Fine-mapping within eQTL Credible Intervals by E-CROPseq
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PROVIDER: PRJNA590167 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Precise identification of causal variants within credible intervals of eQTL associations is needed to identify regulatory GWAS variants. We show that CROPseq, namely multiplex CRISPR-Cas9 genome editing combined with single cell RNAseq, is a viable strategy for fine mapping regulatory SNPs. Mutations were induced nearby 67 SNPs in three genes, two of which, rs2251039 and rs17523802, significantly altered CISD1 and PARK7 expression, respectively, and overlap with chromatin accessibility peaks.
2019-11-28 | GSE140553 | GEO
Project description:The majority of genome-wide association study (GWAS)-identified SNPs are located in noncoding regions of genes and are likely to influence disease risk and phenotypes by affecting gene expression. Since credible intervals responsible for genome-wide associations typically consist of ≥100 variants with similar statistical support, experimental methods are needed to fine map causal variants. We report here a moderate-throughput approach to identifying regulatory GWAS variants, expression CROP-seq, which consists of multiplex CRISPR-Cas9 genome editing combined with single-cell RNAseq to measure perturbation in transcript abundance. Mutations were induced in the HL60/S4 myeloid cell line nearby 57 SNPs in three genes, two of which, rs2251039 and rs35675666, significantly altered CISD1 and PARK7 expression, respectively, with strong replication and validation in single-cell clones. The sites overlap with chromatin accessibility peaks and define causal variants for inflammatory bowel disease at the two loci. This relatively inexpensive approach should be scalable for broad surveys and is also implementable for the fine mapping of individual genes.
| S-EPMC7334875 | biostudies-literature
Project description:Mapping of expression quantitative trait loci (eQTL) is a powerful means for elucidating the genetic architecture of gene regulation. Yet, eQTL mapping has not been applied towards investigating the regulation architecture of genes involved in the process of population divergence, ultimately leading to speciation events. Here, we conducted an eQTL mapping experiment to compare the genetic architecture of transcript regulation in adaptive traits differentiating the recently evolved limnetic (dwarf) and benthic (normal) species pairs of lake whitefish. The eQTL were mapped in three data sets derived from a F1 hybrid-dwarf backcrossed family: the entire set of 66 genotyped individuals, and the two sexes treated separately. We identified strikingly more eQTL in the female dataset (174), compared to both male (54) and combined (33) data sets. The majority of these genes were not differentially expressed between male and female progeny of the backcross family, thus providing evidence for a strong pleiotropic sex-linked effect in transcriptomic regulation. The subtelomeric region of a linkage group segregating in females encompassed more than 50% of all eQTL, which exhibited the most pronounced additive effects. We also conducted a direct comparison of transcriptomic profiles between pure dwarf and normal progeny reared in controlled conditions. We detected 34 differentially expressed transcripts associated with eQTL segregating only in sex-specific data-sets, and mostly belonging to functional groups that differentiate dwarf and normal whitefish in natural populations. Therefore, these eQTL are not related to inter-individual variation, but instead to the adaptive and historical genetic divergence between dwarf and normal whitefish. This study exemplifies how the integration of genetic and transcriptomic data offers a strong means for dissecting the functional genomic response to selection by separating mapping family specific effects from genetic factors under selection, potentially involved in the phenotypic divergence of natural populations. Keywords: eQTL mapping
2008-09-01 | GSE11378 | GEO
Project description:The majority of genetic loci linked to polygenic complex traits are found in non-coding regions of the human genome. These loci often exhibit complex gene regulatory relationships and linkage disequilibrium (LD) configurations, making it challenging to accurately identify causal variants and their target genes. We used multiplexed single-cell CRISPR interference and activation perturbations to investigate cis-regulatory element (CRE) and target gene expression relationships within tight LD in the endogenous chromatin context. We demonstrated the prevalence of multiple causality in perfect LD (pLD) for independent expression quantitative trait locus (eQTL) and uncovered fine-grained genetic effects on gene expression within pLD, which are difficult to decipher using traditional eQTL fine-mapping or existing computational methods. We found that over one third of the causal CREs lack classical epigenetic markers, and we functionally validated one of these hidden regulatory mechanisms. Leveraging Multiome single-cell epigenetic and sequence perturbations, we highlighted the regulatory plasticity of the human genome. Our study will guide the exploration of missing causal mechanisms in molecular trait formation and disease development.
