International Case Control Study of Malignant Glioma

描述（由申请人提供）：胶质瘤约占所有原发性脑肿瘤的40%，每年在美国和欧洲造成多达26,000例死亡。遗传易感性在胶质瘤病例的一级亲属中起着2倍的风险增加的作用。2007年，我们发起了一项多中心的国际关联研究：“神经胶质瘤国际联盟遗传流行病学”，以解决遗传易感性的作用。由于这项研究不允许我们目前解决遗传易感性在散发性病例中的作用，我们提出这个病例对照研究来解决这一差距。该联盟的成员已经进行了基因组全关联研究（GWAS），这将是发现阶段（第一阶段）。虽然具有很高的发现价值，但我们建议招募6000例新诊断的胶质瘤病例和6000例年龄-性别-种族匹配的对照，并从13个参与Gliogene的站点收集详细的流行病学数据和生物样本。我们假设可以通过高密度SNP分析确定影响胶质瘤易感性的新的遗传变异，并提出以下具体目标：SA1：确定与胶质瘤风险相关的常见遗传变异。我们计划进行3阶段方法的第2阶段和第3阶段，具体来说，我们将：（SA1a）验证来自发现集的GWAS发现，并在一系列2000例病例和2000例对照中复制两阶段GWA研究中p值最低的最佳结果（~23,000个snp）（第2阶段，验证）。验证测试集中的顶级命中值（目标1a）。从第二阶段开始，具有与pd10-4相关的snp的基因（包括完整的单倍型标记-大约100个snp）将在进一步的4000例病例和4000例对照中进行基因分型（第三阶段，验证）。在10-7或更高的snp显著性将在最终分析中考虑。(SA1b)。利用炎症中的DNA修复有助于胶质瘤风险的先验假设，我们将使用来自高密度阵列的精选基因数据选择性地询问DNA修复和炎症途径基因。我们将使用功能基因组学和生物信息学工具来查询可用的数据库，在DNA修复和炎症/免疫功能相关基因与胶质瘤风险之间进行假设驱动的遗传关联。（SA2）：在这些探索性分析中，使用机器学习工具（MDR、FITF和CART）评估具有强生物学相关性的基因-基因（G-G）和基因-环境（G-E）相互作用，以确定G-G和G-E相互作用对胶质瘤风险的影响。目前还没有足够数量的生物样本和共同流行病学数据的胶质瘤病例对照研究来进行全面的验证和复制研究，或者像我们在本研究中那样解决肿瘤异质性问题。

DESCRIPTION (provided by applicant): Glioma comprises about 40% of all primary brain tumors accounting for as many as 26,000 U.S. and European deaths annually. Inherited susceptibility plays a role with 2-fold increased risk of glioma among first-degree relatives of glioma cases. We initiated a multi-center, international linkage study: "Genetic Epidemiology of Glioma International Consortium" in 2007 to address the role of inherited susceptibility. Since this study does not allow us currently to address the role of genetic susceptibility in sporadic cases, we propose this case- control study to address this gap. Members of the consortium have conducted genome wide association studies (GWAS) that will be the discovery phase (phase I). While of high value for discovery, we propose to recruit 6,000 newly diagnosed glioma cases and 6,000 age-gender-ethnicity-matched controls, and collect detailed epidemiologic data and biologic samples from the 13 participating Gliogene sites. We hypothesize that novel inherited variation influencing the susceptibility to glioma can be identified by high density SNP analysis, and propose the following specific aims: SA1: Identify common genetic variants contributing to the risk of glioma. We plan to conduct phases 2 and 3 of the 3 phase approach, specifically we will: (SA1a) Validate GWAS findings from the discovery sets, and replicate the top hits (~23,000 SNPs) with lowest p- values from the two phase GWA studies in a series of 2,000 cases and 2,000 controls (phase 2, validation). Validate the top hits from the test set (aim 1a). From stage 2, genes with SNPs displaying an association at pd10-4 (including the complete haplotype tagging - approximately 100 SNPs) will then be genotyped in a further series of 4,000 cases and 4,000 controls (stage 3, validation). SNPs significant at 10-7 or better will be considered in the final analyses. (SA1b). Using an a priori hypothesis that DNA repair in inflammation contribute to glioma risk, we will selectively interrogate DNA repair and inflammation pathway genes using data for select genes derived from the high-density array. We will conduct a hypothesis driven genetic association in DNA repair and in inflammation/immune function related genes with glioma risk using functional genomic and bioinformatic tools to interrogate available databases. (SA2): Evaluate gene-gene (G-G) and gene- environmental (G-E) interactions with strong biologic relevance to identify G-G and G-E interactions for glioma risk using machine-learning tools (MDR, FITF, and CART) in these exploratory analyses. There are no existing glioma case-control studies with sufficient numbers of biological samples and common epidemiologic data to conduct a comprehensive validation and replication study, or to address the issues of tumor heterogeneity as we will in this study. PUBLIC HEALTH RELEVANCE: Gliomas are a rare and understudied disease that is rapidly fatal. The proposed case-control study of primary malignant glioma (Gliogene Consortium) includes 13-centers, and will address the role of genetic susceptibility in sporadic cases. It will identify common genetic variants contributing to the risk of glioma from a genome wide association study.