Genetics Core: Summary/Abstract The overarching goal of the ADNI Genetics Core is to support the identification and validation of genetic markers for use in clinical trials and drug discovery, in alignment with Project 1 and the broader NIA and NAPA goals of effective precision medicine for AD/ADRD. AD is a complex and highly heritable disease, with genetics playing an important role in identifying novel targets for therapeutics, diagnostics, and risk assessment. APOE remains an attractive target and is widely employed for stratification and enrichment of clinical trials, as it influences onset age, amyloid accumulation, and susceptibility to adverse effects of anti-amyloid treatment. Large scale genome- wide association studies (GWAS), most including ADNI data, have identified multiple genes and pathways related to amyloid and tau, immune dysregulation, lipid metabolism, vascular integrity, endocytosis/autophagy, and other biological mechanisms. The core will continue its focus on collaboratively advancing genetics and related omics to discover, validate, and implement markers that can improve the precision and power of AD clinical trials. Specific Aims for ADNI4: (1) Continue longitudinal sample collection, processing, banking, curation, and dissemination in partnership with NCRAD, including baseline PBMC collection to support future iPSCs development or other NIA-approved uses by the scientific community; (2) Continue providing genome-wide genotyping data, including APOE, with new emphasis on diverse ancestry; (3) Support the ADNI4 multiethnic screening strategy (n=4000); (4) Continue to perform and facilitate bioinformatic analyses of genetic and quantitative endophenotype data related to the goals of ADNI, which include improvement of clinical trial design through enrichment using genetic markers. With Project 1, we will assess genetic models including APOE and polygenic risk scores (PRS) as predictors of biomarker and cognitive outcomes. The Core will continue to explore the benefit of adding transcriptional risk scores (TRS) and DNA methylation profiling in candidate gene loci to models. Using a systems biology framework, genetic data will be used to predict trajectories of amyloid, tau, neurodegeneration, and vascular pathways (A/T/N/V) as measured by ADNI biomarkers, as well as clinical course. The Core will also continue to explore how advanced AI methods (machine learning and deep learning) can contribute to AD genetic analyses, working with affiliated NIA programs including ADSP, AMP-AD and AI4AD. With the AD Metabolomics Consortium, we will continue to assess genes related to longitudinal metabolomics/lipidomics changes in AD; and (5) Continue as a “hub” providing organization, collaboration, and support for genomic studies of quantitative biomarker phenotypes and outcomes in ADNI, including fostering new knowledge through team science working groups. Funded or pending collaborations will support sequencing of previously collected AD...