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: 9813293
• 负责人: Nathan L Tintle
• 金额: $ 35.27万
• 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 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9813293
• 关键词: Academic Research Enhancement Awards; Address; Area; Arts; Award; Behavior; Biological Markers; Categories; Complex; Computer software; Counseling; Data; Data Quality; Data Set; Disease; Electronic Health Record; Environment; Etiology; Eye; Fatty Acids; Foundations; Genetic; Genetic Markers; Genetic Research; Genetic Variation; Genetic screening method; Genome; Genotype; Goals; Human Genome; Hypertension; Individual; International; Knowledge; Light; Linear Models; Linear Regressions; Measurement; Methods; Mind; Performance; Phenotype; Privacy; Publications; Recording of previous events; Research; Research Personnel; Role; Source; Statistical Methods; Students; Technology; Testing; Uncertainty; United States National Institutes of Health; Variant; Work; biobank; clinically relevant; college; cost; design; experience; genetic architecture; genetic association; genetic variant; human disease; human genome sequencing; improved; journal article; next generation sequencing; novel; performance tests; phenotypic data; programs; rare variant; repository; statistics; undergraduate student

The technological and computational breakthroughs in the years 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 many thousands of individuals, with widespread access to that data via large repositories of electronic health records (EHRs; e.g,. biobanks). However, major statistical questions remain about biobank-era analysis strategies in order to study the contribution of genetic variation to common diseases. In particular, foundational statistical questions exist in the areas of: (a) a recognition of the need to minimize computational complexity and respect data privacy concerns, (b) random and non-random missing data, (c) data uncertainty and errors (both phenotypic and genotypic), and (d) the role of multi-marker (variant-set) tests, which aggregate evidence from many individual variants into a single test statistic. Research by our group as part of our existing award (R15-HG0006915 (2011-present)) began by developing a framework for evaluating the performance of existing variant-set tests. We then utilized this framework to provide a clear understanding of test performance in a variety of circumstances, developed novel robust and powerful tests, evaluated method performance in light of genotype uncertainty, developed methods to characterize underlying genetic architecture and demonstrated the utility of these methods to understand the genetics of fatty acids and high blood pressure. Recently, we proposed a novel method for utilizing summary statistics from simple one variant – one phenotype linear models to draw inferences about complex phenotypes (in this case, the linear combination of phenotypes) as a first step to provide computationally efficient, biobank era-ready statistical methods for assessing genotype-phenotype association. Moving forward, our research will generalize this initial method to be applicable to any complex phenotype. Additionally, we will continue to build on a strong history of exploration of uncertainty, by considering the impact of random and non-random errors and uncertainty on genotype-phenotype association tests in the biobank era, and extension of these methods to multi-marker test settings. Methods we develop will be tested on both simulated and real data via the CHARGE consortium. 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.

自人类基因组测序以来的几年中的技术和计算突破 已经提供了一个前所未有的机会来了解复杂人类疾病的病因。尤其， 下一代测序的成本降低意味着现在研究人员可以获得 完整的基因组序列信息有关成千上万的人，并访问宽度 通过电子健康记录的大型存储库（EHRS；例如，生物库）的数据。但是，主要统计数据 关于生物银行时代分析策略的问题仍然存在，以研究遗传变异对 常见疾病。特别是，基础统计问题存在于：（a）认识到： 需要最大程度地减少计算复杂性并尊重数据隐私问题，（b）随机和非随机 缺少数据，（c）数据不确定性和错误（表型和基因型），以及（d）多标记的作用 （变体设置）测试，该测试将许多单个变体的证据汇总到单个测试统计数据中。研究 我们的小组作为我们现有奖项的一部分（R15-HG0006915（2011年至今）），首先开发一个框架 用于评估现有变体测试的性能。然后，我们利用此框架提供了一个清晰的 在各种情况下了解测试性能，开发了新颖的强大测试， 根据基因型不确定性，评估的方法性能，开发的方法来表征基础 遗传结构并证明了这些方法的实用性，以了解脂肪酸的遗传学 和高血压。最近，我们提出了一种新的方法，用于使用简单的摘要统计信息 一种变体 - 一种表型线性模型，以提出有关复杂表型的推断（在这种情况下， 表型的线性组合）作为提供计算高效的生物库准备的第一步 评估基因型 - 表型关联的统计方法。向前迈进，我们的研究将 将此初始方法概括为适用于任何复杂表型。此外，我们将继续建造 通过考虑随机错误和非随机错误的影响，探索不确定性的悠久历史 以及生物库时代基因型 - 表型关联测试的不确定性，并扩展这些方法 进行多标记测试设置。我们开发的方法将通过模拟和真实数据进行测试 收费财团。此外，我们将执行的工作将解决NIH R15的三个主要目标 计划：（a）进行有功的研究，以（b）加强自由主义的研究环境 将进行研究的艺术学院，而（c）将本科生暴露于统计数据 遗传学研究。考虑到最后一个目标，我们的建议的第四个目标是提供研究经验 在进行AIM 1、2和3时，本科生。