Epigenome-wide Association Study Identifies Cardiac Develop ^¬ mental Gene Patterning and a Novel Class of Biomarkers for Heart Failure

The present invention relates to a method of determining markers for a disease from a patient, wherein information from epigenomics and/or the transcriptome from peripheral blood and a diseased tissue or information from epigenomics and the transcriptome from peripheral blood or a diseased tissue is used for obtaining the markers, as well as a method of determining a risk for a disease in a patient using the markers obtained thereby.

The finding of markers for diagnosing diseases is a recently growing field due to new high-throughput methods of analysis of samples of patients as well as the availability of suffi ^¬ cient computing power to analyze the vast amount of data gen- erated thereby.

This enables the identification of a variety of markers for a multitude of diseases, e.g. cardiac diseases, cancer, etc. Heart failure (HF) is one major cause of morbidity and mor ^¬ tality in the general population and is the leading cause of hospitalization in individuals older than 65. Currently, 2% of general population suffers from HF, in elderly this increases to about 10%. In all western countries there is addi- tionally an increasing prevalence of clinical manifest HF predicted .

HF is the result of an underlying cardiac disease. The two most common reasons for developing HF are systolic and/or di- astolic dysfunction. For systolic HF, also referred to as HF- rEF the main reasons are ischemic heart disease due to coro ^¬ nary artery disease and myocardial infarction and non- ischemic causes such as Dilated Cardiomyopathy (DCM) . DCM is a frequent heart muscle disease with an estimated prevalence of 1:2500 up to 1:500, which is caused by genetic mechanism, inflammation or infection. The progressive nature of the dis- order is responsible for nearly 50,000 hospitalizations and 10,000 deaths per year in the US alone and is the main cause for heart transplantation in young adults. Overall, the inci ^¬ dence of the disease has continually increased over the past years and it was recognized that DCM has a substantial genet- ic contribution. It is estimated that about 30-40% of all DCM cases show familial aggregation and until now more than 40 different genes were found to cause genetic DCM.

Diagnosis and risk stratification of HF and DCM is still challenging and relies predominantly on symptoms, cardiovas ^¬ cular imaging parameters and biomarkers such as N-terminal pro b-type natriuretic peptide (Nt-ProBNP) . Although highly accurate, Nt-ProBNP has its own caveats. For instance, sever ^¬ al confounding factors can alter plasma level of Nt-ProBNP such as, age, gender, race, obesity, exercise, renal failure and anaemia

For better understanding of diseases like HF and to define therapy and diagnostic strategies, more accurate molecular biomarkers are needed. While several studies have now identi ^¬ fied common genetic polymorphisms that are associated with DCM or heart failure - disclosed in Friedrichs, F. et al . : HBEGF, SRA1 , and IK : Three cosegregating genes as determinants of cardiomyopathy, 395-403 (2009),

doi : 10.1101/gr.076653.108.19; and Villard, E. et al . : A ge ^¬ nome-wide association study identifies two loci associated with heart failure due to dilated cardiomyopathy, Eur. Heart J. 32, 1065-76 (2011); epigenetic alterations - disclosed in Haas, J. et al . : Alterations in cardiac DNA methylation in human dilated cardiomyopathy, EMBO Mol. Med. 5, 413-429

(2013) ; or miRNA expression patterns, there still is an unmet need for reliable markers of HF/DCM, as well as other diseas ^¬ es .

Heart failure is the leading cause of hospitalization and death in Western countries. Over the last decades the genetic causes and molecular events driving the progression of heart failure have only been partially unravelled. Besides genetic predisposition (Meder B, et al . , A genome-wide association study identifies 6p21 as novel risk locus for dilated cardio- myopathy. Eur Heart J. 2014;35:1069-77; Villard E, et al . , A genome-wide association study identifies two loci associated with heart failure due to dilated cardiomyopathy. Eur Heart J. 2011;32:1065-76), it is long known that additional aspects including environmental factors and life-style influence the outbreak and course of myocardial failure (Hang CT, et al . , Chromatin regulation by Brgl underlies heart muscle develop ^¬ ment and disease. Nature. 2010;466:62-7). The precise mode of action how such extrinsic, environmental factors may influ ^¬ ence the phenotype of an individual and his disease is basi- cally unknown.

Most recently, cardiovascular research has made first steps towards elucidating the role of the cardiac epigenome. During cardiac development, a series of dynamic changes in the meth- ylation of gene bodies and Histone marks of developmental and sarcomeric genes were detected, a pattern that is partially re-established in failing cardiomyocytes (Hang CT, et al . , Chromatin regulation by Brgl underlies heart muscle develop ^¬ ment and disease. Nature. 2010;466:62-7; Sergeeva IA, et al . , Identification of a regulatory domain controlling the Nppa-

Nppb gene cluster during heart development and stress. Devel ^¬ opment. 2016;143:2135-46; Greco CM, et al . , DNA hydroxymeth- ylation controls cardiomyocyte gene expression in development and hypertrophy. Nature communications. 2016; 7 : 12418) . In the adaption to stress and during hypertrophy, similar observations were made in engineered heart tissue from rats, point ^¬ ing towards conservation of methylation-based gene patterning across species (Stenzig J, et al . , DNA methylation in an engineered heart tissue model of cardiac hypertrophy: common signatures and effects of DNA methylation inhibitors. Basic Res Cardiol. 2016; 111 : 9) . While these studies indicate a po- tentially central role of epigenetic regulation in the heart and highly sophisticated technologies exist to assess His- tone-modifications or DNA methylation at a base-pair resolu ^¬ tion, the lack of availability of myocardial specimen from patients is a major roadblock for elucidating the impact of such changes on complex cardiovascular traits (Greco CM and Condorelli G. Epigenetic modifications and noncoding RNAs in cardiac hypertrophy and failure. Nat Rev Cardiol.

2015;12:488-97). Hence, mainly animal studies or investiga ^¬ tions of very small clinical cohorts could shed some light onto the presence and role of chemical alterations of cardiac DNA in heart failure or cardiomyopathy.

One of the pioneering studies on DNA methylation in heart failure was published by the group of Roger Foo in 2011 (Mo- vassagh M, et al . , Distinct epigenomic features in endstage failing human hearts. Circulation. 2011;124:2411-22). They identified that epigenetic changes in heart failure occur not uniformly across the genome, but are concentrated in promoter CpG islands, intragenic CpG islands and gene bodies. The lim- itation of this study was the very small sample size of only 4 end-stage heart failure cardiac explants that were investi ^¬ gated. In 2013 Haas et al . were able to identify and repli ^¬ cate genome-wide signatures of lower resolution DNA methyla ^¬ tion changes in living patients suffering from Dilated Cardi- omyopathy (DCM) , which is a major cause of non-ischemic heart failure (Haas J, et al . , Alterations in cardiac DNA methyla ^¬ tion in human dilated cardiomyopathy. EMBO Mol Med.

2013;5:413-29) . In this study, they identified a set of novel candidate genes that are potentially involved in heart fail- ure, such as ADORA2A and LY75. Another of the few available examples identified Methyl-CpG-binding protein 2 (MeCP2), a downstream effector of DNA methylation, to be repressed dur- ing heart failure in humans and reactivated after mechanical unloading of the left ventricle by assist devices (Mayer SC, et al . , Adrenergic Repression of the Epigenetic Reader MeCP2 Facilitates Cardiac Adaptation in Chronic Heart Failure.

Circ. Res. 2015;117:622-33), pointing towards a potential role of targeted epigenetic therapies for heart failure.

Biochemical DNA modification resembles a crucial regulatory layer between genetic information, environmental factors and the transcriptome .

Summary of the Invention

To identify epigenetic susceptibility regions and novel bi- omarkers linked to myocardial dysfunction and heart failure, the inventors performed the first multi-omics study in myo ^¬ cardial tissue and blood of patients with Dilated Cardiomyo ^¬ pathy (DCM) and controls. The present inventors dissected for the first time high- resolution epigenome-wide cardiac and blood DNA methylation in conjunction with mRNA and whole-genome sequencing in a large cohort of densely-phenotyped patients with systolic heart failure due to DCM. They provide the yet largest da- taset of cardiac and blood DNA methylation profiles and iden ^¬ tified key epigenomic patterns that are distinct fingerprints of human heart failure.

The present inventors have found that improved marker finding is possible when more than one characteristic of the sample, e.g. the nucleic acid sequence, is considered. Further, it was found that also improved marker finding is possible when more than one sample from different sources is considered, wherein one if preferably from tissue related to a disease and a further one from peripheral blood. In a first aspect, the present invention is related to a method of determining markers for a disease from a patient, comprising

obtaining or providing at least one sample of peripheral blood and at least one sample of a diseased tissue of the pa ^¬ tient diagnosed with the disease;

obtain an epigenomics profile and/or analyze a tran ^¬ scriptome of the at least one sample of the peripheral blood and the at least one sample of the diseased tissue;

- compare the epigenomics profile and/or the transcriptome to an epigenomics profile and/or a transcriptome of a suita ^¬ ble control, respectively; and

determine one or more alteration in the epigenomics pro ^¬ file and/or the transcriptome in both the at least one sample of the peripheral blood and at least one sample of the dis ^¬ eased tissue of the patient diagnosed with the disease.

Further, the present invention relates to a method of deter ^¬ mining markers for a disease from a patient, comprising

- obtaining or providing at least one sample of peripheral blood or at least one sample of a diseased tissue of the pa ^¬ tient diagnosed with the disease;

obtain an epigenomics profile and analyze a transcrip ^¬ tome of the at least one sample of the peripheral blood or the at least one sample of the diseased tissue;

compare the epigenomics profile and the transcriptome to an epigenomics profile and a transcriptome of a suitable con ^¬ trol, respectively; and

determine one or more alteration in the epigenomics pro- file and the transcriptome in either the at least one sample of the peripheral blood or the at least one sample of the diseased tissue of the patient diagnosed with the disease.

Additionally, a method of determining a risk for a disease in a patient, comprising

obtaining or providing an epigenomics profile and/or a transcriptome of at least one sample of the peripheral blood and/or the a diseased tissue, e.g. the myocard/myocardium, of the patient, and

determining the presence of at least one marker as de ^¬ termined by the method of the first or second aspect is dis- closed.

Further disclosed is a data bank comprising specific markers for heart failure and/or dilated cardiomyopathy in a patient, the use of this databank in a method of determining a risk for heart failure and/or dilated cardiomyopathy in a patient, and the use of the specific markers as a marker for heart failure and/or dilated cardiomyopathy in a patient.

In addition, a method of determining a risk for a disease in a patient, comprising

obtaining or providing data of an epigenomics profile and/or a transcriptome of at least one sample of the periph ^¬ eral blood and/or a diseased tissue of the patient, and

determining the presence of at least one marker as de- termined by the method of the first or second aspect is dis ^¬ closed, as well as a computer program product comprising computer executable instructions which, when executed, perform such a method. Further aspects and embodiments of the invention are dis ^¬ closed in the dependent claims and can be taken from the fol ^¬ lowing description, figures and examples, without being limited thereto. Figures

The enclosed drawings should illustrate embodiments of the present invention and convey a further understanding thereof. In connection with the description they serve as explanation of concepts and principles of the invention. Other embodi ^¬ ments and many of the stated advantages can be derived in re ^¬ lation to the drawings. The elements of the drawings are not necessarily to scale towards each other. Identical, functionally equivalent and acting equal features and components are denoted in the figures of the drawings with the same refer ^¬ ence numbers, unless noted otherwise.

Figs. 1 to 3 show schematically concepts for finding markers for a disease according to a method of the present invention.

Fig. 4 shows the relation between Simes significance level (SL) for association between DNA methylation and gene expression at increasing distances (D) as determined in the present Example 1.

Figures 5 to 21 show data referred to and obtained in present Example 2.

Detailed description of the present invention

Definitions

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The term "nucleic acid molecule" refers to a polynucleotide molecule having a defined sequence. It comprises DNA mole ^¬ cules, RNA molecules, nucleotide analog molecules and combi ^¬ nations and derivatives thereof, such as DNA molecules or RNA molecules with incorporated nucleotide analogs or cDNA.

The term "nucleic acid sequence information" relates to in ^¬ formation which can be derived from the sequence of a nucleic acid molecule, such as the sequence itself or a variation in the sequence as compared to a reference sequence.

The term "mutation" relates to a variation in the sequence as compared to a reference sequence. A mutation is for example a deletion of one or multiple nucleotides, an insertion of one or multiple nucleotides, or substitution of one or multiple nucleotides, duplication of one or a sequence of multiple nu ^¬ cleotides, translocation of one or a sequence of multiple nu- cleotides, and, in particular, a single nucleotide polymor ^¬ phism (SNP) .

In the context of the present invention a "sample" is a sam ^¬ ple which comprises at least epigenetic information and/or information regarding the transcriptome of a patient. Exam ^¬ ples for samples are: cells, tissue, biopsy specimens, body fluids, blood, urine, saliva, sputum, plasma, serum, cell culture supernatant, swab sample and others. An epigenomics profile corresponds to the multitude of all epigenomic modifications, i.e. DNA methylation, Histone meth- ylation, etc., that can occur in a patient.

A transcriptomics profile corresponds to the multitude of all transcribed nucleic acids, i.e. messenger RNA, micro RNAs, non-coding RNAs, etc.

Peripheral blood refers to the circulating pool of blood within the patient.

According to certain embodiments, the patient in the present methods is a vertebrate, more preferably a mammal and most preferred a human patient. A vertebrate within the present invention refers to animals having a vertebrae, which includes mammals - including hu ^¬ mans, birds, reptiles, amphibians and fishes. The present in ^¬ vention thus is not only suitable for human medicine, but al ^¬ so for veterinary medicine.

New and highly efficient methods of sequencing nucleic acids referred to as next generation sequencing have opened the possibility of large scale genomic analysis. The term "next generation sequencing" or "high throughput sequencing" refers to high-throughput sequencing technologies that parallelize the sequencing process, producing thousands or millions of sequences at once. Examples include Massively Parallel Signa ^¬ ture Sequencing (MPSS) , Polony sequencing, 454 pyrosequenc- ing, Illumina (Solexa) sequencing, SOLiD sequencing, Ion semiconductor sequencing, DNA nanoball sequencing, Helioscope (TM) single molecule sequencing, Single Molecule

SMRT(TM) sequencing, Single Molecule real time (RNAP) se ^¬ quencing, Nanopore DNA sequencing, Sequencing By Hybridization, Amplicon Sequencing, GnuBio.

Before the invention is described in exemplary detail, it is to be understood that this invention is not limited to the particular component parts of the process steps of the meth ^¬ ods described herein as such methods may vary. It is also to be understood that the terminology used herein is for purpos ^¬ es of describing particular embodiments only, and is not in- tended to be limiting. It must be noted that, as used in the specification and the appended claims, the singular forms "a," "an" and "the" include singular and/or plural referents unless the context clearly dictates otherwise. For example, the term "a" as used herein can be understood as one single entity or in the meaning of "one or more" entities. It is al ^¬ so to be understood that plural forms include singular and/or plural referents unless the context clearly dictates other ^¬ wise. It is moreover to be understood that, in case parameter ranges are given which are delimited by numeric values, the ranges are deemed to include these limitation values.

In a first aspect, the present invention relates to a method of determining markers for a disease from a patient, compris ^¬ ing

- obtaining or providing at least one sample of peripheral blood and at least one sample of a diseased tissue of the pa ^¬ tient diagnosed with the disease; obtain an epigenomics profile and/or analyze a tran ^¬ scriptome of the at least one sample of the peripheral blood and the at least one sample of the diseased tissue;

compare the epigenomics profile and/or the transcriptome to an epigenomics profile and/or a transcriptome of a suita ^¬ ble control, respectively; and

determine one or more alteration in the epigenomics pro ^¬ file and/or the transcriptome in both the at least one sample of the peripheral blood and at least one sample of the dis- eased tissue of the patient diagnosed with the disease.

In this first aspect, thus at least two different samples are obtained, and these can be analyzed with regard to the epige ^¬ nomics profile, the transcriptome, or both. This is schemati- cally shown in exemplary Figures 1 and 2.

According to Fig. 1, two samples are provided, e.g. from a human, i.e. one sample from a diseased tissue 1, e.g. the my- ocard, and one sample from peripheral blood 2. For both sam- pies the epigenomics profile 3 and the transcriptome 4 are obtained and analyzed with the present method, to obtain one or more markers 5. As an alternative, only the epigenomics profile 3 or the transcriptome 4 can be obtained and analyzed when two samples are provided (not shown) . Preferably, only either the epigenomics profile 3 or the transcriptome 4 are then analyzed from both samples in such a case, i.e. not the epigenomics profile 3 from one sample and the transcriptome 4 from the other sample. In an alternative method shown in Fig. 2, again two samples are provided, e.g. from a human, i.e. one sample from a dis ^¬ eased tissue 1, e.g. the myocard, and one sample from periph ^¬ eral blood 2. For both samples only the epigenomics profile 3 is obtained, though, and analyzed with the present method, to obtain one or more markers 5. Of course, it is also possible to analyze the transcriptome 4 only instead of the epige ^¬ nomics profile 3 in the scheme shown in Fig. 2. In a second aspect, the present invention relates to a method of determining markers for a disease from a patient, compris ^¬ ing

- obtaining or providing at least one sample of peripheral blood or at least one sample of a diseased tissue of the pa ^¬ tient diagnosed with the disease;

obtain an epigenomics profile and analyze a transcrip ^¬ tome of the at least one sample of the peripheral blood or the at least one sample of the diseased tissue;

compare the epigenomics profile and the transcriptome to an epigenomics profile and a transcriptome of a suitable con ^¬ trol, respectively; and

determine one or more alteration in the epigenomics pro- file and the transcriptome in either at least one sample of the peripheral blood or the at least one sample of the dis ^¬ eased tissue of the patient diagnosed with the disease.

In this second aspect, thus at least one sample is obtained, but not from different sources. This sample is then analyzed with regard to both the epigenomics profile and the tran ^¬ scriptome. This is schematically shown in exemplary Figure 3.

According to Fig. 3, one sample is provided, e.g. from a hu- man, i.e. one sample from a diseased tissue 1, e.g. the myo- card. For this sample both the epigenomics profile 3 and the transcriptome 4 are obtained and analyzed with the present method, to obtain one or more markers 5. Of course, it is al ^¬ so possible to provide one sample from the peripheral blood 2 instead of from the diseased tissue 1 in this method, though.

The disease in the present invention is not particularly limited. According to certain embodiments, it is a non ^¬ infectious disease, particularly a cardiovascular disease. According to certain embodiments, the disease is heart fail ^¬ ure (HF) and/or dilated cardiomyopathy (DCM) . In such a case, the sample of the diseased tissue can be obtained from myo ^¬ cardial tissue.

The obtaining of the sample is also not particularly limited, but is preferably non-invasive, e.g. is taken from a stock or from a storage, etc.

Further, also the obtaining of the epigenomics profile as well as the analysis of the transcriptome are not particular- ly limited and can be suitably carried out using known means, including sequencing, bead array or microarray technology.

Also, the comparison to an epigenomics profile and/or a tran ^¬ scriptome of a suitable control is not particularly limited and can be done in any way, e.g. using computational pro ^¬ grams, etc. Further, the alteration in the epigenomics pro ^¬ file and/or the transcriptome is not particularly limited. According to certain embodiments, the alteration is a hyper and/or hypo methylation and/or a change in chromatin marks and/or a change in the RNA (e.g. messenger RNA, micro RNA, non-coding RNA etc.) expression level, e.g. an increase or decrease in RNA expression level, wherein all combinations are possible, e.g. a hyper methylation in combination with a decrease or an increase in RNA expression level, or a hypo methylation in combination with a decrease or an increase in RNA expression level.

The control is not limited as well and can be suitably chosen based on the patient. For example, a control can be obtained from one or more patients not diagnosed with the disease, or from a publicly known control that is not affected by the disease. According to certain embodiments, the one or more alteration is determined with regard to the nucleic acid se ^¬ quence information of the patient, e.g. the genome. According to certain embodiments, the patient is a human. According to certain embodiments, the patient is a human and the control is reference genome hgl9, as provided by e.g. Genome Refer- ence Consortium and the University of California, Santa Cruz (GRCh37/hgl9, downloadable from

http : / /hgdownload . cse . ucsc . edu/goldenPath/hgl 9/bigZips/ and http : //www . ncbi . nlm. nih . gov/proj ects /genome/assembly/grc/huma n/) . Gene regions are based on the GRCh37/hgl9 and the Gen- code 19 gene model (http://www.gencodegenes.org/).

According to certain embodiments a plurality of samples of the peripheral blood and/or the diseased tissue are obtained or provided from patients diagnosed with the disease. This way statistical significance of the found markers can be im ^¬ proved .

In a further aspect, the present invention relates to a meth- od of determining a risk for a disease in a patient, compris ^¬ ing

obtaining or providing an epigenomics profile and/or a transcriptome of at least one sample of the peripheral blood and/or a diseased tissue, e.g. the myocard, of the patient, and

determining the presence of at least one marker as de ^¬ termined by the method of the first or the second aspect.

Again, the obtaining of the sample is not particularly lim- ited, but is preferably non-invasive, e.g. is taken from a stock or from a storage, etc.

According to certain embodiments, the diseased tissue is the myocard, and preferably the disease is heart failure and/or dilated cardiomyopathy.

For heart failure and/or dilated cardiomyopathy, a list of markers for improved determination of a risk for these dis ^¬ eases has been found by the present methods of the first and second aspect. These are shown in the following tables. Thus, according to certain embodiments, the at least one epi- genetic and / or transcriptomic marker for determining a risk for heart failure and/or dilated cardiomyopathy

- is contained in genomic regions with regard to reference genome hgl9 that show coordinated hyper/hypo methylation in HF/DCM in peripheral blood and myocardial tissue and are as ^¬ sociated with RNA expression levels and is chosen from the sequences disclosed in Table 1, preferably Table la, particu ^¬ larly preferably Table lb; and/or

- is contained in genomic regions with regard to reference genome hgl9 that show hyper/hypo methylation in HF/DCM in myocardial tissue and are associated with RNA expression levels and is chosen from the sequences disclosed in Table 2, pref ^¬ erably Table 2a, particularly preferably Table 2b; and/or - is contained in genomic regions with regard to reference genome hgl9 that show coordinated hyper/hypo methylation in HF/DCM in peripheral blood and myocardial tissue and is cho ^¬ sen from the sequences disclosed in Table 3, preferably Table 3a, particularly preferably Table 3b; and/or

- is contained in genomic regions with regard to reference genome hgl9 that show dysmethylation in HF/DCM in peripheral blood and is chosen from the sequences disclosed in Table 4; and/or

- is contained in genomic regions with regard to the refer- ence Infinium HumanMethylation450K database and the reference genome hgl9, respectively, that show dysmethylation in HF/DCM in peripheral blood and is chosen from the cpg IDs or posi ^¬ tions disclosed in Table 5; and/or

- is contained in genomic regions with regard to reference genome hgl9 that show dysmethylation in HF/DCM in peripheral blood and is chosen from the sequences disclosed in Table 6; and/or

- is contained in genomic regions with regard to the refer- genome hgl9, respectively, that show dysmethylation in HF/DCM in peripheral blood and is chosen from the cpg IDs or posi ^¬ tions disclosed in Table 7; and/or - is contained in genomic regions with regard to reference genome hgl9 that show dysmethylation in HF/DCM in peripheral blood and is chosen from the sequences disclosed in Table 8 ; and/or

- is contained in genomic regions with regard to the refer ^¬ ence Infinium HumanMethylation450K database and the reference genome hgl9, respectively, that show dysmethylation in HF/DCM in peripheral blood and is chosen from the cpg IDs or posi ^¬ tions disclosed in Table 9; and/or

- is contained in genomic regions with regard to reference genome hgl9 that show dysmethylation in HF/DCM in peripheral blood and myocardial tissue and are associated with RNA ex ^¬ pression levels and is chosen from the ANF and/or BNP loci and/or the sequences disclosed in Table 10. In the tables 1, la , lb, 2, 2a, 2b, 3, 3a, 3b, 4, 6, 8, and 10 the sequences are the nucleic acid sequences between the positions in the columns titled start and end in the respective chromosomes (chr.), including the positions given under start and end, with regard to reference genome hgl9. Further, in tables 1, la, lb, 2, 2a, 2b, 3, 3a, and 3b sequences are given in col ^¬ umns 1 and 2 as well as in columns 4 and 5 for brevity sake, i.e. one sequence is between and including the positions in columns 1 and 2, and one sequence is between and including the positions in columns 4 and 5. Tables 5, 7 and 9 represent distinct cpg IDs with regard to the reference Infinium Hu- mariMethylatiori450K database and positions with regard to ref ^¬ erence genome hgl9 that show statistically significant dys ^¬ methylation in peripheral blood. The inventors have found that a hyper/hypo methylation can affect both strands and therefore genes on both strands. They further found out that it also does not only affect the gene regions itself, but also the surrounding area, particularly within a region of 10000 base pairs, more particularly within a region of 1000 base pairs. Not only coding regions may be influenced thereby, but also regions surrounding the coding regions, e.g. promoter regions, etc. Thus, while the most significant results may be found in only a very limited re ^¬ gion, hyper/hypo methylation was observed within a broad re ^¬ gion around the position, without a significant change in the significance within 10000 base pairs, as is also shown in e.g. Fig. 4. Tables 1, 2, 3, 4, 6, 8, and 10 thus represent the respective ranges for a gene range -10000 base pairs at the start and +10000 at the end for genes affected by a change in methylation, i.e. a hyper/hypo methylation, whereas tables la, 2a and 3a represent the sequence ranges for the affected gene, and tables lb, 2b and 3b represent the most significant methylation alterations.

