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Genome-wide Mapping of Genetic-Epigenetic Interactions Reveals Constraints Imposed by Three-Dimensional Chromatin Structure in Genetically Diverse Mouse Embryonic Stem Cells

Core Concepts
Genetic variants and chromatin accessibility interact in a three-dimensional chromatin context to jointly influence gene expression, with these interactions preferentially occurring within topologically associated domains.
The authors conducted a genome-wide analysis of genetic-epigenetic interactions in a genetically diverse population of mouse embryonic stem cells (mESCs) derived from the Diversity Outbred (DO) mouse population. They used regression modeling to identify widespread non-additive interactions between genetic variants and chromatin accessibility (as measured by ATAC-seq) that affect gene expression. Key findings: Genetic-epigenetic interactions are pervasive across the genome, with over 27,000 significant models identified. These interactions are strongly biased to occur within the same topologically associated domain (TAD) as the affected gene, rather than being distributed randomly along the linear genome. The likelihood of an interaction is most strongly defined by the 3D domain structure rather than linear DNA sequence, with interactions preferentially occurring between elements within the same TAD. Interacting regulatory elements are enriched for developmental genes and the TAD-forming CTCF binding complex, suggesting a role for 3D genome structure in regulating gene expression during early development. CTCF ChIP-seq in inbred founder strains reveals strain-specific differences in CTCF binding, which are most predictable in regions with significant genetic-epigenetic interactions in the DO mESCs. These findings demonstrate that genetic and epigenetic factors operate within the context of three-dimensional chromatin structure to jointly influence gene expression, and that 3D genome organization can guide the search for regulatory elements and vice versa.
Approximately 50% of significant interacting models included a genetic variant or ATAC peak within 4 Mb of the gene they affected. Models with at least one interacting element within the same TAD as the gene were 3.7 times more likely to reach the significance threshold, compared to models that did not. 26.35% of genetic variants are shared between non-additive interacting models and additive models, and 23.12% are shared with models that contain no ATAC-seq peak contribution. 41.81% of ATAC-seq peaks are exclusive to interacting models.
"Genetic-epigenetic interactions were found across the genome and involve regulatory elements that would not be identified with single-omics data." "TADs contained more ATAC peaks that interact with local genetic variants to affect expression of a resident gene." "ATAC-seq peaks involved in non-additive interactions favored results between CTCF binding sites associated with TAD formation."

Deeper Inquiries

How might the findings from this study inform the design of future experiments to study gene regulation in genetically diverse populations?

The findings from this study provide valuable insights into the interplay between genetic variation, chromatin accessibility, and gene expression in genetically diverse populations. Future experiments could leverage this information by incorporating multi-omics data analysis, such as integrating genotypic variants, open chromatin regions, and gene expression levels. By using regression modeling to identify genetic-epigenetic interactions on a genome-wide scale, researchers can systematically uncover global regulatory networks in genetically diverse populations. This approach allows for a more comprehensive understanding of how genetic and epigenetic factors interact to influence gene expression, providing a nuanced view of the regulatory landscape in diverse genetic backgrounds. Additionally, future experiments could focus on exploring the functional significance of specific genetic-epigenetic interactions identified in this study. By targeting key genes or regulatory elements that show significant interactions, researchers can gain a deeper understanding of how these interactions impact gene expression and cellular function in genetically diverse populations. This targeted approach could help elucidate the molecular mechanisms underlying complex traits and diseases, shedding light on the genetic basis of phenotypic variation in diverse populations.

What are some potential limitations or caveats of the regression modeling approach used in this study, and how could it be improved or extended?

While regression modeling is a powerful tool for analyzing genetic-epigenetic interactions, there are several limitations and caveats to consider. One limitation is the assumption of linearity in the relationship between genetic variants, chromatin accessibility, and gene expression. In reality, these relationships may be more complex and nonlinear, requiring more sophisticated modeling approaches to capture the full extent of their interactions. Another limitation is the potential for overfitting in regression models, especially when dealing with a large number of variables and interactions. To address this, researchers could employ regularization techniques or cross-validation to prevent overfitting and improve the generalizability of the models. Furthermore, the resolution of genetic mapping in genetically diverse populations may impact the accuracy of identifying causal variants in regression models. Improving the resolution of genetic mapping, such as through high-throughput sequencing technologies or fine-mapping approaches, could enhance the precision of identifying causal variants and their interactions with chromatin accessibility. To extend the regression modeling approach used in this study, researchers could consider incorporating additional layers of omics data, such as DNA methylation profiles, histone modification patterns, or transcription factor binding data. Integrating multiple omics datasets could provide a more comprehensive view of the regulatory landscape and enhance the understanding of gene regulation in genetically diverse populations.

Given the insights into 3D genome structure provided by the genetic-epigenetic interaction analysis, how might this information be leveraged to understand the role of chromatin organization in developmental processes or disease states?

The insights into 3D genome structure obtained from the genetic-epigenetic interaction analysis offer a unique opportunity to investigate the role of chromatin organization in developmental processes and disease states. By identifying genetic variants and open chromatin regions that interact to regulate gene expression within specific topologically associated domains (TADs), researchers can uncover the spatial organization of regulatory elements and their impact on gene regulation. In developmental processes, understanding how genetic-epigenetic interactions shape 3D chromatin structure can provide insights into the mechanisms underlying cell differentiation, tissue development, and organogenesis. By mapping the interactions between genetic variants, chromatin accessibility, and gene expression within TADs, researchers can elucidate the regulatory networks that govern developmental gene expression programs. In disease states, aberrant chromatin organization has been implicated in various disorders, including cancer, neurodegenerative diseases, and developmental disorders. By leveraging the information on genetic-epigenetic interactions and 3D genome structure, researchers can investigate how disruptions in chromatin organization contribute to disease pathogenesis. This knowledge could lead to the identification of novel therapeutic targets and the development of precision medicine approaches tailored to individual genetic backgrounds. Overall, the insights gained from genetic-epigenetic interaction analysis can provide a deeper understanding of the role of chromatin organization in controlling gene expression dynamics in both normal development and disease states. This knowledge has the potential to revolutionize our understanding of gene regulation and open new avenues for exploring the molecular basis of complex biological processes.