only the data from one of the three microarray data for each strain gave us on average 36% fewer local eQTLs in comparison to what we had obtained by averaging the expression phenotypes over the three microarrays (Text S1 and Table S6). This difference would not change the overall conclusions regarding the commonality and the differences observed between the peptide and transcript genome-wide mapping results. Discussion We report a comparative analysis of the genetic regulation of the transcriptome and proteome in a mammalian system. By examining the effects of thousands of genetic perturbations simultaneously on transcript and protein levels in the HMDP, we were able to investigate the global nature of relationships between the two. Since the HMDP was typed for numerous clinical/physiologic traits, we were also able to study the relationships of these to transcript levels as compared to protein levels. Finally, we examined the commonality of genetic drivers affecting transcript and protein levels. We discuss these points in turn below. We performed the comparison of protein and transcript levels using two separate approaches. In one approach we comprehen- sively compared the LC-MS peptide measurements to the microarray expression estimates. In the second approach we examined the relationship between the expression of exons representing the peptides identified by the LC-MS to the expression of exons counted in the next generation sequence data. In addition, to address sources of technical and biologic variation in our measurements, we filtered peptides with significant nongenetic variation. In all these analyses we found that the relationship between the protein expression and transcript expression was modest at best and in only 50% of the cases did this relationship reach nominal statistical significance. We also found that the amount of genetic variation is a predictor of concordance between peptides and transcripts. Our data complements the data previously published for yeast and plant indicating similar modest protein-transcript relationship. As compared to yeast, we found a slightly higher estimate of protein-transcript concordance (0.27 vs 0.18 correlation) when considering all the peptide measurements and significantly higher estimates (0.42 vs 0.18 correlation) when considering peptides with large genetic variability. The higher estimates reported here are likely to be more reflective of the true relationship between protein and transcript levels as compared to the previous reports mainly due to the choice of technology used to measure protein levels in our study. We utilized the differential labeling technique as put forth by Qian and colleagues where the label free samples are combined with an internal control labeled with heavy isotope [15]. This mixture is then quantified and the results are reported as the ratio of sample to the pool for each identified peptide during mass spectrometry. This strategy, which offers the advantage of overcoming peptide level variation due to platform robustness, has been shown to more precisely quantify peptides as compared to label free methods [15]. This was evident in our study as well where we showed that in general the variance in technical replicates was low, with an overall narrow distribution across the peptides quantified. Biologically, the modest relationship between the proteins and transcripts is likely to be explained in part by molecular events such as translational efficiency, alternative splicing, folding, assembly into complexes, transport and localization, covalent modification, secretion, and degradation, all of which affect protein levels independently of transcripts. The importance of these post-transcriptional processes is highlighted by a recent Global Mouse mRNA and Protein Levels Comparison report showing that the presence of genetic variation in some of these post-transcriptional processes is associated with certain human diseases [34]. We acknowledge that the design of our study and our most comprehensive dataset, which was generated by Affymetric microarrays capturing the 39 end of transcripts, prevented us from comprehensively addressing the issue of differential splicing. However, using two complementary ap- proaches, NGS and concordance level of peptides, we examined the possibility of differentially regulated isoforms as a predictor for the lack of concordance between microarray data and LC-MS data. In neither case did differential splicing appear to contribute importantly to the lack of transcript-protein correlation. An unexpected finding was the stronger association of transcript levels with clinical traits as compared to protein levels with clinical traits. This is likely due in part to the greater technical difficulties for the quantification of proteins as compared to transcripts, but the differences were quite striking and there may be additional explanations. One possible explanation is that the molecular phenotypes are reactive to the clinical phenotypes (rather than being causal) and that there is increased buffering at the protein level. Apart from the strength of the trait associations, the protein and transcript associations in many cases did not overlap. For example, less than 15% of clinical trait-transcript correlations were replicated when traits were correlated with the corresponding proteins. At the genetic level we also found marked differences in the number and locations of loci controlling protein and transcript levels. When we directly examined protein-transcript pairs corresponding to the same gene, we found that the transcript data had twice as many associations as the protein data. One plausible explanation for the existence of the differential genetic regulation between proteins and transcripts is that of ‘‘phenotypic buffering’’ as put forth previously [11]. An alternative explanation, however, would be that in general the more removed a phenotype is from the DNA variation, the more complex the phenotype becomes. Thus, protein levels would be affected by all the factors influencing transcript levels as well as numerous additional factors. The consequence of increasing complexity in the phenotype is that less of the variation in phenotype would become linked to a single DNA variation. In summary, we highlighted the similarities and differences in genetic regulation of protein and transcript levels. Although a component of the observed differences in regulation is likely to be technical, particularly with respect to the protein levels, it is clear that the proteomics and transcriptomics provide nonoverlapping information. Thus, these data have important implications for systems biology approaches that utilize such high throughput data. They also raise fundamental questions about the complexity of the relationships between various biological scales involved in complex genetic traits. Materials and Methods Ethics statement All animals were handled in strict accordance with good animal practice as defined by the relevant national and/or local animal welfare bodies, and all animal work was approved by the appropriate committee. All experiments in this paper were carried out with UCLA IACUC approval. Animals and clinical phenotype collection Male mice from the HMDP panel, approximately 6–10 weeks of age, were purchased from Jackson Labs and were fed Purina Chow (Ralston-Purina Co., St. Louise, MO) at 16 weeks of age. All mice were maintained on a 12 h light/dark cycle. At 16 weeks PLoS Genetics | www.plosgenetics.org 12 June 2011 | Volume 7 | Issue 6 | e1001393