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，通过遗传关联研究寻找易患人类疾病的遗传变异的研究工作数量显著增加。在这项研究中，我们考虑了遗传关联研究的替代样本设计方法，目的是最大化检测基因型-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、更有效地识别使个体易患复杂人类疾病的遗传变异。我们将从评估收集重复基因型数据的成本效益开始。重复基因型数据是通过使用可能导致分类错误（例如单核苷酸多态性（snp））的方法对研究中某些部分的个体进行两次基因分型来收集的。目前的建议是在基因关联研究中重复5-10%的个体基因型。近年来，人们提出了将重复基因型数据纳入关联基因检测的方法。然而，没有努力评估收集重复基因型数据是否具有成本效益。我们将在固定预算下，通过检查收集重复（或更高重复）基因型数据的样本设计与不收集重复（或更高重复）基因型数据的样本设计的能力，评估收集重复基因型数据的成本效益。以类似的方式，我们将考虑获得条件重复基因型数据的成本效益。条件重复基因型数据是通过对某些个体进行重复基因分型获得的，但根据第一次观察到的基因型，分型率不同。我们还将评估条件双重抽样，即个体的部分被测序（一种近乎完美的基因分型方法），其速率取决于观察到的SNP基因型。我们将把这些设计建议与具有成本效益的双重抽样实施建议综合起来。双重抽样包括对个体的随机部分进行测序。此外，我们将考虑使用产生信息缺失数据的分类方法的成本效益，并演示如何在关联的相关测试中利用信息缺失数据。所有的设计建议都将被整合到免费的网络工具中，这样研究人员就可以快速评估这些可供选择的设计策略的成本效益。研究结论将以数学方式发展，通过计算机模拟加以证实，并根据实际遗传关联研究的数据加以证明。此外，所有的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单体型图计划完成以来，通过遗传关联研究寻找易患人类疾病的遗传变异的研究工作数量显著增加。在这项研究中，我们考虑了遗传关联研究的替代样本设计方法，目的是最大化检测基因型-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、更有效地识别使个体易患复杂人类疾病的遗传变异。

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.