For scRNA-seq datasets in cell type analysis section, please see tutorial for links and references.
| Data source/tool | Used for | Links | Last update | Reference |
|---|---|---|---|---|
| 1000 Genome Project Phase 3 | Reference panel used to compute r2 and MAF. |
Info: http://www.internationalgenome.org/ Data: ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/ |
27 May 2019 |
1000 Genomes Project Consortium, et al. 2015. A global reference for human genetic variation. Nature. 526, 68-74. PMID:26432245 |
| PLINK v1.9 | Used to compute r2 and MAF. | Info and download: https://www.cog-genomics.org/plink2 | 27 May 2019 |
Purcell, S., et al. 2007. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559-575. PMID:17701901 |
| MAGMA v1.08 | Used for gene analysis and gene-set analysis. | Info and download: https://ctg.cncr.nl/software/magma | 9 Sep 2020 |
de Leeuw, C., et al. 2015. MAGMA: Generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, DOI:10.1371/journal.pcbi.1004219. PMCID:PMC4401657 |
| ANNOVAR | A variant annotation tool used to obtain functional consequences of SNPs on gene functions. | Info and download: http://annovar.openbioinformatics.org/en/latest/ | 5 Dec 2016 |
Wang, K., Li, M. and Hakonarson, H. 2010. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38:e164 PMID:20601685 |
| CADD v1.4 | A deleterious score of variants computed by integrating 63 functional annotations. The higher the score, the more deleterious. |
Info: http://cadd.gs.washington.edu/ Data: http://cadd.gs.washington.edu/download |
27 May 2019 |
Kicher, M., et al. 2014. A general framework for estimating the relative pathogeneticity of human genetic variants. Nat. Genet. 46, 310-315. PMID:24487276 |
| RegulomeDB v1.1 | A categorical score to guide interpretation of regulatory variants. |
Info: http://regulomedb.org/index Data: http://regulomedb.org/downloads/RegulomeDB.dbSNP141.txt.gz |
5 Dec 2016 |
Boyle, AP., et al. 2012. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790-7. PMID:22955989 |
| 15-core chromatin state | Chromatin state for 127 epigenomes was learned by ChromHMM derived from 5 chromatin markers (H3K4me3, H3K4me1, H3K36me3, H3K27me3, H3K9me3). |
Info: http://egg2.wustl.edu/roadmap/web_portal/chr_state_learning.html Data: http://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/coreMarks/jointModel/final/all.mnemonics.bedFiles.tgz |
5 Dec 2016 |
Roadmap Epigenomics Consortium, et al. 2015. Integrative analysis of 111 reference human epigenomes. Nature. 518, 317-330. PMID:25693563 Ernst, J. and Kellis, M. 2012. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods. 28, 215-6. PMID:22373907 |
| GTEx v6/v7/v8 | eQTLs and gene expression used in the pipeline were obtained from GTEx. |
Info and data: http://www.gtexportal.org/home/ | 14 Oct 2019 |
GTEx Consortium. 2015. Human genomics, The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 348, 648-60. PMID:25954001 GTEx Consortium. 2017. Genetic effects on gene expression across human tissues. Nature. 550, 204-213. PMID:29022597 Aguet, et al. 2019. The GTEx consortium atlas of genetic regulatory effects across human tissues. bioRxiv. doi: https://doi.org/10.1101/787903. https://doi.org/10.1101/787903 |
| Blood eQTL Browser | eQTLs of blood cells. Only cis-eQTLs with FDR ≤ 0.05 are available in FUMA. | Info and data: http://genenetwork.nl/bloodeqtlbrowser/ | 17 January 2017 |
Westra et al. 2013. Systematic identification of trans eQTLs as putative divers of known disease associations. Nat. Genet. 45, 1238-1243. PMID:24013639 |
| BIOS QTL browser | eQTLs of blood cells in Dutch population. Only cis-eQTLs (gene-level) with FDR ≤ 0.05 are available in FUMA. | Info and data: http://genenetwork.nl/biosqtlbrowser/ | 17 January 2017 |
Zhernakova et al. 2017. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139-145. PMID:27918533 |
| BRAINEAC | eQTLs of 10 brain regions. Cis-eQTLs with nominal P-value < 0.05 are available in FUMA. | Info and data: http://www.braineac.org/ | 26 January 2017 |
