Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases

Cavalli, Marco; Baltzer, Nicholas; Umer, Husen M.; Grau, Jan; Lemnian, Ioana; Pan, Gang; Wallerman, Ola; Spalinskas, Rapolas; Sahlén, Pelin; Große, Ivo; Komorowski, Jan; Wadelius, Claes

doi:10.1038/s41598-019-39633-0

Cited by 24 publications

(23 citation statements)

References 52 publications

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“…Our method is developed based on the established strategy for conducting allele-specific analysis on many types of sequencing data, which specifically focus on the sequencing reads on heterozygous loci. This includes allele-specific gene expression ( Zhang et al, 2014 ; Castel et al, 2015 ), allele-specific alternative splicing analysis ( Nembaware et al, 2008 ; Li et al, 2012 ), allele-specific binding of ChIP-seq ( de Santiago et al, 2017 ) and CLIP-seq data analysis ( Yang et al, 2019 ), allele-specific chromatin interaction ( Cavalli et al, 2019 ), and allele-specific chromatin accessibility ( Harvey et al, 2014 ; Zhang et al, 2019 ). To our knowledge, our method is the first to analyze allele-specific footprint.…”

Section: Discussionmentioning

confidence: 99%

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data

Feng

et al. 2020

Front. Bioeng. Biotechnol.

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Expression quantitative trait loci (eQTL) analysis is useful for identifying genetic variants correlated with gene expression, however, it cannot distinguish between causal and nearby non-functional variants. Because the majority of disease-associated SNPs are located in regulatory regions, they can impact allele-specific binding (ASB) of transcription factors and result in differential expression of the target gene alleles. In this study, our aim was to identify functional single-nucleotide polymorphisms (SNPs) that alter transcriptional regulation and thus, potentially impact cellular function. Here, we present regSNPs-ASB, a generalized linear model-based approach to identify regulatory SNPs that are located in transcription factor binding sites. The input for this model includes ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) raw read counts from heterozygous loci, where differential transposasecleavage patterns between two alleles indicate preferential transcription factor binding to one of the alleles. Using regSNPs-ASB, we identified 53 regulatory SNPs in human MCF-7 breast cancer cells and 125 regulatory SNPs in human mesenchymal stem cells (MSC). By integrating the regSNPs-ASB output with RNA-seq experimental data and publicly available chromatin interaction data from MCF-7 cells, we found that these 53 regulatory SNPs were associated with 74 potential target genes and that 32 (43%) of these genes showed significant allele-specific expression. By comparing all of the MCF-7 and MSC regulatory SNPs to the eQTLs in the Genome-Tissue Expression (GTEx) Project database, we found that 30% (16/53) of the regulatory SNPs in MCF-7 and 43% (52/122) of the regulatory SNPs in MSC were also in eQTL regions. The enrichment of regulatory SNPs in eQTLs indicated that many of them are likely responsible for allelic differences in gene expression (chi-square test, p-value < 0.01). In summary, we conclude that regSNPs-ASB is a useful tool for identifying causal variants from ATAC-seq data. This new computational tool will enable efficient prioritization of

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Section: Discussionmentioning

confidence: 99%

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data

Feng

et al. 2020

Front. Bioeng. Biotechnol.

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“…The allele-specific signals were also searched for in the ChIP-seq data for histone modifications in a study aimed to identify the regulatory variants involved in the development mechanisms of immune and B-cell related diseases [ 175 ]. The SNPs with allele-specific behavior in the available ChIP-seq datasets produced for the histone modifications defining promoters (H3K4me3) and enhancers (H3K4me1 and H3K27ac) and for domain boundary proteins (CTCF and SA.1) were the focus of this study.…”

Section: Rsnps On a Genome-wide Scalementioning

confidence: 99%

Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases

Degtyareva

Antontseva

Merkulova

2021

IJMS

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The vast majority of the genetic variants (mainly SNPs) associated with various human traits and diseases map to a noncoding part of the genome and are enriched in its regulatory compartment, suggesting that many causal variants may affect gene expression. The leading mechanism of action of these SNPs consists in the alterations in the transcription factor binding via creation or disruption of transcription factor binding sites (TFBSs) or some change in the affinity of these regulatory proteins to their cognate sites. In this review, we first focus on the history of the discovery of regulatory SNPs (rSNPs) and systematized description of the existing methodical approaches to their study. Then, we brief the recent comprehensive examples of rSNPs studied from the discovery of the changes in the TFBS sequence as a result of a nucleotide substitution to identification of its effect on the target gene expression and, eventually, to phenotype. We also describe state-of-the-art genome-wide approaches to identification of regulatory variants, including both making molecular sense of genome-wide association studies (GWAS) and the alternative approaches the primary goal of which is to determine the functionality of genetic variants. Among these approaches, special attention is paid to expression quantitative trait loci (eQTLs) analysis and the search for allele-specific events in RNA-seq (ASE events) as well as in ChIP-seq, DNase-seq, and ATAC-seq (ASB events) data.

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“…In support of genetic variation at enhancers influencing transcriptional regulation, Yu et al used HiC data to demonstrate that eQTLs tend to be in close spatial proximity with their target genes [157]. Additionally, by integrating the information of GWAS SNPs, TF binding site prediction and Hi-C/CHi-C data, studies have shown that non-coding SNPs can affect the binding of TFs, and can further potentially affect multiple target genes via 3D interactions [158,159].…”

Section: Functional Interaction Identification Via Omics-based Data Imentioning

confidence: 99%

Seeing the forest through the trees: Identifying functional interactions from Hi-C

Liu

Low

Alinejad‐Rokny

et al. 2020

Preprint

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Eukaryotic genomes are highly organised within the nucleus of a cell, allowing widely dispersed regulatory elements such as enhancers to interact with gene promoters through physical contacts in three-dimensional space. Recent chromosome conformation capture methodologies such as Hi-C have enabled the analysis of interacting regions of the genome providing a valuable insight into the three-dimensional organisation of the chromatin in the nucleus, including chromosome compartmentalisation and gene expression. Complicating the analysis of Hi-C data however is the massive amount of identified interactions, many of which do not directly drive gene function, thus hindering the identification of potentially biologically functional 3D interactions. In this review, we collate and examine the downstream analysis of Hi-C data with particular focus on methods that identify significant functional interactions. We classify three groups of approaches; structurally-associated domain discovery methods e.g. topologically-associated domains and compartments, detection of statistically significant interactions via background models, and the use of epigenomic data integration to identify functional interactions. Careful use of these three approaches is crucial to successfully identifying functional interactions within the genome.

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Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases

Cited by 24 publications

References 52 publications

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data

Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases

Seeing the forest through the trees: Identifying functional interactions from Hi-C

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