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Alzheimer's disease (AD) is highly heterogeneous and driven by diverse molecular and cellular mechanisms. Functional genomics investigates these mechanisms from genetic variants to gene expression and regulation. We performed personalized functional genomics analysis on population-scale single-nucleus RNA-seq data, with cross-cohort validation across multiple cohorts comprising over 1900 individual brains, capturing donor-level cell type interactions and gene regulatory networks. Using a knowledge-guided graph neural network, we learned latent representations of each donor's functional genomics that accurately classified AD phenotypes, identified molecularly defined subpopulations, and traced disease progression trajectories. Our importance scores, derived from graph attentions, identified significant inter-donor differences and prioritized personalized cell type genes and regulatory networks. Finally, we identified gene regulatory QTLs (grQTLs) linking genetic variants to donor-level regulatory changes, providing insights into gene regulatory relationships beyond traditional eQTLs. All results are summarized into a personalized functional genomics atlas for AD, including an open-source framework, iBrainMap, for general use.