Functional Genomics Annotation of a Statistical Epistasis Network Associated with Bladder Cancer Susceptibility

Background: Several different genetic and environmental factors have been identified as independent risk factors for bladder cancer in population-based studies. Recent studies have turned to understanding the role of gene-gene and gene-environment interactions in determining risk. We previously developed the bioinformatics framework of statistical epistasis networks (SEN) to characterize the global structure of interacting genetic factors associated with a particular disease or clinical outcome. By applying SEN to a population-based study of bladder cancer among Caucasians in New Hampshire, we were able to identify a set of connected genetic factors with strong and significant interaction effects on bladder cancer susceptibility. Findings: To support our statistical findings using networks, in the present study, we performed pathway enrichment analyses on the set of genes identified using SEN, and found that they are associated with the carcinogen benzo[a]pyrene, a component of tobacco smoke. We further carried out an mRNA expression microarray experiment to validate statistical genetic interactions, and to determine if the set of genes identified in the SEN were differentially expressed in a normal bladder cell line and a bladder cancer cell line in the presence or absence of benzo[a]pyrene. Significant nonrandom sets of genes from the SEN were found to be differentially expressed in response to benzo[a]pyrene in both the normal bladder cells and the bladder cancer cells. In addition, the patterns of gene expression were significantly different between these two cell types. Conclusions: The enrichment analyses and the gene expression microarray results support the idea that SEN analysis of bladder in population-based studies is able to identify biologically meaningful statistical patterns. These results bring us a step closer to a systems genetic approach to understanding cancer susceptibility that integrates population and laboratory-based studies

Functional Dyadicity and Heterophilicity of Gene-Gene Interactions in Statistical Epistasis Networks

Background: The interaction effect among multiple genetic factors, i.e. epistasis, plays an important role in explaining susceptibility on common human diseases and phenotypic traits. The uncertainty over the number of genetic attributes involved in interactions poses great challenges in genetic association studies and calls for advanced bioinformatics methodologies. Network science has gained popularity in modeling genetic interactions thanks to its structural characterization of large numbers of entities and their complex relationships. However, little has been done on functionally interpreting statistically inferred epistatic interactions using networks. Results: In this study, we propose to characterize gene functional properties in the context of interaction network structure. We used Gene Ontology (GO) to functionally annotate genes as vertices in a statistical epistasis network, and quantitatively characterize the correlation between the distribution of gene functional properties and the network structure by measuring dyadicity and heterophilicity of each functional category in the network. These two parameters quantify whether genetic interactions tend to occur more frequently for genes from the same functional category, i.e. dyadic effect, or more frequently for genes from across different functional categories, i.e. heterophilic effect. Conclusions: By applying this framework to a population-based bladder cancer dataset, we were able to identify several GO categories that have significant dyadicity or heterophilicity associated with bladder cancer susceptibility. Thus, our informatics framework suggests a new methodology for embedding functional analysis in network modeling of statistical epistasis in genetic association studies