A genome-wide association and admixture mapping study of bronchodilator drug response in African Americans with asthma


Short-acting β2-adrenergic receptor agonists (SABAs) are the most commonly prescribed asthma medications worldwide. Response to SABAs is measured as bronchodilator drug response (BDR), which varies among racial/ethnic groups in the United States. However, the genetic variation that contributes to BDR is largely undefined in African Americans with asthma. To identify genetic variants that may contribute to differences in BDR in African Americans with asthma, we performed a genome-wide association study (GWAS) of BDR in 949 African-American children with asthma, genotyped with the Axiom World Array 4 (Affymetrix, Santa Clara, CA) followed by imputation using 1000 Genomes phase III genotypes. We used linear regression models adjusting for age, sex, body mass index (BMI) and genetic ancestry to test for an association between BDR and genotype at single-nucleotide polymorphisms (SNPs). To increase power and distinguish between shared vs. population-specific associations with BDR in children with asthma, we performed a meta-analysis across 949 African Americans and 1830 Latinos (total = 2779). Finally, we performed genome-wide admixture mapping to identify regions whereby local African or European ancestry is associated with BDR in African Americans. We identified a population-specific association with an intergenic SNP on chromosome 9q21 that was significantly associated with BDR (rs73650726, p = 7.69 × 10−9). A trans-ethnic meta-analysis across African Americans and Latinos identified three additional SNPs within the intron of PRKG1 that were significantly associated with BDR (rs7903366, rs7070958 and rs7081864, p ≤ 5 × 10−8). Our results failed to replicate in three additional populations of 416 Latinos and 1615 African Americans. Our findings indicate that both population-specific and shared genetic variation contributes to differences in BDR in minority children with asthma, and that the genetic underpinnings of BDR may differ between racial/ethnic groups.

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  1. 1.

    Naqvi M, Thyne S, Choudhry S, Tsai HJ, Navarro D, Castro RA. et al. Ethnic-specific differences in bronchodilator responsiveness among African Americans, Puerto Ricans, and Mexicans with asthma. J Asthma. 2007;44:639–48.

    Article  Google Scholar 

  2. 2.

    Padhukasahasram B, Yang JJ, Levin AM, Yang M, Burchard EG, Kumar R, et al. Gene-based association identifies SPATA13-AS1 as a pharmacogenomic predictor of inhaled short-acting beta-agonist response in multiple population groups. Pharmacogenomics J 2014;14:365–371.

    CAS  Article  Google Scholar 

  3. 3.

    Palmer LJ, Silverman ES, Weiss ST, Drazen JM. Pharmacogenetics of asthma. Am J Respir Crit Care Med. 2002;165:861–6.

    Article  Google Scholar 

  4. 4.

    Nelson HS. Beta-adrenergic bronchodilators. N Engl J Med. 1995;333:499–506.

    CAS  Article  Google Scholar 

  5. 5.

    Loza MJ, Penn RB. Regulation of T cells in airway disease by beta-agonist. Front Biosci. 2010;2:969–79.

    Google Scholar 

  6. 6.

    Salathe M. Effects of beta-agonists on airway epithelial cells. J Allergy Clin Immunol. 2002;110(6 Suppl):S275–81.

    CAS  Article  Google Scholar 

  7. 7.

    Shore SA, Moore PE. Regulation of beta-adrenergic responses in airway smooth muscle. Respir Physiol Neurobiol. 2003;137:179–95.

    CAS  Article  Google Scholar 

  8. 8.

    Jartti T. Asthma, asthma medication and autonomic nervous system dysfunction. Clin Physiol. 2001;21:260–9.

    CAS  Article  Google Scholar 

  9. 9.

    Drake KA, Torgerson DG, Gignoux CR, Galanter JM, Roth LA, Huntsman S, et al. A genome-wide association study of bronchodilator response in Latinos implicates rare variants. J Allergy Clin Immunol. 2014;133:370–8.

    Article  Google Scholar 

  10. 10.

    Burchard EG, Avila PC, Nazario S, Casal J, Torres A, Rodriguez-Santana JR, et al. Lower bronchodilator responsiveness in Puerto Rican than in Mexican subjects with asthma. Am J Respir Crit Care Med. 2004;169:386–92.

