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A new statistical screening approach for finding pharmacokinetics-related genes in genome-wide studies

The Pharmacogenomics Journal volume 9, pages 137–146 (2009)Cite this article

Abstract

Biomedical researchers usually test the null hypothesis that there is no difference of the population mean of pharmacokinetics (PK) parameters between genotypes by the Kruskal–Wallis test. Although a monotone increasing pattern with a number of alleles is expected for PK-related genes, the Kruskal–Wallis test does not consider a monotonic response pattern. For detecting such patterns in clinical and toxicological trials, a maximum contrast method has been proposed. We show how that method can be used with pharmacogenomics data to a develop test of association. Further, using simulation studies, we compare the power of the modified maximum contrast method to those of the maximum contrast method and the Kruskal–Wallis test. On the basis of the results of those studies, we suggest rules of thumb for which statistics to use in a given situation. An application of all three methods to an actual genome-wide pharmacogenomics study illustrates the practical relevance of our discussion.

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References

  1. Licinio J, Wong M . Pharmacogenomics: The Search for Individualized Therapies. Wiley: Weinheim (Germany), 2002.

    Book  Google Scholar 

  2. Evans WE, McLeod HL . Pharmacogenomics—drug disposition, drug targets, and side effects. N Engl J Med 2003; 348: 538–549.

    Article  CAS  PubMed  Google Scholar 

  3. Ingelman-Sundberg M . Pharmacogenomic biomarkers for prediction of severe adverse drug reactions. N Engl J Med 2008; 358: 637–639.

    Article  CAS  PubMed  Google Scholar 

  4. Efferth T, Volm M . Pharmacogenetics for individualized cancer chemotherapy. Pharmacol Ther 2005; 107: 155–176.

    Article  CAS  PubMed  Google Scholar 

  5. US FDA. Guidance for Industry Pharmacogenomic Data Submissions. 2005.

  6. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature 2004; 431: 931–945.

    Article  Google Scholar 

  7. Risch N, Merikangas K . The future of genetic studies of complex human diseases. Science 1996; 273: 1516–1517.

    Article  CAS  Google Scholar 

  8. Hirschhorn JN, Daly MJ . Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 2005; 6: 95–108.

    Article  CAS  Google Scholar 

  9. Kingsmore SF, Lindquist IE, Mudge J, Gessler DD, Beavis WD . Genome-wide association studies: progress and potential for drug discovery and development. Nat Rev Drug Discov 2008; 7: 221–230.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kruskal HW, Wallis WA . Use of ranks in one-criterion variance analysis. J Am Stat Assoc 1952; 47: 583–621.

    Article  Google Scholar 

  11. Siegel S, Castellan NJ . Nonparametric Statistics for the Behavioral Sciences, 2nd edn. McGraw-Hill: New York, 1988.

    Google Scholar 

  12. Mukerjee H, Robertson T, Wright FT . Comparison of several treatments with a control using multiple contrast. J Am Stat Assoc 1987; 82: 902–910.

    Article  Google Scholar 

  13. Ruberg SJ . Contrasts identifying the minimum effective dose. J Am Stat Assoc 1989; 82: 816–822.

    Article  Google Scholar 

  14. Yoshimura I, Wakana A, Hamada C . A performance comparison of maximum contrast methods to detect dose dependency. Drug Inf J 1997; 31: 423–432.

    Article  Google Scholar 

  15. Stewart H, Ruberg SJ . Detecting dose response with contrasts. Stat Med 2000; 19: 913–921.

    Article  CAS  PubMed  Google Scholar 

  16. Nishiyama H, Yanagihara H, Yoshimura I . SAS/IML program for computing probabilities related to maximum contrast methods. Jpn J Biometrics 2003; 24: 57–70, (in Japanese).

    Article  Google Scholar 

  17. Wakana A, Yoshimura I, Hamada C . A method for therapeutic dose selection in a phase II clinical trial using contrast statistics. Stat Med 2007; 26: 498–511.

    Article  PubMed  Google Scholar 

  18. Jeunemaitre X, Soubrier F, Kotelevtsev YV, Lifton RP, Williams CS, Charru A et al. Molecular basis of human hypertension: role of angiotensinogen. Cell 1992; 71: 169–180.

    Article  CAS  Google Scholar 

  19. Caulfield M, Lavender P, Farrall M, Munroe P, Lawson M, Turner P et al. Linkage of the angiotensinogen gene to essential hypertension. N Engl J Med 1994; 330: 1629–1633.

