V2FKP Epigenetic and Computational Datasets

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Many of the results listed below are generated by computational methods that predict associations between different entities: variants; genes or gene sets; tissues or cell lines; and phenotypes (diseases or traits).

Variantsvariant Genesgene Phenotypesphenotype   Tissuestissue Gene sets or pathwaysGene sets icon

 

Dataset name in V2FKP Description and reference Entities connected Access dataset
ABC Enhancer Maps

Enhancer-gene pairs from roughly 150 cell types, predicted by the ABC method. ABC takes into account measures of enhancer activity and enhancer-promoter 3D contacts to predict enhancer-gene connections in a given cell type based on chromatin state maps.

Activity-by-Contact model of enhancer specificity from thousands of CRISPR perturbations. Fulco CP, Nasser J, et al., 2019, BioRxiv.

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Allele-specific ATAC-seq annotations for lymphoblastoid cell lines

Genome-wide chromatin accessibility landscapes in 24 lymphoblastoid cell lines from the 1000 Genomes GBR population, generated using ATAC-seq.

Fine-mapping Cellular QTLs With RASQUAL and ATAC-seq. Kumasaka N, et al., 2016, PMID:26656845.

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Allele-specific ATAC-seq annotations for activated CD4+ T cells

ATAC-seq on stimulated CD4+ T cells from 105 healthy individuals.

Genetic Determinants of Co-Accessible Chromatin Regions in Activated T Cells Across Humans. GateR, et al., 2018, PMID:29988122.

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dataset name

dataset description.

Title. Authors et al., year, PMID:PMID.

icons for connected entities  
dataset name

dataset description.

Title. Authors et al., year, PMID:PMID.

icons for connected entities  
dataset name

dataset description.

Title. Authors et al., year, PMID:PMID.

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dataset name

dataset description.

Title. Authors et al., year, PMID:PMID.

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UK Biobank Fine-Mapping

Genome-wide association analysis was performed for multiple traits in the UK Biobank population; then fine-mapping was performed to statistically identify causal variants.

Unpublished; see documentation.

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