The benefits of using genetic information to design prevention trials

American Journal of Human Genetics

Volumn 92, Issue 4, 2013, Pages 547-557

  Hu, Youna ^a,b   Li, Li ^c   Ehm, Margaret G ^c   Bing, Nan ^c   Song, Kijoung ^c   Nelson, Matthew R ^c   Talmud, Philippa J ^d   Hingorani, Aroon D ^d   Kumari, Meena ^d   Kivimäki, Mika ^d   Xu, Chun Fang ^e   Waterworth, Dawn M ^c   Whittaker, John C ^e   Abecasis, Gonçalo R ^a   Spino, Cathie ^a   Kang, Hyun Min ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c GLAXOSMITHKLINE   (United States)

^d UNIVERSITY COLLEGE LONDON   (United Kingdom)

^e GLAXOSMITHKLINE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AGED; ARTICLE; CONTROLLED STUDY; COST CONTROL; COST EFFECTIVENESS ANALYSIS; GENETIC DATABASE; GENETIC RISK; GENOTYPE; HEALTH CARE COST; HEART INFARCTION; HUMAN; INSULIN DEPENDENT DIABETES MELLITUS; LONGITUDINAL STUDY; MAJOR CLINICAL STUDY; MEDICAL INFORMATION SYSTEM; NON INSULIN DEPENDENT DIABETES MELLITUS; PREVENTION STUDY; PRIORITY JOURNAL; RANDOMIZED CONTROLLED TRIAL; RETINA MACULA AGE RELATED DEGENERATION;

CLINICAL TRIALS AS TOPIC; COST-BENEFIT ANALYSIS; DIABETES MELLITUS, TYPE 1; DIABETES MELLITUS, TYPE 2; GENETIC TESTING; GENETIC VARIATION; GENOTYPE; HUMANS; MACULAR DEGENERATION; MODELS, STATISTICAL; MYOCARDIAL INFARCTION; PHENOTYPE; RESEARCH DESIGN; RISK FACTORS;

EID: 84875963918     PISSN: 00029297     EISSN: 15376605     Source Type: Journal    
DOI: 10.1016/j.ajhg.2013.03.003     Document Type: Article

References (35)

