UMMS Affiliation
RNA Therapeutics Institute
Publication Date
2015-11-01
Document Type
Article
Disciplines
Computational Biology | Genetics | Genomics | Population Biology
Abstract
Elucidating the consequences of genetic differences between humans is essential for understanding phenotypic diversity and personalized medicine. Although variation in RNA levels, transcription factor binding, and chromatin have been explored, little is known about global variation in translation and its genetic determinants. We used ribosome profiling, RNA sequencing, and mass spectrometry to perform an integrated analysis in lymphoblastoid cell lines from a diverse group of individuals. We find significant differences in RNA, translation, and protein levels suggesting diverse mechanisms of personalized gene expression control. Combined analysis of RNA expression and ribosome occupancy improves the identification of individual protein level differences. Finally, we identify genetic differences that specifically modulate ribosome occupancy-many of these differences lie close to start codons and upstream ORFs. Our results reveal a new level of gene expression variation among humans and indicate that genetic variants can cause changes in protein levels through effects on translation.
Rights and Permissions
© 2015 Cenik et al. This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
DOI of Published Version
10.1101/gr.193342.115
Source
Genome Res. 2015 Nov;25(11):1610-21. doi: 10.1101/gr.193342.115. Epub 2015 Aug 21. Link to article on publisher's site
Journal/Book/Conference Title
Genome research
Related Resources
PubMed ID
26297486
Repository Citation
Cenik C, Cenik ES, Byeon GW, Grubert F, Candille SI, Spacek D, Alsallakh B, Tilgner H, Araya CL, Tang H, Ricci EP, Snyder MP. (2015). Integrative analysis of RNA, translation, and protein levels reveals distinct regulatory variation across humans. Open Access Articles. https://doi.org/10.1101/gr.193342.115. Retrieved from https://escholarship.umassmed.edu/oapubs/2622
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Included in
Computational Biology Commons, Genetics Commons, Genomics Commons, Population Biology Commons