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.

  • Article
  • Published:

Genome-wide meta-analysis of SNP-by9-ACEI/ARB and SNP-by-thiazide diuretic and effect on serum potassium in cohorts of European and African ancestry

Abstract

We evaluated interactions of SNP-by-ACE-I/ARB and SNP-by-TD on serum potassium (K+) among users of antihypertensive treatments (anti-HTN). Our study included seven European-ancestry (EA) (N = 4835) and four African-ancestry (AA) cohorts (N = 2016). We performed race-stratified, fixed-effect, inverse-variance-weighted meta-analyses of 2.5 million SNP-by-drug interaction estimates; race-combined meta-analysis; and trans-ethnic fine-mapping. Among EAs, we identified 11 significant SNPs (P < 5 × 10−8) for SNP-ACE-I/ARB interactions on serum K+ that were located between NR2F1-AS1 and ARRDC3-AS1 on chromosome 5 (top SNP rs6878413 P = 1.7 × 10−8; ratio of serum K+ in ACE-I/ARB exposed compared to unexposed is 1.0476, 1.0280, 1.0088 for the TT, AT, and AA genotypes, respectively). Trans-ethnic fine mapping identified the same group of SNPs on chromosome 5 as genome-wide significant for the ACE-I/ARB analysis. In conclusion, SNP-by-ACE-I /ARB interaction analyses uncovered loci that, if replicated, could have future implications for the prevention of arrhythmias due to anti-HTN treatment-related hyperkalemia. Before these loci can be identified as clinically relevant, future validation studies of equal or greater size in comparison to our discovery effort are needed.

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

Access options

Buy this article

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

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Papademetriou V. Diuretics, hypokalemia, and cardiac arrhythmia: a 20-year controversy. J Clin Hypertens. 2006;8:86–92.

    Article  Google Scholar 

  2. Cohen HW, Madhavan S, Alderman MH. High and low serum potassium associated with cardiovascular events in diuretic-treated patients. J Hypertens. 2001;19:1315–23.

    Article  CAS  Google Scholar 

  3. Salvetti A, Ghiadoni L. Thiazide diuretics in the treatment of hypertension: an update. J Am Soc Nephrol. 2006;17:S25–9.

    Article  CAS  Google Scholar 

  4. Freis ED. The efficacy and safety of diuretics in treating hypertension. Ann Intern Med. 1995;122:223–6.

    Article  CAS  Google Scholar 

  5. Franse LV, Pahor M, Di Bari M, Somes GW, Cushman WC, Applegate WB. Hypokalemia associated with diuretic use and cardiovascular events in the Systolic Hypertension in the Elderly Program. Hypertension. 2000;35:1025–30.

    Article  CAS  Google Scholar 

  6. Alderman MH, Piller LB, Ford CE, Probstfield JL, Oparil S, Cushman WC, et al. Clinical significance of incident hypokalemia and hyperkalemia in treated hypertensive patients in the antihypertensive and lipid-lowering treatment to prevent heart attack trial. Hypertension. 2012;59:926–33.

    Article  CAS  Google Scholar 

  7. Bathum L, Fagnani C, Christiansen L, Christensen K. Heritability of biochemical kidney markers and relation to survival in the elderly–results from a Danish population-based twin study. Clin Chim Acta. 2004;349:143–50.

    Article  CAS  Google Scholar 

  8. Marroni F, Grazio D, Pattaro C, Devoto M, Pramstaller P. Estimates of genetic and environmental contribution to 43 quantitative traits support sharing of a homogeneous environment in an isolated population from South Tyrol, Italy. Hum Hered. 2008;65:175–82.

    Article  Google Scholar 

  9. Nilsson SE, Read S, Berg S, Johansson B. Heritabilities for fifteen routine biochemical values: findings in 215 Swedish twin pairs 82 years of age or older. Scand J Clin Lab Invest. 2009;69:562–9.

    Article  CAS  Google Scholar 

  10. Pilia G, Chen WM, Scuteri A, Orru M, Albai G, Dei M, et al. Heritability of cardiovascular and personality traits in 6,148 Sardinians. PLoS Genet. 2006;2:e132.

    Article  Google Scholar 

  11. Psaty BM, O’Donnell CJ, Gudnason V, Lunetta KL, Folsom AR, Rotter JI, et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet. 2009;2:73–80.

    Article  Google Scholar 

  12. Li Y, Abecasis GR. MACH 1.0: rapid haplotype reconstruction and missing genotype inference. Am J Hum Genet. 2006;S79:2290.

    Google Scholar 

  13. Browning BL, Yu Z. Simultaneous genotype calling and haplotype phasing improves genotype accuracy and reduces false-positive associations for genome-wide association studies. Am J Hum Genet. 2009;85:847–61.

    Article  CAS  Google Scholar 

  14. Servin B, Stephens M. Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genet. 2007;3:e114.

    Article  Google Scholar 

  15. Voorman A, Lumley T, McKnight B, Rice K. Behavior of QQ-plots and genomic control in studies of gene-environment interaction. PLoS ONE. 2011;6:e19416.

