ABSTRACT. Alzheimer's disease (AD) involves a long prodromal stage where β-amyloid (Aβ) plaques accumulate before clinical symptom onset. Yet, the molecular factors that underlie the striking heterogeneity in the onset, accumulation, and response to AD neuropathology are poorly understood. Advances in imaging genetics offer an opportunity to move the field towards precision interventions by deconvolving the clinical heterogeneity by using sensitive imaging biomarkers and genetics to nominate novel targets for intervention. Genome-wide association studies (GWAS) have identified 70+ genetic loci linked to AD, shedding light on disease mechanisms. However, traditional case/control designs are limited by clinical misdiagnosis. My prior work has leveraged imaging-derived traits as a more precise phenotype closer to disease biology in genetic studies. Recent advances in artificial intelligence (AI), computational imaging analysis, and harmonization allow us to extract quantitative features from images that humans cannot see at a scale that can facilitate well-powered GWAS with more precise traits. We will leverage one such advanced modeling algorithm to estimate the age of Aβ+ onset from 16,000+ harmonized Aβ positron emission tomography (PET) from the AD Sequencing Project-Phenotype Harmonization Consortium (Contact-PI: Dr. Timothy Hohman--primary mentor). Characterizing the genetic architecture of Aβ+ onset can provide insight into the AD cascade of our research participants decades before they joined our studies. Our initial work has shown that the age of Aβ+ onset varies significantly (40 to over 90 years) but is a heritable trait. Beyond APOE, in a subset of the cohort we propose here, we have found that genes involved in Aβ clearance pathways seem to influence age of Aβ+ onset. Additionally, biological sex appears to play a significant role. In this proposal, I will expand upon this preliminary work to characterize the association of established AD-risk loci with age of Aβ+ onset, identify novel genetic drivers of age of Aβ+ onset leveraging state-of-the-art genome-wide approaches, and identify sex-specific genetic drivers of age of Aβ+. We will use cutting-edge methods that allow for inclusion of admixed individuals (rather than traditional exclusionary approaches) and that account for sex as a biological variable. I will receive training from world experts in AI and advanced analyses of large-scale image datasets, cutting-edge genomic analyses, the neurobiology of aging and clinical geriatrics, and important career development skills for a successful physician-scientist career. This K76 research proposal – along with the parallel detailed training plan that expands upon my expertise in genetics and radiology– uniquely position me as a leader and expert at the intersection of imaging and genetics to lead the way in developing more precise treatment strategies in AD and related dementias.