Journal
NATURE GENETICS
Volume 54, Issue 7, Pages 940-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41588-022-01102-2
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Funding
- Scientific Computing Core, at the Flatiron Institute
- National Science Foundation Graduate Research Fellowship Program [NSF-GRFP]
- National Institutes of Health (NIH) [R01HG005998, U54HL117798, R01GM071966]
- U.S. Department of Health and Human Services [HHSN272201000054C]
- Simons Foundation [395506]
- Cancer Prevention and Research Institute of Texas [RR190071]
- NIH [DP2GM146336]
- UT Southwestern Endowed Scholars Program
- Terascale Infrastructure for Groundbreaking Research in Science and Engineering high-performance computer center at Princeton University
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Sei is a new framework for integrating human genetics data with a sequence-based mapping of predicted regulatory activities to elucidate mechanisms contributing to complex traits and diseases.
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. Sei is a new framework for integrating human genetics data with a sequence-based mapping of predicted regulatory activities to elucidate mechanisms contributing to complex traits and diseases.
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