Pharmacoepigenetics of hypertension: genome-wide methylation analysis of responsiveness to four classes of antihypertensive drugs using a double-blind crossover study design

References

• GBD 2019. Risk factors collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396:1223–1249.
   Google Scholar
• Beaney T, Schutte AE, Stergiou GS, et al. May measurement month 2019: the global blood pressure screening campaign of the international society of hypertension. Hypertension. 2020;76:333–341.
   Google Scholar
• Turner ST, Boerwinkle E, O’Connell JR, et al. Genomic association analysis of common variants influencing antihypertensive response to hydrochlorothiazide. Hypertension. 2013;62:391–397.
   Google Scholar
• Frau F, Zaninello R, Salvi E, et al. Genome-wide association study identifies CAMKID variants involved in blood pressure response to losartan: the SOPHIA study. Pharmacogenomics. 2014;15:1643–1652.
   Google Scholar
• Hiltunen TP, Donner KM, Sarin AP, et al. Pharmacogenomics of hypertension: a genome‐wide, placebo‐controlled cross‐over study, using four classes of antihypertensive drugs. J Am Heart Assoc. 2015;4:e001521.
   Google Scholar
• Chittani M, Zaninello R, Lanzani C, et al. TET2 and CSMD1 genes affect SBP response to hydrochlorothiazide in never-treated essential hypertensives. J Hypertens. 2015;33:1301–1309.
   Google Scholar
• Gong Y, McDonough CW, Beitelshees AL, et al. PTPRD gene associated with blood pressure response to atenolol and resistant hypertension. J Hypertens. 2015;33:2278–2285.
   Google Scholar
• Salvi E, Wang Z, Rizzi F, et al. Genome-wide and gene-based meta-analyses identify novel loci influencing blood pressure response to hydrochlorothiazide. Hypertension. 2017;69:51–59.
   Google Scholar
• Singh S, Warren HR, Hiltunen TP, et al. Genome-wide meta-analysis of blood pressure response to β1-blockers: results from ICAPS (International Consortium of Antihypertensive Pharmacogenomics Studies). J Am Heart Assoc. 2019;8:e013115.
   Google Scholar
• Evangelou E, Warren HR, Mosen-Ansorena D, et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet. 2018;50:1412–1425.
   Google Scholar
• Sánez Tähtisalo H, Ruotsalainen S, Mars N, et al. Human essential hypertension: no significant association of polygenic risk scores with antihypertensive drug responses. Sci Rep. 2020;10:11940.
   Google Scholar
• Cowley AW Jr, Nadeau JH, Baccarelli A, et al. Report of the national heart, lung, and blood institute working group on epigenetics and hypertension. Hypertension. 2012;59:899–905.
   Google Scholar
• Allis CD, Jenuwein T. The molecular hallmarks of epigenetic control. Nat Rev Genet. 2016;17:487–500.
   Google Scholar
• Feinberg AP. The key role of epigenetics in human disease prevention and mitigation. N Engl J Med. 2018;378:1323–1334.
   Google Scholar
• Dor Y, Cedar H. Principles of DNA methylation and their implications for biology and medicine. Lancet. 2018;392:777–786.
   Google Scholar
• Hiltunen TP, Suonsyrjä T, Hannila-Handelberg T, et al. Predictors of antihypertensive drug responses: initial data from a placebo-controlled, randomized, cross-over study with four antihypertensive drugs (The GENRES Study). Am J Hypertens. 2007;20:311–318.
   Google Scholar
• Rimpelä JM, Kontula KK, Fyhrquist F, et al. Replicated evidence for aminoacylase 3 and nephrin gene variations to predict antihypertensive drug responses. Pharmacogenomics. 2017;18:445–458.
   Google Scholar
• Dahlöf B, Devereux RB, Kjeldsen SE, 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.
   Google Scholar
• Aryee MJ, Jaffe AE, Corrada-Bravo H, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30:1363–1369.
   Google Scholar
• R Core Team. R: a language and environment for statistical computing. Vienna (Austria): R Foundation for Statistical Computing; 2016.
   Google Scholar
• Maksimovic J, Gordon L, Oshlack A. SWAN: subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biol. 2012;13:R44.
   Google Scholar
• Chen YA, Lemire M, Choufani S, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8:203–209.
   Google Scholar
• Chen EY, Tan CM, Kou Y, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14:128.
   Google Scholar
• Kuleshov MV, Jones MR, Rouillard AD, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44:W90–97.
   Google Scholar
• Xie Z, Bailey A, and Kuleshov MV, et al. Gene set knowledge discovery with Enrichr. Curr Protoc. 2021;1. e90.
   Google Scholar
• Houseman EA, Accomando WP, Koestler DC, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86.
   Google Scholar
• Rimpelä JM, Pörsti IH, Jula A, et al. Genome-wide association study of nocturnal blood pressure dipping in hypertensive patients. BMC Med Genet. 2018;19:110.
