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Current Genomics

Editor-in-Chief

ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

Review Article

Available Software for Meta-Analyses of Genome-Wide Expression Studies

Author(s): Diego A. Forero*

Volume 20, Issue 5, 2019

Page: [325 - 331] Pages: 7

DOI: 10.2174/1389202920666190822113912

Price: $65

Abstract

Advances in transcriptomic methods have led to a large number of published Genome- Wide Expression Studies (GWES), in humans and model organisms. For several years, GWES involved the use of microarray platforms to compare genome-expression data for two or more groups of samples of interest. Meta-analysis of GWES is a powerful approach for the identification of differentially expressed genes in biological topics or diseases of interest, combining information from multiple primary studies. In this article, the main features of available software for carrying out meta-analysis of GWES have been reviewed and seven packages from the Bioconductor platform and five packages from the CRAN platform have been described. In addition, nine previously described programs and four online programs are reviewed. Finally, advantages and disadvantages of these available programs and proposed key points for future developments have been discussed.

Keywords: Genomics, transcriptomics, bioinformatics, meta-analysis, genome-wide expression, microarray experiment.

Graphical Abstract
[1]
Athar, A.; Füllgrabe, A.; George, N.; Iqbal, H.; Huerta, L.; Ali, A.; Snow, C.; Fonseca, N.A.; Petryszak, R.; Papatheodorou, I.; Sarkans, U.; Brazma, A. ArrayExpress update - from bulk to single-cell expression data. Nucleic Acids Res., 2019, 47(D1), D711-D715.
[http://dx.doi.org/10.1093/nar/gky964] [PMID: 30357387]
[2]
Brazma, A.; Hingamp, P.; Quackenbush, J.; Sherlock, G.; Spellman, P.; Stoeckert, C.; Aach, J.; Ansorge, W.; Ball, C.A.; Causton, H.C.; Gaasterland, T.; Glenisson, P.; Holstege, F.C.; Kim, I.F.; Markowitz, V.; Matese, J.C.; Parkinson, H.; Robinson, A.; Sarkans, U.; Schulze-Kremer, S.; Stewart, J.; Taylor, R.; Vilo, J.; Vingron, M. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat. Genet., 2001, 29(4), 365-371.
[http://dx.doi.org/10.1038/ng1201-365] [PMID: 11726920]
[3]
Ramasamy, A.; Mondry, A.; Holmes, C.C.; Altman, D.G. Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med., 2008, 5(9)e184
[http://dx.doi.org/10.1371/journal.pmed.0050184] [PMID: 18767902]
[4]
Irizarry, R.A.; Warren, D.; Spencer, F.; Kim, I.F.; Biswal, S.; Frank, B.C.; Gabrielson, E.; Garcia, J.G.; Geoghegan, J.; Germino, G.; Griffin, C.; Hilmer, S.C.; Hoffman, E.; Jedlicka, A.E.; Kawasaki, E.; Martínez-Murillo, F.; Morsberger, L.; Lee, H.; Petersen, D.; Quackenbush, J.; Scott, A.; Wilson, M.; Yang, Y.; Ye, S.Q.; Yu, W. Multiple-laboratory comparison of microarray platforms. Nat. Methods, 2005, 2(5), 345-350.
[http://dx.doi.org/10.1038/nmeth756] [PMID: 15846361]
