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Genome-wide meta-analysis of variant-by-diuretic interactions as modulators of lipid traits in persons of European and African ancestry

Abstract

Hypertension (HTN) is a significant risk factor for cardiovascular morbidity and mortality. Metabolic abnormalities, including adverse cholesterol and triglycerides (TG) profiles, are frequent comorbid findings with HTN and contribute to cardiovascular disease. Diuretics, which are used to treat HTN and heart failure, have been associated with worsening of fasting lipid concentrations. Genome-wide meta-analyses with 39,710 European-ancestry (EA) individuals and 9925 African-ancestry (AA) individuals were performed to identify genetic variants that modify the effect of loop or thiazide diuretic use on blood lipid concentrations. Both longitudinal and cross sectional data were used to compute cohort-specific interaction results, which were then combined through meta-analysis in each ancestry. These ancestry-specific results were further combined through trans-ancestry meta-analysis. Analysis of EA data identified two genome-wide significant (p < 5 × 10−8) loci with single nucleotide variant (SNV)-loop diuretic interaction on TG concentrations (including COL11A1). Analysis of AA data identified one genome-wide significant locus adjacent to BMP2 with SNV-loop diuretic interaction on TG concentrations. Trans-ancestry analysis strengthened evidence of association for SNV-loop diuretic interaction at two loci (KIAA1217 and BAALC). There were few significant SNV-thiazide diuretic interaction associations on TG concentrations and for either diuretic on cholesterol concentrations. Several promising loci were identified that may implicate biologic pathways that contribute to adverse metabolic side effects from diuretic therapy.

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References

  1. Zanchetti A, Hansson L, Menard J, Leonetti G, Rahn KH, Warnold I, et al. Risk assessment and treatment benefit in intensively treated hypertensive patients of the Hypertension Optimal Treatment (HOT) study. J Hypertens. 2001;19:819–25.

    CAS  PubMed  Article  Google Scholar 

  2. James PA, Oparil S, Carter BL, Cushman WC, Dennison-Himmelfarb C, Handler J, et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA. 2014;311:507–20.

    CAS  PubMed  Article  Google Scholar 

  3. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289:2560–72.

    CAS  PubMed  Article  Google Scholar 

  4. Brook RD. Mechanism of differential effects of antihypertensive agents on serum lipids. Curr Hypertens Rep. 2000;2:370–7.

    CAS  PubMed  Article  Google Scholar 

  5. Cutler R. Effect of antihypertensive agents on lipid metabolism. Am J Cardiol. 1983;51:628–31.

    CAS  PubMed  Article  Google Scholar 

  6. Ames RP. The influence of non-beta-blocking drugs on the lipid profile: are diuretics outclassed as initial therapy for hypertension? Am Heart J. 1987;114:998–1006.

    CAS  PubMed  Article  Google Scholar 

  7. Cusi D, Barlassina C, Azzani T, Casari G, Citterio L, Devoto M, et al. Polymorphisms of alpha-adducin and salt sensitivity in patients with essential hypertension. Lancet. 1997;349:1353–7.

    CAS  PubMed  Article  Google Scholar 

  8. Citterio L, Lanzani C, Manunta P. Polymorphisms, hypertension and thiazide diuretics. Pharmacogenomics. 2011;12:1587–604.

    CAS  PubMed  Article  Google Scholar 

  9. Gong Y, McDonough CW, Wang Z, Hou W, Cooper-DeHoff RM, Langaee TY, et al. Hypertension susceptibility loci and blood pressure response to antihypertensives: results from the pharmacogenomic evaluation of antihypertensive responses study. Circ Cardiovasc Genet. 2012;5:686–91.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. McDonough CW, Burbage SE, Duarte JD, Gong Y, Langaee TY, Turner ST, et al. Association of variants in NEDD4L with blood pressure response and adverse cardiovascular outcomes in hypertensive patients treated with thiazide diuretics. J Hypertens. 2013;31:698–704.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Turner ST, Boerwinkle E, O’Connell JR, Bailey KR, Gong Y, Chapman AB, et al. Genomic association analysis of common variants influencing antihypertensive response to hydrochlorothiazide. Hypertension. 2013;62:391–7.

    CAS  PubMed  Article  Google Scholar 

  12. Beeks E, Janssen RG, Kroon AA, Keulen ET, Geurts JM, de Leeuw PW, et al. Association between the alpha-adducin Gly460Trp polymorphism and systolic blood pressure in familial combined hyperlipidemia. Am J Hypertens. 2001;14:1185–90.

    CAS  PubMed  Article  Google Scholar 

  13. Kasiske BL, Ma JZ, Kalil RS, Louis TA. Effects of antihypertensive therapy on serum lipids. Ann Intern Med. 1995;122:133–41.

