Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

Pharmacodynamic genome-wide association study identifies new responsive loci for glucocorticoid intervention in asthma

The Pharmacogenomics Journal volume 15, pages 422–429 (2015)Cite this article

Subjects

Abstract

Asthma is a chronic lung disease that has a high prevalence. The therapeutic intervention of this disease can be made more effective if genetic variability in patients’ response to medications is implemented. However, a clear picture of the genetic architecture of asthma intervention response remains elusive. We conducted a genome-wide association study (GWAS) to identify drug response-associated genes for asthma, in which 909 622 SNPs were genotyped for 120 randomized participants who inhaled multiple doses of glucocorticoids. By integrating pharmacodynamic properties of drug reactions, we implemented a mechanistic model to analyze the GWAS data, enhancing the scope of inference about the genetic architecture of asthma intervention. Our pharmacodynamic model observed associations of genome-wide significance between dose-dependent response to inhaled glucocorticoids (measured as %FEV1) and five loci (P=5.315 × 10−7 to 3.924 × 10−9), many of which map to metabolic genes related to lung function and asthma risk. All significant SNPs detected indicate a recessive effect, at which the homozygotes for the mutant alleles drive variability in %FEV1. Significant associations were well replicated in three additional independent GWAS studies. Pooled together over these three trials, two SNPs, chr6 rs6924808 and chr11 rs1353649, display an increased significance level (P=6.661 × 10−16 and 5.670 × 10−11). Our study reveals a general picture of pharmacogenomic control for asthma intervention. The results obtained help to tailor an optimal dose for individual patients to treat asthma based on their genetic makeup.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

References

  1. Fanta CH . Asthma. N Engl J Med 2009; 360: 1002–1014.

    Article  CAS  Google Scholar 

  2. Szefler SJ . Advancing asthma care: the glass is only half full!. J Allergy Immunol 2011; 128: 485–494.

    Article  Google Scholar 

  3. Apter AJ . Advances in adult asthma diagnosis and treatment and HEDQ in 2010. J Allergy Clin Immunol 2011; 127: 116–122.

    Article  Google Scholar 

  4. National Institutes of Health, National Heart, Lung, and Blood Institute, National Asthma Education and Prevention Program. Expert panel report 3: guidelines for the diagnosis and management of asthma. NIH Publication no. 07-4051. Available at: http://www.nhlbi.nih.gov/guidelines/asthma/index.htm. Accessed July 16, 2011.

  5. Yoshihara S . Early intervention for infantile and childhood asthma. Expert Rev Clin Immunol 2010; 6: 247–255.

    Article  Google Scholar 

  6. Bouzigon E, Corda E, Aschard H, Dizier MH, Boland A, Bousquet J et al. Effect of 17q21 variants and smoking exposure in early-onset asthma. N Engl J Med 2008; 359: 1985–1994.

    Article  CAS  Google Scholar 

  7. Moffatt MF, Kabesch M, Liang L, Dixon AL, Strachan D, Heath S et al. Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature 2007; 448: 470–473.

    Article  CAS  Google Scholar 

  8. Moffatt MF, Gut IG, Demenais F, Strachan DP, Bouzigon E, Heath S et al. A large-scale, consortium-based genomewide association study of asthma. N Engl J Med 2010; 363: 1211–1221.

    Article  CAS  Google Scholar 

  9. Repapi E, Sayers I, Wain LV, Burton PR, Johnson T, Obeidat M et al. Genome-wide association study identifies five loci associated with lung function. Nat Genet 2010; 42: 36–44.

    Article  CAS  Google Scholar 

  10. Hancock DB, Eijgelsheim M, Wilk JB, Gharib SA, Loehr LR, Marciante KD et al. Meta-analyses of genome-wide association studies identify multiple loci associated with pulmonary function. Nat Genet 2010; 42: 45–52.

