%0 Journal Article
%T The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.
%A Rozowsky J
%A Gao J
%A Borsari B
%A Yang YT
%A Galeev T
%A Gürsoy G
%A Epstein CB
%A Xiong K
%A Xu J
%A Li T
%A Liu J
%A Yu K
%A Berthel A
%A Chen Z
%A Navarro F
%A Sun MS
%A Wright J
%A Chang J
%A Cameron CJF
%A Shoresh N
%A Gaskell E
%A Drenkow J
%A Adrian J
%A Aganezov S
%A Aguet F
%A Balderrama-Gutierrez G
%A Banskota S
%A Corona GB
%A Chee S
%A Chhetri SB
%A Cortez Martins GC
%A Danyko C
%A Davis CA
%A Farid D
%A Farrell NP
%A Gabdank I
%A Gofin Y
%A Gorkin DU
%A Gu M
%A Hecht V
%A Hitz BC
%A Issner R
%A Jiang Y
%A Kirsche M
%A Kong X
%A Lam BR
%A Li S
%A Li B
%A Li X
%A Lin KZ
%A Luo R
%A Mackiewicz M
%A Meng R
%A Moore JE
%A Mudge J
%A Nelson N
%A Nusbaum C
%A Popov I
%A Pratt HE
%A Qiu Y
%A Ramakrishnan S
%A Raymond J
%A Salichos L
%A Scavelli A
%A Schreiber JM
%A Sedlazeck FJ
%A See LH
%A Sherman RM
%A Shi X
%A Shi M
%A Sloan CA
%A Strattan JS
%A Tan Z
%A Tanaka FY
%A Vlasova A
%A Wang J
%A Werner J
%A Williams B
%A Xu M
%A Yan C
%A Yu L
%A Zaleski C
%A Zhang J
%A Ardlie K
%A Cherry JM
%A Mendenhall EM
%A Noble WS
%A Weng Z
%A Levine ME
%A Dobin A
%A Wold B
%A Mortazavi A
%A Ren B
%A Gillis J
%A Myers RM
%A Snyder MP
%A Choudhary J
%A Milosavljevic A
%A Schatz MC
%A Bernstein BE
%A Guigó R
%A Gingeras TR
%A Gerstein M
%J Cell
%V 186
%N 7
%D 03 2023 30
%M 37001506
%F 66.85
%R 10.1016/j.cell.2023.02.018
%X 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.