Defining the consequences of genetic variation on a proteome-wide scale
Open accessAbstract
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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A number of studies have shown that while levels of many proteins are strongly correlated with their transcript levels, with coincident eQTL and pQTL, the correlations for most protein-transcript pairs are modest.
“A number of studies have now shown that while levels of many proteins are strongly correlated with their transcript levels, with coincident eQTL and protein QTL (pQTL), the correlations for most protein–transcript pairs are modest [51–58].”
From Multi-omics approaches to disease (2017) · cites this paper as [52]
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Cites
Baron, R. M., & Kenny, D. A. (1986) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.51.6.1173 · Cited by 91,542 · see citing works
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · see citing works
Eng, J. K., McCormack, A. L., & Yates, J. R. (1994) · Journal of the American Society for Mass Spectrometry · doi:10.1016/1044-0305(94)80016-2 · Cited by 6,484 · see citing works
Pfam: the protein families database
Finn, R. D., Bateman, A., Clements, J., Coggill, P., Eberhardt, R. Y., Eddy, S. R., Heger, A., Hetherington, K., Holm, L., Mistry, J., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1223 · Cited by 6,148 · see citing works
Required sample size to detect the mediated effect
Fritz, M. S., & MacKinnon, D. P. (2007) · Psychological Science · doi:10.1111/j.1467-9280.2007.01882.x · Cited by 4,810 · see citing works
Insights into the regulation of protein abundance from proteomic and transcriptomic analyses
Vogel, C., & Marcotte, E. M. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3185 · Cited by 4,195 · see citing works
Elias, J. E., & Gygi, S. P. (2007) · Nature Methods · doi:10.1038/nmeth1019 · Cited by 3,925 · see citing works
Self-citationCorrelation between Protein and mRNA Abundance in Yeast
Gygi, S. P., Rochon, Y., Franza, B. R., & Aebersold, R. (1999) · Molecular and Cellular Biology · doi:10.1128/mcb.19.3.1720 · Cited by 3,819 · see citing works
Self-citationAn efficient algorithm for large-scale detection of protein families
Enright, A. J. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.7.1575 · Cited by 3,536 · see citing works
Central Dogma of Molecular Biology
CRICK, F. (1970) · Nature · doi:10.1038/227561a0 · Cited by 3,444 · see citing works
Open source clustering software
de Hoon, M., Imoto, S., Nolan, J., & Miyano, S. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth078 · Cited by 3,043 · see citing works
A tissue-specific atlas of mouse protein phosphorylation and expression
Huttlin, E. L., Jedrychowski, M. P., Elias, J. E., Goswami, T., Rad, R., Beausoleil, S. A., Villén, J., Haas, W., Sowa, M. E., & Gygi, S. P. (2010) · Cell · doi:10.1016/j.cell.2010.12.001 · Cited by 1,778 · see citing works
Self-citationGenetics of gene expression surveyed in maize, mouse and man
Schadt, E., Monks, S. A., Drake, T., Lusis, A., Che, N., Colinayo, V. V., Ruff, T., Milligan, S. B., Lamb, J., Cavet, G., et al. (2003) · Nature · doi:10.1038/nature01434 · Cited by 1,594 · see citing works
Mouse genomic variation and its effect on phenotypes and gene regulation.
