The DOI matches its Crossref record; 43 of the 44 references it cites were found in a registry or an index; 6 of the 30 sources its claims cite were checked for support.
Impact of regulatory variation from RNA to protein
Open accessAbstract
How genetics affect phenotypic variation: How an individual looks depends on their genes, genetic variation, and interactions with the environment. However, the path from genotype to phenotype remains murky. Battle et al. examine how an individual's genetic variation affects expression of RNA, ribosome occupancy, and protein levels. They find that RNA expression and ribosome occupancy are generally correlated. However, in contrast, protein levels appear not to depend on RNA levels or ribosome occupancy. Protein levels are thus regulated by posttranscriptional mechanisms. Science , this issue p. 664
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20 Own result · 11 Own interpretation · 8 Not checked yet · 6 Own method · 1 Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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“We found that most QTLs are associated with transcript expression levels, with consequent effects on ribosome and protein levels.”
- Also stated in: § Abstract · boosted
“In summary, we have shown that while a substantial fraction of regulatory genetic variants influence gene expression at all levels from mRNA to steady state protein abundance, there are also a number of effects with specific impact on particular expression phenotypes.”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
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“Additionally, we identified a class of cis QTLs that affect protein abundance with little or no effect on mRNA or ribosome levels, suggesting that they may arise from differences in post-translational regulation.”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
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“However, eQTLs tend to have significantly reduced effect sizes on protein levels, suggesting that their potential impact on downstream phenotypes is often attenuated or buffered.”
- Also stated in: § Abstract · hedged
“Yet, both eQTL mapping and effect-size analyses indicate that many eQTLs have attenuated (or absent) effects on steady state protein levels (Fig. S16).”
- Also stated in: § Abstract · plain
“QTLs affecting mRNA levels are on average attenuated or buffered at the protein level, as has been observed between species (14).”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
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“In contrast, eQTLs showed the lowest overlap with pQTLs (35%), as expected (Fig. 1B).”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
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“Using this approach, we observed a reduced mean effect size for the GEUVADIS eQTLs in the protein data compared to either the RNA-seq data (t-test P = 6.7×10-3) or ribosome data (P = 5.6×10-3; Fig. 1C).”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
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“Among the 4,322 genes quantified for all three phenotypes, 54% of the genes with pQTLs also have a significant rQTL and/or eQTL.”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
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“We found that transcript and protein expression levels – which are the furthest removed processes studied here - are the least correlated (Fig. S9, S10).”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
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“In particular, most (90%) genetic variants associated with ribosome occupancy are also associated with transcript levels (Fig. S13).”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
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In the paper
“Yet, for psQTLs, low ribosome effect sizes are observed.”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
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“Considering protein levels, with RNA levels as a covariate, we identified 146 protein-specific QTLs (psQTLs) at FDR = 10% (Fig. 2A).”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
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Every source the paper cites for this claim was checked and supports it.
