Co-expression networks reveal the tissue-specific regulation of transcription and splicing
Name
Genome Res.-2017-Saha-1843-58.pdf
Description
Published version
Size
5.12 MB
Format
Unknown
Checksum (MD5)
9797fab72a92f95f3b10ccf3e7c3be23
Author(s)
Saha, Ashis
Kim, Yungil
Gewirtz, Ariel D.H.
Jo, Brian
Gao, Chuan
McDowell, Ian C.
The GTEx Consortium
Engelhardt, Barbara E.
Battle, Alexis
Hou, Lei
Date Issued
January 1, 2017
Journal
Genome Research
Publisher
Cold Spring Harbor Laboratory
Version
Final published version
Abstract
© 2017 Saha et al. Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1101/gr.216721.116
