title: Use of Bayesian networks to dissect the complexity of genetic disease: application to the Genetic Analysis Workshop 17 simulated data.
abstract: 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.
authors.affiliation: Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, PO Box 208009, New Haven, CT 06520-8114, USA. hongyu.zhao@yale.edu.
authors.forename: Jia
authors.initials: J
authors.lastname: Kang
authors.forename: Wei
authors.initials: W
authors.lastname: Zheng
authors.forename: Lun
authors.initials: L
authors.lastname: Li
authors.forename: Joon Sang
authors.initials: JS
authors.lastname: Lee
authors.forename: Xiting
authors.initials: X
authors.lastname: Yan
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao