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.
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 v7.0 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 14 Oct 2019 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 e96 2019-09-24 A database of reported SNP-trait associations. Info: https://www.ebi.ac.uk/gwas/
Data: https://www.ebi.ac.uk/gwas/downloads
14 Oct 2019 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