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Impact of regulatory variation across human iPSCs and differentiated cells

    • 1Department of Human Genetics, University of Chicago, Chicago, Illinois 60637, USA;
    • 2Department of Genetics, Stanford University, Stanford, California 94305, USA;
    • 3Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA;
    • 4Department of Biomedical Informatics, Stanford University, Stanford, California 94305, USA;
    • 5Department of Biology, Stanford University, Stanford, California 94305, USA;
    • 6Howard Hughes Medical Institute, Stanford University, Stanford, California 94305, USA
    • 7 These authors contributed equally to this work.
    • Present addresses: 8Translational Genomics Research Institute, Phoenix, AZ 85004, USA; 9Department of Medicine, University of Chicago, Chicago, IL 60637, USA; 10Centre for Systems Genomics, University of Melbourne, Parkville, Victoria 3052, Australia
Published December 5, 2017. https://doi.org/10.1101/gr.224436.117
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cover of Genome Research Vol 36 Issue 4
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April 2026, Vol. 36, No. 4
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Abstract

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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