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:
27 May 2019 1000 Genomes Project Consortium, et al. 2015. A global reference for human genetic variation. Nature. 526, 68-74.
PLINK v1.9 Used to compute r2 and MAF. Info and download: 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.
MAGMA v1.08 Used for gene analysis and gene-set analysis. Info and download: 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.
ANNOVAR A variant annotation tool used to obtain functional consequences of SNPs on gene functions. Info and download: 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
CADD v1.4 A deleterious score of variants computed by integrating 63 functional annotations. The higher the score, the more deleterious. Info:
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.
RegulomeDB v1.1 A categorical score to guide interpretation of regulatory variants. Info:
5 Dec 2016 Boyle, AP., et al. 2012. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790-7.
15-core chromatin state Chromatin state for 127 epigenomes was learned by ChromHMM derived from 5 chromatin markers (H3K4me3, H3K4me1, H3K36me3, H3K27me3, H3K9me3). Info:
5 Dec 2016 Roadmap Epigenomics Consortium, et al. 2015. Integrative analysis of 111 reference human epigenomes. Nature. 518, 317-330.
Ernst, J. and Kellis, M. 2012. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods. 28, 215-6.
GTEx v6/v7/v8 eQTLs and gene expression used in the pipeline were obtained from GTEx.
Info and data: 14 Oct 2019 GTEx Consortium. 2015. Human genomics, The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 348, 648-60.
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:
Blood eQTL Browser eQTLs of blood cells. Only cis-eQTLs with FDR ≤ 0.05 are available in FUMA. Info and data: 17 January 2017 Westra et al. 2013. Systematic identification of trans eQTLs as putative divers of known disease associations. Nat. Genet. 45, 1238-1243.
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: 17 January 2017 Zhernakova et al. 2017. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139-145.
BRAINEAC eQTLs of 10 brain regions. Cis-eQTLs with nominal P-value < 0.05 are available in FUMA. Info and data: 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.
MuTHER eQTLs in Adipose, LCL and Skin samples (only cis eQTLs). Info:
21 January 2018 Grundberg et al. 2012. Mapping cis and trans regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084-1089.
xQTLServer eQTLs in dorsolateral prefrontal cortex samples. Info and data: 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.
CommonMind Consortium eQTLs in brain samples. Both cis and trans eQTLs are available Info and data:!Synapse:syn5585484 21 January 2018 Fromer et al. 2016. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 16, 1442-1453.
eQTLGen Meta-analysis of cis and trans eQTLs based on 37 data sets (in total of 31,684 individuals). Info:
20 Oct 2018 Vosa et al. 2018. Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis. bioRxiv
DICE eQTLs of 15 types of immune cells. Info:
27 May 2019 Schmiedel et al. 2018. Impact of genetic polymorphisms on human immune cell gene expression. Cell 175, 1701-1715.e16.
van der Wijst et al. scRNA eQTLs eQTLs based on scRNA-seq of 9 cell types. Info and data: 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.
PsychENCODE SNP annotations (enhancer, H3K27ac markers), eQTLs and HiC based enhancer-promoter interactions. Info and data: 27 May 2019 Wang et al. 2018. Comprehensive functional genomic resource and integrative model for the human brain. Science 14, eaat8464.
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:
16 March 2020 See tutorial
EyeGEx cis-eQTLs from retina. Info:
06 October 2021 See tutorial
FANTOM5 SNP annotations (enhancer and promoter) and enhancer-promoter correlations. Info:
27 May 2019 Andersson et al. 2014. An atlas of active enhancers across human cell types and tissues. Nature 507, 455-461.
FANTOM Consortium. A promoter-level mammalian expression atlas. Nature 507, 462-470.
Bertin et al. 2017. Linking FANTOM5 CAGE peaks to annotations with CAGEscan. Sci. Data 4, 170147.
BrainSpan Gene expression data of developmental brain samples. Info and data: 31 January 2018 Kang et al. 2011. Spatio-temporal transcriptome of the human brain. Nature 478, 483-489.
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: 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.
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.
Enhancer and promoter regions Predicted enhancer and promoter regions (including dyadic) from Roadmap Epigenomics Projects. 111 epigenomes are available. Info:
9 May 2017 Roadmap Epigenomics Consortium, et al. 2015. Integrative analysis of 111 reference human epigenomes. Nature. 518, 317-330.
Ernst, J. and Kellis, M. 2012. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods. 28, 215-6.
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: 14 Oct 2019 Liberzon, A. et al. 2011. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 27, 1739-40.
WikiPathways v20191010 The curated biological pathways. Info:
14 Oct 2019 Kutmon, M., et al. 2016. WikiPathways: capturing the full diversity of pahtway knowledge. Nucleic Acids Res. 44, 488-494.
GWAS-catalog e104_2021-09-15 A database of reported SNP-trait associations. Info:
18 Sept 2021 MacArthur, J., et al. 2016. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. pii:gkw1133.
DrugBank v5.1.4 Targeted genes (protein) of drugs in DrugBank was obtained to assign drug ID for input genes. Info:
14 Oct 2019 Wishart, DS., et al. 2008. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acis Res. 36, D901-6.
pLI A gene score annotated to prioritized genes. The score is the probability of being loss-of-function intolerance. Info:
27 April 2017 Lek, M. et al. 2016. Analyses of protein-coding genetic variation in 60,706 humans. Nature. 536, 285-291.
ncRVIS A gene score annotated to prioritized genes. The score is the non-coding residual variation intolerance score. Info:
27 April 2017 Petrovski, S. et al. 2015. The intolerance of regulatory sequence to genetic variation predict gene dosage sensitivity. PLOS Genet. 11, e1005492.