    Article  CAS  Google Scholar 

  16. Chang AR, Sang Y, Leddy J, Yahya T, Kirchner HL, Inker LA, et al. Antihypertensive medications and the prevalence of hyperkalemia in a large health system. Hypertension. 2016;67:1181–8.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  18. Sitlani CM, Rice KM, Lumley T, McKnight B, Cupples LA, Avery CL, et al. Generalized estimating equations for genome-wide association studies using longitudinal phenotype data. Stat Med. 2015;34:118–30.

    Article  Google Scholar 

  19. Satterthwaite FE. An approximate distribution of estimates of variance components. Biometrics. 1946;2:110–4.

    Article  CAS  Google Scholar 

  20. Pan W, Wall MM. Small-sample adjustments in using the sandwich variance estimator in generalized estimating equations. Stat Med. 2002;21:1429–41.

    Article  Google Scholar 

  21. Morris AP. Transethnic meta-analysis of genomewide association studies. Genet Epidemiol. 2011;35:809–22.

    Article  Google Scholar 

  22. Wang X, Chua HX, Chen P, Ong RT, Sim X, Zhang W, et al. Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies. Hum Mol Genet. 2013;22:2303–11.

    Article  CAS  Google Scholar 

  23. Coetzee SG, Rhie SK, Berman BP, Coetzee GA, Noushmehr H. FunciSNP: an R/bioconductor tool integrating functional non-coding data sets with genetic association studies to identify candidate regulatory SNPs. Nucleic Acids Res. 2012;40:e139.

    Article  CAS  Google Scholar 

  24. Machiela MJ, Chanock SJ. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics. 2015;31:3555–7.

    Article  CAS  Google Scholar 

  25. Rafiq S, Khan S, Tapper W, Collins A, Upstill-Goddard R, Gerty S, et al. A genome wide meta-analysis study for identification of common variation associated with breast cancer prognosis. PLoS ONE. 2014;9:e101488.

    Article  Google Scholar 

  26. Fox CS, Liu Y, White CC, Feitosa M, Smith AV, Heard-Costa N, et al. Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women. PLoS Genet. 2012;8:e1002695.

    Article  CAS  Google Scholar 

  27. Comuzzie AG, Cole SA, Laston SL, Voruganti VS, Haack K, Gibbs RA, et al. Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population. PLoS ONE. 2012;7:e51954.

    Article  CAS  Google Scholar 

  28. Daily K, Patel VR, Rigor P, Xie X, Baldi P. MotifMap: integrative genome-wide maps of regulatory motif sites for model species. BMC Bioinf. 2011;12:495.

    Article  Google Scholar 

  29. Qi S, O’Hayre M, Gutkind JS, Hurley JH. Insights into beta2-adrenergic receptor binding from structures of the N-terminal lobe of ARRDC3. Protein Sci: a Publ Protein Soc. 2014;23:1708–16.

    Article  CAS  Google Scholar 

  30. Moratinos J, Reverte M. Effects of catecholamines on plasma potassium: the role of alpha- and beta-adrenoceptors. Fundam Clin Pharmacol. 1993;7:143–53.

    Article  CAS  Google Scholar 

  31. Haghikia A, Ricke-Hoch M, Stapel B, Gorst I, Hilfiker-Kleiner D. STAT3, a key regulator of cell-to-cell communication in the heart. Cardiovasc Res. 2014;102:281–9.

    Article  CAS  Google Scholar 

  32. Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A, et al. Genome-wide association study of blood pressure and hypertension. Nat Genet. 2009;41:677–87.

    Article  CAS  Google Scholar 

  33. Fox ER, Young JH, Li Y, Dreisbach AW, Keating BJ, Musani SK, et al. Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study. Hum Mol Genet. 2011;20:2273–84.

    Article  CAS  Google Scholar 

  34. Frank DU, Carter KL, Thomas KR, Burr RM, Bakker ML, Coetzee WA, et al. Lethal arrhythmias in Tbx3-deficient mice reveal extreme dosage sensitivity of cardiac conduction system function and homeostasis. Proc Natl Acad Sci USA. 2012;109:E154–63.

    Article  CAS  Google Scholar 

  35. Vandell AG, McDonough CW, Gong Y, Langaee TY, Lucas AM, Chapman AB, et al. Hydrochlorothiazide-induced hyperuricaemia in the pharmacogenomic evaluation of antihypertensive responses study. J Intern Med. 2014;276:486–97.

    Article  CAS  Google Scholar 

  36. Del-Aguila JL, Beitelshees AL, Cooper-Dehoff RM, Chapman AB, Gums JG, Bailey K, et al. Genome-wide association analyses suggest NELL1 influences adverse metabolic response to HCTZ in African Americans. Pharm J. 2014;14:35–40.

    CAS  Google Scholar 

  37. Del-Aguila JL, Cooper-DeHoff RM, Chapman AB, Gums JG, Beitelshees AL, Bailey K, et al. Transethnic meta-analysis suggests genetic variation in the HEME pathway influences potassium response in patients treated with hydrochlorothiazide. Pharm J. 2015;15:153–7.

