From SNP Genotyping to Improved Pediatric Healthcare

  • Jacek W. Biesiada
  • Senthilkumar Sadhasivam
  • Michael Wagner
  • Jaroslaw Meller
Part of the Translational Bioinformatics book series (TRBIO, volume 2)


SNP genotyping arrays have become an important tool for cohort identification and stratification, phenotype-genotype association studies, discovery of disease markers, prediction of molecular phenotypes, and clinical decision support. In this chapter, large-scale SNP genotyping and the resulting informatics challenges are discussed in the context of basic as well as translational studies. Tailored research informatics solutions and integration with clinical informatics systems are illustrated using several specific examples of applications, including: (i) cohort stratification analysis; (ii) prediction of classical HLA alleles from SNP data in the context of pediatric autoimmune diseases; and (iii) predictive decision models for the management of surgical pain and avoidance of opioid-related adverse outcomes in children.


Obstructive Sleep Apnea Human Leukocyte Antigen Juvenile Idiopathic Arthritis Clinical Decision Support System Single Nucleotide Polymorphism Genotype 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Aller SG, et al. Structure of P-glycoprotein reveals a molecular basis for poly-specific drug binding. Science. 2009;323(5922):1718–22.PubMedCrossRefGoogle Scholar
  2. Aureli A, et al. Identification of a novel HLA-B allele, HLA-B*3580, with possible implication in transplantation and CTL response. Tissue Antigens. 2008;71(1):90–1.PubMedGoogle Scholar
  3. Caldas JC, Pais-Ribeiro JL, Carneiro SR. General anesthesia, surgery and hospitalization in children and their effects upon cognitive, academic, emotional and sociobehavioral development – a review. Paediatr Anaesth. 2004;14(11):910–15.PubMedCrossRefGoogle Scholar
  4. Cepeda MS, et al. Side effects of opioids during short-term administration: effect of age, gender, and race. Clin Pharmacol Ther. 2003;74(2):102–12.PubMedCrossRefGoogle Scholar
  5. Chan IS, Ginsburg GS. Personalized medicine: progress and promise. Annu Rev Genomics Hum Genet. 2011;12:217–44.PubMedCrossRefGoogle Scholar
  6. Chou CK, et al. Human insulin receptors mutated at the ATP-binding site lack protein tyrosine kinase activity and fail to mediate postreceptor effects of insulin. J Biol Chem. 1987;262(4):1842–7.PubMedGoogle Scholar
  7. Chou W-Y, et al. Human opioid receptor A118G polymorphism affects intravenous patient-controlled analgesia morphine consumption after total abdominal hysterectomy. Anesthesiology. 2006a;105(2):334–7.PubMedCrossRefGoogle Scholar
  8. Chou W-Y, et al. Association of mu-opioid receptor gene polymorphism (A118G) with variations in morphine consumption for analgesia after total knee arthroplasty. Acta Anaesthesiol Scand. 2006b;50(7):787–92.PubMedCrossRefGoogle Scholar
  9. Coller JK, et al. ABCB1 genetic variability and methadone dosage requirements in opioid-dependent individuals. Clin Pharmacol Ther. 2006;80(6):682–90.PubMedCrossRefGoogle Scholar
  10. de Bakker PI, et al. A high-resolution HLA and SNP haplotype map for disease association studies in the extended human MHC. Nat Genet. 2006;38(10):1166–72.PubMedCrossRefGoogle Scholar
  11. Diatchenko L, et al. Genetic basis for individual variations in pain perception and the development of a chronic pain condition. Hum Mol Genet. 2005;14(1):135–43.PubMedCrossRefGoogle Scholar
  12. Diatchenko L, et al. Catechol-O-methyltransferase gene polymorphisms are associated with multiple pain-evoking stimuli. Pain. 2006;125(3):216–24.PubMedCrossRefGoogle Scholar
  13. Diatchenko L, et al. Genetic architecture of human pain perception. Trends Genet. 2007;23(12):605–13.PubMedCrossRefGoogle Scholar
  14. Dilthey AT, et al. HLA*IMP – an integrated framework for imputing classical HLA alleles from SNP genotypes. Bioinformatics. 2011;27(7):968–72.PubMedCrossRefGoogle Scholar
  15. Duedahl TH, Hansen EH. A qualitative systematic review of morphine treatment in children with postoperative pain. Paediatr Anaesth. 2007;17(8):756–74.PubMedCrossRefGoogle Scholar
