Skip to main content
SpringerLink
Log in

Bioinformatics, Genomics and Diabetes

  • Chapter
  • First Online:
Computational Intelligence Techniques in Health Care

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSFOMEBI))

Abstract

Bioinformatic analysis has been a key in unraveling the genetic basis of diabetes mellitus, which figured predominantly among target diseases for research after the human genome project. Despite extensive research the genetic contribution using current methods explains less than 10 % of predisposition. Data from next generation sequencing is bound to alter diagnosis, pathogenesis and treatment targets. Insight into the fine genetic architecture allows a fine grained classification of the diabetes spectrum, allowing primary preventive methods in at-risk individuals. In this quest the role of computational, statistical and pattern recognition would play increasingly major roles.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Diabetes Atlas (2014) Available online at www.Idf.Org/diabetesatlas

  2. Prasad RB, Groop L (2015) Genetics of type 2 diabetes—pitfalls and possibilities. Genes 6:87–123

    Google Scholar 

  3. Murphy R, Ellard S, Hattersley AT (2008) Clinical implications of molecular genetic classification of monogenic beta-cell diabetes. Nat Clin Pract Endocrinol Metab 4:200–213

    Article  Google Scholar 

  4. Groop L (2015) Genetics and neonatal diabetes: towards precision medicine. Lancet 386:934–935

    Article  Google Scholar 

  5. Lyssenko V, Lupi R, Marchetti P, Del Guerra S, Orho-Melander M, Almgren P, Sjogren M, Ling C, Eriksson KF, Lethagen AL et al (2007) Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 117:2155–2163

    Article  Google Scholar 

  6. Sridhar GR, Duggirala R, Padmanabhan S (2013) Emerging face of genetics, genomics and diabetes. Int J Diab Devel Countries 33:183–185

    Google Scholar 

  7. Tong Y, Lin Y, Zhang Y, Yang J, Zhang Y, Liu H, Zhang B (2009) Association between TCF71.2 gene polymorphisms and susceptibility to type 2 diabetes mellitus: a large human genome epidemiology (huge) review and meta-analysis. BMC Med Genet 10:e15

    Article  Google Scholar 

  8. Majithiaa AR, Flannicka J, Shahiniana P, Guod M, Braya M-A, Fontanillasa P, Gabriela SB, GoT2D Consortium, NHGRI JHS/FHS Allelic Spectrum Project, SIGMA T2D Consortium 2, T2D-GENES Consortium, Rosenc ED, Altshuler D (2014) Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. PNAS 111:13127–13132

    Google Scholar 

  9. Chavali S, Mahajan A, Tabassum R, Dwivedi OP, Chauhan G, Ghosh S, Tandon N, Bharadwaj D (2011) Association of variants in genes involved in pancreatic β-cell development and function with type 2 diabetes in North Indians. J Human Gen 56:695–700

    Google Scholar 

  10. Estus JL, Family Investigation of Nephropathy and Diabetes Research Group, Fardo DW (2013) Combining genetic association study designs: a GWAS case study. Front Genet 4:186. doi:10.3389/fgene.2013.00186. eCollections 2013

  11. Chen Q, Sun F (2013) A unified approach for allele frequency estimation, SNP detection and association studies based on pooled sequencing data using EM algorithms. BMC Genomics 14 (Supplement 1):S1. doi:10.1186/1471-2164-14-S1-S1

    Google Scholar 

  12. Wang Q, Lu Q, Zhao H (2015) A review of study designs and statistical methods for genomic epidemiology studies using next generation sequencing. Front Genet 6:149. doi:10.3389/fgene.2015.00149

  13. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vincent D, Belisle A et al (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445:881–885

    Article  Google Scholar 

  14. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, Novartis Institutes of BioMedical Research, Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H et al (2007) Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316:1331–1336

    Google Scholar 

  15. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WI, Eridos MR, Stringham HM, Chines PS et al (2007) A genome-wide association analysis of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316:1341–1345

    Article  Google Scholar 

  16. The Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3000 shared controls. Nature 447:661–678

    Article  Google Scholar 

  17. Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, Zeggini E, Huth C, Aulchenko YS et al (2010) Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet 42:579–589

    Article  Google Scholar 

  18. Morris AP, Voight BF, Eslovich TM, Ferreria T, Segre AV, Steinthorsdottir V, Strawbridge RJ, Khan H et al (2012) Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 44:981–990

