Design and analysis of adaptive multistage genetic association studies

描述（由申请人提供）：最近已经可以在整个基因组中筛选许多遗传标记，以确定它们与疾病的关联。这些全基因组关联研究（GWAS）为识别常见的疾病易感变异提供了巨大的希望。该项目的目标是开发一个灵活的框架，用于设计具有成本效益的GWAS，并优化后续的复制工作。为此，我们将使用一个通用的框架来设计最佳的多阶段研究。在多阶段设计中，所有标记物在第一阶段进行基因分型和测试。只有有希望的标记物随后在第二阶段使用额外的样品进行基因分型。我们的方法提供了三大优势。首先，由于需要大样本量来发现疾病易感变异，同时控制错误发现，GWAS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和假发现方面实现相同的目标，同时节省50-70%的基因分型量。第二，单级设计完全基于可能不正确的假设，可能导致目标无法实现或目标本可以以更低的成本实现。然而，多阶段设计提供了使用第一阶段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多阶段设计的这种自适应功能的实际相关性，因为许多研究小组可能会在GWAS数据公开发布后开始在自己的样本中进行复制研究。第三，而不是使用任意的规则（例如，P值小于0.05表示复制），我们的框架将提供统计动机的决策规则，用于声明显着性和随后的解释是什么构成复制。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各种各样的研究场景，我们还提出了各种各样的理论和计算扩展。为了确保在实践中的实用性，我们将测试我们的方法对真实的数据。最后，我们计划使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疾病易感变异提供了巨大的希望。该项目的目标是制定一个灵活的框架，以具有成本效益的方式设计这些研究，并优化随后的复制工作。

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.