Keane, T. M., Goodstadt, L., Danecek, P., White, M. A., Wong, K., Yalcin, B., Heger, A., Agam, A., Slater, G., Goodson, M., et al. (2011) · Nature · doi:10.1038/nature10413 · Cited by 1,580 · see citing works
UniProt Knowledgebase: a hub of integrated protein data
Magrane, M. (2011) · Database · doi:10.1093/database/bar009 · Cited by 1,455 · see citing works
The BioPlex network: a systematic exploration of the human interactome
Huttlin, E. L., Ting, L., Bruckner, R. J., Gebreab, F., Gygi, M. P., Szpyt, J., Tam, S., Zarraga, G., Colby, G., Baltier, K., et al. (2015) · Cell · doi:10.1016/j.cell.2015.06.043 · Cited by 1,425 · see citing works
Genetic Dissection of Transcriptional Regulation in Budding Yeast
Brem, R. B., Yvert, G., Clinton, R., & Kruglyak, L. (2002) · Science · doi:10.1126/science.1069516 · Cited by 1,392 · see citing works
McAlister, G. C., Nusinow, D. P., Jedrychowski, M. P., Wühr, M., Huttlin, E. L., Erickson, B. K., Rad, R., Haas, W., & Gygi, S. P. (2014) · Analytical Chemistry · doi:10.1021/ac502040v · Cited by 1,263 · see citing works
Self-citationGenetic analysis of genome-wide variation in human gene expression
Morley, M. P., Molony, C. M., Weber, T. M., Devlin, J. L., Ewens, K. G., Spielman, R. S., & Cheung, V. G. (2004) · Nature · doi:10.1038/nature02797 · Cited by 1,207 · see citing works
Genetical genomics: the added value from segregation
Jansen, R. (2001) · Trends in Genetics · doi:10.1016/s0168-9525(01)02310-1 · Cited by 1,010 · see citing works
MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics
Ting, L., Rad, R., Gygi, S. P., & Haas, W. (2011) · Nature Methods · doi:10.1038/nmeth.1714 · Cited by 1,009 · see citing works
Self-citationGenic Intolerance to Functional Variation and the Interpretation of Personal Genomes
Petrovski, S., Wang, Q., Heinzen, E. L., Allen, A. S., & Goldstein, D. B. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003709 · Cited by 901 · see citing works
Chesler, E. J., Lu, L., Shou, S., Qu, Y., Gu, J., Wang, J., Hsu, H. C., Mountz, J. D., Baldwin, N. E., Langston, M. A., et al. (2005) · Nature Genetics · doi:10.1038/ng1518 · Cited by 719 · see citing works
Genetics of global gene expression
Rockman, M. V., & Kruglyak, L. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1964 · Cited by 666 · see citing works
Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors
Yvert, G., Brem, R. B., Whittle, J., Akey, J. M., Foss, E. J., Smith, E. N., Mackelprang, R., & Kruglyak, L. (2003) · Nature Genetics · doi:10.1038/ng1222 · Cited by 629 · see citing works
Comparative analysis of proteome and transcriptome variation in mouse
Ghazalpour, A., Bennett, B., Petyuk, V. A., Orozco, L., Hagopian, R., Mungrue, I. N., Farber, C. R., Sinsheimer, J., Kang, H. M., Furlotte, N., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001393 · Cited by 624 · see citing works
Impact of regulatory variation from RNA to protein
Battle, A., Khan, Z., Wang, S. H., Mitrano, A., Ford, M. J., Pritchard, J. K., & Gilad, Y. (2015) · Science · doi:10.1126/science.1260793 · Cited by 471 · see citing works
The Diversity Outbred Mouse Population
Churchill, G., Gatti, D. M., Munger, S. C., & Svenson, K. (2012) · Mammalian Genome · doi:10.1007/s00335-012-9414-2 · Cited by 451 · see citing works
Self-citationA genetic and physiological study of impaired glucose homeostasis control in C57BL/6J mice
Toye, A. A., Lippiat, J. D., Proks, P., Shimomura, K., Bentley, L., Hugill, A., Mijat, V., Goldsworthy, M. E., Moir, L., Haynes, A. R., et al. (2005) · Diabetologia · doi:10.1007/s00125-005-1680-z · Cited by 438 · see citing works
Variation and genetic control of protein abundance in humans
Wu, L., Candille, S. I., Choi, Y., Xie, D., Jiang, L., Li-Pook-Than, J., Tang, H., & Snyder, M. (2013) · Nature · doi:10.1038/nature12223 · Cited by 364 · see citing works