Ramasamy et al. 2014. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418-1428. PMID:27918533 |
| MuTHER | eQTLs in Adipose, LCL and Skin samples (only cis eQTLs). |
Info: http://www.muther.ac.uk/ Data: http://www.muther.ac.uk/Data.html |
21 January 2018 |
Grundberg et al. 2012. Mapping cis and trans regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084-1089. PMID:22941192 |
| xQTLServer | eQTLs in dorsolateral prefrontal cortex samples. | Info and data: http://mostafavilab.stat.ubc.ca/xqtl/ | 21 January 2018 |
Ng et al. 2017. An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome. Nat. Neurosci. 20, 1418-1426. PMID:28869584 |
| CommonMind Consortium | eQTLs in brain samples. Both cis and trans eQTLs are available | Info and data: https://www.synapse.org//#!Synapse:syn5585484 | 21 January 2018 |
Fromer et al. 2016. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 16, 1442-1453. PMID:27668389 |
| eQTLGen | Meta-analysis of cis and trans eQTLs based on 37 data sets (in total of 31,684 individuals). |
Info: http://www.eqtlgen.org/index.html Data: https://molgenis26.gcc.rug.nl/downloads/eqtlgen/cis-eqtl/cis-eQTLs_full_20180905.txt.gz, https://molgenis26.gcc.rug.nl/downloads/eqtlgen/trans-eqtl/trans-eQTL_significant_20181017.txt.gz |
20 Oct 2018 |
Vosa et al. 2018. Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis. bioRxiv https://doi.org/10.1101/447367 |
| DICE | eQTLs of 15 types of immune cells. |
Info: https://dice-database.org/landing Data: https://dice-database.org/downloads |
27 May 2019 |
Schmiedel et al. 2018. Impact of genetic polymorphisms on human immune cell gene expression. Cell 175, 1701-1715.e16. PMID:30449622 |
| van der Wijst et al. scRNA eQTLs | eQTLs based on scRNA-seq of 9 cell types. | Info and data: https://molgenis26.target.rug.nl/downloads/scrna-seq/ | 27 May 2019 |
van der Wijst et al. 2018. Single-cell RNA sequencing identifies celltype-specific eQTLs and co-expression QTLs. Nat. Genet. 50, 493-497. PMID:29610479 |
| PsychENCODE | SNP annotations (enhancer, H3K27ac markers), eQTLs and HiC based enhancer-promoter interactions. | Info and data: http://resource.psychencode.org/ | 27 May 2019 |
Wang et al. 2018. Comprehensive functional genomic resource and integrative model for the human brain. Science 14, eaat8464. PMID:30545857 |
| eQTL Catalogue | Gene level eQTL data generated from a variety of studies, where all of the eQTL datasets were produced in a uniform manner. |
Info: https://www.ebi.ac.uk/eqtl/ Data: https://github.com/eQTL-Catalogue/eQTL-Catalogue-resources/blob/master/tabix/tabix_ftp_paths.tsv |
16 March 2020 | See tutorial https://fuma.ctglab.nl/tutorial#eQTLs. |
| EyeGEx | cis-eQTLs from retina. |
Info: https://gtexportal.org/home/datasets Data: https://storage.googleapis.com/gtex_external_datasets/eyegex_data/single_tissue_eqtl_data/Retina.nominal.eQTLs.with_thresholds.tar |
06 October 2021 | See tutorial https://fuma.ctglab.nl/tutorial#eQTLs. |
| InsPIRE | cis-eQTLs from Human pancreatic islets. |
Info: https://zenodo.org/record/3408356 Data: https://zenodo.org/record/3408356/files/InsPIRE_Islets_Gene_eQTLs_Nominal_Pvalues.txt.gz?download=1 |
27 September 2023 | See tutorial https://fuma.ctglab.nl/tutorial#eQTLs. |
| TIGER | cis-eQTLs from Human pancreatic islets. |
Info: http://tiger.bsc.es/downloads Data: http://tiger.bsc.es/assets/tiger_eqtl_stats.tar.gz |
27 September 2023 | See tutorial https://fuma.ctglab.nl/tutorial#eQTLs. |
| FANTOM5 | SNP annotations (enhancer and promoter) and enhancer-promoter correlations. |
Info: http://fantom.gsc.riken.jp/5/ Data: http://fantom.gsc.riken.jp/5/data/, http://slidebase.binf.ku.dk/human_enhancers/presets |
27 May 2019 |
Andersson et al. 2014. An atlas of active enhancers across human cell types and tissues. Nature 507, 455-461. PMID:24670763 FANTOM Consortium. A promoter-level mammalian expression atlas. Nature 507, 462-470. PMID:24670764 Bertin et al. 2017. Linking FANTOM5 CAGE peaks to annotations with CAGEscan. Sci. Data 4, 170147. PMID:28972578 |
