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

人类基因组测序以来在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， 下一代测序成本的降低意味着研究人员现在有可能获得 数千个个体的完整基因组序列信息，可广泛获取 通过大型电子健康记录储存库(EHR；例如，生物库)。然而，主要的统计数据 关于生物库时代的分析策略仍然存在问题，以研究遗传变异对 常见病。特别是，在以下领域存在基本的统计问题：(A)认识到 需要最大限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 数据缺失，(C)数据不确定性和错误(表型和遗传型)，以及(D)多标记的作用 (变量集)测试，将来自许多单个变量的证据聚合到单个测试统计数据中。研究 作为我们现有奖项(R15-HG0006915(2011-现在))的一部分，我们小组首先制定了一个框架 用于评估现有变量集测试的性能。然后，我们利用这个框架提供了一个清晰的 了解各种情况下的测试性能，开发了新的健壮和强大的测试， 根据基因型不确定性评估方法性能，开发方法来表征潜在的 遗传结构，并演示了这些方法在理解脂肪酸遗传学方面的作用 还有高血压。最近，我们提出了一种新的方法来利用简单的摘要统计信息 一个变量-一个表型线性模型，用于推断复杂表型(在本例中， 表型的线性组合)作为第一步，提供计算高效的、适应时代的生物库 评估基因-表型关联的统计方法。展望未来，我们的研究将 将这一初始方法推广到任何复杂的表型。此外，我们将继续建设 通过考虑随机和非随机误差的影响，对不确定性的探索有很强的历史 和在生物库时代的基因-表型关联测试的不确定性，以及这些方法的扩展 到多标记测试设置。我们开发的方法将通过模拟和真实数据进行测试 指控财团。此外，我们将执行的工作解决了NIH R15的三个主要目标 方案：(A)进行有价值的研究，这将(B)加强自由派的研究环境 进行研究的艺术学院，同时(C)让本科生接触统计学 遗传学研究。考虑到最后一个目标，我们建议的第四个目标是提供研究经验 对本科生实施目标1、目标2和目标3。

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.