Epigenome-wide association study for dilated cardiomyopathy in left ventricular heart tissue identifies putative gene sets associated with cardiac development and early indicators of cardiac risk

Epigenome-wide association study for dilated cardiomyopathy in left ventricular heart tissue identifies putative gene sets associated with cardiac development and early indicators of cardiac risk | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Epigenome-wide association study for dilated cardiomyopathy in left ventricular heart tissue identifies putative gene sets associated with cardiac development and early indicators of cardiac risk Konstanze Tan, Darwin Tay, Wilson Tan, Hong Kiat Ng, Eleanor Wong, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5141306/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Mar, 2025 Read the published version in Clinical Epigenetics → Version 1 posted 9 You are reading this latest preprint version Abstract Background: Methylation changes linked to dilated cardiomyopathy (DCM) affect cardiac gene expression. We investigate DCM mechanisms regulated by CpG methylation using multi-omics and causal analyses in the largest cohort of left ventricular tissues available. Methods: We mapped DNA methylation at ~850,000 CpG sites, performed array-based genotyping and RNA sequencing in left-ventricular tissue samples from failing and non-failing hearts across two independent DCM cohorts (discovery n=329, replication n=85). Summary data-based Mendelian Randomization (SMR) was applied to explore the causal contribution of sentinel CpGs to DCM. Fine-mapping of regions surrounding sentinel CpGs revealed additional signals for cardiovascular disease risk factors. Coordinated changes across multiple CpG sites were examined using weighted gene correlation network analysis (WGCNA). Results: We identified 194 epigenome-wide significant CpGs associated with DCM (discovery P<5.96E-08), enriched in active chromatin states in heart tissue. Amongst these, 183 sentinel CpGs significantly influenced the expression of 849 proximal genes (±1Mb). SMR suggested the causal contribution of two sentinel CpGs to DCM and 36 sentinel CpGs to the expression of 43 unique proximal genes (P<0.05). Colocalization analyses indicated that a single causal variant may underlie the methylation-gene expression relationship for three sentinel CpGs. Fine-mapping revealed additional signals linked to cardiovascular traits including hsCRP and blood pressure. Co-methylation modules were enriched in gene sets related to cardiac physiological and pathological processes and their corresponding transcriptional regulators, as well as in novel transcriptional regulators whose cardiac relevance is yet to be determined. Conclusions: Using the largest series of left ventricular tissue to date, this study investigates the causal role of cardiac methylation changes in DCM and suggests targets for experimental studies to probe DCM pathogenesis. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Dilated cardiomyopathy (DCM) is the most common form of cardiomyopathy and a leading indication for heart transplantation. It affects 1 in 250–400 individuals and has an annual incidence of 5–7 cases per 100,000 persons per year. 1 DCM is characterised by thinning and weakening of the left ventricular heart walls, leading to contractile dysfunction. 2 – 4 There is marked heterogeneity in both the prognosis and age of onset for DCM, with most patients becoming symptomatic between 20–50 years of age. 5 DCM accounts for a substantial 2–3% of yearly sudden cardiac death (SCD) events which are characterised by an abrupt loss of cardiac function and death occurring within one hour of symptom onset. 6 , 7 Over the past decade, the genetic basis for DCM has only been partially unraveled. 8 Beyond genetic predisposition, environmental factors could also influence the incidence and course of DCM. 4 DNA methylation, a key regulator of gene expression, as well as a molecular integrator of genetic and environmental influences, has been investigated in DCM to gain insights into its molecular pathogenesis. To date, multiple epigenome-wide association studies (EWAS) of DCM have identified a handful of differentially methylated loci. 9 – 14 These include CpGs at the natriuretic peptide A ( NPPA ) and natriuretic peptide B ( NPPB ) loci, where disease-linked methylation disturbances have been observed to be conserved between myocardial tissue and peripheral blood. 9 The NPPA and NPPB loci encode the cardiac stress markers atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP), which are commonly measured in clinical settings to establish the diagnosis and prognosis of heart failure. 15 These markers are considered gold standard indicators for heart failure prognosis, underscoring the potential clinical relevance of DNA methylation in DCM. Existing epigenome-wide methylation studies of DCM have largely been limited by sample size (n = 8–72) owing to the scarcity of left ventricular tissue and the limited coverage of CpG sites on older methylation arrays. 9 – 11 , 14 The largest existing EWAS of DCM (discovery n = 72) detected only five loci at epigenome-wide significance (P < 5E-8); highlighting the need for larger samples to uncover signals that are robustly associated with disease. 9 While previous DCM methylation studies utilised older Illumina arrays that covered up to 450,000 CpG sites, newer arrays now cover up to 850,000 sites and provide enhanced coverage of key gene regulatory regions, including enhancers. 16 Recent advancements in statistical methodologies, such as Mendelian Randomisation (MR), have allowed researchers to move beyond discovering associated signals to addressing causality. 17 This is particularly relevant for DNA methylation studies, as DNA methylation is affected by various disease-related and environmental factors. In this study, we sought to expand our understanding of myocardial-specific methylation changes in DCM. We analysed intra-operative left ventricular tissue from a cohort of 329 individuals of White and African American ancestries (n = 159 cases, 170 controls). This study builds upon existing research of DNA methylation in DCM by using a larger and more ancestrally-diverse myocardial tissue sample. Additionally, a suite of integrative omics, causal analyses and fine-mapping of putative methylation markers are employed to elucidate the putative causal involvement of methylation alterations to DCM pathogenesis. METHODS Description of samples Myocardial Applied Genomics Network Cohort (MAGNet) Transmural biopsies of the left ventricular free wall (0.5-1.5g per cryovial) were collected during cardiac surgery from subjects with heart failure attributed to DCM undergoing transplantation, and from donor hearts deemed unsuitable for transplantation but with apparently normal ventricular function. DCM was diagnosed based on the American Heart Association guidelines. 18 Specifically, all left ventricular tissue included in this investigation were obtained from subjects with an LVEF of ≤ 40% and with a diagnostic workup that revealed no evidence of familial or pregnancy-associated DCM, cardiac ischemia, significant toxin exposure or myocarditis. Hearts were perfused immediately with cold in-situ high-potassium cardioplegia before cardiectomy to arrest contraction and prevent ischemic damage. Tissue samples were promptly frozen in liquid nitrogen. Bruce McManus Cardiovascular Biobank Cohort (BMCB) Biopsies of the left ventricular free wall (apical portion; 1- to 2-mm diameter) were obtained within five minutes post-cardiac surgery from subjects with DCM undergoing transplantation. Tissue samples were sourced from the Bruce McManus Cardiovascular Biobank (BMCB) at the University of British Columbia (UBC). DCM was diagnosed based on the American Heart Association/American College of Cardiology (AHA/ACC) guidelines during pre-transplant admission, as well as during routine follow-ups and evaluation. Following the heart transplant, cardiac pathologists conducted detailed examinations of the explanted hearts to confirm the diagnosis of DCM. The pathology findings were documented in reports, which were subsequently utilized by the BMCB for DCM stratification. All left ventricular tissue included in this investigation were obtained from subjects with a diagnostic workup that revealed no evidence of familial DCM, cardiac ischemia, significant toxin exposure or myocarditis. Biopsies were washed in ice-cold saline (0.9% NaCl) and stored in liquid nitrogen until DNA extraction. Control tissue samples were recovered within 0–2 hours of circulatory death and were purchased by UBC from the International Institute for the Advancement of Medicine (IIAM). The iHealth-T2D study iHealth-T2D is a prospective study of Indian Asian males and females living in West London. 20 Participants were recruited in 2016. At enrolment, all participants completed a structured assessment of cardiovascular and metabolic health, which included (i) previous medical history of cardiovascular disease (CVD), specifically myocardial infarction, angina, and coronary heart disease (CHD); (ii) risk factors for CVD including hypertension, Framingham Coronary Heart Disease (FramCHD) score and creatinine; as well as (iii) high-sensitivity C-reactive protein (hsCRP). Methylation profiling was performed using genomic DNA from peripheral blood collected at enrolment. The study is approved by the National Research Ethics Service and all participants provided written informed consent. Quantification of DNA methylation Genomic DNA was bisulfite-converted using the EZ DNA methylation kit according to the manufacturer’s instructions (Zymo Research). Case and control samples were randomised in all experiments. Bisulfite-converted genomic DNA was quantified using the Infinium MethylationEPIC BeadChip (EPIC array). Bead intensity retrieval and Illumina background correction were performed using the minfi R package (version 1.40.0). Markers were excluded from analyses if they met any of the following criteria: (i) they were non-CpG; (ii) they were present on sex chromosomes; or (iii) they had a low call-rate (< 95%). Samples were excluded from analyses if they met any of the following criteria: (i) they had a low call-rate (< 98%); (ii) they had mislabelled sex; or (iii) they were present in duplicate. After filtering, the discovery cohort (MAGNet) had 838,624 autosomal CpG probes and 329 samples, while the replication cohort (BMCB) had 841,904 autosomal CpG probes and 85 samples available for analysis. Statistical analyses of epigenome-wide data Epigenome-wide methylation data was analysed using the CPACOR pipeline (incorporating Control Probe Adjustment and reduction of global CORrelation). 21 Quantile-normalised marker intensity values were used to calculate per-CpG methylation beta values. In the CPACOR pipeline, signal intensities from control probes built into the array are used to correct technical biases in methylation data. These control probes assess key aspects of array chemistry, such as bisulfite conversion efficiency. A principal component analysis (PCA) is performed on the control probe intensities, and the resulting principal components (PCs) are included as linear predictors in regression analyses. This method was designed to minimise technical biases when comprehensive information on experimental factors that could cause data variations is lacking. In the current investigation, PCs accounting for ~ 95% of control probe variation were included as predictors in regression models to adjust for technical biases (MAGNet: first 10 PCs; BMCB: first 3 PCs). The association of each autosomal CpG site with DCM was tested using logistic regression, adjusting for age, sex and control probe PCs (Model: DCM ~ Beta + Age + Sex + PCs control−probes ). Within the mixed-ancestry MAGNet cohort, regression was performed separately for each ancestry group, followed by a trans-ancestry inverse variance-weighted meta-analysis using METAL. Test-statistic bias and inflation in association results was adjusted pre- and post- meta-analysis, using a Bayesian algorithm implemented in the bacon R package (version 1.30.0). 22 Enrichment analysis of genomic regulatory features Sentinel CpGs were evaluated for enrichment in genomic regulatory features. This evaluation was conducted against a background set of 1,000 permutations of EPIC array CpGs. Permuted background sites were matched to sentinel CpGs based on similar methylation levels (± 0.025 difference in mean methylation beta) and variability (± 0.25 difference in standard deviation). Instead of using a fixed threshold for mean and standard deviation across all sentinel CpGs, a sliding-window approach was applied. 23 Using this approach, starting values of 0.025 for mean and 0.0025 for standard deviation were gradually incremented up to a maximum of 0.025 for mean and 0.25 for standard deviation, until 1,000 permuted sites were identified for each sentinel CpG. The assessed genomic features included 15 learned chromatin states, deoxyribonuclease I (DNAse I) hotspots, regions marked by 5 core histone modifications, as well as 1,210 transcription factor binding sites (TFBS). Information on chromatin states, DNAse I hotspots and regions enriched in histone modifications in various tissues and cell subsets were obtained from the Roadmap Epigenomic Consortium database, while known TFBS were sourced from the Remap 2022 database. 24 , 25 Transcription Factors (TFs) corresponding to enriched TFBS were classified as follows: ‘Cardiac TF’ if expressed in humans and validated in vivo 26 , 27 ; ‘Computational-based’ if predicted as cardiac TFs computationally due to disruption of TFBS near heart-failure linked loci 28 , 29 , or ‘Cardiac role unknown’ if existing literature has not suggested any cardiac role in humans. Enrichment was determined through a one-tailed permutation test. Permutation p-values were computed by comparing overlap counts in the sentinel CpG set (test set) with the background distribution. RNA sequencing RNA sequencing (RNA-Seq) data for gene expression was obtained from the same left ventricular tissue samples analysed for methylation, if available (n = 306, MAGNet). Prior to downstream analyses, genes were removed if they met any of the following criteria: (i) they had low read counts (< 10 counts in the minimum group size of samples); or (ii) they were present on sex chromosomes. Gene expression levels were normalised using Trimmed Mean of M-values (TMM), recommended for quantitative trait loci analyses. 30 More details on the sequencing, alignment, filtering and normalization of RNA-seq data are available in Additional File 3. Expression quantitative trait methylation (cis - eQTM) analysis Expression quantitative trait methylation ( cis -eQTM) analysis was conducted to identify significant associations between sentinel CpG methylation and proximal ( cis ) gene expression (+/- 1Mb of the sentinel CpG based on gene transcription start site (TSS)). Gene expression was modelled against CpG methylation with adjustment for age, sex, ethnicity, RNA Integrity number (RIN) and the top 5 probabilistic estimation of expression residuals (PEER) factors which represent latent sources of variability in the gene expression data, possibly including technical and unknown confounders. 31 Association testing was performed using a linear model implemented within the MatrixEQTL R package (version 2.3). 32 Following cis -eQTM identification, physical interactions between sentinel CpGs and their target genes were further examined using left ventricular chromatin interaction data from an external dataset at 5kb resolution. 29 Sentinel CpG enrichment in cis -eQTMs was evaluated against matched background CpGs, using the same permutation testing method described in the 'Enrichment analysis of regulatory features' section. Methylation quantitative trait loci analysis (meQTL) DNA samples were obtained from the same left ventricular tissue samples analysed for methylation, if available (n = 304, MAGNet). Genotyping was performed using the Affymetrix Genome-Wide SNP Array 6.0. 