Abstract
The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.
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References
The Genomes Project, C (2015) A global reference for human genetic variation. Nature 526:68–74
Visscher PM, Brown MA, McCarthy MI, Yang J (2012) Five years of GWAS discovery. Am J Hum Genet 90:7–24
Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci 106:9362–9367
Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, Klemm A, Flicek P, Manolio T, Hindorff L et al (2014) The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42:D1001–D1006
Brem RB, Yvert G, Clinton R, Kruglyak L (2002) Genetic dissection of transcriptional regulation in budding yeast. Science 296:752–755
Cheung VG, Spielman RS (2002) The genetics of variation in gene expression. Nat Genet 32(Suppl):522–525
Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N, Colinayo V, Ruff TG, Milligan SB, Lamb JR, Cavet G et al (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature 422:297–302
Bystrykh L, Weersing E, Dontje B, Sutton S, Pletcher MT, Wiltshire T, Su AI, Vellenga E, Wang J, Manly KF et al (2005) Uncovering regulatory pathways that affect hematopoietic stem cell function using ‘genetical genomics’. Nat Genet 37:225–232
Cheung VG, Spielman RS, Ewens KG, Weber TM, Morley M, Burdick JT (2005) Mapping determinants of human gene expression by regional and genome-wide association. Nature 437:1365–1369
Hubner N, Wallace CA, Zimdahl H, Petretto E, Schulz H, Maciver F, Mueller M, Hummel O, Monti J, Zidek V et al (2005) Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet 37:243–253
Mehrabian M, Allayee H, Stockton J, Lum PY, Drake TA, Castellani LW, Suh M, Armour C, Edwards S, Lamb J et al (2005) Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits. Nat Genet 37:1224–1233
Schadt EE, Lamb J, Yang X, Zhu J, Edwards S, Guhathakurta D, Sieberts SK, Monks S, Reitman M, Zhang C et al (2005) An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet 37:710–717
Li H, Lu L, Manly KF, Chesler EJ, Bao L, Wang J, Zhou M, Williams RW, Cui Y (2005) Inferring gene transcriptional modulatory relations: a genetical genomics approach. Hum Mol Genet 14:1119–1125
Bao L, Wei L, Peirce J, Homayouni R, Li H, Zhou M, Chen H, Lu L, Williams R, Pfeffer L et al (2006) Combining gene expression QTL mapping and phenotypic spectrum analysis to uncover gene regulatory relations. Mamm Genome 17:575–583
Li H, Chen H, Bao L, Manly KF, Chesler EJ, Lu L, Wang J, Zhou M, Williams RW, Cui Y (2006) Integrative genetic analysis of transcription modules: towards filling the gap between genetic loci and inherited traits. Hum Mol Genet 15:481–492
Bao L, Peirce JL, Zhou M, Li H, Goldowitz D, Williams RW, Lu L, Cui Y (2007) An integrative genomics strategy for systematic characterization of genetic loci modulating phenotypes. Hum Mol Genet 16:1381–1390
Alberts R, Lu L, Williams R, Schughart K (2011) Genome-wide analysis of the mouse lung transcriptome reveals novel molecular gene interaction networks and cell-specific expression signatures. Respir Res 12:61
MacLellan WR, Wang Y, Lusis AJ (2012) Systems-based approaches to cardiovascular disease. Nat Rev Cardiol 9:172–184
Kadarmideen HN, Von Rohr P, Janss LLG (2006) From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding. Mamm Genome 17:548–564
Sieberts SK, Schadt EE (2007) Moving toward a system genetics view of disease. Mamm Genome 18:389–401
Jansen RC, Nap JP (2001) Genetical genomics: the added value from segregation. Trends Genet 17:388–391
Ha T, Swanson D, Larouche M, Glenn R, Weeden D, Zhang P, Hamre K, Langston M, Phillips C, Song M et al (2015) CbGRiTS: cerebellar gene regulation in time and space. Dev Biol 397:18–30
Mulligan MK, Williams RW (2015) Systems genetics of behavior: a prelude. Curr Opin Behav Sci 2:108–115
van der Sijde MR, Ng A, Fu J (2014) Systems genetics: from GWAS to disease pathways. Biochim Biophys Acta 1842:1903–1909
Civelek M, Lusis AJ (2014) Systems genetics approaches to understand complex traits. Nat Rev Genet 15:34–48
Li Q, Seo J-H, Stranger B, McKenna A, Pe’er I, LaFramboise T, Brown M, Tyekucheva S, Freedman ML (2013) Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell 152:633–641
Li Q, Stram A, Chen C, Kar S, Gayther S, Pharoah P, Haiman C, Stranger B, Kraft P, Freedman ML (2014) Expression QTL-based analyses reveal candidate causal genes and loci across five tumor types. Hum Mol Genet 23:5294–5302
Faraji F, Hu Y, Wu G, Goldberger NE, Walker RC, Zhang J, Hunter KW (2014) An integrated systems genetics screen reveals the transcriptional structure of inherited predisposition to metastatic disease. Genome Res 24:227–240
