4.7 Article

Impact of regulatory variation across human iPSCs and differentiated cells

期刊

GENOME RESEARCH
卷 28, 期 1, 页码 122-131

出版社

COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT
DOI: 10.1101/gr.224436.117

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资金

  1. NIH [GM007197, AG 044948, MH084703, MH101825, HG007036, CA149145, HL092206]
  2. Center for Computational, Evolutionary and Human Genomics Fellowship
  3. EMBO Long-Term Fellowship [ALTF 751-2014]
  4. Marie Curie Actions
  5. Howard Hughes Medical Institute
  6. NATIONAL CANCER INSTITUTE [U54CA149145] Funding Source: NIH RePORTER
  7. NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [R01HL092206, T32HL007381] Funding Source: NIH RePORTER
  8. NATIONAL HUMAN GENOME RESEARCH INSTITUTE [U01HG007036] Funding Source: NIH RePORTER
  9. NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [P30DK042086] Funding Source: NIH RePORTER
  10. NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [T32GM007281, T32GM007197] Funding Source: NIH RePORTER
  11. NATIONAL INSTITUTE OF MENTAL HEALTH [T32MH020065, R01MH101825, R01MH084703] Funding Source: NIH RePORTER
  12. NATIONAL INSTITUTE ON AGING [F31AG044948] Funding Source: NIH RePORTER
  13. NATIONAL LIBRARY OF MEDICINE [T15LM007033] Funding Source: NIH RePORTER

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Induced pluripotent stem cells (iPSCs) are an essential tool for studying cellular differentiation and cell types that are otherwise difficult to access. We investigated the use of iPSCs and iPSC-derived cells to study the impact of genetic variation on gene regulation across different cell types and as models for studies of complex disease. To do so, we established a panel of iPSCs from 58 well-studied Yoruba lymphoblastoid cell lines (LCLs); 14 of these lines were further differentiated into cardiomyocytes. We characterized regulatory variation across individuals and cell types by measuring gene expression levels, chromatin accessibility, and DNA methylation. Our analysis focused on a comparison of inter-individual regulatory variation across cell types. While most cell-type-specific regulatory quantitative trait loci (QTLs) lie in chromatin that is open only in the affected cell types, we found that 20% of cell-type-specific regulatory QTLs are in shared open chromatin. This observation motivated us to develop a deep neural network to predict open chromatin regions from DNA sequence alone. Using this approach, we were able to use the sequences of segregating haplotypes to predict the effects of common SNPs on cell-type-specific chromatin accessibility.

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