The GATA3 gene is essential for T cell differentiation and is surrounded by risk variants for immune traits. Interpretation of these variants is challenging because the regulatory landscape of GATA3 is complex with dozens of potential enhancers spread across a large topological associating domain (TAD) and gene expression quantitative trait locus (eQTL) studies provide limited evidence for variant function. Here, we perform a tiling deletion screen in Jurkat T cells to identify 23 candidate regulatory elements. Using small deletions in primary T helper 2 (Th2) cells, we validate the function of five of these elements, two of which contain risk variants for asthma and allergic diseases. We fine-map genome-wide association study (GWAS) signals in a distal regulatory element, 1 Mb downstream, to identify 14 candidate causal variants. Small deletions spanning candidate rs725861 decrease GATA3 expression in Th2 cells suggesting a causal mechanism for this variant in allergic diseases. Our study demonstrates the power of integrating GWAS signals with deletion mapping and identifies critical regulatory sequences for GATA3.
A single gene may be regulated by multiple enhancers, but how they work in concert to regulate transcription is poorly understood. Prior studies have mostly examined enhancers at single loci and have reached inconsistent conclusions about whether epistatic-like interactions exist between them. To analyze enhancer interactions throughout the genome, we developed a statistical framework for CRISPR regulatory screens that utilizes negative binomial generalized linear models that account for variable guide RNA (gRNA) efficiency. We reanalyzed a single-cell CRISPR interference experiment that delivered random combinations of enhancer-targeting gRNAs to each cell and interrogated interactions between 3,808 enhancer pairs. We found that enhancers act multiplicatively with one another to control gene expression, but our analysis provides no evidence for interaction effects between pairs of enhancers regulating the same gene. Our findings illuminate the regulatory behavior of multiple enhancers and our statistical framework provides utility for future analyses studying interactions between enhancers.
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