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. 2023 Mar 3;15(1):38.
doi: 10.1186/s13148-023-01457-1.

Epigenome-wide association study in Chinese monozygotic twins identifies DNA methylation loci associated with blood pressure

Weijing Wang  1 , Jie Yao  1   2 , Weilong Li  3 , Yili Wu  1 , Haiping Duan  4 , Chunsheng Xu  4 , Xiaocao Tian  4 , Shuxia Li  5 , Qihua Tan  5 , Dongfeng Zhang  6
Affiliations

Epigenome-wide association study in Chinese monozygotic twins identifies DNA methylation loci associated with blood pressure

Weijing Wang et al. Clin Epigenetics. .

Abstract

Background: Hypertension is a crucial risk factor for developing cardiovascular disease and reducing life expectancy. We aimed to detect DNA methylation (DNAm) variants potentially related to systolic blood pressure (SBP) and diastolic blood pressure (DBP) by conducting epigenome-wide association studies in 60 and 59 Chinese monozygotic twin pairs, respectively.

Methods: Genome-wide DNA methylation profiling in whole blood of twins was performed using Reduced Representation Bisulfite Sequencing, yielding 551,447 raw CpGs. Association between DNAm of single CpG and blood pressure was tested by applying generalized estimation equation. Differentially methylated regions (DMRs) were identified by comb-P approach. Inference about Causation through Examination of Familial Confounding was utilized to perform the causal inference. Ontology enrichment analysis was performed using Genomic Regions Enrichment of Annotations Tool. Candidate CpGs were quantified using Sequenom MassARRAY platform in a community population. Weighted gene co-expression network analysis (WGCNA) was conducted using gene expression data.

Results: The median age of twins was 52 years (95% range 40, 66). For SBP, 31 top CpGs (p < 1 × 10-4) and 8 DMRs were identified, with several DMRs within NFATC1, CADM2, IRX1, COL5A1, and LRAT. For DBP, 43 top CpGs (p < 1 × 10-4) and 12 DMRs were identified, with several DMRs within WNT3A, CNOT10, and DAB2IP. Important pathways, such as Notch signaling pathway, p53 pathway by glucose deprivation, and Wnt signaling pathway, were significantly enriched for SBP and DBP. Causal inference analysis suggested that DNAm at top CpGs within NDE1, MYH11, SRRM1P2, and SMPD4 influenced SBP, while SBP influenced DNAm at CpGs within TNK2. DNAm at top CpGs within WNT3A influenced DBP, while DBP influenced DNAm at CpGs within GNA14. Three CpGs mapped to WNT3A and one CpG mapped to COL5A1 were validated in a community population, with a hypermethylated and hypomethylated direction in hypertension cases, respectively. Gene expression analysis by WGCNA further identified some common genes and enrichment terms.

Conclusion: We detect many DNAm variants that may be associated with blood pressure in whole blood, particularly the loci within WNT3A and COL5A1. Our findings provide new clues to the epigenetic modification underlying hypertension pathogenesis.

Keywords: Blood pressure; Causality; DNA methylation; Monozygotic twins.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Differential methylation patterns from the identified differentially methylated regions for systolic blood pressure. The dots represent the CpGs. The x-axis shows the position of CpGs on chromosome and the y-axis shows regression coefficients. BP, base pair; DMR, differentially methylated region
Fig. 2
Fig. 2
Differential methylation patterns from the identified differentially methylated regions for diastolic blood pressure. The dots represent the CpGs. The x-axis shows the position of CpGs on chromosome and the y-axis shows regression coefficients. BP, base pair; DMR, differentially methylated region
See this image and copyright information in PMC

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References

    1. Bundy JD, He J. Hypertension and related cardiovascular disease burden in China. Ann Glob Health. 2016;82(2):227–233. doi: 10.1016/j.aogh.2016.02.002. - DOI - PubMed
    1. Wang Y, Peng X, Nie X, Chen L, Weldon R, Zhang W, et al. Burden of hypertension in China over the past decades: systematic analysis of prevalence, treatment and control of hypertension. Eur J Prev Cardiol. 2016;23(8):792–800. doi: 10.1177/2047487315617105. - DOI - PubMed
    1. Wu Y, Zhang D, Pang Z, Jiang W, Wang S, Li S, et al. Multivariate modeling of body mass index, pulse pressure, systolic and diastolic blood pressure in Chinese twins. Twin Res Hum Genet. 2015;18(1):73–78. doi: 10.1017/thg.2014.83. - DOI - PubMed
    1. Ganesh SK, Tragante V, Guo W, Guo Y, Lanktree MB, Smith EN, et al. Loci influencing blood pressure identified using a cardiovascular gene-centric array. Hum Mol Genet. 2013;22(8):1663–1678. doi: 10.1093/hmg/dds555. - DOI - PMC - PubMed
    1. Franceschini N, Fox E, Zhang Z, Edwards TL, Nalls MA, Sung YJ, et al. Genome-wide association analysis of blood-pressure traits in African-ancestry individuals reveals common associated genes in African and non-African populations. Am J Hum Genet. 2013;93(3):545–554. doi: 10.1016/j.ajhg.2013.07.010. - DOI - PMC - PubMed

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