    Article  Google Scholar 

  11. 11.

    Choudhry S, Ung N, Avila PC, Ziv E, Nazario S, Casal J, et al. Pharmacogenetic differences in response to albuterol between Puerto Ricans and Mexicans with asthma. Am J Respir Crit Care Med. 2005;171:563–70.

    Article  Google Scholar 

  12. 12.

    Burchard EG, Ziv E, Coyle N, Gomez SL, Tang H, Karter AJ. et al. The importance of race and ethnic background in biomedical research and clinical practice. N Engl J Med. 2003;348:1170–5.

    Article  Google Scholar 

  13. 13.

    Blake K, Madabushi R, Derendorf H, Lima J. Population pharmacodynamic model of bronchodilator response to inhaled albuterol in children and adults with asthma. Chest. 2008;134:981–9.

    CAS  Article  Google Scholar 

  14. 14.

    Gorina Y. QuickStats: asthma*death rates, by race and age group - United States, 2007–9. In (MMWR) MaMWR (ed). Centers for Disease Control and Prevention. 2012.

  15. 15.

    Martinez FD, Graves PE, Baldini M, Solomon S, Erickson R. Association between genetic polymorphisms of the beta2-adrenoceptor and response to albuterol in children with and without a history of wheezing. J Clin Invest. 1997;100:3184–8.

    CAS  Article  Google Scholar 

  16. 16.

    Silverman EK, Kwiatkowski DJ, Sylvia JS, Lazarus R, Drazen JM, Lange C, et al. Family-based association analysis of beta2-adrenergic receptor polymorphisms in the childhood asthma management program. J Allergy Clin Immunol. 2003;112:870–6.

    CAS  Article  Google Scholar 

  17. 17.

    Poon AH, Tantisira KG, Litonjua AA, Lazarus R, Xu J, Lasky-Su J, et al. Association of corticotropin-releasing hormone receptor-2 genetic variants with acute bronchodilator response in asthma. Pharm Genom. 2008;18:373–82.

    CAS  Article  Google Scholar 

  18. 18.

    Tantisira KG, Small KM, Litonjua AA, Weiss ST, Liggett SB. Molecular properties and pharmacogenetics of a polymorphism of adenylyl cyclase type 9 in asthma: interaction between beta-agonist and corticosteroid pathways. Hum Mol Genet. 2005;14:1671–7.

    CAS  Article  Google Scholar 

  19. 19.

    Litonjua AA, Lasky-Su J, Schneiter K, Tantisira KG, Lazarus R, Klanderman B, et al. ARG1 is a novel bronchodilator response gene: screening and replication in four asthma cohorts. Am J Respir Crit Care Med. 2008;178:688–94.

    Article  Google Scholar 

  20. 20.

    Duan QL, Du R, Lasky-Su J, Klanderman BJ, Partch AB, Peters SP, et al. A polymorphism in the thyroid hormone receptor gene is associated with bronchodilator response in asthmatics. Pharm J. 2013;13:130–6.

    CAS  Google Scholar 

  21. 21.

    Reihsaus E, Innis M, MacIntyre N, Liggett SB. Mutations in the gene encoding for the beta 2-adrenergic receptor in normal and asthmatic subjects. Am J Respir Cell Mol Biol. 1993;8:334–9.

    CAS  Article  Google Scholar 

  22. 22.

    Duan QL, Lasky-Su J, Himes BE, Qiu W, Litonjua AA, Damask A, et al. A genome-wide association study of bronchodilator response in asthmatics. Pharm J. 2014;14:41–47.

    CAS  Google Scholar 

  23. 23.

    Israel E, Lasky-Su J, Markezich A, Damask A, Szefler SJ, Schuemann B, et al. Genome-wide association study of short-acting beta2-agonists. A novel genome-wide significant locus on chromosome 2 near ASB3. Am J Respir Crit Care Med. 2015;191:530–7.

    CAS  Article  Google Scholar 

  24. 24.

    Himes BE, Jiang X, Hu R, Wu AC, Lasky-Su JA, Klanderman BJ, et al. Genome-wide association analysis in asthma subjects identifies SPATS2L as a novel bronchodilator response gene. PLoS Genet. 2012;8:e1002824.