[5]
Allison, D.B.; Cui, X.; Page, G.P.; Sabripour, M. Microarray data analysis: From disarray to consolidation and consensus. Nat. Rev. Genet., 2006, 7(1), 55-65.
[http://dx.doi.org/10.1038/nrg1749] [PMID: 16369572]
[6]
Hrdlickova, R.; Toloue, M.; Tian, B. RNA-Seq methods for transcriptome analysis. Wiley Interdiscip. Rev. RNA, 2017, 8(1), 8.
[http://dx.doi.org/10.1002/wrna.1364] [PMID: 27198714]
[7]
Masum, H.; Rao, A.; Good, B.M.; Todd, M.H.; Edwards, A.M.; Chan, L.; Bunin, B.A.; Su, A.I.; Thomas, Z.; Bourne, P.E. Ten simple rules for cultivating open science and collaborative R&D. PLOS Comput. Biol., 2013, 9(9)e1003244
[http://dx.doi.org/10.1371/journal.pcbi.1003244] [PMID: 24086123]
[8]
Bero, L. Meta-research matters: Meta-spin cycles, the blindness of bias, and rebuilding trust. PLoS Biol., 2018, 16(4)e2005972
[http://dx.doi.org/10.1371/journal.pbio.2005972] [PMID: 29608562]
[9]
Ioannidis, J.P.; Allison, D.B.; Ball, C.A.; Coulibaly, I.; Cui, X.; Culhane, A.C.; Falchi, M.; Furlanello, C.; Game, L.; Jurman, G.; Mangion, J.; Mehta, T.; Nitzberg, M.; Page, G.P.; Petretto, E.; van Noort, V. Repeatability of published microarray gene expression analyses. Nat. Genet., 2009, 41(2), 149-155.
[http://dx.doi.org/10.1038/ng.295] [PMID: 19174838]
[10]
Barrett, T.; Wilhite, S.E.; Ledoux, P.; Evangelista, C.; Kim, I.F.; Tomashevsky, M.; Marshall, K.A.; Phillippy, K.H.; Sherman, P.M.; Holko, M.; Yefanov, A.; Lee, H.; Zhang, N.; Robertson, C.L.; Serova, N.; Davis, S.; Soboleva, A. NCBI GEO: Archive for functional genomics data sets update. Nucleic Acids Res., 2013, 41(Database issue), D991-D995.
[PMID: 23193258]
[11]
Sayers, E.W.; Agarwala, R.; Bolton, E.E.; Brister, J.R.; Canese, K.; Clark, K.; Connor, R.; Fiorini, N.; Funk, K.; Hefferon, T.; Holmes, J.B.; Kim, S.; Kimchi, A.; Kitts, P.A.; Lathrop, S.; Lu, Z.; Madden, T.L.; Marchler-Bauer, A.; Phan, L.; Schneider, V.A.; Schoch, C.L.; Pruitt, K.D.; Ostell, J. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res., 2019, 47(D1), D23-D28.
[http://dx.doi.org/10.1093/nar/gky1069] [PMID: 30395293]
[12]
Davis, S.; Meltzer, P.S. GEO query: A bridge between the Gene Expression Omnibus (GEO) and Bio conductor. Bioinformatics, 2007, 23(14), 1846-1847.
[http://dx.doi.org/10.1093/bioinformatics/btm254] [PMID: 17496320]
[13]
Dumas, J.; Gargano, M.A.; Dancik, G.M. Shiny GEO: A web-based application for analyzing gene expression omnibus datasets. Bioinformatics, 2016, 32(23), 3679-3681.
[http://dx.doi.org/10.1093/bioinformatics/btw519] [PMID: 27503226]
[14]
Alonso, R.; Salavert, F.; Garcia-Garcia, F.; Carbonell-Caballero, J.; Bleda, M.; Garcia-Alonso, L.; Sanchis-Juan, A.; Perez-Gil, D.; Marin-Garcia, P.; Sanchez, R.; Cubuk, C.; Hidalgo, M.R.; Amadoz, A.; Hernansaiz-Ballesteros, R.D.; Alemán, A.; Tarraga, J.; Montaner, D.; Medina, I.; Dopazo, J. Babelomics 5.0: Functional interpretation for new generations of genomic data. Nucleic Acids Res., 2015, 43(W1)W117-21
