Genome-Wide Association Analysis of Bladder Cancer

膀胱癌的全基因组关联分析

• 批准号: 7935041
• 负责人: Xifeng Wu
• 金额: $ 30.58万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7935041
• 关键词: Adopted; Age; Algorithms; Architecture; Arts; Biological; Biological Assay; Bladder; Cancer Center; Cancer Etiology; Case Series; Case-Control Studies; Cell Cycle Regulation; Chemoprevention; Chemopreventive Agent; Clinic; Clinical; Complex; Computational algorithm; Custom; Cytogenetics; DNA; DNA Repair; Data; Databases; Dietary Practices; Disease; Doctor of Medicine; Environment; Environmental Exposure; Environmental Risk Factor; Epidemiology; Ethnic Origin; Frequencies; Funding; Future; Gender; Genes; Genetic; Genetic Polymorphism; Genetic Risk; Genetic Variation; Genomics; Genotype; Goals; Haplotypes; Health Benefit; Human Genome; Individual; Individual Differences; Inherited; International; Intervention; Joints; Length; Light; Location; Lymphocyte; Machine Learning; Malignant neoplasm of urinary bladder; Maps; Medicine; Modeling; Molecular; Mutagens; New England; New Hampshire; Newly Diagnosed; Occupational Exposure; One-Step dentin bonding system; Outcome; Pattern; Penetrance; Phenotype; Physicians; Plasma Cells; Play; Polymorphism Analysis; Population; Predisposition; Public Health; Recruitment Activity; Reproduction spores; Resources; Review Literature; Risk; Role; Sampling; Screening procedure; Serum; Single Nucleotide Polymorphism; Smoking; Specimen; Staging; Statistical Methods; Subgroup; Suspension substance; Suspensions; TNFRSF5 gene; Technology; University of Texas M D Anderson Cancer Center; Validation; Variant; aggregation pathway; base; cancer risk; carcinogenesis; case control; college; computer based statistical methods; cost; density; design; epidemiologic data; follow-up; gene interaction; genome wide association study; genome-wide; high risk; medical specialties; novel; parent grant; public health relevance; repository; telomere; tool

DESCRIPTION (provided by applicant): This proposal builds on a rich resource of bladder cancer (BC) cases and controls derived from two ongoing BC studies at the University of Texas M.D. Anderson Cancer Center, and from two large independent U.S. BC studies - the New England BC Study and the New Hampshire BC Study. The goal is to identify genetic loci that predispose individuals to BC through a genome-wide scanning approach. There are five specific aims. Aim 1 is to first perform genome-wide, high-density SNP genotyping using the Illumina HumanHap550 SNP platform on 800 cases and 800 controls from M.D. Anderson, with a target of candidate SNPs of about 28,000; followed by an internal validation to narrow down candidate SNPs to about 1,536 using additional 800 pairs of cases and controls. Illumina's Custom Infinium array will be the genotyping format. In this aim, in addition to individual SNP analysis, we will also implement haplotype-based analyses and pathway aggregation analysis to identify additional genetic loci that might have been overlooked using individual SNP analysis. Aim 2 is the first external validation of the 1,536 SNPs from Aim 1 using 1,000 pairs of cases and controls from the New England BC Study. Illumina's GoldenGate assay will be the genotyping format. After this stage, the candidate SNPs will be narrowed down to about 100. Aim 3 is the second independent external validation using 750 pairs of cases and controls form the New Hampshire BC Study. In this aim, the 100 top candidate SNPs passed from Aims 1 and 2 plus additional functional SNPs in genes containing these SNPs will be genotyped. GoldenGate assay will be used for this aim. Aim 4 is to perform fine mapping studies in the flanking regions of each of the top 25 SNP loci confirmed in Aim 3 to identify causative loci. This will utilize all 6,700 cases and controls. An average of 15 additional SNPs (tagging SNPs and functional SNPs) per gene is expected. Aim 5 is to apply novel machine-learning tools to identify any gene-environment and gene-gene interactions greatly influencing risk for BC in all the 6700 subjects. These analyses will be utilized to examine SNP main effect and develop and validate algorithms that will identify individuals at highest risk for BC, given their personal exposure patterns and their genetic risk profiles. This proposal applies state of art technology to perform a multistage, genome-wide SNP analysis in three largest, well-characterized U.S. population of BC cases and controls, and incorporates complete epidemiologic data and rich and unique functional data. In addition, results from this study will be provided to the International Consortium of BC Case Control Studies for future validation. The ability to identify genetically susceptible, high-risk subgroups that would benefit from intensive screening and/or chemopreventive interventions is of immense clinical and public health benefit. PUBLIC HEALTH RELEVANCE Bladder cancer (BC) is a disease mainly caused by smoking and occupational exposure. However, only a small percentage of exposed individuals develop BC. Inherited host genetic factors may play an important role in determining an individual's susceptibility to BC. This proposal builds on three largest well-characterized U.S. populations of BC cases and controls - the M. D. Anderson BC study, the New England BC Study and the New Hampshire BC Study. A total of 6700 cases and controls will be used. The goal is to identify genetic loci that predispose individuals to BC through a non-biased, discovery- driven, genome-wide scanning approach and to incorporate complete epidemiologic data and rich and unique functional data. About 550,000 genetic variations of human genome will be initially screened in an M.D. Anderson population consisting of an equal number of BC cases and normal controls. About 28,000 top candidate variations that are potentially associated with increased BC risk will be first narrowed down and internally replicated in a second M.D. Anderson population, and then be validated in the other two independent U.S. BC populations. Finally, the causative genetic loci that predispose individuals to BC will be mapped. This study is significant because by identifying BC susceptibility loci, it will shed light into the biological mechanisms of BC etiology. Furthermore, it may facilitate identifying high-risk subgroups of individuals for BC, given their genetic makeup and environmental exposures. The ability to identify high-risk subgroups of individuals for BC will provide immense public health benefit for those high- risk people who may be subjected to close surveillance and chemoprevention.

