Relationships among Genetic Regulators of Aging Health and Lifespan

衰老健康与寿命的基因调节因子之间的关系

• 批准号: 9262856
• 负责人: ANATOLIY I YASHIN
• 金额: $ 175.52万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-15 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9262856
• 关键词: Address; Adverse effects; Age; Aging; Aging-Related Process; Area; Biological; Biological Process; Cardiovascular Diseases; Complex; Data; Data Set; Diabetes Mellitus; Discipline; Disease; Elderly; Environment; Epistatic Gene; Future; Genes; Genetic; Genetic Predisposition to Disease; Health; Human; Human body; Individual; Knowledge; Life; Life Cycle Stages; Link; Literature; Longevity; Malignant Neoplasms; Methodology; Methods; Modeling; Molecular; National Institute on Aging; Nature; Organism; Outcome; Pathway interactions; Phenotype; Physiological; Preventive Intervention; Principal Investigator; Process; Public Health; Regulation; Research; Risk; Role; SNP genotyping; Shapes; Statistical Data Interpretation; Statistical Models; Stochastic Processes; Systems Biology; age group; computerized data processing; data management; disability; gene environment interaction; genetic analysis; genetic profiling; genetic variant; genome wide association study; genome-wide; genome-wide analysis; healthy aging; human disease; improved; indexing; medical complication; mortality; next generation sequencing; non-genetic; personalized approach; pleiotropism; preempt; prevent; programs; public health relevance; routine care; trait

DESCRIPTION (provided by applicant): Composite Following the strategic directions proposed by the National Institute on Aging, an overall objective of the proposed research in this program project is to identify genetic and non-genetic factors and mechanisms, which can promote long and healthy life in humans on the basis of better understanding the relationships among regulators of aging, risks, of major diseases and related traits, and lifespan. This objective will be reached on the basis of integrative analyses of genome-wide SNP genotyping data and longitudinal data on life course processes in human organisms. The research in this program project will be performed in three subprojects, supported by the (A) Administrative, and (B) Data Management/Analytic Cores. We will use traditional and advanced methodologies of genetic analyses and statistical modeling, and methods of systems biology, which will be built on knowledge accumulated in the fields of aging, health, and lifespan incorporated into the integrative statistical platform. The methodological concept of the POI stands to advance paradigms of current GWAS and future association studies, using next generation sequencing, by bringing state-of-the-art methods to analyzing traits of late life that breaks new ground in the area of life-course genetics. The project will address three Specific Aims. Aim 1. Conduct comprehensive association analyses using genome-wide SNP genotyping data to identify pleiotropic and specific genetic underpinnings of lifespan, risks of major diseases, health related traits, and physiological aging changes in human body. Aim 2. Conduct analysis of up-to-date information on biological effects of pleiotropic and specific genes for SNPs discovered in Aim 1 to dissect their roles in molecular pathways, and biological processes and functions. Aim 3. Perform dynamic integration of genetic effects revealed in Aims 1 and 2 into the life course processes in individuals by combining methods of systems biology and advanced statistical modeling.

描述（由申请人提供）：复合材料根据国家老龄化研究所提出的战略方向，本计划项目中拟议研究的总体目标是确定遗传和非遗传因素和机制，这些因素和机制可以在更好地理解衰老，风险，主要疾病和相关特征以及寿命的调节因子之间的关系的基础上促进人类长寿和健康的生活。这一目标将在全基因组SNP基因分型数据和人类生物体生命过程纵向数据的综合分析的基础上实现。本计划项目的研究将在三个子项目中进行，由（A）行政和（B）数据管理/分析核心支持。我们将使用传统和先进的遗传分析和统计建模方法，以及系统生物学方法，这些方法将建立在整合到综合统计平台中的衰老，健康和寿命领域积累的知识基础上。POI的方法论概念主张使用下一代测序，通过将最先进的方法引入分析晚年特征来推进当前GWAS和未来关联研究的范式， 生命遗传学领域。该项目将实现三个具体目标。目标1.使用全基因组SNP基因分型数据进行全面的关联分析，以确定寿命、重大疾病风险、健康相关 特征和人体生理老化变化。目标2.对目标1中发现的SNP的多效性和特异性基因的生物学效应的最新信息进行分析，以剖析它们在分子途径、生物学过程和功能中的作用。目标3.通过结合系统生物学和先进的统计建模方法，将目标1和2中揭示的遗传效应动态整合到个体的生命过程中。