Cited by 364Genetic Associations and EpidemiologyAdvanced Proteomics Techniques and ApplicationsGenetic Mapping and Diversity in Plants and Animals

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

Variation and genetic control of protein abundance in humans

Open access

Wu, Linfeng, Candille, Sophie I., Choi, Yoonha, Xie, Dan, Jiang, Lihua, Li-Pook-Than, Jennifer, Tang, Hua, Snyder, Michael·2013·Nature

DOI 10.1038/nature12223PMC PMC3789121

Open this paper in CoChat — free account

Abstract

Gene expression differs among individuals and populations and is thought to be a major determinant of phenotypic variation. Although variation and genetic loci responsible for RNA expression levels have been analysed extensively in human populations, our knowledge is limited regarding the differences in human protein abundance and the genetic basis for this difference. Variation in messenger RNA expression is not a perfect surrogate for protein expression because the latter is influenced by an array of post-transcriptional regulatory mechanisms, and, empirically, the correlation between protein and mRNA levels is generally modest. Here we used isobaric tag-based quantitative mass spectrometry to determine relative protein levels of 5,953 genes in lymphoblastoid cell lines from 95 diverse individuals genotyped in the HapMap Project. We found that protein levels are heritable molecular phenotypes that exhibit considerable variation between individuals, populations and sexes. Levels of specific sets of proteins involved in the same biological process covary among individuals, indicating that these processes are tightly regulated at the protein level. We identified cis-pQTLs (protein quantitative trait loci), including variants not detected by previous transcriptome studies. This study demonstrates the feasibility of high-throughput human proteome quantification that, when integrated with DNA variation and transcriptome information, adds a new dimension to the characterization of gene expression regulation.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/nature12223

Cites

25 of 25 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
An integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
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
An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database
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
A second generation human haplotype map of over 3.1 million SNPs
The International HapMap Consortium (2007) · Nature · doi:10.1038/nature06258 · Cited by 4,438
Integrating common and rare genetic variation in diverse human populations
The International HapMap 3 Consortium (2010) · Nature · doi:10.1038/nature09298 · Cited by 3,026
Mass spectrometry–based proteomics turns quantitative
Ong, S.-E., & Mann, M. (2005) · Nature Chemical Biology · doi:10.1038/nchembio736 · Cited by 1,572
Personal omics profiling reveals dynamic molecular and medical phenotypes
Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y. K., Chen, R., Miriami, E., Karczewski, K. J., Hariharan, M., Dewey, F. E., et al. (2012) · Cell · doi:10.1016/j.cell.2012.02.009 · Cited by 1,291
Self-citation
Understanding mechanisms underlying human gene expression variation with RNA sequencing
Pickrell, J. K., Marioni, J. C., Pai, A. A., Degner, J. F., Engelhardt, B. E., Nkadori, E., Veyrieras, J.-B., Stephens, M., Gilad, Y., & Pritchard, J. K. (2010) · Nature · doi:10.1038/nature08872 · Cited by 1,256
Global signatures of protein and mRNA expression levels
de Sousa Abreu, R., Penalva, L. O., Marcotte, E. M., & Vogel, C. (2009) · Molecular BioSystems · doi:10.1039/b908315d · Cited by 1,248