    Article  CAS  Google Scholar 

  11. Manolio TA . Genomewide association studies and assessment of the risk of disease. N Engl J Med 2010; 363: 166–176.

    Article  CAS  Google Scholar 

  12. Daly AK . Genome-wide association studies in pharmacogenomics. Nat Rev Genet 2010; 11: 241–246.

    Article  CAS  Google Scholar 

  13. Klein TE, Altman RB, Eriksson N, Gage BF, Kimmel SE, Lee MT et al. Estimation of the warfarin dose with clinical and pharmacogenetic data. N Engl J Med 2009; 360: 753–764.

    Article  CAS  Google Scholar 

  14. Daly AK . Pharmacogenomics of anticoagulants: steps toward personal dosage. Genome Med 2009; 1: 10.

    Article  Google Scholar 

  15. Tse SM, Tantisira K, Weiss ST . The pharmacogenetics and pharmacogenomics of asthma therapy. Pharmacogenomics J 2011; 11: 383–392.

    Article  CAS  Google Scholar 

  16. Ge D, Fellay J, Thompson AJ, Simon JS, Shianna KV, Urban TJ et al. Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature 2009; 461: 399–401.

    Article  CAS  Google Scholar 

  17. Suppiah V, Moldovan M, Ahlenstiel G, Berg T, Weltman M, Abate ML et al. IL28B is associated with response to chronic hepatitis C interferon-α and ribavirin therapy. Nat Genet 2009; 41: 1100–1104.

    Article  CAS  Google Scholar 

  18. Tanaka Y, Nishida N, Sugiyama M, Kurosaki M, Matsuura K, Sakamoto N et al. Genome-wide association of IL28B with response to pegylated interferon-α and ribavirin therapy for chronic hepatitis C. Nat Genet 2009; 41: 1105–1109.

    Article  CAS  Google Scholar 

  19. Shuldiner AR, O'Connell JR, Bliden KP, Gandhi A, Ryan K, Horenstein RB et al. Association of cytochrome P450 2C19 genotype with the antiplatelet effect and clinical efficacy of clopidogrel therapy. JAMA 2009; 302: 849–857.

    Article  CAS  Google Scholar 

  20. Cooper GM, Johnson JA, Langaee TY, Feng H, Stanaway IB, Schwarz UI et al. A genome-wide scan for common genetic variants with a large influence on warfarin maintenance dose. Blood 2008; 112: 1022–1027.

    Article  CAS  Google Scholar 

  21. Teichert M, Eijgelsheim M, Rivadeneira F, Uitterlinden AG, van Schaik RH, Hofman A et al. A genome-wide association study of acenocoumarol maintenance dosage. Hum Mol Genet 2009; 18: 3758–3768.

    Article  CAS  Google Scholar 

  22. Takeuchi F, McGinnis R, Bourgeois S, Barnes C, Eriksson N, Soranzo N et al. A genome-wide association stud confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose. PLoS Genet 2009; 5: e1000433.

    Article  Google Scholar 

  23. SEARCH Collaborative Group, Link E, Parish S, Armitage J, Bowman L, Heath S, Matsuda F et al. SLCO1B1 variants and statin-induced myopathy – a genomewide study. N Engl J Med 2008; 359: 789–799.

    Article  Google Scholar 

  24. Daly AK, Donaldson PT, Bhatnagar P, Shen Y, Pe'er I, Floratos A et al. HLA.B*5701 genotype is a major determinant of drug-induced liver injury due to flucloxacillin. Nat Genet 2009; 41: 816–819.

    Article  CAS  Google Scholar 

  25. Van Steen K, McQueen MB, Herbert A, Raby B, Lyon H, Demeo DL et al. Genomic screening and replication using the same data set in family based association testing. Nat Genet 2005; 37: 683–691.

    Article  CAS  Google Scholar 

  26. Herbert A, Gerry NP, McQueen MB, Heid IM, Pfeufer A, Illig T et al. A common genetic variant is associated with adult and childhood obesity. Science 2006; 312: 279–283.

    Article  CAS  Google Scholar 

  27. Lasky-Su J, Lyon HN, Emilsson V, Heid IM, Molony C, Raby BA et al. On the replication of genetic associations: timing can be everything!. Am J Hum Genet 2008; 82: 849–858.