Table 1: Markers, given as nucleic acid sequence with start and end, that show coordinated hyper/hypo methylation in HF/DCM in peripheral blood and myocardial tissue and are as ^¬ sociated with RNA expression levels (with regard to reference genome hgl9)

Table la: Preferred markers, given as nucleic acid sequence with start and end, that show coordinated hyper/hypo methyla tion in HF/DCM in peripheral blood and myocardial tissue and are associated with RNA expression levels (with regard to reference genome hgl9)

start end chr . start end chr .

56408246 56409869 17 129705326 129884119 10

56402812 56493127 17 14772811 14790933 2

77285701 77329673 15 417934 442011 11

82660409 83830204 16 131240374 132206716 11

79412358 80875905 2 19240868 19281495 11

80515484 80531874 2 150999427 151178609 4 217497552 217529159 2

Table lb: Particularly preferred markers, given as nucleic acid sequence with start and end, that show coordinated hy ^¬ per/hypo methylation in HF/DCM in peripheral blood and myo ^¬ cardial tissue and are associated with RNA expression levels (with regard to reference genome hgl9)

Table 2: Markers, given as nucleic acid sequence with start and end, that show hyper/hypo methylation in HF/DCM in myo ^¬ cardial tissue and are associated with RNA expression levels (with regard to reference genome hgl9)

start end chr . start end chr .

119415670 119542179 1 3117166 3150543 20

208185588 208427665 1 52173605 52236446 20

114835997 114860636 12 36150099 37386965 21

114781737 114856247 12 20773529 20860170 22

74954874 75089306 14 38854068 38889452 22

222272748 222448922 2 123318897 123613178 3

11895767 11918402 1 127397910 127552051 3

151013448 151052801 1 15481641 15573258 3

154117785 154177124 1 185813458 186090026 3

16320732 16345302 1 42685177 42719072 3

183888797 184016863 1 43318005 43476256 3

27658514 27690421 1 56751447 57123357 3

53961911 54209877 1 146668780 146869787 4

842246 866396 1 15331443 15457790 4

125455724 125709783 10 186275033 186327053 4 50212291 50333554 10 54315469 54577572 4

71019741 71171638 10 76944836 76972568 4

72962328 73072621 10 138717636 138740885 5

90629492 90744910 10 168078746 168738133 5

10584639 10725535 11 58254866 59827947 5

33870123 33923836 11 71393062 71515395 5

65647876 65669105 11 33229788 33254287 6

68070078 68226743 11 33530330 33558019 6

73009335 73090136 11 106495724 106557590 7

73101533 73319234 11 149554787 149587699 7

93852095 93925138 11 149560058 149587784 7

94429598 94619918 11 47304753 47632156 7

95699763 96086344 11 756339 839190 7

26101963 26242825 12 128796780 129123499 8

102094967 102385456 13 25689247 25912913 8

108860728 108896603 13 116197012 116370018 9

53181606 53227919 13 9701791 9799172 1

96495662 96570417 14 28189056 28223196 1

101830819 102075405 15 198597802 198736545 1

68584051 68734501 15 68582306 68634585 2

74456013 74479213 15 235391686 235415697 2

83766160 83823606 15 47366412 47410127 11

15787030 15960890 16 63964151 64001354 11

27788851 28084830 16 46690056 46796006 13

31119400 31140068 16 89169385 89209714 15

49301829 49325742 16 27314990 27386099 16

17736829 17885736 17 30184149 30210397 16

42102004 42154987 17 31261312 31354213 16

5009734 5088329 17 84589201 84661683 16

62214588 62350661 17 85922410 85966215 16

78183499 78237299 17 7229849 7264797 17

78992934 79018501 17 76116852 76149049 17

8367524 8544079 17 10371512 10407291 19

31755852 31850453 19 36385304 36409197 19

7102267 7304045 19 51864861 51885969 19

176991341 177047830 2 39304489 39327880 20 223054608 223173715 2 46295869 46361904 21

23598089 23941481 2 44558837 44625413 22

55189326 55349757 2

Table 2a: Preferred markers, given as nucleic acid sequence with start and end, that show hyper/hypo methylation in HF/DCM in myocardial tissue and are associated with RNA ex ^¬ pression levels (with regard to reference genome hgl9) start end chr . start end chr .

119425670 119532179 1 3127166 3140543 20

208195588 208417665 1 52183605 52226446 20

114845997 114850636 12 36160099 37376965 21

114791737 114846247 12 20783529 20850170 22

74964874 75079306 14 38864068 38879452 22

222282748 222438922 2 123328897 123603178 3

11905767 11908402 1 127407910 127542051 3

151023448 151042801 1 15491641 15563258 3

154127785 154167124 1 185823458 186080026 3

16330732 16335302 1 42695177 42709072 3

183898797 184006863 1 43328005 43466256 3

27668514 27680421 1 56761447 57113357 3

53971911 54199877 1 146678780 146859787 4

852246 856396 1 15341443 15447790 4

125465724 125699783 10 186285033 186317053 4

50222291 50323554 10 54325469 54567572 4

71029741 71161638 10 76954836 76962568 4

72972328 73062621 10 138727636 138730885 5

90639492 90734910 10 168088746 168728133 5

10594639 10715535 11 58264866 59817947 5

33880123 33913836 11 71403062 71505395 5

65657876 65659105 11 33239788 33244287 6

68080078 68216743 11 33540330 33548019 6

73019335 73080136 11 106505724 106547590 7

73111533 73309234 11 149564787 149577699 7

93862095 93915138 11 149570058 149577784 7

94439598 94609918 11 47314753 47622156 7 95709763 96076344 11 766339 829190 7

26111963 26232825 12 128806780 129113499 8

102104967 102375456 13 25699247 25902913 8

108870728 108886603 13 116207012 116360018 9

53191606 53217919 13 9711791 9789172 1

96505662 96560417 14 28199056 28213196 1

101840819 102065405 15 198607802 198726545 1

68594051 68724501 15 68592306 68624585 2

74466013 74469213 15 235401686 235405697 2

83776160 83813606 15 47376412 47400127 11

15797030 15950890 16 63974151 63991354 11

27798851 28074830 16 46700056 46786006 13

31129400 31130068 16 89179385 89199714 15

49311829 49315742 16 27324990 27376099 16

17746829 17875736 17 30194149 30200397 16

42112004 42144987 17 31271312 31344213 16

5019734 5078329 17 84599201 84651683 16

62224588 62340661 17 85932410 85956215 16

78193499 78227299 17 7239849 7254797 17

79002934 79008501 17 76126852 76139049 17

8377524 8534079 17 10381512 10397291 19

31765852 31840453 19 36395304 36399197 19

7112267 7294045 19 51874861 51875969 19

177001341 177037830 2 t 39314489 39317880 20

223064608 223163715 2 46305869 46351904 21

23608089 23931481 2 44568837 44615413 22

55199326 55339757 2

Table 2b: Particularly preferred markers, given as nucleic acid sequence with start and end, that show hyper/hypo meth ylation in HF/DCM in myocardial tissue and are associated with RNA expression levels (with regard to reference genome hgl9)

start end chr . start end chr .

119526255 119526256 1 78190755 78190756 17

119526882 119526883 1 79012396 79012397 17 119527008 119527009 1 8382941 8382942 17

119527111 119527112 1 31848310 31848311 19

119532189 119532190 1 7224513 7224514 19

119532542 119532543 1 7224713 7224714 19

119534644 119534645 1 177025198 177025199 2

208293478 208293479 1 223164925 223164926 2

208405868 208405869 1 23843711 23843712 2

208412585 208412586 1 55339939 55339940 2

114841202 114841203 12 3148787 3148788 20

114841671 114841672 12 52199729 52199730 20

114841708 114841709 12 52199748 52199749 20

114841792 114841793 12 36577638 36577639 21

114845868 114845869 12 20780298 20780299 22

114846162 114846163 12 38864868 38864869 22

114846162 114846163 12 123372199 123372200 3

114846321 114846322 12 127494852 127494853 3

114846321 114846322 12 15540137 15540138 3

114846399 114846400 12 186080868 186080869 3

114846399 114846400 12 42694144 42694145 3

114846412 114846413 12 42694803 42694804 3

75043777 75043778 14 43405624 43405625 3

75072120 75072121 14 56789178 56789179 3

75086513 75086514 14 146740968 146740969 4

222323493 222323494 2 146841472 146841473 4

222333289 222333290 2 15397288 15397289 4

222367110 222367111 2 186283800 186283801 4

11900652 11900653 1 54357316 54357317 4

151021364 151021365 1 76945459 76945460 4

154164699 154164700 1 138718914 138718915 5

16335452 16335453 1 168139607 168139608 5

184005063 184005064 1 58882753 58882754 5

27677240 27677241 1 71402031 71402032 5

54058616 54058617 1 33240333 33240334 6

854824 854825 1 33551533 33551534 6

125618188 125618189 10 106507474 106507475 7

50289110 50289111 10 149578384 149578385 7 50298306 50298307 10 149578384 149578385 7

71094286 71094287 10 47479433 47479434 7

73026288 73026289 10 811491 811492 7

90712739 90712740 10 128808063 128808064 8

10716164 10716165 11 25908057 25908058 8

33913716 33913717 11 25908279 25908280 8

65659393 65659394 11 25908503 25908504 8

68142234 68142235 11 116359818 116359819 9

73034459 73034460 11 9711791 9789172 1

73108402 73108403 11 28199056 28213196 1

93885254 93885255 11 198607802 198726545 1

94521117 94521118 11 68592306 68624585 2

96071506 96071507 11 235401686 235405697 2

26111821 26111822 12 47376412 47400127 11

102104991 102104992 13 63974151 63991354 11

108867111 108867112 13 46700056 46786006 13

53191046 53191047 13 89179385 89199714 15

96520233 96520234 14 27324990 27376099 16

101932559 101932560 15 30194149 30200397 16

68645969 68645970 15 31271312 31344213 16

74466337 74466338 15 84599201 84651683 16

83776915 83776916 15 85932410 85956215 16

15923487 15923488 16 7239849 7254797 17

28079611 28079612 16 76126852 76139049 17

31129199 31129200 16 10381512 10397291 19

49312543 49312544 16 36395304 36399197 19

17832220 17832221 17 51874861 51875969 19

42151680 42151681 17 39314489 39317880 20

5019989 5019990 17 46305869 46351904 21

62294665 62294666 17 44568837 44615413 22

Table 3: Markers, given as nucleic acid sequence with start and end, that show coordinated hyper/hypo methylation in HF/DCM in peripheral blood and myocardial tissue (with regard to reference genome hgl9)

start end chr . start end chr . 2339980 2409222 11 171459078 171625390 5

2455915 2880339 11 176900396 176934607 5

81762703 82001899 16 33239788 33244287 6

84033273 84086241 16 75784043 75925767 6

82650409 83840204 16 157089064 157541913 6

31421065 31814916 18 99603205 99649312 7

32063255 32481808 18 139198603 139239730 7

30242635 30363025 18 157321751 158390480 7

6041527 6171253 1 37631710 37712414 8

40314411 40848193 2 41500740 41764280 8

239959865 240333348 2 42938659 42988577 8

140762242 141029076 9 54128285 54174257 8

12388733 12562348 11 14071843 14408982 9

98595913 98686551 13 17174254 17254053 10

24795228 24819251 14 42998959 43058270 10

24824880 24858810 14 95316423 95374237 10

3765056 3940727 16 108323422 108934292 10

70107162 70132561 17 7524530 7688358 11

45513264 45827492 20 75100531 75143324 11

50673613 50699834 22 117175274 117293984 11

32073288 32108119 1 49319507 49361334 12

32807123 32839913 1 74862597 74902805 14

41817595 41859262 1 85986489 86105034 14

53182127 53303014 1 45374849 45416542 15

66248198 66850259 1 69442924 69574556 15

111126203 111184096 1 84312839 84718594 15

151010217 151034462 1 47485035 47745434 16

176816439 177144109 1 55928605 56042684 17

28815 56870 2 77896143 78019647 17

5822800 5851516 2 78133792 78193130 17

43854413 44005126 2 5279019 5307052 18

11587545 11772220 3 10656481 11158587 18

42613333 42646606 3 52558741 52636739 18

62236541 62365005 3 36026498 36048428 19

190560667 190620218 3 49288320 49324286 19

166784411 167035047 4 57311446 57362096 19 137657625 137695416 5 40918370 41055064 21

170836661 170894627 5 38291665 38348829 21

Table 3a: Preferred markers, given as nucleic acid sequence with start and end, that show coordinated hyper/hypo methyl tion in HF/DCM in peripheral blood and myocardial tissue (with regard to reference genome hgl9)

start end chr . start end chr .

2349980 2399222 11 171469078 171615390 5

2465915 2870339 11 176910396 176924607 5

81772703 81991899 16 33239788 33244287 6

84043273 84076241 16 75794043 75915767 6

82660409 83830204 16 157099064 157531913 6

31431065 31804916 18 99613205 99639312 7

32073255 32471808 18 139208603 139229730 7

30252635 30353025 18 157331751 158380480 7

6051527 6161253 1 37641710 37702414 8

40324411 40838193 2 41510740 41754280 8

239969865 240323348 2 42948659 42978577 8

140772242 141019076 9 54138285 54164257 8

12398733 12552348 11 14081843 14398982 9

98605913 98676551 13 17184254 17244053 10

24805228 24809251 14 43008959 43048270 10

24834880 24848810 14 95326423 95364237 10

3775056 3930727 16 108333422 108924292 10

70117162 70122561 17 7534530 7678358 11

45523264 45817492 20 75110531 75133324 11

50683613 50689834 22 117185274 117283984 11

32083288 32098119 1 49329507 49351334 12

32817123 32829913 1 74872597 74892805 14

41827595 41849262 1 85996489 86095034 14

53192127 53293014 1 45384849 45406542 15

66258198 66840259 1 69452924 69564556 15

111136203 111174096 1 84322839 84708594 15

151020217 151024462 1 47495035 47735434 16

176826439 177134109 1 55938605 56032684 17 38815 46870 2 77906143 78009647 17

5832800 5841516 2 78143792 78183130 17

43864413 43995126 2 5289019 5297052 18

11597545 11762220 3 10666481 11148587 18

42623333 42636606 3 52568741 52626739 18

62246541 62355005 3 36036498 36038428 19

190570667 190610218 3 49298320 49314286 19

166794411 167025047 4 57321446 57352096 19

137667625 137685416 5 40928370 41045064 21

170846661 170884627 5 38301665 38338829 21

Table 3b: Particularly preferred markers, given as nucleic acid sequence with start and end, that show coordinated hy ^¬ per/hypo methylation in HF/DCM in peripheral blood and myo ^¬ cardial tissue (with regard to reference genome hgl9) start end chr . start end chr .

2368070 2368071 11 170848039 170848040 5

2376275 2376276 11 171469429 171469430 5

2594153 2594154 11 176924827 176924828 5

2594840 2594841 11 33241974 33241975 6

2690304 2690305 11 75798778 75798779 6

81806083 81806084 16 157342220 157342221 6

84076320 84076321 16 99627985 99627986 7

82970452 82970453 16 139208852 139208853 7

31805151 31805152 18 157452656 157452657 7

32173093 32173094 18 37655503 37655504 8

30351983 30351984 18 41625127 41625128 8

6146988 6146989 1 42948547 42948548 8

40678691 40678692 2 54164391 54164392 8

240082420 240082421 2 14313043 14313044 9

140773129 140773130 9 17183411 17183412 10

12524208 12524209 11 43048646 43048647 10

98605951 98605952 13 95326974 95326975 10

24804339 24804340 14 108924398 108924399 10

24836148 24836149 14 7535256 7535257 11

3824553 3824554 16 75110505 75110506 11 70117522 70117523 17 117283767 117283768 11

45523996 45523997 20 49330158 49330159 12

50689804 50689805 22 74892569 74892570 14

32083535 32083536 1 85999731 85999732 14

32827834 32827835 1 85999933 85999934 14

41827960 41827961 1 45404157 45404158 15

53238307 53238308 1 69452537 69452538 15

66259081 66259082 1 84323154 84323155 15

111148984 111148985 1 47494711 47494712 16

151019727 151019728 1 55952063 55952064 17

177034184 177034185 1 77951858 77951859 17

47150 47151 2 78152051 78152052 17

5836181 5836182 2 5295760 5295761 18

43986106 43986107 2 11148769 11148770 18

11623526 11623527 3 52625368 52625369 18

42626083 42626084 3 36036028 36036029 19

62354546 62354547 3 49306842 49306843 19

190580644 190580645 3 57352269 57352270 19

166797526 166797527 4 40984780 40984781 21

137674194 137674195 5 38337780 38337781 22

ID numbers for the methylation (methyl. ID) refer to the In- finium HumanMethylation450 BeadChip Kit probe IDs as listed in the HumanMethylation450 vl.2 Manifest

(http : / /support . illumina . com/downloads/infinium_humanmethylat ion450_product_files.html), preferred reading directions for the respective double helix strand ( str . ; + or -) with regard to the reference genome for the genes as well as gene names, gene ensemble IDs (gene ID) and chromosomes (chr.) are found in Tables lc, 2c and 2d, and 3c for Tables 1, la , lb; 2, 2a, 2b; and 3, 3a, and 3b, respectively. Also, the starts and ends are given, with the respective tables in brackets. It should be noted that table 2, respectively 2a and 2b, has been split in two tables 2c and 2d, since for Table 2d the whole region has been shown to be significantly deregulated on methylation and expression level. Further, gene IDs, gene names and chromosomes are also given in Tables 4, 6, 8 and 10. In Tables 5, 7 and 9 cpg IDs - representing methylation locations (representing either a nucleobase or a paired nu ^¬ cleobase) - are given with regard to the Infinium Hu- manMethylation450K database, and chromosomes and positions (pos) are given with regard to the reference genome.

Table 4: Markers, given as nucleic acid sequence with start and end, that show dysmethylation in HF/DCM in peripheral blood (with regard to reference genome hgl9)

The markers m Table 4 represent genomic regions with lOkb up/downstream of genes that show statistically significant, particularly the statistically most significant, validated dysmethylation in peripheral blood, particularly in independ ent discovery (41 DCM and 31 CTRL) and verification cohorts (9 DCM and 28 CTRL) . (DCM = dilated cardiomyopathy; CTRL = control ) Table 5: Markers, given as cpg ID with regard to the refer ^¬ ence Inf inium HumanMethylation450K database, and as position (pos) , given with regard to the reference genome hgl9, that show dysmethylation in HF/DCM in peripheral blood

The markers in Table 5 represent distinct cpg IDs and genomic positions (particularly top 10) that show statistically sig ^¬ nificant, particularly the statistically most significant, validated dysmethylation in peripheral blood, particularly in independent discovery (41 DCM and 31 CTRL) and verification cohorts (9 DCM and 28 CTRL) .

Table 6: Markers, given as nucleic acid sequence with start and end, that show dysmethylation in HF/DCM in peripheral blood (with regard to reference genome hgl9)

Gene ID gene name chr . start end

ENSG00000167977 KCTD5 16 2722477 2749031

ENSG00000172382 PRSS27 16 2772420 2780552

ENSG00000221866 PLXNA4 7 131818092 132343447

ENSG00000108039 XPNPEP1 10 111634525 111693311

ENSG00000237976 1 151309444 151310503

ENSG00000143390 RFX5 1 151323117 151329833

ENSG00000064115 TM7SF3 12 27136129 27177367

ENSG00000144567 FAM134A 2 220030948 220040201

ENSG00000115649 CNPPD1 2 220046620 220052828 ENSG00000213901 SLC23A3 2 219950052 220045549

ENSG00000100644 HIF1A 14 62152232 62204976

ENSG00000258667 HIF1A-AS2 14 62192277 62227815

ENSG00000070540 WIPI1 17 66427090 66463654

ENSG00000141337 ARSG 17 66245324 66408872

ENSG00000207561 MIR635 17 66430593 66430689

ENSG00000267009 17 66399765 66511090

ENSG00000145216 FIP1L1 4 54233811 55151439

The markers in Table 6 represent genomic regions with lOkb up/downstream of genes that show validated dysmethylation in peripheral blood, particularly in independent discovery (41 DCM and 31 CTRL) and verification cohorts (9 DCM and 28 CTRL) with an area under the curve (AUC) of more than 85% in the discovery and verification cohorts.

Table 7: Markers, given as cpg ID with regard to the refer- ence Infinium HumanMethylation450K database, and as position (pos) , given with regard to the reference genome hgl9, that show dysmethylation in HF/DCM in peripheral blood

The markers in Table 7 represent distinct cpg IDs and genomic positions that show validated dysmethylation in peripheral blood, particularly in independent discovery (41 DCM and 31 CTRL) and verification cohorts (9 DCM and 28 CTRL) with an area under the curve (AUC) of more than 85% in the discovery and verification cohorts.