Flagged first, then by citations

Show all 25 references

Sorted by citations

Items 201–300 of 361

Proteomic analysis in cardiovascular research
Oda, T., & Matsumoto, K. (2015) · Surgery Today · doi:10.1007/s00595-015-1169-4 · Cited by 16
Dysregulated Epigenetic Modifications in the Pathogenesis of NAFLD-HCC
Zhao, F. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-981-10-8684-7_7 · Cited by 17
Placental expression of AChE, α7nAChR and NF-κB in patients with preeclampsia
Zheng, L., Shi, L., Zhou, Z., Chen, X., Wang, L., Lu, Z., & Tang, R. (2018) · Ginekologia Polska · doi:10.5603/gp.a2018.0043 · Cited by 16
Identification of State Markers in Anorexia Nervosa: Replication and Extension of Inflammation-Associated Biomarkers Using Multiplex Profiling
Breithaupt, L. E., Holsen, L. M., Ji, C., Hu, J., Petterway, F., Rosa‐Caldwell, M. E., Nilsson, I. A. K., Thomas, J. J., Williams, K. A., Boutin, R., et al. (2024) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2024.100332 · Cited by 16
Are Environmental Levels of Nonsteroidal Anti-Inflammatory Drugs a Reason for Concern? Chronic Life-Cycle Effects of Naproxen in Zebrafish
Barros, S., Coimbra, A. M., Herath, L. A., Alves, N., Pinheiro, M., Ribeiro, M., Morais, H., Branco, R., Martinez, O., Santos, H. G., et al. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.4c05599 · Cited by 16
Molecular portraits: the evolution of the concept of transcriptome-based cancer signatures
Modelska, A., Quattrone, A., & Re, A. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv013 · Cited by 15
MD-Miner: a network-based approach for personalized drug repositioning
Wu, H., Miller, E. C., Wijegunawardana, D., Regan, K., Payne, P., & Li, F. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0462-9 · Cited by 15
Individualized proteomics
Forler, S., Klein, O., & Klose, J. (2014) · Journal of Proteomics · doi:10.1016/j.jprot.2014.04.003 · Cited by 16
Epigenomes: The missing heritability in human cardiovascular disease?
Monte, E., & Vondriska, T. M. (2014) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.201400031 · Cited by 15
Integrated transcriptomic and proteomic profiling reveals the key molecular signatures of brain endothelial reperfusion injury
Ji, Y., Chen, Y., Tan, X., Huang, X., Gao, Q., Ma, Y., Yang, S., Yin, M., Yu, M., Fang, C., et al. (2024) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.14483 · Cited by 15
Human variation impacting MCOLN2 restricts Salmonella Typhi replication by magnesium deprivation
Gibbs, K. D., Wang, L., Yang, Z. R., Anderson, C. E., Bourgeois, J. S., Cao, Y., Gaggioli, M. R., Biel, M., Puertollano, R., Chen, C., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100290 · Cited by 14
Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics
Shi, Z., Lei, J. T., Elizarraras, J. M., & Zhang, B. (2024) · Nature Cancer · doi:10.1038/s43018-024-00869-z · Cited by 16
GAPP: A Proteogenomic Software for Genome Annotation and Global Profiling of Post-translational Modifications in Prokaryotes
Zhang, J., Yang, M., Zeng, H., & Ge, F. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m116.060046 · Cited by 14
The promise of targeted proteomics for quantitative network biology
Matsumoto, M., & Nakayama, K. I. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2018.02.014 · Cited by 14
A Regulatory Polymorphism in HAVCR2 Modulates Susceptibility to HIV-1 Infection
Sironi, M., Biasin, M., Gnudi, F., Cagliani, R., Saulle, I., Forni, D., Rainone, V., Trabattoni, D., Garziano, M., Mazzotta, F., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106442 · Cited by 14
Post-Translational Decrease in Respiratory Chain Proteins in the Polg Mutator Mouse Brain
Hauser, D. N., Dillman, A., Ding, J., Li, Y., & Cookson, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094646 · Cited by 13
The expression and post-transcriptional regulation of FSTL1 transcripts in placental trophoblasts
