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

Kathleen M. Chen, Aaron K. Wong, Olga G. Troyanskaya, Jian Zhou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)940-949
Number of pages10
JournalNature Genetics
Volume54
Issue number7
DOIs
StatePublished - Jul 2022

All Science Journal Classification (ASJC) codes

  • Genetics

Fingerprint

Dive into the research topics of 'A sequence-based global map of regulatory activity for deciphering human genetics'. Together they form a unique fingerprint.

Cite this