Mapping Enhancer-gene Regulation in Various Cell Types

The human genome encodes over two million DNA regulatory elements called enhancers that control gene expression in specific cell types and states. These enhancers harbor thousands of genetic variants that influence risk for common diseases and traits, each of which could reveal insights into molecular mechanisms of disease risk. Yet, scientists have lacked tools to systematically map which enhancers regulate which genes in each of the thousands of cell types in the human body.

To address this challenge, the team has recently developed CRISPR tools to experimentally test thousands of enhancers in parallel, and discovered a simple computational model that can predict enhancer-gene regulation from single-cell measurements of chromatin state. Together these advances suggest a new strategy to build a comprehensive map of enhancer-gene regulation across many cell types to connect noncoding variants to target genes. This project aims to develop and apply computational pipelines to predict enhancer-gene connections in hundreds of cell types based on single-cell measurements of chromatin accessibility (scATAC-seq).

Specifically, this team will extend their pipeline for their Activity-by-Contact (ABC) model to use scATAC-seq data as inputs and adapt their benchmarking pipeline to aggregate gold-standard CRISPR and eQTL datasets to benchmark enhancer-gene predictions. The team will also compute and benchmark ABC maps in hundreds of cell types using publicly available scATAC-seq datasets. Together, these aims will establish reusable pipelines to build and benchmark models of enhancer-gene regulation, and use the growing corpus of scATAC-seq data to create an unprecedented resource for understanding the functions of genetic variants.