Table 8: Markers, given as nucleic acid sequence with start and end, that show dysmethylation in HF/DCM in peripheral blood (with regard to reference genome hgl9)

gene ID gene name ch . start end

ENSG00000134138 MEIS2 15 37191407 37403504

ENSG00000219438 FAM19A5 22 48875273 49236724

ENSG00000137309 HMGA1 6 34194651 34204008

ENSG00000156466 GDF6 8 97164563 97183020

ENSG00000124766 SOX4 6 21583973 21588847

ENSG00000007520 TSR3 16 1409242 1411912

ENSG00000090581 GNPTG 16 1391925 1403352

ENSG00000007516 BAIAP3 16 1373603 1389439

ENSG00000132535 DLG4 17 7103210 7133021

ENSG00000072778 ACADVL 17 7110445 7118592

ENSG00000199053 MIR324 17 7136617 7136698

ENSG00000004975 DVL2 17 7138661 7147864

ENSG00000236364 1 165875117 165879920

ENSG00000143179 UCK2 1 165786769 165870855

ENSG00000150907 FOXOl 13 41139805 41250734

ENSG00000115840 SLC25A12 2 172650881 172874766

ENSG00000128708 HA 1 2 172768959 172838599

ENSG00000002933 TMEM176A 7 150487492 150492208

ENSG00000106565 TMEM176B 7 150498374 150508448

ENSG00000009830 POMT2 14 77751300 77797227

ENSG00000100577 GSTZ1 14 77777228 77787940

ENSG00000122786 CALD1 7 134419004 134645479

ENSG00000091536 MY015A 17 18002021 18073116

ENSG00000129353 SLC44A2 19 10703134 10745235

ENSG00000129351 ILF3 19 10754938 10793093

ENSG00000267100 ILF3-AS1 19 10772539 10774520

ENSG00000163155 LYSMD1 1 151142225 151148424

ENSG00000163159 VPS72 1 151152464 151177797

ENSG00000163156 SCNM1 1 151119141 151132773 ENSG00000163154 TNFAIP8L2 1 151119106 151122225

ENSG00000234936 2 43446713 43450533

ENSG00000115970 HADA 2 43403801 43833185

ENSG00000152518 ZFP36L2 2 43459542 43463748

ENSG00000198879 SFMBT2 10 7210587 7463450

ENSG00000178814 OPLAH 8 145116168 145128735

ENSG00000128918 ALDH1A2 15 58255623 58800065

ENSG00000109180 OCIAD1 4 48797230 48853834

ENSG00000068383 INPP5A 10 134341325 134586979

ENSG00000072657 TRHDE 12 72471047 73049422

ENSG00000236333 TRHDE-AS 1 12 72657289 72678687

ENSG00000167977 KCTD5 16 2722477 2749031

ENSG00000172382 PRSS27 16 2772420 2780552

ENSG00000137691 Cllorf70 11 101908175 101945291

ENSG00000075618 FSCN1 7 5622440 5636286

ENSG00000011275 RNF216 7 5669679 5831370

ENSG00000165609 NUDT5 10 12217325 12248143

ENSG00000151465 CDC123 10 12227965 12282588

ENSG00000228989 2 242619830 242623704

ENSG00000168395 ING5 2 242631451 242658893

ENSG00000173083 HPSE 4 84223615 84266306

ENSG00000173085 COQ2 4 84192690 84216067

ENSG00000221866 PLXNA4 7 131818092 132343447

ENSG00000240859 7 139598 145465

ENSG00000242474 7 145854 159466

ENSG00000165025 SYK 9 93554070 93650831

ENSG00000125810 CD93 20 23069987 23076977

ENSG00000128917 DLL4 15 41211539 41221237

ENSG00000213719 CLIC1 6 31708359 31717540

ENSG00000211451 GNRHR2 1 145519753 145526076

ENSG00000131795 RBM8A 1 145497599 145503536

ENSG00000197008 ZNF138 7 64244767 64284054

ENSG00000154122 ANKH 5 14714911 14881887

ENSG00000266903 19 45145501 45232031

ENSG00000269834 19 52902096 52911019

ENSG00000167555 ZNF528 19 52891103 52911665 ENSG00000196730 DAPK1 9 90102144 90313548

ENSG00000090273 NUDC 1 27216730 27263353

ENSG00000198746 GPATCH3 1 27226980 27236957

ENSG00000142751 GPN2 1 27212625 27226788

ENSG00000153162 BMP6 6 7717031 7871655

ENSG00000239264 TXNDC5 6 7891484 8036646

ENSG00000137203 TFAP2A 6 10403420 10429892

ENSG00000106333 PCOLCE 7 100189801 100195798

ENSG00000106336 FBX024 7 100171606 100188740

PCOLCE-

ENSG00000224729 AS1 7 100197026 100211829

ENSG00000106330 MOSPD3 7 100199726 100203007

ENSG00000136271 DDX56 7 44615017 44624650

ENSG00000158604 TMED4 7 44627494 44631886

ENSG00000185215 TNFAIP2 14 103579780 103593776

ENSG00000163071 SPATA18 4 52907498 52953458

ENSG00000183060 LYSMD4 15 100265903 100283766

ENSG00000068305 MEF2A 15 100007371 100246671

ENSG00000142453 CARM1 19 10972190 11023453

ENSG00000142444 C19orf52 19 11029410 11034211

ENSG00000130733 YIPF2 19 11043445 11049357

ENSG00000130159 ECSIT 19 11626732 11649989

ENSG00000161914 ZNF653 19 11604243 11626738

ENSG00000135269 TES 7 115840548 115888837

ENSG00000108039 XPNPEP1 10 111634525 111693311

ENSG00000155980 KIF5A 12 57933782 57970415

ENSG00000175203 DCTN2 12 57933886 57951114

ENSG00000162415 ZSWIM5 1 45492072 45781881

ENSG00000233954 1 16143680 16144194

ENSG00000237976 1 151309444 151310503

ENSG00000143390 RFX5 1 151323117 151329833

ENSG00000204581 2 111865923 111883165

ENSG00000153094 BCL2L11 2 111866956 111916024

ENSG00000153093 ACOXL 2 111480151 111865799

ENSG00000159692 CTBP1 4 1215237 1253741

ENSG00000064115 TM7SF3 12 27136129 27177367 ENSG00000113721 PDGFRB 5 149503401 149545435

ENSG00000176095 IP6K1 3 49771728 49833975

ENSG00000204344 STK19 6 31928869 31940598

ENSG00000115339 GALN 3 2 166614102 166661192

ENSG00000170312 CDK1 10 62528090 62544610

ENSG00000005471 ABCB4 7 87041014 87119751

ENSG00000117143 UAP1 1 162521324 162559627

ENSG00000145506 NKD2 5 998945 1029058

ENSG00000169604 ANTXR1 2 69230311 69466459

ENSG00000140939 NOL3 16 67194058 67199643

ENSG00000179044 EXOC3L1 16 67228270 67234107

ENSG00000102878 HSF4 16 67187289 67193848

ENSG00000196123 KIAA0895L 16 67219506 67227943

ENSG00000165138 ANKS6 9 101503612 101569247

ENSG00000133111 RFXAP 13 37383362 37393241

ENSG00000160563 MED27 9 134745495 134965295

ENSG00000184465 WDR27 6 169867308 170112159

ENSG00000135094 SDS 12 113840251 113874106

ENSG00000124831 LRRFIP1 2 238526220 238712325

ENSG00000106012 IQCE 7 2588633 2644368

ENSG00000204463 BAG6 6 31616806 31630482

ENSG00000165355 FBX033 14 39876874 39911704

ENSG00000197757 HOXC6 12 54374409 54414607

ENSG00000114316 USP4 3 49325265 49388145

ENSG00000237641 2 232664192 232664597

ENSG00000156973 PDE6D 2 232607136 232660982

ENSG00000144524 COPS7B 2 232636382 232663963

ENSG00000002587 HS3ST1 4 11404775 11441389

ENSG00000136238 RAC1 7 6404155 6433608

ENSG00000113387 SUB1 5 32521740 32594185

ENSG00000128652 H0XD3 2 176991341 177027830

ENSG00000144567 FAM134A 2 220030948 220040201

ENSG00000115649 CNPPD1 2 220046620 220052828

ENSG00000213901 SLC23A3 2 219950052 220045549

ENSG00000152953 STK32B 4 5043170 5492725

ENSG00000148814 LRRC27 10 134135615 134185010 ENSG00000011105 TSPAN9 12 3176522 3385730

ENSG00000139684 ESD 13 47355392 47381367

ENSG00000182667 NTM 11 131230374 132196716

ENSG00000133313 CNDP2 18 72153052 72178366

ENSG00000140506 LMAN1L 15 75095058 75108099

ENSG00000261606 15 75098565 75114136

ENSG00000140474 ULK3 15 75138458 75145687

ENSG00000144744 UBA3 3 69113882 69139559

ENSG00000244513 3 69053093 69095773

ENSG00000144747 TMF1 3 69078979 69111484

ENSG00000073712 FERM 2 14 53333987 53429153

ENSG00000100644 HIF1A 14 62152232 62204976

ENSG00000258667 HIF1A-AS2 14 62192277 62227815

ENSG00000106066 CPVL 7 29044848 29245067

ENSG00000106069 CHN2 7 29151891 29543944

ENSG00000144649 FAM198A 3 43010760 43091703

ENSG00000267282 19 45395285 45404133

ENSG00000130202 PVRL2 19 45339433 45382485

ENSG00000130204 TOMM40 19 45383827 45396946

ENSG00000126214 KLC1 14 104018234 104157888

ENSG00000162396 PARS2 1 55232572 55240187

ENSG00000139832 RAB20 13 111185418 111224080

ENSG00000182557 SPNS3 17 4326984 4381503

ENSG00000136720 HS6ST1 2 129004291 129086151

ENSG00000179348 GATA2 3 128208271 128222028

ENSG00000244300 3 128198056 128211191

ENSG00000065675 PRKCQ 10 6479106 6632263

ENSG00000172428 MYEOV2 2 241075981 241086224

ENSG00000142459 EVI5L 19 7885120 7919862

ENSG00000086827 ZW10 11 113613910 113654533

ENSG00000176973 FAM89B 11 65329821 65331669

ENSG00000173465 SSSCA1 11 65327902 65331413

SSSCA1-

ENSG00000260233 AS1 11 65347132 65347744

ENSG00000173442 EHBP1L1 11 65333510 65350121

ENSG00000168056 LTBP3 11 65316277 65336401 ENSG00000233527 19 37053973 37075610

ENSG00000186020 ZNF529 19 37035677 37106178

ENSG00000152291 TGOLN2 2 85555148 85565548

ENSG00000198612 COPS 8 2 237983956 237999109

ENSG00000227252 2 237978078 238004460

ENSG00000169398 PTK2 8 141678000 142022315

ENSG00000131473 ACLY 17 40033162 40096795

ENSG00000145247 OCIAD2 4 48897037 48918954

ENSG00000111452 GPR133 12 131428453 131616014

ENSG00000099942 CRKL 22 21261715 21298037

ENSG00000070540 WIPI1 17 66427090 66463654

ENSG00000141337 ARSG 17 66245324 66408872

ENSG00000207561 MIR635 17 66430593 66430689

ENSG00000267009 17 66399765 66511090

ENSG00000154957 ZNF18 17 11890757 11910827

ENSG00000171217 CLDN20 6 155575148 155587682

ENSG00000235381 6 155584274 155587858

ENSG00000146426 IAM2 6 155143832 155568857

ENSG00000029639 TFB1M 6 155588644 155645627

ENSG00000145216 FIP1L1 4 54233811 55151439

The markers in Table 8 represent genomic regions with lOkb up/downstream of genes that show validated dysmethylation in peripheral blood, particularly in independent discovery (41 DCM and 31 CTRL) and verification cohorts (9 DCM and 28 CTRL) with an area under the curve (AUC) of more than 80% in the discovery and verification cohorts.

Table 9: Markers, given as cpg ID with regard to the refer- ence Infinium HumanMethylation450K database, and as position (pos) , given with regard to the reference genome hgl9, that show dysmethylation in HF/DCM in peripheral blood

cg00792966 chr6 21595983 cg01258653 chrl 6 1393103 cg01377644 chrl7 7126609 cg01574241 chrl 165873825 cg01995660 chrl3 41238844 cg02155405 chr2 172776401 cg02244695 chr7 150497346 cg02315508 chrl4 77787366 cg02516134 chr7 134575187 cg02628561 chrl7 18061605 cg03301945 chrl9 10764555 cg03316474 chrl 151138495 cg03443205 chr2 43454133 cg03832371 chrlO 7290545 cg03932271 chr8 145111468 cg04189295 chrl5 58653220 cg04422289 chr4 48833305 cg04716580 chrlO 134546291 cg04775889 chrl2 72665880 cg04880804 chrl 6 2762569 cg05892674 chrl 1 101918304 cg06109226 chr7 5650145 cg06109724 chrlO 12237553 cg06164187 chr2 242641258 cg06168319 chr4 84205972 cg06183123 chr7 132340279 cg06601579 chr7 142966 cg07160163 chr9 93563778 cg07286123 chr20 23067126 cg07431199 chrl5 41218265 cg07584663 chr6 31697834 cg07600211 chrl 145516081 cg08135727 chr7 64254733 cg08482307 chr5 14728684 cg08485918 chrl9 45207541 cg08525430 chrl9 52900882 cg08797471 chr9 90113120 cg09174009 chrl 27216796 cg09245939 chr6 7881428 cg09288780 chr6 10413394 cg09326362 chr7 100202679 cgl0045804 chr7 44621958 cgl0367412 chrl4 103590195 cgl0418567 chr4 52917567 cgl0620429 chrl5 100253266 cgl0706553 chrl9 11039446 cgl0707300 chrl9 11616032 cgl0728469 chr7 115850755 cgll055926 chrlO 111683227 cgll087358 chrl2 57940980 cglll55625 chrl 45769710 cgll650974 chrl 16134399 cgll797228 chrl 151319782 cgl2427896 chr2 111880694 cgl2525219 chr4 1228640 cgl2659065 chrl2 27156738 cgl2727795 chr5 149535695 cgl3033938 chr3 49824475 cgl3116438 chr6 31940606 cgl3169065 chr2 166650947 cgl3227473 chrlO 62538143 cgl3338827 chr7 87104932 cgl3471915 chrl 162531167 cgl3621612 chr5 1021202 cgl3766043 chr2 69396932 cgl4174336 chrl 6 67208654 cgl4281264 chr9 101556171 cgl4522731 chrl3 37393990 cgl4573676 chr9 134954987 cgl4582523 chr6 169952299 cgl5277108 chrl2 113842998 cgl5579587 chr2 238600061 cgl5776929 chr7 2643444 cgl5875502 chr6 31630077 cgl6507511 chrl4 39901950 cgl7026220 chrl2 54410580 cgl7336172 chr3 49377548 cgl7355126 chr2 232651397 cgl7997641 chr4 11401872 cgl8404925 chr7 6413861 cgl8721397 chr5 32584912 cgl8750960 chr2 177016417 cgl8822719 chr2 220035962 cgl8827954 chr4 5053585 cgl8878654 chrlO 134186874 cgl9182035 chrl2 3393005 cgl9196918 chrl3 47371267 cgl9417526 chrl 1 131895599 cgl9523664 chrl8 72160077 cgl9785742 chrl5 75118821 cgl9821425 chr3 69101663 cgl9909334 chrl4 53418212 cg20931965 chrl4 62186141 cg21110052 chr7 29234262 cg21396456 chr3 43021214 cg21549639 chrl9 45394156 cg22353818 chrl4 104095074 cg22693570 chrl 55224579 cg22983760 chrl3 111214246 cg23288535 chrl7 4336494 cg23366762 chr2 128991292 cg23520930 chr3 128206967 cg23875854 chrlO 6531368 cg23973558 chr2 241075520 cg24411648 chrl9 7939467 cg24427944 chrl 1 113644552 cg25010805 chrl 1 65334385 cg25445244 chrl9 37064171 cg25654619 chr2 85555411

cg25656096 chr2 237990400

cg26099902 chr8 141901449

cg26476599 chrl7 40086761

cg26731119 chr4 48908849

cg26829071 chrl2 131590596

cg27088449 chr22 21272634

cg27225708 chrl7 66420734

cg27296352 chrl7 11900707

cg27383562 chr6 155584850

cg27543103 chr4 54975677

The markers in Table 9 represent distinct cpg IDs and genomic positions that show validated dysmethylation in peripheral blood, particularly in independent discovery (41 DCM and 31 CTRL) and verification cohorts (9 DCM and 28 CTRL) with an area under the curve (AUC) of more than 80% in the discovery and verification cohorts.

Table 10: Markers, given as nucleic acid sequence with start and end, that show dysmethylation in HF/DCM in peripheral blood and myocardial tissue and are associated with RNA ex ^¬ pression levels (with regard to reference genome hgl9)

The markers in Table 10 represent markers that show dysmeth- ylation in HF/DCM in peripheral blood and myocardial tissue and are associated with RNA expression levels and represent the genes NPPA and NPPB. The ANF and BNP loci encode atrial natriuretic factor (ANF) and brain natriuretic peptide (BNP) , and the latter represents the present gold-standard biomarker for heart failure. The inventors found the same direction of dysmethylation in DNA, as also shown in Fig. 17 with regard to present Example 2, from heart tissue (red bars) and pe ^¬ ripheral blood (blue bars) . As expected, gene expression of NPPA (ANF) and NPPB is significantly dysregulated in the op ^¬ posite direction in tissue (upregulation, p=0.0001 for both, data not shown) and transcript levels of NPPB highly corre ^¬ late with NT-proBNP levels measured in plasma of the patients (R ² =0.55) . Accordingly, DNA methylation and RNA expression of both loci can serve as a biomarker for heart failure.

Fig. 17 shows therein the DNA methylation of the NPPA and NPPB locus. Natriuretic peptides are the gold-standard bi ^¬ omarkers in HF. In DCM, hypomethylation of the 5' CpG is as ^¬ sociated with increased expression. In blood, the same direc ^¬ tion of dysmethylation is found representing a cross-tissue conservation. Hgl9 coordinates for ANF (NPPA) and NPPB loci with lOkb up/downstream window that can serve as biomarker for heart failure are given in table 10. Thus disclosed is also the usage of DNA methylation and RNA expression of ANF and BNP loci as biomarker for heart failure.

Table lc: Summary of tables 1, la and lb with additional data

Table 2c: Summary of tables 2, 2a and 2b (part 1) with additional data

Table 2d: Summary of tables 2, 2a and 2b (part 2) with additional data

Table 3c: Summary of tables 3, 3a and 3b with additional data

According to certain embodiments, the presence of a plurality of markers is determined, so that the risk of heart failure and/or dilated cardiomyopathy can be determined more accu ^¬ rately.

A further aspect of the present invention is directed to the use of the markers in Table 1, Table 2, Table 3, Table 4, Ta ^¬ ble 5, Table 6, Table 7, Table 8, Table 9 and/or Table 10, preferably Table la, Table 2a, Table 3a, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and/or Table 10, particu ^¬ larly preferably Table lb, Table 2b, Table 3b, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and/or Table 10, e.g. Table la, Table 2a and/or Table 3a, e.g. Table lb, Table 2b and/or Table 3b, as a marker for heart failure and/or dilated cardiomyopathy in a patient.

Furthermore disclosed is a data bank comprising the markers disclosed in Table 1, Table 2, Table 3, Table 4, Table 5, Ta ^¬ ble 6, Table 7, Table 8, Table 9 and/or Table 10, preferably Table la, Table 2a, Table 3a, Table 4, Table 5, Table 6, Ta ^¬ ble 7, Table 8, Table 9 and/or Table 10, particularly preferably Table lb, Table 2b and/or Table 3b, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and/or Table 10, e.g. Ta ^¬ ble la, Table 2a and/or Table 3a, e.g. Table lb, Table 2b and/or Table 3b, .

According to certain embodiments, the data bank can be at a remote location and can be queried from a local client. The present data banks can be used in a variety of applica ^¬ tions. For example, the data bank can then be used, according to an aspect of the invention, in a method of determining a risk for heart failure and/or dilated cardiomyopathy in a patient .

Also disclosed is a data bank comprising markers obtained by the first and/or second aspect of the invention. In addition, the present invention relates in a further aspect to a method of determining a risk for a disease in a pa ^¬ tient, comprising

obtaining or providing data of an epigenomics profile and/or a transcriptome of at least one sample of the periph ^¬ eral blood and/or a diseased tissue of the patient, and

determining the presence of at least one marker as de ^¬ termined by the method of the first and/or second aspect. According to certain embodiments, the disease is heart fail ^¬ ure (HF) and/or dilated cardiomyopathy (DCM) , and the at least one marker as determined by the method of first and/or second aspect is at least a marker disclosed in Table 1, Ta ^¬ ble 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and/or Table 10, preferably Table la, Table 2a, Table 3a, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and/or Table 10, particularly preferably Table lb, Table 2b, Table 3b, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and/or Table 10, e.g. Table la, Table 2a and/or Table 3a, e.g. Table lb, Table 2b and/or Table 3b.

In a still further aspect the present invention relates to a computer program product comprising computer executable instructions which, when executed, perform a method of deter- mining a risk for a disease in a patient.

In certain embodiments the computer program product is one on which program commands or program codes of a computer program for executing said method are stored. According to certain embodiments the computer program product is a storage medium.

The present invention also relates to the use of the computer program product in a method of determining a risk for a disease in a patient.

Further disclosed is a method of prognosis and/or for moni ^¬ toring and/or assisting in drug-based therapy of patients di- agnosed with heart failure and/or dilated cardiomyopathy, wherein a marker as disclosed in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and/or Table 10, preferably Table la, Table 2a, Table 3a, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and/or Table 10, particularly preferably Table lb, Table 2b, Table 3b, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and/or Table 10, e.g. Table la, Table 2a and/or Table 3a, e.g. Table lb, Table 2b and/or Table 3b, is used. The markers disclosed in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table

7, Table 8, Table 9 and/or Table 10, preferably Table la, Ta ^¬ ble 2a, Table 3a, Table 4, Table 5, Table 6, Table 7, Table

8, Table 9 and/or Table 10, particularly preferably Table lb, Table 2b, Table 3b, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and/or Table 10, e.g. Table la, Table 2a and/or Table 3a, e.g. Table lb, Table 2b and/or Table 3b, allow a prognosis of the course of the disease as well as a monitor ^¬ ing thereof and can assist in deriving a conclusion regarding the medication prescription, etc., during the therapeutic treatment thereof.

Examples The present invention will now be described in detail with reference to several examples thereof. However, these exam ^¬ ples are illustrative and do not limit the scope of the in ^¬ vention . Before, some clinical perspectives are briefly discussed with regard to the Examples.

Clinical Perspective

1) What is new?

· The application shows that Multi-omics studies allow detec ^¬ tion of functional patterns in cardiovascular disease. • Epigenetic patterns are associated with heart failure due to dilated cardiomyopathy. The multi-omics studies design furthermore allowed detection of connected functional layers in cardiovascular disease.

· DNA methylation of distinct genomic regions is conserved between heart tissue and peripheral blood. DNA methylation could represent a new class of heart failure biomarkers.

• Transcriptional Rregulation of natriuretic factors ANP and BNP is associated with conserved DNA methylation.

2) What are the clinical implications?

• Epigenetic mechanisms are involved in chronic heart fail ^¬ ure, which opens new perspectives for translational research. Their investigation as diagnostic, predictive of prognostic biomarkers and future drug targets needs further attention.

Example 1

Material and Methods

Patient enrolment and study design

The present study has been approved by the ethics committee, medical faculty of Heidelberg University. All participants have given written informed consent. The diagnosis of non ^¬ ischemic Dilated Cardiomyopathy (DCM) was confirmed by ex ^¬ cluding relevant coronary artery disease (CAD) as determined by coronary angiography. Valvular heart disease was excluded by cMRI and/or echocardiography and myocarditis/inflammatory DCM by histopathology . Patients with history of uncontrolled hypertension, myocarditis, regular alcohol consumption or cardio-toxic chemotherapy were also excluded. To include the whole continuum of systolic heart failure, also early disease stages (EF <55%) who were symptomatic (dyspnoe, ede ^¬ ma/congestion) were included.

After screening of n=135 DCM patients, n=38 met all inclusion and exclusion criteria and had sufficient amounts of high quality left ventricular biopsies (LV free wall) and periph ^¬ eral blood samples available for high-throughput analyses. Control LV-biopsy specimens were obtained from patients after heart transplantation (n=31) that was at least 6 months ago, who had normal systolic and diastolic function and no evi ^¬ dence for relevant vasculopathy or acute/ chronic organ re ^¬ jection as judged by coronary angiography and immuno- histopathology . Additional gender- and age-matched controls for whole blood samples (n=31) had normal systolic and dias ^¬ tolic left ventricular function without evidence for other cardiovascular disease. Additionally for further validation purposes, left ventricu ^¬ lar myocardium of n=ll DCM patients who underwent heart transplantation and left ventricular myocardium (n=5) from previously healthy road accident victims were included. In the mean, patients were 54 years old and disease onset was 11 months prior to inclusion. Detailed basic and clinical characteristics of DCM patients are summarized in the follow ^¬ ing Table 11. Table 11: Detailed information of patients in the examples

Patients' clinical characteris ^¬ All patients

tics at the time of LV-EMB (n=38)

Basic characteristics

Age, mean ± SD, y 53 .7112.6

Age at onset ± SD, y 52 .8±12.8

Males, n. (%) 30 (78.9%)

BMI, mean ± SD, kg/m ² 27±5.6

Diabetes, n. (%) 3 (7.9%)

Atrial fibrillation, n. (%) 5 (13%)

Dyspnoea, n. (%)

NYHA I 6 (16%)

NYHA II 17 (46%)

NYHA III 13 (35%)

NYHA IV 1 (3%)

Family history of SCD or DCM, n. 8 (21%)

(%)

Laboratory tests White blood cell count, mean ± 7.812.4

SD, /nl

Haemoglobin, mean ± SD, g/dl 14.4±1.5

eGFR, mean ± SD, mL/min/1.73 m ² 88.6116.3

NT-proBNP, median (1Q;3Q), ng/1 767 (104;2385)

hs-TNT, median (1Q;3Q), pg/ml 16 (8; 38)

Medications, n. (%)

β-Blocker 36 (95%)

ACE inhibitor or ARB 38 (100%)

Loop diuretic 17 (45%)

Aldosterone antagonist 18 (47%)

Echocardiography

LV ejection fraction, mean ± SD, 32115

o

0

LV-EDD, mean ± SD, mm/m ² 57+ 9

MRI

LV ejection fraction, mean ± SD, 37+15

o

0

LV-EDV index, mean ± SD, mL/m ² 130+54

LV-EDD index, mean ± SD, mm/m ² 31+5

RV-EDD index, mean ± SD, mm/m ² 24+4

ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor blocker; DCM, dilated cardiomyopathy; EDD: end- diastolic diameter; EDV: end-diastolic volume; GFR: Glome ^¬ rular filtration rate; LV: left ventricular; LV-EMB: Left- Ventricular Endomyocardial Biopsy n: number; NYHA, New York Heart Association; SCD: sudden cardiac death; SD: standard deviation; 1Q: first quartile; 3Q: third Quartile.

