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:

Genome-wide association study identifies pharmacogenomic loci linked with specific antihypertensive drug treatment and new-onset diabetes

The Pharmacogenomics Journal volume 18, pages 106–112 (2018)Cite this article

Subjects

Abstract

We conducted a discovery genome-wide association study with expression quantitative trait loci (eQTL) annotation of new-onset diabetes (NOD) among European Americans, who were exposed to a calcium channel blocker-based strategy (CCB strategy) or a β-blocker-based strategy (β-blocker strategy) in the INternational VErapamil SR Trandolapril STudy. Replication of the top signal from the SNP*treatment interaction analysis was attempted in Hispanic and African Americans, and a joint meta-analysis was performed (total 334 NOD cases and 806 matched controls). PLEKHH2 rs11124945 at 2p21 interacted with antihypertensive exposure for NOD (meta-analysis P=5.3 × 10−8). rs11124945 G allele carriers had lower odds for NOD when exposed to the β-blocker strategy compared with the CCB strategy (Odds ratio OR=0.38(0.24−0.60), P=4.0 × 10−5), whereas A/A homozygotes exposed to the β-blocker strategy had increased odds for NOD compared with the CCB strategy (OR=2.02(1.39−2.92), P=2.0 × 10−4). eQTL annotation of the 2p21 locus provides functional support for regulating gene expression.

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

Similar content being viewed by others

References

  1. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M et al. Heart disease and stroke statistics—2015 update: a report from the American Heart Association. Circulation 2015; 131: e29–322.

    PubMed  Google Scholar 

  2. WHO Global Status Report on Noncommunicable Diseases 2014. World Health Organization: Switzerland, 2014.

  3. Weycker D, Nichols GA, O'Keeffe-Rosetti M, Edelsberg J, Vincze G, Khan ZM et al. Excess risk of diabetes in persons with hypertension. J Diabetes Complicat 2009; 23: 330–336.

    Article  Google Scholar 

  4. Major outcomes in high-risk hypertensive patients randomized to angiotensin-converting enzyme inhibitor or calcium channel blocker vs diuretic: the antihypertensive and lipid-lowering treatment to prevent heart attack trial (ALLHAT). JAMA 2002; 288: 2981–2997.

  5. Gupta AK, Dahlof B, Dobson J, Sever PS, Wedel H, Poulter NR . Determinants of new-onset diabetes among 19,257 hypertensive patients randomized in the Anglo-Scandinavian Cardiac Outcomes Trial—blood pressure lowering arm and the relative influence of antihypertensive medication. Diabetes Care 2008; 31: 982–988.

    Article  CAS  PubMed  Google Scholar 

  6. Cooper-Dehoff R, Cohen JD, Bakris GL, Messerli FH, Erdine S, Hewkin AC et al. Predictors of development of diabetes mellitus in patients with coronary artery disease taking antihypertensive medications (findings from the INternational VErapamil SR-Trandolapril STudy [INVEST]). Am J Cardiol 2006; 98: 890–894.

    Article  CAS  PubMed  Google Scholar 

  7. Dahlof B, Devereux RB, Kjeldsen SE, Julius S, Beevers G, de Faire U et al. Cardiovascular morbidity and mortality in the Losartan Intervention for Endpoint reduction in hypertension study (LIFE): a randomised trial against atenolol. Lancet 2002; 359: 995–1003.

    Article  CAS  PubMed  Google Scholar 

  8. Elliott WJ, Meyer PM . Incident diabetes in clinical trials of antihypertensive drugs: a network meta-analysis. Lancet 2007; 369: 201–207.

    Article  CAS  PubMed  Google Scholar 

  9. Shen L, Shah BR, Reyes EM, Thomas L, Wojdyla D, Diem P et al. Role of diuretics, beta blockers, and statins in increasing the risk of diabetes in patients with impaired glucose tolerance: reanalysis of data from the NAVIGATOR study. BMJ 2013; 347: f6745.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Rizos CV, Elisaf MS . Antihypertensive drugs and glucose metabolism. World J Cardiol 2014; 6: 517–530.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Barzilay JI, Davis BR, Cutler JA, Pressel SL, Whelton PK, Basile J et al. Fasting glucose levels and incident diabetes mellitus in older nondiabetic adults randomized to receive 3 different classes of antihypertensive treatment: a report from the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). Arch Intern Med 2006; 166: 2191–2201.

    Article  CAS  PubMed  Google Scholar 

  12. Verdecchia P, Reboldi G, Angeli F, Borgioni C, Gattobigio R, Filippucci L et al. Adverse prognostic significance of new diabetes in treated hypertensive subjects. Hypertension 2004; 43: 963–969.

