Department of Biomedical Informatics, Vanderbilt University in Nashville, TN, USA and Center for Human Genetics Research, Vanderbilt University in Nashville, TN, USA and Department of Molecular Physiology & Biophysics, Vanderbilt University in Nashville, TN, USA

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Daniel R Masys

Department of Biomedical Informatics, Vanderbilt University in Nashville, TN, USA

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Dan M Roden

Department of Medicine, Vanderbilt University in Nashville, TN, USA and Department of Pharmacology, Vanderbilt University in Nashville, TN, USA

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Dana C Crawford

Center for Human Genetics Research, Vanderbilt University in Nashville, TN, USA and Department of Molecular Physiology & Biophysics, Vanderbilt University in Nashville, TN, USA

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Joshua C Denny

* Author for correspondence

Department of Medicine, Vanderbilt University in Nashville, TN, USA and Department of Biomedical Informatics, Vanderbilt University in Nashville, TN, USA and Eskind Biomedical Library, Room 448, 2209 Garland Ave, Nashville, TN 37232, USA.

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Email the corresponding author at joshua.denny@vanderbilt.edu

Published Online:1 Mar 2012https://doi.org/10.2217/pgs.11.164

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Abstract

Aim: Warfarin pharmacogenomic algorithms reduce dosing error, but perform poorly in non-European–Americans. Electronic health record (EHR) systems linked to biobanks may allow for pharmacogenomic analysis, but they have not yet been used for this purpose. Patients & methods: We used BioVU, the Vanderbilt EHR-linked DNA repository, to identify European–Americans (n = 1022) and African–Americans (n = 145) on stable warfarin therapy and evaluated the effect of 15 pharmacogenetic variants on stable warfarin dose. Results: Associations between variants in VKORC1, CYP2C9 and CYP4F2 with weekly dose were observed in European–Americans as well as additional variants in CYP2C9 and CALU in African–Americans. Compared with traditional 5 mg/day dosing, implementing the US FDA recommendations or the International Warfarin Pharmacogenomics Consortium (IWPC) algorithm reduced error in weekly dose in European–Americans (13.5–12.4 and 9.5 mg/week, respectively) but less so in African–Americans (15.2–15.0 and 13.8 mg/week, respectively). By further incorporating associated variants specific for European–Americans and African–Americans in an expanded algorithm, dose-prediction error reduced to 9.1 mg/week (95% CI: 8.4–9.6) in European–Americans and 12.4 mg/week (95% CI: 10.0–13.2) in African–Americans. The expanded algorithm explained 41 and 53% of dose variation in African–Americans and European–Americans, respectively, compared with 29 and 50%, respectively, for the IWPC algorithm. Implementing these predictions via dispensable pill regimens similarly reduced dosing error. Conclusion: These results validate EHR-linked DNA biorepositories as real-world resources for pharmacogenomic validation and discovery.

Original submitted 24 August 2011; Revision submitted 9 November 2011

Keywords:

