Epigenome-wide association study for dilated cardiomyopathy in left ventricular heart tissue identifies putative gene sets associated with cardiac development and early indicators of cardiac risk
Bibliographic record
Abstract
ABSTRACT Background Methylation changes linked to dilated cardiomyopathy (DCM) affect cardiac gene expression. We investigate DCM mechanisms regulated by CpG methylation using multi-omics and causal analyses in the largest cohort of left ventricular tissues available. Methods We mapped DNA methylation at ∼850,000 CpG sites, performed array-based genotyping and RNA sequencing in left-ventricular tissue samples from failing and non-failing hearts across two independent DCM cohorts (discovery n=329, replication n=85). Summary data-based Mendelian Randomization (SMR) was applied to explore the causal contribution of sentinel CpGs to DCM. Fine-mapping of regions surrounding sentinel CpGs revealed additional signals for cardiovascular disease risk factors. Coordinated changes across multiple CpG sites were examined using weighted gene correlation network analysis (WGCNA). Results We identified 194 epigenome-wide significant CpGs associated with DCM (discovery P<5.96E-08), enriched in active chromatin states in heart tissue. Amongst these, 183 sentinel CpGs significantly influenced the expression of 849 proximal genes (±1Mb). SMR suggested the causal contribution of two sentinel CpGs to DCM and 36 sentinel CpGs to the expression of 43 unique proximal genes (P<0.05). Colocalization analyses indicated that a single causal variant may underlie the methylation-gene expression relationship for three sentinel CpGs. Fine-mapping revealed additional signals linked to cardiovascular traits including hsCRP and blood pressure. Co-methylation modules were enriched in genes related to cardiac physiological and pathological processes and their corresponding transcriptional regulators. Conclusions Using the largest series of left ventricular tissue to date, this study investigates the causal role of cardiac methylation changes in DCM and suggests targets for experimental studies to probe DCM pathogenesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".