    Article  CAS  PubMed  Google Scholar 

  13. Almgren T, Wilhelmsen L, Samuelsson O, Himmelmann A, Rosengren A, Andersson OK . Diabetes in treated hypertension is common and carries a high cardiovascular risk: results from a 28-year follow-up. J Hypertens 2007; 25: 1311–1317.

    Article  CAS  PubMed  Google Scholar 

  14. Kostis JB, Wilson AC, Freudenberger RS, Cosgrove NM, Pressel SL, Davis BR . Long-term effect of diuretic-based therapy on fatal outcomes in subjects with isolated systolic hypertension with and without diabetes. Am J Cardiol 2005; 95: 29–35.

    Article  CAS  PubMed  Google Scholar 

  15. Aksnes TA, Kjeldsen SE, Rostrup M, Omvik P, Hua TA, Julius S . Impact of new-onset diabetes mellitus on cardiac outcomes in the Valsartan Antihypertensive Long-term Use Evaluation (VALUE) trial population. Hypertension 2007; 50: 467–473.

    Article  CAS  PubMed  Google Scholar 

  16. Karagiannis A, Tziomalos K, Pagourelias ED, Gossios TD, Athyros VG . Effect of antihypertensive drug-associated diabetes on cardiovascular risk. Hellenic J Cardiol 2010; 51: 195–199.

    PubMed  Google Scholar 

  17. Aksnes TA, Kjeldsen SE, Rostrup M, Storset O, Hua TA, Julius S . Predictors of new-onset diabetes mellitus in hypertensive patients: the VALUE trial. J Hum Hypertens 2008; 22: 520–527.

    Article  CAS  PubMed  Google Scholar 

  18. Billings LK, Florez JC . The genetics of type 2 diabetes: what have we learned from GWAS? Ann N Y Acad Sci 2010; 1212: 59–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhong H, Beaulaurier J, Lum PY, Molony C, Yang X, Macneil DJ et al. Liver and adipose expression associated SNPs are enriched for association to type 2 diabetes. PLoS Genet 2010; 6: e1000932.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Karnes JH, Gong Y, Pacanowski MA, McDonough CW, Arwood MJ, Langaee TY et al. Impact of TCF7L2 single nucleotide polymorphisms on hydrochlorothiazide-induced diabetes. Pharmacogenet Genomics 2013; 23: 697–705.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Karnes JH, McDonough CW, Gong Y, Vo TT, Langaee TY, Chapman AB et al. Association of KCNJ1 variation with change in fasting glucose and new onset diabetes during HCTZ treatment. Pharmacogenomics J 2013; 13: 430–436.

    Article  CAS  PubMed  Google Scholar 

  22. Beitelshees AL, Gong Y, Wang D, Schork NJ, Cooper-Dehoff RM, Langaee TY et al. KCNMB1 genotype influences response to verapamil SR and adverse outcomes in the INternational VErapamil SR/Trandolapril STudy (INVEST). Pharmacogenet Genomics 2007; 17: 719–729.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Pepine CJ, Handberg-Thurmond E, Marks RG, Conlon M, Cooper-DeHoff R, Volkers P et al. Rationale and design of the International Verapamil SR/Trandolapril Study (INVEST): an Internet-based randomized trial in coronary artery disease patients with hypertension. J Am Coll Cardiol 1998; 32: 1228–1237.

    Article  CAS  PubMed  Google Scholar 

  24. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559–575.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Alkes L, Price NJP, Plenge Robert M, Weinblatt Michael E, Shadick Nancy A, Reich David . Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006; 38: 904–909.

    Article  Google Scholar 

  26. Delaneau O, Marchini J, Zagury JF . A linear complexity phasing method for thousands of genomes. Nat Methods 2012; 9: 179–181.

    Article  CAS  Google Scholar 

  27. 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  PubMed  PubMed Central  Google Scholar 

  28. Willer CJ, Li Y, Abecasis GR . METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010; 26: 2190–2191.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ . Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet 2010; 6: e1000888.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Gamazon ER, Huang RS, Cox NJ, Dolan ME . Chemotherapeutic drug susceptibility associated SNPs are enriched in expression quantitative trait loci. Proc Natl Acad Sci USA 2010; 107: 9287–9292.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 2015; 348: 648–660.

    Article  Google Scholar 

  32. Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S et al. The GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet 2013; 45: 580–585.

    Article  CAS  Google Scholar 

  33. Narayan KM, Boyle JP, Thompson TJ, Sorensen SW, Williamson DF . Lifetime risk for diabetes mellitus in the United States. JAMA 2003; 290: 1884–1890.