In the paper
“Comparative studies (9-13) have suggested that protein expression evolves under greater evolutionary constraint than transcript levels (14)”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
What the sources say
- Supportedcites [9] Laurent JM Protein abundances are more conserved than mRNA abundances across diverse taxa Proteomics 10 4209 2010 21089048 PMC3113407 source
The paper reports that steady-state protein abundances are more highly conserved across diverse taxa than the corresponding mRNA abundances (Abstract). It interprets this difference as evidence of strong selective pressure to maintain protein abundances during evolution, supporting the citing sentence’s suggested greater evolutionary constraint. (LLM, conf=0.93)
- Supportedcites [10] Schrimpf SP Comparative Functional Analysis of the Caenorhabditis elegans and Drosophila melanogaster Proteomes PLoS Biol 7 e1000048 2009 PMC2650730 19260763 source
The Discussion proposes that protein levels are under selection while changes in mRNA abundance can accumulate through compensatory changes, leaving protein abundance roughly constant. This supports the citing sentence’s hedged comparison of evolutionary constraint on protein expression and transcript levels. (LLM, conf=0.92)
- Supportedcites [11] Stadler M Fire A Conserved translatome remodeling in nematode species executing a shared developmental transition PLoS genetics 9 e1003739 2013 24098135 PMC3789828 source
The Results/Discussion reports that orthologous protein abundances show greater conservation than mRNA levels. It also finds less between-species divergence in ribosome-footprint changes than in mRNA changes during the L1 diapause program, supporting the cited sentence’s comparative point. (LLM, conf=0.86)
- Supportedcites [12] Artieri CG Fraser HB Evolution at two levels of gene expression in yeast Genome research 24 411 3 2014 24318729 PMC3941106 source
In the Discussion (“Evolution at two regulatory levels”; page not available), the paper says mRNA levels tend to overestimate divergence in protein abundance and identifies protein abundance as the likely target of selection. Its finding that translational changes often buffer mRNA changes supports the citing sentence’s qualified claim of greater constraint on protein expression. (LLM, conf=0.88)
- Supportedcites [13] McManus CJ May GE Spealman P Shteyman A Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast Genome research 24 422 3 2014 24318730 PMC3941107 source
The paper states that prior studies suggest protein abundance is more conserved than mRNA abundance, matching the citing sentence’s hedged comparison. Its Discussion section on buffering also suggests that variation in mRNA abundance can accumulate without substantially changing protein levels. No page numbers are available in our copy. (LLM, conf=0.90)
- Supportedcites [14] Khan Z Primate transcript and protein expression levels evolve under compensatory selection pressures Science (New York, N Y) 342 1100 11 29 2013 PMC3994702 24136357 source
The paper explicitly concludes that protein expression levels evolve under greater evolutionary constraint than mRNA levels (concluding passage in our copy). Its discussion of lineage-specific variation also says the observed pattern supports that conclusion. The citing sentence accurately presents it as a suggestion. (LLM, conf=0.95)
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Probably not a substantial claim.
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In the paper
“At an FDR of 10% we detected 2,355 eQTLs, 939 rQTLs, and 278 pQTLs (Table 1, Fig. S11).”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“In contrast, the average effect sizes observed for the RNA-seq and ribosome data are not significantly different from each other, and their effect sizes are highly correlated across the tested eQTLs (Pearson c = 0.79, P < 10-96, Fig. S14).”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Previous studies have evaluated the association between variation and transcript expression in humans (1-3).”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
What the sources say
- Not checked yetcites [1] Battle A Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals Genome research 24 14 1 2014 24092820 PMC3875855 source
- Not checked yetcites [2] Lappalainen T Transcriptome and genome sequencing uncovers functional variation in humans Nature 501 506 9 26 2013 24037378 PMC3918453 source
- Not checked yetcites [3] Grundberg E Mapping cis- and trans-regulatory effects across multiple tissues in twins Nature genetics 44 1084 10 2012 22941192 PMC3784328 source
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In the paper
“We obtained a median coverage of 12 million mapped reads per sample and, as expected, the ribosome profiling reads are highly concentrated within coding regions and show an enrichment of a 3-bp periodicity, reflecting the progression of a translating ribosome (Figs. S1-S3, Table S1).”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
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In the paper
“These observations, along with the phenotype-specific QTL analysis, indicate a scarcity of translation-specific QTLs, and minimal attenuation of genetic impact between mRNA and ribosome phenotypes.”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
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In the paper
“This possibly reflects the functional role of lysine acetylation in modulating protein degradation (23).”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
What the sources say
- Not checked yetcites [23] Yang XJ Seto E Lysine acetylation: codified crosstalk with other posttranslational modifications Molecular cell 31 449 8 22 2008 18722172 PMC2551738 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Overall, the enrichment results suggest that genetic variants involved in post-transcriptional regulation are functionally distinct from genetic variants that primarily affect transcription – they are more likely to fall within translated regions of the gene, and more likely to occur at non-synonymous sites.”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
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In the paper
“Put together, these results allow us to identify loci where genetic variants have specific impacts on protein levels that are not fully mediated by regulation of either transcription or translation and hence may affect rates of protein degradation.”
