Systems biology analyses of gene expression and genome wide association study data in obstructive sleep apnea

Oct. 27, 2011

The precise molecular etiology of obstructive sleep apnea (OSA) is unknown; however recent research indicates that several interconnected aberrant pathways and molecular abnormalities are contributors to OSA. Identifying the genes and pathways associated with OSA can help to expand our understanding of the risk factors for the disease as well as provide new avenues for potential treatment. Towards these goals, we have integrated relevant high dimensional data from various sources, such as genome-wide expression data (microarray), protein-protein interaction (PPI) data and results from genome-wide association studies (GWAS) in order to define sub-network elements that connect some of the known pathways related to the disease as well as define novel regulatory modules related to OSA. Two distinct approaches are applied to identify sub-networks significantly associated with OSA. In the first case we used a biased approach based on sixty genes/proteins with known associations with sleep disorders and/or metabolic disease to seed a search using commercial software to discover networks associated with disease followed by information theoretic (mutual information) scoring of the sub-networks. In the second case we used an unbiased approach and generated an interactome constructed from publicly available gene expression profiles and PPI databases, followed by scoring of the network with p-values from GWAS data derived from OSA patients to uncover sub-networks significant for the disease phenotype. A comparison of the approaches reveals a number of proteins that have been previously known to be associated with OSA or sleep. In addition, our results indicate a novel association of Phosphoinositide 3-kinase, the STAT family of proteins and its related pathways with OSA.

Figure Network identified by jactivemodule using p-values from GWAS study, color represents the p-values and nodes with grey color indicate that the p-values are missing from GWAS.

Results from: Liu, Y., Patel, S., Nibbe, R., Maxwell, S., Chowdhury, S.A., Koyutürk, M., Zhu, X., Larkin, E.K., Buxbaum, S.G., Punjabi, N.M., Gharib, S.A., Redline, S., Chance, M.R. Pacific Symposium on Biocomputing 14-25, 2011.