Detalle de: The landscape of expression and alternative splicing variation across human traits
| Title | The landscape of expression and alternative splicing variation across human traits |
| Abstract |
Understanding the consequences of individual transcriptome variation is fundamental to deciphering human biology and disease. We implement a statistical framework to quantify the contributions of 21 individual traits as drivers of gene expression and alternative splicing variation across 46 human tissues and 781 individuals from the Genotype-Tissue Expression project. We demonstrate that ancestry, sex, age, and BMI make additive and tissue-specific contributions to expression variability, whereas interactions are rare. Variation in splicing is dominated by ancestry and is under genetic control in most tissues, with ribosomal proteins showing a strong enrichment of tissue-shared splicing events. Our analyses reveal a systemic contribution of types 1 and 2 diabetes to tissue transcriptome variation with the strongest signal in the nerve, where histopathology image analysis identifies novel genes related to diabetic neuropathy. Our multi-tissue and multi-trait approach provides an extensive characterization of the main drivers of human transcriptome variation in health and disease. |
| Apa Reference | García-Pérez, R., Ramirez, J.M., Ripoll-Cladellas, A., Chazarra-Gil, R., Oliveros, W., Soldatkina, O., Bosio, M., Rognon, P.J., Capella-Gutierrez, S., Calvo, M., Reverter, F., Guigó, R., Aguet, F., Ferreira, P.G., Ardlie, K.G. & Melé, M. (2023). The landscape of expression and alternative splicing variation across human traits. Cell Genomics, 3(1), 100244. 10.1016/j.xgen.2022.100244 |
| Doi | 10.1016/j.xgen.2022.100244 |
| Publication Date | Jan 2023 |
| Publication Type | Article |
| Researcher | Salvador Capella |
| Ontology | Transcriptomics, Expression Profiling, Statistical Methods for Biology, Image analysis |
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