Systematic identification of human SNPs affecting regulatory element activity
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA527132 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR8730420_1.fastq.gz | Fastqsanger.gz | |||
| SRR8730420_2.fastq.gz | Fastqsanger.gz | |||
| SRR8730430_1.fastq.gz | Fastqsanger.gz | |||
| SRR8730430_2.fastq.gz | Fastqsanger.gz | |||
| SRR8730440_1.fastq.gz | Fastqsanger.gz |
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Project description:Most of the millions of single-nucleotide polymorphisms (SNPs) in the human genome are non-coding, and many overlap with putative regulatory elements. Genome-wide association studies have linked many of these SNPs to human traits or to gene expression levels, but rarely with sufficient resolution to identify the causal SNPs. Functional screens based on reporter assays have previously been of insufficient throughput to test the vast space of SNPs for possible effects on enhancer and promoter activity. Here, we have leveraged the throughput of the SuRE reporter technology to survey a total of 5.9 million SNPs, including 57% of the known common SNPs world-wide. We identified more than 30 thousand SNPs that alter the activity of putative regulatory elements, often in a cell-type specific manner. These data indicate that a large proportion of human non-coding SNPs may affect gene regulation. Integration of these SuRE data with genome-wide association studies may help to identify causal SNPs.
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Project description:Most of the millions of SNPs in the human genome are non-coding, and many overlap with putative regulatory elements. Genome-wide association studies (GWAS) have linked many of these SNPs to human traits or to gene expression levels, but rarely with sufficient resolution to identify the causal SNPs. Functional screens based on reporter assays have previously been of insufficient throughput to test the vast space of SNPs for possible effects on regulatory element activity. Here we leveraged the throughput and resolution of the survey of regulatory elements (SuRE) reporter technology to survey the effect of 5.9 million SNPs, including 57% of the known common SNPs, on enhancer and promoter activity. We identified more than 30,000 SNPs that alter the activity of putative regulatory elements, partially in a cell-type-specific manner. Integration of this dataset with GWAS results may help to pinpoint SNPs that underlie human traits.
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Project description:Genome-wide association studies implicate multiple loci in risk for systemic lupus erythematosus (SLE), but few contain exonic variants, rendering systematic identification of non-coding variants essential to decoding SLE genetics. We utilized SNP-seq and bioinformatic enrichment to interrogate 2180 single-nucleotide polymorphisms (SNPs) from 87 SLE risk loci for potential binding of transcription factors and related proteins from B cells. 52 SNPs that passed initial screening were tested by electrophoretic mobility shift (EMSA) and luciferase reporter assays. To identify binding of transcription factors and/or other nuclear proteins in an allele-determined manner, we employed pulldown using nuclear extract from Daudi cells and silver staining in SNPs that had exhibited allele-specific differential binding by EMSA. Each pulldown product for each allele of the five high-probability SNPs (rs2297550 C/G, rs13213604 C/G, rs276461 T/C, rs9907955 C/T, rs7302634 T/C) was evaluated by mass spectrometry (MS) to identify binding nuclear proteins, yielding a set of candidate proteins for each.
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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.
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Project description:We adapted the self-transcribing active regulatory region sequencing (Starr-seq) strategy to systematically identify the SNPs that affect gene expression by modulating activities of regulatory elements. Among 10,673 SNPs linked with 996 GWAS-identified cancer-risk SNPs, we found 70 regulatory variants for which the two alleles conferred different regulatory activities. We analyzed one of them in-depth and confirmed its target by CRIPSR-Cas9 technology. Our results will help the interpretation of GWAS results and better cancer risk assessment.
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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:Epistasis and pleiotropy are important features of the genetic architecture of quantitative traits, but are difficult to assess in outbred populations. We performed a diallel cross among Drosophila P-element mutations previously associated with hyper-aggressive behavior, and demonstrated extensive epistatic and pleiotropic effects on aggression, brain morphology, and genome wide gene expression. Epistatic interactions were often of greater magnitude than homozygous effects, and the topology of the epistatic networks varied among the traits. The transcriptional signature of the homozygous and double heterozygous genotypes derived from the six mutations implies a large mutational target size for aggressive behavior and evolutionarily conserved genetic mechanisms and neural signaling pathways affecting aggressive behavior. We used six mutant lines homozygous for a P-element insertion, the 15 possible transheterozygous combinations of the initial six lines and a control line. All lines were isogenic other than the P-element insert. three replicates were done for a total of 66 arrays
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