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Defining the consequences of genetic variation on a proteome-wide scale

An Author Correction to this article was published on 07 June 2022

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Genetic variation modulates protein expression through both transcriptional and post-transcriptional mechanisms. To characterize the consequences of natural genetic diversity on the proteome, here we combine a multiplexed, mass spectrometry-based method for protein quantification with an emerging outbred mouse model containing extensive genetic variation from eight inbred founder strains. By measuring genome-wide transcript and protein expression in livers from 192 Diversity outbred mice, we identify 2,866 protein quantitative trait loci (pQTL) with twice as many local as distant genetic variants. These data support distinct transcriptional and post-transcriptional models underlying the observed pQTL effects. Using a sensitive approach to mediation analysis, we often identified a second protein or transcript as the causal mediator of distant pQTL. Our analysis reveals an extensive network of direct protein–protein interactions. Finally, we show that local genotype can provide accurate predictions of protein abundance in an independent cohort of collaborative cross mice.

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Figure 1: Tandem mass tag (TMT)-based liver proteomics in 192 DO mice.
Figure 2: Global view of the liver proteome reveals distinct genetic models of protein regulation.
Figure 3: Examples of local pQTL that illustrate different models of regulation.
Figure 4: Mediation of distant pQTL reveals network interactions in the liver proteome.
Figure 5: Genotype can be an accurate predictor of protein abundance.

Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD002801 ( Raw RNA-seq fastq files and processed gene-level data are archived at Gene Expression Omnibus (GEO) under accession number GSE72759. We implemented our mediation method as the R package, intermediate, which can be freely downloaded from The Genotyping by RNA-seq (GBRS) software is available for download from

Change history


  1. Crick, F. Central dogma of molecular biology. Nature 227, 561–563 (1970)

    ADS  CAS  Article  PubMed  Google Scholar 

  2. Gygi, S. P., Rochon, Y., Franza, B. R. & Aebersold, R. Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol. 19, 1720–1730 (1999)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011)

    ADS  Article  CAS  PubMed  Google Scholar 

  4. Ghazalpour, A. et al. Comparative analysis of proteome and transcriptome variation in mouse. PLoS Genet. 7, e1001393 (2011)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Skelly, D. A. et al. Integrative phenomics reveals insight into the structure of phenotypic diversity in budding yeast. Genome Res. 23, 1496–1504 (2013)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Wühr, M. et al. Deep proteomics of the Xenopus laevis egg using an mRNA-derived reference database. Curr. Biol. 24, 1467–1475 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Fu, J. et al. System-wide molecular evidence for phenotypic buffering in Arabidopsis . Nat. Genet. 41, 166–167 (2009)

    CAS  Article  PubMed  Google Scholar 

  8. Rockman, M. V. & Kruglyak, L. Genetics of global gene expression. Nat. Rev. Genet. 7, 862–872 (2006)

    CAS  Article  PubMed  Google Scholar 

  9. Brem, R. B., Yvert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002)

    ADS  CAS  Article  PubMed  Google Scholar 

  10. Morley, M. et al. Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743–747 (2004)

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. Schadt, E. E. et al. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003)

    ADS  CAS  Article  PubMed  Google Scholar 

  12. Jansen, R. C. & Nap, J. P. Genetical genomics: the added value from segregation. Trends Genet. 17, 388–391 (2001)

    CAS  Article  PubMed  Google Scholar 

  13. Chesler, E. J. et al. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nat. Genet. 37, 233–242 (2005)

    CAS  Article  PubMed  Google Scholar 

  14. Foss, E. J. et al. Genetic variation shapes protein networks mainly through non-transcriptional mechanisms. PLoS Biol. 9, e1001144 (2011)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. Foss, E. J. et al. Genetic basis of proteome variation in yeast. Nat. Genet. 39, 1369–1375 (2007)

    CAS  Article  PubMed  Google Scholar 

  16. Khan, Z., Bloom, J. S., Garcia, B. A., Singh, M. & Kruglyak, L. Protein quantification across hundreds of experimental conditions. Proc. Natl Acad. Sci. USA 106, 15544–15548 (2009)