    Article  CAS  Google Scholar 

  28. Bertram L, Lange C, Mullin K, Parkinson M, Hsiao M, Hogan MF et al. Genome-wide association analysis reveals putative Alzheimer’s disease susceptibility loci in addition to APOE. Am J Hum Genet 2008; 83: 623–632.

    Article  CAS  Google Scholar 

  29. Tantisira KG, Lasky-Su J, Harada M, Murphy A, Litonjua AA, Himes BE et al. Genomewide association between GLCCI1 and response to glucocorticoid therapy in asthma. N Engl J of Med 2011; 365: 1173–1183.

    Article  CAS  Google Scholar 

  30. Wu RL, Tong CF, Mauger D, Mauger D, Tantisira K, Szefler SJ et al. A conceptual framework for pharmacodynamic genome-wide association studies in pharmacogenomics. Drug Discov Today 2011; 16: 804–890.

    Article  Google Scholar 

  31. Gong Y, Wang Z, Liu T, Zhao W, Zhu Y, Johnson JA et al. A statistical model for functional mapping of quantitative trait loci regulating drug response. Pharmacogenomics J 2004; 4: 315–321.

    Article  CAS  Google Scholar 

  32. Lin M, Aqvilonte C, Johnson JA, Wu R . Sequencing drug response with HapMap. Pharmacogenomics J 2005; 5: 149–156.

    Article  CAS  Google Scholar 

  33. Lin M, Hou W, Li HY, Johnson JA, Wu R . Modeling sequence-sequence interactions for drug response. Bioinformatics 2007; 23: 1251–1257.

    Article  CAS  Google Scholar 

  34. Wu RL, Lin M . Statistical and Computational Pharmacogenomics. Chapman & Hall/CRC: London, 2008.

    Book  Google Scholar 

  35. Martin RJ, Szefler SJ, Chinchilli VM, Kraft M, Dolovich M, Boushey HA et al. Systemic effect comparisons of six inhaled corticosteroid preparations. Am J Resp Crit Care Med 2002; 165: 1377–1383.

    Article  Google Scholar 

  36. Boushey HA, Sorkness CA, King TS, Sullivan SD, Fahy JV, Lazarus SC et al. Regular vs. intermittent controller therapy for mild persistent asthma. N Engl J Med 2005; 352: 1519–1528.

    Article  CAS  Google Scholar 

  37. Lazarus SC, Boushey HA, Fahy JV, Chinchilli VM, Lemanske RF Jr, Sorkness CA et al. Long-acting beta2-agonist monotherapy vs continued therapy with inhaled corticosteroids in patients with persistent asthma: a randomized controlled trial. JAMA 2001; 285: 2583–2593.

    Article  CAS  Google Scholar 

  38. Giraldo J . Empirical models and Hill coefficients. Trend Pharmacolog Sci 2003; 24: 63–65.

    Article  CAS  Google Scholar 

  39. Diggle PJ, Heagerty P, Liang KY et al Analysis of Longitudinal Data. Oxford University Press: Oxford, UK, 2002.

    Google Scholar 

  40. Zimmerman DL, Nunez-Anton V . Parametric modeling of growth curve data: an overview (with discussion). Test 2001; 10: 1–73.

    Article  Google Scholar 

  41. Zhao W, Hou W, Littell RC, Wu R . Structured antedependence models for functional mapping of multivariate longitudinal quantitative traits. Stat Appl Mol Genet Biol 2005; 4: Article 33.

    Article  Google Scholar 

  42. Yap JS, Li Y, Das K, Li J, Wu R . Functional mapping of reaction norms to multiple environmental signals through nonparametric covariance estimation. BMC Plant Biol 2011; 11: 23.

    Article  Google Scholar 

  43. Devlin B, Roeder K . Genomic control for association studies. Biometrics 1999; 55: 997–1004.

    Article  CAS  Google Scholar 

  44. Mustavich LF, Miller P, Kidd KK, Zhao H . Using a pharmacokinetic model to relate an individual's susceptibility to alcohol dependence to genotypes. Hum Hered 2010; 70: 177–193.

    Article  CAS  Google Scholar 

  45. Ahn K, Luo JT, Berg A, Keefe D, Wu R . Functional mapping of drug response with pharmacodynamic-pharmcokinetic principles. Trend Pharmacol Sci 2010; 31: 306–311.

