Statistics requantitates the central dogma

Jingyi Jessica Li, Department of Statistics and Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.
Mark D. Biggin, Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

Abstract

Mammalian proteins are expressed at ∼10³ to 10⁸ molecules per cell (1). Differences between cell types, between normal and disease states, and between individuals are largely defined by changes in the abundance of proteins, which are in turn determined by rates of transcription, messenger RNA (mRNA) degradation, translation, and protein degradation. If the rates for one of these steps differ much more than the rates of the other three, that step would be dominant in defining the variation in protein expression. Over the past decade, system-wide studies have claimed that in animals, differences in translation rates predominate (2–5). On page 1112 of this issue, Jovanovic et al. (6), as well as recent studies by Battle et al. (7) and Li et al. (1), challenge this conclusion, suggesting that transcriptional control makes the larger contribution.

(full article) 

Impact of regulatory variation from RNA to protein

Alexis Battle^1,2
Zia Khan³
Sidney H. Wang³
Amy Mitrano³
Michael J. Ford⁴
Jonathan K. Pritchard^1,2,5
Yoav Gilad³

¹Department of Genetics, Stanford University, Stanford, CA 94305, USA.
²Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
³Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
⁴MS Bioworks, LLC, 3950 Varsity Drive, Ann Arbor, MI 48108, USA.
⁵Department of Biology, Stanford University, Stanford, CA 94305, USA.

Abstract

The phenotypic consequences of expression quantitative trait loci (eQTLs) are presumably due to their effects on protein expression levels. Yet the impact of genetic variation, including eQTLs, on protein levels remains poorly understood. To address this, we mapped genetic variants that are associated with eQTLs, ribosome occupancy (rQTLs), or protein abundance (pQTLs). We found that most QTLs are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, eQTLs tend to have significantly reduced effect sizes on protein levels, which suggests that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we identified a class of cis QTLs that affect protein abundance with little or no effect on messenger RNA or ribosome levels, which suggests that they may arise from differences in posttranslational regulation.

(full article)