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Wu, Y. et al. Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population. Cell 158, 1415–1430 (2014)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. Wu, L. et al. Variation and genetic control of protein abundance in humans. Nature 499, 79–82 (2013)

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Damerval, C., Maurice, A., Josse, J. M. & de Vienne, D. Quantitative trait loci underlying gene product variation: a novel perspective for analyzing regulation of genome expression. Genetics 137, 289–301 (1994)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Albert, F. W., Treusch, S., Shockley, A. H., Bloom, J. S. & Kruglyak, L. Genetics of single-cell protein abundance variation in large yeast populations. Nature 506, 494–497 (2014)

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Ting, L., Rad, R., Gygi, S. P. & Haas, W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 8, 937–940 (2011)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. McAlister, G. C. et al. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 86, 7150–7158 (2014)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Churchill, G. A. et al. The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nat. Genet. 36, 1133–1137 (2004)

    CAS  Article  PubMed  Google Scholar 

  24. Churchill, G. A., Gatti, D. M., Munger, S. C. & Svenson, K. L. The Diversity Outbred mouse population. Mamm. Genome 23, 713–718 (2012)

    Article  PubMed  PubMed Central  Google Scholar 

  25. Threadgill, D. W. & Churchill, G. A. Ten years of the collaborative cross. Genetics 190, 291–294 (2012)

    Article  PubMed  PubMed Central  Google Scholar 

  26. Keane, T. M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011)

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. Gatti, D. M. et al. Quantitative trait locus mapping methods for diversity outbred mice. G3 (Bethesda) 4, 1623–1633 (2014)

    Article  Google Scholar 

  28. Toye, A. A. et al. A genetic and physiological study of impaired glucose homeostasis control in C57BL/6J mice. Diabetologia 48, 675–686 (2005)

    CAS  Article  PubMed  Google Scholar 

  29. Ronchi, J. A. et al. A spontaneous mutation in the nicotinamide nucleotide transhydrogenase gene of C57BL/6J mice results in mitochondrial redox abnormalities. Free Radic. Biol. Med. 63, 446–456 (2013)

    CAS  Article  PubMed  Google Scholar 

  30. Freeman, H. C., Hugill, A., Dear, N. T., Ashcroft, F. M. & Cox, R. D. Deletion of nicotinamide nucleotide transhydrogenase: a new quantitive trait locus accounting for glucose intolerance in C57BL/6J mice. Diabetes 55, 2153–2156 (2006)

    CAS  Article  PubMed  Google Scholar 

  31. Huttlin, E. L. et al. The BioPlex Network: a systematic exploration of the human interactome. Cell 162, 425–440 (2015)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. van Weering, J. R. T. et al. Molecular basis for SNX-BAR-mediated assembly of distinct endosomal sorting tubules. EMBO J. 31, 4466–4480 (2012)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. Liu, Y.-T. & Yin, H. L. Identification of the binding partners for flightless I, A novel protein bridging the leucine-rich repeat and the gelsolin superfamilies. J. Biol. Chem. 273, 7920–7927 (1998)

    CAS  Article  PubMed  Google Scholar 

  34. Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174–1189 (2010)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. Battle, A. et al. Genomic varation. Impact of regulatory variation from RNA to protein. Science 347, 664–667 (2013)

    Google Scholar 

  36. Laurent, J. M. et al. Protein abundances are more conserved than mRNA abundances across diverse taxa. Proteomics 10, 4209–4212 (2010)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. Vogel, C. & Marcotte, E. M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat. Rev. Genet. 13, 227–232 (2012)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Welsh, C. E. et al. Status and access to the Collaborative Cross population. Mamm. Genome 23, 706–712 (2012)

    Article  PubMed  PubMed Central  Google Scholar 

  39. Chesler, E. J. et al. The Collaborative Cross at Oak Ridge National Laboratory: developing a powerful resource for systems genetics. Mamm. Genome 19, 382–389 (2008)

    Article  PubMed  PubMed Central  Google Scholar 

  40. Iraqi, F. A., Churchill, G. & Mott, R. The Collaborative Cross, developing a resource for mammalian systems genetics: a status report of the Wellcome Trust cohort. Mamm. Genome 19, 379–381 (2008)

    Article  PubMed  Google Scholar 

  41. Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976–989 (1994)

