Back

Cell type-specific epigenomic variation and its association with genotype in the human breast

Hauduc, A.; Steif, J.; Bilenky, M.; Moksa, M. M.; Cao, Q.; Ding, S.; Eaves, C. J.; Hirst, M.

2025-04-20 genomics
10.1101/2025.04.16.648998 bioRxiv
Show abstract

BackgroundUnderstanding the interplay between genome variation and epigenomic structure is fundamental to the study of the 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. ResultsWe generated comprehensive genomic and epigenomic measurements in four functionally defined human breast cell types across eight individuals. We developed a method to measure histone modification variance, discovering significantly higher variation in repressive chromatin states marked by H3K27me3 compared to the active states marked by H3K27ac and H3K4me3. Genetic variation linked to variation in chromatin state was highly cell type-specific, with nearly 90% occurring uniquely in a single cell type, and active histone modifications were enriched in these variants relative to repressive modifications. Association with gene transcription allowed for the prioritization of functional candidates, and the regulatory impact of an ANXA1-linked variant, rs75071948, was validated in vitro with CRISPR/Cas9-mediated HDR. ConclusionsWe define structures of epigenomic variability among breast cell types and present evidence of extensive cell type-specific genome-epigenome interactions, highlighting the critical role of cell type in mediating these associations in the breast.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.