Biomaterial Processing

Biopsy specimens were obtained from the apical part of the free left ventricular wall (LV) from DCM patients or cardiac transplant patients (controls) undergoing cardiac catheteri ^¬ zation using a standardized protocol. Biopsies were immedi ^¬ ately washed in ice-cold saline (0.9% NaCl) and immediately transferred and stored in liquid nitrogen until DNA or RNA was extracted. After diagnostic workup of the biopsies (his- topathology) , remaining material was evenly dissected to iso ^¬ late DNA and RNA. DNA was isolated from biopsies and periph ^¬ eral blood using Qiagen DNA Blood Maxi Kit. Total RNA was ex- tracted using the RNeasy kit according to the manufacturer's protocol (Qiagen, Germany) from biopsies and peripheral blood. RNA purity and concentration were determined using the Bioanalyzer 2100 (Agilent Technologies, Berkshire, UK) with a Eukaryote Total RNA Pico assay chip.

DNA methylation profiling and RNA sequencing

Methylation profiles were measured using the Illumina 450k methylation assay, following procedures as described in

Bibikova, M., et al . : High density DNA methylation array with single CpG site resolution, Genomics, 2011, 98(4): p. 288-95. From each patient, we subjected 200ng DNA (blood) and 200ng DNA (biopsy) to the measurements.

Quality control (QC) and removal of unreliable measurements Methylation sites with a detection p-value of > 0.05 in more than 10% of the samples were removed from analysis. Methyla ^¬ tion levels with a detection p-value of > 0.05 in less than 10% of the samples were imputed via knn-imputation, as de ^¬ scribed in Hastie T, T., R, Narasimhan, B Chu, G, impute: im- pute : Imputation for microarray data, R package version

1.46.0, 2016. To reduce the effects of genomic variation on methylation measurements we excluded all methylation sites where we found variants in more than 10% of the DCM patients of the discovery cohort within the 50bp probe region by whole genome sequencing. Methylation levels with variants in less than 10% of the DCM patients were imputed. We further removed all probes on X and Y chromosomes as well as probes which have been identified by Chen et al . (Chen, Y.A., et al . , Dis ^¬ covery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenet- ics, 2013. 8(2): p. 203-9) to cross-hybridize with non- targeted DNA, yielding 394,247 methylation sites that passed QC . It should be noted that the predictive performance may even be increased when e.g. switching from the employed high- throughput Infinium HumanMethylation450 BeadChip screening array to a targeted analysis approach for single methylation sites.

Whole Genome Sequencing

1 yg of total gDNA (genomic DNA) was sheared using the Co- varisTM S220 system, applying 2 treatments of 60 seconds each (peak power=140; duty factor=10) with 200 cycles/burst. 500 ng of sheared gDNA was taken and whole genome libraries were prepared using TruSeq DNA sample preparation kit according to manufacturer's protocols (Illumina, San Diego, US). Sequenc ^¬ ing was performed on an IlluminaHiSeq 2000, using TruSeq SBS Kit v3 and reading two times 100 bp for paired end sequenc ^¬ ing, on four lanes of a sequencing flowcell.

Demultiplexing of the raw sequencing reads and generation of the fastq files was done using CASAVA v.1.82. The raw reads were then mapped to the human reference genome (GRCh37/hgl9, http : / /hgdownload . cse . ucsc . edu/goldenPath/hgl 9/bigZips/ ) with the burrows-wheeler alignment tool (BWA v.0.7.5a) and dupli ^¬ cate reads were marked ( Picard-tools 1.56)

(http://picard.sourceforge.net/). Next, we used the Genome- Analysis-Toolkit according to the recommended protocols for variant recalibration (v. 2.8-l-g932cd3a) and variant calling (v.3.3-0-g37228af) as described in the respective best- practices guidelines

(https : //www.broadinstitute . org/gatk/guide/best-practices ) , as described in DePristo, M.A., et al . : A framework for vari ^¬ ation discovery and genotyping using next-generation DNA sequencing data, Nat Genet, 2011, 43(5): p. 491-8.

Normalization and removal of technical variations and batch effects

To remove unwanted technical variation, we applied a modified danes normalization procedure across all methylation measure- ments. Danes normalization is part of the wateRmelon package. The normalization procedure is based on between-array quan ^¬ tile normalization of methylated and unmethylated raw signal intensities of red and green channels together and thus also accounts for dye bias. However, between-array quantile nor ^¬ malization as initially developed for gene expression data is controversial for methylation data as overall methylation distributions may differ strongly between samples, tissues and diseases states. Consequently, we modified the danes nor- malization approach by not applying quantile normalization for between-array normalization but cyclicloess normalization instead. Cyclicloess normalization is similar in effect and intention to quantile normalization, but with the advantage that it does not as drastically normalize extreme cases and still preserves major distributional differences.

To account for batch effects, we performed duplicate measure ^¬ ments on different chips of in total 8 samples and used the duplicates for bridging the methylation-values of different analysis batches based on the duplicates only using the re- moveBatchEffect function from the limma package, as described in Ritchie, M.E., et al . , limma powers differential expres ^¬ sion analyses for RNA-sequencing and microarray studies, Nu ^¬ cleic Acids Res, 2015, 43(7) : p. e47. Following batch bridg- ing, duplicate measurements were averaged before downstream statistical analysis.

Epigenome-wide association analysis

Deregulated methylation sites were identified by linear mod- elling and moderated t-tests including age and gender using the limma package, as described above.

To also correct for potential genomic inflation in the dis ^¬ covery cohort, we performed principal component analysis on methylation measurements and identified principal components (PCs) which were associated with known confounders (e.g.

technical such as analysis date and biological confounders) at FDR (false discovery rate) <= 0.05. Again, deregulated methylation sites were subsequently identified by linear mod ^¬ elling and moderated t-tests including age and gender all identified PCs as covariates using the limma package. Statis- tical analyses were carried out in R-3.2.2. FDR correction of significance levels was performed using the Benjamini- Hochberg procedure.

Transcriptome analysis

RNAseq libraries were generated using TrueSeq RNA Sample Prep Kit (Illumina), and sequencing was performed 2x75bp on a HiS- eq2000 (Illumina) sequencer. Unstranded paired end raw read files were mapped with STAR v2.4.1c using GRCh37/hgl9 and the Gencode 19 gene model (http://www.gencodegenes.org/). Only uniquely mapped reads were counted into genes using subread's featureCounts program (subread version 1.4.6. pi). Prior to statistical analyses, genes with very low expression levels (average reads <= 1, detected reads in less than 50% of the samples) were removed. Count data was normalized by rlog nor- malization as described in Love, M.I., W. Huber, and S. Anders: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol, 2014, 15(12): p. 550, which is an improved method of the variance stabilization transformation as recommended for eQTL (expression quantita- tive trait loci) by the original MatrixEQTL publication of Shabalin, A. A. : Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics , 2012. 28(10): p. 1353-8. Epigenome-transcriptome association analysis

An eQTL analysis between methylation sites and gene expres ^¬ sions was performed on 34 DCM patients and 25 controls for which high quality transcriptome data from biopsy samples could be obtained (out of the total of 38 DCM patients and 31 controls which were profiled on the methylation level) . Ma ^¬ trixEQTL and linear models were used to correlate the expres ^¬ sion profiles of 19,418 genes with the 311,222 methylation sites in a range of lO.OOObp up- and downstream of the genes as well as in the gene body region out of the 394,247 that passed quality control. Association with the RNA expression level was carried out using the myocard samples.

Epigenetic region of interest definition

DNA methylation of the gene body as well as adjacent non- coding regulatory regions is known to be an important regula ^¬ tion mechanism for gene expression. For aggregated analyses on region level, aggregate significance level was then ob ^¬ tained using the simes procedure for all methylation loci as the simes procedure has been shown to generally perform well, also for correlated significance levels, as described in R0DLAND, E.A.: Simes' procedure is Valid on average', Bio- metrika, 93: p. 742-746. To determine the distance for sig ^¬ nificant associations between DNA methylation and RNA expres ^¬ sion, an aggregate significance level for associations was obtained using the simes procedure for all methylation loci within the gene body and adjacent regions at increased dis- tances, as the simes procedure has been shown to generally perform well as an aggregate measure for significant associa ^¬ tions, also for correlated significance levels. The results thereof are shown in Fig. 4, with SL being the Simes significance level and D the distance for association between DNA methylation and gene expression at increasing distances.

As shown in Fig. 4, the simes measure (-loglO simes signifi ^¬ cance level) only starts to drop significantly when increas ^¬ ing the distance from 10.000 to 100.000 bp as until 10.000 bp the difference from 0 bp distance is less than one standard deviation (horizontal lines in the figure, as estimated by 10-fold random sampling with replacement to estimate the standard deviation) . As a result, a cut-off was chosen at a distance of 10.000 bp.

Epigenetic and transcriptomic marker definition From the discovery cohort first four different categories of biomarkers (Cat. 1-4) were identified which show concordant dysregulation in methylation profiles in DCM either across molecular levels (i.e. epigenetic and transcriptomic; Cat. 4), tissues (i.e. cardiac tissue and blood; Cat. 2 and 3) or even both (Cat. 1) .

The following categories (Cat. 1-4) describe molecular marker of HF and DCM.

• Cat. la describes genomic regions that show coordinated hyper/hypo methylation in HF/DCM in peripheral blood and myo ^¬ cardial tissue and are associated with mRNA expression levels of genes of cardiac relevance in the myocard which are dereg ^¬ ulated in HF/DCM. The genes are given in Table 12.

Table 12: Data for Cat. la

ID CHR POS GENE NAME CARDIAC RELEVANCE

MIR142 Required for Survival Signalling cg03649649 17 56408197

(BZRAPl-ASl) During Adaptive Hypertrophy cg06613515 15 77287656 PSTPIP1 Immune System (Arthritis) cgl0495227 16 82970452 CDH13 Cadherin 13 (Heart)

CTNNA2 Catenin (Cadherin-Associated Pro ^¬ cg02856109 2 80531656

(LRRTM1) tein) , Alpha 2

Insuline-Like Growth Factor Bind ^¬ cgl7033080 2 217508851 IGFBP2

ing Protein 2

Regulates Insulin-induced Tyrosine cg20689294 10 129846082 PTPRE phosphorylation of Insulin Receptor

Cat . lb describes genomic regions that show coordinated hyper/hypo methylation in HF/DCM in peripheral blood and myo- cardial tissue and are associated with mRNA expression levels of genes of unknown cardiac relevance in the myocard which are deregulated in HF/DCM. The genes are given in Table 13.

Table 13: Data for Cat. lb ID CHR POS GENE NAME

cg20720059 2 14772731 FAM84A

cg!6362232 11 430036 AN09

• Cat. 2 describes genomic regions that show coordinated hyper/hypo methylation in HF/DCM in peripheral blood and myo ^¬ cardial tissue and cluster in chromosome bands with heart specific genes. The genes are given in Table 14.

Table 14: Data for Cat. 2

ID CHR POS GENE NAME CHROMOSOME BAND

cg05532869 11 2368070 CD81-AS1 Chrllpl5.5

cgl2121166 11 2376275 CD81-AS1 (Cat2a)

cg20751395 11 2594153 KCNQ1

cgl3145504 11 2594840 KCNQ1

cg22239603 11 2690304 KCNQ1

cg21522797 16 81806083 PLCG2 Chrl6q23.3

cg02516845 16 84076320 SLC38A8 (Cat2b)

cgl0495227 16 82970452 CDH13

cg25794153 18 31805151 NOL4 Chrl8ql2.1

cg26530706 18 32173093 DTNA (Cat2b)

cg22648949 18 30351983 KLHL14

• Cat. 3 describes genomic regions that show coordinated hyper/hypo methylation in HF/DCM in peripheral blood and myo cardial tissue but do not fall within Cat. 1 or 2. Two sub ^¬ categories were identified.

CcLt · 3cL is related to genomic regions in genes with cardiac relevance. The genes are given in Table 15.

Table 15: Data for Cat. 3a

ID CHR POS GENE NAME

cg24068761 1 6146988 KCNAB2

cgl2748607 2 40678691 SLC8A1

cg09283977 2 240082420 HDAC4

cg03912954 9 140773129 CACNA1B cg01744056 11 12524208 PARVA

cgl6932472 13 98605951 IP05

cgl4287235 14 24804339 RIPK3

cg25076767 14 24836148 NFATC4

cg03368634 16 3824553 CREBBP

cg03547745 17 70117522 SOX9

cg09306675 20 45523996 EYA2

cg26943378 22 50689804 HDAC10

Cat. 3b is related to genomic regions in genes with unknown cardiac relevance. The genes are given in Table 16. Table 16: Data for Cat. 3b

ID CHR POS GENE NAME

cg05536984 1 32083535 HCRTR1

cgl5061530 1 32827834 TSSK3

cgl2431729 1 41827960 FOX06

cgll750103 1 53238307 ZYG11B

cg26963271 1 66259081 PDE4B

cg00791468 1 111148984 KCNA2

cgl3072446 1 151019727 Clorf56

cgl3474719 1 177034184 ASTN1

cg23548885 2 47150 FAM110C

cg26659079 2 5836181 SOX11

cg05939149 2 43986106 PLEKHH2

cgl8809126 3 11623526 VGLL4

cg24823485 3 42626083 SS18L2

cg06327727 3 62354546 PTPRG-AS1

cg27338287 3 190580644 GMNC

cg08923494 4 166797526 TLL1

cgl4553364 5 137674194 FAM53C

cgl2364324 5 170848039 FGF18

cg26651429 5 171469429 STK10

cgl3898548 5 176924827 PDLIM7

cg05560494 6 33241974 RPS18

cgl5089846 6 75798778 COL12A1 cg26732340 6 157342220 ARID1B cg00155447 7 99627985 ZKSCAN1 cg08832906 7 139208852 CLEC2L cg00461149 7 157452656 PTPRN2 cg09121695 8 37655503 GPR124 cg09125812 8 41625127 ANK1 cgl6587988 8 42948547 POMK cgl6491617 8 54164391 OPRK1 cg01924448 9 14313043 NFIB cg24701032 10 17183411 TRDMT1 cgl7003301 10 43048646 ZNF37BP cg25497250 10 95326974 FFAR4 cg26554592 10 108924398 SORCS1 cgl2486121 11 7535256 PPFIBP2 cgl9279432 11 75110505 RPS3 cgl4727452 11 117283767 CEP164 cg07732097 12 49330158 ARF3 cgl3222752 14 74892569 SYNDIG1L cg05295297 14 85999731 FLRT2 cgl4400498 14 85999933 FLRT2 cg21883598 15 45404157 DUOX2 cg22381808 15 69452537 GLCE cgll611600 15 84323154 ADAM SL3 cg01852244 16 47494711 PHKB cg07665510 17 55952063 CUEDC1 cgl4787267 17 77951858 TBC1D16 cgl4893129 17 78152051 CARD14 cg24498538 18 5295760 ZBTB14 cg24362812 18 11148769 PIEZ02 cgl2113740 18 52625368 CCDC68 cg03124313 19 36036028 TMEM147 cg09430060 19 49306842 BCAT2 cgl9098268 19 57352269 PEG3 cg08448665 21 40984780 B3GALT5 cg01777170 22 38337780 MICALL1 • Cat. 4 describes genomic regions that show correlated, deregulated methylation and mRNA expression patterns in

HF/DCM in the myocardial tissue. The genes are given in Table 17.

Table 17: Data for Cat. 4

Gene Chr Start End Width Strand

PIK3CD 1 9710791 9790172 77382 +

THEMIS2 1 28198056 28214196 14141 +

PTPRC 1 198606802 198727545 118744 +

PLEK 2 68591306 68625585 32280 +

ARL4C 2 235400686 235406697 4012 -

SPI1 11 47375412 47401127 23716 -

FERMT3 11 63973151 63992354 17204 +

LCP1 13 46699056 46787006 85951 -

ISG20 15 89178385 89200714 20330 +

IL4R 16 27323990 27377099 51110 +

COROIA 16 30193149 30201397 6249 +

ITGAM 16 31270312 31345213 72902 +

COTL1 16 84598201 84652683 52483 -

IRF8 16 85931410 85957215 23806 +

ACAP1 17 7238849 7255797 14949 +

TMC8 17 76125852 76140049 12198 +

ICAM1 19 10380512 10398291 15780 +

TYROBP 19 36394304 36400197 3894 -

NKG7 19 51873861 51876969 1109 -

MAFB 20 39313489 39318880 3392 -

ITGB2 21 46304869 46352904 46036 -

PARVG 22 44567837 44616413 46577 +

Further, the following categories (Ca. 5-7) describe molecu ^¬ lar marker of HF and DCM that were further identified.

• Cat. 5 describes genomic regions that show coordinated hyper/hypo methylation in HF/DCM in peripheral blood and myo cardial tissue and are associated with mRNA expression levels in the myocard. The genes are given in Table 18.

Table 18: Data for Cat. 5

ID Ensemble ID Gene name Chr Pos

cg25943276 ENSG00000182667 NTM 131533284 cg24884140 ENSG00000108641 B9D1 17 19250190 cgl2115081 ENSG00000170390 DCL 2 4 151038391

• Cat. 6 describes genomic regions that show coordinated methylation and gene expression changes in HF/DCM in the myocardial tissue and are also associated with HF/DCM on gene level. The genes are given in Table 19.

Table 19: Data for Cat. 6

ID Ensemble ID Gene name Chr Pos

cg24720355 ENSG00000092607 TBX15 1 119526255 cg24144440 ENSG00000092607 TBX15 1 119526882 cg02829688 ENSG00000092607 TBX15 1 119527008 cg21647227 ENSG00000092607 TBX15 1 119527111 cg05940231 ENSG00000092607 TBX15 1 119532189 cg08942939 ENSG00000092607 TBX15 1 119532542 cg21301805 ENSG00000092607 TBX15 1 119534644 cgl0587082 ENSG00000076356 PLXNA2 1 208 293478 cg01876531 ENSG00000076356 PLXNA2 1 208 405868 cgl6045271 ENSG00000076356 PLXNA2 1 208 412585 cg04685570 ENSG00000255399 TBX5-AS1 12 114 841202 cg00182639 ENSG00000255399 TBX5-AS1 12 114 841671 cg00642359 ENSG00000255399 TBX5-AS1 12 114 841708 cg22045225 ENSG00000255399 TBX5-AS1 12 114 841792 cg21907579 ENSG00000255399 TBX5-AS1 12 114 845868 cg03877376 ENSG00000255399 TBX5-AS1 12 114 846162 cg03877376 ENSG00000089225 12 114 846162 cgl7645823 ENSG00000255399 TBX5-AS1 12 114 846321 cgl7645823 ENSG00000089225 TBX5 12 114 846321 cgl0281002 ENSG00000255399 TBX5-AS1 12 114 846399 cgl0281002 ENSG00000089225 TBX5 12 114 846399 cgl6458436 ENSG00000255399 TBX5-AS1 12 114846412 cgl6056219 ENSG00000119681 LTBP2 14 75043777 cgl4340889 ENSG00000119681 LTBP2 14 75072120 cg08140459 ENSG00000119681 LTBP2 14 75086513 cg09004195 ENSG00000116106 EPHA4 2 222323493 ocfl3364311 ENSG00000116106 EPHA4 2 222333289 cg03850035 ENSG00000116106 EPHA4 2 222367110

• Cat. 7 describes genomic regions that show coordinated methylation and gene expression changes in HF/DCM in the myocardial tissue. The genes are given in Table 20.

Table 20: Data for Cat. 7

ID Ensemble ID Name Chr Pos

cg01179095 ENSG00000175206 NPPA 1 11900652 cg03603260 ENSG00000197622 CDC42SE1 1 151021364 cgl3740187 ENSG00000143549 TPM3 ISIi!l 154164699 cgl4529268 ENSG00000183888 Clorf64 1 16335452 cg09013655 ENSG00000198756 COLGALT2 illfllS 184005063 cg01963906 ENSG00000142765 SYTL1 1 27677240 cgl6254946 ENSG00000174332 GLIS1 liiiiii 54058616 cg08029603 ENSG00000223764 1 854824 cg09608533 ENSG00000121898 CPXM2 10 125618188 cg04109883 ENSG00000165633 VSTM4 10 50289110 cg00857536 ENSG00000165633 VSTM4 10 50298306 cg24699895 ENSG00000156515 HK1 10 71094286 cg02378006 ENSG00000107731 UNC5B 10 73026288 cg07216529 ENSG00000138134 STAMBPL1 10 90712739 cg06595154 ENSG00000072952 MRVI1 11 10716164 cgll822932 ENSG00000135363 LM02 11 33913716 cg02337873 ENSG00000175602 CCDC85B 11 6565939 cg21746120 ENSG00000162337 LRP5 11 68142234 cg08679180 ENSG00000110237 ARHGEF17 11 73034459 cgl0630085 ENSG00000054965 FAM168A 73108402 cgl5542639 ENSG00000110218 PANX1 11 93885254 cg20735050 ENSG00000166025 AMOTL1 94521117 cg24088496 ENSG00000184384 MAML2 11 96071506 cgll513088 ENSG00000123094 RASSF8 12 26111821 cg22070156 ENSG00000198542 ITGBL1 13 102104991 cg07403350 ENSG00000139826 ABHD13 13 108867111 ocf022 5357 ENSG00000139675 HNRNPA1L2 13 53191046 cgl9910802 ENSG00000227051 C14orf132 14 96520233 cg27370471 ENSG00000140479 PCSK6 15 101932559 cg05377733 ENSG00000137809 ITGA11 15 68645969^^^-X7258X 95 ENSG00000129009 ISLR 15 74466337 cg27009545 ENSG00000136404 TM6SF1 15 83776915 cg09284275 ENSG00000133392 MYH11 16 15923487 cg04674421 ENSG00000169181 GSG1L 16 28079611 cg09509739 ENSG00000262766 16 31129199 cg02696327 ENSG00000102924 CBLN1 16 49312543 cg27232494 ENSG00000175662 TOM1L2 17 17832220 cg26535547 ENSG00000161654 LSM12 17 42151680 cg03995300 ENSG00000129204 USP6 17 5019989 cg00864012 ENSG00000136478 TEX2 17 62294665 cg06331359 ENSG00000181045 SLC26A11 17 78190755 cgl2475142 ENSG00000226137 BAIAP2 -AS 1 17 79012396 cg22588546 ENSG00000133026 MYH10 17 8382941 cg01085362 ENSG00000121297 TSHZ3 19 31848310 cg09779027 ENSG00000171105 INSR 19 7224513 cg00428638 ENSG00000171105 INSR 19 7224713 cg07077013 ENSG00000128652 H0XD3 2 177025198 cgl0035294 ENSG00000135903 PAX3 2 223164925 cgl7245125 ENSG00000119771 KLHL29 2 23843711 cg05403316 ENSG00000115310 RTN4 2 55339939 cgl6665041 ENSG00000215251 FASTKD5 20 3148787 cg22164891 ENSG00000171940 ZNF217 20 52199729 cg20979153 ENSG00000171940 ZNF217 20 52199748 cg21172011 ENSG00000159216 RUNX1 21 36577638 cgl4703829 ENSG00000099910 LHL22 22 20780298 cg01640635 ENSG00000100196 KDELR3 22 38864868 cgl3066481 ENSG00000065534 MYLK 3 123372199 cgl8274619 ENSG00000074416 MGLL 3 127494852 cg20950633 ENSG00000206561 COLQ 3 15540137 cg00434119 ENSG00000058866 DGKG 3 186080868 cgl0960375 ⁿ ENSG00000114853 ZBTB47 42694144 cg02316506 ENSG00000114853 ZBTB47 3 42694803 cg24074783 ENSG00000163788 SNRK 3 43405624 cg08052292 ENSG00000163947 ARHGEF3 3 56789178 cg09427605 ENSG00000151612 ZNF827 4 146740968 cgl9116959 ENSG00000151612 ZNF827 4 146841472 cg25924602 ENSG00000163145 C1QTNF7 4 15397288 cgl3832772 ENSG00000109771 LRP2BP 4 186283800 cg23664174 ENSG00000072201 LNX1 4 54357316 cgl4855841 ENSG00000169248 CXCL11 4 76945459 cg21631086 ENSG00000228672 PROB1 5 138718914 cgll462252 ENSG00000184347 SLIT3 5 168139607 cgl3112511 ENSG00000113448 PDE4D 5 58882753 cg02511723 ENSG00000131711 MAPIB 5 71402031 cg25515801 ENSG00000231500 RPS18 6 33240333 cg04201373 ENSG00000030110 BAK1 6 33551533 cg00604356 ENSG00000105851 PIK3CG 7 106507474 cg09374838 ENSG00000204934 ATP6V0E2- 7 149578384

AS1

cg09374838 ENSG00000171130 ATP6V0E2 7 149578384 cg26672672 ENSG00000136205 TNS3 7 47479433 cg03143486 ENSG00000164818 HEATR2 7 811491 cgll201447 ENSG00000249859 PV 1 8 128808063 cg25079691 ENSG00000221818 EBF2 8 25908057 cg04244354 ENSG00000221818 EBF2 ~ 25908279 cgl2563372 ENSG00000221818 EBF2 8 25908503 cgl4523204 ENSG00000138835 RGS3 9 116359818

Example 2

Methods and Results (summary) : Infinium HumanMethylation450 was used for high-density epigenomewide mapping of DNA meth- ylation in left ventricular biopsies and whole peripheral blood of living probands. RNA deep sequencing was performed on the same samples in parallel. Wholegenome sequencing of all patients allowed exclusion of promiscuous genotype- induced methylation calls. In the screening stage, we detect- ed 59 epigenetic loci that are significantly associated with DCM (FDR corrected p≤0.05), with three of them reaching epigenome-wide significance at p≤5xl0-8. Twenty-seven (46%) of these loci could be replicated in independent cohorts, un ^¬ derlining the role of epigenetic regulation of key cardiac transcription regulators. Using a staged multi-omics study design, we link a subset of 517 epigenetic loci with DCM and cardiac gene expression. Furthermore, we identified distinct epigenetic methylation patterns that are conserved across tissues, rendering these CpGs novel epigenetic biomarkers for heart failure.

