Detecting pleiotropic effects through integration of omics data

通过组学数据整合检测多效性效应

• 批准号: 10117042
• 负责人: Andrew DeWan
• 金额: $ 69万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10117042
• 关键词: African; African American; Age; Asians; Asthma; Biolectric Impedance; Biological; Body mass index; Communities; Complex; Computer software; Data; Detection; Disease; Documentation; Drug Targeting; Ensure; Equation; Etiology; Gene Expression; Genes; Genetic Predisposition to Disease; Genetic Risk; Genetic Variation; Genotype-Tissue Expression Project; Goals; Heritability; High Density Lipoproteins; Hispanics; Individual; Lead; Lipids; Logistic Regressions; Low-Density Lipoproteins; Mediating; Methods; Modeling; National Heart, Lung, and Blood Institute; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Phenotype; Play; Population; Public Health; Reporting; Role; Sample Size; Sampling; Site; Smoking; Statistical Methods; Study Subject; Testing; Tissues; Trans-Omics for Precision Medicine; Triglycerides; Validation; Variant; Waist-Hip Ratio; base; biobank; blood lipid; design; differential expression; exome sequencing; genetic architecture; genome-wide; improved; insight; large datasets; lipoprotein triglyceride; low density lipoprotein triglyceride; mRNA Differential Displays; parallel processing; phenotypic data; pleiotropism; population based; programs; rare variant; sex; software development; therapy development; trait; whole genome

Vast amounts of whole genome sequence and imputed sequence data are being generated for many complex traits and diseases. Most studies, e.g. UK10K, National Heart, Lung and Blood Institute-Exome Sequencing Project, have concentrated on detecting main effects. Pleiotropy, although an important phenomenon in genetic etiology, has not been adequately studied and methods are limited to detect pleiotropy for rare and imputed variants. Additionally, although there have been reports of pleiotropic loci it has been difficult to elucidate if these effects underlie disease etiology or are false positives. We will tackle this problem using a multi-prong approach that utilizes pleiotropic association testing, estimating tissue-specific disease heritability and detecting tissue-specific pleiotropy. To meet the goals of this study we will use omics data, implement previously developed methods and extend existing methods to analyze imputed and rare variants. To ensure discoveries for a large variety of complex diseases and traits e.g. asthma, type 2 diabetes, adiposity, and lipids, and to demonstrate that these methods are an effective approach to study pleiotropy, data from the UK Biobank (500,000 study subjects) will be analyzed. A split sample design will be employed in which 350,000 subjects (Release 2) for Discovery and 150,000 subjects (Release 1) for Replication. Secondary replication and fine mapping will be performed using TOPMed data which will have >150,000 individuals with whole genome sequence data with 26% of these individuals being African- American, 10% Hispanic, and 7% Asian. All methods will be implemented in our SEQSpark software which uses parallel processing to make it feasible to analyze hundreds of thousands of samples efficiently and quickly. Not only is this study expected to improve our understanding of the genetic etiology for complex diseases and traits, but it also has high public health significance; understanding pleiotropic effects will improve our ability to estimate genetic risk and provide insight into drug targets for the development of treatments of multiple diseases due to shared genetic architecture. The framework and software developed in this proposal will be available to the scientific community to apply to other large datasets for the identification of pleiotropic loci beyond those phenotypes described here.

正在生成大量的全基因组序列和估算序列数据， 许多复杂的特征和疾病。大多数研究，例如UK 10 K，国家心肺和血液 研究所-外显子组测序项目，集中在检测主效应。多效性， 尽管这是遗传病因学中的一个重要现象，但尚未得到充分研究， 方法仅限于检测罕见和插补变异的多效性。此外，虽然 已经有关于多效性基因座的报道，但是很难阐明这些效应是否 是疾病病因学的基础还是假阳性。我们将采用多管齐下的方法解决这个问题。 一种利用多效性关联检验的方法，估计组织特异性疾病 遗传性和检测组织特异性多效性。为了实现本研究的目标，我们将使用 组学数据，实现以前开发的方法，并扩展现有的方法来分析 插补和罕见变异。确保发现各种复杂疾病， 特征，例如哮喘、2型糖尿病、肥胖和脂质，并证明这些方法 是研究多效性的有效方法，来自英国生物银行的数据（50万人研究 将进行分析。将采用分裂样本设计，其中350，000名受试者 （第2版）用于Discovery，150，000名受试者（第1版）用于Replication。二次 复制和精细映射将使用TOPM数据执行，这些数据将具有> 150，000 有全基因组序列数据的人，其中26%是非洲人， 美国人，10%西班牙裔，7%亚洲人。所有方法都将在我们的SEQSpark中实现 软件使用并行处理，使其能够分析数十万个 快速有效地采样。这项研究不仅有望提高我们对 复杂疾病和性状的遗传病因学，但它也具有很高的公共卫生 意义;了解多效性效应将提高我们估计遗传风险的能力 并为开发多种疾病的治疗方法提供药物靶点， 共同的基因结构。本提案中开发的框架和软件将 可供科学界应用于其他大型数据集， 多效性基因座超出这里描述的那些表型。