| BrainSpan | Gene expression data of developmental brain samples. | Info and data: http://www.brainspan.org/static/download | 31 January 2018 |
Kang et al. 2011. Spatio-temporal transcriptome of the human brain. Nature 478, 483-489. PMID:22031440 |
| GSE87112 (Hi-C) | Hi-C data (significant loops) of 21 tissue/cell types. Pre-processed data (output of Fit-Hi-C) is used in FUMA. | Info and data: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE87112 | 9 May 2017 |
Schmitt, A.D. et al. 2016. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042-2059. PMID:27851967 |
| Giusti-Rodriguez et al. 2019 (Hi-C) | Hi-C data (significant loops) of adult and fetal cortex. Only significant loops after Bonferroni correction (Pbon < 0.001) are available. | The data was kindly shared by Patric F. Sullivan. | 13 Feb 2019 |
Giusti-Rodriguez, P. et al. 2019. Using three-dimentional regulatory chromatin interactions from adult and fetal cortex to interpret genetic results for psychiatric disorders and cognitive traits. bioRxiv. https://doi.org/10.1101/406330 |
| Enhancer and promoter regions | Predicted enhancer and promoter regions (including dyadic) from Roadmap Epigenomics Projects. 111 epigenomes are available. | Info: http://egg2.wustl.edu/roadmap/web_portal/DNase_reg.html Data: http://egg2.wustl.edu/roadmap/data/byDataType/dnase/ |
9 May 2017 |
Roadmap Epigenomics Consortium, et al. 2015. Integrative analysis of 111 reference human epigenomes. Nature. 518, 317-330. PMID:25693563 Ernst, J. and Kellis, M. 2012. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods. 28, 215-6. PMID:22373907 |
| MsigDB v2023.1.Hs | Collection of publicly available gene sets. Data sets include e.g. KEGG, Reactome, BioCarta, GO terms and so on. | Info and data: http://software.broadinstitute.org/gsea/msigdb | 02 Aug 2023 |
Liberzon, A. et al. 2011. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 27, 1739-40. PMID:21546393 |
| WikiPathways v20191010 | The curated biological pathways. |
Info: http://wikipathways.org/index.php/WikiPathways Data: http://data.wikipathways.org/20161110/gmt/wikipathways-20161110-gmt-Homo_sapiens.gmt |
14 Oct 2019 |
Kutmon, M., et al. 2016. WikiPathways: capturing the full diversity of pahtway knowledge. Nucleic Acids Res. 44, 488-494. PMID:26481357 |
| GWAS-catalog e110_r2023-07-20 | A database of reported SNP-trait associations. |
Info: https://www.ebi.ac.uk/gwas/ Data: https://www.ebi.ac.uk/gwas/downloads |
02 Aug 2023 |
MacArthur, J., et al. 2016. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. pii:gkw1133. PMID:27899670 |
| DrugBank v5.1.4 | Targeted genes (protein) of drugs in DrugBank was obtained to assign drug ID for input genes. |
Info: https://www.ncbi.nlm.nih.gov/pubmed/27899670 Data: https://www.drugbank.ca/releases/latest#protein-identifiers |
14 Oct 2019 |
Wishart, DS., et al. 2008. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acis Res. 36, D901-6. PMID:18048412 |
| pLI | A gene score annotated to prioritized genes. The score is the probability of being loss-of-function intolerance. |
Info: http://exac.broadinstitute.org/ Data: ftp://ftp.broadinstitute.org/pub/ExAC_release/release0.3.1/functional_gene_constraint |
27 April 2017 |
Lek, M. et al. 2016. Analyses of protein-coding genetic variation in 60,706 humans. Nature. 536, 285-291. PMID:27535533 |
| ncRVIS | A gene score annotated to prioritized genes. The score is the non-coding residual variation intolerance score. |
Info: http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1005492 Data: http://journals.plos.org/plosgenetics/article/file?type=supplementary&id=info:doi/10.1371/journal.pgen.1005492.s011 |
27 April 2017 |
Petrovski, S. et al. 2015. The intolerance of regulatory sequence to genetic variation predict gene dosage sensitivity. PLOS Genet. 11, e1005492. PMID:26332131 |