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

整合多种内表型和功能注释数据的新颖统计方法和工具，利用测序数据研究罕见变异在复杂人类疾病中的作用

• 批准号: 10372265
• 负责人: Baolin Wu
• 金额: $ 36.25万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10372265
• 关键词: Address; Basic Science; Biological; Cardiometabolic Disease; Cardiovascular Diseases; Clinical Research; Complex; Computer software; Data; Development; Disease; Frequencies; Genes; Genetic; Genetic study; Genome Scan; Heritability; Human; Knowledge; Large-Scale Sequencing; Machine Learning; Measures; Meta-Analysis; Metabolic Diseases; Methods; Modeling; National Heart, Lung, and Blood Institute; Outcome; Phenotype; Play; Prevention strategy; Public Health; Research; Research Personnel; Role; Sampling; Scientist; Software Tools; Source; Statistical Methods; Statistical Models; Testing; Time; Trans-Omics for Precision Medicine; Translations; Variant; Work; analytical tool; base; biobank; cardiometabolism; clinical Diagnosis; cohort; computerized tools; cost effective; design; disease diagnosis; effective therapy; endophenotype; exome sequencing; experience; genetic architecture; genetic variant; genome sequencing; genome wide association study; genome-wide; human disease; improved; insight; novel; programs; rare variant; tool; trait; treatment strategy; web site; whole genome

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).

项目概要 在过去的十五年里，人们为了解复杂的遗传结构付出了巨大的努力。 通过全基因组关联研究来研究人类疾病。尽管许多全基因组显着变异 已经确定，这些变异所解释的遗传力或方差仍然非常小，这表明 大量缺失的遗传性可能可以用效应较小的常见遗传变异来解释 和/或罕见和低频变异，这需要新的统计方法的开发和应用 从深度表型队列收集的全基因组/外显子组测序数据的方法。在这个项目中， 我们将开发利用多种相关内表型并进一步整合功能的方法 注释数据来识别复杂性状的新颖罕见变异。我们将开发一套新的计算 实用且广泛适用于一般测序研究的分析工具，以及 我们的方法的应用可能会识别新颖的罕见变异关联，并为研究提供新的线索 心脏代谢疾病的遗传学。 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的空白 关联研究的实践以及整合内表型以改善的实际需要 了解和诊断临床结果。在目标 2 中，我们将把方法扩展到荟萃分析 跨研究。在目标 3 中，我们将开发一种新颖的内核机器学习方法来集成各种 功能信息来注释整个基因组区域，并进一步整合它们以开发动态的 全基因组扫描测试可检测与多种内表型的罕见变异关联。我们将利用 NHLBI TOPMed 全基因组测序 (WGS) 数据和英国生物银行全外显子组测序 (WES) 数据，并整合功能注释数据来识别和剖析罕见变异对 心脏代谢特征（目标 4）。我们提议的工作具有成本效益，因为它利用了现有的 WGS/WES 样本和功能注释数据，同时提供广泛适用于其他领域的方法和工具 研究，并建立了一支强大的科学家团队，他们在统计遗传学、大规模 遗传学研究和心脏代谢特征。我们期望我们的方法将带来更多的发现 这些特征的罕见和低频变异。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。我们提出的所有方法都将向公众传播 通过经过充分测试的公开软件（目标 5）。