Dataset Information

Exploring SNP-SNP interactions and colon cancer risk using polymorphism interaction analysis.

ABSTRACT: Several single nucleotide polymorphisms (SNPs) in genes derived from distinct pathways are associated with colon cancer risk; however, few studies have examined SNP-SNP interactions concurrently. We explored the association between colon cancer and 94 SNPs, using a novel approach, polymorphism interaction analysis (PIA). We developed PIA to examine all possible SNP combinations, based on the 94 SNPs studied in 216 male colon cancer cases and 255 male controls, employing 2 separate functions that cross-validate and minimize false-positive results in the evaluation of SNP combinations to predict colon cancer risk. PIA identified previously described null polymorphisms in glutathione-S-transferase T1 (GSTT1) as the best predictor of colon cancer among the studied SNPs, and also identified novel polymorphisms in the inflammation and hormone metabolism pathways that singly or jointly predict cancer risk. PIA identified SNPs that may interact with the GSTT1 polymorphism, including coding polymorphisms in TP53 (Arg72Pro in p53) and CASP8 (Asp302His in caspase 8), which may modify the association between this polymorphism and colon cancer. This was confirmed by logistic regression, as the GSTT1 null polymorphism in combination with either the TP53 or the CASP8 polymorphism significantly alter colon cancer risk (p(interaction) < 0.02 for both). GSTT1 prevents DNA damage by detoxifying mutagenic compounds, while the p53 protein facilitates repair of DNA damage and induces apoptosis, and caspase 8 is activated in p53-mediated apoptosis. Our results suggest that PIA is a valid method for suggesting SNP-SNP interactions that may be validated in future studies, using more traditional statistical methods on different datasets.

SUBMITTER: Goodman JE

PROVIDER: S-EPMC1451415 | biostudies-literature | 2006 Apr

REPOSITORIES: biostudies-literature

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Exploring SNP-SNP interactions and colon cancer risk using polymorphism interaction analysis.

Goodman Julie E JE Mechanic Leah E LE Luke Brian T BT Ambs Stefan S Chanock Stephen S Harris Curtis C CC

International journal of cancer 20060401 7

Several single nucleotide polymorphisms (SNPs) in genes derived from distinct pathways are associated with colon cancer risk; however, few studies have examined SNP-SNP interactions concurrently. We explored the association between colon cancer and 94 SNPs, using a novel approach, polymorphism interaction analysis (PIA). We developed PIA to examine all possible SNP combinations, based on the 94 SNPs studied in 216 male colon cancer cases and 255 male controls, employing 2 separate functions that ...[more]

PMID: 16217767

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Project description:Urinary tract cancer is a common cause of cancer-related death. The etiology and pathogenesis of urinary tract cancer remain unclear, with genetic and epigenetic factors playing an important role. Studies of the polymorphism of murine double minute 2 (MDM2) have shown inconclusive trends in the risk of urinary tract cancer.To clarify this inconsistency, we conducted updated meta-analyses to evaluate the role of MDM2 T309G polymorphism in urinary tract cancer susceptibility.Data sources were Pubmed (1966-May 2015), Chinese biomedicine literature database (1978-May 2015), and hand searching of the reference lists of included studies:(1) research categories case-control study or a nested case-control study; (2) information evaluating the association between the MDM2 SNP309 and urinary tract cancer risk; (3) studies with sufficient data to perform a meta-analysis.It included the use of odds ratios (ORs) to assess the strength of the association, and 95% confidence intervals (CIs) give a sense of the precision of the estimate. We used I for the assessment of between-study heterogeneity, and publication bias was assessed using the funnel plot and the Egger test. Statistical analyses were performed by Review Manage, version 5.0 and Stata 11.0.A total of 18 studies met the eligibility criteria and were included in our analyses. Overall, there was no statistical association between MDM2 SNP309 and prostate cancer risk for the allele contrast, the GG genotype, the recessive genetic model, the dominant genetic model, and prostate cancer risk in all subjects (OR = 0.96, 95% CI 0.87-1.05, P = 0.36; OR = 0.93, 95% CI 0.75-1.15, P = 0.50; OR = 1.00, 95% CI 0.87-1.15, P = 0.99; OR = 0.93, 95% CI 0.80-1.07, P = 0.30), and between MDM2 SNP309 and bladder cancer risk (the allele contrast: OR = 1.06, 95% CI 0.89-1.27, P = 0.50; the GG genotype: OR = 1.12, 95% CI 0.79-1.61, P = 0.52; the dominant genetic model: OR = 1.03, 95% CI 0.83-1.28, P = 0.78; the recessive genetic model: OR = 1.12, 95% CI 0.84-1.49, P = 0.45). However, there was positive association between MDM2 SNP309 and kidney cancer risk for the allele contrast (OR = 1.24, 95% CI 1.05-1.46, P = 0.01), the GG genotype (OR = 1.57, 95% CI 1.11-2.20, P = 0.01), dominant model contrast (OR = 1.30, 95% CI 1.00-1.68, P = 0.05), the recessive genetic model (OR = 1.37, 95% CI 1.02-1.83, P = 0.04).First, only the data of published studies were included in this meta-analysis. Unpublished studies tend to show more negative results; therefore, publication bias may be present. Second, because of the lack of the original data, we did not perform stratification analysis by age, hormone levels, dietary habit, or other variables. This might have caused confounding bias. Third, because the number of studies was relatively small for kidney cancer, the results might not have enough statistical power for us to investigate the association of the polymorphism with kidney cancer susceptibility, and we could not perform subgroup analyses. Finally, there were no studies about Africans in this meta-analysis.In summary, the results of our meta-analysis suggest an increased risk role of the MDM2 SNP T309G in renal cancer. However, there was no association between the MDM2 SNP T309G and prostate cancer risk or between the MDM2 SNP T309G and bladder cancer risk. Moreover, well-designed studies should estimate different ethnicities, degree of malignancy and clinical progression on the association between MDM2 SNP309 and urinary cancer risk in the future.

