Dataset Information

Cancer GAMAdb: database of cancer genetic associations from meta-analyses and genome-wide association studies.

ABSTRACT: In the field of cancer, genetic association studies are among the most active and well-funded research areas, and have produced hundreds of genetic associations, especially in the genome-wide association studies (GWAS) era. Knowledge synthesis of these discoveries is the first critical step in translating the rapidly emerging data from cancer genetic association research into potential applications for clinical practice. To facilitate the effort of translational research on cancer genetics, we have developed a continually updated database named Cancer Genome-wide Association and Meta Analyses database that contains key descriptive characteristics of each genetic association extracted from published GWAS and meta-analyses relevant to cancer risk. Here we describe the design and development of this tool with the aim of aiding the cancer research community to quickly obtain the current updated status in cancer genetic association studies.

SUBMITTER: Schully SD

PROVIDER: S-EPMC3172934 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature

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Cancer GAMAdb: database of cancer genetic associations from meta-analyses and genome-wide association studies.

Schully Sheri D SD Yu Wei W McCallum Victoria V Benedicto Camilla B CB Dong Linda M LM Wulf Anja A Clyne Melinda M Khoury Muin J MJ

European journal of human genetics : EJHG 20110413 8

In the field of cancer, genetic association studies are among the most active and well-funded research areas, and have produced hundreds of genetic associations, especially in the genome-wide association studies (GWAS) era. Knowledge synthesis of these discoveries is the first critical step in translating the rapidly emerging data from cancer genetic association research into potential applications for clinical practice. To facilitate the effort of translational research on cancer genetics, we h ...[more]

PMID: 21487441

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Project description:ObjectivesReported associations of the cannabinoid receptor 1 (CNR1) single nucleotide polymorphisms (SNPs) with alcohol dependence (AD) have been inconsistent, prompting a meta-analysis to obtain more precise estimates.MethodsA Boolean search of 4 databases (PubMed, Scopus, Google Scholar, and Mednar) sought articles that evaluated the association between CNR1 polymorphisms and risk of AD. We selected the articles with sufficient genotype frequency data to enable calculation of odds ratios (ORs) and 95% confidence intervals (CIs). Using the Population Intervention Comparators Outcome elements, AD patients (P) were compared by genotype data between AD-participants (I) and non-AD-participants (C) in order to determine the risk of AD (O) attributed to the CNR1 SNPs. Analyzing 4 SNPs (rs1049353, rs1535255, rs2023239, and rs806379) using standard genetic models, we examined associations where multiple comparisons were Holm-Bonferroni corrected. The pooled ORs were assessed for aggregate statistical power and robustness (sensitivity analysis). Subgroups were Caucasians and African-Americans.ResultsFrom 32 comparisons, 14 were significant indicating increased risk, from which 5 outcomes (P-value for association [Pa] = .003 to <.001) survived the Holm-Bonferroni-correction, which were deemed robust. In the rs1535255 outcomes, the codominant effect (OR = 1.43, 95% CIs = 1.24-1.65, Pa < .001) had greater statistical power than the dominant effect (OR = 1.30, 95% CI = 1.08-1.57, Pa = .006). In contrast, the rs2023239 codominant outcome was underpowered. Significance of both rs806379 Caucasian outcomes (ORs = 1.20-1.43, 95% CIs = 1.07-1.57, Pa = .003) contrasted with the null effects in African-Americans (ORs = 0.98-1.08, 95% CIs = 0.70-1.53).ConclusionsThree CNR1 SNPs (rs1535255, rs2023239, and rs806379) were implicated in their associations with development of AD: based on aggregate statistical power, rs1535255 presented greater evidence for associations than rs2023239; rs806379 implicated the Caucasian subgroup. Multiple statistical and meta-analytical features (consistency, robustness, and high significance) underpinned the strengths of these outcomes. Our findings could render the CNR1 polymorphisms useful in the clinical genetics of AD.

Dataset Information

Cancer GAMAdb: database of cancer genetic associations from meta-analyses and genome-wide association studies.

Publications

Cancer GAMAdb: database of cancer genetic associations from meta-analyses and genome-wide association studies.

Similar Datasets

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