insulin resistance, lipodystrophy, type 2 diabetes, and related metabolic phenotypes. Diabetes 56, 714–719 (2007).

    Article  CAS  PubMed  Google Scholar 

  111. Hsu, P. D., Lander, E. S. & Zhang, F. Development and applications of CRISPR–Cas9 for genome engineering. Cell 157, 1262–1278 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Pearson, E. R., Liddell, W. G., Shepherd, M., Corrall, R. J. & Hattersley, A. T. Sensitivity to sulphonylureas in patients with hepatocyte nuclear factor-1α gene mutations: evidence for pharmacogenetics in diabetes. Diabet. Med. 17, 543–545 (2000).

    Article  CAS  PubMed  Google Scholar 

  113. Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009). Reference 114 is a highly cited discussion of potential explanations for the 'missing heritability' that was often debated after the first wave of GWAS.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. McClellan, J. & King, M. C. Genetic heterogeneity in human disease. Cell 141, 210–217 (2010).

    Article  CAS  PubMed  Google Scholar 

  115. Cirulli, E. T. & Goldstein, D. B. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat. Rev. Genet. 11, 415–425 (2010).

    Article  CAS  PubMed  Google Scholar 

  116. The International Schizophrenia Consortium. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).

  117. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Cohen, J. C., Boerwinkle, E., Mosley, T. H. Jr & Hobbs, H. H. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N. Engl. J. Med. 354, 1264–1272 (2006). Reference 119 is one of the most influential papers to demonstrate the power of protective loss-of-function mutations to suggest therapeutic targets.

    Article  CAS  PubMed  Google Scholar 

  119. Jostins, L. & Barrett, J. C. Genetic risk prediction in complex disease. Hum. Mol. Genet. 20, R182–R188 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Risch, N. J. Searching for genetic determinants in the new millennium. Nature 405, 847–856 (2000).

    Article  CAS  PubMed  Google Scholar 

  122. Altshuler, D., Daly, M. J. & Lander, E. S. Genetic mapping in human disease. Science 322, 881–888 (2008). Reference 123 is a thorough review of the history of genetic mapping.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Reich, D. E. & Lander, E. S. On the allelic spectrum of human disease. Trends Genet. 17, 502–510 (2001). Reference 124 is responsible for much of the motivation behind GWAS.

    Article  CAS  PubMed  Google Scholar 

  124. Bamshad, M. J. et al. The Centers for Mendelian Genomics: a new large-scale initiative to identify the genes underlying rare Mendelian conditions. Am. J. Med. Genet. A158A, 1523–1525 (2012).

    Article  PubMed  Google Scholar 

  125. Beaulieu, C. L. et al. FORGE Canada Consortium: outcomes of a 2-year national rare-disease gene-discovery project. Am. J. Hum. Genet. 94, 809–817 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Wright, C. F. et al. Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data. Lancet 385, 1305–1314 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  127. Yang, Y. et al. Clinical whole-exome sequencing for the diagnosis of mendelian disorders. N. Engl. J. Med. 369, 1502–1511 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Stefan Johansson has received a grant from the Novo Nordisk Foundation (#15OC0016108). Pål R. Njølstad has received grants from the European Research Council (AdG; #293574), the Research Council of Norway (#240413), the KG Jebsen Foundation (Translational Research Center), the University of Bergen (Diabetes Group), and the Helse Vest (Strategic Fund).

Author information

Authors and Affiliations

  1. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, 02142, Massachusetts, USA

    Jason Flannick

  2. Center for Human Genetic Research, Massachusetts General Hospital, 185 Cambridge Street, Boston, 02114, Massachusetts, USA

    Jason Flannick

  3. The Department of Clinical Science, K.G. Jebsen Center for Diabetes Research, University of Bergen, Jonas Lies veg 87, Bergen, N-5020, Norway

    Stefan Johansson & Pål R. Njølstad

  4. Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Jonas Lies veg 65, Bergen, N-5021, Norway

    Stefan Johansson

  5. Department of Pediatrics, Haukeland University Hospital, Jonas Lies veg 65, Bergen, N-5021, Norway

    Pål R. Njølstad

Authors
  1. Jason Flannick
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stefan Johansson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pål R. Njølstad
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.F., S.J. and P.R.N. researched data for the article, made substantial contributions to discussions of the content, wrote the article and reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Pål R. Njølstad.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

PowerPoint slides

Glossary

Sulfonylureas

A class of antidiabetic medication that increases insulin secretion by blockage of the ATP-sensitive potassium channel in the pancreas.

Mendelian

A trait whose inheritance pattern obeys the laws proposed by Gregor Mendel; characteristic of a disease caused by mutations in a single gene.

Linkage

An approach for identifying disease genes by testing for co-segregation between DNA segments and phenotype in a familial study design.

Next-generation sequencing

(NGS). A family of technologies for relatively inexpensive and high-throughput sequencing of DNA.

Genome-wide association studies

(GWAS). An approach for identifying disease genes by testing many common genetic variants in different individuals for associations with a trait.

Expression quantitative trait loci

(eQTL). A genomic region that is associated with variation in expression levels of mRNA.

Allelic series

A number of alleles of a gene or chromosome region with a range of phenotypic and/or molecular effects; useful to infer a genetic–phenotypic 'dose-response' curve.

Probands

In a familial study, the first affected family member who seeks attention for a disease presentation.

CRISPR/Cas9

A technique for precise and efficient editing of genetic information within a cell.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Flannick, J., Johansson, S. & Njølstad, P. Common and rare forms of diabetes mellitus: towards a continuum of diabetes subtypes. Nat Rev Endocrinol 12, 394–406 (2016). https://doi.org/10.1038/nrendo.2016.50

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrendo.2016.50

This article is cited by

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