    Article  CAS  PubMed  Google Scholar 

  34. Barner JC, Worchel J, Yang M . Frequency of new-onset diabetes mellitus and use of antipsychotic drugs among Central Texas veterans. Pharmacotherapy 2004; 24: 1529–1538.

    Article  PubMed  Google Scholar 

  35. Perisic L, Lal M, Hulkko J, Hultenby K, Onfelt B, Sun Y et al. Plekhh2, a novel podocyte protein downregulated in human focal segmental glomerulosclerosis, is involved in matrix adhesion and actin dynamics. Kidney Int 2012; 82: 1071–1083.

    Article  CAS  PubMed  Google Scholar 

  36. Greene CN, Keong LM, Cordovado SK, Mueller PW . Sequence variants in the PLEKHH2 region are associated with diabetic nephropathy in the GoKinD study population. Hum Genet 2008; 124: 255–262.

    Article  CAS  PubMed  Google Scholar 

  37. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 2008; 40: 638–645.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Chen ZJ, Zhao H, He L, Shi Y, Qin Y, Li Z et al. Genome-wide association study identifies susceptibility loci for polycystic ovary syndrome on chromosome 2p16.3, 2p21 and 9q33.3. Nat Genet 2011; 43: 55–59.

    Article  PubMed  Google Scholar 

  39. Goodarzi MO, Jones MR, Li X, Chua AK, Garcia OA, Chen YD et al. Replication of association of DENND1A and THADA variants with polycystic ovary syndrome in European cohorts. J Med Genet 2012; 49: 90–95.

    Article  CAS  PubMed  Google Scholar 

  40. Eeles RA, Kote-Jarai Z, Al Olama AA, Giles GG, Guy M, Severi G et al. Identification of seven new prostate cancer susceptibility loci through a genome-wide association study. Nat Genet 2009; 41: 1116–1121.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Rippe V, Drieschner N, Meiboom M, Murua Escobar H, Bonk U, Belge G et al. Identification of a gene rearranged by 2p21 aberrations in thyroid adenomas. Oncogene 2003; 22: 6111–6114.

    Article  CAS  PubMed  Google Scholar 

  42. Simonis-Bik AM, Nijpels G, van Haeften TW, Houwing-Duistermaat JJ, Boomsma DI, Reiling E et al. Gene variants in the novel type 2 diabetes loci CDC123/CAMK1D, THADA, ADAMTS9, BCL11A, and MTNR1B affect different aspects of pancreatic beta-cell function. Diabetes 2010; 59: 293–301.

    Article  CAS  PubMed  Google Scholar 

  43. Santoni M, Andrikou K, Sotte V, Bittoni A, Lanese A, Pellei C et al. Toll like receptors and pancreatic diseases: From a pathogenetic mechanism to a therapeutic target. Cancer Treat Rev 2015; 41: 569–576.

    Article  CAS  PubMed  Google Scholar 

  44. Donath MY, Shoelson SE . Type 2 diabetes as an inflammatory disease. Nat Rev Immunol 2011; 11: 98–107.

    Article  CAS  PubMed  Google Scholar 

  45. Westwell-Roper C, Nackiewicz D, Dan M, Ehses JA . Toll-like receptors and NLRP3 as central regulators of pancreatic islet inflammation in type 2 diabetes. Immunol Cell Biol 2014; 92: 314–323.

    Article  CAS  PubMed  Google Scholar 

  46. Grant RW, Dixit VD . Mechanisms of disease: inflammasome activation and the development of type 2 diabetes. Front Immunol 2013; 4: 50.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Fadista J, Vikman P, Laakso EO, Mollet IG, Esguerra JL, Taneera J et al. Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism. Proc Natl Acad Sci USA 2014; 111: 13924–13929.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Below JE, Gamazon ER, Morrison JV, Konkashbaev A, Pluzhnikov A, McKeigue PM et al. Genome-wide association and meta-analysis in populations from Starr County, Texas, and Mexico City identify type 2 diabetes susceptibility loci and enrichment for expression quantitative trait loci in top signals. Diabetologia 2011; 54: 2047–2055.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Elbein SC, Gamazon ER, Das SK, Rasouli N, Kern PA, Cox NJ . Genetic risk factors for type 2 diabetes: a trans-regulatory genetic architecture? Am J Hum Genet 2012; 91: 466–477.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Das SK, Sharma NK . Expression quantitative trait analyses to identify causal genetic variants for type 2 diabetes susceptibility. World J Diabetes 2014; 5: 97–114.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Sarwar N, Gao P, Seshasai SR, Gobin R, Kaptoge S, Di Angelantonio E et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet 2010; 375: 2215–2222.

