Methodological issues in detecting gene-gene interactions in breast cancer susceptibility: a population-based study in Ontario
Autor: | Laurent Briollais, Yuanyuan Wang, Julia A. Knight, Ellen Shi, Isaac Rajendram, Venus Onay, Hilmi Ozcelik |
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Jazyk: | angličtina |
Předmět: |
Adult
Male lcsh:Medicine Single-nucleotide polymorphism Breast Neoplasms Genome 03 medical and health sciences 0302 clinical medicine Breast cancer Medicine Humans Genetic Predisposition to Disease Gene 030304 developmental biology Aged Genetics Ontario Medicine(all) 0303 health sciences business.industry lcsh:R Case-control study Small sample General Medicine Middle Aged medicine.disease 3. Good health Population based study Genes 030220 oncology & carcinogenesis Case-Control Studies Female Cancer development business Research Article |
Zdroj: | BMC Medicine BMC Medicine, Vol 5, Iss 1, p 22 (2007) |
ISSN: | 1741-7015 |
DOI: | 10.1186/1741-7015-5-22 |
Popis: | Background There is growing evidence that gene-gene interactions are ubiquitous in determining the susceptibility to common human diseases. The investigation of such gene-gene interactions presents new statistical challenges for studies with relatively small sample sizes as the number of potential interactions in the genome can be large. Breast cancer provides a useful paradigm to study genetically complex diseases because commonly occurring single nucleotide polymorphisms (SNPs) may additively or synergistically disturb the system-wide communication of the cellular processes leading to cancer development. Methods In this study, we systematically studied SNP-SNP interactions among 19 SNPs from 18 key genes involved in major cancer pathways in a sample of 398 breast cancer cases and 372 controls from Ontario. We discuss the methodological issues associated with the detection of SNP-SNP interactions in this dataset by applying and comparing three commonly used methods: the logistic regression model, classification and regression trees (CART), and the multifactor dimensionality reduction (MDR) method. Results Our analyses show evidence for several simple (two-way) and complex (multi-way) SNP-SNP interactions associated with breast cancer. For example, all three methods identified XPD-[Lys751Gln]*IL10-[G(-1082)A] as the most significant two-way interaction. CART and MDR identified the same critical SNPs participating in complex interactions. Our results suggest that the use of multiple statistical approaches (or an integrated approach) rather than a single methodology could be the best strategy to elucidate complex gene interactions that have generally very different patterns. Conclusion The strategy used here has the potential to identify complex biological relationships among breast cancer genes and processes. This will lead to the discovery of novel biological information, which will improve breast cancer risk management. |
Databáze: | OpenAIRE |
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