Predicting cell-type-specific non-coding RNA transcription from genome sequence
Predicting cell-type-specific non-coding RNA transcription from genome sequence
SUMMARY Transcription is regulated through complex mechanisms involving non-coding RNAs (ncRNAs). However, because transcription of ncRNAs, especially enhancer RNAs, is often low and cell type-specific, its dependency on genotype remains largely unexplored. Here, we developed mutation effect prediction on ncRNA transcription (MENTR), a quantitative machine learning framework reliably connecting genetic associations with expression of ncRNAs, resolved to the level of cell type. MENTR-predicted mutation effects on ncRNA transcription were concordant with estimates from previous genetic studies in a cell type-dependent manner. We inferred reliable causal variants from 41,223 GWAS variants, and proposed 7,775 enhancers and 3,548 long-ncRNAs as complex trait-associated ncRNAs in 348 major human primary cells and tissues, including plausible enhancer-mediated functional alterations in single-variant resolution in Crohn’s disease. In summary, we present new resources for discovering causal variants, the biological mechanisms driving complex traits, and the sequence-dependency of ncRNA regulation in relevant cell types.
Kamatani Yoichiro、Koido Masaru、Hon Chung-Chau、Koyama Satoshi、Ito Kaoru、Terao Chikashi、Sese Jun、Kawaji Hideya、Ishigaki Kazuyoshi、Carninci Piero、Murakawa Yasuhiro
Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences||Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of TokyoLaboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences||Division of Molecular Pathology, Department of Cancer Biology, Institute of Medical Science, The University of TokyoLaboratory for Genome Information Analysis, RIKEN Center for Integrative Medical SciencesLaboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical SciencesLaboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical SciencesLaboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences||Clinical Research Center, Shizuoka General Hospital||The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of ShizuokaArtificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology||Humanome Lab Inc.Preventive Medicine and Applied Genomics Unit, RIKEN Center for Integrative Medical SciencesLaboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences||Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women?ˉs Hospital, Harvard Medical School||Center for Data Sciences, Harvard Medical School||Program in Medical and Population Genetics, Broad Institute of MIT and HarvardLaboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences||Laboratory for Single Cell Technologies, RIKEN Center for Integrative Medical SciencesRIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences
遗传学分子生物学细胞生物学
Kamatani Yoichiro,Koido Masaru,Hon Chung-Chau,Koyama Satoshi,Ito Kaoru,Terao Chikashi,Sese Jun,Kawaji Hideya,Ishigaki Kazuyoshi,Carninci Piero,Murakawa Yasuhiro.Predicting cell-type-specific non-coding RNA transcription from genome sequence[EB/OL].(2025-03-28)[2025-05-16].https://www.biorxiv.org/content/10.1101/2020.03.29.011205.点此复制
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