Material and Methods

Patient enrolment and study design

Patient inclusion for the present study was approved by the ethics committee, medical faculty of Heidelberg University.

All participants have given written informed consent to allow for molecular analysis of blood and left-over tissue. The di ^¬ agnosis of Dilated Cardiomyopathy (DCM) was confirmed after excluding coronary artery disease (CAD) as determined by cor- onary angiography, valvular heart disease was excluded by cMRI and echocardiography and myocarditis/inflammatory DCM by histopathology (Richardson P, et al . , Report of the 1995 World Health Organization/International Society and Federa ^¬ tion of Cardiology Task Force on the Definition and Classifi- cation of cardiomyopathies. Circulation. 1996;93:841-2). Patients with history of uncontrolled hypertension, myocardi ^¬ tis, regular alcohol consumption, illicit drugs or cardio- toxic chemotherapy were also excluded. To include the clini ^¬ cal continuum of systolic heart failure, also early but symp- tomatic disease stages (LV-EF between >45 and <55~6 ) were in eluded .

After screening of n=135 DCM patients, n=41 met all inclusion and no exclusion criteria and had sufficient amounts of left- over LV ventricular biopsies (LV free wall) and peripheral blood samples available for the laborious high-throughput analyses of DNA methylation, genome- and mRNA sequencing. Control LV-biopsy specimens were obtained from stable and symptom-free patients after heart transplantation (n=31; HTX was at least 6 months ago) , who had normal systolic and dias ^¬ tolic function and no evidence for relevant vasculopathy or acute/chronic organ rejection as judged by coronary angiography and immuno-histopathology . Controls for whole blood samples (n=31) had a cardiovascular risk profile (Hypertension, Hyperlipidemia) , but completely normal systolic and di ^¬ astolic left ventricular function without evidence for heart failure or significant (>50%) coronary artery disease.

As an independent validation cohort, left ventricular myocardium of n=18 DCM patients and n=8 previously healthy road ac ^¬ cident victims were included. The independent validation co- hort for peripheral blood consisted of n=9 DCM patients and n=28 clinical controls. A third replication cohort for top blood-based markers included n=82 DCM patients (Institute for Cardiomyopathies Heidelberg) and n=109 Controls (Noko/normal control project).

Biomaterial Processing

Biopsy specimens were obtained from the apical part of the free left ventricular wall (LV) from DCM patients or cardiac transplant patients (controls) undergoing cardiac catheteri- zation using a standardized protocol. Biopsies were immedi ^¬ ately washed in ice-cold saline (0.9% NaCl) and transferred and stored in liquid nitrogen until DNA and RNA was extract ^¬ ed. After diagnostic workup of the biopsies (histopathology) , remaining material was evenly dissected to isolate DNA and RNA. DNA was extracted from blood with DNA Blood Maxi Kit

(Qiagen) and from biopsies with Allprep Kit (Qiagen) . Total RNA was extracted using the miRNeasy mini Kit (blood) or All ^¬ prep Kit (biopsies) according to the manufacturer's protocol (Qiagen, Germany) from biopsies and peripheral blood. RNA pu- rity and concentration were determined using the Bioanalyzer 2100 (Agilent Technologies, Berkshire, UK) with a Eukaryote Total RNA Pico assay for RNA from biopsies and with Eukaryote Total RNA Nano assay for RNA from blood.

DNA methylation profiling, RNA and whole-genome sequencing Methylation profiles were measured using the Illumina 450k methylation assay, following procedures as described earlier (Bibikova M, et al . , High density DNA methylation array with single CpG site resolution. Genomics. 2011;98:288-95). From each patient, we subjected 200ng DNA (blood and biopsy) for the measurements. Methylation sites with a detection p-value of >0.05 in more than 10% of the samples were removed from analysis. Methylation levels with a detection p-value of >0.05 in less than 10% of the samples were imputed via knn- imputation (Hastie T T, R, Narasimhan, B Chu, G. impute: im- pute : Imputation for microarray data. R package version 1460. 2016) . To reduce the effects of genomic variation on methyla ^¬ tion measurements, we excluded methylation sites that were potentially influenced by genotypes present in more than 10% of the DCM patients and that lie within the 50bp probe region as assessed by whole-genome sequencing. Methylation levels with variants in less than 10% of the DCM patients were im ^¬ puted. We further removed all probes on X and Y chromosomes as well as probes that have been identified by Chen et al . to cross-hybridize with non-targeted DNA (Chen YA, et al . , Dis- covery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenet- ics . 2013; 8:203-9). Finally, 394, 247 methylation sites passed QC. DNA methylation was validated for the top two biomarker candidate loci by the MassARRAY technique as previously de ^¬ scribed (Haas J, et al . , Alterations in cardiac DNA methyla ^¬ tion in human dilated cardiomyopathy. EMBO Mol Med.

2013;5:413-29). Briefly, 400ng genomic DNA was chemically modified with sodium bisulfite. The bisulfite-treated DNA was PCR-amplified by primers designed to cover the Infinium probes cg06688621 and cg01642653 (cg06688621 primer sequences GGTGTTTTTTGTTTAGTATTTTTTAGAG and AGGGTAGATTTGAGGTAGTTTAGGA; cg01642653 primer sequences TAGGTGTTTTTTAGGGTTGTTTTTT and GTTGGGGAATTTGTTGTTTATTAG) . The amplicons were transcribed by T7 polymerase, followed by T-specific-RNAase-A cleavage. The digested fragments were quantified by MALDI-TOF-based tech ^¬ nique (MassARRAY) .

1 μg of total peripheral blood gDNA was sheared using the Co- varis™ S220 system, applying 2 treatments of 60 seconds each (peak power=140; duty factor=10) with 200 cycles/burst. 500 ng of sheared gDNA was taken and whole genome libraries were prepared using TruSeq DNA sample preparation kit according to manufacturer's protocols (Illumina, San Diego, US). Sequenc ^¬ ing was performed on an Illumina HiSeq 2000, using TruSeq SBS Kit v3 and reading two times 100 bp for paired end sequenc ^¬ ing, on four lanes of a sequencing flowcell.

Demultiplexing of the raw sequencing reads and generation of the fastq files was done using CASAVA v.1.82. The raw reads were then mapped to the human reference genome (GRCh37/hgl9) with the burrows-wheeler alignment tool (BWA v.0.7.5a) (Li H and Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics . 2009;25:1754-60) and duplicate reads were marked ( Picard-tools 1.56)

(http://picard.sourceforge.net/). Next, we used the Genome- Analysis-Toolkit according to the recommended protocols for variant recalibration (v. 2.8-l-g932cd3a) and variant calling (v.3.3-0-g37228af) as described in the respective best- practices guidelines

(https : //www.broadinstitute . org/gatk/guide/best-practices )

(DePristo MA, et al . , A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature genetics. 2011;43:491-8). Statistical analysis

Regarding detailed information on normalization and removal of technical and batch effects, association statistics, overrepresentation and gene ontology analyses, the following is applied.

Normalization and removal of technical variations and batch effects

To remove unwanted technical variation, we applied a modified danes normalization procedure across all methylation measure ^¬ ments. Danes normalization is part of the wateRmelon package and was first described by Pidsley (Pidsley R, et al . , A da- ta-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics. 2013; 14 : 293) . The normalization procedure is based on between-array quantile normalization of methylated and unmethylated raw signal intensities of red and green channels together and thus accounts for dye bias. How- ever, between-array quantile normalization as initially de ^¬ veloped for gene expression data is controversial for methyl ^¬ ation data as overall methylation distributions may differ strongly between samples, tissues and diseases states. Conse ^¬ quently, we modified the danes normalization approach by not applying quantile normalization for between-array normaliza ^¬ tion but cyclicloess normalization instead. Cyclicloess nor ^¬ malization is similar in effect and intention to quantile normalization, but with the advantage that it does not as drastically normalize extreme cases and still preserves major distributional differences (Ballman KV, Grill DE, Oberg AL and Therneau TM. Faster cyclic loess: normalizing RNA arrays via linear models. Bioinformatics . 2004;20:2778-86).

All samples were measured in 5 different batches and each batch contained duplicate samples from other batches. To re ^¬ move technical variation possibly introduced by the measure ^¬ ment batch, the duplicate measurements of in total 8 samples were used for bridging the methylation-values (Du P, et al . , Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformat ^¬ ics . 2010;11:587) of different analysis batches using the re- moveBatchEffect function from the limma package (Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 ; 43 : e47 ) . Follow ^¬ ing batch bridging, duplicate measurements were averaged be ^¬ fore downstream statistical analysis.

Epigenome-wide association analysis

To correct for genomic inflation in the discovery cohort, we performed principal component analysis on methylation meas ^¬ urements and identified principal components (PCs) , which were associated with known confounders (e.g. technical such as analysis date and biological confounders such as medica ^¬ tion) at FDR <0.05, see Tables 21 and 22.

Table 21: Confounders for methylation measurements from myo ^¬ cardial tissue in the discovery cohort that are associated with principal components after FDR correction. PCl-4 and 6-7 were subsequently used for correction of potential genomic inflation .

PC Explained Cum. Explained Measurement Medication

Variation Variation Batch

Tacrolimus Mycophenolat

1 0.1 1603 0. .11603 1. .17E-07 0. .87787399 0. .1433775

2 0. .11056 0. .22659 0. .004955317 0. .94099466 0. .94099466

3 0. .08126 0. .30784 0. .000119371 0. .48469009 0. .45229374

4 0. .05514 0. .36298 6. .53E-09 0. .00591254 0. .00195786

5 0. .03721 0. .40019 0. .23171337 0. .6788642 0. .51621221

6 0. .02961 0. .4298 0. .014540305 0. .91277464 0. .88088841

7 0. .02114 0. .45094 0. .485198384 0. .02555192 0. .05004367

8 0. .01917 0. .47012 0. .346068453 0. .9661979 0. .9661979

9 0. .01637 0. .48648 0. .573861536 0. .90992672 0. .87682897

10 0. .01602 0. .5025 0. .431079505 0. .84476548 0. .74247531

Table 22: Known confounders for methylation measurements from peripheral blood in the discovery cohort that was identified to be significantly associated with principal components af- ter FDR correction. PCl-4 as well as age and gender were sub ^¬ sequently included for correction of genomic inflation.

Cum. Ex¬

Explained Measure¬

PC plained Weight BMI Age

Variation ment Batch

Variation

1 0. .17702 0 , .17702 3. .65E-08 0. .74142811 0. .82657779 0 , .88017013

2 0. .074 0 , .25102 0. .17882175 0. .00432245 0. .00881378 0 , .7547449

3 0. .05376 0 , .30478 1. .88E-09 0. .99029277 0. .99029277 0 , .08324938

4 0. .03977 0 , .34455 4. .84E-06 0. .95067972 0. .95183356 0 , .76970735

5 0. .02545 0 , .37001 0. .89104493 0. .90205601 0. .89104493 7 , .34E-05

6 0. .01911 0 , .38912 0. .1419809 0. .98514199 0. .98514199 0 , .97446499

7 0. .01776 0 , .40688 0. .74935875 0. .74935875 0. .74935875 0 , .74935875

8 0. .0155 0 , .42238 0. .83285365 0. .84157735 0. .84157735 0 , .84157735

9 0. .01495 0 , .43732 0. .74629486 0. .08897591 0. .27253148 0 , .74629486

10 0. .01449 0 , .45182 0. .90693645 0. .90693645 0. .93956553 0 , .3294711

Deregulated methylation sites were identified by linear mod- elling and moderated t-tests including age and gender as well as all identified PCs as covariates using the limma package (Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK. limma powers differential expression analyses for RNA- sequencing and microarray studies. Nucleic Acids Res.

2015 ; 43 : e47 ) . Methylation sites were subsequently direction- ally verified in verification cohorts including gender (as age was not available for all samples) as covariates. Statis ^¬ tical analyses were carried out in R-3.2.2 (R: A Language and Environment for Statistical Computing [computer program] .

2008) . FDR correction of significance levels was performed using the Benj amini-Hochberg procedure (Benjamini Y and

Hochberg Y. Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. J Roy Stat Soc B Met. 1995;57:289-300). Significance levels from discovery and verification cohorts were combined using Fisher's method to combine results from independent tests.

Transcriptome analysis RNA sequencing libraries were generated using TrueSeq RNA Sample Prep Kit (Illumina) and sequencing was performed

2x75bp on a HiSeq2000 (Illumina) sequencer. Samples were se ^¬ quenced to a median paired-end read count of 29.85 million. Unstranded paired-end raw read files were mapped with STAR v2.4. lc (Dobin A and Gingeras TR. Mapping RNA-seq Reads with STAR. Curr Protoc Bioinformatics . 2015;51:11 14 1-11 14 19) using GRCh37/hgl9 and the Gencode 19 gene model

(http://www.gencodegenes.org/). Only uniquely mapped reads were counted into genes using subread's feature counts pro ^¬ gram (Liao Y, Smyth GK and Shi W. featureCounts : an efficient general purpose program for assigning sequence reads to ge ^¬ nomic features. Bioinformatics . 2014;30:923-30) (subread ver ^¬ sion 1.4.6. pi) and mapping percentages were median 88.08. Prior to statistical analyses, genes with very low expression levels (average reads <= 1, detected reads in less than 50% of the samples) were removed. Count data was normalized by rlog normalization (Love MI, Huber W and Anders S. Moderated estimation of fold change and dispersion for RNA-seq da- ta with DESeq2. Genome Biol. 2014 ; 15 : 550 ) , which is an im ^¬ proved method of the variance stabilization transformation (Anders S and Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010 ; 11 : RIO 6) as recommend ^¬ ed for eQTL by the original MatrixEQTL publication (Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics . 2012;28:1353-8).

Epigenome-transcriptome association analysis

An eQTL analysis between methylation sites and gene expres- sions was performed on the 34 DCM patients and 25 controls with high quality epigenome and transcriptome data from the same biopsy samples. MatrixEQTL (Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioin- formatics. 2012;28:1353-8) and linear models were used to correlate the expression profiles of 19,418 genes with the 311,222 methylation sites in a range of 10,000bp up- and downstream of the genes as well as in the gene body region. Epigenome-transcriptome associations were subsequently direc- tionally verified in the cardiac tissue verification cohort.

To identify an epigenetic signature for DCM we filtered for methylation loci, which were associated with the disease and gene expression in myocardial discovery and verification cohort at an uncorrected significance level of p≤0.05. Con ^¬ served methylation differences in DCM across myocardial tis ^¬ sue and peripheral blood were identified by filtering for methylation loci that additionally showed conservation across tissues (kendall rank test for direct correlation p≤0.05) and deregulated methylation status in identical directions (di ^¬ rectional p≤0.05) . To minimize the effect of blood cell het ^¬ erogeneity, we excluded all sites which have been shown to be associated with blood cell heterogeneity at a (Holm S. A sim ^¬ ple sequentially rejective multiple test procedure. Scandina ^¬ vian Journal of Statistics. 1979; 6, 65-70) corrected F- statistics significance level p≤0.05 by Jaffe et al . (Jaffe AE and Irizarry RA. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol. 2014 ; 15 : R31 ) . Finally, predictive DCM models were built for myocardial tissue and peripheral blood separately using the glm function of the R stats package based on logistic regres ^¬ sion models and 5-fold cross-validation with 10 repeats in the discovery cohort and subsequently tested in the verifica ^¬ tion cohorts.

For aggregated analyses on gene or multi-gene level, aggre ^¬ gate significance level was then obtained using the simes procedure for all methylation loci (R0DLAND EA. Simes' proce ^¬ dure is Valid on average'. Biometrika. 93:742-746).

Overrepresentation and Gene Ontology analyses

Overrepresentation analyses for deregulated methylation sites in chromosome bands, discovery and verification cohorts as well as for methylation sites associated with disease state and gene expression was based on the fisher exact test on 2x2 contingency tables using a threshold of p≤0.05.

Identification of overrepresented GO terms was performed us- ing the gometh function of the missMethyl package (Phipson B, Maksimovic J and Oshlack A. missMethyl: an R package for analyzing data from Illumina's HumanMethylation450 platform. Bi- oinformatics . 2016;32:286-8), taking into account the proba ^¬ bility of differential methylation based on the number of probes on the 450k array per gene. This is particularly im ^¬ portant, since severe bias when performing gene set analysis for genome-wide methylation data due to the differing numbers of methylation sites profiled for each gene has been reported (Geeleher P, Hartnett L, Egan LJ, Golden A, Raja Ali RA and Seoighe C. Gene-set analysis is severely biased when applied to genome-wide methylation data. Bioinformatics .

2013;29:1851-7). The applied approach models and compensates the effect of selection bias using the methodological frame ^¬ work originally developed by Young et al . (Young MD, Wake- field MJ, Smyth GK and Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol.

2010; 11 :R14) .

Further data regarding the analysis carried out in Example 2 and results obtained therein are found in the following Ta ^¬ bles 23 to 34.

Table 23: Binding-site Overrepresentation in DMR (Tissue Screening) .

Motif P-Value FDR

Smad2 0.00010351269 0.01193747

BMAL1 0.00014737619 0.01193747

Smad4 0.00076668415 0.04657606

01ig2 0.00006106049 0.01193747 Table 24: Overrepresented Gene Ontology Terms of Replicated DCM-associated and geneexpression associated DMR (Tissue Screening+Replication) .

GO Biological Process P-Value FDR

biological adhesion 2.768E-11 3.6969E-07 homophilic cell adhesion via plasma mem- 9.9239E-11 6.6272E-07 brane adhesion molecules

cell adhesion 1.5502E-10 6.9015E-07 cell-cell adhesion via plasma-membrane 3.477E-10 9.7543E-07 adhesion molecules

cell-cell adhesion 3.6517E-10 9.7543E-07 cardiac muscle cell differentiation 3.6617E-06 0.00815094 anatomical structure morphogenesis 4.3621E-06 0.00832288 muscle contraction 5.9186E-06 0.00988107 cardiovascular system development 8.7094E-06 0.01163223 circulatory system development 8.7094E-06 0.01163223 muscle system process 9.7189E-06 0.0118005 cardiac muscle tissue development 1.5042E-05 0.01483882 muscle filament sliding 1.5554E-05 0.01483882 actin-myosin filament sliding 1.5554E-05 0.01483882 multicellular organismal development 1.8194E-05 0.01620023 cardiac muscle cell development 3.4344E-05 0.02675178 myosin filament organization 3.6108E-05 0.02675178 cardiocyte differentiation 3.7612E-05 0.02675178 tissue development 3.8057E-05 0.02675178 cardiac cell development 5.6864E-05 0.03725419 skeletal muscle myosin thick filament as- 6.1365E-05 0.03725419 sembly

striated muscle myosin thick filament as- 6.1365E-05 0.03725419 sembly Tables 25: Baseline Characteristics of Included Patients (Screening stage, cardiac tissue & blood, n=41)

Clinical characteristics

Age, mean ± SD, y 54.1112.3

Age at onset ± SD, y 53.2112.6

Males, n. (%) 31 (75.6%) BMI, mean ± SD, kg/m 27.1±5.7

Atrial fibrillation, n. (%) 6 (14.6%)

Functional Class:

NYHA I, n. (%) 6 (14.6%)

NYHA II, n. (%) 20 (47.8%)

NYHA III, n. (%) 14 (34%)

NYHA IV, n. (%) 1 (2.4%)

Family history of SCD or DCM, n. (%) 9 (21.9%)

Clinical Biomarkers

White blood cell count, mean ± SD, /nl 7.712.3

Haemoglobin, mean ± SD, g/dl 14.311.5 eGFR, mean ± SD, mL/min/1.73 m ² 87.4117.8

Creatinine ± SD, mg/dl 0.910.2

NT-proBNP, median (1Q 3Q), ng/1

812 (109 2255)

hs-TNT, median (1Q 3Q) , pg/ml

12 (8 36)

Medications

β-Blocker 38 (92.7%)

ACE inhibitor or ARB 40 (97.6%) Loop diuretic 18 (43.9%) Aldosterone antagonist 20 (48.9%)

MRI

LV ejection fraction, mean ± SD, % 37+15

LV-EDV index, mean ± SD, mL/m ² 126.1+44.3 LV-EDD mm ± SD, mm 61.2+9.8

RV-EDD mean ± SD, mm 48.017.8

ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor blocker; DCM, dilated cardiomyopathy; EDD: end- diastolic diameter; EDV: end-diastolic volume; GFR: Glomeru ^¬ lar filtration rate; LV: left ventricular; n: number; NYHA, New York Heart Association; SCD: sudden cardiac death; SD: standard deviation; 1Q: first quartile; 3Q: third Quartile.

Table 26: Baseline Characteristics of Included HTX Controls

(Screening stage, cardiac tissue, n=31)

Basic characteristics Age, mean ± SD, y 54.1+11.7 Males, n. (%) 24 (77.4%) BMI, mean ± SD, kg/m ² 24.4+4

Atrial fibrillation, n. (%) 0 (0%)

Laboratory tests

White blood cell count, mean ± SD, /nl 6.6+2.9

Haemoglobin, mean ± SD, g/dl 12.7+2.1

Creatinine ± SD, mg/dl 1.3+0.4

Medications

Aspirin 14 (45.2%) β-Blocker 19 (61.3%)

ACE inhibitor or ARB 25 (80.1%) Diuretic 14 (45.2%) Steroid 9 (29%)

Tacrolimus 21 (74%)

Mycophenolat 27 (87.1%) Everolimus 5 (16.1%) Ciclosporin 5 (16.1%) Sirolimus 1 (3.2%)

Echocardiography

LV ejection fraction, mean ± SD, % 60.613.1

ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor blocker; EMB : endomyocardial biopsy; n: number; SD: standard deviation Table 27: Baseline Characteristics of Included Clinical Con ^¬ trols (Screening stage, blood methylation, n=31)

Basic characteristics

Age, mean ± SD, y 65.71.11

Males, n. (%) 19 (61.3%)

BMI, mean ± SD, kg/m ² 27.9+4.2

Atrial fibrillation, n. (%) 3 (9.7%)

Laboratory tests

White blood cell count, mean ± SD, /nl 7.712.7

Haemoglobin, mean ± SD, g/dl 14.411.1

Creatinine + SD, mg/dl 0.8+0.2

Medications Aspirin 22 (71.0%) β-Blocker 19 (61.3%)

ACE inhibitor or ARB 14 (45.2%)

Diuretic 9 (29.0%)

Statin 13 (41.9%)

Echocardiography

LV ejection fraction, mean ± SD, % 61.5+3.4

ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor blocker; n: number; SD: standard deviation.

