Cell type-specific epigenomic variation and its association with genotype in the human breast
Bibliographic record
Abstract
Abstract Background Understanding the interplay between genomic variation and the epigenome is fundamental to the study of development and mechanisms of disease. Previous studies have leveraged population-scale genotype surveys to associate alleles with epigenomic states in heterogenous tissue types. However, epigenomes are inherently cell type-specific, giving rise to unique genome–epigenome interactions that can influence distinct functional states and susceptibility to disease. Moreover, the extent of individual variation in cell type-specific epigenotypes remains poorly understood, posing additional challenges to accurately link genotypes with epigenomic features. Results We generated comprehensive genomic and epigenomic profiles of four functionally defined human breast epithelial cell types from eight healthy individuals. To quantify inter-individual epigenomic variation, we developed a statistical framework that measures variability in histone modification landscapes across individuals. This analysis revealed substantially greater variation in repressive chromatin marked by H3K27me3 than in active chromatin marked by H3K27ac and H3K4me3. Integrative chromatin state analysis further identified enhancer elements as the principal source of epigenomic divergence between individuals. Stable enhancer states corresponded to high-confidence cis -regulatory elements that underpin cell type-specific transcriptional programs, whereas variable enhancer states were enriched for environmentally responsive regulatory circuits. Mapping genetic variants associated with chromatin state variation uncovered extensive cell type-specificity, with nearly 90% of regulatory variants detected in only a single cell type. These associations were strongly enriched within active regulatory chromatin and, when integrated with gene expression, enabled the prioritization of functional regulatory variants. We experimentally validated one such variant, rs75071948, demonstrating allele-specific regulation of ANXA1 expression using CRISPR/Cas9 genome editing. Conclusions Our study defines the landscape of normal epigenomic variation across the major human breast epithelial cell types and demonstrates that genome–epigenome interactions are highly cell type-specific. These findings establish cell type as a critical determinant of the functional interpretation of regulatory genetic variation and provide a framework for understanding how inherited genetic variation shapes normal breast biology and disease susceptibility.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".