distinct SNPs across the genome. From these 1088 SNPs, 930 were associated with a single protein, 100 were associated with 2 proteins, and 14 SNPs were associated with 5 or more proteins. From the 1368 peak markers associated with protein levels 438 (32%) were also a peak SNP for one or more transcripts. To investigate if the distinct peak SNPs found in the transcript and protein data map near each other, we divided the genome into 2 Mb bins and using a 50 kb sliding window counted the number of associations in each bin. In the transcript data, the median eQTL number/window was 8 and the highest number of associations was found for bins on Chr 4 (from 98.7 Mb to 100.8 Mb) with 71 eQTLs, Chr 5 (from 80 Mb to 83.1 Mb, from 112.4 Mb to 114.4 Mb) with 79 and 75 eQTLs in each respectively, Chr 7 (from 143.2 Mb to 146.2 Mb) with 78 eQTLs, Chr 8 (from 93.0 Mb to 95.1 Mb) with 71 eQTLs, Chr 17 (from 43.8 Mb to 46.4 Mb) with 80 eQTLs, and Chr 18 (from 55.0 Mb to 57.5 Mb) with 76 eQTLs. In the protein data, however, most associations were randomly distributed except for a clustering of associations on Chr 3 (from 36.5 Mb to 38.6 Mb) with 20 pQTLs and Chr 11 (from 94.3 Mb to 96.7 Mb, and from 114.1 Mb to 118.1 Mb) with 19 and 21 pQTLs respectively (Figure 6B). These results contrast to the previously published reports where hotspots containing hundreds or thousands of eQTLs were observed [30,31]. This could be partially explained by both our ability to map molecular phenotypes with higher precision in the HMDP panel and the relatively stringent genome-wide threshold chosen to carry out the analysis. The eQTL hotspots on Chr 4 (from 98.7 Mb to 100.8 Mb) resides 6 Mb proximal and the Chr 5 hotspot (from 80 Mb to 83.1 Mb) resides 25 Mb distal to the Chr 5 hotspot reported recently in mouse-hamster radiation hybrid cell line [32]. Despite the relative close distance in mapping, however, we did not find a significant overlap between the genes mapping to these two loci in the two studies. Genetic regulation of transcript and protein levels The global look at the eQTL profiles of the transcriptome and proteome described above suggested that transcripts are more extensively regulated at the genetic level than proteins. However, since the transcriptome data is more comprehensive than the protein data, the differences observed between two datasets might be due to sampling bias. In order provide a measure of similarity for genetic regulation of proteins and transcripts we restricted the data to the set of 396 genes for which we had both protein and transcript measurements available. As mentioned earlier, in this restricted dataset the 396 genes are represented by twice as many peptides as probesets (1343 peptides and 607 probesets). Similar to the genome-wide global analysis, we avoided the use of single statistical cutoff to compare association results across the transcript and peptide datasets, as each dataset has its own variance properties. Instead, we compiled separate lists of significant associations for each dataset using the same FDR cutoff. Since the FDR threshold is driven by the distribution of p-values in each dataset, this allowed us to compare the two lists directly without setting a single statistical cutoff for both datasets. Limiting the mapping data to those associations that met the 5%FDR cutoff in each dataset (p-value,1.7e-05 for transcripts and p-value,9.6e- 06 for proteins) we found that despite mapping twice as many peptides as probesets the number of significant associations were roughly equal (939 and 1083 significant associations for probesets and peptides, respectively). This suggests that transcripts are twice as likely to be genetically regulated as are peptides. Next, we performed a gene level analysis where we assigned the associations obtained in each data set to their respective genes and for each gene investigated the degree of similarity in genetic regulation Global Mouse mRNA and Protein Levels Comparison across the protein and transcript dataset. As summarized in Table 2 and consistent with the probeset/peptide analysis described earlier, we found that the number of genes under genetic regulation, as judged by fraction of total genes with at least one significant genome-wide association, favors the transcript dataset. Overall, from the initial 396 genes, 75% (297/396) of the genes with transcript products had at least one significant result vs 61% (242/396) of the genes in the LC-MS dataset. The number of genes with multiple eQTL and pQTL was 205 and 171, respectively. We also looked at the comparison across datasets after classifying the mapping results into local and distant eQTL and pQTL. For distant associations, 281 genes mapped to 799 distinct loci in the transcript dataset and 236 genes mapped to 874 unique genomic locations in protein dataset. Overlapping the association results from the two datasets for distant eQTL/ pQTLs, we found that only 25 loci overlap with each other. From these 25 loci, 7 loci had the same peak SNP between the pQTL and the eQTL and in the remaining 18 the distance of peak SNP between the eQTL and pQTL ranged from 2.6 kb to 1.6 Mb. For local eQTLs, we found approximately twice as many local eQTLs as local pQTLs for the 396 genes (79 vs 46). To examine the extent of overlap between local QTLs, we considered a pQTL and an eQTL shared if they mapped within 2 Mb of each other. Using this definition, there were 26 local QTLs shared between the protein and transcript products of the gene. From these common QTLs, 8 mapped to the same peak SNP in the genome-wide association and 18 others mapped in various proximities of each other ranging from 23 bp to 1.8 Mb. The number of shared local QTLs suggests that majority of local pQTLs (26/46 = 56%) are likely to be conserved at the transcript level, and only 1/3 (26/ 79 = 32%) of eQTLs are conserved at the protein level. Since local eQTLs are less likely to contain false positives [33], we utilized them to assess if our definition of significance based on FDR had any effect of the results of the comparative analysis we described above. For this, since the transcript local eQTL counts outnumbered the peptide counts, we set a fixed threshold for significance in the transcript dataset (5% FDR, p-value,1.69e-05), counted the number of pQTL overlaps with the significant eQTLs at varying statistical cutoffs, and asked if the increase in the overlap was more than what would be expected by chance. For this we examined results at 5% FDR cutoff, 10% FDR cutoff, and 25% FDR cutoff. At the 5%FDR (p-value,9.58e-06) there were 46 local pQTLs from which 26 overlapped with the 79 eQTLs. Decreasing the p-value stringency to detect association in the protein data, however, did not significantly increase the overlap between eQTL and pQTL. At the 10% FDR (p-value,2.95e-05) we detected 56 pQTL from which 28 overlapped with eQTLs (2 more than 5%FDR), and at the 25% FDR (p-value,0.0002) we detected 68 pQTLs for which the overlap with eQTL was only 29, one more than 10% FDR cutoff and 3 more than the 5%FDR cutoff. These non-significant changes in overlap between eQTL and pQTL suggest that the lack of overlap between eQTL and pQTL as presented earlier was not due to the genomewide significance thresholds set for each dataset. We should emphasize that one limitation of our study originated from our study design where we utilized different number of mice per strain to estimate the transcript and the peptide levels. For transcript levels we profiled the RNA from 3 mice per strain and estimated transcript levels for genes by averaging over the data obtained for three mice, but for the LC-MS data we only sampled one mouse per strain. This design, by its nature, results in a higher power to detect genome-wide associations and significant correlations with clinical traits for transcript data in comparison to the peptide data. In fact, mapping transcript levels by taking PLoS Genetics | www.plosgenetics.org 11 June 2011 | Volume 7 | Issue 6 | e1001393