Use of Bayesian networks to dissect the complexity of genetic disease: application to the Genetic Analysis Workshop 17 simulated data

BMC Proceedings

Proceedings

Jia Kang¹  Hongyu Zhao²  Lun Li³  Xiting Yan⁴  Wei Zheng⁴  Joon Sang Lee⁴ 

[1] Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, PO Box 208009, 06520-8114, New Haven, CT, USA;Keck Biotechnology Resource Laboratory, Yale University, 300 George Street, Room 2119, 06511, New Haven, CT, USA;Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, PO Box 208009, 06520-8114, New Haven, CT, USA;School of Epidemiology and Public Health, Yale University, 06520-8114, New Haven, CT, USA;Keck Biotechnology Resource Laboratory, Yale University, 300 George Street, Room 2119, 06511, New Haven, CT, USA;School of Epidemiology and Public Health, Yale University, 06520-8114, New Haven, CT, USA;Hubei Bioinformatics and Molecular Imaging Key Laboratory, School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China;Keck Biotechnology Resource Laboratory, Yale University, 300 George Street, Room 2119, 06511, New Haven, CT, USA;School of Epidemiology and Public Health, Yale University, 06520-8114, New Haven, CT, USA;Keck Biotechnology Resource Laboratory, Yale University, 300 George Street, Room 2119, 06511, New Haven, CT, USA;

关键词: Bayesian Network;    Disease Phenotype;    Area Under Curve;    Conditional Independence;    Risk Prediction Model;   

DOI  :  10.1186/1753-6561-5-S9-S37

来源: Springer

【 摘 要 】

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.

【 授权许可】

Unknown   
© Kang et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.