  16. Esclamado RM, et al. Perioperative complications and risk factors in the surgical treatment of obstructive sleep apnea syndrome. Laryngoscope. 1989;99(11):1125–9.PubMedCrossRefGoogle Scholar
  17. Frank E, et al. Data mining in bioinformatics using Weka. Bioinformatics. 2004;20(15):2479–81.PubMedCrossRefGoogle Scholar
  18. Hastie T, et al. The elements of statistical learning. 2nd ed. Dordrecht: Springer; 2009.CrossRefGoogle Scholar
  19. Holmes MV, et al. Fulfilling the promise of personalized medicine? Systematic review and field synopsis of pharmacogenetic studies. PLoS One. 2009;4(12):e7960. doi: 10.1371/journal.pone.960.PubMedCrossRefGoogle Scholar
  20. Hothorn T. Unbiased recursive partioning: a conditional inference framework. J Comput Graph Stat. 2010;15(3):651–74.Google Scholar
  21. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5(6):e129.CrossRefGoogle Scholar
  22. Klepsch F, Chiba P, Ecker GF. Exhaustive sampling of docking poses reveals binding hypotheses for propafenone type inhibitors of P-glycoprotein. PLoS Comput Biol. 2011;7(5):e136.CrossRefGoogle Scholar
  23. Klepstad P, et al. The 118 A > G polymorphism in the human mu-opioid receptor gene may increase morphine requirements in patients with pain caused by malignant disease. Acta Anaesthesiol Scand. 2004;48(10):1232–9.PubMedCrossRefGoogle Scholar
  24. LaFramboise T. Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances. Nucl Acids Res. 2009;37(13):4181–93.PubMedCrossRefGoogle Scholar
  25. Lechler R, Warrens A. HLA in health and disease. San Diego: Academic; 2000.Google Scholar
  26. Leslie S, Donnelly P, McVean G. A statistical method for predicting classical HLA alleles from SNP data. Am J Hum Genet. 2008;82(1):48–56.PubMedCrossRefGoogle Scholar
  27. Li N, Stephens M. Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. Genetics. 2003;165(4):2213–33.PubMedGoogle Scholar
  28. Li M, Boehnke M, Abecasis GR. Joint modeling of linkage and association: identifying SNPs responsible for a linkage signal. Am J Hum Genet. 2005;76(6):934–49.PubMedCrossRefGoogle Scholar
  29. Lindahl E, et al. NOMAD-Ref: visualization, deformation and refinement of macromolecular structures based on all-atom normal mode analysis. Nucleic Acids Res. 2006;34(Web Server issue):W52–6.PubMedCrossRefGoogle Scholar
  30. Loo TW, Clarke DM. Recent progress in understanding the mechanism of P-glycoprotein-mediated drug efflux. J Membr Biol. 2005;206(3):173–85.PubMedCrossRefGoogle Scholar
  31. Manichaikul A, et al. Robust relationship inference in genome-wide association studies. Bioinformatics. 2010;26(22):2867–73.PubMedCrossRefGoogle Scholar
  32. Marchini J, Howie B. Genotype imputation for genome-wide association studies. Nat Rev Genet. 2010;11:499–511.PubMedCrossRefGoogle Scholar
  33. Marchini J, et al. A comparison of phasing algorithms for trios and unrelated individuals. Am J Hum Genet. 2006;78(3):437–50.PubMedCrossRefGoogle Scholar
  34. Marchini J, et al. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007;39(7):906–13.PubMedCrossRefGoogle Scholar
  35. Marsh SGE, et al. Nomenclature for factors of the HLA system, 2010. Tissue Antigens. 2010a;75(4):291–455.PubMedCrossRefGoogle Scholar
  36. Marsh SGE, et al. An update to HLA nomenclature, 2010. Bone Marrow Transplant. 2010b;45(5):846–8.PubMedCrossRefGoogle Scholar
  37. Nackley AG, Diatchenko L. Assessing potential functionality of catechol-O-methyltransferase (COMT) polymorphisms associated with pain sensitivity and temporomandibular joint disorders. Methods Mol Biol. 2010;617:375–93.PubMedCrossRefGoogle Scholar
  38. Nackley AG, et al. Catechol-O-methyltransferase inhibition increases pain sensitivity through activation of both beta2- and beta3-adrenergic receptors. Pain. 2007;128(3):199–208.PubMedCrossRefGoogle Scholar
  39. Oertel BG, et al. The mu-opioid receptor gene polymorphism 118A > G depletes alfentanil-induced analgesia and protects against respiratory depression in homozygous carriers. Pharmacogenet Genomics. 2006;16(9):625–36.PubMedCrossRefGoogle Scholar
  40. Orozco G, et al. Auto-antibodies, HLA and PTPN22: susceptibility markers for rheumatoid arthritis. Rheumatology (Oxford). 2008;47(2):138–41.CrossRefGoogle Scholar