   Google Scholar
• Newman D, Abuladze N, Scholz K, et al. Specificity of aminoacylase III-mediated deacetylation of mercapturic acids. Drug Metab Dispos. 2007;35:43–50.
   Google Scholar
• Liang M. Epigenetic mechanisms and hypertension. Hypertension. 2018;72:1244–1254.
   Google Scholar
• Stoll S, Wang C, Qiu H. DNA methylation and histone modification in hypertension. Int J Mol Sci. 2018;19:1174.
   Google Scholar
• Gonzalez-Jaramillo V, Portilla-Fernandez E, Glisic M, et al. The role of DNA methylation and histone modifications in blood pressure: a systematic review. J Hum Hypertens. 2019;33:703–715.
   Google Scholar
• Irvin MR, Jones AC, Claas SA, et al. DNA methylation and blood pressure phenotypes: a review of the literature. Am J Hypertens. 2021;34:267–273.
   Google Scholar
• Kato N, Loh M, Takeuchi F, et al. Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation. Nat Genet. 2015;47:1282–1293.
   Google Scholar
• Richard MA, Huan T, Ligthart S, et al. DNA methylation analysis identifies loci for blood pressure regulation. Am J Hum Genet. 2017;101:888–902.
   Google Scholar
• Kazmi N, Elliott HR, Burrows K, et al. Associations between high blood pressure and DNA methylation. PLoS One. 2020;15:e0227728.
   Google Scholar
• Huang Y, Ollikainen M, Muniandy M, et al. Identification, heritability, and relation with gene expression of novel DNA methylation loci for blood pressure. Hypertension. 2020;76:195–205.
   Google Scholar
• Ge S, Wang Y, Song M, et al. Type 2 diabetes mellitus: integrative analysis of multiomics data for biomarker discovery. OMICS. 2018;22:514–523.
   Google Scholar
• Ouni M, Saussenthaler S, Eichelmann F, et al. Epigenetic changes in islets of Langerhans preceding the onset of diabetes. Diabetes. 2020;69:2503–2517.
   Google Scholar
• Ruggieri A, Saredi S, Zanotti S, et al. DNAJB6 myopathies: focused review on an emerging and expanding group of myopathies. Front Mol Biosci. 2016;3:63.
   Google Scholar
• Al-Barghouthi BM, Mesner LD, Calabrese GM, et al. Systems genetics in diversity outbred mice inform BMD GWAS and identify determinants of bone strength. Nat Commun. 2021;12:3408.
   Google Scholar
• Nixon TRW, Alexander P, Richards A, et al. Homozygous type IX collagen variants (COL9A1, COL9A2, and COL9A3) causing recessive Stickler syndrome - expanding the phenotype. Am J Med Genet A. 2019;179:1498–1506.
   Google Scholar
• Huang D, Deng X, Ma K, et al. Association of COL9A3 trp3 polymorphism with intervertebral disk degeneration: a meta-analysis. BMC Musculoskelet Disord. 2018;19:381.
   Google Scholar
• Suonsyrjä T, Hannila-Handelberg T, Paavonen KJ, et al. Laboratory tests as predictors of the antihypertensive effects of amlodipine, bisoprolol, hydrochlorothiazide and losartan in men: results from the randomized, double-blind, crossover GENRES Study. J Hypertens. 2008;26:1250–1256.
   Google Scholar
• Turner ST, Schwartz GL, Chapman AB, et al. Plasma renin activity predicts blood pressure responses to beta-blocker and thiazide diuretic as monotherapy and add-on therapy for hypertension. Am J Hypertens. 2010;23:1014–1022.
   Google Scholar
• Lin Q, Zhao G, Fang X, et al. IP3 receptors regulate vascular smooth muscle contractility and hypertension. JCI Insight. 2016;1:e89402.
   Google Scholar
• Eid AH, El-Yazbi AF, Zouein F, et al. Inositol 1,4,5-trisphosphate receptors in hypertension. Front Physiol. 2018;26:1018.
   Google Scholar
• Kichaev G, Bhatia G, Loh PR, et al. Leveraging polygenic functional enrichment to improve GWAS power. Am J Hum Genet. 2019;104:65–75.
   Google Scholar
• Schweda F, Kurtz L, de Wit C, et al. Substitution of connexin40 with connexin45 prevents hyperreninemia and attenuates hypertension. Kidney Int. 2009;75:482–489.
   Google Scholar
• Pushkin A, Carpenito G, Abuladze N, et al. Structural characterization, tissue distribution, and functional expression of murine aminoacylase III. Am J Physiol Cell Physiol. 2004;286:C848–856.
   Google Scholar
• Long PM, Stradecki HM, Minturn JE, et al. Differential aminoacylase expression in neuroblastoma. Int J Cancer. 2011;129:1322–1330.
   Google Scholar
• The Genotype-Tissue Expression (GTEx) Portal. The broad institute of MIT and harvard, Cambridge (MA). 2017. [cited 2021 Aug 21]; http://www.gtexportal.org
   Google Scholar