bioRxiv preprint doi: https://doi.org/10.1101/078741; this version posted October 2, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Co-expression networks reveal the tissue-specific regulation of transcription and splicing Ashis Saha1, Yungil Kim1y, Ariel D. H. Gewirtz2y, Brian Jo2, Chuan Gao3, Ian C. McDowell4, GTEx Consortium, Barbara E. Engelhardt5*, and Alexis Battle1* 1Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA 2Department of Quantitative and Computational Biology, Princeton University, Princeton, NJ, USA 3Department of Statistical Science, Duke University, Durham, NC, USA 4Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA 5Department of Computer Science and Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA yDenotes equal contribution. *Corresponding authors:
[email protected],
[email protected] Abstract Gene co-expression networks capture biologically important patterns in gene expression data, en- abling functional analyses of genes, discovery of biomarkers, and interpretation of regulatory genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single or small sets of tissues. Here, we have reconstructed networks that capture a much more complete set of regulatory relationships, specifically including regulation of rel- ative isoform abundance and splicing, and tissue-specific connections unique to each of a diverse set of tissues. Using the Genotype-Tissue Expression (GTEx) project v6 RNA-sequencing data across 44 tissues in 449 individuals, we evaluated shared and tissue-specific network relationships.