Epistasis pp 269-283
Genome-Wide Epistasis and Pleiotropy Characterized by the Bipartite Human Phenotype Network
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.
Key wordsPleiotropy Epistasis Eye diseases Glaucoma Network GWAS Human Phenotype Network SNPs Pathways
- 2.Plate L (1910) Vererbungslehre und deszendenztheorie. Festschrift für R Hertwig II:537–610Google Scholar
- 6.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, 2000Google Scholar
- 17.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, 2013Google Scholar