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现在，研究人员可以获得有关数千个患者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稀有变体结合测试缺乏能力，激发了许多潜在强大，基于基因的测试的发展，这些测试将几个单个变体的证据汇总为单个测试统计量。提出的基于基因的测试在结合和重量变异的方式上有所不同，从而导致不同遗传模型下的性能差异。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，将仍然存在一些挑战，将这些方法应用于真实的，不完美的序列数据，然后基于统计学意义的测试结果推断潜在的遗传体系结构。我们的研究并非专注于新的测试开发，而是将对基于基因的稀有变体测试的行为，这些测试的现实应用以及分解重要的测试统计数据的方法的开发以更深入了解，以获取可以指导未来研究的方法。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未来测序实验中罕见的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标：我们将（1）开发几何表示，以更好地了解基于基因的稀有变体测试的行为（2）评估基于基因的稀有变体测试在存在不完美数据的情况下，以及（3）开发事后分析以识别因果变体变体和重复研究的研究。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。 公共卫生相关性：旨在鉴定易感人类疾病的遗传变异的遗传关联研究数量。此外，由于测序和基因分型成本，新的统计技术（例如插补）以及对人类基因组的了解增加，进行这些研究的环境正在迅速改变。拟议的研究将为遗传关联研究提供设计和分析策略，以加速研究的步伐，以完全了解常见人类疾病的遗传结构。