Impact of regulatory variation from RNA to protein

Genetic variants that impact gene regulation are important contributors to human phenotypic variation. For this reason, considerable efforts have been made to identify genetic associations with differences in mRNA levels of nearby genes, namely, cis expression quantitative trait loci (eQTLs). The phenotypic consequences of eQTLs are presumably due, in most cases, to their ultimate effects on protein expression levels. Yet, only few studies have quantified the impact of genetic variation on proteins levels directly. It remains unclear how faithfully eQTLs are reflected at the protein level, and whether there is a significant layer of cis regulatory variation acting primarily on translation or steady state protein levels. To address these questions, we measured ribosome occupancy by high-throughput sequencing, and relative protein levels by high-resolution quantitative mass spectrometry, in a panel of lymphoblastoid cell lines (LCLs) in which we had previously measured transcript expression using RNA sequencing. We then mapped genetic variants that are associated with changes in transcript expression (eQTLs), ribosome occupancy (rQTLs), or protein abundance (pQTLs). Most of the QTLs we detected are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, we found that eQTLs tend to have significantly reduced effect sizes on protein levels, suggesting that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we confirmed the presence of a class of cis QTLs that specifically affect protein abundance with little or no effect on mRNA levels; most of these QTLs have little effect on ribosome occupancy, and hence may arise from differences in post-translational regulation. Overall design: We measured level of translation transcriptome-wide in lymphoblastoid cell lines derived from 72 HapMap Yoruba individuals using ribosome profiling assay, for which we have transcript level, protein level (62 out of 72) and genotype information collected.

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Battle A, Khan Z, Wang SH, Mitrano A, Ford MJ, Pritchard JK, Gilad Y