Genomic Translation Across Species Core

摘要-跨物种核心的基因组翻译 跨物种基因组翻译核心（GTASC）的总体目标与更大的使命一致 内森休克中心，以推进生物过程的转化研究有助于老化- 通过跨学科合作取得相关成果。虽然在使用模型方面取得了很大进展， 系统来了解与健康寿命和疾病相关的衰老表型的生物学过程， 从生物学家到临床医生的模式生物中的发现的翻译，以验证遗传，转录组学， 或表观遗传与人类年龄特异性指标的关联发生得非常缓慢， 回到实验操作的模型系统。GTASC拥有独特的资源和专业知识 促进正常老化样品中的翻译过程，具有性别/性别的多样性， 种族/族裔组成，年龄在50至109岁之间，从而有可能加速 许多与衰老有关的疾病的治疗发现率。这一核心利用了方法方面的专业知识， 分析来自大规模，全国代表性，正常老化队列的表型，并使 这些生物学家可以获得的种群水平的数据。我们将使用来自美国健康和医疗保健中心长达28年的数据， 退休研究（HRS），一项50岁以上成年人（N= 18，000）的人群代表性样本， 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（艾尔莎）遗传样本（N= 7，407）。可用的表型包括 从血液、甲基化DNA或端粒中筛选的生物标志物，以及功能能力指标，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）通过以下方式加速人类衰老的转化研究 询问来自人口数据模型系统的调查结果;（2）开发资源， 可以方便地调用定制的翻译分析工具;（3）开发新的研究 方向使用模型系统，这需要设计和开发分析计划的实验和 基于人体数据的机制研究。增加跨翻译的机会 资源，以验证模型系统在人类中的发现，在自然人类中产生额外的假设， 条件，并使用实验驱动的方法进行实验设计将导致加速 获得对衰老过程的新见解，并为干预和治疗研究铺平了更快的道路。的 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、疾病和功能的发展是GTAS核心的独特之处， resource.

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.