Table 28: Baseline Characteristics of Included DCM Patients (Replication stage, cardiac tissue, n=18)

Basic characteristics

Age, mean ± SD, y 58.2±8.8

Age at onset ± SD, y 52.0+11.5

Males, n. (%) 13 (72.2%)

Atrial fibrillation, n. (%) 10 (55.5%)

Functional classes:

NYHA I, n. (%) 1 (5.6%)

NYHA II, n. (%) 4 (22.2%)

NYHA III, n. (%) 10 (55.6%)

NYHA IV, n. (%) 1 (16.7%)

Clinical biomarkers

White blood cell count, mean ± SD, /nl 8.4+3.4

Haemoglobin, mean ± SD, g/dl 13.3+1.9

Creatinine ± SD, mg/dl 1.5+0.8

NT-proBNP, median (1Q,3Q), ng/1 5641 (2201; 10309)

Medications

β-Blocker 15 (83.3%)

ACE inhibitor or ARB 17 (94.4%)

Diuretic 17 (94.4%)

Echocardiography

LV ejection fraction, mean ± SD, % 23+8

LV-EDD, mean ± SD, mm/m ² 61+8

ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor blocker; DCM, dilated cardiomyopathy; EDD: end- diastolic diameter; LV: left ventricular; n: number; NYHA, New York Heart Association; SD: standard deviation; 1Q: first quartile; 3Q: third Quartile.

Table 29: Baseline Characteristics of Included Accident Con ^¬ trols (Replication stage, cardiac tissue, n=8)

Basic characteristic

Males, n. (%) 7 (87.5%) n: number

Table 30: Baseline Characteristics of Included DCM patients (Replication stage I, blood, n=9)

Basic characteristics

Age, mean ± SD, y 53±14.8

Age at onset ± SD, y 52.8±15.1

Males, n. (%) 8 (88.8%)

Atrial fibrillation, n. (%) 6(66.7%)

Functional classes:

NYHA I, n. (%) 1 (22.2%)

NYHA II, n. (%) 1 (22.2%)

NYHA III, n. (%) 5 (55.6%)

NYHA IV, n. (%) 0 (0%)

Clinical biomarkers

White blood cell count, mean ± SD, /nl 8.4±3.4

Haemoglobin, mean ± SD, g/dl 14.6±1.5

Creatinine ± SD, mg/dl 1.0±0.2

NT-proBNP, median (1Q;3Q), ng/1 233 (144;636)

Medications, n. (%)

β-Blocker 8 (88.8%)

ACE inhibitor or ARB 9 (100%)

Diuretic 6 (66.7%)

Echocardiography

LV ejection fraction, mean ± SD, % 32±12

LV-EDD, mean ± SD, mm/m ² 57±6

ACE, angiotensin-converting enzyme; ARB, angiotensin II re ceptor blocker; DCM, dilated cardiomyopathy; EDD: end- diastolic diameter; LV: left ventricular; n: number; NYHA, New York Heart Association; SD: standard deviation; 1Q: first quartile; 3Q: third Quartile.

Table 31: Baseline Characteristics of Included Controls

(Replication stage I, blood, n=28)

Basic characteristics

Age, mean ± SD, y 59.6±.8.5

Males, n. (%) 22 (78.6%)

Table 32: Baseline Characteristics of Included DCM patients (Replication stage II, blood, n=82)

Clinical characteristics

Age, mean ± SD, y 53.0±13.4

Males, n. (%) 64 (78.0%)

BMI, mean ± SD, kg/m ² 28.3±6.5

Atrial fibrillation, n. (%) 21 (25.6%)

Clinical Biomarkers

White blood cell count, mean ± SD, /nl 7.6±2.1

Haemoglobin, mean ± SD, g/dl 14.4±1.6

Creatinine ± SD, mg/dl 1.3±1.5

NT-proBNP, median (1Q;3Q), ng/1 785 (144;2626) hs-TNT, median (1Q;3Q), pg/ml 12 (6;23)

CRP, mean ± SD; mg/1 8.3 (24.6)

Echocradiography

LV ejection fraction, mean ± SD, % 30±13

LV-EDD mean ± SD, mm 61.6±10.2

EDD: end-diastolic diameter; LV: left ventricular; n: number; SD: standard deviation; 1Q: first quartile; 3Q: third Quar ^¬ tile .

Table 33: Baseline Characteristics of Included Controls (Rep- lication stage II, blood, n=109)

Clinical characteristics

Age, mean ± SD, y 62.2±9.1

Males, n. (%) 81 (74.3%)

BMI, mean ± SD, kg/m ² 24.9±2.7

Atrial fibrillation, n. (%) 0 (0%) Clinical Biomarkers

White blood cell count, mean ± SD, /nl 7.6±1.3

Haemoglobin, mean ± SD, g/dl 14.6±1.0

Creatinine ± SD, mg/dl 0.85±0.1

CRP, mean ± SD; mg/1 5.1 (2.6)

Echocradiography

LV ejection fraction, mean ± SD, % 59.1±8.7

LV-EDD mean ± SD, mm 46.8±4.5

EDD: end-diastolic diameter; LV: left ventricular; n: number; SD: standard deviation

Table 34: Loci associated with DCM and RNA expression

CpG Site Nearby Gene p-Value DCM Pearson Corp-Value

Association relation Me- Correlatithylation-RNA on

cgl4523204 ENSG00000138835 4.74E-06 -0.3708 3.84E-03 cgl6254946 ENSG00000174332 7.42E-06 -0.4643 2.12E-04 cg21518947 ENSG00000269913 1.48E-05 -0.3814 2.88E-03 ch.3.1226245F ENSG00000187672 1.50E-05 -0.3289 1.10E-02 cg21363050 ENSG00000134853 1.54E-05 -0.3526 6.16E-03 cg21363050 ENSG00000145216 1.54E-05 -0.3208 1.32E-02 cg25924602 ENSG00000163145 2.92E-05 -0.4888 8.58E-05 cgll822932 ENSG00000135363 3.05E-05 -0.3422 7.98E-03 ch.10.2770541R ENSG00000150760 4.32E-05 -0.2870 2.75E-02 cg03001305 ENSG00000126561 7.10E-05 -0.4174 1.00E-03 cgll970163 ENSG00000135842 7.68E-05 -0.6979 8. lOE-10 cg02801277 ENSG00000101638 1.08E-04 -0.6542 1.92E-08 cg02801277 ENSG00000270112 1.08E-04 -0.6609 1.23E-08 cg03600605 ENSG00000170421 1.16E-04 -0.7376 2.67E-11 cg08732466 ENSG00000177133 1.17E-04 -0.3334 9.86E-03 cg08732466 ENSG00000142611 1.17E-04 -0.3271 1.15E-02 cgl3909178 ENSG00000151702 1.24E-04 0.3906 2.22E-03 cg02215357 ENSG00000139675 1.34E-04 -0.3780 3.16E-03 cg06783197 ENSG00000179364 1.36E-04 0.2631 4.41E-02 cgl9223064 ENSG00000165757 1.37E-04 -0.2907 2.55E-02 cg21144009 ENSG00000076356 1.48E-04 -0.4817 1.12E-04 cg08840665 ENSG00000183011 1.53E-04 -0.2968 2.24E-02 cg08840665 ENSG00000167874 1.53E-04 -0.2636 4.37E-02 cg08840665 ENSG00000182224 1.53E-04 -0.2823 3.03E-02 cg05990080 ENSG00000144677 2.00E-04 -0.6816 2.80E-09 cg23664174 ENSG00000072201 2.11E-04 -0.4015 1.62E-03 cgl9514721 ENSG00000231185 2.25E-04 -0.2675 4.05E-02 cgl6045271 ENSG00000076356 2.26E-04 -0.5452 8.00E-06 cgll702448 ENSG00000105401 2.27E-04 -0.3360 9.27E-03 ch.7.1171004F ENSG00000106070 2.33E-04 -0.3738 3.54E-03 cg09777256 ENSG00000155657 3.15E-04 -0.3471 7.07E-03 cgl7326555 ENSG00000092607 3.22E-04 0.6010 4.84E-07 cg09990481 ENSG00000107796 3.94E-04 -0.3666 4.29E-03 cg09990481 ENSG00000138134 3.94E-04 -0.4206 9.11E-04 cg04430582 ENSG00000267532 3.98E-04 -0.2852 2.86E-02 cg04430582 ENSG00000219200 3.98E-04 0.2680 4.02E-02 cgl9677302 ENSG00000057294 4.11E-04 -0.2904 2.57E-02 cgl4524975 ENSG00000139626 4.46E-04 -0.3850 2.61E-03 cg20950633 ENSG00000206561 4.50E-04 -0.3283 1.11E-02 cg09779027 ENSG00000171105 4.90E-04 -0.3363 9.20E-03 cgl9201144 ENSG00000186684 5.15E-04 -0.2971 2.23E-02 cg23436746 ENSG00000188730 5.37E-04 -0.5435 8.67E-06 cg26512226 ENSG00000175084 5.44E-04 -0.3785 3.12E-03 cgl4174232 ENSG00000178031 5.46E-04 -0.5017 5.16E-05 cg00767058 ENSG00000150401 5.49E-04 -0.6773 3.85E-09 cg00767058 ENSG00000153531 5.49E-04 -0.5235 2.09E-05 cg00857536 ENSG00000165633 5.70E-04 -0.3722 3.70E-03 cg06357561 ENSG00000126561 5.71E-04 -0.3614 4.92E-03 cgl4039237 ENSG00000148339 6.48E-04 -0.2674 4.06E-02 cg01876531 ENSG00000076356 6.81E-04 -0.5424 9.10E-06 cg03721976 ENSG00000266040 6.83E-04 0.2792 3.23E-02 cg03721976 ENSG00000108292 6.83E-04 0.3891 2.32E-03 cg05819249 ENSG00000113504 7.00E-04 -0.5639 3.31E-06 cg07249742 ENSG00000082781 7.21E-04 -0.3176 1.42E-02 cg07654843 ENSG00000133454 7.44E-04 -0.2883 2.68E-02 cg07164133 ENSG00000114541 7.57E-04 -0.3178 1.42E-02 cg21829328 ENSG00000099958 7.71E-04 0.3002 2.09E-02 cg23882945 ENSG00000073331 7.76E-04 0.3002 2.09E-02 cg08569786 ENSG00000119771 7.99E-04 0.3798 3.01E-03 cg09537551 ENSG00000104375 8.03E-04 0.3136 1.56E-02 cgl0587082 ENSG00000076356 8.05E-04 -0.4303 6.69E-04 cgl2563372 ENSG00000221818 8.70E-04 0.4505 3.43E-04 cgl7486234 ENSG00000104332 8.85E-04 -0.3435 7.74E-03 cgll235297 ENSG00000113504 9.30E-04 -0.4271 7.41E-04 cg24128630 ENSG00000182224 9.33E-04 -0.4742 1.48E-04 cg24128630 ENSG00000167874 9.33E-04 -0.4953 6.65E-05 cg24128630 ENSG00000132510 9.33E-04 -0.2906 2.56E-02 cg24128630 ENSG00000183011 9.33E-04 -0.2985 2.17E-02 cg08140459 ENSG00000119681 9.58E-04 -0.5521 5.83E-06 cgl4624207 ENSG00000162337 9.89E-04 -0.3000 2.10E-02 cgl5227911 ENSG00000183011 1.01E-03 -0.2836 2.95E-02 cgl0402018 ENSG00000181754 1.05E-03 0.3441 7.61E-03 cgl2140144 ENSG00000177133 1.08E-03 -0.3694 3.99E-03 cgl2140144 ENSG00000142611 1.08E-03 -0.3294 1.09E-02 cg04201373 ENSG00000030110 1.11E-03 0.3854 2.58E-03 cg05678871 ENSG00000174780 1.18E-03 -0.4142 1.11E-03 cgll909137 ENSG00000101665 1.18E-03 -0.3223 1.28E-02 cgl2475142 ENSG00000226137 1.21E-03 -0.4950 6.72E-05 cgl2475142 ENSG00000175866 1.21E-03 -0.3227 1.27E-02 cg26498574 ENSG00000122367 1.25E-03 -0.5197 2.47E-05 cgl4741228 ENSG00000139146 1.25E-03 -0.6173 1.91E-07 cg04101806 ENSG00000230393 1.27E-03 -0.2700 3.86E-02 cg20462242 ENSG00000142611 1.30E-03 -0.3449 7.46E-03 cg02711479 ENSG00000181817 1.36E-03 -0.2693 3.91E-02 cgl7810966 ENSG00000163110 1.41E-03 -0.3464 7.20E-03 cg00434119 ENSG00000058866 1.41E-03 -0.4576 2.69E-04 cg24678869 ENSG00000198837 1.42E-03 0.3573 5.46E-03 cgl5647725 ENSG00000113504 1.47E-03 -0.4628 2.23E-04 cg04864441 ENSG00000155093 1.52E-03 -0.2869 2.76E-02 cg22219450 ENSG00000166016 1.53E-03 -0.4770 1.34E-04 cgl4703829 ENSG00000244486 1.54E-03 0.4641 2.14E-04 cgl4703829 ENSG00000099910 1.54E-03 0.3490 6.74E-03 cg05905699 ENSG00000155657 1.55E-03 -0.3315 1.03E-02 cg01179095 ENSG00000175206 1.61E-03 -0.3960 1.90E-03 cg01179095 ENSG00000242349 1.61E-03 -0.2912 2.52E-02 cg03221266 ENSG00000107796 1.62E-03 -0.4799 1.20E-04 cg03221266 ENSG00000138134 1.62E-03 -0.4423 4.53E-04 cg20979153 ENSG00000171940 1.66E-03 -0.4400 4.88E-04 cg20979153 ENSG00000197670 1.66E-03 -0.3672 4.23E-03 cg09550083 ENSG00000143995 1.75E-03 -0.2755 3.47E-02 cg09284275 ENSG00000133392 1.85E-03 -0.5835 1.24E-06 cg04685570 ENSG00000255399 1.86E-03 0.4147 1.09E-03 cg04685570 ENSG00000089225 1.86E-03 0.2855 2.84E-02 cgl4711976 ENSG00000186204 1.88E-03 0.3017 2.02E-02 cgl6201146 ENSG00000185052 1.89E-03 -0.5150 3.01E-05 cg04109883 ENSG00000165633 1.89E-03 -0.4309 6.57E-04 cgl3364311 ENSG00000116106 1.91E-03 -0.5640 3.29E-06 cg03850035 ENSG00000116106 1.92E-03 -0.5553 4.99E-06 cgl2509665 ENSG00000075240 2.00E-03 -0.3653 4.44E-03 cgl2509665 ENSG00000100422 2.00E-03 -0.3899 2.27E-03 cg03256938 ENSG00000177133 2.04E-03 -0.3870 2.46E-03 cg03256938 ENSG00000142611 2.04E-03 -0.4447 4.17E-04 cg08127462 ENSG00000197956 2.05E-03 -0.2956 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1.03E-02 cg04254886 ENSG00000165795 2.32E-02 0.3420 8.03E-03 cgll592677 ENSG00000182197 2.33E-02 -0.2653 4.23E-02 cg21980394 ENSG00000185345 2.35E-02 -0.2732 3.63E-02 cg05021796 ENSG00000250900 2.36E-02 0.3004 2.08E-02 cg05021796 ENSG00000146054 2.36E-02 0.2921 2.48E-02 cg04171808 ENSG00000026508 2.36E-02 -0.7314 4.74E-11 cg21907579 ENSG00000255399 2.39E-02 0.3461 7.25E-03 cg21296513 ENSG00000149564 2.40E-02 -0.3369 9.07E-03 cg03110167 ENSG00000167657 2.42E-02 -0.2657 4.20E-02 ch.5.2517577F ENSG00000152377 2.43E-02 -0.4141 1.11E-03 cgl3270873 ENSG00000104447 2.46E-02 -0.4685 1.83E-04 cg02584488 ENSG00000110429 2.46E-02 -0.2862 2.80E-02 cg22291922 ENSG00000120093 2.47E-02 -0.3886 2.35E-03 cg22291922 ENSG00000108511 2.47E-02 -0.3536 6.01E-03 cg01945840 ENSG00000198125 2.47E-02 -0.3554 5.74E-03 cgl6155393 ENSG00000163827 2.48E-02 0.3509 6.43E-03 cg07134316 ENSG00000166888 2.48E-02 -0.4051 1.46E-03 cg21746120 ENSG00000162337 2.48E-02 -0.3573 5.47E-03 cgl6459103 ENSG00000149243 2.48E-02 0.2587 4.78E-02 cgl0169241 ENSG00000119559 2.50E-02 -0.3584 5.32E-03 cgl0057528 ENSG00000162585 2.50E-02 -0.2945 2.36E-02 cgl0057528 ENSG00000067606 2.50E-02 -0.3051 1.88E-02 cgl5542639 ENSG00000110218 2.51E-02 -0.5029 4.92E-05 cgl4688579 ENSG00000135903 2.53E-02 0.5289 1.66E-05 cg24721309 ENSG00000068650 2.54E-02 -0.4419 4.59E-04 cgl4508696 ENSG00000261801 2.58E-02 0.4797 1.21E-04 cg25353436 ENSG00000225383 2.59E-02 -0.4240 8.19E-04 cg05424970 ENSG00000004059 2.60E-02 0.3573 5.47E-03 cg09217157 ENSG00000138185 2.64E-02 -0.4552 2.92E-04 cgl7510121 ENSG00000146197 2.68E-02 -0.2722 3.70E-02 cgl6616918 ENSG00000132386 2.70E-02 -0.3658 4.38E-03 cg04223553 ENSG00000113196 2.71E-02 -0.3010 2.05E-02 cg04543156 ENSG00000223820 2.72E-02 0.2898 2.60E-02 cgl8919660 ENSG00000185070 2.74E-02 -0.2662 4.16E-02 cgl3371883 ENSG00000170485 2.74E-02 -0.3877 2.41E-03 cg27009545 ENSG00000136404 2.74E-02 -0.4472 3.84E-04 cg04112126 ENSG00000171798 2.75E-02 -0.4520 3.26E-04 cg23024136 ENSG00000113196 2.76E-02 -0.3278 1.13E-02 cgl0624914 ENSG00000150401 2.77E-02 -0.5535 5.44E-06 cgl0624914 ENSG00000153531 2.77E-02 -0.4908 7.93E-05 cg03347450 ENSG00000135447 2.80E-02 -0.3173 1.43E-02 cg08936706 ENSG00000160808 2.80E-02 -0.5436 8.62E-06 cg22157087 ENSG00000091831 2.81E-02 -0.3459 7.30E-03 cg08052292 ENSG00000163947 2.86E-02 -0.7347 3.50E-11 cgllll7099 ENSG00000184922 2.86E-02 -0.3892 2.32E-03 cg06607384 ENSG00000133943 2.91E-02 -0.3035 1.94E-02 cgl5161225 ENSG00000198125 2.91E-02 -0.5306 1.54E-05 cg00572843 ENSG00000177374 2.92E-02 0.3733 3.59E-03 cg00572843 ENSG00000070366 2.92E-02 0.2618 4.52E-02 cg00572843 ENSG00000108963 2.92E-02 0.3448 7.49E-03 cg02829688 ENSG00000092607 2.93E-02 0.5381 1.11E-05 cgl6665041 ENSG00000215251 2.95E-02 0.3460 7.27E-03 cgl6665041 ENSG00000185019 2.95E-02 -0.3407 8.28E-03 cg20252015 ENSG00000079739 2.95E-02 -0.4698 1.74E-04 cgl2522898 ENSG00000101019 2.96E-02 0.2967 2.25E-02 cg03942871 ENSG00000128606 2.97E-02 -0.5892 9.15E-07 cg03942871 ENSG00000161040 2.97E-02 -0.3555 5.73E-03 cg21301805 ENSG00000092607 2.99E-02 0.3454 7.38E-03 cgl9709585 ENSG00000196878 3.00E-02 -0.2702 3.85E-02 cgl7800788 ENSG00000142794 3.00E-02 -0.6104 2.85E-07 cg23186333 ENSG00000026508 3.01E-02 -0.6854 2.12E-09 cg02633609 ENSG00000137809 3.02E-02 0.3866 2.49E-03 cgl0380643 ENSG00000235098 3.02E-02 -0.3547 5.85E-03 cgl0380643 ENSG00000225285 3.02E-02 -0.3155 1.49E-02 cgl5428496 ENSG00000144677 3.04E-02 -0.4569 2.75E-04 cgl7253785 ENSG00000175073 3.06E-02 -0.2686 3.97E-02 cgl7253785 ENSG00000213865 3.06E-02 -0.2584 4.81E-02 cg26535547 ENSG00000161654 3.06E-02 0.4037 1.52E-03 cg08679180 ENSG00000110237 3.10E-02 -0.5172 2.73E-05 cg03548463 ENSG00000189339 3.17E-02 -0.3736 3.56E-03 cg21948564 ENSG00000140506 3.18E-02 -0.2803 3.15E-02 cg24621972 ENSG00000135903 3.19E-02 0.5522 5.78E-06 cg07403350 ENSG00000139826 3.21E-02 -0.3363 9.22E-03 cg07403350 ENSG00000174405 3.21E-02 -0.3392 8.58E-03 cg02715006 ENSG00000204956 3.21E-02 0.4467 3.90E-04 cg00343747 ENSG00000156011 3.23E-02 -0.3931 2.07E-03 cg06560379 ENSG00000146232 3.25E-02 -0.3137 1.55E-02 cg22999025 ENSG00000128487 3.28E-02 -0.3023 2.00E-02 cgl3229360 ENSG00000174705 3.29E-02 -0.4048 1.47E-03 cgl6419756 ENSG00000113504 3.32E-02 -0.4504 3.45E-04 cg24074783 ENSG00000160746 3.32E-02 -0.3316 1.03E-02 cg24074783 ENSG00000163788 3.32E-02 0.3956 1.93E-03 cg00781839 ENSG00000150401 3.33E-02 -0.5065 4.25E-05 cg04658601 ENSG00000168993 3.34E-02 -0.2743 3.55E-02 cgl7452384 ENSG00000109339 3.34E-02 -0.4443 4.23E-04 cg00864012 ENSG00000136478 3.36E-02 -0.3269 1.15E-02 cg21163444 ENSG00000161642 3.37E-02 0.4381 5.20E-04 cg24541550 ENSG00000072952 3.38E-02 -0.4374 5.33E-04 cg21919599 ENSG00000162711 3.39E-02 -0.6395 4.97E-08 cgl5641364 ENSG00000158710 3.40E-02 -0.2990 2.14E-02 cg04674421 ENSG00000169181 3.41E-02 -0.5540 5.32E-06 cg01322214 ENSG00000153790 3.46E-02 -0.3622 4.82E-03 cg24843609 ENSG00000160783 3.47E-02 0.2761 3.43E-02 cg04913078 ENSG00000183091 3.49E-02 0.3058 1.85E-02 cg24406898 ENSG00000164692 3.52E-02 -0.4267 7.52E-04 cg23360190 ENSG00000101331 3.55E-02 0.3708 3.84E-03 cg07567308 ENSG00000185019 3.59E-02 -0.2922 2.47E-02 cg02378006 ENSG00000107731 3.62E-02 -0.4740 1.49E-04 cg23931734 ENSG00000074410 3.62E-02 -0.4075 1.36E-03 cg02511723 ENSG00000131711 3.62E-02 -0.3364 9.18E-03 cgl4855841 ENSG00000169248 3.66E-02 -0.4632 2.21E-04 cgl4855841 ENSG00000169245 3.66E-02 -0.4714 1.64E-04 cg03764585 ENSG00000122176 3.68E-02 -0.3760 3.34E-03 cg24699895 ENSG00000156515 3.68E-02 -0.5237 2.07E-05 cgl0986043 ENSG00000173991 3.68E-02 -0.3297 1.08E-02 cg26541218 ENSG00000158683 3.70E-02 0.3105 1.67E-02 cg06069861 ENSG00000082641 3.72E-02 0.3280 1.12E-02 cgl6990168 ENSG00000092607 3.73E-02 0.5532 5.52E-06 cg06786153 ENSG00000167202 3.73E-02 -0.2866 2.78E-02 cg05403316 ENSG00000115310 3.74E-02 -0.5191 2.53E-05 cg06431025 ENSG00000172554 3.75E-02 0.2589 4.77E-02 cg25918166 ENSG00000226674 3.76E-02 -0.3505 6.49E-03 cg08880369 ENSG00000187535 3.79E-02 -0.4697 1.75E-04 cg08880369 ENSG00000131634 3.79E-02 -0.5386 1.08E-05 cgl0634619 ENSG00000227372 3.79E-02 -0.2649 4.26E-02 cg21814178 ENSG00000110934 3.84E-02 -0.4844 1.01E-04 cg00622552 ENSG00000182950 3.85E-02 -0.3225 1.27E-02 cg00364758 ENSG00000106483 3.89E-02 -0.3827 2.78E-03 cg00364758 ENSG00000086289 3.89E-02 -0.3219 1.29E-02 cgl5535174 ENSG00000149639 3.89E-02 -0.4765 1.36E-04 cg01963906 ENSG00000142765 3.91E-02 0.4417 4.61E-04 cgl4678583 ENSG00000133250 3.98E-02 -0.3321 1.02E-02 cg09262100 ENSG00000198752 3.98E-02 -0.3500 6.59E-03 cg09004195 ENSG00000116106 3.99E-02 -0.6464 3.20E-08 cg22941573 ENSG00000240849 4.01E-02 0.2647 4.27E-02 cg09645291 ENSG00000156113 4.02E-02 0.4013 1.63E-03 cg08668662 ENSG00000131044 4.04E-02 -0.3480 6.92E-03 cg00604356 ENSG00000105851 4.04E-02 -0.4268 7.49E-04 cg05318210 ENSG00000226674 4.05E-02 -0.3984 1.78E-03 cg22950111 ENSG00000117020 4.05E-02 -0.3001 2.09E-02 cgl5281283 ENSG00000183486 4.06E-02 -0.2939 2.39E-02 cgl5281283 ENSG00000183844 4.06E-02 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-0.3805 2.95E-03 cg06032021 ENSG00000177791 4.38E-02 -0.3848 2.62E-03 cg24339032 ENSG00000143850 4.43E-02 0.3663 4.33E-03 cg09042277 ENSG00000255399 4.44E-02 0.3090 1.73E-02 cg06728055 ENSG00000018408 4.47E-02 -0.3296 1.08E-02 cgl3873733 ENSG00000152795 4.50E-02 -0.4559 2.85E-04 cgl3873733 ENSG00000145293 4.50E-02 -0.4414 4.66E-04 cg02722596 ENSG00000253910 4.54E-02 0.2653 4.23E-02 cg01941219 ENSG00000152767 4.55E-02 -0.3618 4.87E-03 cg21783442 ENSG00000134375 4.56E-02 0.2724 3.68E-02 cgll027217 ENSG00000073331 4.57E-02 0.3148 1.52E-02 cg03603260 ENSG00000197622 4.58E-02 0.4160 1.05E-03 cg03603260 ENSG00000143443 4.58E-02 -0.3612 4.95E-03 cg23647640 ENSG00000184489 4.61E-02 -0.3413 8.15E-03 cg01709312 ENSG00000150593 4.61E-02 -0.5085 3.93E-05 cgl9542542 ENSG00000163697 4.65E-02 -0.3956 1.93E-03 cg22060817 ENSG00000135547 4.69E-02 -0.4038 1.51E-03 cg08942939 ENSG00000092607 4.72E-02 0.4411 4.71E-04 cg04738151 ENSG00000198812 4.74E-02 -0.2851 2.86E-02 cgl8619300 ENSG00000134321 4.74E-02 -0.2691 3.93E-02 cgl6202734 ENSG00000067191 4.75E-02 -0.2700 3.86E-02 cg27355006 ENSG00000150281 4.78E-02 0.3889 2.33E-03 cgl4637411 ENSG00000053918 4.79E-02 -0.2991 2.14E-02 cgl6003601 ENSG00000105357 4.79E-02 -0.2848 2.88E-02 cg21990700 ENSG00000139178 4.80E-02 -0.7204 1.26E-10 cg21990700 ENSG00000205885 4.80E-02 -0.6747 4.65E-09 cgl6016960 ENSG00000132561 4.80E-02 -0.6237 1.31E-07 cg00589850 ENSG00000253767 4.80E-02 0.5045 4.62E-05 cg00589850 ENSG00000204956 4.80E-02 0.4285 7.11E-04 cg00589850 ENSG00000253537 4.80E-02 0.3184 1.40E-02 cg00589850 ENSG00000253731 4.80E-02 0.4298 6.80E-04 cg00589850 ENSG00000253910 4.80E-02 0.4632 2.20E-04 cg00589850 ENSG00000253485 4.80E-02 0.4167 1.03E-03 cg00589850 ENSG00000262576 4.80E-02 0.3810 2.91E-03 cg00589850 ENSG00000253873 4.80E-02 0.3409 8.24E-03 cg00589850 ENSG00000262209 4.80E-02 0.4029 1.56E-03 cg00589850 ENSG00000253953 4.80E-02 0.2882 2.69E-02 cg00589850 ENSG00000242419 4.80E-02 0.2712 3.78E-02 cg00589850 ENSG00000254245 4.80E-02 0.3041 1.92E-02 cg00589850 ENSG00000253846 4.80E-02 0.2783 3.28E-02 cg00589850 ENSG00000254221 4.80E-02 0.3212 1.31E-02 cg00589850 ENSG00000253305 4.80E-02 0.3182 1.40E-02 cg02696327 ENSG00000102924 4.80E-02 0.4802 1.19E-04 cg06917231 ENSG00000197062 4.82E-02 -0.2939 2.39E-02 cgl8108818 ENSG00000128606 4.82E-02 -0.2806 3.14E-02 cgl2178237 ENSG00000172915 4.84E-02 0.4312 6.51E-04 cg09430976 ENSG00000221818 4.86E-02 0.2895 2.62E-02 cg08771114 ENSG00000184956 4.87E-02 -0.2989 2.15E-02 cgl3654836 ENSG00000153944 4.88E-02 -0.2828 3.00E-02 cg23009419 ENSG00000241186 4.89E-02 -0.3829 2.76E-03 cg07768268 ENSG00000090565 4.91E-02 0.2793 3.22E-02 cgl3054523 ENSG00000261888 4.95E-02 0.3581 5.35E-03 cgl9489885 ENSG00000087116 4.97E-02 -0.3751 3.42E-03 cg05940231 ENSG00000092607 4.97E-02 0.4053 1.45E-03 cg06595154 ENSG00000072952 4.98E-02 -0.4303 6.70E-04 cg00203284 ENSG00000186564 5.00E-02 0.4212 8.94E-04