    CAS  Google Scholar 

  38. Huang CC, Chung CM, Hung SI, Leu HB, Lin LY, Huang PH, et al. Genetic predictors of thiazide-induced serum potassium changes in nondiabetic hypertensive patients. Hypertens Res. 2014;37:759–64.

    Article  CAS  Google Scholar 

  39. Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES, et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 2013;14:365–76.

    Article  CAS  Google Scholar 

  40. Ashton JC. Experimental power comes from powerful theories–the real problem in null hypothesis testing. Nat Rev Neurosci. 2013;14:585.

    Article  CAS  Google Scholar 

  41. Quinlan PT. Misuse of power: in defence of small-scale science. Nat Rev Neurosci. 2013;14:585.

    Article  CAS  Google Scholar 

  42. Bacchetti P. Small sample size is not the real problem. Nat Rev Neurosci. 2013;14:585.

    Article  CAS  Google Scholar 

  43. Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES, et al. Confidence and precision increase with high statistical power. Nat Rev Neurosci. 2013;14:585–6.

    Article  CAS  Google Scholar 

  44. Kraft P, Zeggini E, Ioannidis JP. Replication in genome-wide association studies. Stat Sci. 2009;24:561–73.

    Article  Google Scholar 

Download references

Funding

Cardiovascular Health Study: This CHS research was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086; and NHLBI grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, and R01HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center.

Jackson Heart Study: We thank the Jackson Heart Study (JHS) participants and staff for their contributions to this work. The JHS is supported by contracts HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN268201300050C from the National Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health Disparities.

Rotterdam Study: The RS is supported by the Erasmus Medical Center and Erasmus University Rotterdam; The Netherlands Organization for Scientific Research; The Netherlands Organization for Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly; The Netherlands Heart Foundation; the Ministry of Education, Culture and Science; the Ministry of Health Welfare and Sports; the European Commission; and the Municipality of Rotterdam. Support for genotyping was provided by The Netherlands Organization for Scientific Research (NWO) (175.010.2005.011, 911.03.012) and Research Institute for Diseases in the Elderly (RIDE). This study was supported by The Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) Project No. 050-060-810. This collaborative effort was supported by an award from the National Heart, Lung and Blood Institute (R01-HL-103612, PI BMP).

NEO Study: The authors of the NEO study thank all individuals who participated in the Netherlands Epidemiology in Obesity study, all participating general practitioners for inviting eligible participants and all research nurses for collection of the data. We thank the NEO study group, Pat van Beelen, Petra Noordijk and Ingeborg de Jonge for the coordination, lab and data management of the NEO study. The genotyping in the NEO study was supported by the Centre National de Génotypage (Paris, France), headed by Jean-Francois Deleuze. The NEO study is supported by the participating Departments, the Division and the Board of Directors of the Leiden University Medical Center, and by the Leiden University, Research Profile Area Vascular and Regenerative Medicine. Dennis Mook-Kanamori is supported by Dutch Science Organization (ZonMW-VENI Grant 916.14.023).

Heart and Vascular Health Studies: The Heart and Vascular Health Studies has been funded in part by NHLBI grants R01HL085251 and R01HL073410.

Atherosclerosis Risk in Communities Study: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research.

HyperGEN: Genetics of Left Ventricular Hypertrophy: The HyperGEN: Genetics of Left Ventricular Hypertrophy is ancillary to the Family Blood Pressure Program, http://clinicaltrials.gov/ct/show/NCT00005267. Funding sources included National Heart, Lung, and Blood Institute grant R01HL055673 and cooperative agreements (U01) with the National Heart, Lung, and Blood Institute: U01HL054471, U01HL54515 (UT); U01HL054472, U01HL054496 (MN); U01HL054473 (DCC); U01HL054495 (AL); U01HL054509 (NC).

AGES: Age, Gene, Environment, Susceptibility—Reykjavik Study: This study has been funded by NIH contracts N01-AG-1-2100 and 271201200022C, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). The study is approved by the Icelandic National Bioethics Committee, VSN: 00-063. The researchers are indebted to the participants for their willingness to participate in the study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marguerite R. Irvin.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical statement

All studies were approved by local ethics committees and all participants provided written informed consent. The ethics committees for the individual studies are: AGES: The National Bioethics Committee, Iceland; ARIC: University of North Carolina at Chapel Hill Office of Human Research Ethics; CHS: University of Washington Human Subjects Division IRB; HVH: Group Health Cooperative Human Subjects Review Committee; JHS: The Institutional Review Board of the University of Mississippi Medical Center; Medical Center Rotterdam Study: Medical Ethics Committee of the Erasmus Medical Center; HyperGEN: University of Alabama at Birmingham Office of Human Research Ethics; NEO: Ethical committee of the Leiden University Medical Center (LUMC).

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Irvin, M.R., Sitlani, C.M., Noordam, R. et al. Genome-wide meta-analysis of SNP-by9-ACEI/ARB and SNP-by-thiazide diuretic and effect on serum potassium in cohorts of European and African ancestry. Pharmacogenomics J 19, 97–108 (2019). https://doi.org/10.1038/s41397-018-0021-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41397-018-0021-9

This article is cited by

Search

Quick links