Summary
A single gene may have multiple enhancers, but how they work in concert to regulate transcription is poorly understood. To analyze enhancer interactions throughout the genome, we developed a generalized linear modeling framework, GLiMMIRS, for interrogating enhancer effects from single-cell CRISPR experiments. We applied GLiMMIRS to a published dataset and tested for interactions between 46,166 enhancer pairs and corresponding genes, including 264 ’high-confidence’ enhancer pairs. We found that enhancer effects combine multiplicatively but with limited evidence for further interactions. Only 31 enhancer pairs exhibited significant interactions (FDR < 0.1), of which none came from the high confidence subset and 20 were driven by outlier expression values. Additional analyses of a second CRISPR dataset and in silico enhancer perturbations with Enformer both support a multiplicative model of enhancer effects without interactions. Altogether, our results indicate that enhancer interactions are uncommon or have small effects that are difficult to detect.
Highlights
Analysis of a large single-cell CRISPRi screen finds limited evidence for synergistic or redundant interactions between enhancers
The collective action of multiple enhancers on a common target gene follows a multiplicative model of activity
A new statistical framework for simulating and modeling data from single-cell CRISPRi screens
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Additional analysis of another CRISPR perturbation dataset and an in silico perturbation experiment with Enformer have been added. Outlier detection with Cook's distance has been added as well as a Bayesian analysis of posterior enhancer interaction frequency.