    Article  Google Scholar 

  19. DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium, Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium, South Asian Type 2 Diabetes (SAT2D) Consortium, Mexican American Type 2 Diabetes (MAT2D) Consortium, Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) Consortium (2014) Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 46:234–244

    Google Scholar 

  20. Tipu HN, Shabbir A (2015) Evolution of DNA sequencing. J Coll Phys Surg Pak 25:210–215

    Google Scholar 

  21. Durmaz AA, Karaca E, Demkow U, Toruner G, Schoumans J, Cogulu O (2015) Evolution of genetic techniques: past, present, and beyond. BioMed Res Int Article id: 461524. http://doi.org/10.1155/2015/461524

    Google Scholar 

  22. Behjati S, Tarpey PS (2013) What is next generation sequencing? Arch Dis Child Pract Educ 98:236–238

    Google Scholar 

  23. Ohashi H, Hasegawa M, Wakimoto K, Sato EM (2015) Next-generation technologies for multiomics approaches including interactome sequencing. BioMed Res Int Article id: 104209. http://dx.doi.org/10.1155.2015/104209

  24. Pareek CS, Smoczynski R, Tretyn A (2011) Sequencing technologies and genome sequencing. J Appl Genet 52:413–435

    Google Scholar 

  25. Biesecker LG, Shianna KV, Mullikin JC (2011) Exome sequencing: the expert view. Genome Biol 12:128

    Article  Google Scholar 

  26. Morris JA, Barrett JC (2012) Olorin: combining gene flow with exome sequencing in large family studies of complex disease. Bioinformatics 28:3320–3321

    Google Scholar 

  27. Bickeboller H, Bailey JN, Beyene J, Cantor RM, Cordell HJ, Culverhouse RC, Engelman CD, Fardo DW, Ghosh S, Konig IR et al (2014) Genetic analysis workshop 18: methods and strategies for analyzing human sequence and phenotype data in members of extended pedigrees. BMC Proc 8(Suppl 1):S1. doi:10.1186/1753-6561-8-S1-S1. eCollection

  28. Yorgov D, Edwards KL, Santorico SA (2014) Use of admixture and association for detection of quantitative trait loci in the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples (T2D-GENES) study. BMC Proc 8(Suppl 1):S6. doi:10.1186/1753-6561-8-S1-S6. eCollection

  29. Fang YH, Chiu YF (2013) A novel support vector machine-based approach for rare variation detection. PLoS ONE 8(8):e71114. doi:10.1371/journal.pone.0071114

    Article  Google Scholar 

  30. Yun S, Yun S (2014) Masking as an effective quality control method for next-generation sequencing data analysis. BMC Bioinform 15:382. doi:10.1186/s12859-014-0382-2

    Article  Google Scholar 

  31. Donath MY, Ehses JA (2006) Type 1, type 1.5, and type 2 diabetes: NOD the diabetes we thought it was. PNAS 103(33):12217–12118

    Google Scholar 

  32. Schwitzgebel VM (2014) Many faces of monogenic diabetes. J Diab Invest 5:121–133

    Article  Google Scholar 

  33. Gao R, Liu Y, Gjesing AP, Hollensted M, Wan X, He S, Pedersen O, Yi X, Wang J, Hansen T (2014) Evaluation of a target region capture sequencing platform using monogenic diabetes as a study-model. BMC Genet 15:13. doi:10.1186/1471-2156-15-13

    Article  Google Scholar 

  34. Ellard S, Lango AH, De Franco E, Flangan SE, Hysenaj G, Colclough K, Houghton JA, Shepherd M, Hattersley AT, Weeden MN, Caswell R (2013) Improved genetic testing for monogenic diabetes using targeted next-generation sequencing. Diabetologia 56:1958–1963

    Google Scholar 

  35. Haaland WC, Scaduto DI, Maldonado MR, Mansouri DL, Nalini R, Iyer D, Patel S, Guthikonda A, Hampf CS, Balasubramanyam A, Metzker ML (2009) A-β—subtype of ketosis-prone diabetes is not predominantly a monogenic diabetic syndrome. Diab Care 32:873–877

    Article  Google Scholar 

  36. Bonnefond A, Durand E, Sand O, De Graeve F, Gallina S, Busiah K, Lobbens S, Simon A, Chantelot BC, Letourneau L, Scharfmann R, Delplanque J et al (2010) Molecular diagnosis of neonatal diabetes mellitus using next-generation sequence of the whole exome. PLoS ONE 5:e13630

