Aim: Extreme discordant phenotype and genome-wide association (GWA) approaches were combined to explore the role of genetic variants on warfarin dose requirement in Brazilians. Methods: Patients receiving low (≤20 mg/week; n = 180) or high stable warfarin doses (≥42.5 mg/week; n = 187) were genotyped with Affymetrix Axiom^® Biobank arrays. Imputation was carried out using data from the combined 1000 Genomes project. Results: Genome-wide signals (p ≤ 5 × 10^-8) were identified in the well-known VKORC1 (lead SNP, rs749671; OR: 20.4; p = 1.08 × 10^-33) and CYP2C9 (lead SNP, rs9332238, OR: 6.8 and p = 4.4 × 10^-13) regions. The rs9332238 polymorphism is in virtually perfect LD with CYP2C9*2 (rs1799853) and CYP2C9*3 (rs1057910). No other genome-wide significant regions were identified in the study. Conclusion: We confirmed the important role of VKORC1 and CYP2C9 polymorphisms in warfarin dose.

Original submitted 14 January 2015; Revision submitted 26 May 2015

Keywords:

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

References

1 Sconce EA, Khan TI, Wynne HA et al. The impact of CYP2C9 and VKORC1 genetic polymorphism and patient characteristics upon warfarin dose requirements: proposal for a new dosing regimen. Blood 106, 2329–2333 (2005).
Medline CASGoogle Scholar
2 Anderson JL, Horne BD, Stevens SM et al. Randomized trial of genotype-guided versus standard warfarin dosing in patients initiating oral anticoagulation. Circulation 116, 2563–2570 (2007).
Medline CASGoogle Scholar
3 Wen MS, Lee M, Chen JJ et al. Prospective study of warfarin dosage requirements based on CYP2C9 and VKORC1 genotypes. Clin. Pharmacol. Ther. 84, 83–89 (2008).
Medline CASGoogle Scholar
4 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, 326–331 (2008).
Medline CASGoogle Scholar
5 Perini JA, Struchiner CJ, Silva-Assunção E et al. Pharmacogenetics of warfarin: development of a dosing algorithm for brazilian patients. Clin. Pharmacol. Ther. 84, 722–728 (2008).• First study of warfarin pharmacogenetics in Brazilian patients. The present study enrolled some of these patients.
Medline CASGoogle Scholar
6 International Warfarin Pharmacogenetics Consortium, Klein TE, Altman RB et al. Estimation of the warfarin dose with clinical and pharmacogenetic data. N. Engl. J. Med. 360, 753–764 (2009).
MedlineGoogle Scholar
7 Lenzini P, Wadelius M, Kimmel S et al. Integration of genetic, clinical, and INR data to refine warfarin dosing. Clin. Pharmacol. Ther. 87, 572–578 (2010).
Medline CASGoogle Scholar
8 Limdi NA, Wadelius M, Cavallari L et al. Warfarin pharmacogenetics: a single VKORC1 polymorphism is predictive of dose across 3 racial groups. Blood 115, 3827–3834 (2010).
Medline CASGoogle Scholar
9 Kimmel SE, French B, Kasner SE et al. A pharmacogenetic versus a clinical algorithm for warfarin dosing. N. Engl. J. Med. 369, 2283–2293 (2013).
Medline CASGoogle Scholar
10 Pirmohamed M, Burnside G, Eriksson N et al. A randomized trial of genotype-guided dosing of warfarin. N. Engl. J. Med. 369, 2294–2303 (2013).
Medline CASGoogle Scholar
11 Cooper GM, Johnson JA, Langaee TY et al. A genome-wide scan for common genetic variants with a large influence on warfarin maintenance dose. Blood 112, 1022–1027 (2008).• The first warfarin genome-wide association study GWA study, in which it was suggested that common SNPs with large effects on warfarin dose were unlikely to be discovered outside of the CYP2C9 and VKORC1 genes.
Medline CASGoogle Scholar
12 Takeuchi F, McGinnis R, Bourgeois et al. A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose. PLoS Genet. 5, e1000433 (2009).• Conditional analysis revealed a GWA signal in CYP4F2 (rs2108622).
MedlineGoogle Scholar
13 Cha PC, Mushiroda T, Takahashi A et al. Genome-wide association study identifies genetic determinants of warfarin responsiveness for Japanese. Hum. Mol. Genet. 19, 4735–4744 (2010).
Medline CASGoogle Scholar
14 Perera MA, Cavallari LH, Limdi NA et al. Genetic variants associated with warfarin dose in African–American individuals: a genome-wide association study. Lancet 382, 790–796 (2013).
Medline CASGoogle Scholar
15 Nebert DW. Extreme discordant phenotype methodology: an intuitive approach to clinical pharmacogenetics. Eur. J. Pharmacol. 410, 107–120 (2000).• Description of the extreme discordant phenotype methodology.
Medline CASGoogle Scholar
16 Gurwitz D, McLeod HL. Genome-wide studies in pharmacogenomics: harnessing the power of extreme phenotypes. Pharmacogenomics 14, 337–339 (2013).
CASGoogle Scholar
17 Botton MR, Bandinelli E, Rohde LE, Amon LC, Hutz MH. Influence of genetic, biological and pharmacological factors on warfarin dose in a Southern Brazilian population of European ancestry. Br. J. Clin. Pharmacol. 72, 442–450 (2011).• Patients from this cohort were enrolled in the present GWA study.
Medline CASGoogle Scholar