    CAS  Article  Google Scholar 

  25. 25.

    Torgerson DG, Ampleford EJ, Chiu GY, Gauderman WJ, Gignoux CR, Graves PE, et al. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet. 2011;43:887–92.

    CAS  Article  Google Scholar 

  26. 26.

    Galanter JM, Gignoux CR, Torgerson DG, Roth LA, Eng C, Oh SS, et al. Genome-wide association study and admixture mapping identify different asthma-associated loci in Latinos: the Genes-environments & Admixture in Latino Americans study. J Allergy Clin Immunol. 2014;134:295–305.

    CAS  Article  Google Scholar 

  27. 27.

    White MJ, Risse-Adams O, Goddard P, Contreras MG, Adams J, Hu D, et al. Novel genetic risk factors for asthma in African American children: precision medicine and the SAGE II Study. Immunogenetics. 2016;68:391–400.

    CAS  Article  Google Scholar 

  28. 28.

    Winkler CA, Nelson GW, Smith MW. Admixture mapping comes of age. Annu Rev Genom Hum Genet. 2010;11:65–89.

    CAS  Article  Google Scholar 

  29. 29.

    Nishimura KK, Galanter JM, Roth LA, Oh SS, Thakur N, Nguyen EA, et al. Early-life air pollution and asthma risk in minority children. The GALA II and SAGE II studies. Am J Respir Crit Care Med. 2013;188:309–18.

    Article  Google Scholar 

  30. 30.

    Torgerson DG, Gignoux CR, Galanter JM, Drake KA, Roth LA, Eng C, et al. Case-control admixture mapping in Latino populations enriches for known asthma-associated genes. J Allergy Clin Immunol. 2012;130:76–82 e12.

    CAS  Article  Google Scholar 

  31. 31.

    Gould W, Peterson EL, Karungi G, Zoratti A, Gaggin J, Toma G, et al. Factors predicting inhaled corticosteroid responsiveness in African American patients with asthma. J Allergy Clin Immunol. 2010;126:1131–8.

    CAS  Article  Google Scholar 

  32. 32.

    Moore WC, Bleecker ER, Curran-Everett D, Erzurum SC, Ameredes BT, Bacharier L, et al. Characterization of the severe asthma phenotype by the National Heart, Lung, and Blood Institute’s Severe Asthma Research Program. J Allergy Clin Immunol. 2007;119:405–13.

    Article  Google Scholar 

  33. 33.

    Moore WC, Meyers DA, Wenzel SE, Teague WG, Li H, Li X, et al. Identification of asthma phenotypes using cluster analysis in the Severe Asthma Research Program. Am J Respir Crit Care Med. 2010;181:315–23.

    Article  Google Scholar 

  34. 34.

    Standardization of Spirometry. 1994 Update. American Thoracic Society. Am J Respir Crit Care Med. 1995;152:1107–36.

    Article  Google Scholar 

  35. 35.

    Delaneau O, Zagury JF. Haplotype inference. Methods Mol Biol. 2012;888:177–96.

    Article  Google Scholar 

  36. 36.

    Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5:e1000529.

    Article  Google Scholar 

  37. 37.

    Howie B, Marchini J, Stephens M. Genotype imputation with thousands of genomes. G3. 2011;1:457–70.

    Article  Google Scholar 

  38. 38.

    Genomes Project C, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526:68–74.

    Article  Google Scholar 

  39. 39.

    Maples BK, Gravel S, Kenny EE, Bustamante CD. RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference. Am J Hum Genet. 2013;93:278–88.

    CAS  Article  Google Scholar 

  40. 40.

    Proceedings of the ATS workshop on refractory asthma: current understanding, recommendations, and unanswered questions. American Thoracic Society. Am J Respir Crit Care Med. 2000;162:2341–51.

    Article  Google Scholar 

  41. 41.

    Pino-Yanes M, Thakur N, Gignoux CR, Galanter JM, Roth LA, Eng C, et al. Genetic ancestry influences asthma susceptibility and lung function among Latinos. J Allergy Clin Immunol. 2015;135:228–35.

    Article  Google Scholar 

  42. 42.

