The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.
| Authors | |
| Keywords | |
| 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. |
| Year of Publication | 2023
|
| Journal | Cell
|
| Volume | 186
|
| Issue | 7
|
| Pages | 1493-1511.e40
|
| Date Published | 03/2023
|
| ISSN | 1097-4172
|
| DOI | 10.1016/j.cell.2023.02.018
|
| PubMed ID | 37001506
|
| Links |