描述（由申请人提供）： 该提案建立在来自德克萨斯大学医学博士的两项正在进行的膀胱癌研究的丰富的膀胱癌（BC）病例和对照资源的基础上。安德森癌症中心，并从两个大型独立的美国不列颠哥伦比亚省研究-新英格兰不列颠哥伦比亚省研究和新罕布什尔州不列颠哥伦比亚省研究。其目标是通过全基因组扫描方法确定使个体易患BC的遗传位点。有五个具体目标。目的1是首先使用Illumina HumanHap 550 SNP平台对来自M.D.的800例病例和800例对照进行全基因组、高密度SNP基因分型。安德森，候选SNP的目标约为28，000;随后进行内部验证，使用额外的800对病例和对照将候选SNP缩小到约1，536。Illumina的Custom Infinium阵列将是基因分型格式。在这个目标中，除了个体SNP分析，我们还将实施基于单体型的分析和途径聚集分析，以确定使用个体SNP分析可能被忽略的其他遗传位点。目标2是第一次使用来自新英格兰BC研究的1,000对病例和对照对来自目标1的1,536个SNP进行外部验证。Illumina的GoldenGate测定将是基因分型形式。在此阶段之后，候选SNP将缩小到约100个。目标3是使用来自新罕布什尔州BC研究的750对病例和对照进行的第二次独立外部验证。在这个目标中，将对从目标1和2传递的100个最佳候选SNP加上含有这些SNP的基因中的另外的功能性SNP进行基因分型。GoldenGate试验将用于此目的。目的4是在目的3中确认的前25个SNP基因座的每个基因座的侧翼区域中进行精细定位研究，以鉴定致病基因座。这将利用所有6，700例病例和对照。预计每个基因平均有15个额外的SNP（标签SNP和功能SNP）。目的5是应用新的机器学习工具来识别所有6700名受试者中对BC风险有重大影响的任何基因-环境和基因-基因相互作用。这些分析将用于检查SNP的主要影响，并开发和验证算法，这些算法将根据个人暴露模式和遗传风险特征识别BC风险最高的个体。该提案应用最先进的技术在三个最大的、特征良好的美国BC病例和对照人群中进行多阶段、全基因组SNP分析，并结合了完整的流行病学数据和丰富而独特的功能数据。此外，本研究的结果将提供给BC病例对照研究国际联盟，以供将来验证。识别遗传易感、高风险亚群的能力将受益于强化筛查和/或化学预防干预，这具有巨大的临床和公共卫生益处。膀胱癌是一种主要由吸烟和职业暴露引起的疾病。然而，只有一小部分接触者会发展为BC。遗传宿主遗传因素可能在决定个体对BC的易感性方面发挥重要作用。这项建议建立在三个最大的特征明确的美国BC病例和对照人群的基础上-M。D.安德森公元前研究，新英格兰公元前研究和新罕布什尔州公元前研究。总共将使用6700例病例和对照。其目标是通过无偏见、发现驱动、全基因组扫描方法，确定使个体易患BC的遗传基因座，并纳入完整的流行病学数据和丰富独特的功能数据。大约550，000个人类基因组的遗传变异将在一个医学博士项目中进行初步筛选。安德森人口组成的同等数量的BC病例和正常对照。大约28，000个可能与BC风险增加相关的最佳候选变异将首先缩小范围，并在第二次MD中进行内部复制。安德森人群，然后在其他两个独立的美国BC人群中进行验证。最后，将绘制使个体易患BC的致病基因座。本研究具有重要意义，因为通过确定BC易感基因位点，它将揭示BC病因学的生物学机制。此外，它可能有助于识别BC的高风险亚组，考虑到他们的基因组成和环境暴露。识别BC的高风险个体亚组的能力将为那些可能受到密切监测和化学预防的高风险人群提供巨大的公共卫生益处。