Detecting pleiotropic effects through integration of omics data

大量的全基因组序列和推定的序列数据正在为 许多复杂的特征和疾病。大多数研究，例如UK10K，国家心肺和血液 研究所-外显子组测序项目，主要集中在检测主效应项。多效性， 虽然是遗传病因学中的一个重要现象，但尚未得到充分的研究和 方法仅限于检测稀有和可归因性变异的多效性。此外，尽管 已经有关于多效性基因座的报道，但很难阐明这些效应是否 疾病病因或为假阳性。我们将多管齐下解决这个问题。 利用多效性关联检验，估计特定组织疾病的方法 遗传力和检测组织特异性多效性。为了实现这项研究的目标，我们将使用 组学数据，实施以前开发的方法并扩展现有方法以进行分析 被认为是稀有的变种。确保对多种复杂疾病的发现和 特征，如哮喘、2型糖尿病、肥胖症和血脂，并证明这些方法 是研究多效性的有效方法，来自英国生物库的数据(500,000项研究 主题)将被分析。将采用分裂样本设计，受试者为350,000人 (版本2)用于发现，150,000名受试者(版本1)用于复制。次要的 将使用TOPMed数据执行复制和精细映射，该数据将包含150,000 拥有全基因组序列数据的个人，其中26%是非洲人- 美国人，10%的西班牙人，7%的亚洲人。所有方法都将在我们的SEQSpark中实现 使用并行处理的软件，使其能够分析数十万 高效、快速地采样。这项研究不仅有望提高我们对 复杂疾病和特征的遗传病因，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了药物靶点 到共享的基因架构。在本提案中开发的框架和软件将是 可供科学界应用于其他大型数据集以识别 这里描述的那些表型之外的多效性基因座。

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.