PROJECT SUMMARY Biological aging is the process through which living things accumulate genetic damage, degenerate, and eventually die. Aging manifests itself through the loss of proteostasis, cellular senescence, telomere attrition, genomic instability, among other molecular effects. Various factors influence the rate of aging, such as genetics, smoking status, and diet. Thus, biological age is distinct from chronological age; two individuals born at the same time may have different biological ages. Aging is a significant risk factor for a range of age-related diseases, including neurodegenerative disorders like Alzheimer’s Disease (AD), which is the primary cause of dementia worldwide. Methylation clocks, which leverage the methylation levels of multiple CpG sites across the genome have been developed to measure biological age in an individual. Multiple methylation clocks have been associated with increased risk of age-related health outcomes, including AD. As such, methylation clocks show promise as predictive biomarkers to inform therapeutic interventions. However, existing methylation clocks and their associations with AD have not been validated in genetically admixed populations, who are historically underrepresented in biomedical research and underserved in clinical settings. This is a critical gap, given that other genome-based predictive tools, such as polygenic risk scores (PRS) fail to generalize across humans with diverse ancestries. Thus, there is a need for evaluating whether methylation clocks need to account for genetic admixture and diverse ancestries to be portable across global populations. I hypothesize that existing methylation clocks will not generalize in their AD risk assessment when applied to genetically admixed individuals, and that making them “ancestry aware” will improve their accuracy and portability across populations. To evaluate the accuracy of methylation clocks in admixed populations, I will leverage the MAGENTA study, a large cohort of diverse admixed AD and control individuals with genome-wide methylation data. I will further integrate existing methylation clocks and a new “admixture-aware” clock I develop with polygenic risk scores. The findings of this proposed work will contribute to our knowledge of biological aging and its impact on AD risk and expand existing genome-based predictive tools of personalized medicine to diverse populations, thereby expanding essential inclusive science efforts.