Project Summary: Integrative Analysis Core (IAC) Longevity and age-related traits and diseases such as Alzheimer’s disease and related dementias (ADRD) are multifactorial and ultimately influenced by many interacting genetic and non-genetic factors. This challenges the identification of mechanisms that influence human lifespan and healthspan. Addressing this challenge requires strategies to integrate and triangulate results from different studies. Such integration can lead to verification of a finding, as well as placing a finding into a broader context to gain a deeper understanding of longevity related physiology. The Integrated Analysis Core (IAC) of the Longevity Consortium (LC) will seek to integrate the results of 5 different research projects (P1-P5), each addressing a key objective in the RFA: P1 studies diverse human populations to determine the contexts within which longevity-related factors exhibit associations; P2 studies genomically-mediated evolutionary processes contributing to longevity; P3 studies extreme longevity (EL) and its relationship to ADRD in diverse human populations; P4 studies aging rate indicators in response to candidate geroprotective drugs in mice; and P5 studies novel cell systems and constructs derived from individuals with EL and other longevity related phenotypes. The IAC will seek to integrate results and data across these projects by developing appropriate infrastructure and analytical methods and engaging in analyses with the project researchers, including: 1. Developing connections, collaborations, and infrastructure to access data from different sources to enhance P1-P5 research, and also work with the NIA-funded Data Management and Coordinating Center’s (DMCC) Exceptional Longevity Translational Resources (ELITE) portal team to enable broader access to relevant data sets; 2. Develop and apply methods for harmonizing data when needed, accommodating heterogeneous data sets when harmonization is impossible, and dealing with the analysis of complexities associated with large, diverse data sets; 3. Develop and apply systems biology and result triangulation methods; 4. Develop strategies for leveraging genetics and genomics data to enhance cross population comparisons via individual ancestry assignments, orthology determination to enable integration across studies of different species, and genetic association studies involving complex phenotypes; and 5. Coordinate and support a broad translational workflow leading from the identification of longevity-associated factors to geroprotective drugs, drug targets, clinically meaningful biomarkers, and predictive models of health and longevity.