Abstract
Networks are central to turning the colossal amount of information generated by high-throughput genetic technology into manageable sources of knowledge. They are an intuitive way of representing interaction data, yet they offer a full set of sophisticated quantitative tools to analyze the phenomena they model. When combining genetic information, diseases, and phenotypic traits, networks can reveal and facilitate the analysis of pleiotropic and epistatic effects at the genome-wide scale. Genome-wide association study data is publicly available, and so are gene and pathway databases, and many more, making the global overview next to impossible. Networks allow information from these multiple sources to be encompassed. We use connections between the strata of the network to characterize pleiotropy and epistasis effects taking place between traits and biological pathways. The global graph-theory-based quantitative methods reveal that levels of pleiotropy and epistasis are in-line with theoretical expectations. The results of the magnified “glaucoma” region of the network confirm the existence of well-documented interactions, supported by overlapping genes and biological pathways and more obscure associations. They have the potential to generate new hypotheses for yet uncharacterized interactions. As the amount and complexity of genetic data increase, bipartite and, more generally, multipartite networks that combine human diseases and other physical attributes with layers of genetic information have the potential to become ubiquitous tools in the study of complex genetic, phenotypic interactions, and possibly improve personalized medicine.
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References
Bateson W (1907) Facts limiting the theory of heredity. Science 26(672):649–660
Plate L (1910) Vererbungslehre und deszendenztheorie. Festschrift für R Hertwig II:537–610
Stearns FW (2010) One hundred years of pleiotropy: a retrospective. Genetics 186(3):767–773
Gruneberg H (1938) An analysis of the “pleiotropic” effects of a new lethal mutation in the rat (Mus norvegicus). Proc R Soc Lond B Biol Sci 125(838):123–144
Hodgkin J (1998) Seven types of pleiotropy. Int J Dev Biol 42(3):501–505
Anthony JF Griffiths, Jeffrey H Miller, David T Suzuki, Richard C Lewontin, and William M Gelbart. Introduction to Genetic Analysis. 7th edition. W. H. Freeman, 2000
Moore JH (2003) The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered 56(1–3):73–82
Tyler AL, Asselbergs FW, Williams SM, Moore JH (2009) Shadows of complexity: what biological networks reveal about epistasis and pleiotropy. Bioessays 31(2):220–227
Newman M (2010) Networks: an introduction. Oxford University Press, Inc., New York, NY
Watts DJ, Strogatz SH (1998) Collective dynamics of “small-world” networks. Nature 393:440–442
Zhou T, Ren J, Medo M, Zhang Y-C (2007) Bipartite network projection and personal recommendation. Phys Rev E 76:046115
Goh K-I, Cusick ME, Valle D, Childs B, Vidal M, Barabasi A-L (2007) The human disease network. Proc Natl Acad Sci 104(21):8685–8690
Li H, Lee Y, Chen JL, Rebman E, Li J, Lussier YA (2012) Complex-disease networks of trait-associated single-nucleotide polymorphisms (SNPs) unveiled by information theory. J Am Med Inform Assoc 19(2):295–305
Suthram S, Dudley JT, Chiang AP, Chen R, Hastie TJ, Butte AJ (2010) Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. PLoS Comput Biol 6(2):e1000662
Barrenas F, Chavali S, Holme P, Mobini R, Benson M (2009) Network properties of complex human disease genes identified through genome-wide association studies. PLoS One 4(11):e8090
Schilling CH, Schuster S, Palsson BO, Heinrich R (1999) Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic era. Biotechnol Prog 15(3):296–303
Darabos C, Desai K, Cowper-Sal-lari R, Giacobini M, Lupien M, Moore JH. Inferring human phenotype networks from genome-wide genetic associations. In Giacobini M, Vanneschi L, Bush WS (eds.), Evolutionary computation, machine learning and data mining in bioinformatics—11th European Conference, EvoBIO 2013, Vienna, Austria, April 3–5, 2013. Proceedings, Lecture Notes in Computer Science. Springer, to appear, 2013
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 U S A 106(23):9362–9367
Featherstone DE, Broadie K (2002) Wrestling with pleiotropy: genomic and topological analysis of the yeast gene expression network. Bioessays 24(3):267–274
Li R, Tsaih S-W, Shockley K, Ioannis M, Stylianou JW, Paigen B, Churchill GA (2006) Structural model analysis of multiple quantitative traits. PLoS Genet 2(7):e114
Welch JJ, Waxman D (2003) Modularity and the cost of complexity. Evolution 57(8):1723–1734
Darabos C, White MJ, Graham BE, Leung DN, Williams S, Moore JH (2014) The multiscale backbone of the human phenotype network based on biological pathways. BioData Min 7(1):1
Chae B, Cakiner-Egilmez T, Desai M (2013) Glaucoma medications. Insight 38(1):5–9
He Z, Vingrys AJ, Armitage JA, Bui BV (2011) The role of blood pressure in glaucoma. Clin Exp Optom 94(2):133–149
Oswal KS, Sivaraj RR, Murray PI, Stavrou P (2013) Clinical course and visual outcome in patients with diabetes mellitus and uveitis. BMC Res Notes 6(1):167
Inoue T, Kawaji T, Tanihara H (2013) Elevated levels of multiple biomarkers of Alzheimer’s disease in the aqueous humor of eyes with open-angle glaucoma. Invest Ophthalmol Vis Sci 54(8):5353–8
Wang D, Huang Y, Huang C, Pengfei W, Lin J, Zheng Y, Peng Y, Liang Y, Chen J-H, Zhang M (2012) Association analysis of cigarette smoking with onset of primary open-angle glaucoma and glaucoma-related biometric parameters. BMC Ophthalmol 12:59
Ghiso JA (2013) Alzheimer’s disease and glaucoma: mechanistic similarities and differences. J Glaucoma 22(Suppl 5):S36–S38
Acknowledgements
This work was supported by National Institutes of Health (NIH) grants R01 EY022300, LM009012, LM010098, AI59694.
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Darabos, C., Moore, J.H. (2015). Genome-Wide Epistasis and Pleiotropy Characterized by the Bipartite Human Phenotype Network. In: Moore, J., Williams, S. (eds) Epistasis. Methods in Molecular Biology, vol 1253. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2155-3_14
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DOI: https://doi.org/10.1007/978-1-4939-2155-3_14
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