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: 9087202
• 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/9087202
• 关键词: Address; Architecture; Archives; Cations; Characteristics; Colorectal; Colorectal Cancer; Complex; Computer software; Data; Databases; Development; Disease; Elements; Encyclopedia of DNA Elements; Environment; Environmental Risk Factor; Epidemiology; Epigenetic Process; Gene Expression; Genes; Genetic; Genetic Predisposition to Disease; Genetic Variation; Genome; Genomics; Genotype; Goals; Health; Heritability; Human Genome Project; Information Networks; Inherited; Lead; Linkage Disequilibrium; Malignant Neoplasms; Maps; 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 epidemiology; genetic variant; genome sequencing; genome wide association study; genome-wide; high throughput technology; insight; lifestyle factors; method development; next generation sequencing; novel; open source; personalized strategies; prevent; 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（应用程序提供）：人类基因组项目的最新发展和不同类型的高吞吐量技术的突破已经改变了研究人员通过朝着跨学科研究迈进的复杂疾病，收集有关疾病所有方面的数据。该应用的目的是开发有效的统计和计算方法，以整合遗传学，基因组学和流行病学数据，以理解复杂疾病中遗传学和环境的相互作用，并具有为预防和治疗这些疾病的个性化策略的长期目标。全基因组关联研究已经确定了数千种相关的遗传变异，并为这些特征的遗传结构提供了宝贵的见解。但是，大多数变体都确定了迄今为止的风险相对较小的会议，并且仅解释了一小部分o遗传力，这使许多人质疑如何解释其余的“缺失”遗传力。该应用程序从几个方面解决了这种“缺失”的遗传力：罕见的变体关联分析，基因环境相互作用以及超出加性遗传效应的遗传力估计。根据，我们提出以下特定目标。目的1是开发将功能信息整合到稀有变体关联分析中的方法。为了实现这一目标，AIM 1包括开发组织特异性功能注释的数据库，并从大型协作项目（例如DNA元素的百科全书和基因型组织表达）产生的公共数据中构建监管表达网络（EQTL）。稀有变体分析的理论特性也将是研究的 考虑到基因组特征的强大测试，例如链接二动和稀疏信号。 AIM 2是开发包含功能信息的稀有变体基因环境相互作用（GXE）的方法。还将开发出高效且多才多艺的筛选策略，以发现全基因组的GXE。即使该目标集中在GXE上，这些方法也适用于Gene-Gene相互作用（GXG）。目的3是开发估计遗传力的方法，该方法结合了GXE和GXG，以了解遗传易感性与环境之间的复杂相互作用。拟议的工作是由大型结合结构癌的财团激励的，该联盟对结直肠癌的大型财团有超过40,000名参与者来自良好的特征性研究，以及有关环境风险因素和GWAS和GWAS和整个基因序列数据的详细数据。开发的方法将应用于财团，以获得大肠癌的新见解，并证明了方法的可行性。由于这些方法适用于其他复杂疾病和特征，因此将开发基于R的开源软件并将其提交给综合的R档案网络以进行广泛传播。