ArticleBioData mining2014
Functional genomics annotation of a statistical epistasis network associated with bladder cancer susceptibility.
Article in BioData mining, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Gene and pathway analysis of genome-wide genetic associations of bladder cancer.Current urology · 2025Article
- Genetic interactions effects for cancer disease identification using computational models: a review.Medical & biological engineering & computing · 2021Review
- CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions.BMC bioinformatics · 2016Article
- Studying the Genetics of Complex Disease With Ancestry-Specific Human Phenotype Networks: The Case of Type 2 Diabetes in East Asian Populations.Genetic epidemiology · 2016Article
- A screening-testing approach for detecting gene-environment interactions using sequential penalized and unpenalized multiple logistic regression.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2015Article
- Functional dyadicity and heterophilicity of gene-gene interactions in statistical epistasis networks.BioData mining · 2015Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundSeveral 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.
findingsTo 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.
conclusionsThe 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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Registered trials
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