Integrative Comparison of GeneHancer and Single-Cell Co-Accessibility Reveals Active Enhancer–Gene Interactions

Linking enhancers to their target genes remains challenging due to the context-independent nature of curated annotations and the noise inherent in data-driven predictions. GeneHancer provides a comprehensive catalogue of enhancer–gene associations, but many elements are inactive in specific biological settings. Conversely, co-accessibility inferred from single-cell chromatin accessibility data captures sample-specific regulatory structure but may reflect indirect or non-functional interactions. This work integrates these complementary perspectives by comparing GeneHancer annotations with co-accessibility networks derived from a human PBMC Multiome dataset. Using Circe to infer peak–peak co-accessibility and GRAIGH to map peaks onto GeneHancer elements, this approach identifies enhancer–gene associations supported both by prior evidence and by accessibility patterns in the dataset. Only a small subset of GeneHancer links is validated by co-accessibility, yet these conserved associatio ns display substantially higher cell-type specificity and stronger accessibility–expression concordance than either the full or “Elite” GeneHancer sets. This refined subset isolates regulatory interactions that are both biologically plausible and active in the sample, reducing redundancy and improving interpretability. Our results show that integrating curated enhancer annotations with single-cell epigenomic evidence yields a focused, high-confidence regulatory map suited for analyzing transcriptional regulation and cell identity in a dataset-specific manner.