Analyzing the behavior and interpreting the results of gene based tests of rare variant association

分析罕见变异关联的行为并解释基于基因的测试结果

• 批准号: 9099474
• 负责人: Nathan L Tintle
• 金额: $ 38.59万
• 依托单位: DORDT COLLEGE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-20 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9099474
• 关键词: Academic Research Enhancement Awards; Address; Architecture; Arts; Attention; Award; Behavior; Complex; Computer Simulation; Computer software; Counseling; Data; Data Set; Development; Disease; Disease model; Environment; Etiology; Eye; Fatty Acids; Foundations; Future; Gene Frequency; Genetic; Genetic Models; Genetic Research; Genetic screening method; Genome; Genotype; Goals; Hereditary Disease; Human Genome; Hypertension; Individual; Lead; Light; Methods; Mind; Modeling; Performance; Phenotype; Public Health Practice; Publications; Research; Research Design; Research Personnel; Sample Size; Students; Technology; Test Result; Testing; Translating; Uncertainty; United States National Institutes of Health; Variant; Weight; Work; base; case control; college; cost; cost effective; data modeling; design; experience; genetic association; genetic variant; genome sequencing; human disease; human genome sequencing; insight; journal article; method development; next generation sequencing; novel; novel strategies; performance tests; programs; prospective; public health relevance; rare variant; research study; risk variant; simulation; statistics; technological innovation; tool; undergraduate student; vector

 DESCRIPTION (provided by applicant): The technological and computational breakthroughs in the decade since the sequencing of the human genome have provided an unprecedented opportunity to understand the etiology of complex human diseases. Notably, the diminishing cost of next-generation sequencing means that it is now possible for researchers to obtain complete genome sequence information on thousands of diseased individuals. However, major statistical questions remain about optimal design and analysis of studies using next-generation sequencing data to study the contribution of rare variation to common diseases. At the foundation of many such questions is the lack of power for single marker, rare variant tests of association, motivating the development of many, potentially more powerful, variant-set based tests, which aggregate evidence from several individual variants into a single test statistic. Current and newly proposed variant-set based tests which attempt to address large variant situations vary in how they combine and weight variants, leading to poorly understood differences in performance under different genetic models. Much of the current focus is on developing an all-around "best" rare variant test, typically through assessment on simulated data. Regardless of which test--or, more likely, tests--emerge as optimal, several challenges will remain toward applying these methods to real, imperfect sequence data and then inferring underlying genetic architecture based on a statistically significant test result. Thus, rather than focus exclusively on novel test development, our research will center on gaining a deeper understanding of the behavior of rare variant set tests, the realistic application of these tests, and the development of methods to decompose significant test statistics to gain information that can guide future studies. We will pay specific attention to the interplay of various underlying disease models, test statistics, and study designs. This work will provide a critical step towards successfully identifying rare risk variants in future sequencing experiments and translating the results into public health practice. To achieve these goals, we propose the following specific aims: We will (1) develop a framework to understand the behavior of rare variant set tests, (2) evaluate rare variant set tests in the presence of imperfect data and (3) develop post-hoc analyses to identify causal variants and inform replication study design. We will conduct the research using a combination of analytic, computational and simulation approaches. Additionally, the work we will perform addresses the three main goals of NIH's R15 program: (a) to conduct meritorious research that will (b) strengthen the research environment of the liberal arts college where the research will be conducted, while (c) exposing undergraduate students to statistical genetics research. With this last goal in mind, the fourth aim of our proposal is to provide research experiences to undergraduate students when conducting aims 1, 2 and 3.

 描述（由适用提供）：自人类基因组测序以来十年来的技术和计算突破，为了解复杂人类疾病的病因提供了前所未有的机会。值得注意的是，下一代测序的成本降低意味着研究人员现在有可能获得有关数千个失职个体的完整基因组序列信息。但是，使用下一代测序数据来研究罕见变异对常见疾病的贡献，有关最佳设计和研究的主要统计问题仍然存在。许多这样的问题的基础是单个标记，罕见的关联变体测试缺乏能力，许多基于更强大的，基于变体的测试的动机，这些测试将几个单个变体的证据汇总到单个测试统计数据中。试图解决较大变体情况的当前和新提出的基于变体的测试在结合和重量变异方面有所不同，从而导致不同遗传模型下的性能差异很差。当前的许多重点是开发全方位的“最佳”稀有变体测试，通常是通过对模拟数据进行评估。无论哪种测试（或更有可能的测试）是最佳的，将在将这些方法应用于真实的，不完美的序列数据上，然后基于统计学意义的测试结果来推断潜在的遗传体系结构，将仍然存在一些挑战。那，而不是 我们的研究专注于新的测试开发，将集中在对稀有变体设置测试的行为，这些测试的现实应用以及分解重要测试统计量的方法的开发以获得可以指导未来研究的信息的过程中更深入地了解。我们将特别注意各种潜在疾病模型，测试统计和研究设计的相互作用。这项工作将为成功识别以后的测序实验中成功识别罕见风险变体的关键步骤，并将结果转化为公共卫生实践。为了实现这些目标，我们提出以下特定目的：我们将（1）开发一个框架来了解稀有变体集测试的行为，（2）在存在不完美的数据的情况下评估稀有变体设置测试，（3）开发事后分析以识别因果变体，并识别因果变体并为复制研究设计。我们将使用分析，计算和仿真方法的组合进行研究。此外，我们将执行的工作解决了NIH R15计划的三个主要目标：（a）进行有罪的研究，该研究将（b）加强将进行研究的研究环境，在该学院进行研究，而（c）将本科生的学生暴露于统计遗传学研究中。考虑到最后一个目标，我们的提案的第四个目标是在进行AIM 1、2和3时为本科生提供研究经验。