Epistasis Blog

From the Artificial Intelligence Innovation Lab at Cedars-Sinai Medical Center (www.epistasis.org)

Tuesday, January 23, 2007

MDR Analysis of Bladder Cancer

A new paper by Huang et al. in CEBP reports results from an MDR analysis of gene-gene interactions in bladder cancer. This paper is a nice example of the use of interaction dendrograms to interpret MDR models.

Huang M, Dinney CP, Lin X, Lin J, Grossman HB, Wu X. High-Order Interactions among Genetic Variants in DNA Base Excision Repair Pathway Genes and Smoking in Bladder Cancer Susceptibility. Cancer Epidemiol Biomarkers Prev. 2007 Jan;16(1):84-91. [PubMed]

Cancer is a common multifactor human disease resulting from complex interactions between many genetic and environmental factors. In this study, we used a multifaceted analytic approach to explore the relationship between eight single nucleotide polymorphisms in base excision repair (BER) pathway genes, smoking, and bladder cancer susceptibility in a hospital-based case-control study. Overall, we did not find an association between any single BER gene single nucleotide polymorphism and bladder cancer risk. However, in stratified analysis, the OGG1 S326C variant genotypes in ever smokers (odds ratio, 0.74; 95% confidence interval, 0.56-0.99) and ADP-ribosyltransferase (ADPRT) V762A variant genotypes in never smokers (odds ratio, 0.58; 95% confidence interval, 0.37-0.91) conferred a significantly reduced risk. Using logistic regression, we observed that there was a two-way interaction between ADPRT V762A and smoking status. We next used classification and regression tree analysis to explore high-order gene-gene and gene-environment interactions. We found that smoking is the most important influential factor for bladder cancer risk. Consistent with the above findings, we found that the ADPRT V762A was only significantly involved in bladder cancer risk in never smokers and the OGG1 S326C was only significantly involved in ever smokers. We also observed gene-gene interactions among OGG1 S326C, XRCC1 R194W, and MUTYH H335Q in ever smokers. Using multifactor dimensionality reduction approach, the four-factor model, including smoking status, OGG1 S326C (rs1052133), APEX1 D148E (rs3136820), and ADPRT762 (rs1136410), had the best ability to predict bladder cancer risk with the highest cross-validation consistency (100%) and the lowest prediction error (37.02%; P < 0.001). These results support the hypothesis that genetic variants in BER genes contribute to bladder cancer risk through gene-gene and gene-environmental interactions. (Cancer Epidemiol Biomarkers Prev 2007;16(1):84-91).


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