    Baran Y, Pasaniuc B, Sankararaman S, Torgerson DG, Gignoux C, Eng C, et al. Fast and accurate inference of local ancestry in Latino populations. Bioinformatics. 2012;28:1359–67.

    CAS  Article  Google Scholar 

  43. 43.

    Kumar R, Nguyen EA, Roth LA, Oh SS, Gignoux CR, Huntsman S, et al. Factors associated with degree of atopy in Latino children in a nationwide pediatric sample: the Genes-environments and Admixture in Latino Asthmatics (GALA II) study. J Allergy Clin Immunol. 2013;132:896–905 e891.

    Article  Google Scholar 

  44. 44.

    Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009;19:1655–64.

    CAS  Article  Google Scholar 

  45. 45.

    McGarry ME, Castellanos E, Thakur N, Oh SS, Eng C, Davis A, et al. Obesity and bronchodilator response in black and Hispanic children and adolescents with asthma. Chest. 2015;147:1591–8.

    Article  Google Scholar 

  46. 46.

    Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genome-wide association scans. Bioinformatics. 2010;26:2190–1.

    CAS  Article  Google Scholar 

  47. 47.

    PLUMMER MBN, COWLES K, VINES K. CODA: convergence diagnosis and output analysis for MCMC. R News. 2012;6:7–11.

    Google Scholar 

  48. 48.

    Sobota RS, Shriner D, Kodaman N, Goodloe R, Zheng W, Gao YT, et al. Addressing population-specific multiple testing burdens in genetic association studies. Ann Hum Genet. 2015;79:136–47.

    CAS  Article  Google Scholar 

  49. 49.

    Marcus JH, Novembre J. Visualizing the geography of genetic variants. Bioinformatics 2016;33:594–595.

  50. 50.

    Consortium GT. The genotype-tissue expression (GTEx) project. Nat Genet. 2013;45:580–5.

    Article  Google Scholar 

  51. 51.

    Li Z, Xi X, Gu M, Feil R, Ye RD, Eigenthaler M, et al. A stimulatory role for cGMP-dependent protein kinase in platelet activation. Cell. 2003;112:77–86.

    CAS  Article  Google Scholar 

  52. 52.

    Tamura N, Itoh H, Ogawa Y, Nakagawa O, Harada M, Chun TH, et al. cDNA cloning and gene expression of human type I alpha cGMP-dependent protein kinase. Hypertension. 1996;27(3 Pt 2):552–7.

    CAS  Article  Google Scholar 

  53. 53.

    Orstavik S, Natarajan V, Tasken K, Jahnsen T, Sandberg M. Characterization of the human gene encoding the type I alpha and type I beta cGMP-dependent protein kinase (PRKG1). Genomics. 1997;42:311–8.

    CAS  Article  Google Scholar 

  54. 54.

    Burgoyne JR, Madhani M, Cuello F, Charles RL, Brennan JP, Schroder E, et al. Cysteine redox sensor in PKGIa enables oxidant-induced activation. Science. 2007;317:1393–7.

    CAS  Article  Google Scholar 

  55. 55.

    Pfeifer A, Klatt P, Massberg S, Ny L, Sausbier M, Hirneiss C, et al. Defective smooth muscle regulation in cGMP kinase I-deficient mice. EMBO J. 1998;17:3045–51.

    CAS  Article  Google Scholar 

  56. 56.

    Dawes M, Chowienczyk PJ, Ritter JM. Effects of inhibition of the L-arginine/nitric oxide pathway on vasodilation caused by beta-adrenergic agonists in human forearm. Circulation. 1997;95:2293–7.

    CAS  Article  Google Scholar 

  57. 57.

    Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22:1790–7.

    CAS  Article  Google Scholar 

  58. 58.

    Lima JJ, Thomason DB, Mohamed MH, Eberle LV, Self TH, Johnson JA. Impact of genetic polymorphisms of the beta2-adrenergic receptor on albuterol bronchodilator pharmacodynamics. Clin Pharmacol Ther. 1999;65:519–25.

    CAS  Article  Google Scholar 

  59. 59.

    Taylor DR, Drazen JM, Herbison GP, Yandava CN, Hancox RJ, Town GI. Asthma exacerbations during long term beta agonist use: influence of beta(2) adrenoceptor polymorphism. Thorax. 2000;55:762–7.