    Article  Google Scholar 

  37. Asha HS, Chapla A, Shetty S, Thomas N (2015) Next-generation sequencing-based genetic testing for familial partial lipodystrophy. AACE Clin Case Rep 1(1):e28–e31

    Google Scholar 

  38. Erlich HA, Valdes AM, McDevitt SL, Simen BB, Blake LA, McGowan KR, Todd JA, Rich SS, Noble JA, Type 1 Diabetes Genetics Consortium (T1DGC) (2013) Next generation sequencing reveals the association of DRB3*02:02 with type 1 diabetes. Diabetes 62:2618–2622

    Article  Google Scholar 

  39. Lee HS, Briese T, Winkler C, Rewers M, Bonifacio E, Hyoty H, Pflueger M, Simell O, She JX, Hagopian W, Lernmark A et al (2013) Next-generation sequencing for viruses in children with rapid-onset type 1 diabetes. Diabetologia 56:1705–1711

    Article  Google Scholar 

  40. Kramna L, Kalarova K, Oikarinen S, Purusiheimo JP, Ilonen J, Simelll O, Knip M, Veijola R, Hyoty H, O Cinek (2015) Gut virome sequencing in children with early islet autoimmunity. Diab Care 38:930–933

    Google Scholar 

  41. Talmud PJ, Cooper JA, Morris RW, Dudbridge F, Shah T, Engmann J, Dale C, White J, McLachlan S, Zabaneh D, Wong A, Ong KK, Gaunt T, Holmes MV, Lawlor DA et al (2015) Sixty-five common genetic variants and prediction of type 2 diabetes. Diabetes 64:1830–1840

    Google Scholar 

  42. Tanaka D, Nagashima K, Sasaki M, Funakoshi S, Kondo Y, Yasuda K, Koizumi A, Inagaki N (2013) Exome sequencing identifies a new candidate mutation for susceptibility to diabetes in a family with highly aggregated type 2 diabetes. Mol Genet Metab 109:112–117

    Article  Google Scholar 

  43. Rung J, Cauchi S, Albrechtsen A, Shen L, Rocheleau G, Cavalcanti-Proenca C, Bacot F, Balkau B, Belisle A et al (2009) Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat Genet 41:1110–1115

    Article  Google Scholar 

  44. Lyssenko V, Laakso M (2013) Genetic screening for the risk of type 2 diabetes. Diab Care 36:S120–S126

    Google Scholar 

  45. Kwak SH, Jang HC, Park KS (2012) Finding genetic risk factors of gestational diabetes. Genomics Inf 10:2390243

    Google Scholar 

  46. Bonnefond A, Philippe J, Durand E, Muller J, Saeed S, Arsian M, Martinez R, De Graeve F, Dhennin V, Rabearivelo I, Polak M, Cave H et al (2014) Highly sensitive diagnosis of 43 monogenic forms of diabetes or obesity through one-step PCR-based enrichment in combination with next-generation sequencing. Diab Care 37:460–467

    Google Scholar 

  47. Lieber DS, Vafai SB, Horton LC, Slate NG, Liu S, Borowsky ML, Calvo SE, Schmahmann JD, Mootha VK (2012) A typical case of Wolfram syndrome revealed through targeted exome sequencing in a patient with suspected mitochondrial disease. BMC Med Genet 13:3. doi:10.1186/1471-2350-13-3

    Article  Google Scholar 

  48. Komers R, Xu B, Fu Y, McCelland A, Kantharidis P, Mittal A, Cohen HT, Cohen DM (2014) Transcriptome-based analysis of kidney gene expression changes associated with diabetes in OVE26 mice, in the presence and absence of losartan treatment. PLoS ONE 9(5):e96987. doi:10.1371/journal.pone.0096987

    Article  Google Scholar 

  49. Kelly K, Liu Y, Zhang J, Goswami C, Lin H, Dominguez JH (2014) Comprehensive genomic profiling in diabetic nephropathy reveals the predominance of proinflammatory pathways. Physiol Genomics 45:710–719

    Article  Google Scholar 

  50. Brennan EP, Morine MJ, Walsh DW, Roxburgh SA, Lindenmeyer MT, Brazil DP, Gaora PO, Roche HM, Sadlier DM, Cohen CD, GENIE Consortium, Godson C, Martin F (2012) Next-generation sequencing identified TGF-β1-associated gene expression profiles in renal epithelial cells reiterated in human diabetic nephropathy. Biochim Biophys Acta 1822:589–599