1. M. Dickson, and J.P. Gagnon Key factors in the rising cost of new drug discovery and development Nat. Rev. Drug Discov. 3 2004 417 429
2. J.L. Cummings, R. Doody, and C. Clark Disease-modifying therapies for Alzheimer disease: challenges to early intervention Neurology 69 2007 1622 1634
3. J.C. Florez, K.A. Jablonski, N. Bayley, T.I. Pollin, P.I. de Bakker, A.R. Shuldiner, W.C. Knowler, D.M. Nathan, D. Altshuler Diabetes Prevention Program Research Group TCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program N. Engl. J. Med. 355 2006 241 250
4. P.M. Ridker, E. Danielson, F.A. Fonseca, J. Genest, A.M. Gotto Jr., J.J. Kastelein, W. Koenig, P. Libby, A.J. Lorenzatti, J.G. MacFadyen JUPITER Study Group Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein N. Engl. J. Med. 359 2008 2195 2207
5. R. Simon The use of genomics in clinical trial design Clin. Cancer Res. 14 2008 5984 5993
6. W. Burke, and B.M. Psaty Personalized medicine in the era of genomics JAMA 298 2007 1682 1684
7. P.D.P. Pharoah, A.C. Antoniou, D.F. Easton, and B.A.J. Ponder Polygenes, risk prediction, and targeted prevention of breast cancer N. Engl. J. Med. 358 2008 2796 2803
8. J. Yang, B. Benyamin, B.P. McEvoy, S. Gordon, A.K. Henders, D.R. Nyholt, P.A. Madden, A.C. Heath, N.G. Martin, and G.W. Montgomery Common SNPs explain a large proportion of the heritability for human height Nat. Genet. 42 2010 565 569
9. S. Sanna, B. Li, A. Mulas, C. Sidore, H.M. Kang, A.U. Jackson, M.G. Piras, G. Usala, G. Maninchedda, and A. Sassu Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability PLoS Genet. 7 2011 e1002198
10. X. Lin, K. Song, N. Lim, X. Yuan, T. Johnson, A. Abderrahmani, P. Vollenweider, H. Stirnadel, S.S. Sundseth, and E. Lai Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score - the CoLaus Study Diabetologia 52 2009 600 608
11. P.M. Ridker, E. Danielson, F.A. Fonseca, J. Genest, A.M. Gotto Jr., J.J. Kastelein, W. Koenig, P. Libby, A.J. Lorenzatti, J.G. Macfadyen JUPITER Trial Study Group Reduction in C-reactive protein and LDL cholesterol and cardiovascular event rates after initiation of rosuvastatin: a prospective study of the JUPITER trial Lancet 373 2009 1175 1182
12. W. Rathmann, and G. Giani Global prevalence of diabetes: estimates for the year 2000 and projections for 2030 Diabetes Care 27 2004 2568 2569 author reply 2569
13. N.R. Wray, and P.M. Visscher Narrowing the boundaries of the genetic architecture of schizophrenia Schizophr. Bull. 36 2010 14 23
14. V. Hyttinen, J. Kaprio, L. Kinnunen, M. Koskenvuo, and J. Tuomilehto Genetic liability of type 1 diabetes and the onset age among 22,650 young Finnish twin pairs: a nationwide follow-up study Diabetes 52 2003 1052 1055
15. J.J. Nora, R.H. Lortscher, R.D. Spangler, A.H. Nora, and W.J. Kimberling Genetic - epidemiologic study of early-onset ischemic heart disease Circulation 61 1980 503 508
16. S.K. Das, and S.C. Elbein The genetic basis of type 2 diabetes Cellscience 2 2006 100 131
17. J.M. Seddon, J. Cote, W.F. Page, S.H. Aggen, and M.C. Neale The US twin study of age-related macular degeneration: relative roles of genetic and environmental influences Arch. Ophthalmol. 123 2005 321 327
18. B.F. Voight, L.J. Scott, V. Steinthorsdottir, A.P. Morris, C. Dina, R.P. Welch, E. Zeggini, C. Huth, Y.S. Aulchenko, G. Thorleifsson MAGIC investigators; GIANT Consortium Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis Nat. Genet. 42 2010 579 589
19. W. Chen, D. Stambolian, A.O. Edwards, K.E. Branham, M. Othman, J. Jakobsdottir, N. Tosakulwong, M.A. Pericak-Vance, P.A. Campochiaro, M.L. Klein Complications of Age-Related Macular Degeneration Prevention Trial Research Group Genetic variants near TIMP3 and high-density lipoprotein-associated loci influence susceptibility to age-related macular degeneration Proc. Natl. Acad. Sci. USA 107 2010 7401 7406
20. J.C. Barrett, D.G. Clayton, P. Concannon, B. Akolkar, J.D. Cooper, H.A. Erlich, C. Julier, G. Morahan, J. Nerup, C. Nierras Type 1 Diabetes Genetics Consortium Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes Nat. Genet. 41 2009 703 707
21. S. Kathiresan, B.F. Voight, S. Purcell, K. Musunuru, D. Ardissino, P.M. Mannucci, S. Anand, J.C. Engert, N.J. Samani, H. Schunkert Myocardial Infarction Genetics Consortium; Wellcome Trust Case Control Consortium Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants Nat. Genet. 41 2009 334 341
22. H.C. Gerstein, S. Yusuf, J. Bosch, J. Pogue, P. Sheridan, N. Dinccag, M. Hanefeld, B. Hoogwerf, M. Laakso, V. Mohan DREAM (Diabetes REduction Assessment with ramipril and rosiglitazone Medication) Trial Investigators Effect of rosiglitazone on the frequency of diabetes in patients with impaired glucose tolerance or impaired fasting glucose: a randomised controlled trial Lancet 368 2006 1096 1105
23. J.M. Seddon, R. Reynolds, J. Maller, J.A. Fagerness, M.J. Daly, and B. Rosner Prediction model for prevalence and incidence of advanced age-related macular degeneration based on genetic, demographic, and environmental variables Invest. Ophthalmol. Vis. Sci. 50 2009 2044 2053
24. M. Reimann, E. Bonifacio, M. Solimena, P.E. Schwarz, B. Ludwig, M. Hanefeld, and S.R. Bornstein An update on preventive and regenerative therapies in diabetes mellitus Pharmacol. Ther. 121 2009 317 331
25. S.R. Jayasinghe, A. Mishra, A. Van Daal, and E. Kwan Genetics and cardiovascular disease: Design and development of a DNA biobank Exp. Clin. Cardiol. 14 2009 33 37
26. D.M. Evans, J. Marchini, A.P. Morris, and L.R. Cardon Two-stage two-locus models in genome-wide association PLoS Genet. 2 2006 e157
27. P.J. Talmud, A.D. Hingorani, J.A. Cooper, M.G. Marmot, E.J. Brunner, M. Kumari, M. Kivimäki, and S.E. Humphries Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study BMJ 340 2010 b4838
28. J.A. Hanley, and B.J. McNeil The meaning and use of the area under a receiver operating characteristic (ROC) curve Radiology 143 1982 29 36
29. J.M. Lachin Introduction to sample size determination and power analysis for clinical trials Control. Clin. Trials 2 1981 93 113
30. B. Rosner, and R.J. Glynn Power and sample size estimation for the Wilcoxon rank sum test with application to comparisons of C statistics from alternative prediction models Biometrics 65 2009 188 197
31. P.W.F. Wilson, J.B. Meigs, L. Sullivan, C.S. Fox, D.M. Nathan, and R.B. D'Agostino Sr. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study Arch. Intern. Med. 167 2007 1068 1074
32. M.L. Klein, P.J. Francis, F.L. Ferris 3rd, S.C. Hamon, and T.E. Clemons Risk assessment model for development of advanced age-related macular degeneration Arch. Ophthalmol. 129 2011 1543 1550
33. L.C. McCarthy, P.J. Newcombe, J.C. Whittaker, J.I. Wurzelmann, M.A. Fries, N.R. Burnham, G. Cai, S.W. Stinnett, T.M. Trivedi, and C.-F. Xu Predictive models of choroidal neovascularization and geographic atrophy incidence applied to clinical trial design Am. J. Ophthalmol. 154 2012 568 578 e12
34. R. Moonesinghe, M.J. Khoury, T. Liu, and A.C. Janssens Discriminative accuracy of genomic profiling comparing multiplicative and additive risk models Eur. J. Hum. Genet. 19 2011 180 185
35. T. Orban, J.M. Sosenko, D. Cuthbertson, J.P. Krischer, J.S. Skyler, R. Jackson, L. Yu, J.P. Palmer, D. Schatz, G. Eisenbarth Diabetes Prevention Trial-Type 1 Study Group Pancreatic islet autoantibodies as predictors of type 1 diabetes in the Diabetes Prevention Trial-Type 1 Diabetes Care 32 2009 2269 2274