Undefined
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB64479 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR11748912_1.fastq.gz | Fastqsanger.gz | |||
| ERR11748912_2.fastq.gz | Fastqsanger.gz | |||
| ERR11748913_1.fastq.gz | Fastqsanger.gz | |||
| ERR11748913_2.fastq.gz | Fastqsanger.gz | |||
| ERR11748914_1.fastq.gz | Fastqsanger.gz |
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Project description:Genome-wide association studies (GWAS) have linked hundreds of loci to cardiac diseases. However, in most loci the causal variants and their target genes remain unknown. We developed a combined experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes. We profiled accessible chromatin in single cells obtained from human hearts and leveraged the data to study genetics of Atrial Fibrillation (AF), the most common cardiac arrhythmia. Enrichment analysis of AF risk variants using cell-type-resolved open chromatin regions (OCRs) implicated cardiomyocytes as the main mediator of AF risk. We then performed statistical fine-mapping, leveraging the information in OCRs, and identified putative causal variants in 122 AF-associated loci. Taking advantage of the fine-mapping results, our novel statistical procedure for gene discovery prioritized 45 high-confidence risk genes, highlighting transcription factors and signal transduction pathways important for heart development. We further leveraged our single-cell data to study genetics of gene expression. An unexpected finding from earlier studies is that expression QTLs (eQTLs) are often shared across tissues even though most regulatory elements are cell-type specific. We found that this sharing is largely driven by the limited power of eQTL studies using bulk tissues to detect cell-type-specific regulatory variants. This finding points to an important limitation of using eQTLs to interpret GWAS of complex traits. In summary, our analysis provides a comprehensive map of AF risk variants and genes, and a general framework to integrate single-cell genomics with genetic studies of complex traits.
2023-02-12 | GSE224996 | GEO
Project description:Genome-wide association studies (GWAS) have linked hundreds of loci to cardiac diseases. However, in most loci the causal variants and their target genes remain unknown. We developed a combined experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes. We profiled accessible chromatin in single cells obtained from human hearts and leveraged the data to study genetics of Atrial Fibrillation (AF), the most common cardiac arrhythmia. Enrichment analysis of AF risk variants using cell-type-resolved open chromatin regions (OCRs) implicated cardiomyocytes as the main mediator of AF risk. We then performed statistical fine-mapping, leveraging the information in OCRs, and identified putative causal variants in 122 AF-associated loci. Taking advantage of the fine-mapping results, our novel statistical procedure for gene discovery prioritized 45 high-confidence risk genes, highlighting transcription factors and signal transduction pathways important for heart development. We further leveraged our single-cell data to study genetics of gene expression. An unexpected finding from earlier studies is that expression QTLs (eQTLs) are often shared across tissues even though most regulatory elements are cell-type specific. We found that this sharing is largely driven by the limited power of eQTL studies using bulk tissues to detect cell-type-specific regulatory variants. This finding points to an important limitation of using eQTLs to interpret GWAS of complex traits. In summary, our analysis provides a comprehensive map of AF risk variants and genes, and a general framework to integrate single-cell genomics with genetic studies of complex traits.
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Project description:Comparative muscle transcriptome associated with carcass traits of Nellore cattle
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Project description:Systematic characterization of how genetic variation modulates gene regulation in a cell type specific context is essential for understanding complex traits. To address this question, we profiled gene expression and chromatin state of cells from healthy retinae of 20 human donors with a single-cell multiomics approach, and performed genomic sequencing. sc-eQTLs, sc-caQTL, sc-ASCA and sc-ASE were mapped in major retinal cell types. By integrating these results, we identified and characterized regulatory elements and genetic variants effective on gene regulation in individual cell types. Most of the identified sc-eQTLs and sc-caQTLs exhibit cell type-specific effects. Interestingly, the cis-elements harboring genetic variants with cell type-specific effects tend to be accessible in multiple cell types. Lastly, we identified the enriched cell types, fine-mapped candidate causal variants and genes, and uncovered cell type-specific regulatory mechanism underlying GWAS loci.
