This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data
Use of Bayesian networks to dissect the complexity of genetic disease: application to the Genetic Analysis Workshop 17 simulated data
1 Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, PO Box 208009, New Haven, CT 06520-8114, USA
2 School of Epidemiology and Public Health, Yale University, New Haven, CT, 06520-8114, USA
3 Hubei Bioinformatics and Molecular Imaging Key Laboratory, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
4 Keck Biotechnology Resource Laboratory, Yale University, 300 George Street, Room 2119, New Haven, CT 06511, USA
BMC Proceedings 2011, 5(Suppl 9):S37 doi:10.1186/1753-6561-5-S9-S37Published: 29 November 2011
Complex diseases are often the downstream event of a number of risk factors, including both environmental and genetic variables. To better understand the mechanism of disease onset, it is of great interest to systematically investigate the crosstalk among various risk factors. Bayesian networks provide an intuitive graphical interface that captures not only the association but also the conditional independence and dependence structures among the variables, resulting in sparser relationships between risk factors and the disease phenotype than traditional correlation-based methods. In this paper, we apply a Bayesian network to dissect the complex regulatory relationships among disease traits and various risk factors for the Genetic Analysis Workshop 17 simulated data. We use the Bayesian network as a tool for the risk prediction of disease outcome.