Global Mouse mRNA and Protein Levels Comparison Figure 5. Relationships between the peptide data and transcript data with clinical traits and biological pathways. A) Correlations of transcriptome and proteome with clinical traits. A scatter plot of correlation coefficients between 607 probesets and 1343 peptides with 42 clinical traits (peptide-trait correlations are plotted on the x-axis and probeset-trait correlations are plotted on the y-axis). Red points are those correlations which were significant for transcripts only, green points are those correlations which were significant for protein data only and black points are those which were not significant in either of the two datasets. B) Concordance of transcripts and proteins in 115 KEGG biological pathways. doi:10.1371/journal.pgen.1001393.g005 to the same pathway, 2) correlation between probesets belonging to the same pathway, 3) correlation between probesets and peptides belonging to the same pathway. Comparing the results of these three analyses suggests that overall within-transcript correlation of biological pathway genes is higher than within- protein correlations (0.20 vs 0.14 mean Spearman correlation coefficients) (Figure 5B), and transcript-protein correlations are the weakest of all (mean Spearman correlation coefficient of 0.11). From the 99 pathways, 79 pathways had better between-transcript correlations than between-protein correlations and 20 had better between-protein correlations. We also observed that for most pathways when there was good concordance between the transcripts there was also good concordance between the peptides of that pathway (r = 0.39, Figure 5B). Modest concordance of genetic loci controlling transcript and protein levels Next, we examined the genetic loci regulating protein and transcript levels. It is known that the presence of SNP within probe sequence can affect hybridization of the mRNA [26], leading to both type-I and type-II errors in the genomewide association analysis. In our Affymetrix dataset, as expected, we also observed a significant effect of SNPs on genomewide association results for fraction of the probes, as judged by comparing the significance level for local eQTLs between probesets before and after masking of probes containing publicly available SNPs (see Text S1 and Table S5 for details, and Dataset S3 for the list of probes which were masked from each probeset due to the presence of SNP). To minimize this technical artifact, we removed all the SNP- containing probes from their corresponding probeset before normalization of the data and eliminated all the probesets which contained 8 or more probes with SNPs (,300 probesets fell in this category). Therefore, all the data reported below were generated from masked probesets. We performed genomewide association on both transcriptome and proteome datasets using 95,854 SNPs with minor allele frequencies greater than 10% obtained from the Broad Institute (http://www.broadinstitute.org/mouse/hapmap) and Wellcome Trust Center (WTCHG) (see Materials and Methods for details). To account for the population structure and genetic relatedness among strains in the genome-wide association mapping, we utilized the Efficient Mixed Modeling Algorithm (EMMA) [27]. Furthermore, haplotype analysis of the inbred strains has shown the presence of over 60,000 haplotype blocks of varying size throughout the genome of inbred strains [28]. Since the presence of these blocks could be a source for overestimation of extent of genetic regulation and false positive associations, for each transcript and protein we removed significant associations due to high linkage disequilibrium (defined as R-squared of 0.5 or larger between genotypes). Since the transcript and protein data have different variance properties, which may subsequently affect our statistical power to detect associations in the two different datasets, we avoided the use of the same statistical cutoff for each dataset. Instead, in order to achieve a comparable genome-wide cutoff across the two datasets, we made use of false discovery rate and compared the two association results by restricting the genome- wide mapping results of each dataset to a list of associations with a similar false discovery rates. The results are summarized in Table 2 and the eQTL profile for the combined set is depicted in Figure 6A. At the 5% genome-wide FDR cutoff (p-value,1.7e-05) we identified 14463 associations for the transcript data (referred to as ‘‘eQTL’’ for expression QTL). At this cutoff stringency, 63% of the transcripts (6299 out of 9896) mapped to at least one locus and roughly one third of the transcripts (3651 out of 9896) mapped to two or more loci (Table 2). In contrast, at the same 5% FDR (p- value,9.6e-06), we only found 1368 significant associations for the proteins (referred to as ‘‘pQTL’’ for protein QTL). The fraction of total proteins with significant association was 672 genes (43%) PLoS Genetics | www.plosgenetics.org 9 June 2011 | Volume 7 | Issue 6 | e1001393