[http://dx.doi.org/10.1093/nar/gkv384] [PMID: 25897133]
[15]
Walsh, C.J.; Hu, P.; Batt, J.; Santos, C.C. Microarray meta-analysis and cross-platform normalization: Integrative genomics for robust biomarker discovery. Microarrays (Basel), 2015, 4(3), 389-406.
[http://dx.doi.org/10.3390/microarrays4030389] [PMID: 27600230]
[16]
Kontou, P.I.; Pavlopoulou, A.; Bagos, P.G. Methods of analysis and meta-analysis for identifying differentially expressed genes. Methods Mol. Biol., 2018, 1793, 183-210.
[http://dx.doi.org/10.1007/978-1-4939-7868-7_12] [PMID: 29876898]
[17]
Waldron, L.; Riester, M. Meta-analysis in gene expression studies. Methods Mol. Biol., 2016, 1418, 161-176.
[http://dx.doi.org/10.1007/978-1-4939-3578-9_8] [PMID: 27008014]
[18]
Forero, D.A.; Lopez-Leon, S.; González-Giraldo, Y.; Bagos, P.G. Ten simple rules for carrying out and writing meta-analyses. PLOS Comput. Biol., 2019, 15(5)e1006922
[http://dx.doi.org/10.1371/journal.pcbi.1006922] [PMID: 31095553]
[19]
Chang, L.C.; Lin, H.M.; Sibille, E.; Tseng, G.C. Meta-analysis methods for combining multiple expression profiles: Comparisons, statistical characterization and an application guideline. BMC Bioinformatics, 2013, 14, 368.
[http://dx.doi.org/10.1186/1471-2105-14-368] [PMID: 24359104]
[20]
Hong, F.; Breitling, R.; McEntee, C.W.; Wittner, B.S.; Nemhauser, J.L.; Chory, J. Rank Prod: A bio conductor package for detecting differentially expressed genes in meta-analysis. Bioinformatics, 2006, 22(22), 2825-2827.
[http://dx.doi.org/10.1093/bioinformatics/btl476] [PMID: 16982708]
[21]
Lottaz, C.; Yang, X.; Scheid, S.; Spang, R. Ordered list: A bio conductor package for detecting similarity in ordered gene lists. Bioinformatics, 2006, 22(18), 2315-2316.
[http://dx.doi.org/10.1093/bioinformatics/btl385] [PMID: 16844712]
[22]
Stevens, J.R.; Nicholas, G. metahdep: Meta-analysis of hierarchically dependent gene expression studies. Bioinformatics, 2009, 25(19), 2619-2620.
[http://dx.doi.org/10.1093/bioinformatics/btp468] [PMID: 19648140]
[23]
Zhou, G.; Soufan, O.; Ewald, J.; Hancock, R.E.W.; Basu, N.; Xia, J. Network analyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res., 2019, 47(W1), W234-W241.
[http://dx.doi.org/10.1093/nar/gkz240] [PMID: 30931480]
[24]
Tusher, V.G.; Tibshirani, R.; Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA, 2001, 98(9), 5116-5121.
[http://dx.doi.org/10.1073/pnas.091062498] [PMID: 11309499]
[25]
Falagas, M.E.; Pitsouni, E.I.; Malietzis, G.A.; Pappas, G. Comparison of PubMed, scopus, web of science and google scholar: Strengths and weaknesses. FASEB J., 2008, 22(2), 338-342.
[http://dx.doi.org/10.1096/fj.07-9492LSF] [PMID: 17884971]
[26]
Gentleman, R.C.; Carey, V.J.; Bates, D.M.; Bolstad, B.; Dettling, M.; Dudoit, S.; Ellis, B.; Gautier, L.; Ge, Y.; Gentry, J.; Hornik, K.; Hothorn, T.; Huber, W.; Iacus, S.; Irizarry, R.; Leisch, F.; Li, C.; Maechler, M.; Rossini, A.J.; Sawitzki, G.; Smith, C.; Smyth, G.; Tierney, L.; Yang, J.Y.; Zhang, J. Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol., 2004, 5(10), R80.