33 SNPs were removed from the current analyses if they met any of the following criteria: (i) they were multiallelic; (ii) they were present in duplicate; (iii) they had low imputation quality (R2 < 0.3); or (iv) they were rare, defined as having an ancestry-specific minor allele frequency (MAF) of less than 0.05. Methylation quantitative trait loci (meQTL) analysis was conducted to identify significant associations between SNPs and sentinel CpG methylation (+/-500kb). CpG methylation was regressed against SNP dosage values with adjustment for age, sex and methylation array control probe PCs capturing 95% of control probe variation. Association testing was performed using a linear model implemented within the MatrixEQTL R package (version 2.3). 32 Regression was performed separately for each ancestry group, followed by a trans-ancestry inverse variance-weighted meta-analysis using METAL. Causal analyses (Mendelian Randomisation and Colocalisation) Summary data-based Mendelian Randomisation (SMR) was performed to investigate the putative causal contribution of sentinel CpGs to DCM. SMR uses summary-level association statistics from independent association studies to compute a causal estimate for the influence of an exposure (e.g. sentinel CpG methylation) on an outcome (e.g. DCM disease status, or gene expression). 17 The significance of the causal estimate is determined using the Wald Test. Separate SMR analyses were conducted to examine causal relationships between sentinel CpG methylation and (i) DCM, as well as (ii) proximal gene expression (+/-1Mb). For each sentinel CpG, the SNP that was most strongly associated with the CpG in meQTL analysis (P < 0.05), that was also analysed for association with the respective outcome, was chosen to be the instrumental variable (IV) for assessing causal relationships. Genetic associations for DCM were obtained from the largest published GWAS of DCM (n = 355,381, UKBioBank), while genetic associations for gene expression were obtained from the largest left ventricular tissue cis -expression quantitative trait loci ( cis -eQTL) study (n = 386; GTEx v8 release; SNPs within +/- 1Mb of gene transcription start sites). 34 , 35 We validated SMR-significant associations using one-sample Mendelian Randomisation (one-sample MR), and also conducted colocalisation analyses to evaluate the posterior probabilities of a shared causal variant for the assessed traits. One-sample MR was performed with individual-level genotype, methylation, and outcome data (MAGNet) using the 2-stage least squares regression method implemented in the AER R package (version: 1.2–12). Colocalisation analysis was conducted using the coloc.abf function in the coloc R package (version 5.2.3), based on a +/-500kb region around each sentinel CpG as this was the window size used for identifying meQTL. 36 Weighted gene co-expression network analysis (WGCNA) We employed weighted gene co-expression network analysis (WGCNA) to construct co-methylation modules using DCM-associated CpGs from discovery-stage EWAS. 37 These CpGs were (i) associated with DCM (FDR P 0.02). Using 32,198 CpGs, we built a signed consensus co-methylation network using the blockwiseconsensusModules function in the WGCNA R package (version 1.72-5), using settings recommended for our sample size (soft thresholding power = 12, merge cut height = 0.25 and minimum module size = 30). Methylation levels within consensus modules were summarised as module eigengenes (ME) and tested for correlation with DCM separately by ancestry (Model: DCM ~ ME + Age + Sex + 10PCs). Ancestry-specific results were combined using inverse variance-weighted meta-analysis to determine module associations with DCM. Additionally, a hypergeometric test was applied to assess module enrichment in DCM sentinel CpGs (P < 0.05) against all CpGs used for network construction. Genes mapped to CpGs within co-methylation modules for overrepresentation of Gene Ontology (GO) terms and biological processes from KEGG and REACTOME databases using the clusterProfiler R package (version 4.10.0). GO gene sets were obtained from org.Hs.eg.db (version 3.18.0), while KEGG and REACTOME datasets were accessed via the msigdbr R package (version 7.5.1). Targeted methylation sequencing Targeted methylation sequencing was performed using genomic DNA from peripheral blood samples collected from participants of the iHealth-T2D study. Targeted methylation sequencing was performed for regions defined as +/-500bp of DCM sentinel CpGs. The choice of window size is supported by previous publications demonstrating a decay in correlation between methylation sites beyond 1-2kb, 38 , 39 and from our in-house whole-genome bisulfite sequencing dataset where we observed that |r|>0.2 was within +/-500bp for most of the assessed CpGs (data unpublished). Additional details on probe design, sample processing and library preparation, sequencing and quality control steps employed are available in Additional File 3. Statistical analyses of targeted methylation sequencing data Within each sequenced region, the association of each CpG site with CVD-related traits was tested using linear regression for continuous traits and logistic regression for binary traits, adjusting for age, sex and white blood cell proportion of six white blood cell sub-populations (CD8 T cells, CD4 T cells, Natural Killer Cells, B-cells, Monocytes, Granulocytes) estimated by the Houseman algorithm. 40 In parallel, pairwise correlation in methylation levels between CpGs within regions was calculated using the R function cor . For a given pair of CpGs, only samples where methylation data was available for both CpGs being compared were used to calculate correlation. Regions represented by sentinel CpGs were assessed for enrichment in significant associations to CVD traits, compared regions represented by a background set of 1,000 permutations of non-sentinel CpGs. The background set consisted of EPIC array CpGs matched by methylation levels and variability to sentinel CpGs using the same sliding-window approach that was described in ‘Methods: Enrichment analysis of genomic regulatory features . ’ Construction of methylation risk score (MRS) We constructed a methylation risk score (MRS) from CpGs in fine-mapped regions and examined the association of these scores with CVD traits. Effect sizes of association between individual CpG loci and CVD traits were used as weights for constructing the methylation score. Specifically, we defined trait-specific MRS within each region as a linear combination of k CpG site beta values b and weights w : Within each region, the association of each MRS with CVD-related traits was assessed, adjusting for the same covariates as in single loci association testing described in ‘ Statistical analyses of targeted methylation sequencing data’. Permutation testing was conducted using the same methodology as that applied to single CpG associations. RESULTS Overview of study design Our study design is summarised in Fig. 1 . In brief, we first carried out an epigenome-wide association investigation of DCM using 414 left ventricular samples obtained from two repositories: the Myocardial Applied Genomics network (MAGNet; n = 329 [discovery]) and the Bruce McManus Cardiovascular Biobank (BMCB; n = 85 [replication]) (Additional File 1: Table S1 ). Integrative omics analyses were performed on the identified sentinel CpGs. To discover additional signals beyond CpG sites captured by the methylation array, we conducted fine-mapping of selected top-ranking loci in blood samples obtained from a population-based cohort (iHealth-T2D, n = 1,974). Epigenome-wide association analysis We performed EWAS of DCM using genomic DNA extracted from left ventricular free-wall tissue. Separate EWAS were carried out for Whites and African Americans within the discovery cohort (MAGNet), followed by inverse-variance meta-analysis (Methods; Additional File 2: Figure S1 ). From discovery-stage EWAS, 196 CpG sites were associated with DCM at a Bonferroni-corrected threshold of P < 5.96E-08 (0.05/838,624) (Fig. 2 A). Subsequent targeted replication testing in the BMCB cohort (N = 36,925 CpGs, discovery FDR P < 0.05) confirmed consistent directionality of effect size estimates for all 196 Bonferroni-significant discovery CpGs, as well as previously reported CpG associations with DCM (Additional File 1: Tables S2 and S3). The 196 CpG sites were distributed across 171 genetic loci, with 150/171(88%) genetic loci containing a single sentinel CpG and 21/171 (12%) loci containing two or more sentinel CpGs (Additional File 1: Table S2 , Additional File 2: Figure S2 ). Conditional analyses at each locus identified a total of 194 robustly associated and conditionally independent signals (‘sentinel CpGs’), which were further analysed for functional relevance and causal contribution to DCM pathogenesis (Fig. 2 B). Unsupervised hierarchical clustering based on the methylation levels of the 194 sentinel CpGs resulted in two distinct clusters, segregating samples by their respective case and control status (binomial P < 2.2E-16) (Fig. 3 ). DCM cases comprised the majority of one cluster (128/145;88%), while controls constituted the majority of the second cluster (153/184; 83%). This finding supports a perturbation of DNA methylation in DCM. Clustering by case and control status persisted in ancestry-specific unsupervised hierarchical clustering of methylation levels of the sentinel CpGs (Additional File 2: Figure S3 ). Enrichment of Sentinel CpGs in active gene regulatory regions and impact on proximal gene expression To understand the regulatory role of sentinel CpGs, we first examined the enrichment of chromatin states. Compared to a background of array CpGs matched by methylation levels and variability, sentinel CpGs were enriched in transcriptionally active chromatin states of left ventricular tissue, including weakly-transcribed regions (permutation test P < 0.001), actively transcribed regions and enhancers (permutation test P < 0.05; Additional File 2: Figure S4A). Conversely, sentinel CpGs exhibited depletion in polycomb-repressed regions relative to the background (permutation test P < 0.001). In addition to enrichment for transcriptionally active chromatin states, sentinel CpGs were also enriched in deoxyribonuclease I (DNase I) hotspots (Additional File 2: Figure S4B). DNase I hotspots are genomic regions that exhibit a significantly high frequency of cleavage by the enzyme DNase I, indicating areas of increased accessibility within the chromatin. Sentinel CpGs were not only enriched in cardiac tissue-specific DNase I hotspots, but also in DNAse I hotspots across various other tissue types and cell subsets, suggesting their gene regulatory role across multiple tissues. We also analysed the overlap between sentinel CpGs and regions marked by histone modifications associated with gene regulation. Sentinel CpGs were enriched in H3K4me1-marked regions indicative of primed enhancer and promoters. Conversely, sentinel CpGs were depleted in H3K4me3-marked regions associated with active promoters. This enrichment in primed, rather than active regulatory elements, suggests that sentinel CpGs could contribute to an epigenetic priming mechanism that facilitates changes in gene expression in response to pathological stressors. To identify sentinel CpGs that impacted proximal gene expression, hereon referred to as cis -expression quantitative methylation loci ( cis -eQTM), we examined the association between methylation levels of the 194 sentinel CpGs and expression of their proximal genes (< 1Mb from the gene transcription start site (TSS)) present in the discovery (MAGNet) and replication (BMCB) RNA-seq datasets. The 194 sentinel CpGs were enriched for association with proximal gene expression in left ventricular tissue (3.80-fold compared to expectations under the null hypothesis; P < 0.001) (Additional File 2: Figure S5). Subsequent targeted replication testing on sentinel CpG-gene pairs reaching FDR P < 0.05 in discovery-stage association testing confirmed consistent directionality of effect size estimates between 183 sentinel CpGs and 849 unique proximal genes (964 pairs; ‘replicated cis -eQTMs’) (Fig. 4 , Additional File 1: Table S4). Bulk left ventricular Hi-C data further supported physical interactions between 174 sentinel CpGs and 686 unique proximal genes (772/964 pairs, 80.1%) (Fig. 4 , Additional File 1: Table S4). For most replicated cis -eQTMs, sentinel CpGs were located 5’ upstream of their target genes (Fig. 4 A) and were inversely correlated with target gene expression (Fig. 4 B). Summary data-based Mendelian Randomisation to infer disease causation Having obtained initial evidence of sentinel CpG contribution to transcriptional regulation, we next leveraged upon Summary data-based Mendelian Randomisation (SMR) to further evaluate potential causal relationship of the sentinel CpGs with both DCM and proximal gene expression. Separate causal analyses were conducted for DCM and gene expression. We found two sentinel CpGs that were potentially causally linked to DCM (cg08140459 and cg12359658; p < 0.05), with subsequent validation testing via one-sample Mendelian Randomisation (one-sample MR) confirming consistent directionality of causal estimate for cg08140459-DCM (p < 0.05) (Additional File 1: Table S5). To gain further insight into the molecular mechanisms underpinning the contribution of sentinel CpGs to DCM, we conducted a separate SMR of gene expression, focusing on replicated cis -eQTMs. After excluding sentinel CpGs without suitable instrumental variables (e.g., the SNP with the strongest association to CpG methylation was not assessed for association with proximal gene expression in the GTEx cis -eQTL analysis), 931 cis -eQTMs (181 unique sentinel CpGs, 828 unique genes) could be analysed in the SMR of gene expression. Out of the 181 sentinel CpGs analysed, 36 sentinel CpGs showed putative causal relationships with 43 unique proximal genes (p < 0.05) (Additional File 1: Table S6). We further integrated evidence from multi-omics analyses to identify the most relevant sentinel CpGs for DCM pathogenesis. Among the 36 sentinel CpGs causally linked to proximal gene expression, we selected the three CpGs with the highest posterior probability for a shared causal variant influencing both CpG methylation and proximal gene expression (cg09862509- IER5 , coloc.abf-PP.H4 = 0.91; cg11793257- ENTPD6 , coloc.abf-PP.H4 = 0.69; cg11793257- ABHD12 , coloc.abf-PP.H4 = 0.47; cg06807905 -KCNC4 , coloc.abf-PP.H4 = 0.51) (Fig. 5 , Additional File 2: Figure S6). Causal estimates for these pairs were validated by one-sample MR, showing consistent direction of association (Additional File 1: Table S6). We illustrate the integration of SMR and colocalisation analyses to assess causality with cg09862509- IER5 , which showed the highest probability of genetic colocalisation (Fig. 5 ). To further assess the likelihood of a causal regulatory relationship between cg09862509 methylation and Immediate Early Response 5 ( IER5 ) expression, we additionally examined an external dataset of left ventricular H3K27ac-based HiChip chromatin interactions. 29 This analysis supported a physical interaction between the chromatin regions containing cg09862509 and the putative promoter region of IER5 (Fig. 5 C). Although the cardiac roles of the target genes have not been well-studied, existing investigations suggest functions with potential relevance to DCM pathogenesis. Immediate Early Response 5 ( IER5 ) is a transcription factor regulating cell proliferation, possibly via the crosstalk between Notch and DNA damage response pathways. 41 Ectonucleoside Triphosphate Diphosphohydrolase 6 ( ENTPD6 ) is a nucleotide-metabolising enzyme that is predominantly expressed in the heart and functions in platelet recruitment and aggregation (Additional File 2: Figure S6A). 42 Potassium voltage-gated channel Subfamily C Member 4 ( KCNC4 ) influences neural cell survival and apoptosis under oxidative stress conditions in mice and may also play a role in regulating myocardial action potential (Additional File 2: Figure S6B). 