Genomic Translation Across Species Core

跨物种核心的摘要-基因组翻译 跨物种基因组翻译核心(GTASC)的总体目标与更大的使命保持一致 以推动对导致衰老的生物过程的翻译研究- 通过跨学科协作取得相关成果。虽然在使用模型方面已经有了很大的进步 了解与健康寿命和疾病相关的衰老表型潜在的生物学过程的系统， 将模型生物中的发现从生物学家翻译给临床医生，以验证遗传，转录， 在人类中，表观遗传学与特定年龄指标的关联发生得非常慢，带来的发现也是如此 回到模型系统进行实验操作。GTASC拥有一套独特的资源和专业知识 用它来促进正常老化样本的翻译过程，性别/性别和 种族/族裔构成，年龄在50岁至109岁之间，从而有可能加速 许多衰老相关疾病的治疗发现率。这一核心利用了方法和 来自大规模、具有全国代表性的正常老龄化队列的表型分析，以及 这些种群水平的数据是生物学家可以获得的。我们将使用来自美国卫生与公众服务部的长达28年的数据 退休研究(HRS)，一项具有人口代表性的样本，50岁(N=18,000)的成年人具有高- 密度基因型数据，转录和甲基化数据(n~4,000)，以及来自 英国老龄化纵向研究(ELSA)遗传样本(N=7,407)。可用的表型包括 来自血液、甲基化DNA或端粒的生物标记物，以及功能能力指数，包括 身体状况、疾病状况、认知功能、虚弱或死亡指标，以及环境和 行为表型。GTASC的目标是：(1)通过以下方式加快人类老龄化的翻译研究 在人类人口数据中询问从模型系统得出的结果；(2)开发资源和 可以方便地调用工具进行定制的翻译分析；以及(3)开发新的研究 使用模型系统的指导，这需要设计和开发实验和 以人体数据为基础的基于机制的研究。更多地获得交叉翻译 在人类身上验证模型系统结果的资源，在自然人类下产生额外的假设 条件，并使用经验驱动的方法进行实验设计将导致加快步伐 获得对衰老过程的新见解，并为干预和治疗研究铺平一条更快的途径。这个 群体基因组学、生物标记、生物信息学和表型专业知识的创新组合 标记死亡率、疾病和功能的开发使GTA Core具有独特性 资源。

ABSTRACT- Genomic Translation Across Species Core The overarching goal of the Genomic Translation Across Species Core (GTASC) aligns with the larger mission of the Nathan Shock Center, to advance translational research on biological processes contributing to aging- related outcomes through interdisciplinary collaboration. While there has been much progress in using model systems to understand biological processes underlying aging phenotypes related to healthspan and disease, the translation of findings in model organisms from biologists to clinicians in order to validate genetic, transcriptomic, or epigenetic associations with age-specific indicators in humans occurs very slowly, as does bringing findings back to model systems for experimental manipulation. The GTASC has a unique set of resources and expertise with which to facilitate translational processes in normal aging samples, with diversity in sex/gender and race/ethnic composition, and with age ranges from 50 to 109, thereby enabling the potential to accelerate the rate of therapeutic discovery for many aging-related diseases. This core leverages expertise in the methods and analysis of phenotypes derived from large-scale, nationally representative, normal aging cohorts, and makes these population-level data accessible to biologists. We will use up to 28 years of data from the U.S. Health and Retirement Study (HRS), a population representative sample of adults aged 50+ (N=18,000) who have high- density genotypic data, and transcription and methylation data (n~4,000), and over 16 years of data from the English Longitudinal Study of Ageing (ELSA) genetic sample (N=7,407). Available phenotypes include biomarkers curated from blood, methylated DNA or telomeres, as well as indices of functional ability, including physical status, disease conditions, cognitive functioning, frailty or mortality indicators, and environmental and behavioral phenotypes. Aims of the GTASC are to: (1) Accelerate translational research on human aging by interrogating findings derived from model systems in human population data; (2) Develop the resources and tools that can be expediently invoked for customized translational analyses; and (3) Develop new research directions using model systems, which entails designing and developing analytical plans for experimental and mechanism-based studies based on findings from human data. Increased access to cross-translational resources to validate model system findings in humans, generate additional hypotheses under natural human conditions, and use empirically-driven approaches for experimental designs will lead to an accelerated pace for gaining novel insights into processes in aging, and pave a faster route to intervention and treatment studies. The innovative combination of population genomics, biological markers, bioinformatics, and expertise in phenotype development to mark mortality, disease, and functionality is what gives this GTAS Core its uniqueness as a resource.