    Article  CAS  PubMed  Google Scholar 

  52. Torres JM, Cox NJ, Philipson LH . Genome wide association studies for diabetes: perspective on results and challenges. Pediatr Diabetes 2013; 14: 90–96.

    Article  CAS  PubMed  Google Scholar 

  53. Prasad RB, Groop L . Genetics of type 2 diabetes-pitfalls and possibilities. Genes (Basel) 2015; 6: 87–123.

    Article  CAS  Google Scholar 

  54. Maranville JC, Cox NJ . Pharmacogenomic variants have larger effect sizes than genetic variants associated with other dichotomous complex traits. Pharmacogenomics J 2015; 16: 388–392.

    Article  PubMed  PubMed Central  Google Scholar 

  55. 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  PubMed  PubMed Central  Google Scholar 

  56. Niu Y, Gong Y, Langaee TY, Davis HM, Elewa H, Beitelshees AL et al. Genetic variation in the beta2 subunit of the voltage-gated calcium channel and pharmacogenetic association with adverse cardiovascular outcomes in the INternational VErapamil SR-Trandolapril STudy GENEtic Substudy (INVEST-GENES). Circ Cardiovasc Genet 2010; 3: 548–555.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. McDonough CW, Gong Y, Padmanabhan S, Burkley B, Langaee TY, Melander O et al. Pharmacogenomic association of nonsynonymous SNPs in SIGLEC12, A1BG, and the selectin region and cardiovascular outcomes. Hypertension 2013; 62: 48–54.

    Article  CAS  PubMed  Google Scholar 

  58. Limdi NA, Wadelius M, Cavallari L, Eriksson N, Crawford DC, Lee MT et al. Warfarin pharmacogenetics: a single VKORC1 polymorphism is predictive of dose across 3 racial groups. Blood 2010; 115: 3827–3834.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Yin T, Miyata T . Pharmacogenomics of clopidogrel: evidence and perspectives. Thromb Res 2011; 128: 307–316.

    Article  CAS  PubMed  Google Scholar 

  60. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 2010; 26: 2336–2337.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This project was supported by the National Institute of Health Pharmacogenetics Research Network Grant U01-GM074492, NIH R01 HL074730, UF Opportunity Fund and Abbott Laboratories. The genotyping and imputation were performed by the RIKEN Center for Integrative Medical Sciences. INVEST was supported by the University of Florida and Grants from BASF Pharma and Abbott Laboratories.

Author contributions

S-WC wrote the manuscript and all co-authors critically evaluated and reviewed the manuscript. CJP, JAJ and RCD designed and conducted the INVEST-GENES and secured funding. S-WC, YG, CWM, JAJ and RCD designed the research. TAJ, TT, AT, TT and MK facilitated genotyping and imputation. ERG and MAP provided guidance on the eQTL analysis. S-WC, YG, CWM, RMC-D, CJP and JAJ performed the research, and S-WC analyzed the data.

Author information

Authors and Affiliations

  1. Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, College of Pharmacy, University of Florida, Gainesville, FL, USA

    S-W Chang, C W McDonough, Y Gong, J A Johnson & R M Cooper-DeHoff

  2. Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan

    T A Johnson & T Tsunoda

  3. Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan

    T Tsunoda

  4. Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA

    E R Gamazon

  5. Department of Medicine, University of Chicago, Chicago, IL, USA

    M A Perera

  6. Laboratory for Statistical Analysis, SNP Research Center, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    A Takahashi

  7. Laboratory for Cardiovascular Diseases, SNP Research Center, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    T Tanaka

  8. Laboratory for Genotyping Development, SNP Research Center, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    M Kubo

  9. Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA

    C J Pepine, J A Johnson & R M Cooper-DeHoff

Authors
  1. S-W Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C W McDonough
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Y Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. T A Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. T Tsunoda
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. E R Gamazon
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. M A Perera
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. A Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. T Tanaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. M Kubo
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. C J Pepine
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. J A Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. R M Cooper-DeHoff
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R M Cooper-DeHoff.

Ethics declarations

Competing interests

CWM, YG, MAP, JAJ, CJP and RMC-D have received support from NIH. CJP, JAJ and RMC-D also received support for this project from Abbott Laboratories. The remaining 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

Chang, SW., McDonough, C., Gong, Y. et al. Genome-wide association study identifies pharmacogenomic loci linked with specific antihypertensive drug treatment and new-onset diabetes. Pharmacogenomics J 18, 106–112 (2018). https://doi.org/10.1038/tpj.2016.67

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/tpj.2016.67

This article is cited by

Search

Advanced search

Quick links