High-throughput functional dissection of noncoding SNPs with biased allelic enhancer activity for insulin resistance-relevant phenotypes
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ABSTRACT:
PROVIDER: PRJNA813523 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR18254633.fastq.gz | Fastqsanger.gz | |||
| SRR18254633_1.fastq.gz | Fastqsanger.gz | |||
| SRR18254633_2.fastq.gz | Fastqsanger.gz | |||
| SRR18254634.fastq.gz | Fastqsanger.gz | |||
| SRR18254634_1.fastq.gz | Fastqsanger.gz |
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Project description:The majority of single nucleotide polymorphisms (SNPs) associated with insulin resistance (IR)-relevant phenotypes by genome-wide association studies (GWASs) are located in noncoding regions, complicating their functional interpretation. Here, we utilized an adapted STARR-seq to systematically detect regulatory activity of 5,987 noncoding GWASs SNPs in three IR-relevant cell lines. We identified 876 SNPs with biased allelic enhancer activity across 133 loci, and further uncovered the genetic regulatory mechanisms underlying functional SNPs through integrating multi-omics analyses.
2023-07-28 | GSE198047 | GEO
Project description:Most of the single-nucleotide polymorphisms (SNPs) associated with insulin resistance (IR)-relevant phenotypes by genome-wide association studies (GWASs) are located in noncoding regions, complicating their functional interpretation. Here, we utilized an adapted STARR-seq to evaluate the regulatory activities of 5,987 noncoding SNPs associated with IR-relevant phenotypes. We identified 876 SNPs with biased allelic enhancer activity effects (baaSNPs) across 133 loci in three IR-relevant cell lines (HepG2, preadipocyte, and A673), which showed pervasive cell specificity and significant enrichment for cell-specific open chromatin regions or enhancer-indicative markers (H3K4me1, H3K27ac). Further functional characterization suggested several transcription factors (TFs) with preferential allelic binding to baaSNPs. We also incorporated multi-omics data to prioritize 102 candidate regulatory target genes for baaSNPs and revealed prevalent long-range regulatory effects and cell-specific IR-relevant biological functional enrichment on them. Specifically, we experimentally verified the distal regulatory mechanism at IRS1 locus, in which rs952227-A reinforces IRS1 expression by long-range chromatin interaction and preferential binding to the transcription factor HOXC6 to augment the enhancer activity. Finally, based on our STARR-seq screening data, we predicted the enhancer activity of 227,343 noncoding SNPs associated with IR-relevant phenotypes (fasting insulin adjusted for BMI, HDL cholesterol, and triglycerides) from the largest available GWAS summary statistics. We further provided an open resource (http://www.bigc.online/fnSNP-IR) for better understanding genetic regulatory mechanisms of IR-relevant phenotypes.
| S-EPMC10432149 | biostudies-literature
Project description:Most variants associated to diseases are located in non-coding regions of the genome, and are thought to be regulatory in nature. Cis-regulatory SNPs (cis-rSNPs) impacting transcription can be identified through the mapping of differences in allelic expression (AE). We mapped common cis-rSNPs of protein coding and non-coding genes in 3 distinct cell-types. We show 70% sharing across tissues and similar genetically controlled transcription for protein-coding genes and lincRNAs. Candidate cis-rSNPs altering the expression of 42 non-coding RNA overlap SNPs underlying GWAS associations for 39 diseases. We uncover a new class of cis-rSNPs leading to disruption of footprint-derived de novo motifs, predominantly bind by repressive factors and implicated in disease susceptibility through overlaps with GWAS SNPs. Finally, we provide proof-of-principle for a new approach for genome-wide functional validation of transcription factor – SNP interactions. We perturbed NFκB action in lymphoblasts and identified 489 cis-regulated transcripts with altered AE after NFkB perturbation. Altogether, we performed a comprehensive analysis of cis-variation in four cell-populations, and provide new tools for the identification of functional variants associated to complex diseases.
