Title | A sequence-based global map of regulatory activity for deciphering human genetics. |
Publication Type | Journal Article |
Year of Publication | 2022 |
Authors | Chen, KM, Wong, AK, Troyanskaya, OG, Zhou, J |
Journal | Nat Genet |
Volume | 54 |
Issue | 7 |
Pagination | 940-949 |
Date Published | 2022 Jul |
ISSN | 1546-1718 |
Keywords | Chromatin, Epigenomics, Human Genetics, Humans, Quantitative Trait Loci, Regulatory Sequences, Nucleic Acid |
Abstract | <p>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.</p> |
DOI | 10.1038/s41588-022-01102-2 |
Alternate Journal | Nat Genet |
PubMed ID | 35817977 |
PubMed Central ID | PMC9279145 |
Grant List | HHSN272201000054C / AI / NIAID NIH HHS / United States R01 HG005998 / HG / NHGRI NIH HHS / United States R01 GM071966 / GM / NIGMS NIH HHS / United States U54 HL117798 / HL / NHLBI NIH HHS / United States DP2 GM146336 / GM / NIGMS NIH HHS / United States |