Novel statistical methods and tools to integrate multiple endophenotypes and functional annotation data to study the roles of rare variants in complex human diseases using sequencing data

项目摘要 在过去的15年里，人们对复杂结构的遗传结构进行了大量的研究。 通过全基因组关联研究发现人类疾病。尽管许多全基因组范围的显着变异 ，这些变异解释的遗传力或方差仍然很小，这表明 大量缺失的遗传力，可能还可以用效应大小较小的常见遗传变异来解释 和/或稀有和低频变量，这就需要开发和应用新的统计方法 方法从表型较深的队列中收集全基因组/外显子组测序数据。在这个项目中， 我们将开发利用多种相关内表型的方法，并进一步整合功能 注解数据，以确定复杂性状的新的稀有变异。我们将开发一套新的计算 实际有用并广泛适用于一般测序研究的分析工具，以及 我们的方法的应用将可能识别新的稀有变异关联，并为 心脏代谢性疾病遗传学。 在目标1中，我们建议开发新的统计方法来整合多种内表型来研究 罕见变异对复杂人类疾病的影响。我们的方法将填补当前 关联性研究的实践和整合内表型以改进的实际需要 了解和诊断临床结果。在目标2中，我们将把这些方法扩展到Meta分析 不同的研究。在目标3中，我们将开发一种新的核机器学习方法来集成不同的 功能信息来注释整个基因组区域，并进一步整合它们来开发动态的 全基因组扫描测试，以检测与多种内表型相关的罕见变异。我们将利用 NHLBI TOPMed全基因组测序(WGS)数据和英国生物库全外显子组测序(WES) 数据，并集成功能注释数据，以识别和剖析稀有变体在 心脏代谢特征(目标4)。我们拟议的工作具有成本效益，因为它利用了现有的WGS/WES 样例和功能注解数据，同时提供广泛适用于其他 研究并建立了一支强大的科学家团队，他们在统计遗传学方面有良好的记录，并大规模 基因研究和心脏新陈代谢特征。我们希望我们的方法将导致更多的发现 这些特征的罕见和低频率变异。这些结果将提供新的见解，帮助设计更多 有效的治疗和预防策略。我们所有建议的方法都会向公众传播。 通过经过良好测试和公开提供的软件(AIM 5)。

Project Summary In the past fifteen years, great efforts have been made to understand the genetic architecture of complex human diseases through genome-wide association studies. Although many genome-wide significant variants have been identified, the heritability or variance explained by these variants remains very small, suggesting substantial missing heritability that may yet be explained by common genetic variants with smaller effect sizes and/or rare and low frequency variants, which calls for the development and application of novel statistical methods to whole genome/exome sequencing data collected from deeply phenotyped cohorts. In this project, we will develop methods that leverage multiple correlated endophenotypes and further integrate functional annotation data to identify novel rare variants for complex traits. We will develop a set of new computational and analytical tools that are practically useful and broadly applicable to general sequencing studies, and the applications of our methods will likely identity novel rare variant associations and shed new lights on the genetics of cardiometabolic diseases. In Aim 1, we propose to develop novel statistical methods to integrate multiple endophenotypes to study the impact of rare variants on complex human diseases. Our methods will fill in the gap between the current practice of association studies and the practical needs of integrating endophenotypes for improved understanding and diagnosis of clinical outcomes. In Aim 2, we will extend the methods to meta-analyses across studies. In Aim 3, we will develop a novel kernel machine learning approach to integrating various functional information to annotate the whole genome region, and further integrate them to develop a dynamic whole-genome scan test to detect rare variant associations with multiple endophenotypes. We will leverage the NHLBI TOPMed whole genome sequencing (WGS) data and the UK Biobank whole exome sequencing (WES) data, and integrate the functional annotation data to identify and dissect the role of rare variants on the cardiometabolic traits (Aim 4). Our proposed work is cost-effective as it leverages the existing WGS/WES samples and functional annotation data while providing methods and tools that are broadly applicable to other studies, and builds on a strong team of scientists with proven track record in statistical genetics, large-scale genetic studies, and cardiometabolic traits. We expect our methods will lead to the discoveries of many more rare and low frequency variants for these traits. These results will offer new insights to help design more effective treatment and prevention strategies. All our proposed methods will be disseminated to the public through well-tested and publicly available software (Aim 5).