Genetic basis of proteome variation in yeast
Foss, E. J., Radulovic, D., Shaffer, S. A., Ruderfer, D. M., Bedalov, A., Goodlett, D. R., & Kruglyak, L. (2007) · Nature Genetics · doi:10.1038/ng.2007.22 · Cited by 358 · see citing works
Ronchi, J. A., Figueira, T. R., Ravagnani, F. G., Oliveira, H. C. F., Vercesi, A. E., & Castilho, R. F. (2013) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2013.05.049 · Cited by 283 · see citing works
System-wide molecular evidence for phenotypic buffering in Arabidopsis
Fu, J., Keurentjes, J. J. B., Bouwmeester, H., America, T., Verstappen, F. W. A., Ward, J. L., Beale, M. H., de Vos, R. C. H., Dijkstra, M., Scheltema, R. A., et al. (2009) · Nature Genetics · doi:10.1038/ng.308 · Cited by 265 · see citing works
Deep Proteomics of the Xenopus laevis Egg using an mRNA-Derived Reference Database
Wühr, M., Freeman, R. M., Presler, M., Horb, M. E., Peshkin, L., Gygi, S. P., & Kirschner, M. W. (2014) · Current Biology · doi:10.1016/j.cub.2014.05.044 · Cited by 257 · see citing works
Self-citationChesler, E. J., Miller, D. R., Branstetter, L. R., Galloway, L., Jackson, B. L., Philip, V. M., Voy, B. H., Culiat, C. T., Threadgill, D. W., Williams, R. W., et al. (2008) · Mammalian Genome · doi:10.1007/s00335-008-9135-8 · Cited by 257 · see citing works
Self-citationMultilayered Genetic and Omics Dissection of Mitochondrial Activity in a Mouse Reference Population
Wu, Y., Williams, E. G., Dubuis, S., Mottis, A., Jovaisaite, V., Houten, S. M., Argmann, C. A., Faridi, P., Wolski, W., Kutalik, Z., et al. (2014) · Cell · doi:10.1016/j.cell.2014.07.039 · Cited by 244 · see citing works
Damerval, C., Maurice, A. I., Josse, J.-M., & de Vienne, D. (1994) · Genetics · doi:10.1093/genetics/137.1.289 · Cited by 234 · see citing works
Quantitative Trait Locus Mapping Methods for Diversity Outbred Mice
Gatti, D. M., Svenson, K. L., Shabalin, A. A., Wu, L.-Y., Valdar, W. S. J., Šimeček, P., Goodwin, N. C., Cheng, R., Pomp, D., Palmer, A. A., et al. (2014) · G3 Genes Genomes Genetics · doi:10.1534/g3.114.013748 · Cited by 211 · see citing works
Self-citationMolecular basis for SNX‐BAR‐mediated assembly of distinct endosomal sorting tubules
van Weering, J. R. T., Sessions, R. B., Traer, C. J., Kloer, D. P., Bhatia, V. K., Stamou, D. G., Carlsson, S. R., Hurley, J. H., & Cullen, P. J. (2012) · The EMBO Journal · doi:10.1038/emboj.2012.283 · Cited by 193 · see citing works
Ten Years of the Collaborative Cross
Threadgill, D. W., & Churchill, G. A. (2012) · Genetics · doi:10.1534/genetics.111.138032 · Cited by 185 · see citing works
Self-citationDudbridge, F., & Koeleman, B. P. C. (2004) · The American Journal of Human Genetics · doi:10.1086/423738 · Cited by 166 · see citing works
Protein abundances are more conserved than mRNA abundances across diverse taxa
Laurent, J. M., Vogel, C., Kwon, T., Craig, S. A., Boutz, D. R., Huse, H. K., Nozue, K., Walia, H., Whiteley, M., Ronald, P. C., et al. (2010) · PROTEOMICS · doi:10.1002/pmic.201000327 · Cited by 156 · see citing works
Genetics of single-cell protein abundance variation in large yeast populations
Albert, F. W., Treusch, S., Shockley, A. H., Bloom, J. S., & Kruglyak, L. (2014) · Nature · doi:10.1038/nature12904 · Cited by 141 · see citing works
Integrative phenomics reveals insight into the structure of phenotypic diversity in budding yeastPreprint
Skelly, D. A., Merrihew, G. E., Riffle, M. E., Connelly, C. F., Kerr, E. O., Johansson, M., Jaschob, D., Graczyk, B., Shulman, N., Wakefield, J., et al. (2013) · Genome Research · doi:10.1101/gr.155762.113 · Cited by 140 · see citing works
Status and access to the Collaborative Cross population
Welsh, C. E., Miller, D. R., Manly, K. F., Wang, J., McMillan, L., Morahan, G., Mott, R., Iraqi, F. A., Threadgill, D. W., & de Villena, F. P. (2012) · Mammalian Genome · doi:10.1007/s00335-012-9410-6 · Cited by 134 · see citing works
