Evaluating the Cost Effectiveness of Alternative Sample Designs for Genetic Assoc

评估遗传关联替代样本设计的成本效益

• 批准号: 7363067
• 负责人: Nathan L Tintle
• 金额: $ 19.35万
• 依托单位: HOPE COLLEGE
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7363067
• 关键词: Academic Research Enhancement Awards; Accounting; Address; Area; Budgets; Classification; Complex; Computer Simulation; Data; Disease; Enrollment; Evaluation; Genetic; Genetic screening method; Genotype; Goals; Gold; Human Genome Project; Individual; International; Internet; Methodology; Methods; Numbers; Online Systems; Phenotype; Rate; Recommendation; Research; Research Personnel; Resource Allocation; Resources; SNP genotyping; Sampling; Single Nucleotide Polymorphism; Software Tools; Staging; Standards of Weights and Measures; Statistical Methods; Statistical Models; Students; Testing; Time; base; college; cost; cost effectiveness; design; genetic association; genetic variant; genome wide association study; genotyping technology; high school; human disease; interest; software development; time use; tool

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.

描述（由申请人提供）：自从人类基因组项目和国际HAPMAP项目完成以来，通过遗传关联研究倾向于发现人类疾病的遗传变异的研究数量已显着增长。在这项研究中，我们考虑了用于遗传关联研究的替代样品设计方法，目的是最大化测试基因型 - 表型关联的统计能力。最大化统计能力将使研究人员能够更快速有效地识别易于人类疾病的遗传变异。我们将首先评估收集重复基因型数据的成本效益。重复的基因型数据是通过使用可能导致分类误差的方法（例如单核苷酸多态性（SNP））两倍的基因分型来收集的。当前的建议是针对研究中5-10％的基因型的遗传关联研究。最近，提出方法将重复的基因型数据包括在关联的基因检验中。但是，没有努力评估收集重复的基因型数据是否具有成本效益。我们将通过检查样本设计的功率来收集重复基因型数据的成本效益，这些设计的能力收集了重复（或更高复制）基因型数据，而不是在固定预算的情况下进行的。以类似的方式，我们将考虑获得有条件重复基因型数据的成本效益。有条件的重复基因型数据是通过重复基因分型来获得某些个体获得的，但速率不同，取决于第一个观察到的基因型。我们还将评估条件双重抽样，以根据观察到的SNP基因型的速率对个体的分数进行测序（一种接近完美的基因分型方法）。我们将与这些设计建议合成这些建议，并建议对双重抽样的具有成本效益的实施。双重抽样涉及对个体的随机分数进行测序。此外，我们将考虑使用分类方法的成本效益，这些分类方法创造了信息性丢失的数据，并证明如何在关联的相关测试中使用信息性丢失的数据。所有设计建议都将集成到免费可用的网络工具中，以便研究人员可以快速评估这些替代设计策略的成本效益。研究结论将以数学开发，通过计算机模拟确认，并在实际遗传关联研究的数据中证明。此外，所有研究都将在本科研究专业的学生积极参与下进行。 自从人类基因组项目和国际HAPMAP项目完成以来，寻求通过遗传关联研究使人类疾病的遗传变异的研究数量显着增长。在这项研究中，我们考虑了用于遗传关联研究的替代样品设计方法，目的是最大化测试基因型 - 表型关联的统计能力。最大化统计能力将使研究人员能够更快速有效地识别易于人类疾病的遗传变异。