§ Impact of Regulatory Variation from RNA to Protein > Abstract
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In the citing paper
“Genetic variants play a major role in phenotypic differences between individuals38 and how these sequences affect translation is only beginning to be understood39,40.”
From Human 5′ UTR design and variant effect prediction from a massively parallel translation assay (2019) · cites this paper as [39]
Experimental — accuracy not yet measuredIn the citing paper
“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 [51]
In the citing paper
“Human biology is tightly linked to proteins, and mass spectrometry-based proteomics has established a strong linkage between phenotype and protein-level biology (1, 2).”
From The Blood Proteoform Atlas: A reference map of proteoforms in human hematopoietic cells (2022) · cites this paper as [2]
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Tensor decomposition for multiple-tissue gene expression experiments
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“Nevertheless, a gene ’ s RNA and protein levels do not necessarily correlate [15 – 18] and the long-standing question of how RNA expression patterns covary with and are driven by the levels and activities of various protein species remains underexplored [10, 15, 19, 20].”
BackgroundEnhancing GTEx by bridging the gaps between genotype, gene expression, and disease
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Evolutionary Convergence of Pathway-Specific Enzyme Expression Stoichiometry
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“From the evolutionary perspective, our observation of divergent regulatory strategies underlying conserved protein stoichiometry dramatically extends recent findings based on human individuals and other closely related eukaryotic species (<106 years of separation) (Artieri and Fraser, 2014; Battle et al., 2014; Khan e…”
BackgroundStatistics requantitates the central dogma
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The ribosome-engaged landscape of alternative splicing
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Reduced changes in protein compared to mRNA levels across non-proliferating tissues
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“Hence we can speculate that the translation rate is the factor that is changing between the two tissues, although in a different context, of expression quantitative trait locis in LCLs, the buffering observed between protein and mRNA was attributed mainly to protein degradation [26].”
BackgroundTranscriptomic signatures across human tissues identify functional rare genetic variation
Ferraro, N. M., Strober, B. J., Einson, J. E., Abell, N. S., Aguet, F., Barbeira, A., Brandt, M. K., Bućan, M., Castel, S. E., Davis, J. R., et al. (2020) · Science · doi:10.1126/science.aaz5900 · Cited by 162
Self-citationCo-regulation map of the human proteome enables identification of protein functions
Kustatscher, G., Grabowski, P., Schrader, T. A., Passmore, J. B., Schrader, M., & Rappsilber, J. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0298-5 · Cited by 155
The role of alternative splicing in adaptation and evolution
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Methods, Tools and Current Perspectives in Proteogenomics
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Quantifying the regulatory effect size of cis -acting genetic variation using allelic fold changePreprint
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Insights into the mechanisms of eukaryotic translation gained with ribosome profiling
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“Novel applications of RiboSeq continue to emerge, recently RiboSeq was used for the analysis of translation in subcellular compartments (26,27), in certain cell types in the context of a whole organism (28,29) and for the detection of translation quantitative trait loci (30,31).”
OSCA: a tool for omic-data-based complex trait analysis
Zhang, F., Chen, W., Zhu, Z., Zhang, Q., Nabais, M. F., Qi, T., Deary, I. J., Wray, N. R., Visscher, P. M., McRae, A. F., et al. (2019) · Genome biology · doi:10.1186/s13059-019-1718-z · Cited by 147
“These advances include the identification of a large number of genetic variants associated with gene expression [4, 5], DNA methylation [6, 7], histone modification [8, 9], and protein abundance [10, 11]; the discovery of omic measures associated with complex traits [12, 13]; the improved accuracy in predicting a trai…”
BackgroundThe missing link between genetic association and regulatory function
Connally, N. J., Nazeen, S., Lee, D., Shi, H., Stamatoyannopoulos, J. A., Chun, S., Cotsapas, C. J., Cassa, C. A., & Sunyaev, S. R. (2022) · eLife · doi:10.7554/elife.74970 · Cited by 153
Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling
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“Ribosome footprint profiling experiments and sequencing data processing were performed as previously described ( Battle et al., 2015 ), with the exception of a harringtonine treatment step to arrest ribosomes at the sites of translation initiation.”
