Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics. [Display omitted] • EN-TEx includes 1,635 datasets mapped to 4 personal genomes, ∼30 tissues × ∼15 assays • Comprehensive catalog of allele-specific activity, decorating regulatory elements • Model to transfer known eQTLs to difficult-to-profile tissues (e.g., skin→heart) • Transformer model for predicting allelic activity based on local sequence context Understanding the impact of genetic variants is important to functional genomics. EN-TEx provides epigenomes across tissues, coupled with long-read genome assemblies, to build generalizable models of variant impact. [ABSTRACT FROM AUTHOR]