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Genetic Variation, Not Cell Type of Origin, Underlies Regulatory Differences in iPSCs

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE65079 | GEO | 2015/01/19

SECONDARY ACCESSION(S): PRJNA272898

REPOSITORIES: GEO

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Genetic Variation, Not Cell Type of Origin, Underlies Regulatory Differences in iPSCs

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Genetic Variation, Not Cell Type of Origin, Underlies Regulatory Differences in iPSCs [Methylation]

Project description:Analysis of contribution of cell type of origin and individual to gene expression differences in iPSCs. The hypothesis tested in the present study was that cell type of origin affects iPSC gene expression. Results show that individual has a much stronger effect than cell type of origin on differences between iPSCs derived from multiple individuals. DNA obtained from feeder-free iPSCs derived from multiple individuals and tissues and their corresponding cell type of origin

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Genetic Variation, Not Cell Type of Origin, Underlies Regulatory Differences in iPSCs

Project description:Genetic Variation, Not Cell Type of Origin, Underlies Regulatory Differences in iPSCs

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Impact of regulatory variation across human iPSCs and differentiated cells

Project description:This SuperSeries is composed of the SubSeries listed below.

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Project description: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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