- 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.
