Author Summary An old dogma in biology states that, in every cell, the flow of biological information is from DNA to RNA to proteins and that the latter act as a working force to determine the organism’s phenotype. This model predicts that changes in DNA that affect the clinical phenotype should also similarly change the cellular levels of RNA and protein levels. In this report, we test this prediction by looking at the concordance between DNA variation in population of mouse inbred strains, the RNA and protein variation in the liver tissue of these mice, and variation in metabolic phenotypes. We show that the relationship between various biological traits is not simple and that there is relatively little concordance of RNA levels and the corresponding protein levels in response to DNA pertur- bations. In addition, we also find that, surprisingly, metabolic traits correlate better to RNA levels than to protein levels. In light of current efforts in searching for the molecular bases of disease susceptibility in humans, our findings highlight the complexity of information flow that underlies clinical outcomes. protein levels. The extent to which this occurs, however, remains poorly understood and understanding the relationships across scales, from DNA to phenotype, has both practical and basic implications. For example, ‘‘genetical genomics’’ studies examine transcript levels as a function of genetic variation and use this information to construct models, such as interaction networks, to explain complex phenotypes [1–8]. Systems based approaches, in particular, have relied heavily on transcriptome data [9]. Concordance of protein and transcript levels has been studied in yeast and plants. A recent comparative study in a yeast segregating population showed that there is a significant but modest correlation between transcript and protein levels [10]. Moreover, this report also found that, in general, loci that influence protein abundance are different from those affecting transcript abun- dance. A similar comparative analysis of molecular phenotype mapping in Arabidopsis [11] was reported subsequently. In this report the authors investigated the commonality of hotspot loci (defined as loci affecting a large number of traits within each biological class) across various biological scales and observed a general theme consistent with the phenotypic buffering of perturbations affecting molecular phenotypes as one looks to scales further away from the DNA variation (e.g. proteome vs transcriptome). Both of these reports emphasize the value gained from bringing together information from various biological scales, as each dataset will add new information to the phenotypic effect of DNA variation. We now report global analysis of transcript-protein relationships in mice using a genetic approach involving thousands of naturally occurring perturbations. For this, we have utilized a recently developed panel of permanent inbred strains of mice, termed the Hybrid Mouse Diversity Panel (HMDP), that allows high resolution mapping of complex traits [12]. We chose to examine protein and transcript levels in liver given the importance of the organ in metabolic traits relevant to disease. Results Study design The experimental design of our study is depicted in Figure 1. To study the relationship between transcript and protein levels globally, we examined 97 inbred strains of mice of the HMDP Global Mouse mRNA and Protein Levels Comparison representing a wide range of genetic diversity, including ,11,000,000 single nucleotide polymorphisms as well as copy number variations [13,14]. As we have shown previously, this population includes thousands of expression quantitative trait loci (eQTL) that can be mapped in the population using association analysis with correction for population structure using a mixed model algorithm [12]. The resolution achieved in this way is, on average, one to two orders of magnitude narrower than that using linkage analysis [12]. Livers from the 97 strains were quantitatively analyzed for global transcript levels using the Affymetrix HT-MG- 430A platform and for protein levels using LC-MS employing AMT tag approach for identification and 16O/18O labeling for quantification [12,15]. In the latter, each individually processed and unlabeled sample is spiked with the 18O labeled ‘‘universal’’ reference pool (i.e. the pool made from mixing together the same amount of isolated proteins from all samples) providing an internal standard for accurate measurement of protein abundance across biological samples. This dual-quantification, which combines the label-free and isotope labeling techniques, has been shown to be significantly superior over label-free methods in terms of quantification precision [15] and offers a simple, robust, and a more precise alternative to other proteomic techniques for studying variations in protein levels across large biological samples. In the LC-MS dataset, we also included 10 technical replicates from the C57BL/6J strain to measure the reproducibility of the sample preparation and technology which we describe in detail below. Peptide and microarray data quantification and quality One technical issue for proteomic analysis relates to how peptides with non-synonymous coding SNPs would present themselves in the correlation and association analyses. In order to annotate peptides detected by the tandem LC- MS/MS fragmentation patterns, the mass spectra are matched against a pre-established known reference database. In our case this database, which was built from the pool of all the inbred strains in the HMDP panel, was created by annotating the peptides against the reference sequence (C57BL/6J strain) followed by filtering out those peptides which have non-synonymous coding SNPs documented in public database for any of the HMDP strains. As a result of this preprocessing step, the peptides identified by LC-MS were limited to those that did not contain known non- synonymous SNPs in their amino acid sequences. From the original 5363 peptides measured, we selected peptides that a) had less than 50% missing measurements in the whole population, b) had no internal lysine or arginine, and c) aligned uniquely to one Ensembl gene. Fifty four percent of peptides (2893 peptides) passed these initial selection criteria. To assess the quality of the measurements, we investigated the amount of technical noise in the peptides selected. Having the control technical replicates allowed us to measure the reproducibility of the LC-MS measurement and assess whether the variation in the levels of the selected peptides in the HMDP population was due to technical or genetic variation. The distribution of the variance in the control mice and in the HMDP panel are shown in Figure 2A (the blue histogram). The mean and median across the ten replicates were 0.19 and 0.08 (the grey histogram), respectively, suggesting that, for most peptides, the measurements were robust. In contrast, the distribution of the variance was much broader in the genetic population where the mean and median of variances across all the peptides were 0.2 and 0.3 respectively (Figure 2A, the blue histogram). The relationship between RNA levels and peptide levels across the HMDP genetic perturbations would be a function of the PLoS Genetics | www.plosgenetics.org 2 June 2011 | Volume 7 | Issue 6 | e1001393