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A comparison of gene expression and DNA methylation patterns across tissues and species
Previously published comparative functional genomic data sets from primates using frozen tissue samples, including many data sets from our own group, were often collected and analyzed using nonoptimal study designs and analysis approaches. In addition, when samples from multiple tissues were studied...
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Published in: | Genome research 2020-02, Vol.30 (2), p.250-262 |
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Main Authors: | , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Previously published comparative functional genomic data sets from primates using frozen tissue samples, including many data sets from our own group, were often collected and analyzed using nonoptimal study designs and analysis approaches. In addition, when samples from multiple tissues were studied in a comparative framework, individuals and tissues were confounded. We designed a multitissue comparative study of gene expression and DNA methylation in primates that minimizes confounding effects by using a balanced design with respect to species, tissues, and individuals. We also developed a comparative analysis pipeline that minimizes biases attributable to sequence divergence. Thus, we present the most comprehensive catalog of similarities and differences in gene expression and DNA methylation levels between livers, kidneys, hearts, and lungs, in humans, chimpanzees, and rhesus macaques. We estimate that overall, interspecies and inter-tissue differences in gene expression levels can only modestly be accounted for by corresponding differences in promoter DNA methylation. However, the expression pattern of genes with conserved inter-tissue expression differences can be explained by corresponding interspecies methylation changes more often. Finally, we show that genes whose tissue-specific regulatory patterns are consistent with the action of natural selection are highly connected in both gene regulatory and protein-protein interaction networks. |
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ISSN: | 1088-9051 1549-5469 |
DOI: | 10.1101/gr.254904.119 |