[http://dx.doi.org/10.1186/gb-2004-5-10-r80] [PMID: 15461798]
[27]
Breitling, R.; Armengaud, P.; Amtmann, A.; Herzyk, P. Rank products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett., 2004, 573(1-3), 83-92.
[http://dx.doi.org/10.1016/j.febslet.2004.07.055] [PMID: 15327980]
[28]
Del Carratore, F.; Jankevics, A.; Eisinga, R.; Heskes, T.; Hong, F.; Breitling, R. Rank Prod 2.0: A refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets. Bioinformatics, 2017, 33(17), 2774-2775.
[http://dx.doi.org/10.1093/bioinformatics/btx292] [PMID: 28481966]
[29]
Choi, J.K.; Yu, U.; Kim, S.; Yoo, O.J. Combining multiple microarray studies and modeling inter study variation. Bioinformatics, 2003, 19(Suppl. 1), i84-i90.
[http://dx.doi.org/10.1093/bioinformatics/btg1010] [PMID: 12855442]
[30]
Choi, H.; Shen, R.; Chinnaiyan, A.M.; Ghosh, D. A latent variable approach for meta-analysis of gene expression data from multiple microarray experiments. BMC Bioinformatics, 2007, 8, 364.
[http://dx.doi.org/10.1186/1471-2105-8-364] [PMID: 17900369]
[31]
Tarazona, S.; García-Alcalde, F.; Dopazo, J.; Ferrer, A.; Conesa, A. Differential expression in RNA-seq: A matter of depth. Genome Res., 2011, 21(12), 2213-2223.
[http://dx.doi.org/10.1101/gr.124321.111] [PMID: 21903743]
[32]
Polanin, J.R.; Hennessy, E.A.; Tanner-Smith, E.E. A review of meta-analysis packages in R. J. Educ. Behav. Stat., 2017, 42, 206-242.
[http://dx.doi.org/10.3102/1076998616674315]
[33]
Pihur, V.; Datta, S.; Datta, S. Rank Aggreg, an R package for weighted rank aggregation. BMC Bioinformatics, 2009, 10, 62.
[http://dx.doi.org/10.1186/1471-2105-10-62] [PMID: 19228411]
[34]
Marot, G.; Foulley, J.L.; Mayer, C.D.; Jaffrézic, F. Moderated effect size and p-value combinations for microarray meta-analyses. Bioinformatics, 2009, 25(20), 2692-2699.
[http://dx.doi.org/10.1093/bioinformatics/btp444] [PMID: 19628502]
[35]
Shen, K.; Tseng, G.C. Meta-analysis for pathway enrichment analysis when combining multiple genomic studies. Bioinformatics, 2010, 26(10), 1316-1323.
[http://dx.doi.org/10.1093/bioinformatics/btq148] [PMID: 20410053]
[36]
Rau, A.; Marot, G.; Jaffrézic, F. Differential meta-analysis of RNA-seq data from multiple studies. BMC Bioinformatics, 2014, 15, 91.
[http://dx.doi.org/10.1186/1471-2105-15-91] [PMID: 24678608]
[37]
Haynes, W.A.; Vallania, F.; Liu, C.; Bongen, E.; Tomczak, A.; Andres-Terrè, M.; Lofgren, S.; Tam, A.; Deisseroth, C.A.; Li, M.D.; Sweeney, T.E.; Khatri, P. Empowering multi-cohort gene expression analysis to increase reproducibility. Pac. Symp. Biocomput., 2017, 22, 144-153.
[http://dx.doi.org/10.1142/9789813207813_0015] [PMID: 27896970]
[38]
Bisognin, A.; Coppe, A.; Ferrari, F.; Risso, D.; Romualdi, C.; Bicciato, S.; Bortoluzzi, S. A-MADMAN: Annotation-based microarray data meta-analysis tool. BMC Bioinformatics, 2009, 10, 201.
