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）：遵循国家衰老研究所提出的战略方向的综合，该计划项目中拟议研究的总体目标是识别遗传和非遗传因素和机制，这些因素和机制可以更好地促进人类的长期健康生活，以更好地理解主要疾病，主要疾病和相关特质和相关特质和生活的人之间的调节剂之间的关系。将根据对人类生物的生命过程过程的全基因组SNP基因分型数据和纵向数据的综合分析来实现此目标。该计划项目中的研究将以（a）管理和（b）数据管理/分析核心支持的三个副本进行。我们将使用遗传分析和统计建模的传统和先进的方法论，以及系统生物学方法，这些方法将建立在集成统计平台中的老化，健康和寿命领域中积累的知识上。 POI的方法论概念是通过将最新方法带入分析后期生活的特征，以促进当前GWAS和未来关联研究的范式，并在 生命遗传学领域。该项目将解决三个具体目标。 AIM 1。使用全基因组SNP基因分型数据进行全面关联分析，以识别寿命的多效和特定遗传基础，主要疾病的风险，与健康相关的风险 人体的特征和生理衰老变化。 AIM 2。对AIM 1中发现的多效性和特定基因生物学作用的最新信息进行最新信息，以剖析其在分子途径中的作用以及生物学过程和功能。目标3。通过结合系统生物学方法和高级统计建模方法，在目标1和2中揭示了在目标1和2中揭示的遗传效应的动态整合。