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Abstract
Background
There is widespread interest in the three-dimensional chromatin conformation of the genome and its impact on gene expression. However, these studies frequently do not consider parent-of-origin differences, such as genomic imprinting, which result in monoallelic expression. In addition, genome-wide allele-specific chromatin conformation associations have not been extensively explored. There are few accessible bioinformatic workflows for investigating allelic conformation differences and these require pre-phased haplotypes which are not widely available.
Results
We developed a bioinformatic pipeline, “HiCFlow,” that performs haplotype assembly and visualization of parental chromatin architecture. We benchmarked the pipeline using prototype haplotype phased Hi-C data from GM12878 cells at three disease-associated imprinted gene clusters. Using Region Capture Hi-C and Hi-C data from human cell lines (1-7HB2, IMR-90, and H1-hESCs), we can robustly identify the known stable allele-specific interactions at the IGF2-H19 locus. Other imprinted loci (DLK1 and SNRPN) are more variable and there is no “canonical imprinted 3D structure,” but we could detect allele-specific differences in A/B compartmentalization. Genome-wide, when topologically associating domains (TADs) are unbiasedly ranked according to their allele-specific contact frequencies, a set of allele-specific TADs could be defined. These occur in genomic regions of high sequence variation. In addition to imprinted genes, allele-specific TADs are also enriched for allele-specific expressed genes. We find loci that have not previously been identified as allele-specific expressed genes such as the bitter taste receptors (TAS2Rs).
Conclusions
This study highlights the widespread differences in chromatin conformation between heterozygous loci and provides a new framework for understanding allele-specific expressed genes.
There is widespread interest in the three-dimensional chromatin conformation of the genome and its impact on gene expression. However, these studies frequently do not consider parent-of-origin differences, such as genomic imprinting, which result in monoallelic expression. In addition, genome-wide allele-specific chromatin conformation associations have not been extensively explored. There are few accessible bioinformatic workflows for investigating allelic conformation differences and these require pre-phased haplotypes which are not widely available.
Results
We developed a bioinformatic pipeline, “HiCFlow,” that performs haplotype assembly and visualization of parental chromatin architecture. We benchmarked the pipeline using prototype haplotype phased Hi-C data from GM12878 cells at three disease-associated imprinted gene clusters. Using Region Capture Hi-C and Hi-C data from human cell lines (1-7HB2, IMR-90, and H1-hESCs), we can robustly identify the known stable allele-specific interactions at the IGF2-H19 locus. Other imprinted loci (DLK1 and SNRPN) are more variable and there is no “canonical imprinted 3D structure,” but we could detect allele-specific differences in A/B compartmentalization. Genome-wide, when topologically associating domains (TADs) are unbiasedly ranked according to their allele-specific contact frequencies, a set of allele-specific TADs could be defined. These occur in genomic regions of high sequence variation. In addition to imprinted genes, allele-specific TADs are also enriched for allele-specific expressed genes. We find loci that have not previously been identified as allele-specific expressed genes such as the bitter taste receptors (TAS2Rs).
Conclusions
This study highlights the widespread differences in chromatin conformation between heterozygous loci and provides a new framework for understanding allele-specific expressed genes.
Original language | English |
---|---|
Article number | 40 |
Journal | Genome Biology |
Volume | 24 |
Issue number | 1 |
DOIs | |
Publication status | Published - 3 Mar 2023 |
Bibliographical note
FundingThis work has been supported by the Medical Research Council (MR/P000711/1 to A.M. and L.H.), the Leverhulme Trust
(RPG-2020-327 to A.M.), and the EPSRC DTP studentship (2106811 to S.R.).
Availability of data and materials
The Region Capture Hi-C datasets that we generated in this work are available in NCBI repository at the accession number PRJNA926951 [147].
Scripts used for downstream bioinformatics analysis are available under MIT license at Github: https://github.com/Steph
enRicher/HiCFlow [148] and https://github.com/StephenRicher/AS-HiC-Analysis [149]. These scripts are also deposited in
Zenodo: https://zenodo.org/record/7563515 [150] and https://zenodo.org/record/6510198 [151].
Further details of the HiCFlow workfow are provided below.
• Project name: HiCFlow
• Project home page: https://github.com/StephenRicher/HiCFlow
• Archived version: 10.5281/zenodo.7563515
• Operating system: Unix-based operating systems
• Programming language: Snakemake (Python)
• Other requirements: Snakemake 7.3.1 or higher, Conda
• License: MIT License
• Any restrictions to use by non-academics: None
Datasets supporting the conclusions of this study include public available Hi-C Data (GSE63525 [98, 99] GSE163666
[100])/ Phased Variant Data (PRJEB338 [133])/ CTCF ChIP Data (GSE30263 [115, 116], GSE31477 [118], GSE29611 [119],
PRJEB3073 [121], GSE51334 [117])/ CpG Data ((GSE86765 [142], GSE17312 [143], GSE80911 [144])/ Allele-Specifc Expression Data (NA12878 [110], GSE16256 [102–108])/Allele-Specifc Methylation Data (GSE40832 [112, 113])/ Chromatin Loop
Data (http://3dgenome.fsm.northwestern.edu/downloads/loops-hg19.zip) [53]/ Chromatin State Data (15-core) (https://
egg2.wustl.edu/roadmap/web_portal/) [122].
ASJC Scopus subject areas
- Genetics
- Ecology, Evolution, Behavior and Systematics
- Cell Biology
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How Does the expression of One Gene Affect that of its Neighbour?
Murrell, A. (PI) & Hurst, L. (CoI)
1/01/17 → 28/02/21
Project: Research council