Evaluating the Cost Effectiveness of Alternative Sample Designs for Genetic Assoc

描述(由申请人提供)：自人类基因组计划和国际HapMap计划完成以来，寻求通过基因关联研究寻找易患人类疾病的基因变异的研究工作数量显着增加。在这项研究中，我们考虑用于遗传关联研究的替代样本设计方法，目的是最大化检验基因-表型关联的统计能力。最大限度地提高统计能力将使研究人员能够更快、更有效地识别导致个人患复杂人类疾病的基因变异。我们将首先评估收集重复基因数据的成本效益。重复的基因数据是通过在一项研究中使用可能造成分类错误的方法(例如单核苷酸多态(SNPs))对部分个体进行两次基因分型来收集的。目前的建议是进行遗传关联研究，以复制研究中5%-10%的个体的基因。最近，有人提出了将重复的基因数据纳入关联基因测试的方法。然而，没有做出任何努力来评估收集重复的基因数据是否具有成本效益。我们将通过检查样本设计的能力来评估收集重复基因数据的成本效益，这些样本设计在固定预算下收集重复(或更高复制)的基因数据与不收集重复基因数据的样本设计相比。我们将以类似的方式考虑获得有条件的重复基因数据的成本效益。条件重复基因数据是通过对某些个体进行重复基因分型而获得的，但根据第一个观察到的基因以不同的速率进行。我们还将评估有条件的双重抽样，即根据观察到的SNP基因型对部分个体进行测序(一种近乎完美的基因分型方法)。我们将综合这些设计建议和以符合成本效益的方式实施双重抽样的建议。双重抽样涉及对随机部分个体进行测序。此外，我们将考虑使用分类方法创建信息性缺失数据的成本效益，并演示如何在相关的关联测试中利用信息性缺失数据。所有设计建议都将被整合到免费提供的网络工具中，以便研究人员能够快速评估这些替代设计战略在他们的研究中的成本效益。研究结论将从数学上得到发展，通过计算机模拟得到确认，并在实际遗传关联研究的数据上进行演示。此外，所有研究都将在本科生的积极参与下进行。自人类基因组计划和国际HapMap计划完成以来，寻求通过基因关联研究寻找易患人类疾病的基因变异的研究努力的数量显着增加。在这项研究中，我们考虑用于遗传关联研究的替代样本设计方法，目的是最大化检验基因-表型关联的统计能力。最大限度地提高统计能力将使研究人员能够更快、更有效地识别导致个人患复杂人类疾病的基因变异。

DESCRIPTION (provided by applicant): The number of research efforts seeking to find genetic variants that predispose to human disease via genetic association studies has grown significantly since the completion of both the Human Genome Project and the International HapMap Project. In this research we consider alternate sample design methodologies for genetic association studies, with the goal of maximizing statistical power for testing genotype-phenotype association. Maximizing statistical power will allow researchers to more quickly and efficiently identify genetic variants predisposing individuals to complex human diseases. We will start by evaluating the cost-effectiveness of gathering duplicate genotype data. Duplicate genotype data is collected by twice genotyping some portion of individuals in a study using a method that may make classification errors (e.g. Single Nucleotide Polymorphisms (SNPs)). Current recommendations are for genetic association studies to duplicate genotype 5-10% of the individuals in the study. Recently, methods were proposed to include duplicate genotype data into genetic tests of association. However, no effort was made to evaluate whether or not gathering duplicate genotype data is cost-effective. We will evaluate the cost-effectiveness of gathering duplicate genotype data by examining power of sample designs which gather duplicate (or higher replicate) genotype data versus those that don't, on a fixed budget. In a similar manner we will consider the cost-effectiveness of obtaining conditional duplicate genotype data. Conditional duplicate genotype data is obtained by duplicate genotyping some individuals but at different rates, dependent upon the first observed genotype. We will also evaluate conditional double sampling, whereby fractions of individuals are sequenced (a near perfect method of genotyping) at rates dependent on the observed SNP genotype. We will synthesize these design recommendations with recommendations for the cost-effective implementation of double sampling. Double sampling involves sequencing a random fraction of individuals. Additionally, we will consider the cost- effectiveness of using classification methods which create informative missing data and demonstrate how informative missing data can be utilized in related tests of association. All design recommendations will be integrated into freely available web-tools so that researchers can quickly assess the cost-effectiveness of these alternative design strategies for their study. Research conclusions will be developed mathematically, confirmed via computer simulation and demonstrated on data from actual genetic association studies. Additionally, all research will be conducted with the active involvement of undergraduate research students. The number of research efforts seeking to find genetic variants that predispose to human disease via genetic association studies has grown significantly since the completion of both the Human Genome Project and the International HapMap Project. In this research we consider alternate sample design methodologies for genetic association studies, with the goal of maximizing statistical power for testing genotype-phenotype association. Maximizing statistical power will allow researchers to more quickly and efficiently identify genetic variants predisposing individuals to complex human diseases.