Mouillet, J., Mishima, T., Paffaro, A. M. D. A., Parks, W. T., Ziegler, J. A., Chu, T., & Sadovsky, Y. (2015) · Placenta · doi:10.1016/j.placenta.2015.09.005 · Cited by 13
Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra
Halloran, J. T., Bilmes, J., & Noble, W. S. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b00290 · Cited by 13
A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics
Halloran, J. T., & Rocke, D. M. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00767 · Cited by 13
Immuno-Nanoparticles for Multiplex Protein Imaging in Cells and Tissues
Lee, H., Gao, X., & Kim, Y. (2018) · BioChip Journal · doi:10.1007/s13206-018-2201-8 · Cited by 13
Plasma proteomic changes during hypothermic and normothermic cardiopulmonary bypass in aortic surgeries
Oda, T., Yamaguchi, A., Yokoyama, M., Shimizu, K., Toyota, K., Nikai, T., & Matsumoto, K. (2014) · International Journal of Molecular Medicine · doi:10.3892/ijmm.2014.1855 · Cited by 12
Integrative Omics Analysis Reveals Post-Transcriptionally Enhanced Protective Host Response in Colorectal Cancers with Microsatellite Instability
Liu, Q., & Zhang, B. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00847 · Cited by 12
The Impact of Multiparity on Uterine Gene Expression and Decidualization in Mice
Liu, J., Zuo, R.-J., Peng, Y., & Fu, Y.-S. (2015) · Reproductive Sciences · doi:10.1177/1933719115612131 · Cited by 12
Exploring genome-wide DNA methylation patterns in Aicardi syndrome
Piras, I. S., Mills, G., Llaci, L., Naymik, M., Ramsey, K. M., Belnap, N., Balak, C., Jepsen, W. M., Szelinger, S., Siniard, A. L., et al. (2017) · Epigenomics · doi:10.2217/epi-2017-0060 · Cited by 12
Uncovering changes in proteomic signature of rat pelvic floor muscles in pregnancy
Burnett, L. A., Boscolo, F. S., Laurent, L. C., Wong, M., & Alperin, M. (2019) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2019.04.025 · Cited by 12
Splice-site variant in ACSL5: a marker promoting opposing effect on cell viability and protein expression
Pérez-Núñez, I., Karaky, M., Fedetz, M., Barrionuevo, C., Izquierdo, G., Matesanz, F., & Alcina, A. (2019) · European Journal of Human Genetics · doi:10.1038/s41431-019-0414-5 · Cited by 12
Can “Normal” Protein Expression Ranges Be Estimated with High-Throughput Proteomics?
Higdon, R., & Kolker, E. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00176 · Cited by 11
Cellular and Molecular Anesthesia: from Bench to Bedside
Dabbagh, A., & Elyassi, H. (2016) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.22037/jcma.v1i1.10853 · Cited by 10
The immunomodulation of outer membrane vesicles from Vibrio parahaemolyticus causing acute hepatopancreatic necrosis disease in Pacific white shrimp Litopenaeus vannamei
Wang, L., Yang, J., Li, S., Qu, Z., & Wang, M. (2025) · Fish & Shellfish Immunology · doi:10.1016/j.fsi.2025.110274 · Cited by 10
A Poisson Log-Normal Model for Constructing Gene Covariation Network Using RNA-seq Data
Choi, Y., Coram, M., Peng, J., & Tang, H. (2017) · Journal of Computational Biology · doi:10.1089/cmb.2017.0053 · Cited by 10
Self-citation
High levels of human epididymis protein 4 mRNA and protein expression are associated with chemoresistance and a poor prognosis in pancreatic cancer
Ohkuma, R., Yada, E., Ishikawa, S., Komura, D., Kubota, Y., Hamada, K., Horiike, A., Ishiguro, T., Hirasawa, Y., Ariizumi, H., et al. (2020) · International Journal of Oncology · doi:10.3892/ijo.2020.5147 · Cited by 10
Identification of host factors binding to dengue and Zika virus subgenomic RNA by efficient yeast three-hybrid screens of the human ORFeome
Jansen, S., Smlatic, E., Copmans, D., Debaveye, S., Tangy, F., Vidalain, P., Neyts, J. H., & Dallmeier, K. (2021) · RNA Biology · doi:10.1080/15476286.2020.1868754 · Cited by 10
A Bioinformatics Perspective on the Links Between Tetraspanin-Enriched Microdomains and Cardiovascular Pathophysiology
Sun, G., Chen, J., Ding, Y., Wren, J. D., Xu, F., Lu, L., Wang, Y., Wang, D., & Zhang, X. A. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.630471 · Cited by 10
