Functional mapping and annotation of genetic associations with FUMA
Tóm tắt
A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations.
Từ khóa
Tài liệu tham khảo
Welter, D. et al. The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).
Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).
Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).
Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, 1–10 (2015).
Breen, G. et al. Translating genome-wide association findings into new therapeutics for psychiatry. Nat. Neurosci. 19, 1392–1396 (2016).
Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).
The GTEx Consortium. The genotype-tissue expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science 348, 648–660 (2015).
The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016).
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).
Boyle, A. P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).
Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).
Westra, H.-J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).
Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2016).
Ramasamy, A. et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418–1428 (2014).
Amberger, J. S., Bocchini, C. A., Schiettecatte, F., Scott, A. F. & Hamosh, A. OMIM.org: online mendelian inheritance in man (OMIM), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43, D789–D798 (2015).
Wishart, D. S. et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34, D668–D672 (2006).
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
Kutmon, M. et al. WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Res. 44, D488–D494 (2016).
Edwards, S. L., Beesley, J., French, J. D. & Dunning, M. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 93, 779–797 (2013).
Marjoram, P., Zubair, A. & Nuzhdin, S. V. Post-GWAS: where next? More samples, more SNPs or more biology? Heredity (Edinb.) 112, 79–88 (2014).
Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
Hou, L. & Zhao, H. A review of post-GWAS prioritization approaches. Front. Genet. 4, 2009–2014 (2013).
Ward, L. D. & Kellis, M. HaploReg v4: Systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 44, D877–D881 (2016).
Tak, Y. G. & Farnham, P. J. Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome. Epigenet. Chromatin 8, 57 (2015).
Liu, J. Z. et al. A versatile gene-based test for genome-wide association studies. Am. J. Hum. Genet. 87, 139–145 (2010).
Mishra, A. & Macgregor, S. VEGAS2: Software for more flexible gene-based testing. Twin Res. Hum. Genet. 18, 1–6 (2014).
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, 1–19 (2015).
Lamparter, D., Marbach, D., Rueedi, R., Kutalik, Z. & Bergmann, S. Fast and rigorous computation of gene and pathway scores from SNP-based summary statistics. PLOS Comput. Biol. 12, e1004714 (2016).
Ayellet, V. S., Groop, L., Mootha, V. K., Daly, M. J. & Altshuler, D. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 6, e1001058 (2010).
Lee, P. H., O’dushlaine, C., Thomas, B. & Purcell, S. M. INRICH: Interval-based enrichment analysis for genome-wide association studies. Bioinformatics 28, 1797–1799 (2012).
Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).
Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 27, 2336–2337 (2011).
Cuellar-Partida, G., Renteria, M. E. & MacGregor, S. LocusTrack: integrated visualization of GWAS results and genomic annotation. Source Code Biol. Med. 10, 1 (2015).
Wang, Y. et al. The 3D genome browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions. Preprint at http://biorxiv.org/content/early/2017/02/27/112268 (2017).
Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).
Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).
Sato, M., Kawakami, T., Kadota, Y., Mori, M. & Suzuki, S. Obesity and metallothionein. Curr. Pharm. Biotechnol. 14, 432–440 (2013).
Raza, S. et al. Association of glutathione-S-transferase (GSTM1 and GSTT1) and FTO gene polymorphisms with type 2 diabetes mellitus cases in Northern India. Balkan J. Med. Genet. 17, 47–54 (2014).
Franke, A. et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nat. Genet. 42, 1118–1125 (2010).
Turner, M. D., Nedjai, B., Hurst, T. & Pennington, D. J. Cytokines and chemokines: at the crossroads of cell signalling and in fl ammatory disease. Biochim. Biophys. Acta 1843, 2563–2582 (2014).
Braat, H., Peppelenbosch, M. P. & Hommes, D. W. Immunology of Crohn’s disease. Ann. N. Y. Acad. Sci. 1072, 135–154 (2006).
Gusev, A.. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Preprint at http://biorxiv.org/content/early/2016/08/02/067355 (2016).
Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).
Martin, L. F. & Freedman, R. Schisophrenia and the alpha 7 nicotinic acetylcholine receptor. Int. Rev. Neurobiol. 78, 225–246 (2007).
Freedman, R. α 7-Nicotinic acetylcholine receptor agonists for cognitive enhancement in schizophrenia. Annu. Rev. Med. 65, 245–261 (2014).
Mifsud, B. et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat. Genet. 47, 598–606 (2015).
Martin, P. et al. Capture Hi-C reveals novel candidate genes and complex long-range interactions with related autoimmune risk loci. Nat. Commun. 6, 10069 (2015).
Mcgovern, A. et al. Capture Hi-C identifies a novel causal gene, IL20RA, in the pan-autoimmune genetic susceptibility region 6q23. Genome Biol. 17, 212 (2016).
Promoters, T. G. et al. Lineage-specific genome architecture lnks enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384 (2016).
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).