[http://dx.doi.org/10.1186/1471-2105-10-201] [PMID: 19563634]
[39]
Conlon, E.M.; Song, J.J.; Liu, J.S. Bayesian models for pooling microarray studies with multiple sources of replications. BMC Bioinformatics, 2006, 7, 247.
[http://dx.doi.org/10.1186/1471-2105-7-247] [PMID: 16677390]
[40]
Rajaram, S. A novel meta-analysis method exploiting consistency of high-throughput experiments. Bioinformatics, 2009, 25(5), 636-642.
[http://dx.doi.org/10.1093/bioinformatics/btp007] [PMID: 19176547]
[41]
Gan, Z.; Wang, J.; Salomonis, N.; Stowe, J.C.; Haddad, G.G.; McCulloch, A.D.; Altintas, I.; Zambon, A.C. MAAMD: A work flow to standardize meta-analyses and comparison of affy-metrix microarray data. BMC Bioinformatics, 2014, 15, 69.
[http://dx.doi.org/10.1186/1471-2105-15-69] [PMID: 24621103]
[42]
Emig, D.; Salomonis, N.; Baumbach, J.; Lengauer, T.; Conklin, B.R.; Albrecht, M. AltAnalyze and DomainGraph: Analyzing and visualizing exon expression data. Nucleic Acids Res., 2010, 38(Web Server issue), W755-W762.
[http://dx.doi.org/10.1093/nar/gkq405] [PMID: 20513647]
[43]
Borozan, I.; Chen, L.; Paeper, B.; Heathcote, J.E.; Edwards, A.M.; Katze, M.; Zhang, Z.; McGilvray, I.D. MAID: An effect size based model for microarray data integration across laboratories and platforms. BMC Bioinformatics, 2008, 9, 305.
[http://dx.doi.org/10.1186/1471-2105-9-305] [PMID: 18616827]
[44]
Ma, T.; Huo, Z.; Kuo, A.; Zhu, L.; Fang, Z.; Zeng, X.; Lin, C.W.; Liu, S.; Wang, L.; Liu, P.; Rahman, T.; Chang, L.C.; Kim, S.; Li, J.; Park, Y.; Song, C.; Oesterreich, S.; Sibille, E.; Tseng, G.C. MetaOmics: Analysis pipeline and browser-based software suite for transcriptomic meta-analysis. Bioinformatics, 2018, 35(9), 1597-1599.
[PMID: 30304367]
[45]
Zintzaras, E.; Ioannidis, J.P. Meta-analysis for ranked discovery datasets: Theoretical framework and empirical demonstration for microarrays. Comput. Biol. Chem., 2008, 32(1), 38-46.
[http://dx.doi.org/10.1016/j.compbiolchem.2007.09.003] [PMID: 17988949]
[46]
Ma, S.; Huang, J. Regularized gene selection in cancer microarray meta-analysis. BMC Bioinformatics, 2009, 10, 1.
[http://dx.doi.org/10.1186/1471-2105-10-1] [PMID: 19118496]
[47]
Xia, J.; Fjell, C.D.; Mayer, M.L.; Pena, O.M.; Wishart, D.S.; Hancock, R.E. INMEX--a web-based tool for integrative meta-analysis of expression data. Nucleic Acids Res., 2013, 41(Web Server issue), W63-W70.
[http://dx.doi.org/10.1093/nar/gkt338] [PMID: 23766290]
[48]
Xia, J.; Benner, M.J.; Hancock, R.E. NetworkAnalyst--integrative approaches for protein-protein interaction network analysis and visual exploration. Nucleic Acids Res., 2014, 42, W167-W174.
[http://dx.doi.org/10.1093/nar/gku443]
[49]
Xia, J.; Gill, E.E.; Hancock, R.E. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat. Protoc., 2015, 10(6), 823-844.
[http://dx.doi.org/10.1038/nprot.2015.052] [PMID: 25950236]
[50]
