bioRxivGenomicsPreprint
A systematic survey of human tissue-specific gene expression and splicing reveals new opportunities for therapeutic target identification and evaluation
Robert Yang, Jie QuaniD, Reza Sodaei, François AguetiD, Ayellet V. SegrèiD, John AlleniD, Thomas A. LanziD, Veronica Reinhart, Matthew E. Crawford, Samuel A. HassoniD, Kristin ArdlieiD, Roderic GuigóiD, Hualin Simon XiiD
- Citations
- 35
Abstract
Differences in the expression of genes and their splice isoforms across human tissues are fundamental factors to consider for therapeutic target evaluation. To this end, we conducted a transcriptome-wide survey of tissue-specific gene expression and splicing events in the unprecedented collection of 8527 high-quality RNA-seq samples from the GTEx project, covering 36 human peripheral tissues and 13 brain subregions. We derived a weighted tissue-specificity scoring scheme accounting for the similarity of related tissues and inherent variability across individual samples. We showed that ~50.6% of all annotated human genes show tissue-specific expression, including many low abundance transcripts vastly underestimated by previous array-based expression atlases. As utilities for drug discovery, we demonstrated that tissue-specificity is a highly desirable attribute of validated drug targets and tissue-specificity can be used to prioritize disease-associated genes from genome-wide association studies (GWAS). Using brain striatum-specific gene expression as an example, we provided a template to leverage tissue-specific gene expression to identify novel therapeutic targets. Mining of tissue-specific splicing further reveals new opportunities for tissue-specific targeting. Thus, the high quality transcriptome atlas provided by the GTEx is an invaluable resource for drug discovery and systematic analysis anchored on the human tissue specific gene expression provides a promising avenue to identify novel therapeutic target hypotheses.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: GWAS / association mapping
- approach: statistical model
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