    CAS  Article  Google Scholar 

  60. 60.

    Israel E, Drazen JM, Liggett SB, Boushey HA, Cherniack RM, Chinchilli VM, et al. The effect of polymorphisms of the beta(2)-adrenergic receptor on the response to regular use of albuterol in asthma. Am J Respir Crit Care Med. 2000;162:75–80.

    CAS  Article  Google Scholar 

  61. 61.

    Bustamante CD, Burchard EG, De la Vega FM. Genomics for the world. Nature. 2011;475:163–5.

    CAS  Article  Google Scholar 

  62. 62.

    Popejoy AB, Fullerton SM. Genomics is failing on diversity. Nature. 2016;538:161–4.

    CAS  Article  Google Scholar 

  63. 63.

    Editors PM, Rid A, Johansson MA, Leung G, Valantine H, Burchard EG, et al. Towards equity in health: researchers take stock. PLoS Med. 2016;13:e1002186.

    Article  Google Scholar 

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This work was supported in part by the Sandler Family Foundation, the American Asthma Foundation, the RWJF Amos Medical Faculty Development Program, National Institutes of Health 1R01HL117004, R01Hl128439, National Institute of Health and Environmental Health Sciences R01 ES015794, R21ES24844, National Institute on Minority Health and Health Disparities 1P60 MD006902, U54MD009523, 1R01MD010443, and the National Institutes of Health National Heart, Lung, and Blood Institute K08 HL118128, U10 HL109164, HL69116, R01 HL69167, HL69170, HL69174 and U10 HL098103. This project has been funded part with federal funds from the National Cancer Institute, National Institutes of Health, under contract HHSN26120080001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the U.S. Government. This research was supported in part by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research. MLS was supported in part by a National Science Foundation Graduate Research Fellowship under grant no. 1144247. MP-Y was funded by the Ramón y Cajal Program (RYC-2015–17205) by the Spanish Ministry of Economy and Competitiveness. MP-Y was also supported by award number AC15/00015 by Instituto de Salud Carlos III thorough AES and EC within AAL framework, and the SysPharmPedia grant awarded from the ERACoSysMed 1st Joint Transnational Call from the European Union under the Horizon 2020; DGT was supported in part by the California Institute for Quantitative Biosciences (QB3). JG was supported in part by NIH Training Grant T32 (5T32GM007546) and career development awards from the NHLBI K23 (5K23HL111636) and NCATS KL2 (5KL2TR000143), as well as the Hewett Fellowship; NT was supported in part by a institutional training grant from the NIGMS (T32GM007546) and career development awards from the NHLBI (5K12HL119997 and K23-HL125551-01A1), Parker B. Francis Fellowship Program, and the American Thoracic Society; RK was supported with a career development award from the NHLBI (5K23HL093023); HJF was supported in part by the GCRC (RR00188); PCA was supported in part by the Ernest S. Bazley Grant. LKW received grant support from the Fund for Henry Ford Hospital, the American Asthma Foundation and the following NIH institutes: NHLBI (R01HL118267, R01HL079055), NIAID (R01AI079139) and NIDDK (R01DK064695). Study accession numbers in dbGaP are phs000921.v2.p1 and phs001180.v1.p1. The authors acknowledge the patients, families, recruiters, health-care providers and community clinics for their participation in SAGE and GALA II. In particular, we thank study coordinator Sandra Salazar and the recruiters who obtained the data: Duanny Alva, MD; Gaby Ayala-Rodriguez; Lisa Caine; Elizabeth Castellanos; Jaime Colon; Denise DeJesus; Blanca Lopez; Brenda Lopez, MD; Louis Martos; Vivian Medina; Juana Olivo; Mario Peralta; Esther Pomares, MD; Jihan Quraishi; Johanna Rodriguez; Shahdad Saeedi; Dean Soto; and Ana Taveras. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

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Spear, M.L., Hu, D., Pino-Yanes, M. et al. A genome-wide association and admixture mapping study of bronchodilator drug response in African Americans with asthma. Pharmacogenomics J 19, 249–259 (2019). https://doi.org/10.1038/s41397-018-0042-4

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