    CAS  Article  PubMed  Google Scholar 

  42. Elias, J. E. & Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007)

    CAS  Article  PubMed  Google Scholar 

  43. de Hoon, M. J. L., Imoto, S., Nolan, J. & Miyano, S. Open source clustering software. Bioinformatics 20, 1453–1454 (2004)

    CAS  Article  PubMed  Google Scholar 

  44. Welsh, C. E. & McMillan, L. Accelerating the inbreeding of multi-parental recombinant inbred lines generated by sibling matings. G3 (Bethesda) 2, 191–198 (2012)

    Article  Google Scholar 

  45. Broman, K. W. et al. Haplotype probabilities in advanced intercross populations. G3 (Bethesda) 2, 199–202 (2012)

    Article  Google Scholar 

  46. Munger, S. C. et al. RNA-Seq alignment to individualized genomes improves transcript abundance estimates in multiparent populations. Genetics 198, 59–73 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Cheng, R., Abney, M., Palmer, A. A. & Skol, A. D. QTLRel: an R package for genome-wide association studies in which relatedness is a concern. BMC Genet. 12, 66 (2011)

    Article  PubMed  PubMed Central  Google Scholar 

  48. Dudbridge, F. & Koeleman, B. P. C. Efficient computation of significance levels for multiple associations in large studies of correlated data, including genomewide association studies. Am. J. Hum. Genet. 75, 424–435 (2004)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. Storey, J. D., Taylor, J. E. & Siegmund, D. Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: A unified approach. J. R. Stat. Soc. Series B 66, 187–205 (2004)

    MathSciNet  MATH  Article  Google Scholar 

  50. Baron, R. M. & Kenny, D. A. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51, 1173–1182 (1986)

    CAS  Article  PubMed  Google Scholar 

  51. Fritz, M. S. & Mackinnon, D. P. Required sample size to detect the mediated effect. Psychol. Sci. 18, 233–239 (2007)

    Article  PubMed  Google Scholar 

  52. Yvert, G. et al. Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nat. Genet. 35, 57–64 (2003)

    CAS  Article  PubMed  Google Scholar 

  53. Petrovski, S., Wang, Q., Heinzen, E. L., Allen, A. S. & Goldstein, D. B. Genic intolerance to functional variation and the interpretation of personal genomes. PLoS Genet. 9, e1003709 (2013)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. Enright, A. J., Van Dongen, S. & Ouzounis, C. A. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 30, 1575–1584 (2002)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  55. Finn, R. D. et al. Pfam: the protein families database. Nucleic Acids Res. 42, D222–D230 (2014)

    CAS  Article  PubMed  Google Scholar 

  56. Magrane, M. & UniProt Consortium. UniProt Knowledgebase: a hub of integrated protein data. Database (Oxford) 2011, bar009 (2011)

    Article  CAS  Google Scholar 

  57. Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  58. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing on JSTOR. J. R. Stat. Soc. B 57, 289–300 (1995)

    MATH  Google Scholar 

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The authors thank L. Somes and M. Strobel for breeding the mice; S. Ciciotte, S. Daigle, J. Pereira, C. Snow, R. Lynch and H. Munger for extracting RNA and performing RNA-seq experiments; and A. Manichaikul for discussion on mediation analysis. Collaborative Cross strains used in this study were imported to JAX from the Systems Genetics Core Facility at the University of North Carolina (USA)38. Previous to their relocation to UNC, CC lines CC001, CC003 and CC017 were generated and bred at Oak Ridge National Laboratory (USA)39; CC line CC004 was generated and bred at Tel Aviv University (Israel)40. Research reported here was supported by Harvard Medical School, The Jackson Laboratory, and National Institutes of Health (NIH) grants under awards P50GM076468 (to G.A.C.), F32HD074299 (to S.C.M.), GM67945 (to S.P.G.) and U41HG006673 (to S.P.G. and E.L.H).

Author information

Authors and Affiliations



C.M. developed the methodology for analysing the convection models, conducted the plate analysis, contributed to the interpretation and wrote the manuscript. N.C. conducted the convection calculations, contributed to the development of the methodology and analysis, contributed to the interpretation and wrote the manuscript. M.S. and R.D.M. provided guidance with GPlates and scripts, contributed to the interpretation and wrote the manuscript. P.J.T. provided the StagYY convection code, guidance on using it and wrote the manuscript.