Nominal p-values for Correlation. For DCM association, justment for gender, age and PCA. Results

Epigenome-wide Association Study of DCM

For the inclusion in this study, it was required that pa ^¬ tients with systolic dysfunction and suspicion for DCM underwent extensive clinical phenotyping. Excluded were all pa ^¬ tients who had hints for secondary causes of DCM from the de- tailed clinical work-up (see Materials and Methods section) . A total of n=135 patients were included in the study. Since we only were interested in complete datasets and sufficient cardiac biomaterial as left-over, we excluded 94 individuals. In the final core cohort, n=41 patients for whom we were able to generate high quality DNA methylation profiles from heart tissue and peripheral blood were used in the screening stage of this study. None of these patients or controls did overlap with previous studies on DNA methylation (Haas J, et al . , Al ^¬ terations in cardiac DNA methylation in human dilated cardio ^¬ myopathy. EMBO Mol Med. 2013;5:413-29). The mean age of pa ^¬ tients was 54.1112.3 and 63% were in early NYHA stages. As such, the median NT-proBNP was 812 ng/1, see Table 25. As control samples, we used left-ventricular biopsies from 31 patients free of heart failure with regular systolic and di ^¬ astolic heart function who underwent routine left-heart myo ^¬ cardial biopsy after receiving heart transplantation, see Table 26. For an overview on patients, controls and molecular phenotyping, please see Figures 5 and 6, which show an overview of the study cohort in the multi-omics screening stage. Figure 5 shows therein the screening in an abstract way, wherein N=41 for DCM. RNA 6, methylation 7, phenotype 8, bi- omarkers 9, and genome 10 have been determined for heart tis- sue H and blood B, respectively, as well as for HTX controls HTX, wherein N=31, and for clinical controls CC, wherein N=31. These data were used for epigenome-wide association study 100, as also shown in Figure 7, identification of heart failure associated epigenetic patterns 101, as also shown in Figures 8-10, epigenetic regulation of cardiac RNA transcrip ^¬ tion 102, as also shown in Figures 11-14, and identification of conserved epigenetic patterns, as also shown in Figures 15-19. Fig. 6 shows data for a replication experiment R I with DCM (N=18) for heart tissue H and DCM (N=9) for blood B, wherein again RNA 6, methylation 7 and phenotype 8 were determined, as well as for healthy controls HC with N=8 for H and N=28 for B. In a replication experiment R II shown in Figure 6 as well, DCM was N=82 and HC was N=109 for blood B, wherein methylation 7 and phenotype 8 were determined. These experiments enabled a validation of epigenome-wide associa ^¬ tion loci 104, as also shown in Table 28, a validation of DCM and mRNA associated methylation signatures 105, as also shown in Figures 11-19, and a validation of potential methylation biomarkers 106, as also shown in Figures 15-21.

After performing data quality control and normalization, we calculated genome-wide associations for each CpG site. Gono ^¬ somes were prima vista excluded from the analysis. To adjust for potential epigenomic inflation, we performed principal component (PC) analysis on methylation measurements and iden ^¬ tified PCs, which were associated with confounders (methodo- logical confounders as batch effects and biological confound ^¬ ers such as medication; FDR ≤0.05), see Tables 21 and 22. Dysregulated methylation sites were identified by linear mod ^¬ elling and moderated t-tests including age, gender as well as the identified principal components as covariates (Meder B, et al . , Influence of the confounding factors age and sex on microRNA profiles from peripheral blood. Clin Chem.

2014; 60 : 1200-8) .

From 485,000 methylation sites, 394,247 passed QC in myocar- dial tissue and blood. Genotype-associated methylation chang ^¬ es were excluded. 42,745 CpG-sites (9.5%) were found differ ^¬ entially methylated (raw-p≤0.05) in left-ventricle myocardium when comparing DCM vs. controls (33,396 of them being in lOkb windows around annotated genes with expression in the cardiac tissue) . The ratio of hypo-methytlated vs hyper-methylated

CpG sites was 0.92. In blood samples, 35,566 (9%) were asso ^¬ ciated with DCM (raw p≤0,05; 28,153 being in a lOkb window of annotated genes) . Figure 7 shows a Manhattan plot of the epigenome-wide associ ^¬ ation study for Dilated Cardiomyopathy, showing an epigenome- wide association scan in cardiac tissue. Minus loglO p-values are shown for single CpGs that passed the quality control criteria for the screening cohort. They are plotted against the chromosomes Chr on the x-axis. Probability values were based on linear modelling and moderated t-tests including age, gender and PCs as covariates. The solid line indicates the epigenome-wide significance level of p=5xl0-8 and the dotted line indicates the false discovery (FDR) significance threshold of p=0.05. In the plot, N is 41 for DCM and N is 31 for controls C.

As summarized in the Manhattan plot in Figure 7, after cor ^¬ recting for multiple testing we find 59 CpGs to be significantly differentially methylated in the myocardium of DCM pa ^¬ tients (FDR-corrected p≤0.05; dotted line), with 30 sites that were hypomethylated and 29 sites hypermethylated in DCM. The delta of the methylation difference for FDR significant sites was in the median 14.34% (2.75% - 69.9%) . With the most stringent cut-off, we find 3 epigenome-wide significant loci with p-value ≤5xl0-8 (solid line) . The first of these loci (cgl6318181, p=2.3x10-8) is on Chromosome 3, position

99,717,882. It is located within the gene body of CMSS1, the 5 ' UTR region of FILIPIL and part of the promoter region of miR-548G (within 1500bp upstream of the transcription starting site). The second locus (cg01977762, p=2.8x10-8) is lo- cated on chromosome 19, position 4,909,193. It is within the promoter region of UHRF1 and part of a CpG island

hrl9:4, 909,262-4, 910,256. The third locus (cg23296652, p=4.8x10-8) is on chromosome 8, position 142,852,938 and not located near any known gene within a range of 10,000bp.

To replicate these findings, we epigenotyped DNA from n=18 independent DCM patients and n=8 previously healthy control individuals that were casualties of roadside accidents. To the best of our knowledge, these control individuals were free of any heart condition and did not take regular medica ^¬ tion. As shown in Table 35, we could successfully replicate 27 of the 59 loci (46%) in the independent cohorts. The most significant hit from the screening stage (cgl6318181) could also be validated (replication p=0.004), resulting in a com- bined Fisher's p=2.23x10-09. In total, 5 hits superseded stringent genome-wide significance in the combined analysis. Table 35: Replicated DNA methylation sites

CpG Chr Genes within lOkb Discovery Replication Fisher' s combined

& cardiac expression p-value p-value p-value

cgl6318181 3 FILIP1L;CMSS1 2.31728E-08 0.003988992 2.22813E-09 cg25838968 1 PLXNA2 1.62572E-07 0.000191836 7.85636E-10 cg01726792 14 NDRG2 ; TPPP2 ; RNASE7 1.31022E-06 0.000818940 2.32333E-08 cg05978306 17 MY01C;CRK 1.54725E-06 0.001279220 4.16450E-08 cgl8251389 7 - 1.83860E-06 0.012516509 4.27745E-07 cg00586700 19 FCGRT 2.13918E-06 0.010759738 4.27818E-07 cgl8601596 6 KCNK17 2.44359E-06 0.022232480 9.63125E-07 cg03426023 16 IRX5;CRNDE 2.47814E-06 0.044349724 1.87098E-06 cgll763830 17 TTYH2 2.48453E-06 0.040090963 1.70547E-06 cg24415066 4 HAND2 ; HAND2 -AS 1 2.95582E-06 0.044796249 2.22943E-06 cgl7912835 2 POU3F3 3.51740E-06 0.020237071 1.24270E-06 cgl9567891 15 LINC00925 3.93465E-06 0.021478299 1.46087E-06 ch.16.406779 16 CLEC16A 4.25310E-06 0.022104431 1.61512E-06 R

cgl7291767 6 TRERF1 4.35941E-06 0.001627922 1.40258E-07 cg02581963 10 LINC00263; SCD 4.55249E-06 0.010207249 8.31064E-07 cgl7399647 6 TRERF1 4.67027E-06 0.007516221 6.37642E-07 cgl4523204 9 RGS3 4.73687E-06 0.000876207 8.42549E-08 cg24366665 13 - 5.19845E-06 0.003078146 3.03239E-07 cgl9194167 15 CGNL1 5.21526E-06 0.019165689 1.71107E-06

cg01294686 1 CEP85;UBXN11; 3BGRL3 5.24878E-06 0.020249042 1.81288E-06 cg08755532 2 KCNIP3 5.49783E-06 0.015581898 1.47970E-06 ch.1.1170576 1 - 5.53713E-06 0.005718672 5.78458E-07

66F

cgl4504418 11 BIRC3 5.54343E-06 0.035203372 3.21008E-06 cgl9683073 5 SERINC5 5.83025E-06 0.003635288 3.95694E-07 cg26941823 5 STK10 6.21768E-06 0.009873932 1.08088E-06 cg08281084 15 HERC2 6.27797E-06 0.040257891 4.09205E-06 cgl6254946 1 GLIS1 7.42353E-06 3.56842E-06 6.71640E-10

Conserved DNA Methylation Sites in Heart Failure In previous studies, mainly low-resolution approaches or very small cohorts were used to identify DNA methylation patterns for DCM and/or heart failure. Hence, to see if these findings can be reproduced in the current study, we compared methyla ^¬ tion changes from the available previous studies (34 loci) and the current dataset. Since the methods varied largely and CpGs were not uniformly measured in the former studies, we used simes p-value aggregation of our dataset for the loci described previously. Using a cutoff of p≤0.05, we could rep ^¬ licate DNA methylation changes in the same direction in the genes LY75, PTGES, CTNNAL1, TNFSF14 , MRPL16, KIF17, see Table 36 (Haas J, et al . , Alterations in cardiac DNA methylation in human dilated cardiomyopathy. EMBO Mol Med. 2013;5:413-29.; Koczor CA, et al . , Thymidine kinase and mtDNA depletion in human cardiomyopathy: epigenetic and translational evidence for energy starvation. Physiol Genomics. 2013;45:590-6; Mo- vassagh M, et al . , Differential DNA methylation correlates with differential expression of angiogenic factors in human heart failure. PLoS One. 2010 ; 5 : e8564 ; Garagnani P, et al . ,

Methylation of ELOVL2 gene as a new epigenetic marker of age. Aging Cell. 2012;11:1132-4), which supports the fact that heart failure is associated with certain defined, robust DNA methylation patterns. From all replicated loci, the LY75 methylation pattern showed the highest significance (simes p=0.002) .

Table 36: Replication of DNA gene methylation from previous studies .

Gene Reference Methylation in p- -value

DCM/HF

LY75 Haas et al . 2013 Hyper-methylation 0 .0006

PTGES Koczor et al . 2013 Hypo-methylation 0 .0028

CTNNAL1 Haas et al . 2013 Hypo-methylation 0 .0099

TNFSF14 Koczor et al . 2013 Hyper-methylation 0 .0100

MRPL16 Koczor et al . 2013 Hypo-methylation 0 .0274

KIF17 Koczor et al . 2013 Hyper-methylation 0 .0471 DCM = Dilated Cardiomyopathie; HF = heart failure.

Besides confirming hypermethylation of the LY75 gene locus, we also replicated the associated downregulation of LY75 ex- pression levels in DCM, as seen in Fig. 8. Fig. 8 therein shows the methylation and expression of LY75 in myocardi- al/cardiac tissue. The diagram shows the correlation of cgl0107725 in the promoter region and LY75 expression levels. Plotted is the LY75 mRNA expression (LY75 mRNA exp) on the y- axis versus cgl0107725 methylation beta (cgl0107725 meth) on the x-axis, with values plotted for DCM and control (CTRL) . As for LY75, we could find a significant correlation between DNA methylation and mRNA expression, which underlines the regulatory role of the epigenetic code in the heart

(*=p<0.05, **=p<0.01, ***=p<0.001) .

As for the successful replication of previous findings in tissue, we successfully replicated known age-dependent pat ^¬ terns in CpG islands within EL0VL2, FHL2 and PENK (Garagnani et al . , 2012) in the DNA derived from wholeperipheral blood samples of our cohort (simes significance level <10-14) .

Detection of Methylation Patterns in DCM

In unsupervised cluster analysis, showing DNA methylation in cardiac tissue - as seen in Fig. 9, we found that DNA methyl ^¬ ation differences are able to cluster DCM patients and con ^¬ trols, underlining a disturbance or reprogramming of DNA methylation in heart failure. Fig. 9 therein shows cluster analysis in myocardial tissue, showing a correlation coeffi- cient with a certain color key CK for a flow z-score FZS. As shown, cases and controls group very well together, indicat ^¬ ing conserved methylation changes in DCM.

To test for possible functional methylation patterns, we first performed overrepresentation analysis for genome-wide transcription- and enhancer factor binding sites (Mathelier A, et al . , JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 2016; 44 : D110-5) and their potential affection by DNA methylation. From 158,979 CpGs within annotated sequence motifs, we detected 4 motifs significantly as- sociated with methylation alterations in DCM (FDR-p≤0.05) , as shown in Table 23. Of interest, three of the motif-binding factors (Smad2, Smad4 and Bmall) are known to be involved in cardiac remodeling during DCM and heart failure (Lefta M, Campbell KS, Feng HZ, Jin JP and Esser KA. Development of di- lated cardiomyopathy in Bmall -deficient mice. Am J Physiol Heart Circ Physiol. 2012 ; 303 : H475-85) .

There is ample evidence that larger stretches of DNA methyla ^¬ tion cluster together and exhibit repression of cis- regulatory elements. Hence, we carried out an overrepresenta ^¬ tion analysis for clustering of differentially methylated sites at raw-p≤0.05 in specific chromosomal bands and found 6 regions to be significantly differentially methylated in DCM (Bonferroni level p≤0.05), as seen in Fig. 10. Fig. 10 there- in shows a Chromosome Band Overrepresentation Analysis plot, particularly an epigenome-wide association chromosome band scan in cardiac tissue. Minus loglO p-values are shown for overrepresentation analysis (pORA) based on chromosome bands in the screening cohort. The solid line indicates the Bonfer- roni-corrected significance level of 0.05 and the dotted line indicates the FDR-corrected significance threshold of p=0.05.

These regions host noticeable numbers of genes associated with cardiac development, heart function and cardiomyopathy. As an example, we found the gene locus 12q24.21 to be differ ^¬ entially methylated in DCM (78 out of 425 methylation sites show association with DCM at raw-p≤0.05, fisher's exact p=2xl0-6) . The 12q24.21 locus is harbouring several genes that have previously been linked to cardiomyopathies or car- diac development. One of the genes is TBX5, coding for a pro ^¬ tein that is part of the T-Box family, known to be implicated in embryonic development and cardiogenesis (Papaioannou VE . The T-box gene family: emerging roles in development, stem cells and cancer. Development. 2014;141:3819-33). Mutations in TBX5 could lately been shown in patients suffering from familial, as well as sporadic dilated cardiomyopathy (Zhou W, Zhao L, Jiang JQ, Jiang WF, Yang YQ and Qiu XB . A novel TBX5 loss-of-function mutation associated with sporadic dilated cardiomyopathy. Int J Mol Med. 2015;36:282-8). Another gene within this locus is MED13L, which is part of the Mediator complex family, which is also known to be involved in cardio- vascular disease (Schiano C, Casamassimi A, Vietri MT, Rienzo M and Napoli C. The roles of mediator complex in cardiovascu ^¬ lar diseases. Biochim Biophys Acta. 2014;1839:444-51) and early heart development, leading to a variety of inborn car ^¬ diac abnormalities when disturbed (Samanek M. Congenital heart malformations: prevalence, severity, survival, and quality of life. Cardiol Young. 2000;10:179-85). Additional ^¬ ly, we find the MYL2 gene within close vicinity to the

12q24.21 locus, which is coding for the ventricular regulatory Myosin Light Chain. It has an essential role during early embryonic cardiac development and represents one of the ear ^¬ liest markers of ventricular specification. Mutations in MYL2 are furthermore associated with Dilated and Hypertrophic Car ^¬ diomyopathy (O'Brien TX, Lee KJ and Chien KR. Positional specification of ventricular myosin light chain 2 expression in the primitive murine heart tube. Proc Natl Acad Sci U S A. 1993;90:5157-61). Together, we found evidence for coordinated DNA methylation patterning in key cardiac developmental ge ^¬ nomic regions. Impact of Differential DNA Methylation on Cardiac Gene Ex ^¬ pression

To test if the observed alterations in the degree of DNA methylation also act on global gene expression, we performed poly-A enriched mRNA sequencing in isolated RNA from the same biopsies that were taken for the methylation analysis in our discovery cohort. To link expression and DNA methylation, we performed _met eQTL-analysis and identified a wide range of DNA methylation sites acting on cardiac transcription across the entire genome, as shown in Figures 11 and 12 Figs. 11 and 12 depict Manhattan plots for methylation loci associated with down- and upregulation of mRNA expression in cardiac tissue, with Figure 11 showing an epigenome-wide methQTL scan for negative association in cardiac tissue, and Figure 12 showing an epigenome-wide methQTL scan for positive association in cardiac tissue. The solid line indicates the epigenome-wide significance level of p=5xl0-8 and the dotted line indicates the (FDR) significance threshold of FDR-p=0.05.

