Skip to main content
SpringerLink
Log in

What Will Diabetes Genomes Tell Us?

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

Abstract

A new generation of genetic studies of diabetes is underway. Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes. Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk. Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants. We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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. Voight BF, Scott LJ, Steinthorsdottir V, et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet. 2010;42:579–89.

    Article  PubMed  CAS  Google Scholar 

  2. Dupuis J, Langenberg C, Prokopenko I, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42:105–16.

    Article  PubMed  CAS  Google Scholar 

  3. Kooner JS, Saleheen D, Sim X, et al. Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat Genet. 2011;43:984–9.

    Article  PubMed  CAS  Google Scholar 

  4. Cho YS, Chen CH, Hu C, et al. Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat Genet. 2012;44:67–72.

    Article  CAS  Google Scholar 

  5. Barrett JC, Clayton DG, Concannon P, et al. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat Genet. 2009;41:703–7.

    Article  PubMed  CAS  Google Scholar 

  6. •1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature. 2010;467:1061–73. Provided a comprehensive resource on human genetic variation based on the genomes of hundreds of people.

    Article  Google Scholar 

  7. Marth GT, Yu F, Indap AR, et al. The functional spectrum of low-frequency coding variation. Genome Biol. 2011;12:R84.

    Article  PubMed  Google Scholar 

  8. Mills RE, Walter K, Stewart C, et al. Mapping copy number variation by population-scale genome sequencing. Nature. 2011;470:59–65.

    Article  PubMed  CAS  Google Scholar 

  9. • MacArthur DG, Balasubramanian S, Frankish A, et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science. 2012;335:823–8. Evaluated loss-of-function variants from 185 human genomes and determined their prevalence and properties.

    Article  PubMed  CAS  Google Scholar 

  10. The International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007;449:851–61.

    Article  Google Scholar 

  11. Li Y, Willer CJ, Ding J, et al. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 2010;34:816–34.

    Article  PubMed  Google Scholar 

  12. Marchini J, Howie B. Genotype imputation for genome-wide association studies. Nat Rev Genet. 2010;11:499–511.

    Article  PubMed  CAS  Google Scholar 

  13. HumanOmni2.5S Data Sheet. Available at http://www.illumina.com/documents/products/datasheets/datasheet.omni25S.pdf. Accessed July 2012.

  14. Keating BJ, Tischfield S, Murray SS, et al. Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies. PLoS One. 2008;3:e3583.

    Article  PubMed  Google Scholar 

  15. Saxena R, Elbers CC, Guo Y, et al. Large-scale gene-centric meta-analysis across 39 studies identifies type 2 diabetes loci. Am J Hum Genet. 2012;90:410–25.

    Article  PubMed  CAS  Google Scholar 

  16. Cortes A, Brown MA. Promise and pitfalls of the Immunochip. Arthroplast Res Ther. 2011;13:101.

    Article  Google Scholar 

  17. Buyske S, Wu Y, Carty CL, et al. Evaluation of the metabochip genotyping array in African Americans and implications for fine mapping of GWAS-identified loci: the PAGE study. PLoS One. 2012;7:e35651.

    Article  PubMed  CAS  Google Scholar 

  18. Voight BF, Kang HM, Ding J, et al. The Metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 2012;8:e1002793.

    Article  PubMed  CAS  Google Scholar 

  19. •• 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. Used the metabochip to perform the largest yet meta-analysis of type 2 diabetes and identified novel loci.

  20. Guan W, Boehnke M, Pluzhnikov A, et al. Identifying plausible genetic models based on association and linkage results: Application to type 2 diabetes. Genet Epidemiol 2012, In press.

  21. •• Scott RA, Lagou V, Welch RP, 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. 2012;44:991–1005. Used the metabochip to perform the largest yet meta-analysis of glycemic traits and identified 35 loci not previously described in European genome-wide approaches.

  22. Exome Chip Design. Available at http://genome.sph.umich.edu/wiki/ExomeChipDesign. Accessed July 2012.

  23. Marchini J, Howie B, Myers S, et al. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007;39:906–13.

    Article  PubMed  CAS  Google Scholar 

  24. Browning BL, Browning SR. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet. 2009;84:210–23.

    Article  PubMed  CAS  Google Scholar 

  25. Zeggini E, Scott LJ, Saxena R, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008;40:638–45.

    Article  PubMed  CAS  Google Scholar 

  26. Huang J, Ellinghaus D, Franke A, et al. 1000 Genomes-based imputation identifies novel and refined associations for the Wellcome Trust Case Control Consortium phase 1 Data. Eur J Hum Genet. 2012;20:801–5.

    Article  PubMed  CAS  Google Scholar 

  27. Prokopenko I, Ma C, Magi R, et al. Search for novel type 2 diabetes susceptibility loci using genome-wide association studies imputed from a 1000 Genomes reference panel [abstract 139]. Presented at American Diabetes Association 72nd Scientific Sessions. Philadelphia, PA; June 8–12, 2012.