  41. Pajeva IK, Globisch C, Wiese M. Comparison of the inward- and outward-open homology models and ligand binding of human P-glycoprotein. FEBS J. 2006;276(23):7016–26.CrossRefGoogle Scholar
  42. Price AL, et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38(8):904–9.PubMedCrossRefGoogle Scholar
  43. Purcell S, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.PubMedCrossRefGoogle Scholar
  44. Quinlan JR. Improved use of continuous attributes in c4.5. J Artif Intell Res. 1996;4:77–90.Google Scholar
  45. Rakvåg TT, et al. The Val158Met polymorphism of the human catechol-O-methyltransferase (COMT) gene may influence morphine requirements in cancer pain patients. Pain. 2005;116(1–2):73–8.PubMedCrossRefGoogle Scholar
  46. Rakvåg TT, et al. Genetic variation in the catechol-O-methyltransferase (COMT) gene and morphine requirements in cancer patients with pain. Mol Pain. 2008;4:64.PubMedCrossRefGoogle Scholar
  47. Robinson J, et al. IMGT/HLA and IMGT/MHC: sequence databases for the study of the major histocompatibility complex. Nucleic Acids Res. 2003;31(1):311–14.PubMedCrossRefGoogle Scholar
  48. Robinson J, et al. The IMGT/HLA database. Nucleic Acids Res. 2009;37(Database issue):D1013–17.PubMedCrossRefGoogle Scholar
  49. Rosenfeld RJ, et al. Automated docking of ligands to an artificial active site: augmenting crystallographic analysis with computer modeling. J Comput Aided Mol Des. 2003;17(8):525–36.PubMedCrossRefGoogle Scholar
  50. Sadhasivam S, et al. Race and unequal burden of perioperative pain and opioid related adverse effects in children. Pediatrics. 2012;129(5):832–8.PubMedCrossRefGoogle Scholar
  51. Sampaio-Barros PD, et al. Frequency of HLA-B27 and its alleles in patients with Reiter syndrome: comparison with the frequency in other spondyloarthropathies and a healthy control population. Rheumatol Int. 2008;28(5):483–6.PubMedCrossRefGoogle Scholar
  52. Schaaf CP, Wiszniewska J, Beaudet AL. Copy number and SNP arrays in clinical diagnostics. Annu Rev Genomics Hum Genet. 2011;12:25–51.PubMedCrossRefGoogle Scholar
  53. Scheet P, Stephens M. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet. 2006;78(4):629–44.PubMedCrossRefGoogle Scholar
  54. Seco J, Luque FJ, Barril X. Binding site detection and druggability index from first principles. J Med Chem. 2009;52(8):2363–71.PubMedCrossRefGoogle Scholar
  55. Stephens M, Donnelly P. A comparison of Bayesian methods for haplotype reconstruction from population genotype data. Am J Hum Genet. 2003;73(5):1162–9.PubMedCrossRefGoogle Scholar
  56. Stephens M, Smith NJ, Donnelly P. A new statistical method for haplotype reconstruction from population data. Am J Hum Genet. 2001;68(4):978–89.PubMedCrossRefGoogle Scholar
  57. The International HapMap Consortium. The international HapMap project. Nature. 2003;426:789–96.CrossRefGoogle Scholar
  58. Therneu TM, Atkinson B. rpart: recursive partitioning. 2009.
  59. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661–78.CrossRefGoogle Scholar
  60. Witten IH, Frank E. Data mining: practical machine learning tools and techniques. 2nd ed. San Francisco: Morgan Kaufmann; 2005.Google Scholar
  61. Yin Y, et al. Structure of the multidrug transporter EmrD from Escherichia coli. Science. 2006;312(5774):741–4.PubMedCrossRefGoogle Scholar
  62. Zavodszky MI, et al. Distilling the essential features of a protein surface for improving protein-ligand docking, scoring, and virtual screening. J Comput Aided Mol Des. 2002;16(12):883–902.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Jacek W. Biesiada
    • 1
    • 2
  • Senthilkumar Sadhasivam
    • 3
    • 4
  • Michael Wagner
    • 1
    • 3
  • Jaroslaw Meller
    • 1
    • 5
  1. 1.Division of Biomedical InformaticsCincinnati Children’s Hospital Medical CenterCincinnatiUSA
  2. 2.Division of Management and InformaticsTechnical University of SilesiaGliwicePoland
  3. 3.Department of PediatricsUniversity of Cincinnati College of MedicineCincinnatiUSA
  4. 4.Perioperative and Acute Pain Service, Division of Pediatric AnesthesiaCincinnati Children’s Hospital Medical CenterCincinnatiUSA
  5. 5.Department of Environmental HealthUniversity of Cincinnati College of MedicineCincinnatiUSA

Personalised recommendations