    CAS  PubMed  Article  Google Scholar 

  14. Lakshman MR, Reda DJ, Materson BJ, Cushman WC, Freis ED. Diuretics and beta-blockers do not have adverse effects at 1 year on plasma lipid and lipoprotein profiles in men with hypertension. Department of Veterans Affairs Cooperative Study Group on Antihypertensive Agents. Arch Intern Med. 1999;159:551–8.

    CAS  PubMed  Article  Google Scholar 

  15. Ott SM, LaCroix AZ, Ichikawa LE, Scholes D, Barlow WE. Effect of low-dose thiazide diuretics on plasma lipids: results from a double-blind, randomized clinical trial in older men and women. J Am Geriatr Soc. 2003;51:340–7.

    PubMed  Article  Google Scholar 

  16. Savage PJ, Pressel SL, Curb JD, Schron EB, Applegate WB, Black HR, et al. Influence of long-term, low-dose, diuretic-based, antihypertensive therapy on glucose, lipid, uric acid, and potassium levels in older men and women with isolated systolic hypertension: The Systolic Hypertension in the Elderly Program. SHEP Cooperative Research Group. Arch Intern Med. 1998;158:741–51.

    CAS  PubMed  Article  Google Scholar 

  17. Maitland-van der Zee AH, Turner ST, Schwartz GL, Chapman AB, Klungel OH, Boerwinkle E. Demographic, environmental, and genetic predictors of metabolic side effects of hydrochlorothiazide treatment in hypertensive subjects. Am J Hypertens. 2005;18:1077–83.

    CAS  PubMed  Article  Google Scholar 

  18. Jentzer JC, DeWald TA, Hernandez AF. Combination of loop diuretics with thiazide-type diuretics in heart failure. J Am Coll Cardiol. 2010;56:1527–34.

    CAS  Article  PubMed  Google Scholar 

  19. 1000 Genomes Project Consortium, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56–65.

    Article  CAS  Google Scholar 

  20. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 2010;34:816–34.

    PubMed  PubMed Central  Article  Google Scholar 

  21. Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet. 2012;44:955–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 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.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  23. Browning BL, Browning SR. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet. 2009;84:210–23.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18:499–502.

    CAS  Article  PubMed  Google Scholar 

  25. Peloso GM, Auer PL, Bis JC, Voorman A, Morrison AC, Stitziel NO, et al. Association of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks. Am J Hum Genet. 2014;94:223–32.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Avery CL, Sitlani CM, Arking DE, Arnett DK, Bis JC, Boerwinkle E, et al. Drug-gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval. Pharmacogenomics J. 2014;14:6–13.

    CAS  PubMed  Article  Google Scholar 

  27. 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.

    PubMed  Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  PubMed  Article  Google Scholar 

  31. Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012;40:D930–934.

    CAS  PubMed  Article  Google Scholar 

  32. 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  PubMed  PubMed Central  Article  Google Scholar 

  33. Lim JM, Sherling D, Teo CF, Hausman DB, Lin D, Wells L. Defining the regulated secreted proteome of rodent adipocytes upon the induction of insulin resistance. J Proteome Res. 2008;7:1251–63.

    CAS  PubMed  Article  Google Scholar 

  34. Hung SC, Chang CF, Ma HL, Chen TH, Low-Tone Ho L. Gene expression profiles of early adipogenesis in human mesenchymal stem cells. Gene. 2004;340:141–50.

    CAS  PubMed  Article  Google Scholar 

  35. Kamsteeg EJ, Wormhoudt TA, Rijss JP, van Os CH, Deen PM. An impaired routing of wild-type aquaporin-2 after tetramerization with an aquaporin-2 mutant explains dominant nephrogenic diabetes insipidus. EMBO J. 1999;18:2394–2400.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Wang KS, Liu X, Zheng S, Zeng M, Pan Y, Callahan K. A novel locus for body mass index on 5p15.2: a meta-analysis of two genome-wide association studies. Gene. 2012;500:80–84.

    CAS  PubMed  Article  Google Scholar 

  37. Kushwaha D, Ramakrishnan V, Ng K, Steed T, Nguyen T, Futalan D, et al. A genome-wide miRNA screen revealed miR-603 as a MGMT-regulating miRNA in glioblastomas. Oncotarget. 2014;5:4026–39.

