Activity-by-Contact model of enhancer-promoter regulation from thousands of CRISPR perturbations

By C. Fulco, J. Nasser, Thouis Raymond Jones, Glen Munson, Drew T. Bergman, V. Subramanian, S. Grossman, Rockwell Anyoha, Benjamin R. Doughty, Tejal A. Patwardhan and 9 more. Nature Genetics, 2019

970 citations recorded by the source corpus.

Enhancer elements in the human genome control how genes are expressed in specific cell types and harbor thousands of genetic variants that influence risk for common diseases1–4. Yet, we still do not know how enhancers regulate specific genes, and we lack general rules to predict enhancer–gene connections across cell types5,6. We developed an experimental approach, CRISPRi-FlowFISH, to perturb enhancers in the genome, and we applied it to test >3,500 potential enhancer–gene connections for 30 genes. We found that a simple activity-by-contact model substantially outperformed previous methods at predicting the complex connections in our CRISPR dataset. This activity-by-contact model allows us to construct genome-wide maps of enhancer–gene connections in a given cell type, on the basis of chromatin state measurements. Together, CRISPRi-FlowFISH and the activity-by-contact model provide a systematic approach to map and predict which enhancers regulate which genes, and will help to interpret the functions of the thousands of disease risk variants in the noncoding genome. Combining CRISPRi-FlowFISH to perturb enhancers with an activity-by-contact model to predict complex connections allows systematic mapping of enhancer–gene connections in a given cell type, on the basis of chromatin-state measurements.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b489f5a877438e993ac26a04accc4e2459d5d8f4

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Citations in the corpus

Genome-wide enhancer maps link risk variants to disease genes (2021, methodology): “We validated these predictions by comparing them to a compendium of CRISPR perturbations that included 5,755 tested element– gene pairs in 11 cell types and states (including previous data 4,12 as well as additional CRISPR experiments that we performed here (Supplementary Tables 3, 4)).”
Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases (2020, background): “…leverage local GWAS data by connecting the causal variants to the causal gene(s) using protein-coding variants, genomic distance, enhancer–gene maps 11–16 or expression quantitative trait loci (eQTLs) 7,8 ; and second, similarity-based meth-ods that search for global patterns in associated genes…”