Kogelman LJA, Zhernakova DV, Westra HJ, Cirera S, Fredholm M, Franke L, Kadarmideen HN (2015) An integrative systems genetics approach reveals potential causal genes and pathways related to obesity. Genome Med 7:1–15
Dobrin R, Zhu J, Molony C, Argman C, Parrish M, Carlson S, Allan M, Pomp D, Schadt E (2009) Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease. Genome Biol 10:R55
Ghosh S, Vivar J, Nelson CP, Willenborg C, Segrè AV, Mäkinen VP, Nikpay M, Erdmann J, Blankenberg S, O'Donnell C et al (2015) Systems genetics analysis of genome-wide association study reveals novel associations between key biological processes and coronary artery disease. Arterioscler Thromb Vasc Biol 35:1712–1722
Lusis AJ, Weiss JN (2010) Cardiovascular networks: systems-based approaches to cardiovascular disease. Circulation 121:157–170
Andreux PA, Williams EG, Koutnikova H, Houtkooper RH, Champy MF, Henry H, Schoonjans K, Williams RW, Auwerx J (2012) Systems genetics of metabolism: the use of the BXD murine reference panel for multiscalar integration of traits. Cell 150:1287–1299
Taneera J, Lang S, Sharma A, Fadista J, Zhou Y, Ahlqvist E, Jonsson A, Lyssenko V, Vikman P, Hansson O et al (2012) A systems genetics approach identifies genes and pathways for type 2 diabetes in human islets. Cell Metab 16:122–134
Ziebarth JD, Cook MN, Wang X, Williams RW, Lu L, Cui Y (2012) Treatment- and population-dependent activity patterns of behavioral and expression QTLs. PLoS One 7, e31805
Palmer RHC, McGeary JE, Francazio S, Raphael BJ, Lander AD, Heath AC, Knopik VS (2012) The genetics of alcohol dependence: advancing towards systems-based approaches. Drug Alcohol Depend 125:179–191
Ziebarth JD, Cook MN, Li B, Williams RW, Lu L, Cui Y (2010) Biomedical sciences and engineering conference (BSEC), 2010. IEEE 2010:1–4
Kollmus H, Wilk E, Schughart K (2014) Systems biology and systems genetics – novel innovative approaches to study host-pathogen interactions during influenza infection. Curr Opin Virol 6:47–54
Miyairi I, Ziebarth J, Laxton JD, Wang X, van Rooijen N, Williams RW, Lu L, Byrne GI, Cui Y (2012) Host genetics and chlamydia disease: prediction and validation of disease severity mechanisms. PLoS One 7, e33781
Emery FD, Parvathareddy J, Pandey AK, Cui Y, Williams RW, Miller MA (2014) Genetic control of weight loss during pneumonic Burkholderia pseudomallei infection. Pathog Dis 71:249–264
Ziebarth JD, Bhattacharya A, Cui Y (2013) Bayesian Network Webserver: a comprehensive tool for biological network modeling. Bioinformatics 29:2801–2803
Pearl J (2000) Causality: models, reasoning, and inference. Cambridge University Press, Cambridge
Cui Y (2007) In: Deng HW (ed) Current topics in human genetics: studies of complex diseases. World Scientific, Singapore, pp 433–448
Cui Y (2006) In: Shannon F, Rao S (eds) Microarrays and transcription networks. Landes Bioscience, Georgetown, KY, pp 114–126
Tasaki S, Sauerwine B, Hoff B, Toyoshiba H, Gaiteri C, Chaibub Neto E (2015) Bayesian network reconstruction using systems genetics data: comparison of MCMC methods., Genetics
Shipley B (2000) Cause and correlation in biology. Cambridge University Press, Cambridge
Bøttcher SG, Dethlefsen C (2003) Deal: a package for learning bayesian networks. J Stat Softw 8:1–19
Tian J, He R, Ram L (2010) Bayesian model averaging using the k-best Bayesian network structures. Proc Conf Uncertain Artif Intel 2010:589–597
Bolouri H, Davidson EH (2002) Modeling transcriptional regulatory networks. Bioessays 24:1118–1129
Davidson EH (2010) Emerging properties of animal gene regulatory networks. Nature 468:911–920
Mitra K, Carvunis A-R, Ramesh SK, Ideker T (2013) Integrative approaches for finding modular structure in biological networks. Nat Rev Genet 14:719–732
Aittokallio T, Schwikowski B (2006) Graph-based methods for analysing networks in cell biology. Brief Bioinform 7:243–255
Bao L, Xia X, Cui Y (2010) Expression QTL modules as functional components underlying higher-order phenotypes. PLoS One 5, e14313
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Ziebarth, J.D., Cui, Y. (2017). Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver. In: Schughart, K., Williams, R. (eds) Systems Genetics. Methods in Molecular Biology, vol 1488. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6427-7_15
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DOI: https://doi.org/10.1007/978-1-4939-6427-7_15
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