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

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…

• NS
  National Science Foundation
• UD
  U.S. Department of Health and Human Services

  Award: HHSN272201000054C
• SF
  Simons Foundation

  Award: 395506
• CP
  Cancer Prevention and Research Institute of Texas

  Award: RR190071
• PU
  Princeton University
• CI
  Canadian Institute for Advanced Research
• UO
  University of Texas Southwestern Medical Center
• FH
  Flatiron Health
• NI
  National Institutes of Health

  Awards: R01HG005998, U54HL117798, R01GM071966, DP2GM146336