    Article  CAS  Google Scholar 

  46. Yeh TY, Sbodio JI, Nguyen MT, Meyer TN, Lee RM, Chi NW . Tankyrase-1 overexpression reduces genotoxin-induced cell death by inhibiting PARP1. Mol Cell Biochem 2005; 276: 183–192.

    Article  CAS  Google Scholar 

  47. Kuschel L, Hansel A, Schonherr R, Weissbach H, Brot N, Hoshi T et al. Molecular cloning and functional expression of a human peptide methionine sulfoxide reductase (hMsrA). FEBS Lett 1999; 456: 17–21.

    Article  CAS  Google Scholar 

  48. Lanuza GM, Gosgnach S, Pierani A, Jessell TM, Goulding M . Genetic identification of spinal interneurons that coordinate left-right locomotor activity necessary for walking movements. Neuron 2004; 42: 375–386.

    Article  CAS  Google Scholar 

  49. Maes T, Barcelo A, Buesa C . Neuron navigator: a human gene family with homology to unc-53, a cell guidance gene from Caenorhabditis elegans. Genomics 2002; 80: 21–30.

    Article  CAS  Google Scholar 

  50. Persson B, Kallberg Y, Bray JE, Bruford E, Dellaporta SL, Favia AD et al. The SDR (short-chain dehydrogenase/reductase and related enzymes) nomenclature initiative. Chem Biol Interact 2009; 178: 94–98.

    Article  CAS  Google Scholar 

  51. Tang J, Gary JD, Clarke S, Herschman HR . PRMT 3, a type I protein arginine N-methyltransferase that differs from PRMT1 in its oligomerization, subcellular localization, substrate specificity, and regulation. J Biol Chem 1998; 273: 16935–16945.

    Article  CAS  Google Scholar 

  52. Hasson T, Skowron JF, Gilbert DJ, Avraham KB, Perry WL, Bement WM et al. Mapping of unconventional myosins in mouse and human. Genomics 1997; 36: 431–439.

    Article  Google Scholar 

  53. Rastogi D, Suzuki M, Greally JM . Differential epigenome-wide DNA methylation patterns in childhood obesity-associated asthma. Sci Rep 2013; 3: 2164.

    Article  Google Scholar 

Download references

Acknowledgements

We thank Kwangmi Ahn for her assistance in data management. This research was funded by grants NIH/NHLBI-1U10HL098115, UL1 TR000127, U10 HL-51810, U10 HL-51834, U10 HL-51831, U10 HL-51823, U10 HL-51845, U10 HL-51843, and U10 HL-56443. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Author information

Author notes

  1. Y Wang and C Tong: These authors contributed to this work equally.

Authors and Affiliations

  1. Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State College of Medicine, PA, USA, Hershey

    Y Wang, C Tong, Z Wang, D Mauger, V M Chinchilli & R Wu

  2. Division of Biostatistics, Yale School of Public Health, New Haven, CT, USA

    Z Wang

  3. Channing Laboratory, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    K G Tantisira & E Israel

  4. National Jewish Health and University of Colorado School of Medicine, Denver, CO, USA

    S J Szefler

  5. Department of Medicine, University of California, San Francisco, CA, USA

    H A Boushey & S C Lazarus

  6. Department of Medicine, University of Wisconsin, Madison, WI, USA

    R F Lemanske

Authors
  1. Y Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Z Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Z Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. D Mauger
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. K G Tantisira
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. E Israel
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. S J Szefler
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. V M Chinchilli
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. H A Boushey
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. S C Lazarus
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. R F Lemanske
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. R Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to D Mauger or R Wu.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on the The Pharmacogenomics Journal website

Supplementary information

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Tong, C., Wang, Z. et al. Pharmacodynamic genome-wide association study identifies new responsive loci for glucocorticoid intervention in asthma. Pharmacogenomics J 15, 422–429 (2015). https://doi.org/10.1038/tpj.2014.83

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/tpj.2014.83

This article is cited by

Search

Advanced search

Quick links