The Collaborative Cross, a community resource for the genetic analysis of complex traits
T. Consortium (2004) · Nature Genetics · doi:10.1038/ng1104-1133 · Cited by 121 · see citing works
Iraqi, F. A., Churchill, G. A., & Mott, R. (2008) · Mammalian Genome · doi:10.1007/s00335-008-9113-1 · Cited by 117 · see citing works
Self-citationGenetic Variation Shapes Protein Networks Mainly through Non-transcriptional Mechanisms
Foss, E. J., Radulovic, D., Shaffer, S. A., Goodlett, D. R., Kruglyak, L., & Bedalov, A. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1001144 · Cited by 103 · see citing works
Liu, Y.-T., & Yin, H. L. (1998) · Journal of Biological Chemistry · doi:10.1074/jbc.273.14.7920 · Cited by 90 · see citing works
Munger, S. C., Raghupathy, N., Choi, K., Simons, A. K., Gatti, D. M., Hinerfeld, D. A., Svenson, K. L., Keller, M. P., Attie, A. D., Hibbs, M. A., et al. (2014) · Genetics · doi:10.1534/genetics.114.165886 · Cited by 85 · see citing works
Self-citationQTLRel: an R Package for Genome-wide Association Studies in which Relatedness is a Concern
Cheng, R., Abney, M., Palmer, A. A., & Skol, A. D. (2011) · BMC Genetics · doi:10.1186/1471-2156-12-66 · Cited by 68 · see citing works
Protein quantification across hundreds of experimental conditions
Khan, Z., Bloom, J. S., Garcia, B. A., Singh, M., & Kruglyak, L. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0904100106 · Cited by 42 · see citing works
Haplotype Probabilities in Advanced Intercross Populations
Broman, K. W. (2012) · G3 Genes Genomes Genetics · doi:10.1534/g3.111.001818 · Cited by 31 · see citing works
Accelerating the Inbreeding of Multi-Parental Recombinant Inbred Lines Generated By Sibling Matings
Welsh, C. E., & McMillan, L. (2012) · G3 Genes Genomes Genetics · doi:10.1534/g3.111.001784 · Cited by 17 · see citing works
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- [30]Freeman HC Hugill A Dear NT Ashcroft FM Cox RD Deletion of nicotinamide nucleotide transhydrogenase: a new quantitive trait locus accounting for glucose intolerance in C57BL/6J mice Diabetes 2006 55 2153 2156 16804088
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Boyle, E. A., Li, Y. I., & Pritchard, J. K. (2017) · Cell · doi:10.1016/j.cell.2017.05.038 · Cited by 2,851 · see citing works
Multi-omics approaches to disease
Hasin, Y., Seldin, M., & Lusis, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1215-1 · Cited by 2,953 · see citing works
mRNAs, proteins and the emerging principles of gene expression control
Buccitelli, C., & Selbach, M. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0258-4 · Cited by 1,243 · see citing works
Quirós, P. M., Prado, M. A., Zamboni, N., D’Amico, D., Williams, R. W., Finley, D., Gygi, S. P., & Auwerx, J. (2017) · Journal of Cell Biology · doi:10.1083/jcb.201702058 · Cited by 832 · see citing works
Self-citationYao, C., Chen, G., Song, C., Keefe, J., Mendelson, M., Huan, T., Sun, B. B., Laser, A., Maranville, J. C., Wu, H., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05512-x · Cited by 431 · see citing works
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Umans, B. D., Battle, A., & Gilad, Y. (2021) · Trends in Genetics · doi:10.1016/j.tig.2020.08.009 · Cited by 296 · see citing works
A Review on Quantitative Multiplexed Proteomics
Pappireddi, N., Martin, L., & Wühr, M. (2019) · ChemBioChem · doi:10.1002/cbic.201800650 · Cited by 299 · see citing works
Untangling Genetic Risk for Alzheimer’s Disease
Pimenova, A. A., Raj, T., & Goate, A. M. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.05.014 · Cited by 223 · see citing works
Niu, M., Cho, J.-H., Kodali, K., Pagala, V., High, A. A., Wang, H., Wu, Z., Li, Y., Bi, W., Zhang, H., et al. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.6b04415 · Cited by 118 · see citing works