Corresponding authors

Correspondence to Gary A. Churchill or Steven P. Gygi.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Proteomic profiling of the eight founder strains used to create the DO mouse population.

a, A multiplexed TMT proteomics method was used to characterize protein expression for the eight founder strains with two biological replicates for each strain using both sexes. In total, just over 400,000 peptides were quantified corresponding to 7,699 proteins. b, Hierarchical clustering and principal component analysis determined that the major source of variation in protein expression is due to genetic variation among the eight strains and the sex within strains. c, K-means clustering and gene set enrichment determined that each of the clusters was specifically enriched for metabolic pathways, biological process or cellular components. d, Proteins representing each of the displayed clusters from c. These proteins have specific patterns of expression as exemplified by PCK1, which was highly expressed in the NOD strain. Other examples include SCD1, which was highly expressed in C57BL/6J and NZO strains (n = 4 mice for each founder, 2 male and 2 female, black bars represent median values). Protein abundance is shown as the percentage contribution of that mouse’s protein levels to its respective 10-plex.

Extended Data Figure 2 The influence of sex and diet on protein and transcript abundance.

a, Principal component analysis aligns well with sex and diet as major experimental contributors of variation in protein abundance. b, Female-specific protein abundance profiles for SULT2A1 and FMO3. c, Male-specific protein abundance profiles for CYP4A12A and MUP3. d, e, Diet also resulted in the regulation of many proteins, which are represented by proteins such as SCD1 and ACACA that increased in abundance and proteins such as HMGCR and SQLE that decreased in abundance. f, Principal component analysis aligns well with sex and diet as major experimental contributors of variation in transcript abundance. gj. Transcript scatter plots for the proteins in be. Transcript abundance data were transformed to rank normal scores for plotting.

Extended Data Figure 3 Genetic effects drive much of the observed expression variance in the RNA-seq and proteomics data.

Liver transcript and protein abundance are highly variable in the DO population. Among the discovery set (n = 6,707 proteins, 6,647 genes), much of this variance can be attributed to one or more experimental variables and/or genetic effects. ac, The experimental covariates sex and diet influence many transcripts and proteins in an additive manner, however, the interaction of sex and diet does not seem to affect many genes. The effects from sex and diet are not biased towards one molecular species—that is, similar numbers of transcripts and proteins are similarly affected by these experimental variables. Genetic variation underlies many of the most variable transcripts and proteins. d, e, Local genetic variation in particular is a strong driver of expression variation for many genes, while distant genetic effects are observed but more subtle. Among the discovery set, we observe more and larger genetic effects (both local and distant) on transcript abundance than protein abundance. f, For most transcripts and proteins detected in this study, expression variation is minimal, cannot be attributed to a known experimental or genetic variable, and is plotted as noise. g, pQTL map for all 6,707 proteins tested from genetic linkage analysis. h, i, QTL mapping identified the genetic loci that underlie variability in transcript abundance (eQTL). For the discovery set of transcripts with detected proteins and the larger set of all expressed genes, the location of the eQTL is plotted on the x axis and the location of the controlled gene is plotted on the y axis. Most genetic effects are local and map to the same location as the gene, as evidenced by the prominent diagonal line in both maps.

Extended Data Figure 4 Replication rates for eQTL are highly correlated with effect size, and local eQTL replicate at higher rates than distant eQTL.