  28. Bamshad MJ, Ng SB, Bigham AW, et al. Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet. 2011;12:745–55.

    Article  PubMed  CAS  Google Scholar 

  29. Johansson S, Irgens H, Chudasama KK, et al. Exome sequencing and genetic testing for MODY. PLoS One. 2012;7:e38050.

    Article  PubMed  CAS  Google Scholar 

  30. • Bonnefond A, Philippe J, Durand E, et al. Whole-exome sequencing and high throughput genotyping identified KCNJ11 as the thirteenth MODY gene. PLoS One. 2012;7:e37423. Used exome sequencing to identify KCNJ11 as a MODY gene.

    Article  PubMed  CAS  Google Scholar 

  31. Thanabalasingham G, Pal A, Selwood MP, et al. Systematic assessment of etiology in adults with a clinical diagnosis of young-onset type 2 diabetes is a successful strategy for identifying maturity-onset diabetes of the young. Diabetes Care. 2012;35:1206–12.

    Article  PubMed  CAS  Google Scholar 

  32. Kiezun A, Garimella K, Do R, et al. Exome sequencing and the genetic basis of complex traits. Nat Genet. 2012;44:623–30.

    Article  PubMed  CAS  Google Scholar 

  33. Cohen JC, Kiss RS, Pertsemlidis A, et al. Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science. 2004;305:869–72.

    Article  PubMed  CAS  Google Scholar 

  34. Ahituv N, Kavaslar N, Schackwitz W, et al. Medical sequencing at the extremes of human body mass. Am J Hum Genet. 2007;80:779–91.

    Article  PubMed  CAS  Google Scholar 

  35. Nejentsev S, Walker N, Riches D, et al. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science. 2009;324:387–9.

    Article  PubMed  CAS  Google Scholar 

  36. Jafar-Mohammadi B, Groves CJ, Gjesing AP, et al. A role for coding functional variants in HNF4A in type 2 diabetes susceptibility. Diabetologia. 2011;54:111–9.

    Article  PubMed  CAS  Google Scholar 

  37. Zhu Q, Yamagata K, Miura A, et al. T130I mutation in HNF-4 alpha gene is a loss-of-function mutation in hepatocytes and is associated with late-onset Type 2 diabetes mellitus in Japanese subjects. Diabetologia. 2003;46:567–73.

    PubMed  CAS  Google Scholar 

  38. Ek J, Rose CS, Jensen DP, et al. The functional Thr130Ile and Val255Met polymorphisms of the hepatocyte nuclear factor-4 alpha (HNF4A): gene associations with type 2 diabetes or altered beta-cell function among Danes. J Clin Endocrinol Metab. 2005;90:3054–9.

    Article  PubMed  CAS  Google Scholar 

  39. Stitziel NO, Kiezun A, Sunyaev S. Computational and statistical approaches to analyzing variants identified by exome sequencing. Genome Biol. 2011;12:227.

    Article  PubMed  Google Scholar 

  40. Li Y, Sidore C, Kang HM, et al. Low-coverage sequencing: implications for design of complex trait association studies. Genome Res. 2011;21:940–51.

    Article  PubMed  CAS  Google Scholar 

  41. Kang HM, Gaulton K, Voight BF, et al. Sequencing and genotyping thousands of European genomes and exomes to better understand the genetic architecture of type 2 diabetes: the GoT2D Study [abstract 190]. Presented at International Congress of Human Genetics, Montreal, Canada, October 11–15, 2011.

  42. Almeida M, Jun G, Teslovich TM, et al. Whole genome sequencing to discover type 2 diabetes risk genes in Mexican American pedigrees: T2D-GENES consortium project 2 [abstract 141]. Presented at American Diabetes Association 72nd Scientific Sessions. Philadelphia, PA; June 8–12, 2012.

  43. Cooper GM, Shendure J. Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nat Rev Genet. 2011;12:628–40.

    Article  PubMed  CAS  Google Scholar 

  44. •• Bonnefond A, Clement N, Fawcett K, et al. Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes. Nat Genet. 2012;44:297–301. Used exon sequencing to identify 40 nonsynonymous variants in MTNR1B and functionally characterized the variant proteins.

    Article  PubMed  CAS  Google Scholar 

  45. •• Rees MG, Ng D, Ruppert S, et al. Correlation of rare coding variants in the gene encoding human glucokinase regulatory protein with phenotypic, cellular, and kinetic outcomes. J Clin Invest. 2012;122:205–17. Used exon sequencing to identify 19 nonsynonymous variants in GCKR and functionally characterized the variant proteins.

    Article  PubMed  CAS  Google Scholar 

  46. Bouatia-Naji N, Bonnefond A, Cavalcanti-Proenca C, et al. A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk. Nat Genet. 2009;41:89–94.