A panoramic view of proteomics and multiomics in precision health
Zilocchi, M., Wang, C., Babu, M., & Li, J. (2021) · iScience · doi:10.1016/j.isci.2021.102925 · Cited by 10
Higher‐order modular regulation of the human proteome
Kustatscher, G., Hödl, M., Rullmann, E., Grabowski, P., Fiagbedzi, E. W., Groth, A., & Rappsilber, J. (2023) · Molecular Systems Biology · doi:10.15252/msb.20209503 · Cited by 10
Colorectal cancer biomarker discovery and validation using LC-MS/MS-based proteomics in blood: truth or dare?
Reumer, A., Maes, E., Mertens, I., Cho, W. C. S., Landuyt, B., Valkenborg, D., Schoofs, L., & Baggerman, G. (2014) · Expert Review of Proteomics · doi:10.1586/14789450.2014.905743 · Cited by 10
High-speed Analysis of Large Sample Sets – How Can This Key Aspect of the Omics Be Achieved?
Cramer, R. (2020) · Molecular & Cellular Proteomics · doi:10.1074/mcp.p120.001997 · Cited by 11
New technology on the horizon: Fast analytical screening technique FNA (FAST‐FNA) enables rapid, multiplex biomarker analysis in head and neck cancers
Pai, S. I., Faquin, W. C., Sadow, P. M., Pittet, M. J., & Weissleder, R. (2020) · Cancer Cytopathology · doi:10.1002/cncy.22305 · Cited by 10
High nocturnal sleep fragmentation is associated with low T lymphocyte P2Y11 protein levels in narcolepsy type 1
Viste, R., Viken, M. K., Lie, B. A., Juvodden, H. T., Nordstrand, S. E. H., Thorsby, P. M., Rootwelt, T., Kornum, B. R., & Knudsen, S. (2021) · SLEEP · doi:10.1093/sleep/zsab062 · Cited by 10
Paired proteomics, transcriptomics and miRNomics in non-small cell lung cancers: known and novel signaling cascades
Backes, C., Ludwig, N., Leidinger, P., Huwer, H., Tenzer, S., Fehlmann, T., Franke, A., Meese, E. U., Lenhof, H., & Keller, A. (2016) · Oncotarget · doi:10.18632/oncotarget.11723 · Cited by 10
The genetic architecture of molecular traits
Claringbould, A., de Klein, N., & Franke, L. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.01.002 · Cited by 10
Polymer-coated cardiopulmonary bypass circuit attenuates upregulation of both proteases/protease inhibitors and platelet degranulation in pigs
Suehiro, S., Shimizu, K., Imai, K., Niii, A., Akeho, K., Nakata, H., Yamaguchi, A., Matsumoto, K., & Oda, T. (2017) · Perfusion · doi:10.1177/0267659117715506 · Cited by 10
Personalized Radioproteomics: Identification of a Protein Biomarker Signature for Preemptive Rescue by Tocopherol Succinate in CD34+ Irradiated Progenitor Cells Isolated from a Healthy Control Donor
Leighton, A. S. X. (2015) · Journal of Proteomics & Bioinformatics · doi:10.4172/jpb.1000349 · Cited by 9
Plasma proteomic changes during therapeutic hypothermia in resuscitated patients after cardiac arrest
Oda, T., Yamaguchi, A., Ishida, R., Nikai, T., Shimizu, K., & Matsumoto, K. (2019) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2019.7649 · Cited by 9
Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits
Okamoto, J., Yin, X., Ryan, B., Chiou, J., Luca, F., Piqué-Regi, R., Im, H. K., Morrison, J., Burant, C., Fauman, E. B., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03480-2 · Cited by 9
Sex‐dependent and sex‐independent regulatory systems of size variation in natural populations
Okada, H., Yagi, R., Gardeux, V., Deplancke, B., & Hafen, E. (2019) · Molecular Systems Biology · doi:10.15252/msb.20199012 · Cited by 9
Prolactin-Releasing Peptide Differentially Regulates Gene Transcriptomic Profiles in Mouse Bone Marrow-Derived Macrophages
Sun, Y., Zuo, Z., & Kuang, Y. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22094456 · Cited by 9
Age-stratified proteomic characteristics and identification of promising precise clinical treatment targets of colorectal cancer
Wang, Q., Zhou, Y.-C., Zhou, G., Qin, G., Tan, C., Yin, T., Zhao, D., & Yao, S. (2023) · Journal of Proteomics · doi:10.1016/j.jprot.2023.104863 · Cited by 9
Multiple sclerosis treatment effects on plasma cytokine receptor levels
