Secrets of the perioptome: New tools for a new concept

Current Opinion in Critical Care

Volumn 16, Issue 4, 2010, Pages 365-370

  Shaw, Andrew ^a   VandeVen, Thomas ^a  

^a DUKE UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

Complex trait genomics; Gene sequencing; Genome wide assocation study; Perioperative genomics

Indexed keywords

CLOPIDOGREL; CYTOCHROME P450 1A2; CYTOCHROME P450 2A5; CYTOCHROME P450 2B6; CYTOCHROME P450 2C19; CYTOCHROME P450 2C9; POLYPEPTIDE;

ACUTE DISEASE; ANTIGEN RECOGNITION; BLOOD CLOTTING; BODY HEIGHT; DIABETES MELLITUS; ECONOMIC ASPECT; FUNCTIONAL GENOMICS; GENE SEQUENCE; GENETIC ASSOCIATION; GENETIC RESISTANCE; GENETIC SELECTION; GENETIC VARIABILITY; GENOME; HERITABILITY; HUMAN; INFLAMMATION; INTENSIVE CARE UNIT; KIDNEY INJURY; OPERATING ROOM; PERIOPERATIVE GENOMICS; PERIOPTOME; PHARMACODYNAMICS; PHENOTYPE; PROTEOMICS; REVIEW; SEPSIS; SINGLE NUCLEOTIDE POLYMORPHISM; STENT THROMBOSIS; THROMBOCYTE AGGREGATION;

BODY HEIGHT; COMPUTATIONAL BIOLOGY; DIABETES MELLITUS; GENETIC PREDISPOSITION TO DISEASE; GENETIC VARIATION; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; HUMANS; PERIOPERATIVE CARE; PROTEOMICS; RISK FACTORS;

EID: 78449292606     PISSN: 10705295     EISSN: 15317072     Source Type: Journal    
DOI: 10.1097/MCC.0b013e32833b0466     Document Type: Review

References (24)

1. Mendel G. Experiments in plant hybridization. Royal Horticultural Society of London, translation 1938. 1866.
2. Fisher RA. Has Mendel's workbeen rediscovered? Ann Sci 1936; 1:115-137.
3. McCarthy MI, Abecasis GR, Cardon LR, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 2008; 9:356-369.
4. Bodmer W, Bonilla C. Common and rare variants in multifactorial suscept-ibility to common diseases. Nat Genet 2008; 40:695-702.
5. Tonjes A, Koriath M, Schleinitz DK, et al. Genetic variation in GPR133 is associated with height: genome wide association study in the self-contained population ofSorbs. Hum Mol Genet 2009; 18:4662-4668.
6. Tabara Y, Osawa H, Kawamoto R, et al. Replication study of candidate genes associated with type 2 diabetes based on genome-wide screening. Diabetes 2009; 58:493-498.
7. Bouchard C. Defining thegenetic architecture of the predispositiontoobesity: a challenging but not insurmountable task. Am J Clin Nutr 2010; 91:5-6.
8. Kim H, Ramsay E, Lee H, et al. Genome-wide association study of acute postsurgical pain in humans. Pharmacogenomics 2009; 10:171-179.
9. Le Fanu J. The disappointments of the double helix: a master theory. J R Soc Med 2010; 103:43-45.
10. Goldstein DB. Common genetic variation and human traits. N Engl J Med 2009; 360:1696-1698.
11. Hirschhorn JN. Genomewide association studies: illuminating biologic pathways. N Engl J Med 2009; 360:1699-1701.
12. Shaw A. Exploring the perioptome: the role of genomics in thoracic surgery and anaesthesia. Curr Opin Anaesthesiol 2007; 20:32-36.
13. Podgoreanu MV, Schwinn DA. New paradigms in cardiovascular medicine: emerging technologies and practices: perioperative genomics. J Am Coll Cardiol 2005; 46:1965-1977.
14. Voog J, Jones DL. Stem cells and the niche: a dynamic duo. Cell Stem Cell 2010; 6:103-115.
15. Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome. Nature 2001; 409:860-921.
16. Weedon MN, Lango H, Lindgren CM, et al. Genome-wide association analysis identifies 20 loci that influence adult height. Nat Genet 2008; 40:575-583.
17. Hahn LW, Ritchie MD, Moore JH. Multifactor dimensionality reduction soft-ware for detecting gene-gene and gene-environment interactions. Bioinfor-matics 2003; 19:376-382.
18. Shaw AD, Vaporciyan AA, Wu X, et al. Inflammatory gene polymorphisms influence riskofpostoperative morbidityafterlung resection. Ann Thorac Surg 2005; 79:1704-1710.
19. Mega JL, Close SL, Wiviott SD, et al. Cytochrome p-450 polymorphisms and response to clopidogrel. N Engl J Med 2009; 360:354-362.
20. Gerszten RE, Carr SA, Sabatine M. Integration of proteomic-based tools for improved biomarkers of myocardial injury. Clin Chem 2010; 56:194-201.
21. Lucas J, Carvalho C, Wang Q, et al. Sparse statistical modelling in gene expression genomics. Bayesian Inference for Gene Expression and Proteo-mics 2006; 155-176.
22. Diatchenko L, Slade GD, Nackley AG, et al. Genetic basis for individual variations in pain perception and the development of a chronic pain condition. Hum Mol Genet 2005; 14:135-143.
23. Caldwell JC, Tracey WDJ. Alternatives to mammalian pain models 2: using Drosophilato identify novel genes involved in nociception. Methods Mol Biol 2010; 617:19-29.
24. Minami K, Hasegawa M, Ito H, et al. Morphine, oxycodone, and fentanyl exhibit different analgesic profiles in mouse pain models. J Pharmacol Sci 2009; 111:60-72.