Design and analysis of adaptive multistage genetic association studies

适应性多阶段遗传关联研究的设计和分析

• 批准号: 7929795
• 负责人: EDWIN VAN DEN OORD
• 金额: $ 14.77万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-24 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7929795
• 关键词: Accounting; Biological; Collaborations; Complex; Computers; Confusion; Data; Disease; Documentation; Economic Inflation; Ensure; Feedback; Follow-Up Studies; Framingham Heart Study; Funding; Generations; Genes; Genetic; Genetic Markers; Genome; Genomics; Genotype; Goals; Grant; Institutes; Joints; Methods; Parkinson Disease; Pathway interactions; Population; Probability; Procedures; Public Domains; Reporting; Research; Research Design; Research Personnel; Sample Size; Sampling; Schizophrenia; Simulate; Specific qualifier value; Staging; Stratification; Techniques; Testing; Trust; United States National Institutes of Health; Variant; Work; age related; base; case control; computer human interaction; cost; design; flexibility; follow-up; genetic association; genome wide association study; improved; macula; novel; open source; simulation; statistics; success; theories; trend; user-friendly

DESCRIPTION (provided by applicant): It has recently become possible to screen many genetic markers across the whole genome for their association with a disease. These genome-wide association studies (GWAS) offer great promise to identify common disease-predisposing variants. The goal of this project is to develop a flexible framework for designing cost-effective GWAS and optimize subsequent replication efforts. For this purpose we will use a general framework for designing optimal multistage studies. In multistage designs all the markers are genotyped and tested in a first stage. Only the promising markers are subsequently genotyped in a second stage using additional samples. Our approach offers three broad advantages. First, because of the large sample sizes that are required to discover disease-predisposing variants while controlling false discoveries, GWAS cost millions of dollars. Compared to single-stage GWAS, optimized multistage designs can achieve the same goals in terms of true and false discoveries with a 50-70% saving in the amount of genotyping. Second, single-stage designs are entirely based on assumptions that may be incorrect potentially leading to goals not being achieved or goals which could have been achieved at much lower costs. Multistage designs, however, offer the possibility to use information collected at the first stage(s) to design optimal follow-up studies. The trend to release GWAS data in the public domain will further increase the practical relevance of this adaptive feature of multistage designs because many research groups are likely to start performing replication studies in their own samples after GWAS data are publicly released. Third, rather than using arbitrary rules (e.g. P-values smaller than 0.05 suggest a replication) our framework will provide statistically motivated decision rules for declaring significance and the subsequent interpretation of what consitues a replication . Specific aims of our proposal include evaluating and improving the basic framework we already developed. To make the approach applicable across a wide variety of research scenarios, we also propose a wide variety of theoretical and computational extensions. To ensure the utility in practice, we will test our methods on real data. Finally, we plan to make the computer implementation available to a broad spectrum of researchers. Genome-wide association studies offer great promise to identify common disease- predisposing variants. The goal of this project is to develop a flexible framework for designing these studies in a cost-effective way and optimize subsequent replication efforts.

描述(由申请人提供)：最近有可能对整个基因组中的许多遗传标记进行筛选，以确定它们与疾病的关联。这些全基因组关联研究(GWAS)为识别常见的致病变异提供了巨大的希望。该项目的目标是为设计具有成本效益的全球气候变化系统制定一个灵活的框架，并优化后续的复制工作。为此，我们将使用一个通用框架来设计最佳的多阶段研究。在多阶段设计中，所有标记都在第一阶段进行了基因分型和测试。只有有希望的标记随后才会在第二阶段使用额外的样本进行基因分型。我们的方法提供了三大优势。首先，由于在控制错误发现的同时发现易患疾病的变异所需的样本数量很大，GWA花费了数百万美元。与单阶段GWAS相比，优化的多阶段设计可以在真假发现方面达到相同的目标，并节省50%-70%的基因分型量。其次，单阶段设计完全基于可能不正确的假设，这些假设可能会导致目标无法实现或本可以以低得多的成本实现的目标。然而，多阶段设计提供了利用第一阶段(S)收集的信息来设计最佳随访研究的可能性。在公共领域发布GWAS数据的趋势将进一步增加这一多阶段设计的适应性特征的实际相关性，因为许多研究小组可能在GWAS数据公开后开始在自己的样本中进行复制研究。第三，我们的框架不是使用任意规则(例如，小于0.05的P值意味着复制)，而是提供统计激励的决策规则，用于声明重要性和随后对什么构成复制的解释。我们建议的具体目标包括评估和改进我们已经制定的基本框架。为了使该方法适用于广泛的研究场景，我们还提出了各种理论和计算扩展。为了确保在实践中的实用性，我们将在真实数据上测试我们的方法。最后，我们计划将计算机实现提供给广泛的研究人员。全基因组关联研究为识别常见的致病变异提供了极大的希望。该项目的目标是建立一个灵活的框架，以符合成本效益的方式设计这些研究，并优化后续的复制工作。