{Reference Type}: Journal Article
{Title}: The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.
{Author}: Rozowsky 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 GB;Chee S;Chhetri SB;Cortez Martins GC;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 KZ;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 LH;Sherman RM;Shi X;Shi M;Sloan CA;Strattan JS;Tan Z;Tanaka FY;Vlasova A;Wang J;Werner J;Williams B;Xu M;Yan C;Yu L;Zaleski C;Zhang J;Ardlie K;Cherry JM;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;
{Journal}: Cell
{Volume}: 186
{Issue}: 7
{Year}: 03 2023 30
{Factor}: 66.85
{DOI}: 10.1016/j.cell.2023.02.018
{Abstract}: 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.