43 , 44 Abhydrolase Domain Containing 12 ( ABDH12 ) is an enzyme that hydrolyses endocannabinoids, a class of lipids regulating a wide range of pathologies including platelet aggregation, vasodilation, and the maintenance of energy balance (Additional File 2: Figure S6C). 45 Genes mapped to CpG sites demonstrating coordinated changes in methylation patterns are enriched in disease-relevant pathways Beyond analysing single CpG associations, examining coordinated methylation changes across multiple CpG sites and their linked genes could reveal disease-relevant pathways regulated by DCM methylation. To achieve this, we conducted weighted gene co-expression network analysis (WGCNA), constructing co-methylation modules using methylation levels of 32,918 DCM-associated CpG sites (discovery EWAS FDR P 0.02 across all samples). Seven co-methylation modules were identified (Fig. 6 A). Regressing the module eigengene (the first principal component of module-specific methylation intensities) against DCM confirmed an association with DCM for all modules (Additional File 1: Table S7). To investigate the biological relevance of co-methylation modules, we looked for over-represented gene sets and enrichment in transcription factor binding sites (TFBS). Gene set overrepresentation of gene ontology terms and pathways (KEGG/REACTOME) was analysed using genes belonging to replicated cis -eQTM pairs of module-specific CpGs (Fig. 6 B-F, Additional File 1: Table S8). Five of the seven identified co-methylation modules had enriched gene sets (FDR P < 0.05). The most enriched gene sets and pathways in each module reflected distinct aspects of DCM pathogenesis, including alterations in extracellular matrix composition (Module 1; Fig. 6 B), cell signalling (Module 2; Fig. 6 C), and immune-mediated pathways (Modules 3,4,6) (Figs. 6 D-F). Five of the seven identified modules showed significant enrichment in TFBS relative to background CpGs (permutation test p < 0.001) (Additional File 1: Table S9, Additional File 2: Figure S7). Examining the most enriched TFBS for each module, two modules featured TFBS with known or likely cardiac roles, namely Thyroid Hormone Receptor Alpha ( THRA ) (Module 1) and Homeobox protein Hox-B8 ( HOXB8 ) (Module 2). THRA contributes to cardiac function and contractility and HOXB8 contributes to cardiac development. 46 , 47 The top enriched TFBS for the other modules corresponded to TFs which are not currently known to specifically contribute to cardiac pathways, including zinc finger proteins (Modules 4 and 5) and a TF linked to oncogenesis, LMO1 (Module 7). 48 Overall, module-specific enriched gene sets and TFBS demonstrated alignment in regulated pathways, exemplified by Module 2’s enrichment in TFBS corresponding to TFs known to regulate developmental pathways and concomitant enrichment in the ‘cartilage development involved in endochondral bone morphogenesis' term (GO:0060351, 3.72-fold, p = 2.79E-05) (Fig. 6 C, Additional File 1: Tables S8 and S9). This GO term encompassed genes contributing to collagen biosynthesis and Wnt signaling, which are processes relevant to both cartilage development and DCM-related cardiac phenotypes like cardiac development and remodelling. Taken together, findings from gene set and TFBS enrichment analyses highlight diverse aspects of DCM pathogenesis driven by coordinated changes in methylation patterns. Fine-mapping sentinel CpGs to investigate regional associations with traits related to cardiac disease and disease risk As the methylation array covers only 2–3% of CpG sites in the epigenome, we sought to improve our investigation of regional associations using an existing target methylation sequencing dataset to increase coverage of CpG sites (+/- 500bp) surrounding the top-performing DCM sentinel CpGs. Targeted methylation sequencing was performed using blood samples of individuals from the iHealth-T2D study, which is well phenotyped for various traits relevant to cardiovascular disease (CVD) (n = 1974). We assessed regional associations with: (i) previous medical history of CVD, specifically myocardial infarction, angina, and coronary heart disease (CHD); (ii) risk factors for CVD including hypertension, Framingham Coronary Heart Disease (FramCHD) score and the renal marker creatinine, which has been associated with a greater risk of heart disease and early death in the general population 49 ; as well as (iii) high-sensitivity C-reactive protein (hsCRP), an inflammatory marker that has been independently associated with increased risk of CVD in asymptomatic individuals. 50 Targeted sequencing was performed on regions surrounding 28 DCM sentinel CpGs (28 regions) (Additional File 1: Table S10). A total of 293 CpG sites were captured, with two to 24 CpG sites captured in each region. Seventeen regions had significant associations with at least one of the 10 unique CVD traits (Bonferroni-corrected P < 0.05) (Additional File 1: Table S11). At 14 regions, the CpGs with the strongest association with our CVD traits were not CpGs on the EPIC array, further illustrating the added value brought upon by targeted sequencing. Multiple independent signals were also found for two regions, whereby conditioning on the lead signal revealed secondary signals in both regions. Compared to a background of non-sentinel CpGs matched by methylation levels and variability to sentinel CpGs and with +/- 500 bp regions captured in the targeted sequencing experiment, the 28 sentinel CpGs had a greater number of regions containing significant associations with creatinine (6/28 regions; permutation test P < 2.20E-02) and FramCHD score (5/28 regions; permutation test P < 3.80E-02) (Additional File 1: Table S13). To illustrate the utility of targeted sequencing to improve regional associations with DCM, we highlight a region surrounding the DCM sentinel CpG, cg1179325 (EWAS P = 1.94E-09). Targeted sequencing of this region revealed stronger signals for creatinine and FramCHD score (Bonferroni-corrected P < 0.05) from CpGs that were not present on the EPIC array (chr20_25218304 with creatinine, P = 1.26E-03; chr20_25218276 with FramCHD score, P = 4.08E-03) (Figs. 7 A,B; Additional File 1: Table S11.) The newly identified CpGs exhibited methylation levels that correlated with cg11793257 (chr20_25218304 with cg11793257 |r|=0.42; chr20_25218276 with cg11793257 |r|=0.46) (Additional File 1: Table S11.) We next combined methylation information from multiple CpGs in each region into a weighted methylation risk score (MRS) to investigate associations with CVD. Of the 28 sequenced regions, 25 regions had significant MRS (Bonferroni P < 0.05) for at least one investigated CVD trait (Additional File 1: Table S12). In 23 of these 25 regions, individual CpGs were not significantly associated with CVD-related traits (Bonferroni P < 0.05). However, combining the methylation patterns of multiple CpGs within these regions into an MRS revealed significant associations with at least one of the investigated CVD-related traits, indicating that the combined effect of multiple CpGs provides stronger association with CVD traits than individual CpGs alone. Nonetheless, unlike regional single CpG association tests, the regional MRS of sentinel CpGs did not show enrichment for significant associations with specific CVD traits when compared to the regions surrounding permuted CpG sites (Additional File 1: Table S13). DISCUSSION We perform the largest EWAS of DCM in cardiac tissues to date (discovery n = 159 DCM, 170 control), extending on previous EWAS of DCM in terms of sample size and coverage of CpG sites. Using independent DCM cohorts, we identified and replicated 36,925 CpG associations with DCM. We further performed comprehensive multi-omics and causal analyses on the top signals: 194 independent CpG signals for DCM that reached epigenome-wide significance (P < 5.96E-08). These integrative omics analyses suggested the causal contribution of a subset of the 194 sentinel CpGs to DCM pathogenesis and transcriptional regulation. Fine-mapping of putative methylation markers and network analysis of coordinated changes across multiple DCM-associated CpG loci supported the relevance of regions containing DCM-linked methylation changes to cardiac development, disease pathogenesis as well as early indicators of CVD risk. In addition to identifying novel CpG associations with DCM, our study confirmed strong associations reported by the most comprehensive existing EWAS of DCM conducted by Meder et al. on a predominantly White cohort (discovery n = 41 cases, n = 31 controls) utilizing the older 450k methylation array. 9 Among Meder et al. ’s reported associations that were confirmed in the current investigation (discovery FDR P < 0.05, consistent directionality of effect between MAGNet and BMCB cohorts) were two CpG sites (cg25838968 and cg16254946) that had reached epigenome-wide significance in Meder et al. ’s investigation, as well as an additional CpG site (cg24884140) that had been singled out as a promising DCM diagnostic biomarker demonstrating consistent hypomethylation in both myocardial tissue and blood samples from individuals with DCM compared to control subjects, as well as superior classification accuracy for DCM compared to the clinical gold standard biomarker NT-proBNP. Further to describing robust CpG associations with DCM, our study expands previous investigations with causal analyses of sentinel CpG contribution to DCM pathogenesis. While individual-level genotype data avails the option of conducting one-sample MR as the primary analysis to avoid issues arising from population heterogeneity, we opted instead to conduct SMR as our primary analysis to leverage genetic association data from a large-scale external GWAS of DCM. Additionally, utilizing two samples minimises the risk of false positives. For CpG-DCM or CpG-gene pairs with SMR causal estimates that reached nominal significance (P < 0.05), one-sample MR was subsequently performed for validation. We show putative causal relationships between cg08140459 and DCM. In our investigation, cg08140459 was also robustly linked to the expression of LTBP2 , a recently discovered prognostic biomarker for DCM, in independent cohorts. 51 To gain insight into the molecular pathways in DCM pathogenesis that involve sentinel CpGs, we conducted a separate SMR analysis for gene expression, revealing the putative contribution of three sentinels to nearby genes (cg09862509- IER5 , cg11793257- ENTPD6 , cg11793257- ABHD12 , cg06807905- KCNC4 ) with functions relevant to DCM pathogenesis, thus warranting further investigation in a cardiac context. Using targeted methylation sequencing data from a population-based cohort phenotyped for multiple cardiac traits, we discovered independent CVD risk factor signals near DCM sentinels that were not captured by the EPIC array, particularly for creatinine and FramCHD. We presented an example of new signals identified for both creatinine and FramCHD in the cg11793257 locus. Notably, this locus was also highlighted in our SMR of gene expression, which supported a putative causal relationship between the methylation of cg11793257 and the expression of ENTPD6 and ABHD12. These genes are involved in metabolism and physiological processes that are potentially relevant to cardiac pathology and CVD risk, making this locus worthy of further investigation. While we also found that aggregating methylation data from CpGs on a regional level improved regional associations with CVD traits, permutation analysis did not reveal enrichment for specific CVD traits among sentinel CpG regions. Further validation is required to ascertain the biological relevance of regions with MRS that are associated with CVD traits. Our study has some limitations. Firstly, the cell type heterogeneity of left ventricular tissue makes it challenging to delineate the specific cell types driving the associations between CpG methylation and DCM. Although bioinformatics cell type deconvolution methods exist for this task, the lack of reference methylation profiles for heart cell types means that only reference-free approaches can be applied. While reference-free deconvolution algorithms can predict distinct cell classes using major variations in methylation profiles, finding a biological basis to justify assigning these output classes to specific heart cell types (e.g. cardiomyocytes or cardiofibroblasts) currently poses a significant challenge. As single-cell profiling techniques for methylation and gene expression in cardiac cell types advance, future methylation studies of DCM should prioritise elucidating the cell type specificity of DCM-linked CpG methylation and the genes they regulate. Concerning our MR analyses, one limitation would be the potential bias in the causal relationships estimated by two-sample MR owing to population heterogeneity between the multi-ancestry MAGNet cohort (White, African American) used to generate meQTL and the predominantly White cohorts in which the GWAS of DCM and left ventricular eQTL analyses were conducted. Despite this, we did not restrict the meQTL analysis to only samples from subjects of White ancestry in our cohort with methylation and genotype data available (n = 194) to maximise power for detecting left ventricular meQTL associations. A second limitation of the MR analyses would be the limited sample size of the cohort used to generate meQTL, despite the current meQTL analysis being the largest to be conducted in left ventricular tissue to date. Nonetheless, our study has important strengths. Besides being the largest existing study of DCM-linked methylation disturbances in left ventricular tissue to date, the current investigation extends previous methylation studies of DCM by seeking evidence for the causal contribution of DCM-linked sentinel CpGs and by investigating regional associations with traits indicative of CVD risk. CONCLUSION This is the largest investigation of perturbed CpG methylation in DCM to be conducted in disease-relevant left ventricular tissue obtained from patients and controls. We identify CpGs independently and robustly associated with DCM and suggest molecular players in new, putative causal mechanisms by which DNA methylation may impact DCM. We also provide preliminary indication of the prognostic potential of regions containing DCM-linked methylation alterations that are associated with CVD-relevant traits in the general population. Declarations ETHICS APPROVAL AND CONSENT TO PARTICIPATE For each cohort, written informed consent for the research use of donated left ventricular (LV) tissue were obtained. For heart transplant recipients, consent was obtained from the transplant recipient. For brain-dead organ donors, consent was obtained from the next-of-kin. All analyses and study protocols were approved by the relevant institutional review boards. AVAILABILITY OF DATA AND MATERIALS The datasets supporting the conclusions of this article are included within the article and its additional files. Original R scripts are available in GitHub (https://github.com/KonstanzeTan/tan_etal_DCM). Gene regulatory features for enrichment analysis were downloaded from the Roadmap Epigenomics database (https://egg2.wustl.edu/roadmap/web_portal/processed_data.html#ChipSeq_DNaseSeq). Binding sites of known transcription factors are available at the ReMap2022 database (https://remap.univ-amu.fr/download_page). Raw RNAseq counts and accompanying metadata from the MAGNet cohort are available at the following URL: https://github.com/mpmorley/MAGNet?tab=readme-ov-file. Full summary statistics for the GWAS of DCM can be accessed from the NBDC Human Database (https://humandbs.dbcls.jp/en/hum0197-v3-220 ; dataset ID: hum0197.v3.EUR.DC.v1). The full set of left ventricular eQTL associations (v8) were downloaded from Google Cloud’s requester pay buckets (https://console.cloud.google.com/storage/browser/gtex-resources;tab=objects?pli=1&prefix=&forceOnObjectsSortingFiltering=false; dataset ID: Heart_Left_Ventricle.v8.EUR.allpairs.chr*.parquet), using Google’s command-line tool, gsutil. Left ventricular chromatin interaction data (H3K27ac-ChIPseq, bulk tissue Hi-C and H3K27ac-based HiChIP data) were requested from authors of Tan et al. (doi: 10.1161/CIRCRESAHA.120.317254). Other datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. COMPETING INTERESTS None. FUNDING This work was funded by a start-up grant awarded to Asst Prof Marie Loh [PI] by the Ministry of Education, Singapore (MOE; Grant ID: #002751-00001 ) AUTHORS’ CONTRIBUTIONS This study was conceived, designed, and interpreted by KT and ML. KT performed the statistical analysis. KT drafted the manuscript, and ML contributed to the manuscript writing. DT, WT, HN, PJ and CJM pre-processed the data used for key analyses. EW, MM, GS, CJM, FT, PH, TC, KM, RF supplied and/or processed samples and provided phenotype data. All authors read and approved the final manuscript. ACKNOWLEDGEMENTS None References Reichart D, Magnussen C, Zeller T, Blankenberg S. Dilated cardiomyopathy: from epidemiologic to genetic phenotypes: A translational review of current literature. J Intern Med. 2019;286:362–372. doi: 10.1111/joim.12944 Cuenca S, Ruiz-Cano MJ, Gimeno-Blanes JR, Jurado A, Salas C, Gomez-Diaz I, Padron-Barthe L, Grillo JJ, Vilches C, Segovia J, et al. 