Project description:Genome-wide association studies have identified several genomic regions that are associated with stroke risk, but these provide an explanation for only a small fraction of familial stroke aggregation. Genotype by environment interactions may contribute further to such an explanation. The Women's Health Initiative (WHI) clinical trial found increased stroke risk with postmenopausal hormone therapy (HT) and provides an efficient setting for evaluating genotype-HT interaction on stroke risk.We examined HT by genotype interactions for 392 SNPs selected from candidate gene studies, and 2,371 SNPs associated with changes in blood protein concentrations after hormone therapy, in analyses that included 2,045 postmenopausal women who developed stroke during WHI clinical trial and observational study follow-up and one-to-one matched controls. A two-stage procedure was implemented where SNPs passing the first stage screening based on marginal association with stroke risk were tested in the second stage for interaction with HT using case-only analysis.The two-stage procedure identified two SNPs, rs2154299 and rs12194855, in the coagulation factor XIII subunit A (F13A1) region and two SNPs, rs630431 and rs560892, in the proprotein convertase subtilisin kexin 9 (PCSK9) region, with an estimated false discovery rate <0.05 based on interaction tests. Further analyses showed significant stroke risk interaction between these F13A1 SNPs and estrogen plus progestin (E+P) treatment for ischemic stroke and for ischemic and hemorrhagic stroke combined, and suggested interactions between PCSK9 SNPs with either E+P or estrogen-alone treatment.Genotype by environment interaction information may help to define genomic regions relevant to stroke risk. Two-stage analysis among postmenopausal women generates novel hypotheses concerning the F13A1 and PCSK9 genomic regions and the effects of hormonal exposures on postmenopausal stroke risk for subsequent independent validation.

Project description:BackgroundBreast cancer predisposition genes identified to date (e.g., BRCA1 and BRCA2) are responsible for less than 5% of all breast cancer cases. Many studies have shown that the cancer risks associated with individual commonly occurring single nucleotide polymorphisms (SNPs) are incremental. However, polygenic models suggest that multiple commonly occurring low to modestly penetrant SNPs of cancer related genes might have a greater effect on a disease when considered in combination.MethodsIn an attempt to identify the breast cancer risk conferred by SNP interactions, we have studied 19 SNPs from genes involved in major cancer related pathways. All SNPs were genotyped by TaqMan 5'nuclease assay. The association between the case-control status and each individual SNP, measured by the odds ratio and its corresponding 95% confidence interval, was estimated using unconditional logistic regression models. At the second stage, two-way interactions were investigated using multivariate logistic models. The robustness of the interactions, which were observed among SNPs with stronger functional evidence, was assessed using a bootstrap approach, and correction for multiple testing based on the false discovery rate (FDR) principle.ResultsNone of these SNPs contributed to breast cancer risk individually. However, we have demonstrated evidence for gene-gene (SNP-SNP) interaction among these SNPs, which were associated with increased breast cancer risk. Our study suggests cross talk between the SNPs of the DNA repair and immune system (XPD-[Lys751Gln] and IL10-[G(-1082)A]), cell cycle and estrogen metabolism (CCND1-[Pro241Pro] and COMT-[Met108/158Val]), cell cycle and DNA repair (BARD1-[Pro24Ser] and XPD-[Lys751Gln]), and within carcinogen metabolism (GSTP1-[Ile105Val] and COMT-[Met108/158Val]) pathways.ConclusionThe importance of these pathways and their communication in breast cancer predisposition has been emphasized previously, but their biological interactions through SNPs have not been described. The strategy used here has the potential to identify complex biological links among breast cancer genes and processes. This will provide novel biological information, which will ultimately improve breast cancer risk management.

Dataset Information

Exploring SNP-SNP interactions and colon cancer risk using polymorphism interaction analysis.

Publications

Exploring SNP-SNP interactions and colon cancer risk using polymorphism interaction analysis.

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