The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.

TitleThe EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.
Publication TypeJournal Article
Year of Publication2023
AuthorsRozowsky, J, Gao, J, Borsari, B, Yang, YT, Galeev, T, Gürsoy, G, Epstein, CB, Xiong, K, Xu, J, Li, T, Liu, J, Yu, K, Berthel, A, Chen, Z, Navarro, F, Sun, MS, Wright, J, Chang, J, Cameron, CJF, Shoresh, N, Gaskell, E, Drenkow, J, Adrian, J, Aganezov, S, Aguet, F, Balderrama-Gutierrez, G, Banskota, S, Corona, GBarreto, Chee, S, Chhetri, SB, Martins, GConte Cort, Danyko, C, Davis, CA, Farid, D, Farrell, NP, Gabdank, I, Gofin, Y, Gorkin, DU, Gu, M, Hecht, V, Hitz, BC, Issner, R, Jiang, Y, Kirsche, M, Kong, X, Lam, BR, Li, S, Li, B, Li, X, Lin, KZin, Luo, R, Mackiewicz, M, Meng, R, Moore, JE, Mudge, J, Nelson, N, Nusbaum, C, Popov, I, Pratt, HE, Qiu, Y, Ramakrishnan, S, Raymond, J, Salichos, L, Scavelli, A, Schreiber, JM, Sedlazeck, FJ, See, LHoon, Sherman, RM, Shi, X, Shi, M, Sloan, CAlicia, J Strattan, S, Tan, Z, Tanaka, FY, Vlasova, A, Wang, J, Werner, J, Williams, B, Xu, M, Yan, C, Yu, L, Zaleski, C, Zhang, J, Ardlie, K, J Cherry, M, Mendenhall, EM, Noble, WS, Weng, Z, Levine, ME, Dobin, A, Wold, B, Mortazavi, A, Ren, B, Gillis, J, Myers, RM, Snyder, MP, Choudhary, J, Milosavljevic, A, Schatz, MC, Bernstein, BE, Guigó, R, Gingeras, TR, Gerstein, M
Date Published2023 Mar 30
KeywordsEpigenome, Genome-Wide Association Study, Genomics, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci

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.

Alternate JournalCell
PubMed ID37001506
PubMed Central IDPMC10074325
Grant ListR01 MH101814 / MH / NIMH NIH HHS / United States
U54 HG007004 / HG / NHGRI NIH HHS / United States
U54 HG006991 / HG / NHGRI NIH HHS / United States
U01 CA253481 / CA / NCI NIH HHS / United States
U24 HG006620 / HG / NHGRI NIH HHS / United States
R01 MH113005 / MH / NIMH NIH HHS / United States
R01 HG009318 / HG / NHGRI NIH HHS / United States
U24 HG009397 / HG / NHGRI NIH HHS / United States
UM1 HG009442 / HG / NHGRI NIH HHS / United States
R01 LM012736 / LM / NLM NIH HHS / United States
U24 HG009446 / HG / NHGRI NIH HHS / United States
P30 CA045508 / CA / NCI NIH HHS / United States
U24 HG009649 / HG / NHGRI NIH HHS / United States

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