Papers of special note have been highlighted as: ▪ of interest

References

1 Wysowski DK, Nourjah P, Swartz L. Bleeding complications with warfarin use: a prevalent adverse effect resulting in regulatory action. Arch. Intern. Med.167(13),1414–1419 (2007).
MedlineGoogle Scholar
2 Gage BF, Eby C, Johnson JA et al. Use of pharmacogenetic and clinical factors to predict the therapeutic dose of warfarin. Clin. Pharmacol. Ther.84(3),326–331 (2008).
Medline CASGoogle Scholar
3 Kurnik D, Loebstein R, Halkin H, Gak E, Almog S. 10 years of oral anticoagulant pharmacogenomics: what difference will it make? A critical appraisal. Pharmacogenomics10(12),1955–1965 (2009).
CASGoogle Scholar
4 Aithal GP, Day CP, Kesteven PJ, Daly AK. Association of polymorphisms in the cytochrome P450 CYP2C9 with warfarin dose requirement and risk of bleeding complications. Lancet353(9154),717–719 (1999).
Medline CASGoogle Scholar
5 Rost S, Fregin A, Ivaskevicius V et al. Mutations in VKORC1 cause warfarin resistance and multiple coagulation factor deficiency type 2. Nature427(6974),537–541 (2004).
Medline CASGoogle Scholar
6 McClain MR, Palomaki GE, Piper M, Haddow JE. A Rapid-ACCE review of CYP2C9 and VKORC1 alleles testing to inform warfarin dosing in adults at elevated risk for thrombotic events to avoid serious bleeding. Genet. Med.10(2),89–98 (2008).
Medline CASGoogle Scholar
7 The International Warfarin Pharmacogenetics Consortium. Estimation of the warfarin dose with clinical and pharmacogenetic data. N. Engl. J. Med.360(8),753–764 (2009).▪ Algorithm derived and validated across multiple centers using three variants and clinical data.
Medline CASGoogle Scholar
8 Kidd RS, Curry TB, Gallagher S, Edeki T, Blaisdell J, Goldstein JA. Identification of a null allele of CYP2C9 in an African–American exhibiting toxicity to phenytoin. Pharmacogenetics11(9),803–808 (2001).
Medline CASGoogle Scholar
9 Blaisdell J, Mohrenweiser H, Jackson J et al. Identification and functional characterization of new potentially defective alleles of human CYP2C19. Pharmacogenetics12(9),703–711 (2002).
Medline CASGoogle Scholar
10 Voora D, Koboldt DC, King CR et al. A polymorphism in the VKORC1 regulator calumenin predicts higher warfarin dose requirements in African Americans. Clin. Pharmacol. Ther.87(4),445–451 (2010).
Medline CASGoogle Scholar
11 Takeuchi F, McGinnis R, Bourgeois S et al. A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose. PLoS Genet.5(3),e1000433 (2009).
MedlineGoogle Scholar
12 Loebstein R, Vecsler M, Kurnik D et al. Common genetic variants of microsomal epoxide hydrolase affect warfarin dose requirements beyond the effect of cytochrome P450 2C9. Clin. Pharmacol. Ther.77(5),365–372 (2005).
Medline CASGoogle Scholar
13 Wadelius M, Chen LY, Downes K et al. Common VKORC1 and GGCX polymorphisms associated with warfarin dose. Pharmacogenomics J.5(4),262–270 (2005).
Medline CASGoogle Scholar
14 Cavallari LH, Langaee TY, Momary KM et al. Genetic and clinical predictors of warfarin dose requirements in African Americans. Clin. Pharmacol. Ther.87(4),459–464 (2010).
Medline CASGoogle Scholar
15 Pautas E, Moreau C, Gouin-Thibault I et al. Genetic factors (VKORC1, CYP2C9, EPHX1, and CYP4F2) are predictor variables for warfarin response in very elderly, frail inpatients. Clin. Pharmacol. Ther.87(1),57–64 (2009).
MedlineGoogle Scholar
16 Rieder MJ, Reiner AP, Rettie AE. Gamma-glutamyl carboxylase (GGCX) tagSNPs have limited utility for predicting warfarin maintenance dose. J. Thromb. Haemost.5(11),2227–2234 (2007).
Medline CASGoogle Scholar
17 Limdi NA, Wadelius M, Cavallari L et al. Warfarin pharmacogenetics: a single VKORC1 polymorphism is predictive of dose across 3 racial groups. Blood115(18),3827–3834 (2010).
Medline CASGoogle Scholar
18 Cavallari LH, Langaee TY, Momary KM et al. Genetic and clinical predictors of warfarin dose requirements in African Americans. Clin. Pharmacol. Ther.87(4),459–464 (2010).▪ Describes African–American-specific associations.
Medline CASGoogle Scholar
19 Schelleman H, Chen Z, Kealey C et al. Warfarin response and vitamin K epoxide reductase complex 1 in African Americans and Caucasians. Clin. Pharmacol. Ther.81(5),742–747 (2007).
Medline CASGoogle Scholar
20 Limdi NA, Arnett DK, Goldstein JA et al. Influence of CYP2C9 and VKORC1 on warfarin dose, anticoagulation attainment and maintenance among European–Americans and African–Americans. Pharmacogenomics9(5),511–526 (2008).