Sharov, A.A.; Schlessinger, D.; Ko, M.S. ExAtlas: An interactive online tool for meta-analysis of gene expression data. J. Bioinform. Comput. Biol., 2015, 13(6)1550019
[http://dx.doi.org/10.1142/S0219720015500195] [PMID: 26223199]
[51]
Blanck, S.; Marot, G. SMAGEXP: A galaxy tool suite for transcriptomics data meta-analysis. Gigascience, 2019, 8(2), 8.
[http://dx.doi.org/10.1093/gigascience/giy167] [PMID: 30698691]
[52]
Goecks, J.; Nekrutenko, A.; Taylor, J.; Galaxy, T. Galaxy: A comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol., 2010, 11(8), R86.
[http://dx.doi.org/10.1186/gb-2010-11-8-r86] [PMID: 20738864]
[53]
Hillman-Jackson, J.; Clements, D.; Blankenberg, D.; Taylor, J.; Nekrutenko, A.; Galaxy, T. Using galaxy to perform large-scale interactive data analyses. In: Curr. Protoc. Bioinformatics ; , 2012. Unit10.5.
[54]
Wang, Q.; Li, W.X.; Dai, S.X.; Guo, Y.C.; Han, F.F.; Zheng, J.J.; Li, G.H.; Huang, J.F. Meta-analysis of parkinson’s disease and alzheimer’s disease revealed commonly impaired pathways and dysregulation of NRF2-dependent genes. J. Alzheimers Dis., 2017, 56(4), 1525-1539.
[http://dx.doi.org/10.3233/JAD-161032] [PMID: 28222515]
[55]
Jha, P.K.; Vijay, A.; Sahu, A.; Ashraf, M.Z. Comprehensive Gene expression meta-analysis and integrated bioinformatic approaches reveal shared signatures between thrombosis and myeloproliferative disorders. Sci. Rep., 2016, 6, 37099.
[http://dx.doi.org/10.1038/srep37099] [PMID: 27892526]
[56]
Piras, I.S.; Manchia, M.; Huentelman, M.J.; Pinna, F.; Zai, C.C.; Kennedy, J.L.; Carpiniello, B. Peripheral biomarkers in schizophrenia: A meta-analysis of microarray gene expression datasets. Int. J. Neuropsychopharmacol., 2019, 22(3), 186-193.
[http://dx.doi.org/10.1093/ijnp/pyy103] [PMID: 30576541]
[57]
Forero, D.A.; Guio-Vega, G.P.; González-Giraldo, Y. A comprehensive regional analysis of genome-wide expression profiles for major depressive disorder. J. Affect. Disord., 2017, 218, 86-92.
[http://dx.doi.org/10.1016/j.jad.2017.04.061] [PMID: 28460316]
[58]
Manchia, M.; Piras, I.S.; Huentelman, M.J.; Pinna, F.; Zai, C.C.; Kennedy, J.L.; Carpiniello, B. Pattern of gene expression in different stages of schizophrenia: Down-regulation of NPTX2 gene revealed by a meta-analysis of microarray datasets. Eur. Neuropsychopharmacol., 2017, 27(10), 1054-1063.
[http://dx.doi.org/10.1016/j.euroneuro.2017.07.002] [PMID: 28732597]
[59]
Conesa, A.; Madrigal, P.; Tarazona, S.; Gomez-Cabrero, D.; Cervera, A.; McPherson, A.; Szcześniak, M.W.; Gaffney, D.J.; Elo, L.L.; Zhang, X.; Mortazavi, A. A survey of best practices for RNA-seq data analysis. Genome Biol., 2016, 17, 13.
[http://dx.doi.org/10.1186/s13059-016-0881-8] [PMID: 26813401]
[60]
Brazma, A. Minimum Information About a Microarray Experiment (MIAME)--successes, failures, challenges. ScientificWorldJournal, 2009, 9, 420-423.
[http://dx.doi.org/10.1100/tsw.2009.57] [PMID: 19484163]
[61]