    Article  PubMed  CAS  Google Scholar 

  47. Prokopenko I, Langenberg C, Florez JC, et al. Variants in MTNR1B influence fasting glucose levels. Nat Genet. 2009;41:77–81.

    Article  PubMed  CAS  Google Scholar 

  48. van de Bunt M, Gloyn AL. From genetic association to molecular mechanism. Curr Diab Rep. 2010;10:452–66.

    Article  PubMed  Google Scholar 

  49. Rees MG, Wincovitch S, Schultz J, et al. Cellular characterisation of the GCKR P446L variant associated with type 2 diabetes risk. Diabetologia. 2012;55:114–22.

    Article  PubMed  CAS  Google Scholar 

  50. Pearson ER, Starkey BJ, Powell RJ, et al. Genetic cause of hyperglycaemia and response to treatment in diabetes. Lancet. 2003;362:1275–81.

    Article  PubMed  CAS  Google Scholar 

  51. Slingerland AS, Hattersley AT. Mutations in the Kir6.2 subunit of the KATP channel and permanent neonatal diabetes: new insights and new treatment. Ann Med. 2005;37:186–95.

    Article  PubMed  CAS  Google Scholar 

  52. Talmud PJ, Hingorani AD, Cooper JA, et al. Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ. 2010;340:b4838.

    Article  PubMed  Google Scholar 

  53. Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med. 2008;359:2220–32.

    Article  PubMed  CAS  Google Scholar 

  54. de Miguel-Yanes JM, Shrader P, Pencina MJ, et al. Genetic risk reclassification for type 2 diabetes by age below or above 50 years using 40 type 2 diabetes risk single nucleotide polymorphisms. Diabetes Care. 2011;34:121–5.

    Article  PubMed  Google Scholar 

  55. Perry JR, Voight BF, Yengo L, et al. Stratifying type 2 diabetes cases by BMI identifies genetic risk variants in LAMA1 and enrichment for risk variants in lean compared to obese cases. PLoS Genet. 2012;8:e1002741.

    Article  PubMed  CAS  Google Scholar 

  56. Gibson G. Rare and common variants: twenty arguments. Nat Rev Genet. 2011;13:135–45.

    Article  Google Scholar 

  57. Ingelsson E, Langenberg C, Hivert MF, et al. Detailed physiologic characterization reveals diverse mechanisms for novel genetic loci regulating glucose and insulin metabolism in humans. Diabetes. 2010;59:1266–75.

    Article  PubMed  CAS  Google Scholar 

  58. Gaulton KJ, Nammo T, Pasquali L, et al. A map of open chromatin in human pancreatic islets. Nat Genet. 2010;42:255–9.

    Article  PubMed  CAS  Google Scholar 

  59. Stitzel ML, Sethupathy P, Pearson DS, et al. Global epigenomic analysis of primary human pancreatic islets provides insights into type 2 diabetes susceptibility loci. Cell Metab. 2010;12:443–55.

    Article  PubMed  CAS  Google Scholar 

  60. Gamazon ER, Badner JA, Cheng L, et al. Enrichment of cis-regulatory gene expression SNPs and methylation quantitative trait loci among bipolar disorder susceptibility variants. Mol Psychiat 2012, In press.

  61. Cookson W, Liang L, Abecasis G, et al. Mapping complex disease traits with global gene expression. Nat Rev Genet. 2009;10:184–94.

    Article  PubMed  CAS  Google Scholar 

  62. Liu DJ, Leal SM. A novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associations with rare variants due to gene main effects and interactions. PLoS Genet. 2010;6:e1001156.

    Article  PubMed  Google Scholar 

  63. Darnell G, Duong D, Han B, Eskin E. Incorporating prior information into association studies. Bioinformatics. 2012;28:i147–53.

    Article  PubMed  Google Scholar 

  64. Lee S, Wu MC, Lin X. Optimal tests for rare variant effects in sequencing association studies. Biostatistics 2012, In press.

Download references

Acknowledgments

We acknowledge support from National Institutes of Health Grants DK62370, DK72193, DK88389, HG000376, and DK93757.

Disclosure

No potential conflicts of interest relevant to this article were reported.

Author information

Authors and Affiliations

  1. University of North Carolina, 5096 Genetic Medicine, 120 Mason Farm Drive, Chapel Hill, NC, 27599-7264, USA

    Karen L. Mohlke

  2. University of Michigan, M4134 SPH II, 1415 Washington Heights, Ann Arbor, MI, 48109-2029, USA

    Laura J. Scott

Authors
  1. Karen L. Mohlke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laura J. Scott
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Karen L. Mohlke.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mohlke, K.L., Scott, L.J. What Will Diabetes Genomes Tell Us?. Curr Diab Rep 12, 643–650 (2012). https://doi.org/10.1007/s11892-012-0321-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11892-012-0321-4

Keywords

Search

Navigation