    Google Scholar 

  51. Pezzolesi MG, Krolewski AS (2013) The genetic risk of kidney disease in type 2 diabetes. Med Clin N Am 97:91–107

    Article  Google Scholar 

  52. Kandpal RP, Rajasimha HK, Brooks MJ, Nellissery J, Wan J, Qian J, Kern TS, Swaroop A (2012) Transcriptome analysis using next generation sequencing reveals molecular signatures of diabetic retinopathy and efficacy of candidate drugs. Mol Vis 18:1123–1146

    Google Scholar 

  53. Sridhar GR, Lakshmi G (2015) Epigenetics and diabetes. In: Sridhar GR (ed) Advances in diabetes: Novel Insights. The Health Sciences Pub, N Delhi p 81–91

    Google Scholar 

  54. Wang J, Wu Z, Lif D, Li N, Dindot SV, Satterfield MC et al (2012) Nutrition, epigenetics and metabolic syndrome. Antioxidation Redox Signal 17:282–301

    Article  Google Scholar 

  55. Ong FS, Lin JC, Das K, Grosu DS, Fan JB (2013) Translational utility of next-generation sequencing. Genomics 102:137–139

    Google Scholar 

  56. Salbaum JM, Kappen C (2011) Diabetic embryopathy: a role for the epigenome? Birth Defects Res A Clin Mol Teratol 91:770–780

    Article  Google Scholar 

  57. Reddy MA, Natarajan R (2011) Epigenetic mechanisms in diabetic vascular complications. Cardiovasc Res 90:421–429

    Article  Google Scholar 

  58. Latrelle M, Hausser J, Stutzer I, Zhang Q, Hastoy B, Gargani S et al (2014) MicroRna-7a regulates pancreatic β-cell function. J Clin Invest 124:2722–2735

    Article  Google Scholar 

  59. Wren JD, Garner HR (2005) Data-mining analysis suggests an epigenetic pathogenesis for type 2 diabetes. J Biomed Biotechnol 2005(2):104–112

    Article  Google Scholar 

  60. Miao P, Chen Z, Genuth S, Paterson A, Zhang L, Wu X et al (2014) Evaluating the role of epigenetic histone modifications in the metabolic memory of type 1 diabetes. Diabetes 63:1748–1762

    Article  Google Scholar 

  61. Zhong X, Liao Y, Chen L, Liu G, Feng Y, Zeng T, Zhang J (2015) The microRNs in the pathogenesis of metabolic memory. Endocrinology 156(9):3157–3168. doi: 10.1210/en.2015-1063

    Google Scholar 

  62. Karlsson FH, Tremaroli V, Nookaew I, Bergstrom G, Behre CJ et al (2013) Gut metagenome in European women with normal impaired and diabetic glucose control. Nature 498:99–103

    Article  Google Scholar 

  63. Sridhar GR (2015) Microbiota and metabolic syndrome. In: Bajaj S et al (eds) ESI handbook of endocrinology. Jaypee Pub, Delhi, pp 122–138

    Google Scholar 

  64. Sekirov I, Shannon L, Russell SL, Caetano MA, Finlay BB (2010) Gut microbiota in health and disease. Physiol Rev 90:859–904

    Article  Google Scholar 

  65. Karlsson F, Tremaroli V, Nielsen J, Backhed F (2013) Assessing the human gut microbiota in metabolic diseases. Diabetes 62:3341–3349

    Google Scholar 

  66. Kim BS, Jeon YS, Chun J (2013) Current status and future promise of the human microbiome. Ped Gastreoenterol Hepatol Nutr 16:71–79

    Article  Google Scholar 

  67. Teutsch SM, Bradley LA, Palomaki GE, Haddow JE, Piper M, Calonge N, Dotson WD, Douglas MP, Berg AO (2009) The evaluation of genomic applications in practice and prevention (EGAPP) initiative: methods of the EGAPP working group. Genet Med 11:3–14

    Article  Google Scholar 

  68. Tang Y, Axelsson AS, Spegel P, Andersson LE, Mulder H, Groop LC, Renstrom E, Rosengren AH (2014) Genotype-based treatment of type 2 diabetes with an alpha2α-adrenergic receptor antagonist. Science Transl Med 6:257ra139

    Google Scholar 

  69. Jyothi KS, Srinivas K, Sridhar GR, Rao BS, Apparao A (2010) Plant insulin: an in silico approach. Intl J Diab Dev Countries 30:191–193