    Article  CAS  PubMed  Google Scholar 

  20. Hegele RA, Brunt JH, Connelly PW . A polymorphism of the angiotensinogen gene associated with variation in blood pressure in a genetic isolate. Circulation 1994; 90: 2207–2212.

    Article  CAS  PubMed  Google Scholar 

  21. Vasku A, Soucek M, Znojil V, Rihacek I, Tschoplova S, Strelcova L et al. Angiotensin I-converting enzyme and angiotensinogen gene interaction and prediction of essential hypertension. Kidney Int 1998; 53: 1479–1482.

    Article  CAS  PubMed  Google Scholar 

  22. Pereira TV, Nunes AC, Rudnicki M, Yamada Y, Pereira AC, Krieger JE . Meta-analysis of the association of 4 angiotensinogen polymorphisms with essential hypertension: a role beyond M235T? Hypertension 2008; 51: 778–783.

    Article  PubMed  Google Scholar 

  23. Pei Y, Scholey J, Thai K, Suzuki M, Cattran D . Association of angiotensinogen gene T235 variant with progression of immunoglobulin A nephropathy in Caucasian patients. J Clin Invest 1997; 100: 814–820.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Genz A, Bretz F . Numerical computation of multivariate t-probabilities with application to power calculation of multiple contrasts. J Stat Comput Simulat 1999; 63: 361–378.

    Article  Google Scholar 

  25. Westfall PH, Young SS . Resampling-Based Multiple Testing: Examples and Methods for P-value Adjustment. Wiley: New York, 1993.

    Google Scholar 

  26. Gabrielsson J, Weiner D . Pharmacokinetic and Pharmacodynamic Data Analysis: Concepts and Applications. Taylor & Francis: Sweden, 2000.

    Google Scholar 

  27. Sugiyama E, Kaniwa N, Kim SR, Kikura-Hanajiri R, Hasegawa R, Maekawa K et al. Pharmacokinetics of gemcitabine in Japanese cancer patients: the impact of a cytidine deaminase polymorphism. J Clin Oncol 2007; 25: 32–42.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank Professor Isao Yoshimura and Professor Chikuma Hamada at Tokyo University of Science for their valuable advice, and Dr Hiromi Sakamoto and Ms Sumiko Ohnami for the SNP genotyping. Yasunori Sato is a recipient of Official Trainee of the Foreign Clinical Pharmacology Training Program, Japanese Society of Clinical Pharmacology and Therapeutics. This work was initiated while Yasunori Sato was a postdoctoral fellow at the Department of Biostatistics, Harvard School of Public Health, and partially supported in Japan by the program for promotion of Fundamental Studies in Health Sciences of the National Institute of Biomedical Innovation (NiBio).

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Authors and Affiliations

  1. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

    Y Sato & N M Laird

  2. Genetics Division, National Cancer Center Research Institute, Tokyo, Japan

    Y Sato & T Yoshida

  3. Faculty of Engineering, Tokyo University of Science, Tokyo, Japan

    K Nagashima & R Kato

  4. Hamano Statistical Analysis Ltd., Tokyo, Japan

    T Hamano

  5. Clinic Sendagaya, Tokyo, Japan

    A Yafune

  6. Project Team for Pharmacogenetics, National Institute of Health Sciences, Tokyo, Japan

    N Kaniwa, Y Saito, E Sugiyama, S-R Kim & J-i Sawada

  7. Division of Medicinal Safety Science, National Institute of Health Sciences, Tokyo, Japan

    N Kaniwa & E Sugiyama

  8. Division of Functional Biochemistry and Genomics, National Institute of Health Sciences, Tokyo, Japan

    Y Saito & J-i Sawada

  9. Hepatobiliary and Pancreatic Oncology Division, National Cancer Center Hospital East, Chiba, Japan

    J Furuse & H Ishii

  10. Hepatobiliary and Pancreatic Oncology Division, National Cancer Center Hospital, Tokyo, Japan

    H Ueno & T Okusaka

  11. National Cancer Center Hospital East, Chiba, Japan

    N Saijo

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  1. Y Sato
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  2. N M Laird
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  15. N Saijo
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  16. J-i Sawada
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  17. T Yoshida
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Corresponding author

Correspondence to T Yoshida.

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Sato, Y., Laird, N., Nagashima, K. et al. A new statistical screening approach for finding pharmacokinetics-related genes in genome-wide studies. Pharmacogenomics J 9, 137–146 (2009). https://doi.org/10.1038/tpj.2008.17

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