a, To assess replication of eQTL, an independent set of 192 DO liver RNA-seq samples was analysed (‘replication set’) and compared to the discovery set. A total of 16,839 genes were expressed in half or more samples in both data sets. For each gene, the most significant proximal locus (within ± 10 Mb of gene) and distant locus (located on a different chromosome from the gene) were identified from the discovery set—LOD scores at these loci are plotted on the x axis (local in red; distant in blue). Next, the most significant loci within a 10-Mb window flanking the local and distant loci from the discovery set were identified in the replication set and plotted on the y axis. LOD scores are highly correlated at these peak loci (local Pearson r = 0.91; distant r = 0.84). b, For the core set of 6,707 proteins (6,647 gene ids), pQTL and eQTL overlap were compared at multiple genome-wide P value thresholds from 0.01 to 0.2. Again, one maximum proximal locus and one maximum distant locus were identified for each gene/protein, and recorded if it met the P value cut off. Local pQTL exhibit high overlap with both the discovery eQTL set and replication eQTL set, regardless of P value threshold (67–80%). Distant pQTL exhibit slightly higher overlap with eQTL at the most stringent P value cut off, however, overlap is consistently low for distant pQTL (<1–2%). Local eQTL overlap well with the replication eQTL set regardless of P value threshold (75–77%). Distant eQTL replicate poorly overall (3–31%), but overlap rate is highest (31%) at the most stringent P value threshold, suggesting that larger sample sizes will be required to fully and accurately characterize distant effects on gene expression. c, The maximum proximal locus and distant locus were identified for each of the 6,707 proteins and transcripts, and the cumulative distribution of their LOD scores is plotted (blue = proteins, green = transcripts). LOD score is plotted on the x axis, and the proportion of total QTL is plotted on the y axis. Local eQTL as a group exhibit higher LOD scores (consistent with higher effect sizes) than local pQTL (ninetieth percentile LOD = 23.9 for local eQTL, 13.6 for pQTL), while distant eQTL and pQTL are of similar scale (ninetieth percentile LOD = 7.9 for distant eQTL, 8.2 for distant pQTL). d, Comparison of pQTL from the discovery set to eQTL from the discovery set (left set of Venn diagrams) and eQTL from the replication set (right). As expected given that they derive from the same samples, local pQTL and eQTL overlap is observed to be higher in the discovery set (1,392 out of 1,736 = 80%), however, local pQTL still overlap well with eQTL from the replication set (1,273 out of 1,736 = 73%). Distant pQTL overlap poorly with both eQTL sets (9 out of 1,048 in discovery set); 8 out of 1,048 in replication set), however, 6 of 9 distant pQTL that do overlap with eQTL in the discovery set are also identified as overlapping in the replication set.

Extended Data Figure 5 BIC model selection reveals transcriptional mechanisms driving most local pQTL and post-transcriptional mechanisms underlying most distant pQTL.

We identified the local and distant QTL with the maximum LOD score (regardless of significance) for each of the 6,707 proteins, and used BIC to assess eight models linking QTL genotype to transcript and protein abundance. Most proteins are not affected by the local or distant QTL, and fall in one of the three groups below outlined by the dotted line. Among the five models where a QTL effect on protein abundance is detected, two are transcriptional in nature (L1, L2; D1, D2); the QTL effect on protein abundance is conferred at least partially through the transcript. The remaining three genetic models are post-transcriptional (L3–5; D3–5); the QTL effect on protein abundance is not mediated through the transcript. The transcriptional L1 and L2 models are identified as the best models for most local pQTL, while the post-transcriptional D3 and D4 models are optimal for most distant pQTL.

Extended Data Figure 6 Examples of local pQTL that are due to an underlying eQTL and those that are due to post-transcriptional mechanisms.

a, The protein DHTKD1 contained a local acting eQTL and pQTL, which was associated with increased transcript and protein abundance derived from 129S1/SvImJ, CAST/EiJ, PWK/PhJ and WSB/EiJ strains. Mice were divided into three groups depending on whether or not their genomes contained 0, 1 or 2 of the alleles found to be associated with the pQTL. These increases in protein abundance were further validated using the proteomic analysis of the founder strains. b, c, Similarly, Ces2h and Pipox had both a local acting eQTL and pQTL that could be associated with specific strains (CAST/EiJ, PWK/PhJ and WSB/EiJ). These protein abundance measurements were further validated using the founder strains data set. d, e, Alternatively, 10% of the genes had local pQTL but lacked local eQTLs, which is evident in proteins such as ENTPD5 and OMA1. The founder allele expression patterns inferred at the pQTL were validated by protein abundance measurements in the founder strains, which could be explained CAST/EiJ specific missense mutations in both genes. f, Likewise, Lars2 also contained a pQTL that had no observable eQTL that showed a decrease in protein abundance in the 129S1/SvImJ, CAST/EiJ, PWK/PhJ and WSB/EiJ strains. Genome sequencing determined that these strains share four missense mutations (*P < 0.01 using a Student’s t-test; for founder strains, n = 4 mice for each founder, 2 male and 2 female, error bars represent s.d.).

