Statistical Genetic Methods for Cancer Gene Studies

癌症基因研究的统计遗传学方法

• 批准号: 6458646
• 负责人: Susan L Slager
• 金额: $ 13.41万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6458646
• 关键词: clinical research; computer human interaction; computer program /software; computer system design /evaluation; data collection; experimental designs; family genetics; gene environment interaction; human data; mathematical model; method development; model design /development; molecular biology information system; neoplasm /cancer epidemiology; neoplasm /cancer genetics; statistics /biometry

DESCRIPTION (provided by applicant): Finding the genetic determinants of common complex disorders is a formidable task, since many genetic and environmental factors, either individually or in combination, influence trait expression. The use of association studies to tackle this challenge is becoming more widespread, especially with the release of the human genome sequence. However, large-scale association studies with sufficient power will be required to find these genes with modest effect sizes. Family-based association studies have consistently been shown to be less powerful than case-control studies, but case-control studies do not efficiently use the data collected for linkage studies and national registries. Therefore, the overall goal of this mentored proposal is to develop and investigate novel study designs and statistical analysis methods for association studies that are a combination of the two aforementioned designs. Specifically, we will: (1) examine sampling strategies for our proposed hybrid design in order to provide guidelines that enable researchers to choose the most informative subjects. (2) derive and evaluate statistical methods to be used for our proposed hybrid design. We will consider methods for stratified analyses, for matched designs, for DNA pooling, and for testing interaction effects. (3) develop and examine statistical methods that correct for population stratification, since our proposed designs may be susceptible to the effects of population stratification. (4) develop user-friendly software that can be used for the design and analysis of our proposed hybrid designs. Additionally, through the planned didactic training in areas including statistical genetics and molecular biology, this mentored proposal will provide me the opportunity to build a strong foundation upon which to build career conducting methodological research involving genetic studies of cancer.

描述（由申请人提供）：寻找遗传决定因素 常见的复杂疾病是一项艰巨的任务，因为许多遗传和 环境因素，无论是单独还是组合，都会影响特质 表达。使用关联研究来应对这一挑战是 变得更加广泛，特别是随着人类基因组的发布 顺序。然而，具有足够功效的大规模关联研究将 需要找到这些效应大小适中的基因。以家庭为基础 关联研究一直被证明不如 病例对照研究，但病例对照研究并没有有效地利用数据 收集用于关联研究和国家登记。因此，总体 这个指导提案的目标是开发和调查新颖的研究 关联研究的设计和统计分析方法 上述两种设计的结合。具体来说，我们将： (1) 检查我们提出的混合设计的抽样策略，以便 提供指导方针，使研究人员能够选择信息最丰富的 科目。 (2) 推导出并评估用于我们提出的混合的统计方法 设计。我们将考虑分层分析的方法，以进行匹配 设计、DNA 池和测试相互作用效应。 (3) 开发和检验针对总体进行校正的统计方法 分层，因为我们提出的设计可能容易受到影响 人口分层。 （4）开发用户友好的软件，可用于设计和 分析我们提出的混合设计。 此外，通过计划的教学培训，包括 统计遗传学和分子生物学，这个指导提案将 为我提供了打下坚实基础的机会 从事涉及癌症遗传学研究的方法学研究的职业。