Bedri, S. K., Fink, K., Manouchehrinia, A., Lundström, W., Kockum, I. S., Olsson, T. P., Hillert, J. A., & Glaser, A. (2017) · Clinical Immunology · doi:10.1016/j.clim.2017.08.023 · Cited by 9
Molecular Biology of Endometriosis
Sengupta, J., Geethadevi, A., Bhat, M. A., & Ghosh, D. (2016) · Human Reproduction · doi:10.1002/9781118849613.ch3 · Cited by 10
Deep quantitative proteomics of North American Pacific coast star tunicate (Botryllus schlosseri)
Kültz, D., Gardell, A. M., DeTomaso, A. W., Stoney, G., Rinkevich, B., Rinkevich, Y., Qarri, A., Dong, W., Luu, B., & Lin, M. (2024) · PROTEOMICS · doi:10.1002/pmic.202300628 · Cited by 8
GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations
Chen, H., Petty, L. E., Bush, W. S., Naj, A. C., & Below, J. E. (2019) · Current Genetic Medicine Reports · doi:10.1007/s40142-019-0159-z · Cited by 8
Mouse Organ-Specific Proteins and Functions
Sun, B., Lorang, C. G., Qin, S., Zhang, Y., Liu, K., Li, G., Sun, Z., Francke, A., Utleg, A. G., Flores, P., et al. (2021) · Cells · doi:10.3390/cells10123449 · Cited by 8
Unravelling post‐transcriptional PrmC‐dependent regulatory mechanisms in Pseudomonas aeruginosa
Krueger, J., Pohl, S., Preuße, M., Kordes, A., Rugen, N., Schniederjans, M., Pich, A., & Häußler, S. (2016) · Environmental Microbiology · doi:10.1111/1462-2920.13435 · Cited by 8
Modularity Versus Emergence: How to Cope with Complexity in Whole-Plant Physiology?
Wegner, L. H. (2019) · Springer eBooks · doi:10.1007/978-3-030-06128-9_4 · Cited by 8
New Method Enhanced Extraction of Protein Signatures of Renal Cell Carcinoma from Proteomics Data
Liu, H., Ma, Z., Lih, T. M., Chen, L., Hu, Y., Wang, Y., Sun, Z., Huang, Y., Xu, Y., & Zhang, H. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5c00414 · Cited by 7
Unveiling novel features and phylogenomic assessment of indigenous Priestia megaterium AB-S79 using comparative genomics
Adeniji, A. A., Chukwuneme, C. F., Conceição, E. C., Ayangbenro, A. S., Wilkinson, E., Maasdorp, E., de Oliveira, T., & Babalola, O. O. (2025) · Microbiology Spectrum · doi:10.1128/spectrum.01466-24 · Cited by 7
Impact of allele‐specific peptides in proteome quantification
Wu, L., & Snyder, M. P. (2015) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.201400126 · Cited by 7
Self-citation
QTL Analysis Beyond eQTLs
Wen, J., Nodzak, C., & Shi, X. M. (2019) · Methods in molecular biology · doi:10.1007/978-1-0716-0026-9_14 · Cited by 7
Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits
Kubota, N., & Suyama, M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010436 · Cited by 7
A map of cis-regulatory modules and constituent transcription factor binding sites in 80% of the mouse genome
Ni, P., Wilson, D., & Su, Z. (2022) · BMC Genomics · doi:10.1186/s12864-022-08933-7 · Cited by 7
Combining bioinformatics and MS-based proteomics: clinical implications
Acosta‐Martin, A. E., & Lane, L. (2014) · Expert Review of Proteomics · doi:10.1586/14789450.2014.900446 · Cited by 7
A Transcriptomic Study of Maternal Thyroid Adaptation to Pregnancy in Rats
Liu, J., Wang, T.-S., Zhao, M., Peng, Y. H., & Fu, Y.-S. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms161126030 · Cited by 7
PExFInS: An Integrative Post-GWAS Explorer for Functional Indels and SNPs
Cheng, Z., Chu, H., Fan, Y., Li, C., Song, Y., Zhou, J., & Yuen, K. (2015) · Scientific Reports · doi:10.1038/srep17302 · Cited by 7
Do differences in the activities of carbohydrate metabolism enzymes between Lake Whitefish ecotypes match predictions from transcriptomic studies?
Dalziel, A. C., Laporte, M., Guderley, H., & Bernatchez, L. (2017) · Comparative Biochemistry and Physiology Part B Biochemistry and Molecular Biology · doi:10.1016/j.cbpb.2017.08.001 · Cited by 7
Prediction of survival risks with adjusted gene expression through risk-gene networks
Lee, M., Han, S. W., & Seok, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz399 · Cited by 7
