Relationships among Genetic Regulators of Aging Health and Lifespan

描述(由申请人提供)：综合遵循国家老龄研究所提出的战略方向，该计划项目中拟议研究的总体目标是在更好地了解衰老调节因素、风险、重大疾病和相关特征以及寿命之间的关系的基础上，识别能够促进人类长寿和健康的遗传和非遗传因素和机制。这一目标将在对全基因组SNP基因分型数据和关于人类生物体生命过程的纵向数据进行综合分析的基础上实现。该方案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/分析核心提供支持。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，这些方法将建立在老龄化、健康、寿命等领域积累的知识基础上，纳入综合统计平台。POI的方法论概念将使用下一代测序，通过将最先进的方法引入分析晚年生活的特征，从而推动当前GWAS和未来关联研究的范式，这在 生命历程遗传学领域。该项目将解决三个具体目标。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、与健康相关的多效性和特定的遗传基础 特征，以及人体生理衰老的变化。目的2.对目标1中发现的SNPs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。目的3.结合系统生物学和高级统计建模的方法，将目标1和目标2中揭示的遗传效应动态地整合到个体的生命过程中。

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.