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Project description:Profiles of sequence variants that influence gene transcription are very important for understanding mechanisms that affect phenotypic variation and disease susceptibility. Using genotypes at 1.4 million SNPs and a comprehensive transcriptional profile of 15,454 coding genes and 6,113 lincRNA genes obtained from peripheral blood cells of 298 Japanese individuals, we mapped expression quantitative trait loci (eQTLs). We identified 3,804 cis-eQTLs (within 500 kb from target genes) and 165 trans-eQTLs (>500 kb away or on different chromosomes). Cis-eQTLs were often located in transcribed or adjacent regions of genes; among these regions, 5’ untranslated regions and 5’ flanking regions had the largest effects. Epigenetic evidence for regulatory potential accumulated in public databases explained the magnitude of the effects of our eQTLs. Cis-eQTLs were often located near the respective target genes, if not within genes. Large effect sizes were observed with eQTLs near target genes, and effect sizes were obviously attenuated as the eQTL distance from the gene increased. Using a very stringent significance threshold, we identified 165 large-effect trans-eQTLs. We used our eQTL map to assess 8,069 disease-associated SNPs identified in 1,436 genome-wide association studies (GWAS). We identified genes that might be truly causative, but GWAS might have failed to identify for 148 out of the GWAS-identified SNPs; for example, TUFM (P=3.3E-48) was identified for inflammatory bowel disease (early onset); ZFP90 (P=4.4E-34) for ulcerative colitis; and IDUA (P=2.2E-11) for Parkinson's disease. We identified four genes (P<2.0E-14) that might be related to three diseases and two hematological traits; each expression is regulated by trans-eQTLs on a different chromosome than the gene. The study subjects were 301 apparently healthy individuals residing in Nagahama City, Japan. Whole blood was collected from each participant when in a non-stimulated state.
2014-06-23 | E-GEOD-53351 | biostudies-arrayexpress
Project description:In recent years genome-wide association studies (GWAS) have uncovered numerous chromosomal loci associated with various electrocardiographic traits and cardiac arrhythmia predisposition. A considerable fraction of these loci lie within inter-genic regions. Trait-associated SNPs located in putative regulatory regions likely exert their effect by modulating gene expression. Hence, the key to unraveling the molecular mechanisms underlying cardiac traits is to interrogate variants for association with differential transcript abundance by expression quantitative trait locus (eQTL) analysis. In this study we conducted an eQTL analysis of human heart. To this end, left ventricular mycardium samples from non-diseased human donor hearts were hybridized to Illumina HumanOmniExpress BeadChips for genotyping (n = 129) and Illumina Human HT12 Version 4 BeadChips (n = 129) for transcription profiling. To assess the genotypes of 129 human donor hearts from the study, genome-wide SNP genotyping was carried out using Illumina HumanOmniExpress Beadchips interrogating 733,202 genetic markers.
2014-05-21 | E-GEOD-55230 | biostudies-arrayexpress
Project description:Profiles of sequence variants that influence gene transcription are very important for understanding mechanisms that affect phenotypic variation and disease susceptibility. Using genotypes at 1.4 million SNPs and a comprehensive transcriptional profile of 15,454 coding genes and 6,113 lincRNA genes obtained from peripheral blood cells of 298 Japanese individuals, we mapped expression quantitative trait loci (eQTLs). We identified 3,804 cis-eQTLs (within 500 kb from target genes) and 165 trans-eQTLs (>500 kb away or on different chromosomes). Cis-eQTLs were often located in transcribed or adjacent regions of genes; among these regions, 5’ untranslated regions and 5’ flanking regions had the largest effects. Epigenetic evidence for regulatory potential accumulated in public databases explained the magnitude of the effects of our eQTLs. Cis-eQTLs were often located near the respective target genes, if not within genes. Large effect sizes were observed with eQTLs near target genes, and effect sizes were obviously attenuated as the eQTL distance from the gene increased. Using a very stringent significance threshold, we identified 165 large-effect trans-eQTLs. We used our eQTL map to assess 8,069 disease-associated SNPs identified in 1,436 genome-wide association studies (GWAS). We identified genes that might be truly causative, but GWAS might have failed to identify for 148 out of the GWAS-identified SNPs; for example, TUFM (P=3.3E-48) was identified for inflammatory bowel disease (early onset); ZFP90 (P=4.4E-34) for ulcerative colitis; and IDUA (P=2.2E-11) for Parkinson's disease. We identified four genes (P<2.0E-14) that might be related to three diseases and two hematological traits; each expression is regulated by trans-eQTLs on a different chromosome than the gene.
2014-06-23 | GSE53351 | GEO
Project description:In recent years genome-wide association studies (GWAS) have uncovered numerous chromosomal loci associated with various electrocardiographic traits and cardiac arrhythmia predisposition. A considerable fraction of these loci lie within inter-genic regions. Trait-associated SNPs located in putative regulatory regions likely exert their effect by modulating gene expression. Hence, the key to unraveling the molecular mechanisms underlying cardiac traits is to interrogate variants for association with differential transcript abundance by expression quantitative trait locus (eQTL) analysis. In this study we conducted an eQTL analysis of human heart. To this end, left ventricular mycardium samples from non-diseased human donor hearts were hybridized to Illumina HumanOmniExpress BeadChips for genotyping (n = 129) and Illumina Human HT12 Version 4 BeadChips (n = 129) for transcription profiling. To assess the gene expression levels of 129 human donor hearts from the study, genome-wide transcription profiling was carried out using Illumina Human HT12 Version 4 Beadchips interrogating over 47,000 unique transcripts (total of 47323 probes including controls).
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