MethodInfluentialSelf-citationConditional eQTL analysis reveals allelic heterogeneity of gene expression
Jansen, R., Hottenga, J.-J., Nivard, M. G., Abdellaoui, A., Laport, B., de Geus, E. J., Wright, F. A., Penninx, B. W. J. H., & Boomsma, D. I. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx043 · Cited by 141
Systematic proteome and proteostasis profiling in human trisomy 21 fibroblast cells
Liu, Y., Borel, C., Li, L., Müller, T., Williams, E. G., Germain, P.-L., Buljan, M., Sajic, T., Boersema, P. J., Shao, W., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01422-6 · Cited by 141
The Blood Proteoform Atlas: A reference map of proteoforms in human hematopoietic cells
Melani, R. D., Gerbasi, V. R., Anderson, L. C., Sikora, J. W., Toby, T. K., Hutton, J. E., Butcher, D. S., Negrão, F., Seckler, H. S., Srzentić, K., et al. (2022) · Science · doi:10.1126/science.aaz5284 · Cited by 140
Alterations of the translation apparatus during aging and stress response
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“Some reports provide evidence that protein levels are primarily regulated by mRNA abundance and thus by the rate of transcription (Battle et al., 2015; Jovanovic et al., 2015) while others came to the exact opposite conclusion (de Souza, 2011; Schwanhäusser et al., 2011; Zhang et al., 2014).”
Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD
Sun, W., Kechris, K. J., Jacobson, S. R., Drummond, M., Hawkins, G. A., Yang, J., Chen, T., Quibrera, P. M., Anderson, W. H., Barr, R. G., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006011 · Cited by 140
“, [5] suggests that variants affecting gene expression and protein level may be distinct, so identifying the genetic features that affect protein variation [protein quantitative trait loci (pQTLs)] and gene expression for disease-relevant biomarkers will be important.”
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Multiple repressive mechanisms in the hippocampus during memory formation
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Imaging mRNA In Vivo, from Birth to Death
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“Given that prior studies have reported protein and mRNA levels to be weakly correlated, even in large datasets [49], the absence of strong correspondence between RNA-Seq and MS3 proteomics analyses may be a consequence of the complexity of the mRNA-to-protein signal conversion.”
The state of play in higher eukaryote gene annotation
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Targeted m6A Reader Proteins To Study Epitranscriptomic Regulation of Single RNAs
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Widespread Post-transcriptional Attenuation of Genomic Copy-Number Variation in Cancer
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“These results provide examples and putative mechanistic explanations for how variation in copy number or gene expression of a protein can have trans effects in the abundance of interacting proteins, as seen in protein quantitative trait loci analyses (Battle et al., 2015; Chick et al., 2016).”
BackgroundDynamic single-cell phenotyping of immune cells using the microfluidic platform DropMap
Bounab, Y., Eyer, K., Dixneuf, S., Rybczynska, M., Chauvel, C., Mistretta, M., Tran, T., Aymerich, N., Chenon, G., Llitjos, J., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0354-0 · Cited by 111
The Werner syndrome RECQ helicase targets G4 DNA in human cells to modulate transcription
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An Atlas of Protein-Protein Interactions Across Mouse Tissues
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Genetic control of the human brain proteome
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Exploring regulation in tissues with eQTL networks
Fagny, M., Paulson, J. N., Kuijjer, M. L., Sonawane, A. R., Chen, C.-Y., Lopes‐Ramos, C. M., Glass, K., Quackenbush, J. F., & Platig, J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707375114 · Cited by 107
“A number of studies have shown that eQTL SNPs are overrepresented in regulatory regions (3, 14, 27), but not all eQTL SNPs have obvious regulatory roles.”
