Integrative multivariate association and genomic analyses

综合多变量关联和基因组分析

• 批准号: 8612912
• 负责人: Lin Chen
• 金额: $ 32.23万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-15 至 2019-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8612912
• 关键词: Address; Architecture; Area; Arsenic; Bangladesh; Biological; Blood Pressure; Body mass index; Cardiovascular Diseases; Characteristics; Clinical Treatment; Clinical Trials; Communities; Complex; Coupled; Data; Data Set; Development; Disease; Environment; Environmental Risk Factor; Epidemiology; Epigenetic Process; Etiology; Gene Expression; Gene Expression Profile; Genes; Genetic; Genetic Structures; Genetic Variation; Genome; Genomics; Genotype; Goals; Group Structure; Health; Hormones; Human; Individual; Investigation; Joints; Licensing; Measures; Methods; Modeling; Multivariate Analysis; Network-based; Noise; Outcome; Pathway interactions; Pattern; Phenotype; Portraits; Positioning Attribute; Procedures; Quantitative Trait Loci; Research; Research Personnel; Resources; Risk Factors; Role; Sampling; Signal Transduction; Skin Cancer; Staging; Statistical Methods; System; Testing; Toxic effect; Transcript; Variant; Work; base; computerized tools; cost effective; data integration; flexibility; functional group; gene environment interaction; genetic variant; genome-wide; human disease; improved; insight; member; method development; non-genomic; novel; novel strategies; open source; population based; public health relevance; rare variant; screening; software development; statistics; tool; trait; treatment effect

Summary In order to understand the genomic architecture and etiology for complex human diseases, great efforts have been extended in the past decades on research involving genome-wide genetic variation, transcriptome, and other genomic information. To date, rich resources have been generated and most are made publicly available after being analyzed for respective primary goals/hypotheses. Yet our understandings of human disease mechanisms are just beginning, and those understandings would require both the identification of a cadre of genetic and epigenetic risk factors, and the integration of key factors into a synergistic system. To best utilize existing data and facilitate research on complex human diseases, the long-term objective of the proposed research is to develop powerful and efficient statistical methods and computational tools for multivariate analyses in mainly two areas: association studies with the integration of genomic and non-genomic information in order to further identify genetic variation for complex diseases; and integrative genomic analyses that jointly analyze genetic variation, transcriptome, and other information in the genome. In Aim 1, we propose novel and powerful methods for gene-based association tests, for identification of genetic variation associated with multivariate disease profiles, and for gene-based gene-environment interaction tests. In Aim 2, we develop regularized methods for construction and comparison of eQTL networks. The later can also be used to reveal important genetic variants and regulatory relationships through characterizing the changes in genetic regulatory patterns across different phenotypic or environmental groups. Much of our proposed work is motivated by and will be applied to a genetic-genomic study on arsenic toxicity, Gene-Environment Multi- phenotype Study (GEMS). In Aim 3, we propose methods tailored for the characteristics of this data set; we will also test novel scientific hypotheses on this unique and large arsenic toxicity study. Our proposal is cost- effective as it analyzes existing data from GEMS while providing methods and tools for new research directions. We anticipate that the proposed method development, when applied to and beyond the arsenic toxicity data, would yield valuable insights on clinical trial treatment effects, and on disease etiology for several complex diseases/traits, including but not limited to, arsenic-related skin cancer, cardiovascular diseases, hormone measures, body mass index and blood pressure.

概括 为了了解复杂人类疾病的基因组结构和病因学，人们付出了巨大的努力 在过去的几十年中，涉及全基因组遗传变异、转录组和 其他基因组信息。迄今为止，已经产生了丰富的资源，并且大部分资源已公开 在对各自的主要目标/假设进行分析之后。然而我们对人类疾病的理解 机制才刚刚开始，这些谅解需要确定一支骨干队伍 遗传和表观遗传风险因素，以及将关键因素整合到协同系统中。充分利用 现有数据并促进对复杂人类疾病的研究，是拟议的长期目标 研究的目的是开发强大而高效的多元统计方法和计算工具 主要分析两个领域：基因组和非基因组信息整合的关联研究 为了进一步识别复杂疾病的遗传变异；和综合基因组分析，共同 分析基因组中的遗传变异、转录组和其他信息。在目标 1 中，我们提出新颖且 基于基因的关联测试的强大方法，用于识别与 多变量疾病概况，以及基于基因的基因-环境相互作用测试。在目标 2 中，我们开发 eQTL 网络构建和比较的正则化方法。后者也可以用来揭示 通过表征遗传的变化来确定重要的遗传变异和调控关系 不同表型或环境群体的监管模式。我们提议的大部分工作是 受到砷毒性基因-环境多基因组研究的推动并将应用于砷毒性的基因-环境多基因组研究 表型研究（GEMS）。在目标 3 中，我们提出了针对该数据集特征量身定制的方法；我们将 还在这项独特的大型砷毒性研究中测试新颖的科学假设。我们的建议是成本- 它有效地分析了 GEMS 的现有数据，同时为新的研究方向提供了方法和工具。 我们预计所提出的方法开发当应用于砷毒性数据并超出砷毒性数据时， 将为临床试验治疗效果以及多种复杂疾病的病因学提供有价值的见解 疾病/特征，包括但不限于砷相关皮肤癌、心血管疾病、激素 测量、体重指数和血压。