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Results of targeted replication testing for DCM-associated CpGs (disc FDR P<0.05, N = 36,925 CpGs). Table S4. Replicated expression quantitative trait methylation relationships (964 pairs; 183 Sentinel CpGs and 849 unique genes within ± 1Mb). Table S5. Significant results (P<0.05) from Summary Data Mendelian Randomisation analysis of sentinel CpG-DCM relationships. Table S6. Significant results (P<0.05) from Summary Data Mendelian Randomisation analysis of sentinel CpG-gene expression relationships which were identified as replicated cis-eQTMs. Table S7. Co-methylated modules identified by WGCNA. Table S8. Enriched gene sets (FDR P<005) in co-methylated modules. Table S9. Enriched transcription factor binding sites (p<0.001) in co-methylated modules. Table S10. Regions included in targeted methylation sequencing analysis. Table S11. Associations of individual CpG sites with CVD-related traits for regions included in targeted methylation sequencing (Bonferroni P<0.05). Table S12. Associations of methylation risk scores (MRS) with CVD-related traits for regions included in targeted methylation sequencing analysis. (.xlsx) 20240924additionalfile2.docx Additional File 2: Figure S1 to S6. Figure S1. Identification of sentinel CpGs from epigenome-wide significant CpGs in discovery-stage EWAS. Figure S2. Ancestry-specific clustering using methylation levels of 194 DCM sentinel CpGs. Figure S3. Enrichment analysis for genomic regulatory regions. Figure S4. Enrichment analysis for cis-eQTMs. Figure S5. Regional plots for colocalized sentinel-CpG gene pairs (PP.H4>0.4). Figure S6. Bar plots of transcription factors (TFs) with enriched binding sites (permutation test P<0.001) in co-methylation modules. (.docx) 20240924additionalfile3.docx Additional File 3: Detailed Methods. 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Medicine, Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Darwin","middleName":"","lastName":"Tay","suffix":""},{"id":377932376,"identity":"f1edf775-0fd4-4103-8c1e-25386a6f5f70","order_by":2,"name":"Wilson Tan","email":"","orcid":"","institution":"Cardiovascular Research Institute, National University Health System","correspondingAuthor":false,"prefix":"","firstName":"Wilson","middleName":"","lastName":"Tan","suffix":""},{"id":377932378,"identity":"d7da1813-b567-4d16-acfd-58b223ef9bf3","order_by":3,"name":"Hong Kiat Ng","email":"","orcid":"","institution":"Lee Kong Chian School of Medicine, Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"Kiat","lastName":"Ng","suffix":""},{"id":377932379,"identity":"8b635a0b-c379-420c-947c-cf4a30e0e21e","order_by":4,"name":"Eleanor Wong","email":"","orcid":"","institution":"Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR)","correspondingAuthor":false,"prefix":"","firstName":"Eleanor","middleName":"","lastName":"Wong","suffix":""},{"id":377932380,"identity":"0dd51a80-b621-41b6-907b-5723fd264f87","order_by":5,"name":"Michael P Morley","email":"","orcid":"","institution":"Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"P","lastName":"Morley","suffix":""},{"id":377932381,"identity":"c8205bc3-fe79-4913-ac41-725e8eb01c04","order_by":6,"name":"Gurpreet K Singhera","email":"","orcid":"","institution":"Bruce McManus Cardiovascular Biobank, UBC- Centre for Heart Lung Innovation","correspondingAuthor":false,"prefix":"","firstName":"Gurpreet","middleName":"K","lastName":"Singhera","suffix":""},{"id":377932382,"identity":"2ab5cfcd-0063-44a9-88d1-480cbce95e84","order_by":7,"name":"Chang Jie Mick Lee","email":"","orcid":"","institution":"Cardiovascular-Metabolic Disease Translational Research Programme, National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"Jie Mick","lastName":"Lee","suffix":""},{"id":377932383,"identity":"32907549-3ef4-44ec-a4cf-727c2e8e1825","order_by":8,"name":"Pritesh R Jain","email":"","orcid":"","institution":"Lee Kong Chian School of Medicine, Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Pritesh","middleName":"R","lastName":"Jain","suffix":""},{"id":377932384,"identity":"c6297043-e953-41aa-92a1-b7ed6b599a64","order_by":9,"name":"Fei Li Tai","email":"","orcid":"","institution":"Lee Kong Chian School of Medicine, Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"Li","lastName":"Tai","suffix":""},{"id":377932385,"identity":"075e3227-aa02-48bc-8058-dcaa0c7c326b","order_by":10,"name":"Paul J Hanson","email":"","orcid":"","institution":"Bruce McManus Cardiovascular Biobank, UBC- Centre for Heart Lung Innovation","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"J","lastName":"Hanson","suffix":""},{"id":377932386,"identity":"7df0514b-e46e-4061-b068-e4e4783643a1","order_by":11,"name":"Thomas P Cappola","email":"","orcid":"","institution":"Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"P","lastName":"Cappola","suffix":""},{"id":377932387,"identity":"12b32a6f-8df6-4dcb-af11-552c70357dbe","order_by":12,"name":"Kenneth B Margulies","email":"","orcid":"","institution":"Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Kenneth","middleName":"B","lastName":"Margulies","suffix":""},{"id":377932388,"identity":"fe30bacf-e088-400a-8810-655677e2e21a","order_by":13,"name":"Roger Foo","email":"","orcid":"","institution":"Cardiovascular Research Institute, National University Health System","correspondingAuthor":false,"prefix":"","firstName":"Roger","middleName":"","lastName":"Foo","suffix":""},{"id":377932389,"identity":"549529b2-711b-49b3-a981-b548020f42a0","order_by":14,"name":"Marie Loh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIie3PMQrCMBiG4U8KdskB4mBvIEQKVUE8i0Woizg7Cg4u4qzoIXqESIcuFdeKi1IoDgodHURNCi4OsaNg3imBPPz5AZ3uR+NAE8wc55fSuCChYITnr4sR5IR2C5LadHviGajVqFzDhKBd9bmZnFXEifpsswC1W6uhOyHwbJ8Tp6kk3ENAQF3/MKgLEriClJmS7FIEd0n2kSRPQcxUTWIxBZLERBIuCOyjmqTYzMQuLBrUl2vWs5cBcVRCfMwzshvaFgsjll1Gneo8nCaZ0shKj/dJLmGgTL+Sz4zvU3Q6ne6fegHNi0yR3beS5QAAAABJRU5ErkJggg==","orcid":"","institution":"Lee Kong Chian School of Medicine, Nanyang Technological University","correspondingAuthor":true,"prefix":"","firstName":"Marie","middleName":"","lastName":"Loh","suffix":""}],"badges":[],"createdAt":"2024-09-24 03:08:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5141306/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5141306/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13148-025-01854-8","type":"published","date":"2025-03-08T15:58:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70592782,"identity":"22386b58-b288-4c29-8b80-f8fe51501985","added_by":"auto","created_at":"2024-12-04 17:26:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":522694,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Design. \u003c/strong\u003eKey abbreviations used: (Analyses) EWAS=epigenome-wide association study; eQTM=expression quantitative trait methylation analysis; (Ancestries) AA=African American; (Cohorts) MAGNet=Myocardial Applied Genomics Network; BMCB=Bruce McManus Cardiovascular Biobank; UKBB=UK Biobank.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5141306/v1/555379efc217ea7ff53d828d.png"},{"id":70592861,"identity":"b232a537-f846-4700-8127-1582889d7542","added_by":"auto","created_at":"2024-12-04 17:34:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":135432,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDCM Sentinel CpGs. A. Manhattan plot for discovery-stage EWAS of DCM. \u003c/strong\u003eThe horizontal significance line (red) corresponds to the epigenome-wide significance threshold (P\u0026lt;5.96E-08, 0.05/838,624 tests). The 194 DCM sentinel CpGs are highlighted. At genomic loci with \u0026gt;1 epigenome-wide significant signal, secondary signals (green) were identified by conditioning on the lead signal (lowest P in region; red). \u003cstrong\u003eB. Distribution of DCM Sentinel CpGs across various gene features. \u003c/strong\u003eThe 194 Sentinel CpGs were annotated to gene features based on the Illumina manifest file (version b2).\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5141306/v1/942d360e7d024bd684d4d68a.png"},{"id":70592862,"identity":"634597eb-a6e0-4af1-bc34-1ce3b03eb8bc","added_by":"auto","created_at":"2024-12-04 17:34:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":310257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUnsupervised clustering using methylation levels of 194 sentinel CpGs. \u003c/strong\u003eDCM, dilated cardiomyopathy.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5141306/v1/3359dc3536fe90014e87ddb5.png"},{"id":70592784,"identity":"a5075a06-0f3c-4c6f-9295-50a169288dad","added_by":"auto","created_at":"2024-12-04 17:26:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74528,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of sentinel CpG methylation with gene expression. A. cis-eQTMs by genomic region.\u003c/strong\u003e Sentinel CpGs are annotated for their genic locations: 5' upstream (the region from upstream to +100bp downstream of the gene TSS), gene body, and 3' downstream (the region following the gene body). Replicated cis-eQTMs were defined using two criteria: discovery FDR P\u0026lt;0.05 (MAGNet) and confirmed directionality in replication testing (BMCB).\u003cstrong\u003e B. Directionality of replicated cis-eQTMs by genic location of sentinel CpGs. \u003c/strong\u003eIn each region-specific plot, replicated cis-eQTMs are ordered by chromosomal location of sentinel CpGs along the x-axis. The y-axis represents beta values of individual cis-eQTM relationships based on discovery-stage analysis. The table below each plot summarizes the inverse and positive cis-eQTMs by genomic region, including their ratio (inverse/positive). Gene coordinates and TSS are based on the GENCODE version 19 annotation.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5141306/v1/dac41a35c5d9a678a916fd8d.png"},{"id":70592864,"identity":"d55a60c0-4651-477f-ae3b-cf7f87b85c01","added_by":"auto","created_at":"2024-12-04 17:34:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":54907,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional plots for colocalized sentinel-CpG gene pair. A. Associations of CpGs surrounding cg09862509 with DCM from discovery-stage EWAS (MAGNet). \u003c/strong\u003eThe displayed region (2Mb) is centered on cg09862509. \u003cstrong\u003eB. Regional genetic associations with cg09862509 and IER5. \u003c/strong\u003eGenetic associations were obtained for a region (+/-500kb) centered on the genetic variant (rs12406046) that was used as an instrumental variable in SMR to assess causal relationships between cg09862509 methylation and IER5 gene expression. The posterior probability (coloc PP.H4) of colocalization between cg09862509 meQTLs and IER5 eQTLs was 0.91.\u003cstrong\u003e \u003c/strong\u003eGene tracks for all regional plots consist of protein-coding genes obtained from Ensembl Release 75 (last release to be based on hg19 assembly). \u003cstrong\u003eC. Left ventricular chromatin interactions. \u003c/strong\u003eAt 5kb resolution, cis-interactions were identified between a chromatin region containing rs12406046 and cg09862509 and a putative promoter at the 5’ end of IER5 that contained H3K27ac peaks.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5141306/v1/98c7ed74478153456d5ad461.png"},{"id":70592781,"identity":"71537114-18a6-4cc2-8738-5c4d69196e2f","added_by":"auto","created_at":"2024-12-04 17:26:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":114538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCo-methylation modules identified using DCM-associated CpGs. A. Hierarchical cluster tree (dendogram) of co-methylation modules.\u003c/strong\u003e WGCNA identified seven modulesthat exhibit conservation between co-methylation networks constructed independently within the White (n=209) and African American (n=118) ancestries. The color band underneath the tree indicates distinct modules (grey =CpG sites that are not clustered into any module). \u003cstrong\u003eB-F.\u003c/strong\u003e \u003cstrong\u003eBar plots of over-represented gene sets (FDR P\u0026lt;0.05) in co-methylation modules.\u003c/strong\u003e Genes were assigned to CpGs based on replicated cis-eQTM relationships. Gene set enrichment within a module was assessed against a background set consisting of all genes mapped to CpG sites that were used to identify co-methylation patterns (9,321 unique genes). Enriched gene sets are displayed by decreasing order of \u0026nbsp;significance (-log10p value). GO = gene ontology; KEGG = Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5141306/v1/0e91d63d9de2d131bc9638cf.png"},{"id":70592787,"identity":"70aa2bbe-77f6-40d7-81c7-92315c8eada0","added_by":"auto","created_at":"2024-12-04 17:26:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":51062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional associations of top DCM sentinel CpGs with CVD-related traits. \u003c/strong\u003eRegions surrounding DCM sentinel CpGs (+/-500bp) were fine-mapped to discover signals for CVD-related trait using blood samples from a population-based cohort (iHealth). To highlight the value of fine-mapping disease-associated loci, these plots illustrate the regional associations for cg13173392 and cg16429725, which are the sentinel CpGs with the strongest associations with DCM amongst those that underwent fine-mapping.\u003cstrong\u003e \u003c/strong\u003eThree types of plots are included in each panel: (top) pairwise correlation in methylation levels between CpGs within a region; (middle) regional associations between CVD traits and CpGs; (bottom) protein-coding genes in the region. \u003cstrong\u003eA. Regional associations with coronary heart disease at the cg13173392 locus. \u003c/strong\u003eThis region consists of 24 CpGs. The lead signal is chr11_69245325_69245327 (P=1.93 E-03). \u003cstrong\u003eB. Regional associations with diastolic blood pressure at the cg13173392 locus. \u003c/strong\u003eThis region consists of 24 CpGs. The lead signal is chr11_69245239_69245241 (P=1.29 E-03). A secondary signal chr11_69245748_69245750 (P=1.55E-03) was revealed after conditioning on the lead signal. \u003cstrong\u003eC. Regional associations with the inflammatory mark high-sensitivity C-reactive protein at the cg16429725 locus. \u003c/strong\u003eThis region consists of 15 CpGs. The lead signal is chr16_57798305_57798307 (P=2.79E-05) (Bonferroni P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5141306/v1/cf1306c945be4a7c5b79809a.png"},{"id":78190856,"identity":"12275e0e-3694-4f29-8889-fbbd87185dec","added_by":"auto","created_at":"2025-03-10 19:51:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2802508,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5141306/v1/22a4d87c-b198-4d41-9198-a965e033ac63.pdf"},{"id":70592790,"identity":"3281def6-a33c-4f16-b179-5e61fbddec72","added_by":"auto","created_at":"2024-12-04 17:26:39","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5084032,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 1: Table S1-S12. Table S1. \u003c/strong\u003eCharacteristics of DCM cohorts. \u003cstrong\u003eTable S2. \u003c/strong\u003eIdentification of 194 DCM sentinel CpGs\u003cstrong\u003e. Table S3. \u003c/strong\u003eResults of targeted replication testing for DCM-associated CpGs (disc FDR P\u0026lt;0.05, N = 36,925 CpGs). \u003cstrong\u003eTable S4.