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

 描述（由申请人提供）：自人类基因组测序以来的十年中，技术和计算方面的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，下一代测序成本的降低意味着研究人员现在可以获得数千个患病个体的完整基因组序列信息。然而，主要的统计问题仍然是关于使用下一代测序数据研究罕见变异对常见疾病的贡献的最佳设计和分析。在许多这样的问题的基础是缺乏权力的单一标志物，罕见的变异测试的关联，激发了许多，潜在的更强大的，基于变量集的测试，从几个单独的变量汇总成一个单一的测试统计的证据的发展。当前和新提出的基于变体集的测试试图解决大的变体情况，它们如何联合收割机和权重变体不同，导致在不同的遗传模型下的性能差异知之甚少。目前的重点是开发一个全面的“最佳”罕见变异测试，通常是通过对模拟数据的评估。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，然后根据统计学上显著的测试结果推断潜在的遗传结构仍将面临一些挑战。因此， 专注于新的测试开发，我们的研究将集中在更深入地了解罕见变异集测试的行为，这些测试的实际应用，以及开发方法来分解显着的测试统计数据，以获得可以指导未来研究的信息。我们将特别关注各种基础疾病模型、检验统计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的风险变异并将结果转化为公共卫生实践迈出关键一步。为了实现这些目标，我们提出了以下具体目标：我们将（1）开发一个框架来理解罕见变异集检验的行为，（2）在存在不完美数据的情况下评估罕见变异集检验，（3）开发事后分析来识别因果变异并为复制研究设计提供信息。我们将使用分析，计算和模拟方法相结合进行研究。此外，我们将执行的工作解决了NIH的R15计划的三个主要目标：（a）进行有价值的研究，将（B）加强文科学院的研究环境，研究将进行，同时（c）暴露本科生统计遗传学研究。考虑到最后一个目标，我们提案的第四个目标是在进行目标1，2和3时为本科生提供研究经验。

 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.