    Google Scholar 

  70. Annadurai RS, Jayakumaar V, Mugasimangalam RC, Katta MA, Anand S, Gopinathan S, Sarma SP, Fernandes SJ, Mullapudi N, Murugesan S, Rao SN (2012) Next generation sequencing and de novo transcriptome analysis of Costus pictus D. Don, a non-model plant with potent anti-diabetic properties. BMC Genom 13:663. doi:10.1186/1471-2164-13-663

    Article  Google Scholar 

  71. Tang ZH, Fang Z, Zhou L (2013) Human genetics of diabetic vascular complications. J Genet 92(3):677–694

    Article  Google Scholar 

  72. Jameson JL, Longo DL (2015) Precision medicine-personalized, problematic, and promising. N Engl J Med 372:2229–2234

    Google Scholar 

  73. Fall T, Xie W, Poon W, Yaghootkar H, Magi R, The GENESIS Consortium, Knowles JW, Lyssenko V, Weedon et al (2015) Using genetic variants to assess the relationship between circulating lipids and type 2 diabetes. Diabetes 64:2676–2684

    Google Scholar 

  74. Swerdlow DL, Sattar N (2015) Blood lipids and type 2 diabetes risk: can genetics help untangle the web? Diabetes 2015(64):2344–2345

    Article  Google Scholar 

  75. Phimister EC (2015) Curating the way to better determinants of genetic risk. N Engl J Med 372:2227–2228

    Article  Google Scholar 

  76. Rehm HL, Berg JS, Brooks LD, Bustamante CD, Evans JP, Landrum MJ, Ledbetter DH, Maglott DR, Martin CL et al (2015) ClinGen-the clinical genome resource. N Engl J Med 372:2235–2242

    Article  Google Scholar 

  77. Kohane IS (2015) Ten things we have to do to achieve precision medicine. Science 349:37–38

    Article  Google Scholar 

  78. Bielinski SJ, Pathak J, Weinshilboum RM, Wang L, Lyke KJ, Ryu E, Targonski PV, Van Norstrand MD, Hathcock MA, Takahashi PY, McCormick JB, Johnson KJ et al (2014) Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualise treatment protocol. Mayo Clin Proc 89:25–33

    Article  Google Scholar 

  79. Editorial (2015) Data overprotection. Nature 522:391–392

    Google Scholar 

  80. Sarewitz D (2015) Science can’t solve it. Nature 522:413–414

    Article  Google Scholar 

  81. Veltman JA, Lupski JR (2015) From genes to genomes in the clinic. Genome Med 7:78

    Article  Google Scholar 

  82. Vincent AT, Charette S (2015) Who qualifies to be a bioinformatician? Front Genet 6:164. doi:10.3389/fgene.2015.00164

  83. Smith DR (2015) Broadening the definition of a bioinformatician. Front Genet 6:258. doi:10.3389/fgene.2015.00258

  84. Middha S, Lindor NM, McDonnell SK et al (2015) How well do whole exome sequencing results correlate with medical findings? A study of 89 Mayo Clinic Biobank samples. Front Genet 6:244. doi:10.3389/fgene.2015.00244

Download references

Author information

Authors and Affiliations

  1. Endocrine and Diabetes Centre, 15-12-15 Krishnanagar, Visakhapatnam, 530 002, India

    Gumpeny Ramachandra Sridhar & Gumpeny Lakshmi

Authors
  1. Gumpeny Ramachandra Sridhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gumpeny Lakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gumpeny Ramachandra Sridhar .

Editor information

Editors and Affiliations

  1. Dept Of IT, GIT, GITAM University, Visakhapatnam, Andhra Pradesh, India

    P.V. Lakshmi

  2. DuPont Pioneer, Johnston, Iowa, USA

    Wengang Zhou

  3. MVGR College, Vizianagaram, Andhra Pradesh, India

    P Satheesh

Rights and permissions

Reprints and permissions

Copyright information

© 2016 The Author(s)

About this chapter

Cite this chapter

Ramachandra Sridhar, G., Lakshmi, G. (2016). Bioinformatics, Genomics and Diabetes. In: Lakshmi, P., Zhou, W., Satheesh, P. (eds) Computational Intelligence Techniques in Health Care. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-10-0308-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-0308-0_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0307-3

  • Online ISBN: 978-981-10-0308-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation