Methods for Integrating Functional Data into Complex Disease Genetic Analyses

将功能数据整合到复杂疾病遗传分析中的方法

• 批准号: 9308935
• 负责人: Li Hsu
• 金额: $ 46.42万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9308935
• 关键词: Address; Architecture; Archives; Characteristics; Colorectal Cancer; Complex; Computer software; Data; Databases; Development; Disease; Elements; Encyclopedia of DNA Elements; Environment; Environmental Risk Factor; Epigenetic Process; Gene Expression; Genes; Genetic; Genetic Predisposition to Disease; Genetic Variation; Genome; Genome Mappings; Genomics; Genotype; Goals; Hereditary Disease; Heritability; Human Genome Project; Information Networks; Inherited; Lead; Linkage Disequilibrium; Malignant Neoplasms; Methods; Molecular; Nucleotides; Participant; Property; Research Personnel; Resources; Risk; Sample Size; Signal Transduction; Techniques; Technology; Testing; Theoretical Studies; Tissues; Variant; Work; base; epidemiologic data; gene discovery; gene environment interaction; gene interaction; genetic analysis; genetic association; genetic epidemiology; genetic variant; genome sequencing; genome wide association study; genome-wide; genomic data; high throughput technology; insight; lifestyle factors; method development; next generation sequencing; novel; open source; personalized strategies; prevent; public health relevance; rare variant; screening; statistics; trait; whole genome

 DESCRIPTION (provided by applicant): Recent developments in The Human Genome Project and breakthroughs in different types of high throughput technologies have changed how researchers approach complex diseases by moving toward cross- disciplinary studies, collecting data on all facets of disease. The objective of this application is to develop efficient statistica and computational approaches to integrating genetics, genomics and epidemiologic data for understanding the interplay of genetics and environment in complex diseases, with the long-term goal of devising personalized strategies to prevent and treat these diseases. Genome-wide association studies have identified thousands of trait associated genetic variants, and provided valuable insights into the genetic architecture of these traits. However, most variants identified so far confer relatively small increments in risk, and explain only a small proportion o heritability, leading many to question how the remaining 'missing' heritability can be explained. This application addresses this 'missing' heritability from several aspects: rare variant association analysis, gene-environment interaction, and heritability estimation beyond additive genetic effects. Accordingly, we propose the following specific aims. Aim 1 is to develop methods for integrating functional information into rare variants association analysis. To achieve this goal, Aim 1 includes developing databases of tissue-specific functional annotation and constructing regulatory expression networks (eQTL) from public data generated from large collaborative projects such as the Encyclopedia of DNA Elements and the Genotype Tissue Expression. The theoretical properties of the rare variants analysis will also be studied to devise powerful tests in consideration of genomic features such as linkage disequilibrium and sparse signals. Aim 2 is to develop methods for rare variants gene-environment interaction (GxE) that incorporates functional information. Efficient and versatile screening strategies will also be developed for genome-wide discovery of GxE. Even though this aim is focused on GxE, the methods are also applicable to gene-gene interaction (GxG). Aim 3 is to develop methods for estimating heritability that incorporates GxE and GxG to understand the complex interplay between genetic susceptibility and environment The proposed work is motivated by a large consortium on colorectal cancer, which has over 40,000 participants from well-characterized studies with detailed data on both environmental risk factors and GWAS and whole genome sequencing data. The developed methods will be applied to the consortium to gain new insights in colorectal cancer and demonstrate the feasibility of the methods. Since the methods are applicable to other complex diseases and traits, R-based open source software will be developed and submitted to the Comprehensive R Archive Network for broad dissemination.

 描述（由申请人提供）：人类基因组计划的最新发展和不同类型高通量技术的突破已经改变了研究人员通过跨学科研究、收集疾病各个方面的数据来处理复杂疾病的方式。应用程序是开发有效的统计和计算方法来整合遗传学、基因组学和流行病学数据，以了解复杂疾病中遗传学和环境的相互作用，长期目标是制定个性化策略来预防和治疗这些疾病全基因组关联研究已经发现了数千种与性状相关的遗传变异，并为这些性状的遗传结构提供了有价值的见解。然而，迄今为止发现的大多数变异带来的风险增量相对较小，并且只能解释一小部分遗传性。 ，导致许多人质疑如何解释剩余的“缺失”遗传力，该应用程序从几个方面解决了这种“缺失”遗传力：稀有变异关联分析、基因-环境相互作用以及超越加性遗传效应的遗传力估计。具体如下目标 1 是开发将功能信息整合到稀有关联分析中的方法，为了实现这一目标，目标 1 包括开发组织特异性功能注释数据库并根据大型合作项目生成的公共数据构建调控表达网络 (eQTL)。作为 DNA 元素和基因型组织表达百科全书，稀有变异分析的理论特性也将被研究和设计。 考虑到连锁不平衡和稀疏信号等基因组特征的强大测试，目标 2 是开发包含功能信息的罕见变异基因-环境相互作用 (GxE) 的方法，用于全基因组发现。尽管该目标主要针对 GxE，但这些方法也适用于基因-基因相互作用 (GxG)。目标 3 是开发结合 GxE 的遗传力估计方法。和 GxG 来了解遗传易感性和环境之间复杂的相互作用这项拟议的工作是由结直肠癌大型联盟推动的，该联盟拥有超过 40,000 名来自具有良好特征的研究的参与者，这些研究提供了有关环境风险因素和 GWAS 的详细数据以及全基因组测序数据所开发的方法将应用于该联盟，以获得对结直肠癌的新见解，并证明该方法的可行性，因为这些方法适用于其他复杂的疾病和特征，因此基于 R 的开源软件将被采用。开发并提交给综合 R 档案网络以供广泛传播。