Development of chemokine network inhibitors using combinatorial saturation mutagenesis
Kryukova, J., Vales, S., Payne, M., Smagurauskaite, G., Chandra, S., Clark, C. J., Davies, G., & Bhattacharya, S. (2025) · Communications Biology · doi:10.1038/s42003-025-07778-6 · Cited by 6
A phenome-wide approach to identify causal risk factors for deep vein thrombosis
Constantinescu, A., Bull, C. J., Goudswaard, L. J., Zheng, J., Elsworth, B., Timpson, N. J., Moore, S., Hers, I., & Vincent, E. E. (2023) · BMC Medical Genomics · doi:10.1186/s12920-023-01710-9 · Cited by 6
Differential response of C-type natriuretic peptide to estrogen and dexamethasone in adult bone
Prickett, T. C. R., Wellby, M. P., Barrell, G. K., Richards, M., & Espiner, E. A. (2014) · Steroids · doi:10.1016/j.steroids.2014.05.007 · Cited by 6
Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals
Gedik, H., Peterson, R. E., Riley, B. P., Vladimirov, V. I., & Bacanu, S. (2023) · Complex Psychiatry · doi:10.1159/000530223 · Cited by 6
Bipartite matching generalizations for peptide identification in tandem mass spectrometry
Bai, W., Bilmes, J. A., & Noble, W. S. (2016) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2975167.2975201 · Cited by 6
Does the Rewarmed Heart Restore the Myocardial Proteome to That of the Pre-Cooled State? – A Proteomic Analysis of Surgical Samples –
Oda, T., Yamaguchi, A., Shimizu, K., Nikai, T., & Matsumoto, K. (2015) · Circulation Journal · doi:10.1253/circj.cj-15-0541 · Cited by 6
Multiomics strategy-based obesity biomarkers discovery for precision medicine
Ye, Z.-W., Yang, Q.-Y., Xu, W., Li, F., Luo, P., Qin, S., & Wang, F. (2025) · International Journal of Obesity · doi:10.1038/s41366-025-01906-2 · Cited by 7
A large-scale polygenic risk score analysis identified candidate proteins associated with anxiety, depression and neuroticism
Cheng, B., Yang, X., Cheng, S., Li, C., Zhang, H., Liu, L., Meng, P., Jia, Y., Wen, Y. F., & Zhang, F. (2022) · Molecular Brain · doi:10.1186/s13041-022-00954-3 · Cited by 6
Comparative proteomic based analysis of grain nutrients in two hulless barley cultivars
Wang, Y., Yuan, H., Sang, Z., Jabu, T., & Xu, Q. (2021) · Agronomy Journal · doi:10.1002/agj2.20804 · Cited by 5
Human Lymphoblastoid Cell Lines in Pharmacogenomics
Jeon, J. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-386882-4.00006-2 · Cited by 5
Genomics of chicken domestication and feralisation
Johnsson, M. (2015) · Linköping studies in science and technology. Dissertations · doi:10.3384/diss.diva-122280 · Cited by 5
Comprehensive Cis-Regulation Analysis of Genetic Variants in Human Lymphoblastoid Cell Lines
Wang, Y., He, B., Zhao, Y., Reiter, J. L., Chen, S., Simpson, E. R., Feng, W., & Liu, Y. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00806 · Cited by 5
Gene-environment interactions modulate the phenotype severity in mouse models of congenital craniofacial syndromes
Fitriasari, S., Fiorino, R., Truong, T. H. K., McKinney, M. C., Dixon, J., Dixon, M. J., & Trainor, P. A. (2025) · Journal of Clinical Investigation · doi:10.1172/jci181705 · Cited by 4
Comparative Analysis of Testicular Transcriptional and Translational Landscapes in Yak and Cattle–Yak: Implications for Hybrid Male Sterility
Cao, M., Guo, S., Ding, Z., Hu, L., Xiong, L., Ge, Q., Pei, J., & Guo, X. (2025) · Biomolecules · doi:10.3390/biom15081080 · Cited by 4
Data‐Independent Acquisition Mass Spectrometry in Tumor Classification and Cancer Biomarker Research
Simonik, J., Lapčík, P., Bouchalová, P., & Bouchal, P. (2025) · Mass Spectrometry Reviews · doi:10.1002/mas.70014 · Cited by 4
Proteomics Studies on Extracellular Vesicles Derived from Glioblastoma: Where Do We Stand?
Giuliani, P., Simone, C. D., Febo, G., Bellasame, A., Tupone, N., Virglio, V. D., Giuseppe, F. D., Ciccarelli, R., Iorio, P. D., & Angelucci, S. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25189778 · Cited by 4