\u003c/strong\u003e Replicated expression quantitative trait methylation relationships (964 pairs; 183 Sentinel CpGs and 849 unique genes within ± 1Mb). \u003cstrong\u003eTable S5. \u003c/strong\u003eSignificant results (P\u0026lt;0.05) from Summary Data Mendelian Randomisation analysis of sentinel CpG-DCM relationships.\u003cstrong\u003e Table S6. \u003c/strong\u003eSignificant results (P\u0026lt;0.05) from Summary Data Mendelian Randomisation analysis of sentinel CpG-gene expression relationships which were identified as replicated cis-eQTMs. \u003cstrong\u003eTable S7. \u003c/strong\u003eCo-methylated modules identified by WGCNA. \u003cstrong\u003eTable S8. \u003c/strong\u003eEnriched gene sets (FDR P\u0026lt;005) in co-methylated modules. \u003cstrong\u003eTable S9. \u003c/strong\u003eEnriched transcription factor binding sites (p\u0026lt;0.001) in co-methylated modules. \u003cstrong\u003eTable S10. \u003c/strong\u003eRegions included in targeted methylation sequencing analysis. \u003cstrong\u003eTable S11. \u003c/strong\u003eAssociations of individual CpG sites with CVD-related traits for regions included in targeted methylation sequencing (Bonferroni P\u0026lt;0.05). \u003cstrong\u003eTable S12. \u003c/strong\u003eAssociations of methylation risk scores (MRS) with CVD-related traits for regions included in targeted methylation sequencing analysis. (.xlsx)\u003c/p\u003e","description":"","filename":"20240924additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5141306/v1/6035ee8dfaba539ab3e600ee.xlsx"},{"id":70592791,"identity":"3e2220ff-c6f4-4b71-818d-cacb09fd8901","added_by":"auto","created_at":"2024-12-04 17:26:39","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10852427,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 2: Figure S1 to S6. Figure S1. \u003c/strong\u003eIdentification of sentinel CpGs from epigenome-wide significant CpGs in discovery-stage EWAS.\u003cstrong\u003e Figure S2. \u003c/strong\u003eAncestry-specific clustering using methylation levels of 194 DCM sentinel CpGs. \u003cstrong\u003eFigure S3. \u003c/strong\u003eEnrichment analysis for genomic regulatory regions. \u003cstrong\u003eFigure S4. \u003c/strong\u003eEnrichment analysis for cis-eQTMs. \u003cstrong\u003eFigure S5. \u003c/strong\u003eRegional plots for colocalized sentinel-CpG gene pairs (PP.H4\u0026gt;0.4). \u003cstrong\u003eFigure S6. \u003c/strong\u003eBar plots of transcription factors (TFs) with enriched binding sites (permutation test P\u0026lt;0.001) in co-methylation modules. (.docx)\u003c/p\u003e","description":"","filename":"20240924additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5141306/v1/cbc4c68206305eca6b5e389f.docx"},{"id":70592863,"identity":"ef4cf8d9-9689-491d-b10b-5c9374a515ad","added_by":"auto","created_at":"2024-12-04 17:34:38","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":40949,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 3: Detailed Methods. \u003c/strong\u003eDetails accompanying ‘Methods’ section in this manuscript\u003cstrong\u003e (.docx)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"20240924additionalfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5141306/v1/67852ff1d27691426e34d584.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epigenome-wide association study for dilated cardiomyopathy in left ventricular heart tissue identifies putative gene sets associated with cardiac development and early indicators of cardiac risk","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDilated cardiomyopathy (DCM) is the most common form of cardiomyopathy and a leading indication for heart transplantation. It affects 1 in 250\u0026ndash;400 individuals and has an annual incidence of 5\u0026ndash;7 cases per 100,000 persons per year.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e DCM is characterised by thinning and weakening of the left ventricular heart walls, leading to contractile dysfunction.\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e There is marked heterogeneity in both the prognosis and age of onset for DCM, with most patients becoming symptomatic between 20\u0026ndash;50 years of age.\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e DCM accounts for a substantial 2\u0026ndash;3% of yearly sudden cardiac death (SCD) events which are characterised by an abrupt loss of cardiac function and death occurring within one hour of symptom onset.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOver the past decade, the genetic basis for DCM has only been partially unraveled.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Beyond genetic predisposition, environmental factors could also influence the incidence and course of DCM.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e DNA methylation, a key regulator of gene expression, as well as a molecular integrator of genetic and environmental influences, has been investigated in DCM to gain insights into its molecular pathogenesis. To date, multiple epigenome-wide association studies (EWAS) of DCM have identified a handful of differentially methylated loci.\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e These include CpGs at the natriuretic peptide A (\u003cem\u003eNPPA\u003c/em\u003e) and natriuretic peptide B (\u003cem\u003eNPPB\u003c/em\u003e) loci, where disease-linked methylation disturbances have been observed to be conserved between myocardial tissue and peripheral blood.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e The \u003cem\u003eNPPA\u003c/em\u003e and \u003cem\u003eNPPB\u003c/em\u003e loci encode the cardiac stress markers atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP), which are commonly measured in clinical settings to establish the diagnosis and prognosis of heart failure.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e These markers are considered gold standard indicators for heart failure prognosis, underscoring the potential clinical relevance of DNA methylation in DCM.\u003c/p\u003e \u003cp\u003eExisting epigenome-wide methylation studies of DCM have largely been limited by sample size (n\u0026thinsp;=\u0026thinsp;8\u0026ndash;72) owing to the scarcity of left ventricular tissue and the limited coverage of CpG sites on older methylation arrays.\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e The largest existing EWAS of DCM (discovery n\u0026thinsp;=\u0026thinsp;72) detected only five loci at epigenome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5E-8); highlighting the need for larger samples to uncover signals that are robustly associated with disease.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e While previous DCM methylation studies utilised older Illumina arrays that covered up to 450,000 CpG sites, newer arrays now cover up to 850,000 sites and provide enhanced coverage of key gene regulatory regions, including enhancers.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Recent advancements in statistical methodologies, such as Mendelian Randomisation (MR), have allowed researchers to move beyond discovering associated signals to addressing causality.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e This is particularly relevant for DNA methylation studies, as DNA methylation is affected by various disease-related and environmental factors.\u003c/p\u003e \u003cp\u003eIn this study, we sought to expand our understanding of myocardial-specific methylation changes in DCM. We analysed intra-operative left ventricular tissue from a cohort of 329 individuals of White and African American ancestries (n\u0026thinsp;=\u0026thinsp;159 cases, 170 controls). This study builds upon existing research of DNA methylation in DCM by using a larger and more ancestrally-diverse myocardial tissue sample. Additionally, a suite of integrative omics, causal analyses and fine-mapping of putative methylation markers are employed to elucidate the putative causal involvement of methylation alterations to DCM pathogenesis.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDescription of samples\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eMyocardial Applied Genomics Network Cohort (MAGNet)\u003c/h2\u003e \u003cp\u003eTransmural biopsies of the left ventricular free wall (0.5-1.5g per cryovial) were collected during cardiac surgery from subjects with heart failure attributed to DCM undergoing transplantation, and from donor hearts deemed unsuitable for transplantation but with apparently normal ventricular function. DCM was diagnosed based on the American Heart Association guidelines.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Specifically, all left ventricular tissue included in this investigation were obtained from subjects with an LVEF of \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;40% and with a diagnostic workup that revealed no evidence of familial or pregnancy-associated DCM, cardiac ischemia, significant toxin exposure or myocarditis. Hearts were perfused immediately with cold in-situ high-potassium cardioplegia before cardiectomy to arrest contraction and prevent ischemic damage. Tissue samples were promptly frozen in liquid nitrogen.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eBruce McManus Cardiovascular Biobank Cohort (BMCB)\u003c/h3\u003e\n\u003cp\u003eBiopsies of the left ventricular free wall (apical portion; 1- to 2-mm diameter) were obtained within five minutes post-cardiac surgery from subjects with DCM undergoing transplantation. Tissue samples were sourced from the Bruce McManus Cardiovascular Biobank (BMCB) at the University of British Columbia (UBC). DCM was diagnosed based on the American Heart Association/American College of Cardiology (AHA/ACC) guidelines during pre-transplant admission, as well as during routine follow-ups and evaluation. Following the heart transplant, cardiac pathologists conducted detailed examinations of the explanted hearts to confirm the diagnosis of DCM. The pathology findings were documented in reports, which were subsequently utilized by the BMCB for DCM stratification. All left ventricular tissue included in this investigation were obtained from subjects with a diagnostic workup that revealed no evidence of familial DCM, cardiac ischemia, significant toxin exposure or myocarditis. Biopsies were washed in ice-cold saline (0.9% NaCl) and stored in liquid nitrogen until DNA extraction. Control tissue samples were recovered within 0\u0026ndash;2 hours of circulatory death and were purchased by UBC from the International Institute for the Advancement of Medicine (IIAM).\u003c/p\u003e\n\u003ch3\u003eThe iHealth-T2D study\u003c/h3\u003e\n\u003cp\u003eiHealth-T2D is a prospective study of Indian Asian males and females living in West London.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Participants were recruited in 2016. At enrolment, all participants completed a structured assessment of cardiovascular and metabolic health, which included (i) previous medical history of cardiovascular disease (CVD), specifically myocardial infarction, angina, and coronary heart disease (CHD); (ii) risk factors for CVD including hypertension, Framingham Coronary Heart Disease (FramCHD) score and creatinine; as well as (iii) high-sensitivity C-reactive protein (hsCRP). Methylation profiling was performed using genomic DNA from peripheral blood collected at enrolment. The study is approved by the National Research Ethics Service and all participants provided written informed consent.\u003c/p\u003e\n\u003ch3\u003eQuantification of DNA methylation\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was bisulfite-converted using the EZ DNA methylation kit according to the manufacturer\u0026rsquo;s instructions (Zymo Research). Case and control samples were randomised in all experiments. Bisulfite-converted genomic DNA was quantified using the Infinium MethylationEPIC BeadChip (EPIC array). Bead intensity retrieval and Illumina background correction were performed using the \u003cem\u003eminfi\u003c/em\u003e R package (version 1.40.0). Markers were excluded from analyses if they met any of the following criteria: (i) they were non-CpG; (ii) they were present on sex chromosomes; or (iii) they had a low call-rate (\u0026lt;\u0026thinsp;95%). Samples were excluded from analyses if they met any of the following criteria: (i) they had a low call-rate (\u0026lt;\u0026thinsp;98%); (ii) they had mislabelled sex; or (iii) they were present in duplicate. After filtering, the discovery cohort (MAGNet) had 838,624 autosomal CpG probes and 329 samples, while the replication cohort (BMCB) had 841,904 autosomal CpG probes and 85 samples available for analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses of epigenome-wide data\u003c/h2\u003e \u003cp\u003eEpigenome-wide methylation data was analysed using the CPACOR pipeline (incorporating Control Probe Adjustment and reduction of global CORrelation).\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Quantile-normalised marker intensity values were used to calculate per-CpG methylation beta values. In the CPACOR pipeline, signal intensities from control probes built into the array are used to correct technical biases in methylation data. These control probes assess key aspects of array chemistry, such as bisulfite conversion efficiency. A principal component analysis (PCA) is performed on the control probe intensities, and the resulting principal components (PCs) are included as linear predictors in regression analyses. This method was designed to minimise technical biases when comprehensive information on experimental factors that could cause data variations is lacking. In the current investigation, PCs accounting for ~\u0026thinsp;95% of control probe variation were included as predictors in regression models to adjust for technical biases (MAGNet: first 10 PCs; BMCB: first 3 PCs). The association of each autosomal CpG site with DCM was tested using logistic regression, adjusting for age, sex and control probe PCs (Model: DCM\u0026thinsp;~\u0026thinsp;Beta\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Sex\u0026thinsp;+\u0026thinsp;PCs\u003csub\u003econtrol\u0026minus;probes\u003c/sub\u003e). Within the mixed-ancestry MAGNet cohort, regression was performed separately for each ancestry group, followed by a trans-ancestry inverse variance-weighted meta-analysis using METAL. Test-statistic bias and inflation in association results was adjusted pre- and post- meta-analysis, using a Bayesian algorithm implemented in the \u003cem\u003ebacon\u003c/em\u003e R package (version 1.30.0).\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEnrichment analysis of genomic regulatory features\u003c/h3\u003e\n\u003cp\u003eSentinel CpGs were evaluated for enrichment in genomic regulatory features. This evaluation was conducted against a background set of 1,000 permutations of EPIC array CpGs. Permuted background sites were matched to sentinel CpGs based on similar methylation levels (\u0026plusmn;\u0026thinsp;0.025 difference in mean methylation beta) and variability (\u0026plusmn;\u0026thinsp;0.25 difference in standard deviation). Instead of using a fixed threshold for mean and standard deviation across all sentinel CpGs, a sliding-window approach was applied.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Using this approach, starting values of 0.025 for mean and 0.0025 for standard deviation were gradually incremented up to a maximum of 0.025 for mean and 0.25 for standard deviation, until 1,000 permuted sites were identified for each sentinel CpG.\u003c/p\u003e \u003cp\u003eThe assessed genomic features included 15 learned chromatin states, deoxyribonuclease I (DNAse I) hotspots, regions marked by 5 core histone modifications, as well as 1,210 transcription factor binding sites (TFBS). Information on chromatin states, DNAse I hotspots and regions enriched in histone modifications in various tissues and cell subsets were obtained from the Roadmap Epigenomic Consortium database, while known TFBS were sourced from the Remap 2022 database.