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

View ORCID ProfileKathleen M. Chen, View ORCID ProfileAaron K. Wong, View ORCID ProfileOlga G. Troyanskaya, View ORCID ProfileJian Zhou
doi: https://doi.org/10.1101/2021.07.29.454384
Kathleen M. Chen
1Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
2Flatiron Institute, Simons Foundation, New York, New York, United States of America
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  • ORCID record for Kathleen M. Chen
Aaron K. Wong
2Flatiron Institute, Simons Foundation, New York, New York, United States of America
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Olga G. Troyanskaya
1Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
2Flatiron Institute, Simons Foundation, New York, New York, United States of America
3Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
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  • For correspondence: ogt@cs.princeton.edu jian.zhou@utsouthwestern.edu
Jian Zhou
4Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
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  • ORCID record for Jian Zhou
  • For correspondence: ogt@cs.princeton.edu jian.zhou@utsouthwestern.edu
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Abstract

Sequence is at the basis of how the genome shapes chromatin organization, regulates gene expression, and impacts traits and diseases. Epigenomic profiling efforts have enabled large-scale identification of regulatory elements, yet we still lack a sequence-based map to systematically identify regulatory activities from any sequence, which is necessary for predicting the effects of any variant on these activities. We address this challenge with Sei, a new framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Our framework systematically learns a vocabulary for the regulatory activities of sequences, which we call sequence classes, using a new deep learning model that predicts a compendium of 21,907 chromatin profiles across >1,300 cell lines and tissues, the most comprehensive to-date. Sequence classes allow for a global view of sequence and variant effects by quantifying diverse regulatory activities, such as loss or gain of cell-type-specific enhancer function. We show that sequence class predictions are supported by experimental data, including tissue-specific gene expression, expression QTLs, and evolutionary constraints based on population allele frequencies. Finally, we applied our framework to human genetics data. Sequence classes uniquely provide a non-overlapping partitioning of GWAS heritability by tissue-specific regulatory activity categories, which we use to characterize the regulatory architecture of 47 traits and diseases from UK Biobank. Furthermore, the predicted loss or gain of sequence class activities suggest specific mechanistic hypotheses for individual regulatory pathogenic mutations. We provide this framework as a resource to further elucidate the sequence basis of human health and disease.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/FunctionLab/sei-framework

  • https://github.com/FunctionLab/sei-manuscript

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 30, 2021.
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A sequence-based global map of regulatory activity for deciphering human genetics
Kathleen M. Chen, Aaron K. Wong, Olga G. Troyanskaya, Jian Zhou
bioRxiv 2021.07.29.454384; doi: https://doi.org/10.1101/2021.07.29.454384
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A sequence-based global map of regulatory activity for deciphering human genetics
Kathleen M. Chen, Aaron K. Wong, Olga G. Troyanskaya, Jian Zhou
bioRxiv 2021.07.29.454384; doi: https://doi.org/10.1101/2021.07.29.454384

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