18 Purcell S, Neale B, Todd-Brown K et al. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am. J. Hum. Genet. 81, 559–575 (2007).
Medline CASGoogle Scholar
19 Patterson N, Price AL, Reich D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).
MedlineGoogle Scholar
20 Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).
Medline CASGoogle Scholar
21 Marchini J, Howie B, Myers S, McVean G, Donnelly. A new multipoint method for genome-wide association studies via imputation of genotypes. Nat. Genet. 39, 906–913 (2007).
Medline CASGoogle Scholar
22 Delaneau O, Marchini J, Zagury JF. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2012).
CASGoogle Scholar
23 Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).
Medline CASGoogle Scholar
24 Marchini J, Howie B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).
Medline CASGoogle Scholar
25 Jorgensen AL, Al-Zubiedi S, Zhang JE et al. Genetic and environmental factors determining clinical outcomes and cost of warfarin therapy, a prospective study. Pharmacogenet. Genomics 19, 800–812 (2009).
Medline CASGoogle Scholar
26 Wadelius M, Chen LY, Lindh JD et al. The largest prospective warfarin-treated cohort supports genetic forecasting. Blood 113, 784–792 (2009).
Medline CASGoogle Scholar
27 Hillman MA, Wilke RA, Caldwell MD, Berg RL, Glurich I, Burmester JK. Relative impact of covariates in prescribing warfarin according to CYP2C9 genotype. Pharmacogenetics 14, 539–547 (2004).
Medline CASGoogle Scholar
28 Higashi MK, Veenstra DL, Kondo LM et al. Association between CYP2C9 genetic variants and anticoagulation-related outcomes during warfarin therapy. JAMA 287, 1690–1698, (2002).
Medline CASGoogle Scholar
29 Limdi NA, McGwin G, Goldstein JA et al. Influence of CYP2C9 and VKORC1 1173C/T genotype on the risk of hemorrhagic complications in African–American and European-American patients on warfarin. Clin. Pharmacol. Ther. 83, 312–321 (2008).
Medline CASGoogle Scholar
30 Limdi NA, Beasley TM, Baird MF et al. Kidney function influences warfarin responsiveness and hemorrhagic complications. J. Am. Soc. Nephrol. 20, 912–921 (2009).
Medline CASGoogle Scholar
31 Cavallari LH, Langaee TY, Momary KM et al. Genetic and clinical predictors of warfarin dose requirements in African Americans. Clin. Pharmacol. Ther. 87, 459–464 (2010).
Medline CASGoogle Scholar
32 KGG: A systematic biological knowledge-based mining system for genome-wide association studies. http://statgenpro.psychiatry.hku.hk/limx/kgg/.
Google Scholar
33 Li MX, Gui HS, Kwan JS, Sham PC. GATES, a rapid and powerful gene-based association test using extended Simes procedure. Am. J. Hum. Genet. 88, 283–293, (2011).
Medline CASGoogle Scholar
34 Li MX, Kwan JS, Sham PC. HYST, A hybrid set-based test for genome-wide association studies, with application to protein-protein interaction-based association analysis. Am. J. Hum. Genet. 91, 478–488 (2012).
Medline CASGoogle Scholar
35 Nam D, Kim J, Kim SY, Kim S. GSA-SNP, a general approach for gene set analysis of polymorphisms. Nucleic Acids Res. 28, W749–W754, (2010).
Google Scholar
36 Kim S-Y, Volsky DJ. PAGE, parametric analysis of gene set enrichment. BMC Bioinformatics 6, 144 (2005).
MedlineGoogle Scholar
37 NCBI eQTL browser. http://www.ncbi.nlm.nih.gov/projects/gap/eqtl/index.cgi#.
Google Scholar
38 Montgomery SB, Sammeth M, Gutierrez-Arcelus M et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 464, 773–777 (2010).
Medline CASGoogle Scholar
39 Stranger BE, Nica AC, Forrest MS et al. Population genomics of human gene expression. Nat. Genet. 39, 1217–1224 (2007).
Medline CASGoogle Scholar
40 Schadt EE, Molony C, Chudin E et al. Mapping the genetic architecture of gene expression in human liver. PLoS Biol. 6, e107 (2008).
MedlineGoogle Scholar
41 SNP nexus. http://www.snp-nexus.org/.
Google Scholar
42 SNVrap: Quick annotation web portal. http://jjwanglab.org/snvrap.
Google Scholar
43 Kraft P, Zeggini E, Ioannidis JP. Replication in genome-wide association studies. Stat. Sci. 24, 561–573 (2009).
MedlineGoogle Scholar
44 Suarez-Kurtz G. Pharmacogenetics in the Brazilian population. Front Pharmacol. 1, 118 (2010).• An analysis of the pharmacogenomic implications of the Brazilian population structure.
Medline CASGoogle Scholar

Vol. 16, No. 11

Supplemental Materials

Metrics

Downloaded 238 times

History

Published online 12 August 2015

Published in print July 2015

Information

Keywords

Financial & competing interests disclosure

The research was supported in part by grants from Financiadora de Estudos e Projetos (Finep 01.08.01230.00), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (Faperj) to GS-K. TK was supported by NIH NIGMS R24 GM61374. 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. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

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.

PDF download

Genome-wide association study of warfarin maintenance dose in a Brazilian sample

Abstract

References