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Transcription Factors (TFs) corresponding to enriched TFBS were classified as follows: \u0026lsquo;Cardiac TF\u0026rsquo; if expressed in humans and validated \u003cem\u003ein vivo\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e; \u0026lsquo;Computational-based\u0026rsquo; if predicted as cardiac TFs computationally due to disruption of TFBS near heart-failure linked loci\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, or \u0026lsquo;Cardiac role unknown\u0026rsquo; if existing literature has not suggested any cardiac role in humans. Enrichment was determined through a one-tailed permutation test. Permutation p-values were computed by comparing overlap counts in the sentinel CpG set (test set) with the background distribution.\u003c/p\u003e\n\u003ch3\u003eRNA sequencing\u003c/h3\u003e\n\u003cp\u003eRNA sequencing (RNA-Seq) data for gene expression was obtained from the same left ventricular tissue samples analysed for methylation, if available (n\u0026thinsp;=\u0026thinsp;306, MAGNet). Prior to downstream analyses, genes were removed if they met any of the following criteria: (i) they had low read counts (\u0026lt;\u0026thinsp;10 counts in the minimum group size of samples); or (ii) they were present on sex chromosomes. Gene expression levels were normalised using Trimmed Mean of M-values (TMM), recommended for quantitative trait loci analyses.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e More details on the sequencing, alignment, filtering and normalization of RNA-seq data are available in Additional File 3.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExpression quantitative trait methylation (cis\u003c/b\u003e \u003cb\u003e-\u003c/b\u003e \u003cb\u003eeQTM) analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eExpression quantitative trait methylation (\u003cem\u003ecis\u003c/em\u003e-eQTM) analysis was conducted to identify significant associations between sentinel CpG methylation and proximal (\u003cem\u003ecis\u003c/em\u003e) gene expression (+/- 1Mb of the sentinel CpG based on gene transcription start site (TSS)). Gene expression was modelled against CpG methylation with adjustment for age, sex, ethnicity, RNA Integrity number (RIN) and the top 5 probabilistic estimation of expression residuals (PEER) factors which represent latent sources of variability in the gene expression data, possibly including technical and unknown confounders.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Association testing was performed using a linear model implemented within the \u003cem\u003eMatrixEQTL\u003c/em\u003e R package (version 2.3).\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Following \u003cem\u003ecis\u003c/em\u003e-eQTM identification, physical interactions between sentinel CpGs and their target genes were further examined using left ventricular chromatin interaction data from an external dataset at 5kb resolution.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Sentinel CpG enrichment in \u003cem\u003ecis\u003c/em\u003e-eQTMs was evaluated against matched background CpGs, using the same permutation testing method described in the 'Enrichment analysis of regulatory features' section.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMethylation quantitative trait loci analysis (meQTL)\u003c/h2\u003e \u003cp\u003eDNA samples were obtained from the same left ventricular tissue samples analysed for methylation, if available (n\u0026thinsp;=\u0026thinsp;304, MAGNet). Genotyping was performed using the Affymetrix Genome-Wide SNP Array 6.0.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e SNPs were removed from the current analyses if they met any of the following criteria: (i) they were multiallelic; (ii) they were present in duplicate; (iii) they had low imputation quality (R2\u0026thinsp;\u0026lt;\u0026thinsp;0.3); or (iv) they were rare, defined as having an ancestry-specific minor allele frequency (MAF) of less than 0.05. Methylation quantitative trait loci (meQTL) analysis was conducted to identify significant associations between SNPs and sentinel CpG methylation (+/-500kb). CpG methylation was regressed against SNP dosage values with adjustment for age, sex and methylation array control probe PCs capturing 95% of control probe variation. Association testing was performed using a linear model implemented within the \u003cem\u003eMatrixEQTL\u003c/em\u003e R package (version 2.3).\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Regression was performed separately for each ancestry group, followed by a trans-ancestry inverse variance-weighted meta-analysis using METAL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCausal analyses (Mendelian Randomisation and Colocalisation)\u003c/h2\u003e \u003cp\u003eSummary data-based Mendelian Randomisation (SMR) was performed to investigate the putative causal contribution of sentinel CpGs to DCM. SMR uses summary-level association statistics from independent association studies to compute a causal estimate for the influence of an exposure (e.g. sentinel CpG methylation) on an outcome (e.g. DCM disease status, or gene expression).\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e The significance of the causal estimate is determined using the Wald Test. Separate SMR analyses were conducted to examine causal relationships between sentinel CpG methylation and (i) DCM, as well as (ii) proximal gene expression (+/-1Mb). For each sentinel CpG, the SNP that was most strongly associated with the CpG in meQTL analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), that was also analysed for association with the respective outcome, was chosen to be the instrumental variable (IV) for assessing causal relationships. Genetic associations for DCM were obtained from the largest published GWAS of DCM (n\u0026thinsp;=\u0026thinsp;355,381, UKBioBank), while genetic associations for gene expression were obtained from the largest left ventricular tissue \u003cem\u003ecis\u003c/em\u003e-expression quantitative trait loci (\u003cem\u003ecis\u003c/em\u003e-eQTL) study (n\u0026thinsp;=\u0026thinsp;386; GTEx v8 release; SNPs within +/- 1Mb of gene transcription start sites).\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e We validated SMR-significant associations using one-sample Mendelian Randomisation (one-sample MR), and also conducted colocalisation analyses to evaluate the posterior probabilities of a shared causal variant for the assessed traits. One-sample MR was performed with individual-level genotype, methylation, and outcome data (MAGNet) using the 2-stage least squares regression method implemented in the \u003cem\u003eAER\u003c/em\u003e R package (version: 1.2\u0026ndash;12). Colocalisation analysis was conducted using the \u003cem\u003ecoloc.abf\u003c/em\u003e function in the \u003cem\u003ecoloc\u003c/em\u003e R package (version 5.2.3), based on a +/-500kb region around each sentinel CpG as this was the window size used for identifying meQTL.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eWeighted gene co-expression network analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eWe employed weighted gene co-expression network analysis (WGCNA) to construct co-methylation modules using DCM-associated CpGs from discovery-stage EWAS.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e These CpGs were (i) associated with DCM (FDR P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); (ii) replicated with consistent association direction; and (iii) displayed variability (methylation beta SD\u0026thinsp;\u0026gt;\u0026thinsp;0.02). Using 32,198 CpGs, we built a signed consensus co-methylation network using the \u003cem\u003eblockwiseconsensusModules\u003c/em\u003e function in the \u003cem\u003eWGCNA\u003c/em\u003e R package (version 1.72-5), using settings recommended for our sample size (soft thresholding power\u0026thinsp;=\u0026thinsp;12, merge cut height\u0026thinsp;=\u0026thinsp;0.25 and minimum module size\u0026thinsp;=\u0026thinsp;30). Methylation levels within consensus modules were summarised as module eigengenes (ME) and tested for correlation with DCM separately by ancestry (Model: DCM\u0026thinsp;~\u0026thinsp;ME\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Sex\u0026thinsp;+\u0026thinsp;10PCs). Ancestry-specific results were combined using inverse variance-weighted meta-analysis to determine module associations with DCM. Additionally, a hypergeometric test was applied to assess module enrichment in DCM sentinel CpGs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) against all CpGs used for network construction. Genes mapped to CpGs within co-methylation modules for overrepresentation of Gene Ontology (GO) terms and biological processes from KEGG and REACTOME databases using the \u003cem\u003eclusterProfiler\u003c/em\u003e R package (version 4.10.0). GO gene sets were obtained from \u003cem\u003eorg.Hs.eg.db\u003c/em\u003e (version 3.18.0), while KEGG and REACTOME datasets were accessed via the \u003cem\u003emsigdbr\u003c/em\u003e R package (version 7.5.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTargeted methylation sequencing\u003c/h2\u003e \u003cp\u003eTargeted methylation sequencing was performed using genomic DNA from peripheral blood samples collected from participants of the iHealth-T2D study. Targeted methylation sequencing was performed for regions defined as +/-500bp of DCM sentinel CpGs. The choice of window size is supported by previous publications demonstrating a decay in correlation between methylation sites beyond 1-2kb,\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and from our in-house whole-genome bisulfite sequencing dataset where we observed that |r|\u0026gt;0.2 was within +/-500bp for most of the assessed CpGs (data unpublished). Additional details on probe design, sample processing and library preparation, sequencing and quality control steps employed are available in Additional File 3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses of targeted methylation sequencing data\u003c/h2\u003e \u003cp\u003eWithin each sequenced region, the association of each CpG site with CVD-related traits was tested using linear regression for continuous traits and logistic regression for binary traits, adjusting for age, sex and white blood cell proportion of six white blood cell sub-populations (CD8 T cells, CD4 T cells, Natural Killer Cells, B-cells, Monocytes, Granulocytes) estimated by the Houseman algorithm.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e In parallel, pairwise correlation in methylation levels between CpGs within regions was calculated using the R function \u003cem\u003ecor\u003c/em\u003e. For a given pair of CpGs, only samples where methylation data was available for both CpGs being compared were used to calculate correlation.\u003c/p\u003e \u003cp\u003eRegions represented by sentinel CpGs were assessed for enrichment in significant associations to CVD traits, compared regions represented by a background set of 1,000 permutations of non-sentinel CpGs. The background set consisted of EPIC array CpGs matched by methylation levels and variability to sentinel CpGs using the same sliding-window approach that was described in \u003cb\u003e\u0026lsquo;Methods: Enrichment analysis of genomic regulatory features\u003c/b\u003e.\u003cb\u003e\u0026rsquo;\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of methylation risk score (MRS)\u003c/h2\u003e \u003cp\u003eWe constructed a methylation risk score (MRS) from CpGs in fine-mapped regions and examined the association of these scores with CVD traits. Effect sizes of association between individual CpG loci and CVD traits were used as weights for constructing the methylation score. Specifically, we defined trait-specific MRS within each region as a linear combination of \u003cem\u003ek\u003c/em\u003e CpG site beta values \u003cem\u003eb\u003c/em\u003e and weights \u003cem\u003ew\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eWithin each region, the association of each MRS with CVD-related traits was assessed, adjusting for the same covariates as in single loci association testing described in \u003cb\u003e\u0026lsquo;\u003c/b\u003e\u003cb\u003eStatistical analyses of targeted methylation sequencing data\u0026rsquo;.\u003c/b\u003e Permutation testing was conducted using the same methodology as that applied to single CpG associations.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eOverview of study design\u003c/h2\u003e \u003cp\u003eOur study design is summarised in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In brief, we first carried out an epigenome-wide association investigation of DCM using 414 left ventricular samples obtained from two repositories: the Myocardial Applied Genomics network (MAGNet; n\u0026thinsp;=\u0026thinsp;329 [discovery]) and the Bruce McManus Cardiovascular Biobank (BMCB; n\u0026thinsp;=\u0026thinsp;85 [replication]) (Additional File 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Integrative omics analyses were performed on the identified sentinel CpGs. To discover additional signals beyond CpG sites captured by the methylation array, we conducted fine-mapping of selected top-ranking loci in blood samples obtained from a population-based cohort (iHealth-T2D, n\u0026thinsp;=\u0026thinsp;1,974).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEpigenome-wide association analysis\u003c/h2\u003e \u003cp\u003eWe performed EWAS of DCM using genomic DNA extracted from left ventricular free-wall tissue. Separate EWAS were carried out for Whites and African Americans within the discovery cohort (MAGNet), followed by inverse-variance meta-analysis (Methods; Additional File 2: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). From discovery-stage EWAS, 196 CpG sites were associated with DCM at a Bonferroni-corrected threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5.96E-08 (0.05/838,624) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Subsequent targeted replication testing in the BMCB cohort (N\u0026thinsp;=\u0026thinsp;36,925 CpGs, discovery FDR P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) confirmed consistent directionality of effect size estimates for all 196 Bonferroni-significant discovery CpGs, as well as previously reported CpG associations with DCM (Additional File 1: Tables S2 and S3). The 196 CpG sites were distributed across 171 genetic loci, with 150/171(88%) genetic loci containing a single sentinel CpG and 21/171 (12%) loci containing two or more sentinel CpGs (Additional File 1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Additional File 2: Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Conditional analyses at each locus identified a total of 194 robustly associated and conditionally independent signals (\u0026lsquo;sentinel CpGs\u0026rsquo;), which were further analysed for functional relevance and causal contribution to DCM pathogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnsupervised hierarchical clustering based on the methylation levels of the 194 sentinel CpGs resulted in two distinct clusters, segregating samples by their respective case and control status (binomial P\u0026thinsp;\u0026lt;\u0026thinsp;2.2E-16) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). DCM cases comprised the majority of one cluster (128/145;88%), while controls constituted the majority of the second cluster (153/184; 83%). This finding supports a perturbation of DNA methylation in DCM. Clustering by case and control status persisted in ancestry-specific unsupervised hierarchical clustering of methylation levels of the sentinel CpGs (Additional File 2: Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment of Sentinel CpGs in active gene regulatory regions and impact on proximal gene expression\u003c/h2\u003e \u003cp\u003eTo understand the regulatory role of sentinel CpGs, we first examined the enrichment of chromatin states. Compared to a background of array CpGs matched by methylation levels and variability, sentinel CpGs were enriched in transcriptionally active chromatin states of left ventricular tissue, including weakly-transcribed regions (permutation test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), actively transcribed regions and enhancers (permutation test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Additional File 2: Figure S4A). Conversely, sentinel CpGs exhibited depletion in polycomb-repressed regions relative to the background (permutation test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition to enrichment for transcriptionally active chromatin states, sentinel CpGs were also enriched in deoxyribonuclease I (DNase I) hotspots (Additional File 2: Figure S4B). DNase I hotspots are genomic regions that exhibit a significantly high frequency of cleavage by the enzyme DNase I, indicating areas of increased accessibility within the chromatin. Sentinel CpGs were not only enriched in cardiac tissue-specific DNase I hotspots, but also in DNAse I hotspots across various other tissue types and cell subsets, suggesting their gene regulatory role across multiple tissues. We also analysed the overlap between sentinel CpGs and regions marked by histone modifications associated with gene regulation. Sentinel CpGs were enriched in H3K4me1-marked regions indicative of primed enhancer and promoters. Conversely, sentinel CpGs were depleted in H3K4me3-marked regions associated with active promoters. This enrichment in primed, rather than active regulatory elements, suggests that sentinel CpGs could contribute to an epigenetic priming mechanism that facilitates changes in gene expression in response to pathological stressors.