    PubMed  PubMed Central  Article  Google Scholar 

  38. Mussnich P, D’Angelo D, Leone V, Croce CM, Fusco A. The high mobility group A proteins contribute to thyroid cell transformation by regulating miR-603 and miR-10b expression. Mol Oncol. 2013;7:531–42.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. D’Angelo D, Palmieri D, Mussnich P, Roche M, Wierinckx A, Raverot G, et al. Altered microRNA expression profile in human pituitary GH adenomas: down-regulation of miRNA targeting HMGA1, HMGA2, and E2F1. J Clin Endocrinol Metab. 2012;97:E1128–1138.

    PubMed  Article  CAS  Google Scholar 

  40. Duttagupta R, DiRienzo S, Jiang R, Bowers J, Gollub J, Kao J, et al. Genome-wide maps of circulating miRNA biomarkers for ulcerative colitis. PLoS ONE. 2012;7:e31241.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Longo UG, Denaro L, Spiezia F, Forriol F, Maffulli N, Denaro V. Symptomatic disc herniation and serum lipid levels. Eur Spine J. 2011;20:1658–62.

    PubMed  PubMed Central  Article  Google Scholar 

  42. Jhawar BS, Fuchs CS, Colditz GA, Stampfer MJ. Cardiovascular risk factors for physician-diagnosed lumbar disc herniation. Spine J. 2006;6:684–91.

    PubMed  Article  Google Scholar 

  43. Heintzman ND, Ren B. Finding distal regulatory elements in the human genome. Curr Opin Genet Dev. 2009;19:541–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Siegel DH, Ashton GH, Penagos HG, Lee JV, Feiler HS, Wilhelmsen KC, et al. Loss of kindlin-1, a human homolog of the Caenorhabditis elegans actin-extracellular-matrix linker protein UNC-112, causes Kindler syndrome. Am J Hum Genet. 2003;73:174–87.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Jaffe IZ, Mendelsohn ME. Angiotensin II and aldosterone regulate gene transcription via functional mineralocortocoid receptors in human coronary artery smooth muscle cells. Circ Res. 2005;96:643–50.

    CAS  PubMed  Article  Google Scholar 

  46. Al-Aly Z. Arterial calcification: a tumor necrosis factor-alpha mediated vascular Wnt-opathy. Transl Res. 2008;151:233–9.

    CAS  PubMed  Article  Google Scholar 

  47. Tseng YH, Kokkotou E, Schulz TJ, Huang TL, Winnay JN, Taniguchi CM, et al. New role of bone morphogenetic protein 7 in brown adipogenesis and energy expenditure. Nature. 2008;454:1000–4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. Garcia-Rivas G, Jerjes-Sanchez C, Rodriguez D, Garcia-Pelaez J, Trevino V. A systematic review of genetic mutations in pulmonary arterial hypertension. BMC Med Genet. 2017;18:82.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  49. Golan T, Yaniv A, Bafico A, Liu G, Gazit A. The human Frizzled 6 (HFz6) acts as a negative regulator of the canonical Wnt. beta-catenin signaling cascade. J Biol Chem. 2004;279:14879–88.

    CAS  PubMed  Article  Google Scholar 

  50. Mao B, Wu W, Li Y, Hoppe D, Stannek P, Glinka A, et al. LDL-receptor-related protein 6 is a receptor for Dickkopf proteins. Nature. 2001;411:321–5.

    CAS  PubMed  Article  Google Scholar 

  51. Kahle KT, Wilson FH, Leng Q, Lalioti MD, O’Connell AD, Dong K, et al. WNK4 regulates the balance between renal NaCl reabsorption and K+ secretion. Nat Genet. 2003;35:372–6.

    CAS  PubMed  Article  Google Scholar 

  52. Lu D, Liu JX, Endo T, Zhou H, Yao S, Willert K, et al. Ethacrynic acid exhibits selective toxicity to chronic lymphocytic leukemia cells by inhibition of the Wnt/beta-catenin pathway. PLoS ONE. 2009;4:e8294.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  53. Lyons JP, Miller RK, Zhou X, Weidinger G, Deroo T, Denayer T, et al. Requirement of Wnt/beta-catenin signaling in pronephric kidney development. Mech Dev. 2009;126:142–59.

    CAS  PubMed  Article  Google Scholar 

  54. Pietila I, Ellwanger K, Railo A, Jokela T, Barrantes Idel B, Shan J, et al. Secreted Wnt antagonist Dickkopf-1 controls kidney papilla development coordinated by Wnt-7b signalling. Dev Biol. 2011;353:50–60.

    PubMed  Article  CAS  Google Scholar 

  55. Dai C, Stolz DB, Kiss LP, Monga SP, Holzman LB, Liu Y. Wnt/beta-catenin signaling promotes podocyte dysfunction and albuminuria. J Am Soc Nephrol. 2009;20:1997–2008.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. Kato H, Susztak K. Repair problems in podocytes: Wnt, Notch, and glomerulosclerosis. Semin Nephrol. 2012;32:350–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. Azzolin L, Panciera T, Soligo S, Enzo E, Bicciato S, Dupont S, et al. YAP/TAZ incorporation in the beta-catenin destruction complex orchestrates the Wnt response. Cell. 2014;158:157–70.

