Host: Takis Benos

Abstract: Network and pathway-based analyses are commonly used as powerful tools to interpret genotype-phenotype associations in a functional context.
In particular,
identification of disease-associated functional modules, i.e., highly connected protein-protein interaction (PPI) subnetworks with high aggregate disease association is shown to be promising in uncovering the functional relationships among disease-associated genes and proteins.
However, the connectivity of the product of these genes in PPI network is also likely to provide insights into the functional mechanisms of disease development and progression. Furthermore, collective evaluation of the disease association of multiple functionally related genetic variants may be more likely to detect association signals that may not be detectable via analysis of individual variants. Motivated by these considerations, we develop network-based algorithms to collectively score the association of multiple genetic variants with the disease and functional association with each other, and efficiently detect such groups of variants with statistically significant scores. In this talk, we will present our results on data for several complex diseases. Our results suggest that network analysis can extract knowledge from genomic data beyond the scope of the data itself.