\u003c/p\u003e \u003cp\u003eTo identify sentinel CpGs that impacted proximal gene expression, hereon referred to as \u003cem\u003ecis\u003c/em\u003e-expression quantitative methylation loci (\u003cem\u003ecis\u003c/em\u003e-eQTM), we examined the association between methylation levels of the 194 sentinel CpGs and expression of their proximal genes (\u0026lt;\u0026thinsp;1Mb from the gene transcription start site (TSS)) present in the discovery (MAGNet) and replication (BMCB) RNA-seq datasets. The 194 sentinel CpGs were enriched for association with proximal gene expression in left ventricular tissue (3.80-fold compared to expectations under the null hypothesis; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Additional File 2: Figure S5). Subsequent targeted replication testing on sentinel CpG-gene pairs reaching FDR \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in discovery-stage association testing confirmed consistent directionality of effect size estimates between 183 sentinel CpGs and 849 unique proximal genes (964 pairs; \u0026lsquo;replicated \u003cem\u003ecis\u003c/em\u003e-eQTMs\u0026rsquo;) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Additional File 1: Table S4). Bulk left ventricular Hi-C data further supported physical interactions between 174 sentinel CpGs and 686 unique proximal genes (772/964 pairs, 80.1%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Additional File 1: Table S4). For most replicated \u003cem\u003ecis\u003c/em\u003e-eQTMs, sentinel CpGs were located 5\u0026rsquo; upstream of their target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) and were inversely correlated with target gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSummary data-based Mendelian Randomisation to infer disease causation\u003c/h2\u003e \u003cp\u003eHaving obtained initial evidence of sentinel CpG contribution to transcriptional regulation, we next leveraged upon Summary data-based Mendelian Randomisation (SMR) to further evaluate potential causal relationship of the sentinel CpGs with both DCM and proximal gene expression. Separate causal analyses were conducted for DCM and gene expression. We found two sentinel CpGs that were potentially causally linked to DCM (cg08140459 and cg12359658; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with subsequent validation testing via one-sample Mendelian Randomisation (one-sample MR) confirming consistent directionality of causal estimate for cg08140459-DCM (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Additional File 1: Table S5).\u003c/p\u003e \u003cp\u003eTo gain further insight into the molecular mechanisms underpinning the contribution of sentinel CpGs to DCM, we conducted a separate SMR of gene expression, focusing on replicated \u003cem\u003ecis\u003c/em\u003e-eQTMs. After excluding sentinel CpGs without suitable instrumental variables (e.g., the SNP with the strongest association to CpG methylation was not assessed for association with proximal gene expression in the GTEx \u003cem\u003ecis\u003c/em\u003e-eQTL analysis), 931 \u003cem\u003ecis\u003c/em\u003e-eQTMs (181 unique sentinel CpGs, 828 unique genes) could be analysed in the SMR of gene expression. Out of the 181 sentinel CpGs analysed, 36 sentinel CpGs showed putative causal relationships with 43 unique proximal genes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Additional File 1: Table S6).\u003c/p\u003e \u003cp\u003eWe further integrated evidence from multi-omics analyses to identify the most relevant sentinel CpGs for DCM pathogenesis. Among the 36 sentinel CpGs causally linked to proximal gene expression, we selected the three CpGs with the highest posterior probability for a shared causal variant influencing both CpG methylation and proximal gene expression (cg09862509-\u003cem\u003eIER5\u003c/em\u003e, coloc.abf-PP.H4\u0026thinsp;=\u0026thinsp;0.91; cg11793257-\u003cem\u003eENTPD6\u003c/em\u003e, coloc.abf-PP.H4\u0026thinsp;=\u0026thinsp;0.69; cg11793257-\u003cem\u003eABHD12\u003c/em\u003e, coloc.abf-PP.H4\u0026thinsp;=\u0026thinsp;0.47; cg06807905\u003cem\u003e-KCNC4\u003c/em\u003e, coloc.abf-PP.H4\u0026thinsp;=\u0026thinsp;0.51) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Additional File 2: Figure S6). Causal estimates for these pairs were validated by one-sample MR, showing consistent direction of association (Additional File 1: Table S6). We illustrate the integration of SMR and colocalisation analyses to assess causality with cg09862509-\u003cem\u003eIER5\u003c/em\u003e, which showed the highest probability of genetic colocalisation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). To further assess the likelihood of a causal regulatory relationship between cg09862509 methylation and Immediate Early Response 5 (\u003cem\u003eIER5\u003c/em\u003e) expression, we additionally examined an external dataset of left ventricular H3K27ac-based HiChip chromatin interactions.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e This analysis supported a physical interaction between the chromatin regions containing cg09862509 and the putative promoter region of \u003cem\u003eIER5\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlthough the cardiac roles of the target genes have not been well-studied, existing investigations suggest functions with potential relevance to DCM pathogenesis. Immediate Early Response 5 (\u003cem\u003eIER5\u003c/em\u003e) is a transcription factor regulating cell proliferation, possibly via the crosstalk between Notch and DNA damage response pathways.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e Ectonucleoside Triphosphate Diphosphohydrolase 6 (\u003cem\u003eENTPD6\u003c/em\u003e) is a nucleotide-metabolising enzyme that is predominantly expressed in the heart and functions in platelet recruitment and aggregation (Additional File 2: Figure S6A).\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Potassium voltage-gated channel Subfamily C Member 4 (\u003cem\u003eKCNC4\u003c/em\u003e) influences neural cell survival and apoptosis under oxidative stress conditions in mice and may also play a role in regulating myocardial action potential (Additional File 2: Figure S6B).\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e Abhydrolase Domain Containing 12 (\u003cem\u003eABDH12\u003c/em\u003e) is an enzyme that hydrolyses endocannabinoids, a class of lipids regulating a wide range of pathologies including platelet aggregation, vasodilation, and the maintenance of energy balance (Additional File 2: Figure S6C).\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenes mapped to CpG sites demonstrating coordinated changes in methylation patterns are enriched in disease-relevant pathways\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBeyond analysing single CpG associations, examining coordinated methylation changes across multiple CpG sites and their linked genes could reveal disease-relevant pathways regulated by DCM methylation. To achieve this, we conducted weighted gene co-expression network analysis (WGCNA), constructing co-methylation modules using methylation levels of 32,918 DCM-associated CpG sites (discovery EWAS FDR \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, with consistent direction of association in replication EWAS, SD\u0026thinsp;\u0026gt;\u0026thinsp;0.02 across all samples). Seven co-methylation modules were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Regressing the module eigengene (the first principal component of module-specific methylation intensities) against DCM confirmed an association with DCM for all modules (Additional File 1: Table S7).\u003c/p\u003e \u003cp\u003eTo investigate the biological relevance of co-methylation modules, we looked for over-represented gene sets and enrichment in transcription factor binding sites (TFBS). Gene set overrepresentation of gene ontology terms and pathways (KEGG/REACTOME) was analysed using genes belonging to replicated \u003cem\u003ecis\u003c/em\u003e-eQTM pairs of module-specific CpGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-F, Additional File 1: Table S8). Five of the seven identified co-methylation modules had enriched gene sets (FDR P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The most enriched gene sets and pathways in each module reflected distinct aspects of DCM pathogenesis, including alterations in extracellular matrix composition (Module 1; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), cell signalling (Module 2; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), and immune-mediated pathways (Modules 3,4,6) (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFive of the seven identified modules showed significant enrichment in TFBS relative to background CpGs (permutation test p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Additional File 1: Table S9, Additional File 2: Figure S7). Examining the most enriched TFBS for each module, two modules featured TFBS with known or likely cardiac roles, namely Thyroid Hormone Receptor Alpha (\u003cem\u003eTHRA\u003c/em\u003e) (Module 1) and Homeobox protein Hox-B8 (\u003cem\u003eHOXB8\u003c/em\u003e) (Module 2). \u003cem\u003eTHRA\u003c/em\u003e contributes to cardiac function and contractility and \u003cem\u003eHOXB8\u003c/em\u003e contributes to cardiac development.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e The top enriched TFBS for the other modules corresponded to TFs which are not currently known to specifically contribute to cardiac pathways, including zinc finger proteins (Modules 4 and 5) and a TF linked to oncogenesis, \u003cem\u003eLMO1\u003c/em\u003e (Module 7).\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOverall, module-specific enriched gene sets and TFBS demonstrated alignment in regulated pathways, exemplified by Module 2\u0026rsquo;s enrichment in TFBS corresponding to TFs known to regulate developmental pathways and concomitant enrichment in the \u0026lsquo;cartilage development involved in endochondral bone morphogenesis' term (GO:0060351, 3.72-fold, p\u0026thinsp;=\u0026thinsp;2.79E-05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, Additional File 1: Tables S8 and S9). This GO term encompassed genes contributing to collagen biosynthesis and Wnt signaling, which are processes relevant to both cartilage development and DCM-related cardiac phenotypes like cardiac development and remodelling. Taken together, findings from gene set and TFBS enrichment analyses highlight diverse aspects of DCM pathogenesis driven by coordinated changes in methylation patterns.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFine-mapping sentinel CpGs to investigate regional associations with traits related to cardiac disease and disease risk\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs the methylation array covers only 2\u0026ndash;3% of CpG sites in the epigenome, we sought to improve our investigation of regional associations using an existing target methylation sequencing dataset to increase coverage of CpG sites (+/- 500bp) surrounding the top-performing DCM sentinel CpGs. Targeted methylation sequencing was performed using blood samples of individuals from the iHealth-T2D study, which is well phenotyped for various traits relevant to cardiovascular disease (CVD) (n\u0026thinsp;=\u0026thinsp;1974). We assessed regional associations with: (i) previous medical history of CVD, specifically myocardial infarction, angina, and coronary heart disease (CHD); (ii) risk factors for CVD including hypertension, Framingham Coronary Heart Disease (FramCHD) score and the renal marker creatinine, which has been associated with a greater risk of heart disease and early death in the general population\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e; as well as (iii) high-sensitivity C-reactive protein (hsCRP), an inflammatory marker that has been independently associated with increased risk of CVD in asymptomatic individuals.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTargeted sequencing was performed on regions surrounding 28 DCM sentinel CpGs (28 regions) (Additional File 1: Table S10). A total of 293 CpG sites were captured, with two to 24 CpG sites captured in each region. Seventeen regions had significant associations with at least one of the 10 unique CVD traits (Bonferroni-corrected P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Additional File 1: Table S11). At 14 regions, the CpGs with the strongest association with our CVD traits were not CpGs on the EPIC array, further illustrating the added value brought upon by targeted sequencing. Multiple independent signals were also found for two regions, whereby conditioning on the lead signal revealed secondary signals in both regions. Compared to a background of non-sentinel CpGs matched by methylation levels and variability to sentinel CpGs and with +/- 500 bp regions captured in the targeted sequencing experiment, the 28 sentinel CpGs had a greater number of regions containing significant associations with creatinine (6/28 regions; permutation test P\u0026thinsp;\u0026lt;\u0026thinsp;2.20E-02) and FramCHD score (5/28 regions; permutation test P\u0026thinsp;\u0026lt;\u0026thinsp;3.80E-02) (Additional File 1: Table S13).\u003c/p\u003e \u003cp\u003eTo illustrate the utility of targeted sequencing to improve regional associations with DCM, we highlight a region surrounding the DCM sentinel CpG, cg1179325 (EWAS P\u0026thinsp;=\u0026thinsp;1.94E-09). Targeted sequencing of this region revealed stronger signals for creatinine and FramCHD score (Bonferroni-corrected P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from CpGs that were not present on the EPIC array (chr20_25218304 with creatinine, P\u0026thinsp;=\u0026thinsp;1.26E-03; chr20_25218276 with FramCHD score, P\u0026thinsp;=\u0026thinsp;4.08E-03) (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA,B; Additional File 1: Table S11.) The newly identified CpGs exhibited methylation levels that correlated with cg11793257 (chr20_25218304 with cg11793257 |r|=0.42; chr20_25218276 with cg11793257 |r|=0.46) (Additional File 1: Table S11.)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next combined methylation information from multiple CpGs in each region into a weighted methylation risk score (MRS) to investigate associations with CVD. Of the 28 sequenced regions, 25 regions had significant MRS (Bonferroni P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for at least one investigated CVD trait (Additional File 1: Table S12). In 23 of these 25 regions, individual CpGs were not significantly associated with CVD-related traits (Bonferroni P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, combining the methylation patterns of multiple CpGs within these regions into an MRS revealed significant associations with at least one of the investigated CVD-related traits, indicating that the combined effect of multiple CpGs provides stronger association with CVD traits than individual CpGs alone. Nonetheless, unlike regional single CpG association tests, the regional MRS of sentinel CpGs did not show enrichment for significant associations with specific CVD traits when compared to the regions surrounding permuted CpG sites (Additional File 1: Table S13).