Genomic variation. Impact of regulatory variation from RNA to protein

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.

Figure 1. Comparisons of QTLs at three levels of gene regulation

a Many QTLs exhibit shared effects across mRNA, ribosome occupancy and protein. This example illustrates a shared QTL for the schlafen family member 5 (SLFN5) gene (24). The upper panels show mean sequence depth (per bp) for mRNA and ribosome occupancy, averaged among individuals with each genotype at the QTL SNP. The lower panel shows median log₂ SILAC ratios at each detected peptide, relative to the shared internal standard. b. Replication rates between independently tested cis-QTLs for each phenotype pair, at FDR=10%. QTLs detected for the phenotype labeled on each row were tested in the phenotype listed for each column, considering only the 4,322 genes quantified in all three phenotypes. c. On average, eQTLs exhibit attenuated effects on protein abundance but not on ribosome occupancy. We used eQTLs detected by the GEUVADIS study to avoid ascertainment bias, and we polarized the alleles according to the direction of effect in GEUVADIS. The plot shows mean effect sizes and standard errors on the means, measured as expected fold-change per allele copy on a log₂ scale.

Figure 2. Protein-specific and RNA-specific QTLs

a An example of a protein-specific QTL, for the apolipoprotein L, 2 (APOL2) gene, detected by both the interaction model and the conditional models, indicating both larger effect (LRT, P = 3.3×10^-6, interaction model; P = 5.1×10^-13, conditional model) in protein than mRNA, and that the effect on protein is not mediated by either mRNA or ribosome occupancy (LRT, P = 2.1×10^-12, conditional model). Plotting details as in Figure 1A. While the causal variant underlying this pQTL is unknown, several linked variants near the 3′ end of APOL2 are all strongly associated with protein levels, including rs8142325 shown here and missense variant rs7285167 (β_g = 0.83, P = 9.8×10^-9; LRT, P = 2.1×10^-5, interaction model; P = 5.5×10^-13, conditional model). b. Effect sizes for ribosome occupancy tend to track with RNA, not protein. Top panel: effect sizes in all three phenotypes are shown for protein-specific QTLs. Effect sizes were estimated using linear regression in each of the phenotypes independently. The signs of the effects were set to be positive in protein. Solid lines reflect predicted effects based on a linear model. Bottom panel: Similarly, effect sizes in all three phenotypes for esQTLs. Here, signs of the effects were set to be positive in RNA.