Over the past years, tremendous efforts have been made to elucidate the molecular basis of the initiation and progression of ovarian cancer. However, most existing studies have been focused on individual genes or a single type of data, which may lack the power to detect the complex mechanisms of cancer formation by overlooking the interactions of different genetic and epigenetic factors.We propose an integrative framework to identify genetic and epigenetic features related to ovarian cancer and to quantify the causal relationships among these features using a probabilistic graphical model based on the Cancer Genome Atlas (TCGA) data. In the feature selection, we first defined a set of seed genes by including 48 candidate tumor suppressors or oncogenes and an additional 20 ovarian cancer related genes reported in the literature. The seed genes were then fed into a stepwise correlation-based selector to identify 271 additional features including 177 genes, 82 copy number variation sites, 11 methylation sites and 1 somatic mutation (at gene TP53). We built a Bayesian network model with a logit link function to quantify the causal relationships among these features and discovered a set of 13 hub genes including ARID1A, C19orf53, CSKN2A1 and COL5A2. The directed graph revealed many potential genetic pathways, some of which confirmed the existing results in the literature. Clustering analysis further suggested four gene clusters, three of which correspond to well-defined cellular processes including cell division, tumor invasion and mitochondrial system. In addition, two genes related to glycoprotein synthesis, PSG11 and GALNT10, were found highly predictive for the overall survival time of ovarian cancer patients.The proposed framework is effective in identifying possible important genetic and epigenetic features that are related to complex cancer diseases. The constructed Bayesian network has identified some new genetic/epigenetic pathways, which may shed new light into the molecular mechanisms of ovarian cancer.