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWe perform the largest EWAS of DCM in cardiac tissues to date (discovery n\u0026thinsp;=\u0026thinsp;159 DCM, 170 control), extending on previous EWAS of DCM in terms of sample size and coverage of CpG sites. Using independent DCM cohorts, we identified and replicated 36,925 CpG associations with DCM. We further performed comprehensive multi-omics and causal analyses on the top signals: 194 independent CpG signals for DCM that reached epigenome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5.96E-08). These integrative omics analyses suggested the causal contribution of a subset of the 194 sentinel CpGs to DCM pathogenesis and transcriptional regulation. Fine-mapping of putative methylation markers and network analysis of coordinated changes across multiple DCM-associated CpG loci supported the relevance of regions containing DCM-linked methylation changes to cardiac development, disease pathogenesis as well as early indicators of CVD risk.\u003c/p\u003e \u003cp\u003eIn addition to identifying novel CpG associations with DCM, our study confirmed strong associations reported by the most comprehensive existing EWAS of DCM conducted by Meder \u003cem\u003eet al.\u003c/em\u003e on a predominantly White cohort (discovery n\u0026thinsp;=\u0026thinsp;41 cases, n\u0026thinsp;=\u0026thinsp;31 controls) utilizing the older 450k methylation array.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Among Meder \u003cem\u003eet al.\u003c/em\u003e\u0026rsquo;s reported associations that were confirmed in the current investigation (discovery FDR P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, consistent directionality of effect between MAGNet and BMCB cohorts) were two CpG sites (cg25838968 and cg16254946) that had reached epigenome-wide significance in Meder \u003cem\u003eet al.\u003c/em\u003e\u0026rsquo;s investigation, as well as an additional CpG site (cg24884140) that had been singled out as a promising DCM diagnostic biomarker demonstrating consistent hypomethylation in both myocardial tissue and blood samples from individuals with DCM compared to control subjects, as well as superior classification accuracy for DCM compared to the clinical gold standard biomarker NT-proBNP.\u003c/p\u003e \u003cp\u003eFurther to describing robust CpG associations with DCM, our study expands previous investigations with causal analyses of sentinel CpG contribution to DCM pathogenesis. While individual-level genotype data avails the option of conducting one-sample MR as the primary analysis to avoid issues arising from population heterogeneity, we opted instead to conduct SMR as our primary analysis to leverage genetic association data from a large-scale external GWAS of DCM. Additionally, utilizing two samples minimises the risk of false positives. For CpG-DCM or CpG-gene pairs with SMR causal estimates that reached nominal significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), one-sample MR was subsequently performed for validation. We show putative causal relationships between cg08140459 and DCM. In our investigation, cg08140459 was also robustly linked to the expression of \u003cem\u003eLTBP2\u003c/em\u003e, a recently discovered prognostic biomarker for DCM, in independent cohorts.\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e To gain insight into the molecular pathways in DCM pathogenesis that involve sentinel CpGs, we conducted a separate SMR analysis for gene expression, revealing the putative contribution of three sentinels to nearby genes (cg09862509-\u003cem\u003eIER5\u003c/em\u003e, cg11793257-\u003cem\u003eENTPD6\u003c/em\u003e, cg11793257-\u003cem\u003eABHD12\u003c/em\u003e, cg06807905-\u003cem\u003eKCNC4\u003c/em\u003e) with functions relevant to DCM pathogenesis, thus warranting further investigation in a cardiac context.\u003c/p\u003e \u003cp\u003eUsing targeted methylation sequencing data from a population-based cohort phenotyped for multiple cardiac traits, we discovered independent CVD risk factor signals near DCM sentinels that were not captured by the EPIC array, particularly for creatinine and FramCHD. We presented an example of new signals identified for both creatinine and FramCHD in the cg11793257 locus. Notably, this locus was also highlighted in our SMR of gene expression, which supported a putative causal relationship between the methylation of cg11793257 and the expression of \u003cem\u003eENTPD6\u003c/em\u003e and \u003cem\u003eABHD12.\u003c/em\u003e These genes are involved in metabolism and physiological processes that are potentially relevant to cardiac pathology and CVD risk, making this locus worthy of further investigation. While we also found that aggregating methylation data from CpGs on a regional level improved regional associations with CVD traits, permutation analysis did not reveal enrichment for specific CVD traits among sentinel CpG regions. Further validation is required to ascertain the biological relevance of regions with MRS that are associated with CVD traits.\u003c/p\u003e \u003cp\u003eOur study has some limitations. Firstly, the cell type heterogeneity of left ventricular tissue makes it challenging to delineate the specific cell types driving the associations between CpG methylation and DCM. Although bioinformatics cell type deconvolution methods exist for this task, the lack of reference methylation profiles for heart cell types means that only reference-free approaches can be applied. While reference-free deconvolution algorithms can predict distinct cell classes using major variations in methylation profiles, finding a biological basis to justify assigning these output classes to specific heart cell types (e.g. cardiomyocytes or cardiofibroblasts) currently poses a significant challenge. As single-cell profiling techniques for methylation and gene expression in cardiac cell types advance, future methylation studies of DCM should prioritise elucidating the cell type specificity of DCM-linked CpG methylation and the genes they regulate. Concerning our MR analyses, one limitation would be the potential bias in the causal relationships estimated by two-sample MR owing to population heterogeneity between the multi-ancestry MAGNet cohort (White, African American) used to generate meQTL and the predominantly White cohorts in which the GWAS of DCM and left ventricular eQTL analyses were conducted. Despite this, we did not restrict the meQTL analysis to only samples from subjects of White ancestry in our cohort with methylation and genotype data available (n\u0026thinsp;=\u0026thinsp;194) to maximise power for detecting left ventricular meQTL associations. A second limitation of the MR analyses would be the limited sample size of the cohort used to generate meQTL, despite the current meQTL analysis being the largest to be conducted in left ventricular tissue to date.\u003c/p\u003e \u003cp\u003eNonetheless, our study has important strengths. Besides being the largest existing study of DCM-linked methylation disturbances in left ventricular tissue to date, the current investigation extends previous methylation studies of DCM by seeking evidence for the causal contribution of DCM-linked sentinel CpGs and by investigating regional associations with traits indicative of CVD risk.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis is the largest investigation of perturbed CpG methylation in DCM to be conducted in disease-relevant left ventricular tissue obtained from patients and controls. We identify CpGs independently and robustly associated with DCM and suggest molecular players in new, putative causal mechanisms by which DNA methylation may impact DCM. We also provide preliminary indication of the prognostic potential of regions containing DCM-linked methylation alterations that are associated with CVD-relevant traits in the general population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each cohort, written informed consent for the research use of donated left ventricular (LV) tissue were obtained. For heart transplant recipients, consent was obtained from the transplant recipient. For brain-dead organ donors, consent was obtained from the next-of-kin. All analyses and study protocols were approved by the relevant institutional review boards.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVAILABILITY OF DATA AND MATERIALS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are included within the article and its additional files. Original R scripts are available in GitHub (https://github.com/KonstanzeTan/tan_etal_DCM). Gene regulatory features for enrichment analysis were downloaded from the Roadmap Epigenomics database (https://egg2.wustl.edu/roadmap/web_portal/processed_data.html#ChipSeq_DNaseSeq). Binding sites of known transcription factors are available at the ReMap2022 database (https://remap.univ-amu.fr/download_page). Raw RNAseq counts and accompanying metadata from the MAGNet cohort are available at the following URL: \u0026nbsp;https://github.com/mpmorley/MAGNet?tab=readme-ov-file. \u0026nbsp;Full summary statistics for the GWAS of DCM can be accessed from the NBDC Human Database (https://humandbs.dbcls.jp/en/hum0197-v3-220\u003cu\u003e;\u003c/u\u003edataset ID: hum0197.v3.EUR.DC.v1). The full set of left ventricular eQTL associations (v8) were downloaded from Google Cloud\u0026rsquo;s requester pay buckets (https://console.cloud.google.com/storage/browser/gtex-resources;tab=objects?pli=1\u0026amp;prefix=\u0026amp;forceOnObjectsSortingFiltering=false; dataset ID: Heart_Left_Ventricle.v8.EUR.allpairs.chr*.parquet), using Google\u0026rsquo;s command-line tool, gsutil. Left ventricular chromatin interaction data (H3K27ac-ChIPseq, bulk tissue Hi-C and H3K27ac-based HiChIP data) were requested from authors of Tan et al. (doi: 10.1161/CIRCRESAHA.120.317254). Other datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by a start-up grant awarded to Asst Prof Marie Loh [PI]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eby the Ministry of Education, Singapore (MOE; Grant ID: #002751-00001\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORS\u0026rsquo; CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conceived, designed, and interpreted by KT and ML. KT performed the statistical analysis. KT drafted the manuscript, and ML contributed to the manuscript writing. DT, WT, HN, PJ and CJM pre-processed the data used for key analyses. EW, MM, GS, CJM, FT, PH, TC, KM, RF supplied and/or processed samples and provided phenotype data. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eReichart D, Magnussen C, Zeller T, Blankenberg S. Dilated cardiomyopathy: from epidemiologic to genetic phenotypes: A translational review of current literature. 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Can J Cardiol. 2023;39:1436\u0026ndash;1445. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cjca.2023.05.015\u003c/span\u003e\u003cspan address=\"10.1016/j.cjca.2023.05.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clinical-epigenetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clep","sideBox":"Learn more about [Clinical Epigenetics](http://clinicalepigeneticsjournal.biomedcentral.com/)","snPcode":"13148","submissionUrl":"https://submission.nature.com/new-submission/13148/3","title":"Clinical Epigenetics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5141306/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5141306/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Methylation changes linked to dilated cardiomyopathy (DCM) affect cardiac gene expression. We investigate DCM mechanisms regulated by CpG methylation using multi-omics and causal analyses in the largest cohort of left ventricular tissues available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe mapped DNA methylation at ~850,000 CpG sites, performed array-based genotyping and RNA sequencing in left-ventricular tissue samples from failing and non-failing hearts across two independent DCM cohorts (discovery n=329, replication n=85). Summary data-based Mendelian Randomization (SMR) was applied to explore the causal contribution of sentinel CpGs to DCM. Fine-mapping of regions surrounding sentinel CpGs revealed additional signals for cardiovascular disease risk factors. Coordinated changes across multiple CpG sites were examined using weighted gene correlation network analysis (WGCNA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe identified 194 epigenome-wide significant CpGs associated with DCM (discovery P\u0026lt;5.96E-08), enriched in active chromatin states in heart tissue. Amongst these, 183 sentinel CpGs significantly influenced the expression of 849 proximal genes (±1Mb). SMR suggested the causal contribution of two sentinel CpGs to DCM and 36 sentinel CpGs to the expression of 43 unique proximal genes (P\u0026lt;0.05). Colocalization analyses indicated that a single causal variant may underlie the methylation-gene expression relationship for three sentinel CpGs. Fine-mapping revealed additional signals linked to cardiovascular traits including hsCRP and blood pressure. Co-methylation modules were enriched in gene sets related to cardiac physiological and pathological processes and their corresponding transcriptional regulators, as well as in novel transcriptional regulators whose cardiac relevance is yet to be determined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Using the largest series of left ventricular tissue to date, this study investigates the causal role of cardiac methylation changes in DCM and suggests targets for experimental studies to probe DCM pathogenesis.\u003c/p\u003e","manuscriptTitle":"Epigenome-wide association study for dilated cardiomyopathy in left ventricular heart tissue identifies putative gene sets associated with cardiac development and early indicators of cardiac risk","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-04 17:26:34","doi":"10.21203/rs.3.rs-5141306/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-14T04:27:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-14T04:17:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-05T14:29:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52017727804103447661622405555873857076","date":"2024-10-31T00:48:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165440394740569525976001038922251655023","date":"2024-09-27T13:17:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-26T15:59:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-26T15:54:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-26T08:11:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical Epigenetics","date":"2024-09-24T02:59:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-epigenetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clep","sideBox":"Learn more about [Clinical Epigenetics](http://clinicalepigeneticsjournal.biomedcentral.com/)","snPcode":"13148","submissionUrl":"https://submission.nature.com/new-submission/13148/3","title":"Clinical Epigenetics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"84eb7715-bcf2-47ef-ad24-534eb0b15261","owner":[],"postedDate":"December 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T19:49:30+00:00","versionOfRecord":{"articleIdentity":"rs-5141306","link":"https://doi.org/10.1186/s13148-025-01854-8","journal":{"identity":"clinical-epigenetics","isVorOnly":false,"title":"Clinical Epigenetics"},"publishedOn":"2025-03-08 15:58:50","publishedOnDateReadable":"March 8th, 2025"},"versionCreatedAt":"2024-12-04 17:26:34","video":"","vorDoi":"10.1186/s13148-025-01854-8","vorDoiUrl":"https://doi.org/10.1186/s13148-025-01854-8","workflowStages":[]},"version":"v1","identity":"rs-5141306","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5141306","identity":"rs-5141306","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}