Taschuk, M.; Wilson, G. Ten simple rules for making research software more robust. PLOS Comput. Biol., 2017, 13(4)e1005412
[http://dx.doi.org/10.1371/journal.pcbi.1005412] [PMID: 28407023]
[62]
Wang, N.; Zhang, Y.; Xu, L.; Jin, S. Relationship between alzheimer’s disease and the immune system: A meta-analysis of differentially expressed genes. Front. Neurosci., 2019, 12, 1026.
[http://dx.doi.org/10.3389/fnins.2018.01026] [PMID: 30705616]
[63]
Naz, S.; Khan, R.A.; Giddaluru, J.; Battu, S.; Vishwakarma, S.K.; Subahan, M.; Satti, V.; Khan, N.; Khan, A.A. Transcriptome meta-analysis identifies immune signature comprising of RNA binding proteins in ulcerative colitis patients. Cell. Immunol., 2018, 334, 42-48.
[http://dx.doi.org/10.1016/j.cellimm.2018.09.003] [PMID: 30327138]
[64]
Li, M.D.; Burns, T.C.; Morgan, A.A.; Khatri, P. Integrated multi-cohort transcriptional meta-analysis of neurodegenerative diseases. Acta Neuropathol. Commun., 2014, 2, 93.
[http://dx.doi.org/10.1186/s40478-014-0093-y] [PMID: 25187168]
[65]
Ratanatharathorn, A.; Boks, M.P.; Maihofer, A.X.; Aiello, A.E.; Amstadter, A.B.; Ashley-Koch, A.E.; Baker, D.G.; Beckham, J.C.; Bromet, E.; Dennis, M.; Garrett, M.E.; Geuze, E.; Guffanti, G.; Hauser, M.A.; Kilaru, V.; Kimbrel, N.A.; Koenen, K.C.; Kuan, P.F.; Logue, M.W.; Luft, B.J.; Miller, M.W.; Mitchell, C.; Nugent, N.R.; Ressler, K.J.; Rutten, B.P.F.; Stein, M.B.; Vermetten, E.; Vinkers, C.H.; Youssef, N.A.; Uddin, M.; Nievergelt, C.M.; Smith, A.K.; Nievergelt, C.M.; Smith, A.K. Epigenome-wide association of PTSD from heterogeneous cohorts with a common multi-site analysis pipeline. Am. J. Med. Genet. B. Neuropsychiatr. Genet., 2017, 174(6), 619-630.
[http://dx.doi.org/10.1002/ajmg.b.32568] [PMID: 28691784]
[66]
Ramanan, V.K.; Shen, L.; Moore, J.H.; Saykin, A.J. Pathway analysis of genomic data: Concepts, methods, and prospects for future development. Trends Genet., 2012, 28(7), 323-332.
[http://dx.doi.org/10.1016/j.tig.2012.03.004] [PMID: 22480918]
[67]
Tranchevent, L.C.; Capdevila, F.B.; Nitsch, D.; De Moor, B.; De Causmaecker, P.; Moreau, Y. A guide to web tools to prioritize candidate genes. Brief. Bioinform., 2011, 12(1), 22-32.
[http://dx.doi.org/10.1093/bib/bbq007] [PMID: 21278374]
[68]
Evangelou, E.; Ioannidis, J.P. Meta-analysis methods for genome-wide association studies and beyond. Nat. Rev. Genet., 2013, 14(6), 379-389.
[http://dx.doi.org/10.1038/nrg3472] [PMID: 23657481]
[69]
Huang, W.; Sherman, B.T.; Lempicki, R.A. Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res., 2009, 37(1), 1-13.
[http://dx.doi.org/10.1093/nar/gkn923] [PMID: 19033363]
[70]
Guio-Vega, G.P.; Forero, D.A. Functional genomics of candidate genes derived from genome-wide association studies for five common neurological diseases. Int. J. Neurosci., 2017, 127(2), 118-123.
[http://dx.doi.org/10.3109/00207454.2016.1149172] [PMID: 26829381]

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