DNA hypermethylation within in the promoter region and the vicinity of transcription start-sites was found to be strong ^¬ ly associated with transcriptional downregulation and hypo- methylation with upregulation. For 3' downstream regions as well as towards the end of the gene body we find an equal ra ^¬ tio of positive and negative correlation between methylation status and gene expression levels, as seen in Fig. 13. Fig. 13 shows a correlation analysis of DNA methylation and mRNA expression depending on the position of the CpG relative to the associated gene, particularly methylation-mRNA association in cardiac tissue. Plotted is the correlation coeffi ^¬ cient for - from left to right 100-0% 10 kbp for 5' upstream TSS (5' U TSS), 0-100% for gene body (GB) , and 0-100% lOkbp for 3' downstream (3' D) CpGs with an uncorrected p-value <0.05 are depicted in grey hatched from top left to bottom right, FDR corrected <0.05 are dark grey hatched from top right to bottom left, and genome-wide significant ones are black. Also shown are the ratios of mRNA and methylation Met for upslope and downslope as well as the ratio r thereof.

From the 33,396 CpG-sites found to be differentially methyl ^¬ ated (raw-p≤0.05) in DCM and within lOkb of genes expressed in the cardiac tissue, 8,420 CpGs were also significantly as- sociated with gene expression in the discovery cohort (raw- p≤0.05) . The observed overlap between DNA methylation and mRNA abundancy is far higher than expected by chance (Fisher exact p=7xl0-67), which indicates that DNA methylation has a considerably strong functional impact on gene transcription in the heart. To dissect the role of these changes during DCM and also take into account the most valid candidates, we performed an inde ^¬ pendent validation study. The controls of the validation co ^¬ hort, which were casualties of road accidents, were to the best of our knowledge free of any heart condition and did not take medication. To not only eliminate potential biological confounders, we chose a different mRNA sequencing protocol using random primers instead of poly-A enrichment. Samples were sequenced to a median paired-end read count of 37.17 million and mapping percentages were in the median 88.09. By combining these two independent study cohorts, we could gen ^¬ erate a set of high confidence DNA methylation and expression sites for DCM. In detail, 517 different CpGs were direction- ally replicated on two levels (Fisher exact p=l .2x10-134) , (i) to be associated with DCM and (ii) to act on mRNA tran- scription, as can be seen from Fig. 14 and Table 34. Fig. 14 therein shows a diagram of DNA methylation sites with DCM and/or RNA association in myocardial tissue. Shown on the left is the screening S of cardiac tissue with N=41 for DCM and N=31 for control C, and on the right the replication R of cardiac tissue with N=18 for DCM and N=8 for control C. For each DCM association DCM ass and mRNA association mRNA ass are shown, as well as the overlap, and at the bottom the overlap of the respective overlaps for DCM & mRNA association DCM & mRNA ass. The diagram indicates cardiac methylation sites that are linked to DCM and/or are associated with car ^¬ diac gene expression in the discovery and the replication cohorts for which both DNA methylation and mRNA expression where available (all at nominal p-value <0.05) . The 517 rep ^¬ licated CpGs are associated with DCM and mRNA expression (p=l .2x10-134) . As shown by gene ontology overrepresentation analysis, the host genes of the methylation sites are mostly related to pathways linked to cardiac development and muscle function, as also shown in Table 24, further indicating that coordina- tion of the expression of important functional genes in the course of (early) heart failure is driven by DNA methylation.

Two of the genome-wide significantly replicated methylation sites (see Table 35) were found to also be associated with expression of neighboring genes in the discovery and verification cohorts. Methylation status of cg25838968 was associ ^¬ ated with PLXNA2 expression level (combined p=0.02), which is also differentially expressed in DCM (combined p=3xl0-5) . Methylation status of cgl4523204 is associated with RGS3 (Regulator Of G-Protein Signaling 3) expression (combined p=0.0004), which we found to be differentially expressed in DCM as well (combined p=0.02).

Conservation of DNA Methylation Patterns Across Tissues

The methylation and expression analyses resolved interesting new loci potentially involved in the pathogenesis of heart failure. As shown above, we for instance could replicate the strong association of myocardial LY75 methylation and expression with DCM. However, LY75 methylation is different in pe- ripheral blood, hampering the immediate use as peripheral blood marker.

Hence, to search for potential peripheral biomarkers, we in ^¬ vestigated if DNA methylation changes are conserved across different tissues. As shown by an exploratory analysis there is indeed a set of conserved directionally-dysmethylated re ^¬ gions in heart tissue and blood, as seen in Figs. 15 and 16. Figures 15 and 16 as well as Figures 17 and 18 and 19 show the conservation of DNA methylation signatures across tis- sues. Figs. 15 and 16 show an exploratory analysis on the overlap between cardiac tissue and blood DMRs . Fig. 15 par ^¬ ticularly shows DCM-associated DMR conserved across tissues for the heart H and the blood B, wherein the relative delta- beta in tissue ≥5%, cardiac tissue (N=41 DCM, N=31 controls) , blood (N=41 DCM, N=31 controls) . Resulting in the table below are overrepresented gene ontology categories OGOC, particu- larly contractile fiber part CFP, sarcomere SAR, contractile fiber CF, I band IB, myofibril MF, and Z disc ZD. Fig. 16 particularly shows DCM-associated DMR conserved across tis ^¬ sues for the heart H and the blood B, wherein the relative delta-beta in tissue & blood≥10%, cardiac tissue (N=41 DCM, N=31 controls), blood (N=41 DCM, N=31 controls). Resulting in the table below are overrepresented gene ontology categories OGOC, particularly hemophilic cell adhesion HCA, cell-cell adhesion via pm CCVP, cell-cell adhesion CCA, biological adhesion BA, calcium ion binding CIB, and cell adhesion CA. Venn diagrams indicating the directional overlap of methyla ^¬ tion differences (raw-p≤0.05) in tissue and blood for CpGs with ≥5% or ≥10% relative methylation beta are shown in Fig. 15 and 16. In the attached tables, overrepresentation analy ^¬ sis on gene ontology categories was performed (FDR-corrected p-values) . Fig. 17 depicts the DNA methylation of the NPPA and NPPB locus, particularly for methylation in tissue Meth T (left) and methylation in blood Meth B (right) for each. Natriuretic peptides are the gold-standard biomarkers in HF. In DCM, hypomethylation of the 5' CpG is associated with in- creased expression (not shown) . In blood, the same direction of dysmethylation is found representing a cross-tissue conservation due to an unknown mechanism. Figs. 18 and 19 demonstrate that the methylation of cg24884140 is a conserved methylation locus in myocardial tissue and blood. Methylation is shown as methylation beta for tissue Meth beta T on the top and methylation beta for blood Meth beta B at the bottom for screening S and replication R in Fig. 18, whereas Fig. 19 shows a conserved marker panel in blood for screening S at the top and replication R on the bottom, wherein each time sensitivity sens (y-axis) is plotted against specificity spec (x-axis) , and the area under the curve AUC is given. Differ ^¬ ential methylation is illustrated using nominal p-values. ROC analysis of a DNA methylation signature comprising three CpGs with differential and directed methylation difference in tis ^¬ sue and blood for the detection of DCM/heart failure (B9D1: cg24884140, DCLK2 : cgl2115081 and NTM: cg25943276).

When using 5% dysmethylation in tissue as a cut-off, we find as many as 3,798 conserved methylation sites that are changed in the same direction in tissue and blood (raw-p≤0.05 in both groups) . Very interestingly, the overlapping genes are highly enriched for myofilament components, as seen in the table in ^¬ sets in Figs. 15 and 16. When further increasing the stringency (10% relative dysmethylation in tissue and blood) 217 conserved methylation sites remain. This is by far higher than expected by chance (p=3.2x10-13) , demonstrating a poten- tially conserved regulation of a relevant number of methyla ^¬ tion sites, which further supports the idea to use them as novel biomarkers.

Following this interesting hypothesis, we next explored the epigenetic regulation of the NPPA and NPPB locus. This locus encodes atrial natriuretic factor (ANF) and brain natriuretic peptide (BNP) , the latter represents the gold-standard bi- omarker for heart failure. Astoundingly, we find the same di ^¬ rection of dysmethylation in DNA from heart tissue (Fig. 17, hatched bars top right to bottom left) and peripheral blood (Fig. 17, hatched bars top left to bottom right) . As ex ^¬ pected, gene expression of NPPA and NPPB is significantly dysregulated in the opposite direction in tissue (upregula- tion, p=0.0001 for both, data not shown) and transcript lev- els of NPPB highly correlate with NT-proBNP levels measured in plasma of the patients (R2=0.55) . Accordingly, DNA methyl ^¬ ation of both loci could already serve as a peripheral bi- omarker for heart failure. Epigenetic Loci as Potential Novel Biomarkers for Heart Fail ^¬ ure In order to embark on the power of connected biological lay ^¬ ers captured by the present multistage, multi-omics study de ^¬ sign, we then compared the methylation patterns from myocardial tissue and peripheral blood of the screening and repli- cation cohorts after we removed CpG sites that are directly hit by genetic variation (SNP or INDEL within the 50bp probe region) or are associated with genetic variation within a lOkb region ( ≤0.05) . We also removed all CpG sites that have been shown to be associated with blood cell heterogeneity (Holm S. A simple sequentially rejective multiple test proce ^¬ dure. Scandinavian Journal of Statistics. 1979;6, 65-70) . From 90,935 remaining DNA methylation sites, 17,709 were conserved between cardiac tissue and blood, of which 6 (OR=1.38, fisher's exact p=NS) are associated with DCM in heart tissue and 612 (OR=0.89, fisher's exact p=0.01) had disease associa ^¬ tion in blood. Three epigenetic loci highly significantly overlapped between tissue and blood (OR=28, fisher's exact p<0.001) on all investigated levels, showing disease associa ^¬ tion and concordant dysmethylation across tissues.

The resolved genes were "B9 Protein Domain 1" (B9D1, hypo- methylated in DCM in heart tissue and blood) , "Doublecortin like kinase 2" (DCLK2, hypomethylated) and "Neurotrimin" (NTM, hypermethylated) . For Neurotrimin (NTM) , which belongs to the so-called IgLONS, there is a reported association of its protein blood levels with heart failure and prognosis of affected patients undergoing pharmacotherapy (Cao TH, et al . , Identification of novel biomarkers in plasma for prediction of treatment response in patients with heart failure. Lancet. 2015;385 Suppl 1:S26) . B9D1 (cross-validation median

p=4.55x10- 6), which is also one of the 517 CpGs, as seen in Fig. 14, identified to be robustly associated with DCM in tissue, is one of the most significantly associated hits in blood, as seen in Fig. 20, as well as associated with mRNA transcription in cardiac tissue. Fig. 20 and 21 show graphs representing the top 8 individual blood methylation-sites that were verified in the validation cohort. In Fig. 20, the diagram illustrates the verified methylation blood biomarker candidates (*=p≤0.05, **=p≤0.01, ***=p<o .001) , showing DNA methylation in blood for screening S (DCM N=41, controls N=31) and replication R (DCM N=9, con ^¬ trols N=28; replication I), wherein each time methylation beta Meth beta is plotted on the y-axis. While cg06688621 is a DMR in blood only, cg01642653 is dysmethylated in tissue and blood. cg24884140 near B9D1 is also identified by a complete ^¬ ly different strategy comprising all assessed levels of mul- ti-omics data. Fig. 21 shows a fine-mapping of the Top-2 marker candidates using mass-spectrometry, particularly showing a finemapping of DNA methalytion in blood (replication II) (DCM N=82, control C N=109) . Spider plots show the degree of methylation and significance levels of the lead-CpG and neighboring CpGs for the most significant blood-based DMRs . Dashed line = DCM cases, fat black = healthy controls (NS = not significant) .

Mutations in B9D1 result in disturbed heart development due to disrupted cliogenesis and the protein is highly expressed in myocardium and cardiomyocytes (Dowdle WE, et al . , Disrup ^¬ tion of a ciliary B9 protein complex causes Meckel syndrome. Am J Hum Genet. 2011;89:94-110). We now show that the methyl ^¬ ation state of B9D1 could serve as a diagnostic biomarker for DCM, as exemplified in Figs. 18 and 19, as we found an AUC of greater 87% in peripheral blood discovery cohort and robust replication in myocardial tissue as well as the peripheral blood verification cohorts. For a 3-marker peripheral blood methylation panel (B9D1: cg24884140, DCLK2 : cgl2115081 and NTM: cg25943276), we find and AUC of 91.5% in the discovery cohort and 86.9% in the validation cohort, as seen in Figs. 18 and 19. The single B9D1 DNA methylation as well as the methylation marker panel outperformed NT-proBNP as gold standard marker (AUC of 85%) in this cohort. Finally, we investigated the DNA dysmethylation sites with highest significance in blood alone and replicated them in the validation cohorts, as seen in Fig. 20. The mean AUC of the best ten markers by this strategy was 0.89 in the screen- ing stage and 0.78 in the replication. The most significant marker with DCM association in blood was cg06688621, which is hypermethylated in DCM. This marker is not differentially methylated in tissue. The second most significant blood mark ^¬ er (raw-p=8.5x10-10) that was successfully replicated is cg01642653 (BDNF, brain-derived neurotrophic factor, which is a cardioprotective factor; Hang P, et al . , Brain-derived neu ^¬ rotrophic factor attenuates doxorubicin-induced cardiac dys ^¬ function through activating Akt signalling in rats. J Cell Mol Med. 2017;21:685-696). This methylation site additionally shows - as other markers in this list - conserved methylation in cardiac tissue (raw-p=9.9x10-4) .

By using mass-spectrometry-based DNA methylation quantifica ^¬ tion as an alternative method in another independent set of 82 DCM cases and 109 controls, as seen in Tables 32 and 33, we were able to fine-map and fully replicate the directional, significant dysmethylation of our Top-2 markers (cg06688621 and cg01642653) and their neighbouring CpGs within the same CpG island.

Discussion

The present study on the epigenetics of heart failure due to DCM identified a significant role of DNA methylation patterns on cardiac gene transcription in myocardial disease. The re- producible DNA methylation patterns identified in this study as well as the successful replication of previous epigenetic loci from other studies, underline the robustness of the findings and support a role in diagnosis and potentially prognostication of heart failure.

The cardiac epigenome is far from being understood. Basical ^¬ ly, only very few studies could reliably map DNA methylation changes in human tissue. While in oncology, the surgical re ^¬ section of the tumour is integral part of the therapy and hence explanted tissue is readily available for research, the therapy of heart failure does mostly not require surgical in- tervention and only in rare conditions (e.g. obstructive hy ^¬ pertrophic cardiomyopathy) the resection of myocardium (Kim LK, et al . , Hospital Volume Outcomes After Septal Myectomy and Alcohol Septal Ablation for Treatment of Obstructive Hy ^¬ pertrophic Cardiomyopathy: US Nationwide Inpatient Database, 2003-2011. JAMA Cardiol. 2016;1:324-32). In this study, we were able to refine existing methods for high-quality DNA/RNA extraction and consecutive state-of-the-art sequencing and methylation mapping to assess left-over myocardial tissue from biopsies taken during diagnostics of patients suffering from heart failure due to DCM. By including the largest sam ^¬ ple set yet, we were able to detect disease-associated meth ^¬ ylation marks at epigenome-wide significance level, replicate them in independent cohorts and show their effect on global cardiac gene expression.

Heart failure is an epidemic threat in industrialized na ^¬ tions. The prevalence is already 37.7 million individuals globally, which comes at total medical costs of more than 20.9 billion $ annually in the US alone (Ziaeian B and

Fonarow GC . Epidemiology and aetiology of heart failure. Nat Rev Cardiol. 2016;13:368-78). To better stratify affected pa ^¬ tients or individuals at risk, new molecular biomarkers are desired. By a very systematic approach, we found an intri ^¬ guing overlap of DNA methylation changes in myocardial tissue and blood. Such an overlap is not expected by chance and the replication of diagnostic statistical performance along with the stringent filtering procedure to avoid confounding from blood cell heterogeneity and genomic variation points to ro ^¬ bust epigenetic biomarker patterns. In this early-stage sys- tolic dysfunction cohort, we find methylation markers that outperform NT-proBNP. However, the value of the methylation markers in prognostication, therapy monitoring and decision- making must be rigorously evaluated before concluding any su ^¬ periority to existing biomarkers.

Applying a very stringent cut-off (5x10-8), five epigenome- wide significant hits were found in this study located on Chr . 1, 3, 14, and 17. When using a lower cut-off for ge- nomewide significance used in other epigenome-wide associa ^¬ tion (EWA) studies (10-6) (Tsai PC and Bell J . Power and sample size estimation for epigenome-wide association scans to detect differential DNA methylation. Int J Epidemiol.

2015) , as many as 15 loci could be reliably linked to DCM and heart failure. Genes up- or downstream of the five most- stringent methylation marks all show expression in myocardial tissue. While the top hit from the discovery cohort

cgl6318181 was replicated in the verification cohort, there is no significant interaction between methylation status and expression of the genes within 10,000bp distance. However, two of the epigenome-wide significant hits showed direct as ^¬ sociation with mRNA expression levels, namely cg25838968 (gene body region of PLXNA2) and cgl6254946 (within the gene body region of GLIS1) . PLXNA2 is a member of the Plexin-A family and a receptor for the guiding molecule Semaphorin 3C and has been described in the context of neural crest and cardiac outflow tract development in the sense of GATA6- (Ko- do K, et al . , GATA6 mutations cause human cardiac outflow tract defects by disrupting semaphorinplexin signaling. Proc Natl Acad Sci U S A. 2009;106:13933-8) and HAND2-related sig ^¬ nalling pathways (Morikawa Y and Cserjesi P. Cardiac neural crest expression of Hand2 regulates outflow and second heart field development. Circ Res. 2008;103:1422-9).

During heart failure pathogenesis, the re-expression of the fetal gene programme is thought to be a central element of initial adaptation to stressors, but ultimately leads to mal- adaptation and disease progression. The exact mechanisms by which this concerted switch is realized, is unclear. It is known that non-coding RNAs and several promoter elements and transcription factors are involved. In our analysis, we found and replicated DNA methylation changes in the vicinity of several key-regulators of cardiac development. The transcrip ^¬ tion factor HAND2, for instance, is implicated in cardiomyo- cyte differentiation and proliferation in the second heart field (McFadden DG, et al . , The Handl and Hand2 transcription factors regulate expansion of the embryonic cardiac ventri ^¬ cles in a gene dosage-dependent manner. Development.

2005;132:189-201). During heart failure, Calcineurin/Nfat signalling as well as certain miRNAs (e.g. miR-25) are thought to control HAND2 activation (Dirkx E, et al . , Nfat and miR-25 cooperate to reactivate the transcription factor Hand2 in heart failure. Nat Cell Biol. 2013;15:1282-93). In our study, we found a change in DNA methylation of the

HAND2 locus significantly associated to the regulation of its transcript. IRX5, TBX5, TBX3 and TBX15 and several of their downstream effectors are also altered in the setting of DCM. Altogether 517 CpGs were directionally replicated to be asso- ciated with DCM and mRNA transcription. 307 of the 517 were hypomethylated in DCM and 210 were hypermethylated in DCM. The hypomethylated sites correlated with an upregulation of 374 genes and a downregulation of 173 genes corresponding to an upregulation ratio of 2.16. The hypermethylated sites cor- related with an upregulation of 204 genes and a downregula ^¬ tion of 171 genes (upregulation ratio of 1.19) . Hence, DNA methylation may be involved in the functional reorganisation of important genes during heart failure and these numbers il ^¬ lustrate that the effect of hypomethylation in DCM seems to result mainly in gene (re) activation, while the effect of hy- permethylation is balanced (Movassagh M, et al . , Distinct epigenomic features in endstage failing human hearts. Circu ^¬ lation. 2011;124:2411-22). Only a few regulatory principles have been identified that drive gene expression during development and under pathologi ^¬ cal conditions in vivo (Sergeeva IA, et al . , Identification of a regulatory domain controlling the Nppa-Nppb gene cluster during heart development and stress. Development.

2016;143:2135-46). Our data indicate that DNA methylation may act alone or in concert with other mechanisms in this con- text. As an example may serve the NPPA-NPPB gene cluster.

NPPA and -B descend from a common ancestral gene by duplica ^¬ tion and hence share common chromatin-regulatory mechanisms (Hohl M, et al . , HDAC4 controls histone methylation in response to elevated cardiac load. J Clin Invest.

2013;123:1359-70). Similarly, we found orchestrated hypometh- ylation of 5' -flanking CpGs of NPPA and NPPB, which is associated with the upregulation of the transcripts atrial natri ^¬ uretic factor (ANF) and brain natriuretic peptide (BNP) .

Strikingly, we find the same direction of hypomethylation in peripheral blood, supporting the intriguing finding of conserved heart failure associated DNA methylation patterning across different tissues.

The bimodality of DNA methylation (two copies of homologous DNA) implies a binary on-off control over gene expression, yet a significant number of intermediate methylated loci throughout the genome do not fit within this model (Elliott G, et al . , Intermediate DNA methylation is a conserved signa ^¬ ture of genome regulation. Nature communications.

2015; 6: 6363) . To our knowledge, this is the first study that identified a cross-tissue conservation of such epigenetic patterns occurring during heart failure. Due to our cohort and study design, we can exclude that the observed regulation is only due to medication or other confounders . As shown by the example of NPPA/-B, we postulate that heart failure as a syndrome can impose DNA methylation changes due to mechanisms that are sensitive in different cell types representing an epigenomic signature of context-dependent function (Pai AA, et al . , A genome-wide study of DNA methylation patterns and gene expression levels in multiple human and chimpanzee tis ^¬ sues. PLoS Genet. 2011 ; 7 : el 001316) . Potential limitations of this study are confounders that in ^¬ fluence the epigenetic pattern and DNA methylation. From a technical perspective, we found that genomic variants within the probe region and batch effects are important aspects that need to be considered. To best address this issue, we con ^¬ ducted whole-genome sequencing of patients to identify those sites and measured a random sample of patients multiple times on different arrays on the Infinium platform to define the strata introduced by batches. On the biological level, phar- macotherapy of cases and controls and heterogeneity of tissue are known to be potential confounders, for which we corrected by Principal Component analysis. Using completely independent replication cohorts, we eliminated confounders such as medi ^¬ cation of controls, RNA-seq library generation protocols and methylation measurement batch effects. Using mass- spectrometry based DNA methylation measurement, we further substantiated the reliability of our approach

for a selection of markers. The present study provides to our knowledge the most compre ^¬ hensive mapping of DNA methylation in the human heart and identifies novel loci associated with heart failure and DCM using a comprehensive approach covering genetic variation, DNA methylation and whole transcriptome analyses. To propel epigenetic studies in cardiovascular diseases, it is neces ^¬ sary to develop novel concepts for statistics (power calcula ^¬ tion (Tsai PC and Bell JT . Power and sample size estimation for epigenome-wide association scans to detect differential DNA methylation. Int J Epidemiol. 2015), epigenome-wide sig- nificance levels, differential methylation models (Wang S. Method to detect differentially methylated loci with case- control designs using Illumina arrays. Genet Epidemiol.

2011;35:686-94)), appropriate study designs incorporating different biological levels (multi-omics ) and definition of adequate controls and confounders. Especially for myocardial tissue, lack of healthy controls constrains the elucidation of cardiac epigenetics. In the present study, we compared failing myocardium against non-failing tissue derived from transplanted hearts showing regular function and a smaller control group of donors that suffered road accidents. Im ^¬ portantly, we show that it is worth studying DNA methylation in peripheral blood, for which adequate controls are often available .

It will be interesting to systematically evaluate DNA methyl ^¬ ation markers in longitudinal cohorts of heart failure due to different etiologies including ischemic heart disease. The potential indication of the here detected methylation markers point towards earlier detection of systolic dysfunction and heart failure, but they could also be evaluated for therapy selection and monitoring.

The presently described method allows an efficient and im ^¬ proved tool for finding markers in patients, particularly for non-infectious diseases, like HF and DCM.

With the presently found markers, an improved, early detec ^¬ tion and prognosis of HF/DCM, patient stratification for therapy decision support, and optimized, personalized treat ^¬ ment is possible.

This invention reports molecular markers which are indicative of HF/DCM or of the risk developing HF/DCM or for a prediction of therapy effects or therapy outcome. The present study provides to the knowledge of the inventors the first epigenome-wide association study in living patients with heart failure using a multi-omics approach.