Genetic control of the human brain proteome
Robins, C., Wingo, A. P., Fan, W., Duong, D. M., Meigs, J., Gerasimov, E. S., Dammer, E. B., Cutler, D. J., Jager, P. L. D., Bennett, D. A., et al. (2019) · doi:10.1101/816652 · Cited by 104 · see citing works
Kim, D. K., Park, J., Han, D., Yang, J., Kim, A., Woo, J., Kim, Y., & Mook-Jung, I. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-017-0234-4 · Cited by 95 · see citing works
Study, D. (. D. I. S. A. C., Liu, J., Zhou, Y., Liu, S., Song, X., Yang, X.-Z., Fan, Y., Chen, W., Akdemir, Z. C., Yan, Z., et al. (2018) · Human Genetics · doi:10.1007/s00439-018-1910-3 · Cited by 77 · see citing works
Wingo, T. S., Duong, D. M., Zhou, M., Dammer, E. B., Wu, H., Cutler, D. J., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00324 · Cited by 51 · see citing works
Zhang, Q., Linke, V., Overmyer, K. A., Traeger, L. L., Kasahara, K., Miller, I. J., Manson, D. E., Polaske, T. J., Kerby, R. L., Kemis, J. H., et al. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01326-w · Cited by 43 · see citing works
Self-citationTeyssonnière, E. M., Trébulle, P., Muenzner, J., Loegler, V., Ludwig, D., Amari, F., Mülleder, M., Friedrich, A., Hou, J., Ralser, M., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2319211121 · Cited by 40 · see citing works
Peck Justice, S. A., Barron, M. P., Qi, G. D., Wijeratne, H. R. S., Victorino, J. F., Simpson, E. R., Vilseck, J. Z., Wijeratne, A. B., & Mosley, A. L. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.ra120.014576 · Cited by 28 · see citing works
Peck Justice, S. A., Qi, G., Wijeratne, H. R. S., Victorino, J. F., Simpson, E. R., Wijeratne, A. B., & Mosley, A. L. (2019) · doi:10.1101/2019.12.30.891267 · Cited by 28 · see citing works
Philtjens, S., Acri, D. J., Kim, B., Kim, H., & Kim, J. (2021) · doi:10.21203/rs.3.rs-171899/v1 · Cited by 1 · see citing works
“To overcome the lack of genetic diversity in mouse models, the Diversity Outbred (DO) mouse model was established through a multiparent paradigm from eight founder strains, comprising ve inbred and three wild-derived strains [10, 11].”
Method
- Authors
- Chick, J. M., Munger, S. C., Simecek, P., Huttlin, E. L., Choi, K., Gatti, D. M., Raghupathy, N., Svenson, K. L., Churchill, G. A., & Gygi, S. P.
- Affiliations
- Harvard University (US)
- Jackson Laboratory (US)
- Corresponding author
- Churchill, Gary A.
- Gygi, Steven P.
- ORCID
- Venue
- Nature
- Published
- 2016-06-15
- Volume
- 534
- Issue
- 7608
- Pages
- 500-505
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC5292866
- OpenAlex
- W2434017435
- Cited by
- 361
- References
- 55
- Citations by year
- 2026: 20 · 2025: 30 · 2024: 26 · 2023: 36 · 2022: 35 · 2021: 45 · 2020: 51 · 2019: 36 · 2018: 29 · 2017: 43 · 2016: 9
- FWCI
- 31.51
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-06-07)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion)
- Funders
- National Human Genome Research Institute (U41HG006673)
- National Cancer Institute (P30CA034196)
- National Institute of General Medical Sciences (GM 67945, P50GM076468, R01GM067945, R01GM070683)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (F32HD074299)
- Oak Ridge National Laboratory
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- genetic variation, proteome-wide analysis, protein quantitative trait loci, protein expression, post-transcriptional regulation, transcriptional regulation, mediation analysis, protein-protein interaction, liver transcriptome, Diversity Outbred mice, Collaborative Cross mice, genotype-phenotype prediction, natural genetic variation
- MeSH terms
- Animals, Female, Genotype, Liver, Male, Models, Genetic, RNA, Messenger, Mass Spectrometry, Genetic Variation, Genome, Proteome, Quantitative Trait Loci, Proteomics, Mice, Transcriptome, Protein Interaction Maps
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