Impact of regulatory variation across human iPSCs and differentiated cells
- Nicholas E. Banovich1,7,8,
- Yang I. Li2,7,9,
- Anil Raj2,7,
- Michelle C. Ward1,3,
- Peyton Greenside4,
- Diego Calderon4,
- Po Yuan Tung1,3,
- Jonathan E. Burnett1,
- Marsha Myrthil1,
- Samantha M. Thomas1,
- Courtney K. Burrows1,
- Irene Gallego Romero1,10,
- Bryan J. Pavlovic1,
- Anshul Kundaje2,
- Jonathan K. Pritchard2,5,6 and
- Yoav Gilad1,3
- 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
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↵7 These authors contributed equally to this work.
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.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.224436.117.
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Freely available online through the Genome Research Open Access option.
- Received April 28, 2017.
- Accepted November 20, 2017.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