MI-PVT: A Tool for Visualizing the Chromosome-Centric Human Proteome
Panwar, B., Menon, R., Eksi, R., Omenn, G. S., & Guan, Y. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00525 · Cited by 4
Detection of Regulator Genes and eQTLs in Gene Networks
Wang, L., & Michoel, T. (2016) · Springer eBooks · doi:10.1007/978-3-319-43335-6_1 · Cited by 5
High-throughput omics in the precision medicine ecosystem
Tebani, A., & Bekri, S. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-819178-1.00003-4 · Cited by 5
CONQUER: an interactive toolbox to understand functional consequences of GWAS hits
Bouland, G. A., Beulens, J. W. J., Nap, J., van der Slik, A. R., Zaldumbide, A., Hart, L. M. ‘., & Slieker, R. C. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa085 · Cited by 4
A proteomics approach for the development of sarcoma biomarkers
Kondo, T., & Kawai, A. (2014) · EuPA Open Proteomics · doi:10.1016/j.euprot.2014.06.004 · Cited by 4
Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism
Schwahn, K., & Nikoloski, Z. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.00538 · Cited by 4
The Advent of Mass Spectrometry-Based Proteomics in Systems Biology Research
Blattmann, P., & Aebersold, R. H. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-394447-4.40030-1 · Cited by 5
Integration of transcriptome, proteome and phosphoproteome data elucidates the genetic control of molecular networksPreprint
Großbach, J., Gillet, L. C. J., Clément‐Ziza, M., Schmalohr, C. L., Schubert, O. T., Barnes, C. A., Bludau, I., Aebersold, R. H., & Beyer, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/703140 · Cited by 5
Prognostic significance of p27 in colorectal cancer: a meta-analysis and bioinformatics analysis
Zou, J., Wang, D., Yin, G., Lu, K., Chang, K., & Li, H. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1495476 · Cited by 3
Polysomal Profiling Coupled to Allele-Specific Proteomics Reveals an EIF4H TranSNP Allele Possessing Higher mRNA Translation Potential
Hamadou, M. H., Alunno, L., Peroni, D., Pancher, M., Mazza, F., Venturelli, T., Belli, R., Dassi, E., Romanel, A., & Inga, A. (2026) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2026.101535 · Cited by 3
Les promesses des sciences omiques à l’ère de la médecine de précision
Tebani, A., & Bekri, S. (2022) · La Revue de Médecine Interne · doi:10.1016/j.revmed.2022.07.009 · Cited by 7
YMLA: A comparative platform to carry out functional enrichment analysis for multiple gene lists in yeast
Yang, T.-H., Hsu, C., Wang, Y., Yu, C., Rathod, J., Tseng, Y. Y., & Wu, W.-S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106314 · Cited by 3
A proteomic glimpse into the oncogenesis of prostate cancer
Martiny, P. B., Alcoba, D. D., Neto, B. S., Carvalho, P. C., & Brum, I. S. (2018) · Journal of Applied Biomedicine · doi:10.1016/j.jab.2018.05.003 · Cited by 3
Molecular interaction networks in the analyses of sequence variation and proteomics data
Stelzl, U. (2013) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.201300039 · Cited by 3
Pro-Inflammatory Activation Promotes Atherogenic Endothelial Phenotype in Male and Female Human Umbilical Endothelial Vein Cells (HUVECs)
Lorenz, M., Palant, R., Oscherowa, E., Hamoud, W. K.-H., Kirwan, J., Trajkovski, S., Wiebach, J., Fritsche‐Guenther, R., Wu, H., Haritonow, N., et al. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27073079 · Cited by 2
Absolute Quantification of TGF-β Signaling Proteins Using Quantitative Western Blot
Deng, D., & Zi, Z. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2277-3_1 · Cited by 2
GEO Combined with Quantitative Protein Trait Loci Identify Causative Proteins in Hypertrophic Cardiomyopathy
Li, B., Zhao, X., Ding, Y., & Zhang, Y. X. (2025) · ESC Heart Failure · doi:10.1002/ehf2.15287 · Cited by 2
Measurement and meaning in gene expression evolution
Diaz, R., Wang, Z., & Townsend, J. P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91810-7.00008-x · Cited by 2

