Rozowsky, JoelBorsari, Beatrice, 1992-Guigó Serra, RodericGerstein, Mark B.2023-05-232023-05-232023Rozowsky J, Gao J, Borsari B, Yang YT, Galeev T, Gürsoy G, et al. The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models. Cell. 2023 Mar 30;186(7):1493-1511.e40. DOI: 10.1016/j.cell.2023.02.0180092-8674http://hdl.handle.net/10230/56942Understanding 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.application/pdfeng© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).info:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.cell.2023.02.018Personal genomeAllele-specific activityFunctional epigenomesPredictive modelseQTLsGenome annotationsTransformer modelFunctional genomicsENCODEGTExStructural variantsTissue specificityinfo:eu-repo/semantics/openAccess