Dataset Information

BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes.

ABSTRACT: Allele-specific measurements of transcription factor binding from ChIP-seq data are key to dissecting the allelic effects of non-coding variants and their contribution to phenotypic diversity. However, most methods of detecting an allelic imbalance assume diploid genomes. This assumption severely limits their applicability to cancer samples with frequent DNA copy-number changes. Here we present a Bayesian statistical approach called BaalChIP to correct for the effect of background allele frequency on the observed ChIP-seq read counts. BaalChIP allows the joint analysis of multiple ChIP-seq samples across a single variant and outperforms competing approaches in simulations. Using 548 ENCODE ChIP-seq and six targeted FAIRE-seq samples, we show that BaalChIP effectively corrects allele-specific analysis for copy-number variation and increases the power to detect putative cis-acting regulatory variants in cancer genomes.

SUBMITTER: de Santiago I

PROVIDER: S-EPMC5326502 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes.

de Santiago Ines I Liu Wei W Yuan Ke K O'Reilly Martin M Chilamakuri Chandra Sekhar Reddy CS Ponder Bruce A J BA Meyer Kerstin B KB Markowetz Florian F

Genome biology 20170224 1

Allele-specific measurements of transcription factor binding from ChIP-seq data are key to dissecting the allelic effects of non-coding variants and their contribution to phenotypic diversity. However, most methods of detecting an allelic imbalance assume diploid genomes. This assumption severely limits their applicability to cancer samples with frequent DNA copy-number changes. Here we present a Bayesian statistical approach called BaalChIP to correct for the effect of background allele frequen ...[more]

PMID: 28235418

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Project description:The secreted phospholipase A2 (sPLA2) isoform, sPLA2-IIA, has been implicated in a variety of diseases and conditions, including bacteremia, cardiovascular disease, COVID-19, sepsis, adult respiratory distress syndrome, and certain cancers. Given its significant role in these conditions, understanding the regulatory mechanisms impacting its levels is crucial. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs), including rs11573156, that are associated with circulating levels of sPLA2-IIA. The work in the manuscript leveraged 4 publicly available datasets to investigate the mechanism by which rs11573156 influences sPLA2-IIA levels via bioinformatics and modeling analysis. Through genotype-tissue expression (GTEx), 234 expression quantitative trait loci (eQTLs) were identified for the gene that encodes for sPLA2-IIA, PLA2G2A. SNP2TFBS was used to ascertain the binding affinities between transcription factors (TFs) to both the reference and alternative alleles of identified eQTL SNPs. Subsequently, candidate TF-SNP interactions were cross-referenced with the ChIP-seq results in matched tissues from ENCODE. SP1-rs11573156 emerged as the significant TF-SNP pair in the liver. Further analysis revealed that the upregulation of PLA2G2A transcript levels through the rs11573156 variant was likely affected by tissue SP1 protein levels. Using an ordinary differential equation based on Michaelis-Menten kinetic assumptions, we modeled the dependence of PLA2G2A transcription on SP1 protein levels, incorporating the SNP influence. Collectively, our analysis strongly suggests that the difference in the binding dynamics of SP1 to different rs11573156 alleles may underlie the allele-specific PLA2G2A expression in different tissues, a mechanistic model that awaits future direct experimental validation. This mechanism likely contributes to the variation in circulating sPLA2-IIA protein levels in the human population, with implications for a wide range of human diseases.

Project description: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 transposase-cleavage 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 genetic variants identified as eQTL for further studies to validate their causal regulatory function. Ultimately, identifying causal genetic variants will further our understanding of the underlying molecular mechanisms of disease and the eventual development of potential therapeutic targets.

Dataset Information

BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes.

Publications

BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes.

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