Robust enhancer-gene regulation identified by single-cell transcriptomes and epigenomes.
Document Type
Article
Publication Date
7-12-2023
Original Citation
Xie F,
Armand E,
Yao Z,
Liu H,
Bartlett A,
Behrens M,
Li Y,
Lucero J,
Luo C,
Nery J,
Pinto-Duarte A,
Poirion O,
Preissl S,
Rivkin A,
Tasic B,
Zeng H,
Ren B,
Ecker J,
Mukamel E.
Robust enhancer-gene regulation identified by single-cell transcriptomes and epigenomes. Cell Genom. 2023;3(7):100342.
Keywords
JGM
JAX Source
Cell Genom. 2023;3(7):100342.
ISSN
2666-979X
PMID
37492103
DOI
https://doi.org/10.1016/j.xgen.2023.100342
Grant
This work was funded by the NIH BRAIN Initiative (RF1 MH120015 to E.A.M., U19MH114830 to H.Z., and U19MH121282 to J.R.E.; J.R.E is an Inves- tigator of the Howard Hughes Medical Institute) and by CZI Collaborative Computational Tools for the Human Cell Atlas (to E.A.M.).
Abstract
Single-cell sequencing could help to solve the fundamental challenge of linking millions of cell-type-specific enhancers with their target genes. However, this task is confounded by patterns of gene co-expression in much the same way that genetic correlation due to linkage disequilibrium confounds fine-mapping in genome-wide association studies (GWAS). We developed a non-parametric permutation-based procedure to establish stringent statistical criteria to control the risk of false-positive associations in enhancer-gene association studies (EGAS). We applied our procedure to large-scale transcriptome and epigenome data from multiple tissues and species, including the mouse and human brain, to predict enhancer-gene associations genome wide. We tested the functional validity of our predictions by comparing them with chromatin conformation data and causal enhancer perturbation experiments. Our study shows how controlling for gene co-expression enables robust enhancer-gene linkage using single-cell sequencing data.