Development of "Evidential" Methodology for the Analysis of Genetic Data

开发遗传数据分析的“证据”方法

• 批准号: 7546976
• 负责人: Lisa Joanna Strug
• 金额: $ 6.18万
• 依托单位: HOSPITAL FOR SICK CHLDRN (TORONTO)
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7546976
• 关键词: Backcrossings; Belief; Characteristics; Complex; Computer Simulation; Data; Data Set; Development; Disease model; Genes; Genetic; Genetic Models; Germ Cells; Heterogeneity; Investigation; Knowledge; Measures; Methodology; Methods; Modeling; Nuclear Family; Penetrance; Probability; Research Personnel; Sample Size; Sampling; Siblings; Specific qualifier value; Testing; Variant; design; genetic analysis; genetic linkage analysis; genome wide association study; research study; trait

DESCRIPTION (provided by applicant): The "evidential" paradigm is a statistical paradigm, an alternative to frequentist and Bayesian paradigms for statistical inference. The evidential paradigm provides 4 major advantages over these other paradigms when analyzing genetic data: It (1) provides an objective measure of evidence; (2) has good operating characteristics (low error probabilities); (3) decouples the error probabilities from the measure of evidence; and - perhaps the advantage with the greatest potential impact - (4) provides better approaches to deal with the multiple testing problem. In Strug & Hodge (2006a, b) we quantified these advantages for linkage analysis of several simple genetic models, mostly with known parameters. Here we propose to extend our investigations to more complex genetic models, also with unknown parameters, and to association analysis: Specifically, we will (1) Extend linkage findings to association analysis, including genome-wide association studies; (2) Quantify error probabilities for linkage of more complex disease models; and (3) Develop and test new evidential methodology for linkage analysis of complex unknown traits. We will test and characterize new methods via rigorous theoretical analyses, supplemented by realistic computer simulations. /Relevance The long-term objective is to make the evidential paradigm, with its multiple testing advantages, available for use when analyzing all types of genetic data. As a result, some current problems imposed by conducting multiple tests on genetic data, for example, investigators' reluctance to thoroughly analyze their collected data, will no longer bedevil the field or stunt the advancement of knowledge.

描述（由申请人提供）：“证据”范式是一种统计范式，是统计推断的频率论和贝叶斯范式的替代方案。在分析遗传数据时，证据范式提供了4个主要优势：（1）提供了客观的证据衡量标准;（2）具有良好的操作特性（3）从证据的度量中计算错误概率;并且--也许是具有最大潜在影响的优点--（4）提供了处理多重测试问题的更好方法。在Strug & Hodge（2006 a，B）中，我们量化了几个简单遗传模型的连锁分析的这些优势，大多数是已知参数的。在这里，我们建议将我们的研究扩展到更复杂的遗传模型，也有未知的参数，并关联分析：具体来说，我们将（1）将连锁发现扩展到关联分析，包括全基因组关联研究;（2）量化更复杂的疾病模型的连锁错误概率;（3）开发和测试新的证据方法，用于复杂未知性状的连锁分析。我们将通过严格的理论分析来测试和表征新方法，并辅以现实的计算机模拟。长期目标是使证据范式具有多种测试优势，可用于分析所有类型的遗传数据。因此，目前对基因数据进行多重测试所带来的一些问题，例如调查人员不愿意彻底分析他们收集的数据，将不再困扰该领域或阻碍知识的进步。