A sequence-based global map of regulatory activity for deciphering human genetics

Nat Genet. 2022 Jul;54(7):940-949. doi: 10.1038/s41588-022-01102-2. Epub 2022 Jul 11.


Epigenomic profiling has enabled large-scale identification of regulatory elements, yet we still lack a systematic mapping from any sequence or variant to regulatory activities. We address this challenge with Sei, a framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Sei learns a vocabulary of regulatory activities, called sequence classes, using a deep learning model that predicts 21,907 chromatin profiles across >1,300 cell lines and tissues. Sequence classes provide a global classification and quantification of sequence and variant effects based on diverse regulatory activities, such as cell type-specific enhancer functions. These predictions are supported by tissue-specific expression, expression quantitative trait loci and evolutionary constraint data. Furthermore, sequence classes enable characterization of the tissue-specific, regulatory architecture of complex traits and generate mechanistic hypotheses for individual regulatory pathogenic mutations. We provide Sei as a resource to elucidate the regulatory basis of human health and disease.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

MeSH terms

  • Chromatin / genetics
  • Epigenomics
  • Human Genetics
  • Humans
  • Quantitative Trait Loci* / genetics
  • Regulatory Sequences, Nucleic Acid* / genetics


  • Chromatin