    CAS  Article  PubMed  Google Scholar 

  58. An Y, Kang Q, Zhao Y, Hu X, Li N. Lats2 modulates adipocyte proliferation and differentiation via hippo signaling. PLoS ONE. 2013;8:e72042.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. Zhou D, Strakovsky RS, Zhang X, Pan YX. The skeletal muscle Wnt pathway may modulate insulin resistance and muscle development in a diet-induced obese rat model. Obesity. 2012;20:1577–84.

    CAS  PubMed  Article  Google Scholar 

  60. Tsika RW, Schramm C, Simmer G, Fitzsimons DP, Moss RL, Ji J. Overexpression of TEAD-1 in transgenic mouse striated muscles produces a slower skeletal muscle contractile phenotype. J Biol Chem. 2008;283:36154–67.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. He J, Kelly TN, Zhao Q, Li H, Huang J, Wang L, et al. Genome-wide association study identifies 8 novel loci associated with blood pressure responses to interventions in Han Chinese. Circ Cardiovasc Genet. 2013;6:598–607.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. Slavin TP, Feng T, Schnell A, Zhu X, Elston RC. Two-marker association tests yield new disease associations for coronary artery disease and hypertension. Hum Genet. 2011;130:725–33.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. Debette S, Bis JC, Fornage M, Schmidt H, Ikram MA, Sigurdsson S, et al. Genome-wide association studies of MRI-defined brain infarcts: meta-analysis from the CHARGE Consortium. Stroke. 2010;41:210–7.

    CAS  PubMed  Article  Google Scholar 

  64. Weidmann P, Uehlinger DE, Gerber A. Antihypertensive treatment and serum lipoproteins. J Hypertens. 1985;3:297–306.

    CAS  PubMed  Article  Google Scholar 

  65. Ferrari P, Rosman J, Weidmann P. Antihypertensive agents, serum lipoproteins and glucose metabolism. Am J Cardiol. 1991;67:26B–35B.

    Article  Google Scholar 

  66. Krone W, Nagele H. Effects of antihypertensives on plasma lipids and lipoprotein metabolism. Am Heart J. 1988;116:1729–34.

    CAS  PubMed  Article  Google Scholar 

  67. Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, Clarke R, et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 2008;40:161–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. Aulchenko YS, Ripatti S, Lindqvist I, Boomsma D, Heid IM, Pramstaller PP, et al. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat Genet. 2009;41:47–55.

    CAS  PubMed  Article  Google Scholar 

  69. 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.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. Newton-Cheh C, Johnson T, Gateva V, Tobin MD, Bochud M, Coin L, et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet. 2009;41:666–76.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 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.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  72. Kato N, Takeuchi F, Tabara Y, Kelly TN, Go MJ, Sim X, et al. Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians. Nat Genet. 2011;43:531–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. Wain LV, Verwoert GC, O’Reilly PF, Shi G, Johnson T, Johnson AD, et al. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nat Genet. 2011;43:1005–11.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. Guo Y, Tomlinson B, Chu T, Fang YJ, Gui H, Tang CS, et al. A genome-wide linkage and association scan reveals novel loci for hypertension and blood pressure traits. PLoS ONE. 2012;7:e31489.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. Warren HR, Evangelou E, Cabrera CP, Gao H, Ren M, Mifsud B, et al. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat Genet. 2017;49:403–15.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. Ehret GB, Ferreira T, Chasman DI, Jackson AU, Schmidt EM, Johnson T, et al. The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat Genet. 2016;48:1171–84.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  77. Andaleon A, Mogil LS, Wheeler HE. Gene-based association study for lipid traits in diverse cohorts implicates BACE1 and SIDT2 regulation in triglyceride levels. PeerJ. 2018;6:e4314.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  78. Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, Chasman DI, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478:103–9.

    CAS  PubMed  Article  Google Scholar 

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Acknowledgements

This work was partially supported by National Institutes of Health National Heart, Lung, Blood Institute grants R01HL103612 (BMP) and K25HL121091 (YJS). Study-specific support included in the Supplementary Material.

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de las Fuentes, L., Sung, Y.J., Sitlani, C.M. et al. Genome-wide meta-analysis of variant-by-diuretic interactions as modulators of lipid traits in persons of European and African ancestry. Pharmacogenomics J 20, 482–493 (2020). https://doi.org/10.1038/s41397-019-0132-y

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