Epistasis Blog

From the Computational Genetics Laboratory at Dartmouth Medical School (www.epistasis.org)

Sunday, April 27, 2014

Functional genomics annotation of a statistical epistasis network associated with bladder cancer susceptibility

We have previously published a statistical epistasis network associated with bladder cancer in a population based study (Hu et al., BMC Bioinformatics). This network was highly non-random and contained many genes that were part of the aryl hydrocarbon receptor pathway. Below is a short paper reporting functional genomics annotation of the network.

Hu T, Pan Q, Andrew AS, Langer JM, Cole MD, Tomlinson CR, Karagas MR, Moore JH. Functional genomics annotation of a statistical epistasis network associated with bladder cancer susceptibility. BioData Min. 2014 Apr 11;7(1):5. [BioData Mining]

Abstract

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.


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