2014-11-03 | GSE53837 | GEO
Project description:Most variants associated to diseases are located in non-coding regions of the genome, and are thought to be regulatory in nature. Cis-regulatory SNPs (cis-rSNPs) impacting transcription can be identified through the mapping of differences in allelic expression (AE). We mapped common cis-rSNPs of protein coding and non-coding genes in 3 distinct cell-types. We show 70% sharing across tissues and similar genetically controlled transcription for protein-coding genes and lincRNAs. Candidate cis-rSNPs altering the expression of 42 non-coding RNA overlap SNPs underlying GWAS associations for 39 diseases. We uncover a new class of cis-rSNPs leading to disruption of footprint-derived de novo motifs, predominantly bind by repressive factors and implicated in disease susceptibility through overlaps with GWAS SNPs. Finally, we provide proof-of-principle for a new approach for genome-wide functional validation of transcription factor M-bM-^@M-^S SNP interactions. We perturbed NFM-NM-:B action in lymphoblasts and identified 489 cis-regulated transcripts with altered AE after NFkB perturbation. Altogether, we performed a comprehensive analysis of cis-variation in four cell-populations, and provide new tools for the identification of functional variants associated to complex diseases. Mapping cis-rSNPs across 3 distinct cell types in humans
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2010-06-10 | E-GEOD-2258 | biostudies-arrayexpress
Project description:Most loci contributing to breast cancer susceptibility identified by the recent genome-wide association studies (GWAS) are predicted to have a regulatory function. Additionally, the early phases of the GWAS studies have generated long lists of possible candidates that need to be prioritised for follow-up studies. Integration of allelic expression mapping and disease association data should enable the identification of those GWAS hits with higher cis-regulatory potential. Our aims are (1) to assess how well functional data and disease risk are correlated and (2) to help prioritise and select the strongest candidates from the GWAS scan for further follow-up and functional analysis. We are performing genome-wide differential allelic expression (DAE) analysis to identify loci with cis-regulatory potential in sixty-four normal breast tissue samples. Using the Illumina Exon510S-Duo BeadChips, we have identified 34K informative SNPs of which approximately 8K (23.5%) displayed DAE. Two SNPs showed monoallelic expression suggesting possible new imprinted loci. We are mapping the cis-regulatory loci by fitting a linear regression model with permutations to DAE ratios vs genotype at SNPs within ±250kb of the RefSeq gene corresponding to the DAE SNP. We will combine our data with the UK GWAS1 and GWAS2 studies for breast cancer susceptibility, to generate a list of genes that show regulatory variation for further evaluation as candidates. This is the first genome-wide differential allelic expression study in normal breast tissue, and one of the first in a primary tissue. We predict that this will be a powerful approach to validate/identify susceptibility loci and to unravel some of the biology underlying breast cancer susceptibility. Allelic gene expression of normal breast sample from healthy controls.
2012-05-02 | E-GEOD-35023 | biostudies-arrayexpress
Project description:Background: Expression quantitative trait loci (eQTL) studies are a valuable approach for identifying genetic variants correlated with gene expression. However, identifying the causal variants is challenging due to linkage disequilibrium amongst variants in the same haplotype block. In this study, we aim to identify functional SNPs in key regulatory regions that alter transcriptional regulation and thus, potentially impact cellular function. The majority of disease-associated single-nucleotide polymorphisms (SNPs) are located in regulatory regions, which can result in allele-specific binding (ASB) of transcription factors and differential expression of the target gene alleles. Here, we present regSNPs-ASB, a generalized linear model-based approach to accurately identify regulatory SNPs that are located in transcription factor binding sites from ATAC-seq data. Results: 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 (ASE). 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 eQTLs. 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 sum, 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.
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Project description:We identified 9325 reliable single nucleotide polymorphisms (SNPs) distributed throughout the genome, of which nearly 68% were represented in R9308 versus Xieqingzao B by CT and GA SNPs. This may indicate that DNA methylation, a heritable epigenetic mark, exists in the parents and their F1 hybrid. Of 2793 identified transcripts with consistent allelic biases, only 480 (17%) showed significant allelic biases during at least one stage, suggesting that trans effects may mediate most transcriptional differences in hybrid offspring. Approximately 67% and 62% of the 480 transcripts showed allelic expression biases towards the R9308 allele at tillering and heading stages, respectively. Transcripts with higher levels of gene expression in R9308 also exhibited R9308 allelic biases in the hybrid. In addition, 125 transcripts were identified with significant allelic expression biases at both stages, of which 74% showed allele expression biases towards the R9308 allele. R9308 alleles may tend to preserve their characteristic states of activity in the hybrid and may play an important role in hybrid vigor at both stages. The allelic expression of 355 transcripts was highly stage-specific, with divergent allelic expression patterns observed at different developmental stages. Many transcripts associated with stress resistance were differently regulated in the F1 hybrid. The results of this study would provide valuable information for further understanding molecular mechanisms of heterosis. Root mRNA profiles of a super-hybrid rice Xieyou 9308 and its parents at tillering and heading stages were generated by deep sequencing, in duplicate, on Illumina Hiseq 2000 platform.
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