2025-01-10 | GSE286224 | GEO
Project description:The majority of genetic loci linked to polygenic complex traits are found in non-coding regions of the human genome. These loci often exhibit complex gene regulatory relationships and linkage disequilibrium (LD) configurations, making it challenging to accurately identify causal variants and their target genes. We used multiplexed single-cell CRISPR interference and activation perturbations to investigate cis-regulatory element (CRE) and target gene expression relationships within tight LD in the endogenous chromatin context. We demonstrated the prevalence of multiple causality in perfect LD (pLD) for independent expression quantitative trait locus (eQTL) and uncovered fine-grained genetic effects on gene expression within pLD, which are difficult to decipher using traditional eQTL fine-mapping or existing computational methods. We found that over one third of the causal CREs lack classical epigenetic markers, and we functionally validated one of these hidden regulatory mechanisms. Leveraging Multiome single-cell epigenetic and sequence perturbations, we highlighted the regulatory plasticity of the human genome. Our study will guide the exploration of missing causal mechanisms in molecular trait formation and disease development.
2025-01-10 | GSE286205 | GEO
Project description:The majority of genetic loci linked to polygenic complex traits are found in non-coding regions of the human genome. These loci often exhibit complex gene regulatory relationships and linkage disequilibrium (LD) configurations, making it challenging to accurately identify causal variants and their target genes. We used multiplexed single-cell CRISPR interference and activation perturbations to investigate cis-regulatory element (CRE) and target gene expression relationships within tight LD in the endogenous chromatin context. We demonstrated the prevalence of multiple causality in perfect LD (pLD) for independent expression quantitative trait locus (eQTL) and uncovered fine-grained genetic effects on gene expression within pLD, which are difficult to decipher using traditional eQTL fine-mapping or existing computational methods. We found that over one third of the causal CREs lack classical epigenetic markers, and we functionally validated one of these hidden regulatory mechanisms. Leveraging Multiome single-cell epigenetic and sequence perturbations, we highlighted the regulatory plasticity of the human genome. Our study will guide the exploration of missing causal mechanisms in molecular trait formation and disease development.
2025-01-03 | GSE285760 | GEO
Project description:The majority of genetic loci linked to polygenic complex traits are found in non-coding regions of the human genome. These loci often exhibit complex gene regulatory relationships and linkage disequilibrium (LD) configurations, making it challenging to accurately identify causal variants and their target genes. We used multiplexed single-cell CRISPR interference and activation perturbations to investigate cis-regulatory element (CRE) and target gene expression relationships within tight LD in the endogenous chromatin context. We demonstrated the prevalence of multiple causality in perfect LD (pLD) for independent expression quantitative trait locus (eQTL) and uncovered fine-grained genetic effects on gene expression within pLD, which are difficult to decipher using traditional eQTL fine-mapping or existing computational methods. We found that over one third of the causal CREs lack classical epigenetic markers, and we functionally validated one of these hidden regulatory mechanisms. Leveraging Multiome single-cell epigenetic and sequence perturbations, we highlighted the regulatory plasticity of the human genome. Our study will guide the exploration of missing causal mechanisms in molecular trait formation and disease development.
