Analyzing the behavior and interpreting the results of gene based tests of rare v

分析稀有病毒的行为并解释基于基因的测试结果

• 批准号: 8367623
• 负责人: Nathan L Tintle
• 金额: $ 39.16万
• 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 2016-06-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8367623
• 关键词: Academic Research Enhancement Awards; Address; Architecture; Arts; Attention; Behavior; Complex; Computer Simulation; Data; Data Set; Development; Disease; Disease model; Doctor of Philosophy; Environment; Etiology; Foundations; Frequencies; Future; Gene Frequency; Genes; Genetic; Genetic Models; Genetic Research; Genotype; Goals; Hereditary Disease; Human Genome; Individual; Knowledge; Length; Mentors; Methods; Mind; Modeling; Pattern; Performance; Population; Positioning Attribute; Public Health Practice; Publications; Research; Research Design; Research Personnel; Risk; Sampling; Scheme; Simulate; Source; Students; Technology; Test Result; Testing; Translating; Uncertainty; Variant; Weight; Work; base; case control; college; cost; cost effective; design; experience; genetic association; genetic variant; genome sequencing; human disease; insight; method development; next generation; novel; performance tests; programs; prospective; research study; simulation; statistics; 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, gene-based tests, which aggregate evidence from several individual variants into a single test statistic. The proposed gene-based tests 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. Ths, rather than focus exclusively on novel test development, our research will center on gaining a deeper understanding of the behavior of gene-based rare variant 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 geometric representation to better understand the behavior of gene-based rare variant tests (2) evaluate gene-based rare variant 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. PUBLIC HEALTH RELEVANCE: The number of genetic association studies seeking to identify genetic variants that predispose to human diseases continues to grow. Furthermore, the environment for conducting these studies is rapidly changing due to declining sequencing and genotyping costs, new statistical technologies (e.g. imputation) and increasing understanding of the human genome. The proposed research will provide design and analysis strategies for genetic association studies in order to accelerate the pace of research towards the goal of a complete understanding of the genetic architecture of common human diseases.

描述（由申请人提供）：自人类基因组测序以来的十年间，技术和计算方面的突破为了解复杂人类疾病的病因学提供了前所未有的机会。值得注意的是，下一代测序成本的下降意味着研究人员现在可以获得数千名患病个体的完整基因组序列信息。然而，关于使用下一代测序数据研究罕见变异对常见疾病的影响的研究的优化设计和分析，主要的统计问题仍然存在。许多此类问题的基础是缺乏单一标记罕见变异关联测试的能力，这激发了许多潜在更强大的基于基因的测试的开发，这些测试将来自多个个体变异的证据汇总到单个测试统计数据中。所提出的基于基因的测试在组合方式和权重变异方面各不相同，导致人们对不同遗传模型下的性能差异知之甚少。当前的大部分重点是开发全面的“最佳”稀有变异测试，通常是通过对模拟数据进行评估。无论哪种测试（或者更有可能的是测试）成为最佳测试，将这些方法应用于真实的、不完美的序列数据，然后根据统计上显着的测试结果推断潜在的遗传结构，仍然存在一些挑战。因此，我们的研究将集中于更深入地了解基于基因的稀有变异测试的行为、这些测试的实际应用以及开发分解重要测试统计数据以获得结果的方法，而不是仅仅关注新的测试开发。可以指导未来学习的信息。我们将特别关注各种潜在疾病模型、测试统计数据和研究设计的相互作用。这项工作将为在未来的测序实验中成功识别罕见风险变异并将结果转化为公共卫生实践提供关键的一步。为了实现这些目标，我们提出以下具体目标：我们将（1）开发几何表示以更好地理解基于基因的稀有变异测试的行为（2）在存在不完美数据的情况下评估基于基因的稀有变异测试以及(3) 开展事后分析以识别因果变异并为复制研究设计提供信息。我们将结合分析、计算和模拟方法进行研究。此外，我们将开展的工作涉及 NIH R15 计划的三个主要目标：(a) 进行有价值的研究，(b) 加强将进行研究的文理学院的研究环境，同时 (c) 揭露本科生进行统计遗传学研究。考虑到最后一个目标，我们提案的第四个目标是在实现目标 1、2 和 3 时为本科生提供研究经验。 公共卫生相关性：寻求识别易患人类疾病的遗传变异的遗传关联研究数量持续增长。此外，由于测序和基因分型成本下降、新的统计技术（例如插补）以及对人类基因组了解的不断加深，进行这些研究的环境正在迅速变化。拟议的研究将为遗传关联研究提供设计和分析策略，以加快研究步伐，实现全面了解人类常见疾病的遗传结构的目标。