Disease-associated variants reside frequently in noncoding cis-regulatory elements (CREs), yet their functional consequences remain poorly understood. We performed a large-scale lentiMPRA in human excitatory neurons, quantifying the impact of >46,000 naturally occurring variants across >27,000 candidate CREs near 524 disease-associated genes. These data improved regulatory variant effect predictions beyond state-of-the-art models. Significant allelic effects occurred at comparable rates across common, rare, and singleton variants, demonstrating that, within MPRA-measurable effects, population frequency carries limited information about per-variant regulatory impact. Variant effect detectability and magnitude were governed primarily by baseline activity of the enclosing regulatory element and local sequence context. Regulatory effects were distributed across numerous transcription factors rather than concentrated in master regulators, consistent with a combinatorial enhancer architecture. We establish a large-scale functional variant catalog and provide a complementary benchmark and resource for developing and evaluating models of noncoding regulatory variation.
Kilian Salomon, Chengyu Deng, P. Dash et al.· bioRxiv· 0 citations
Gene regulation depends on coordinated interactions between promoters and distal cis-regulatory elements, yet understanding how these regulatory elements communicate remains a fundamental challenge in mammalian genomics. Chromatin interaction assays provide one approach for identifying potential regulatory relationships, but interpreting the biological significance of individual interactions remains difficult; chromatin interactions comprise multiple biologically distinct classes that are only partially captured by any single assay. Here, we integrate Hi-C, RNAPII ChIA-PET, and CTCF ChIA-PET with the ENCODE Registry of candidate cis-regulatory elements (cCREs) and complementary functional genomic datasets to develop an integrative framework for classifying and interpreting promoter-centric chromatin interactions. Using this framework, we identify a distinct class of candidate architectural promoter-enhancer interactions that are characterized by increased recurrence across cellular contexts, broader promoter connectivity, and reduced dependence on linear genomic proximity. We further show that many regulatory elements anchoring these interactions transition between enhancer and CTCF-only states while maintaining stable chromatin interactions. These dual-state regulatory elements also acquire context-specific transcription factor inputs within evolutionarily conserved architectural scaffolds, suggesting that stable chromatin architecture can be repeatedly repurposed for new regulatory functions. Genes connected to these dual-state regulatory elements are enriched for developmental and signaling pathways and exhibit increased expression specificity across cell types, consistent with specialized roles in context-dependent gene regulation. Together, our findings provide a biologically informed framework for classifying and interpreting chromatin interactions and support a model in which conserved chromatin architecture provides a stable foundation upon which new regulatory programs evolve.
A family of classification models, scE2G, is introduced that predict enhancer–gene regulatory interactions from single-cell datasets and enable mapping of these interactions across diverse cell types and tissues and will enable accurate mapping of enhancer–gene regulatory interactions across thousands of human cell types.
Maya U. Sheth, Wei-Lin Qiu, X. Ma et al.· Nature Genetics· 1 citation
An encyclopedia of enhancer–gene regulatory interactions in the human genome is built, revealing global properties of enhancer networks, identifying differences in regulatory complexity across genes, and improving analyses linking noncoding variants to target genes and cell types for common, complex diseases.
A. Gschwind, Kristy S. Mualim, Alireza Karbalayghareh et al.· Nature· 6 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