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Project description:The majority of genetic loci linked to polygenic complex traits are found in non-coding regions of the human genome. These loci often exhibit complex gene regulatory relationships and linkage disequilibrium (LD) configurations, making it challenging to accurately identify causal variants and their target genes. We used multiplexed single-cell CRISPR interference and activation perturbations to investigate cis-regulatory element (CRE) and target gene expression relationships within tight LD in the endogenous chromatin context. We demonstrated the prevalence of multiple causality in perfect LD (pLD) for independent expression quantitative trait locus (eQTL) and uncovered fine-grained genetic effects on gene expression within pLD, which are difficult to decipher using traditional eQTL fine-mapping or existing computational methods. We found that over one third of the causal CREs lack classical epigenetic markers, and we functionally validated one of these hidden regulatory mechanisms. Leveraging Multiome single-cell epigenetic and sequence perturbations, we highlighted the regulatory plasticity of the human genome. Our study will guide the exploration of missing causal mechanisms in molecular trait formation and disease development.
2025-01-03 | GSE285718 | GEO
Project description:The majority of genetic loci linked to polygenic complex traits are found in non-coding regions of the human genome. These loci often exhibit complex gene regulatory relationships and linkage disequilibrium (LD) configurations, making it challenging to accurately identify causal variants and their target genes. We used multiplexed single-cell CRISPR interference and activation perturbations to investigate cis-regulatory element (CRE) and target gene expression relationships within tight LD in the endogenous chromatin context. We demonstrated the prevalence of multiple causality in perfect LD (pLD) for independent expression quantitative trait locus (eQTL) and uncovered fine-grained genetic effects on gene expression within pLD, which are difficult to decipher using traditional eQTL fine-mapping or existing computational methods. We found that over one third of the causal CREs lack classical epigenetic markers, and we functionally validated one of these hidden regulatory mechanisms. Leveraging Multiome single-cell epigenetic and sequence perturbations, we highlighted the regulatory plasticity of the human genome. Our study will guide the exploration of missing causal mechanisms in molecular trait formation and disease development.
2024-12-31 | GSE285461 | GEO
Project description:Mapping of expression quantitative trait loci (eQTL) is a powerful means for elucidating the genetic architecture of gene regulation. Yet, eQTL mapping has not been applied towards investigating the regulation architecture of genes involved in the process of population divergence, ultimately leading to speciation events. Here, we conducted an eQTL mapping experiment to compare the genetic architecture of transcript regulation in adaptive traits differentiating the recently evolved limnetic (dwarf) and benthic (normal) species pairs of lake whitefish. The eQTL were mapped in three data sets derived from a F1 hybrid-dwarf backcrossed family: the entire set of 66 genotyped individuals, and the two sexes treated separately. We identified strikingly more eQTL in the female dataset (174), compared to both male (54) and combined (33) data sets. The majority of these genes were not differentially expressed between male and female progeny of the backcross family, thus providing evidence for a strong pleiotropic sex-linked effect in transcriptomic regulation. The subtelomeric region of a linkage group segregating in females encompassed more than 50% of all eQTL, which exhibited the most pronounced additive effects. We also conducted a direct comparison of transcriptomic profiles between pure dwarf and normal progeny reared in controlled conditions. We detected 34 differentially expressed transcripts associated with eQTL segregating only in sex-specific data-sets, and mostly belonging to functional groups that differentiate dwarf and normal whitefish in natural populations. Therefore, these eQTL are not related to inter-individual variation, but instead to the adaptive and historical genetic divergence between dwarf and normal whitefish. This study exemplifies how the integration of genetic and transcriptomic data offers a strong means for dissecting the functional genomic response to selection by separating mapping family specific effects from genetic factors under selection, potentially involved in the phenotypic divergence of natural populations. Keywords: eQTL mapping Dissected white muscle tissue (250-350 mg) was sampled for 76 individuals from the hybrid x dwarf backcross mapping family. We used a loop design (YANG and SPEED 2002; CHURCHILL 2002) to maximize the number of sampled meioses. Each of 76 samples was technically replicated on two distinct slides, while performing dye swapping (Cy3 and Alexa) to estimate the dye intensity variation bias. After correcting for local background, raw intensity values were both log2 transformed and normalized using the regional LOWESS method implemented in the R/MANOVA software (KERR et al. 2000). The design file used for the maanova analysis is linked below as a supplementary file.
2008-09-01 | E-GEOD-11378 | biostudies-arrayexpress