Extended Data Figure 7 The causal relationship between genetic variation and protein expression was determined for over 700 proteins as inferred by mediation analysis.

ad, Many of the causal relationships between proteins have been previously documented such as the associations between SNX7–SNX4, PGAM1–PGAM2, LRRFIP1–FLII and PPIF–PPIE. eh, In addition, many of the protein associations had not be previously documented such as UPB1–MTR, FOCAD–AVEN, AGPAT9–CHP1 and ANXA1–ARAD1A. il, Protein associations were also identified for multimeric complexes such as ECSIT–NDUFAF1–TMEM126B, DMXL2–ROGDI–WDR7, PIGU–PIGT–PIGS and IKBKAP–ELP2–ELP3.

Extended Data Figure 8 Mediation analysis for CCT complex members details the effects of a QTL in Cct6a on protein abundance through post-transcriptional protein buffering.

a–f, Mediation analysis for each of the Cct complex identifies Cct6a as the causal intermediate. A local QTL for Cct6a affects transcript and protein abundance, and CCT6A abundance sets the abundance of other CCT proteins regardless of variation in their transcripts. For each of the complex members tested, all other complex members are confirmed to be co-regulated providing additional supporting evidence for stoichiometric buffering.

Extended Data Figure 9 Distant pQTL and co-regulated proteins frequently correspond to complexes of physically interacting proteins.

a, Distant pQTL and co-regulated proteins assemble to form a regulatory network, which is defined by protein clusters with distinct topologies. A total of 3,938 proteins/QTL are linked by 5,794 associations. Distant pQTL are depicted as purple arrows pointing from the inferred causal protein to its regulated pair. Co-regulated proteins are connected with green arrows emanating from the primary target protein. b, MCL clustering decomposes the distant pQTL network into 671 clusters. Cluster size varies considerably, although most clusters contain fewer than 20 proteins. c, Clusters extracted from the distant pQTL network frequently associate proteins with shared biological functions. More than half of clusters are enriched for at least one GO category, as depicted in the bar chart above. df, Three selected clusters of distant pQTL and co-regulated proteins. g, To understand the relationship between the distant pQTL associations and protein interactions, each distant pQTL and its co-regulated proteins were mapped to their human homologues in the BioPlex network of human protein interactions. To assess the tendency for these co-regulated proteins to cluster together, the median graph distance separating all pairs of co-regulated proteins was determined. The distribution of median distances observed for equal numbers of randomly selected proteins was also determined and used to assign a Z-score to each distant pQTL and its co-regulated proteins. h, Histogram depicting the Z-score distribution for distant pQTL and co-regulated proteins. Z-scores below −2.5 (highlighted in red) indicated that co-regulated proteins were unusually close within the BioPlex network. il, Selected distant pQTL and co-regulated proteins, mapped onto the BioPlex network of protein interactions. All shortest paths connecting distant pQTL and their regulated proteins have been extracted from the BioPlex network and displayed. Proteins inferred to be responsible for each QTL are purple, while primary regulated proteins are red and secondary co-regulated proteins are green. Grey circles represent neighbouring proteins in the BioPlex network that were not found to be co-regulated. Grey edges indicate BioPlex interactions, while Blue edges denote co-regulation uncovered from trans-QTL analysis.

Extended Data Figure 10 Comparison of protein abundance in the DO and founder strains reveals a positive correlation between pQTL significance and predictive power.

a, b, For all detected liver pQTL in the DO population, founder strain allelic contributions were derived from the mapping model and compared to protein abundance measured directly from the eight founder strains. Pearson correlations are plotted against the LOD score of the pQTL for both local and distant pQTL. Predictive power tracks well with pQTL significance. Local pQTL tend to be more significant and yield higher predictive power than distant pQTL, however highly significant distant pQTL (>10 LOD) have comparable predictive power to local pQTL of similar significance.

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Chick, J., Munger, S., Simecek, P. et al. Defining the consequences of genetic variation on a proteome-wide scale. Nature 534, 500–505 (2016).

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