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).
|Variants||Genes||Phenotypes||Tissues||Gene sets or pathways|
|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; DNAse I hypersensitivity annotations; CTCF binding motifs; and promoters.
Activity-by-Contact model of enhancer specificity from thousands of CRISPR perturbations. Fulco CP, Nasser J, et al., 2019, PMID:31784727.
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.
|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. Gate R, et al., 2018, PMID:29988122.
|Allele-specific DNAse I hypersensitivity annotations for lymphoblastoid cell lines||
Chromatin accessibility in 70 Yoruba lymphoblastoid cell lines (LCLs) measured by DNaseI sequencing and compared with genotype to generate DNaseI sensitivity Quantitative Trait Loci, dsQTLs.
DNase I Sensitivity QTLs Are a Major Determinant of Human Expression Variation. Degner JF, et al., 2012, PMID:22307276.
|Allele-specific DNAse I hypersensitivity annotations for multiple cell lines||
Integration of DNaseI footprinting data with sequence-based transcription factor (TF) motif models to predict the impact of a genetic variant on TF binding across multiple tissues.
Which Genetics Variants in DNase-Seq Footprints Are More Likely to Alter Binding? Moyerbrailean GA, et al., 2016, PMID:26901046.
|Allele-specific H3K27ac annotations for brain tissues||
Histone H3K27ac modifications in cerebellum, pre-frontal cortex, and temporal cortex.
Histone Acetylome-wide Association Study of Autism Spectrum Disorder. Sun W, et al., 2016, PMID:27863250.
|Allele-specific H3K27ac annotations for immune cells||
Histone H3K27ac modifications in CD14+ monocytes, CD16+ neutrophils, and naive CD4+ T cells.
Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells. Chen L, et al., 2016, PMID:27863251.
|Allele-specific H3K27ac annotations for lymphoblastoid cell lines||
Histone H3K27ac modifications in lymphoblastoid cell lines.
Sensitive Detection of Chromatin-Altering Polymorphisms Reveals Autoimmune Disease Mechanisms. Cruz-Herrera del Rosario R, et al., 2015, PMID:25799442.
|Catalog of Inferred Sequence Binding Preferences (CIS-BP)||
CIS-BP is an online library of transcription factors and their DNA binding motifs.
Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity. Weirauch MT, et al., 2014, PMID:25215497.
HaploReg is a tool for exploring annotations of the noncoding genome at variants on haplotype blocks.
HaploReg v4: Systematic Mining of Putative Causal Variants, Cell Types, Regulators and Target Genes for Human Complex Traits and Disease. Ward LD, Kellis M, 2016, PMID:26657631.
|LD score regression annotations||
LD score regression (LDSR) uses cell type-specific annotations and genetic
Partitioning Heritability by Functional Annotation Using Genome-Wide Association Summary Statistics. Finucane H, et al., 2015. PMID:26414678.
Roadmap Epigenomics and ENCODE ChromHMM chromatin states
Regions delineated using observed DNaseI data across 53 epigenomes, annotated with the 5-mark 15-state model based on imputed data across 127 epigenomes (Roadmap + ENCODE).
Mapping accessible chromatin profiles of individual islet cells using snATAC-seq.
Single cell chromatin accessibility reveals pancreatic islet cell type2 and state-specific regulatory programs of diabetes risk. Chiou J, et al., 2020, BioRxiv.
|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.