CASGoogle Scholar
21 Limdi NA, Beasley TM, Crowley MR et al.VKORC1 polymorphisms, haplotypes and haplotype groups on warfarin dose among African–Americans and European–Americans. Pharmacogenomics9(10),1445–1458 (2008).
CASGoogle Scholar
22 Beyth RJ, Quinn L, Landefeld CS. A multicomponent intervention to prevent major bleeding complications in older patients receiving warfarin. Ann. Intern. Med.133(9),687–695 (2000).
Medline CASGoogle Scholar
23 Anderson JL, Horne BD, Stevens SM et al. Randomized trial of genotype-guided versus standard warfarin dosing in patients initiating oral anticoagulation. Circulation116(22),2563–2570 (2007).
Medline CASGoogle Scholar
24 Wadelius M, Chen LY, Lindh JD et al. The largest prospective warfarin-treated cohort supports genetic forecasting. Blood113(4),784–792 (2009).
Medline CASGoogle Scholar
25 Epstein RS, Moyer TP, Aubert RE et al. Warfarin genotyping reduces hospitalization rates results from the MM-WES (Medco-Mayo Warfarin Effectiveness study). J. Am. Coll. Cardiol.55(25),2804–2812 (2010).▪ Demonstrates a large improvement in outcomes of bleeding-specific and all-cause hospitalizations using pharmacogenomic-based dosing, although used historical controls and did not track prescriber behavior changes.
Medline CASGoogle Scholar
26 Ritchie MD, Denny JC, Crawford DC et al. Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. Am. J. Hum. Genet.86(4),560–572 (2010).▪ First demonstration of using electronic health record data to replicate genome-wide association study associations from traditional clinical trials.
Medline CASGoogle Scholar
27 Roden DM, Pulley JM, Basford MA et al. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin. Pharmacol. Ther.84(3),362–369 (2008).
Medline CASGoogle Scholar
28 Pulley J, Clayton E, Bernard GR, Roden DM, Masys DR. Principles of human subjects protections applied in an opt-out, de-identified biobank. Clin. Transl. Sci.3(1),42–48 (2010).
MedlineGoogle Scholar
29 Xu H, Stenner SP, Doan S, Johnson KB, Waitman LR, Denny JC. MedEx: a medication information extraction system for clinical narratives. J. Am. Med. Inform. Assoc.17(1),19–24 (2010).
Medline CASGoogle Scholar
30 Dumitrescu L, Ritchie MD, Brown-Gentry K et al. Assessing the accuracy of observer-reported ancestry in a biorepository linked to electronic medical records. Genet. Med.12(10),648–650 (2010).
MedlineGoogle Scholar
31 Scott SA, Jaremko M, Lubitz SA, Kornreich R, Halperin JL, Desnick RJ. CYP2C9*8 is prevalent among African–Americans: implications for pharmacogenetic dosing. Pharmacogenomics10(8),1243–1255 (2009).
CASGoogle Scholar
32 Grady BJ, Torstenson E, Dudek SM, Giles J, Sexton D, Ritchie MD. Finding unique filter sets in plato: a precursor to efficient interaction analysis in gwas data. Pac. Symp. Biocomput.315–326 (2010).
MedlineGoogle Scholar
33 Purcell S, Neale B, Todd-Brown K et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet.81(3),559–575 (2007).
Medline CASGoogle Scholar
34 Delaney J, Ramirez A, Bowton E et al. Predicting clopidogrel response using DNA samples linked to an electronic health record. Clin. Pharmacol. Ther.91(2),257–263 (2012).▪ Demonstrates clopidogrel response can also be replicated in electronic health records.
Medline CASGoogle Scholar
35 Shin J, Cao D. Comparison of warfarin pharmacogenetic dosing algorithms in a racially diverse large cohort. Pharmacogenomics12(1),125–134 (2011).
CASGoogle Scholar
36 Shin J, Kayser SR. Accuracy of the pharmacogenetic dosing table in the warfarin label in predicting initial therapeutic warfarin doses in a large, racially diverse cohort. Pharmacotherapy31(9),863–870 (2011).
Medline CASGoogle Scholar
37 Roper N, Storer B, Bona R, Fang M. Validation and comparison of pharmacogenetics-based warfarin dosing algorithms for application of pharmacogenetic testing. J. Mol. Diagn.12(3),283–291 (2010).
Medline CASGoogle Scholar
38 Zambon C-F, Pengo V, Padrini R et al.VKORC1, CYP2C9 and CYP4F2 genetic-based algorithm for warfarin dosing: an Italian retrospective study. Pharmacogenomics12(1),15–25 (2011).
CASGoogle Scholar
39 Wells PS, Majeed H, Kassem S et al. A regression model to predict warfarin dose from clinical variables and polymorphisms in CYP2C9, CYP4F2, and VKORC1: derivation in a sample with predominantly a history of venous thromboembolism. Thromb. Res.125(6),e259–e264 (2010).
Medline CASGoogle Scholar