Authors: Wu, L., Candille, S. I., Choi, Y., Xie, D., Jiang, L., Li-Pook-Than, J., Tang, H., & Snyder, M.
Affiliations: Stanford Medicine (US)
Corresponding author: Tang, Hua
Snyder, Michael
ORCID: Choi, Yoonha
Xie, Dan
Jiang, Lihua
Tang, Hua
Snyder, Michael
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2013-05-15
Volume: 499
Issue: 7456
Pages: 79-82
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/nature12223
PMC: PMC3789121
OpenAlex: W2073109785
Cited by: 364
References: 25
Citations by year: 2026: 6 · 2025: 16 · 2024: 25 · 2023: 18 · 2022: 26 · 2021: 25 · 2020: 21 · 2019: 37 · 2018: 31 · 2017: 24 · 2016: 31 · 2015: 46 · 2014: 44 · 2013: 14
FWCI: 29.86
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: National Institutes of Health
National Heart, Lung, and Blood Institute (U01HL107393)
National Human Genome Research Institute (P50HG002357)
National Institute of General Medical Sciences (R01GM073059)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords: protein abundance, protein quantitative trait loci, cis-pQTL, interindividual variability, heritability, post-transcriptional regulation, mRNA-protein correlation, gene expression regulation, lymphoblastoid cell lines, sex differences, population variation, HapMap
MeSH terms: Cell Line, Ethnicity, Female, Gene Expression Regulation, Genotype, Humans, Male, Phenotype, RNA, Messenger, Mass Spectrometry, Protein Biosynthesis, Genetic Variation, Proteome, Gene Expression Profiling, Quantitative Trait Loci, Proteomics, Transcriptome, HapMap Project

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Variation and genetic control of protein abundance in humans

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks