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  • 1
    Keywords: RISK ; MEN ; GLIOMA ; JAPANESE ; GENOME-WIDE ASSOCIATION ; COMMON VARIANTS ; MYOSIN VI ; 22Q13
    Abstract: Genome-wide association studies (GWAS) have identified 76 variants associated with prostate cancer risk predominantly in populations of European ancestry. To identify additional susceptibility loci for this common cancer, we conducted a meta-analysis of 〉10 million SNPs in 43,303 prostate cancer cases and 43,737 controls from studies in populations of European, African, Japanese and Latino ancestry. Twenty-three new susceptibility loci were identified at association P 〈 5 x 10(-8); 15 variants were identified among men of European ancestry, 7 were identified in multi-ancestry analyses and 1 was associated with early-onset prostate cancer. These 23 variants, in combination with known prostate cancer risk variants, explain 33% of the familial risk for this disease in European-ancestry populations. These findings provide new regions for investigation into the pathogenesis of prostate cancer and demonstrate the usefulness of combining ancestrally diverse populations to discover risk loci for disease.
    Type of Publication: Journal article published
    PubMed ID: 25217961
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  • 2
    Keywords: CANCER ; MODEL ; PATHWAY ; INFORMATION ; DISEASE ; RISK ; GENE ; GENES ; MARKER ; IMPACT ; SEQUENCE ; ASSOCIATION ; polymorphism ; POLYMORPHISMS ; single nucleotide polymorphism ; FORM ; STAGE ; HEALTH ; DESIGN ; NUMBER ; smoking ; BLADDER ; bladder cancer ; BLADDER-CANCER ; MARKERS ; FRANCE ; PRODUCT ; Jun ; case-control studies ; TOBACCO ; CANCER-RESEARCH ; TOBACCO SMOKING ; SINGLE ; ONCOLOGY ; case control study ; case-control study ; REGRESSION ; ASSOCIATIONS ; RE ; SINGLE NUCLEOTIDE POLYMORPHISMS ; CANDIDATE GENES ; CATECHOL-O-METHYLTRANSFERASE ; EMPIRICAL-BAYES ; ENVIRONMENTAL EXPOSURES ; interaction ; ISSUES ; MATRICES ; MYELOPEROXIDASE ; SUPEROXIDE-DISMUTASE ; XRCC1 POLYMORPHISMS
    Abstract: Background: Genetic association studies are generating much information, usually in the form of single nucleotide polymorphisms in candidate genes. Analyzing such data is challenging, and raises issues of multiple comparisons and potential false-positive associations. Using data from a case-control study of bladder cancer, we showed how to use hierarchical modeling in genetic epidemiologic studies with multiple markers to control overestimation of effects and potential false-positive associations. Methods: The data were first analyzed with the conventional approach of estimating each main effect individually. We subsequently employed hierarchical modeling by adding a second stage (prior) model that incorporated information on the potential function of the genes. We used an empirical-Bayes approach, estimating the residual effects of the genes from the data. When the residual effect was set to zero, we instead used a semi-Bayes approach, in which they were pre-specified. We also explored the impact of using different second-stage design matrices. Finally, we used two approaches for assessing gene-environment interactions. The first approach added product terms into the first-stage model. The second approach used three indicators for subjects exposed to gene-only, environment-only, and both genetic and environmental factors. Results: By pre-specifying the prior second-stage covariates, the estimates were shrunk to the mean of each pathway. The conventional model detected a number of positive associations, which were reduced with the hierarchical model. For example, the odds ratio for myeloperoxidase (G/G, G/A) genotype changed from 3.17 [95% confidence interval (0), 1.32-7.59] to 1.64 (95% CI, 0.81-3.34). A similar phenomenon was observed for the gene-environment interactions. The odds ratio for the gene-environment interaction between tobacco smoking and N-acetyltransferase 1 fast genotype was 2.74 (95% Cl, 0.68-11.0) from the conventional analysis and 1.24 (95% CI, 0.80-1.93) from the hierarchical model. Conclusion: Adding a second-stage hierarchical modeling can reduce the likelihood of false positive via shrinkage toward the prior mean, improve the risk estimation by increasing the precision, and, therefore, represents an alternative to conventional methods for genetic association studies
    Type of Publication: Journal article published
    PubMed ID: 15184258
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