40 Lubitz SA, Scott SA, Rothlauf EB et al. Comparative performance of gene-based warfarin dosing algorithms in a multiethnic population. J. Thromb. Haemost.8(5),1018–1026 (2010).
Medline CASGoogle Scholar
41 Finkelman BS, Gage BF, Johnson JA, Brensinger CM, Kimmel SE. Genetic warfarin dosing: tables versus algorithms. J. Am. Coll. Cardiol.57(5),612–618 (2011).▪ Additional detailed comparison of US FDA genotype-only tables versus more complicated algorithms incorporating clinical and genetic information.
Medline CASGoogle Scholar
42 Eckman MH, Rosand J, Greenberg SM, Gage BF. Cost–effectiveness of using pharmacogenetic information in warfarin dosing for patients with nonvalvular atrial fibrillation. Ann. Intern. Med.150(2),73–83 (2009).
MedlineGoogle Scholar
43 Khan N, Peterson J. A surveillance tool to support quality assuranceand research in personalized medicine. Presented at: The 2011 American Medical Informatics Association Annual Symposium. Washington, DC, USA, 22–26 October 2011.
Google Scholar
44 Schildcrout J, Denny J, Bowton E et al. Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping. Presented at: The 12th International Congress of Human Genetics/61st Annual Meeting of The American Society of Human Genetics. Montreal, Canada 11–15 October 2011.
Google Scholar
45 Connolly SJ, Ezekowitz MD, Yusuf S et al. Dabigatran versus warfarin in patients with atrial fibrillation. N. Engl. J. Med.361(12),1139–1151 (2009).
Medline CASGoogle Scholar
46 Perera MA, Gamazon E, Cavallari LH et al. The missing association: sequencing-based discovery of novel SNPs in VKORC1 and CYP2C9 that affect warfarin dose in African Americans. Clin. Pharmacol. Ther.89(3),408–415 (2011).
Medline CASGoogle Scholar
47 Ansell J, Hirsh J, Hylek E, Jacobson A, Crowther M, Palareti G. Pharmacology and management of the vitamin K antagonists: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines (8th Edition). Chest133(Suppl. 6),160S–198S (2008).
Medline CASGoogle Scholar
48 Caraco Y, Blotnick S, Muszkat M. CYP2C9 Genotype-guided warfarin prescribing enhances the efficacy and safety of anticoagulation: a prospective randomized controlled study. Clin. Pharmacol. Ther.83(3),460–470 (2007).
MedlineGoogle Scholar
49 Wen M-S, Lee M, Chen J-J et al. Prospective study of warfarin dosage requirements based on CYP2C9 and VKORC1 genotypes. Clin. Pharmacol. Ther.84(1),83–89 (2008).
Medline CASGoogle Scholar
50 Burmester JK, Berg RL, Yale SH et al. A randomized controlled trial of genotype-based Coumadin initiation. Genet. Med.13(6),509–518 (2011).
Medline CASGoogle Scholar
51 McMillin GA, Melis R, Wilson A et al. Gene-based warfarin dosing compared with standard of care practices in an orthopedic surgery population: a prospective, parallel cohort study. Ther. Drug Monit.32(3),338–345 (2010).
Medline CASGoogle Scholar
52 French B, Joo J, Geller NL et al. Statistical design of personalized medicine interventions: the Clarification of Optimal Anticoagulation through Genetics (COAG) trial. Trials11,108 (2010).
MedlineGoogle Scholar
53 van Schie RMF, Wadelius MIA, Kamali F et al. Genotype-guided dosing of coumarin derivatives: the European pharmacogenetics of anticoagulant therapy (EU-PACT) trial design. Pharmacogenomics10(10),1687–1695 (2009).
CASGoogle Scholar
101 Bristol-Meyers Squibb Company, Coumadin^® tablets (warfarin sodium tablets, USP) crystalline; Coumadin^® for injection (warfarin sodium for injection, USP). www.accessdata.fda.gov/drugsatfda_docs/label/2010/009218s108lbl.pdf
Google Scholar
102 Vanderbilt Biomedical Language Processing Laboratory. http://knowledgemap.mc.vanderbilt.edu/research/warfarin-dose-schedule
Google Scholar

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Acknowledgements

The Vanderbilt DNA Resources Core that houses all of the samples also performed the genotyping under the supervision of C Sutcliffe and H Dilks. The Vanderbilt University Center for Human Genetics Research, Computational Genomics Core provided computational and analytical support for this work.

Financial & competing interests disclosure

This study was funded in part by RC2 GM092618, U19 HL065962 and GM007569. The datasets used for the analyses described were obtained from Vanderbilt University Medical Center’s BioVU, which is supported by institutional funding and by the Vanderbilt CTSA grant 1UL1RR024975-01 from NCRR/NIH. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.

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