BackgroundA comprehensive catalog of predicted functional upstream open reading frames in humans
McGillivray, P. D., Ault, R., Pawashe, M., Kitchen, R. R., Balasubramanian, S., & Gerstein, M. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky188 · Cited by 106
Determinants of genome-wide distribution and evolution of uORFs in eukaryotes
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“First, though the exact correlation between eQTLs and pQTLs has not been fully determined [42], mRNA expression is known to be poorly correlated with protein levels for many genes [43], which is in part due to many post-transcriptional factors, such as sequence features implicated in protein translation and degradatio…”
BackgroundMolecular quantitative trait loci
Aguet, F., Alasoo, K., Li, Y. I., Battle, A., Im, H. K., Montgomery, S. B., & Lappalainen, T. (2023) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00188-6 · Cited by 98
Self-citationTranslational regulation shapes the molecular landscape of complex disease phenotypes
Schäfer, S., Adami, E., Heinig, M., Rodrigues, K. E. C., Kreuchwig, F., Šilhavý, J., van Heesch, S., Šimaitė, D., Rajewsky, N., Cuppen, E. P. J. G., et al. (2015) · Nature Communications · doi:10.1038/ncomms8200 · Cited by 96
“It remains a matter of debate how well transcript abundances explain protein levels 4,5 , to what extent RNA expression differences are mirrored by the proteome 6–10 and how many genes are under translational control in mammalian tissues.”
Pervasive coexpression of spatially proximal genes is buffered at the protein level
Kustatscher, G., Grabowski, P., & Rappsilber, J. (2017) · Molecular Systems Biology · doi:10.15252/msb.20177548 · Cited by 93
“However, protein levels are buffered against certain transcript fluctuations (Liu et al, 2016), such as those caused by stochastic transcription initiation (Raj et al, 2006; Gandhi et al, 2011) and genetic variation between individuals (Battle et al, 2015) and species (Khan et al, 2013).”
InfluentialEpigenetic and Transcriptional Variability Shape Phenotypic Plasticity
Ecker, S., Pancaldi, V., Valencia, A., Beck, S., & Paul, D. S. (2017) · BioEssays · doi:10.1002/bies.201700148 · Cited by 93
Genome-wide pQTL analysis of protein expression regulatory networks in the human liver
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“However, mRNA expression correlates poorly with protein levels for many genes [3], which is in part due to many posttranscriptional factors, such as sequence features implicated in protein translation and degradation [4, 5].”
BackgroundFunctional Architectures of Local and Distal Regulation of Gene Expression in Multiple Human Tissues
Liu, X., Finucane, H. K., Gusev, A., Bhatia, G., Gazal, S., O’Connor, L. J., Bulik-Sullivan, B. K., Wright, F. A., Sullivan, P. F., Neale, B. M., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.03.002 · Cited by 87
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Integrated omics dissection of proteome dynamics during cardiac remodeling
Lau, E., Cao, Q., Lam, M. P. Y., Wang, J., Ng, D. C. M., Bleakley, B. J., Lee, J. M., Liem, D. A., Wang, D., Hermjakob, H., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02467-3 · Cited by 85
Genetic mapping uncovers cis-regulatory landscape of RNA editing
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“The following protocol was performed to map edQTLs in cis 35 : (1) For each editing site, we fit linear models without any covariates between normalized editing levels and genotypes of each variant in the same gene as that editing site using Plink 34 .”
High Throughput Multi-Omics Approaches for Clinical Trial Evaluation and Drug Discovery
Zielinski, J., Luke, J. J., Guglietta, S., & Krieg, C. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.590742 · Cited by 84
De novo genes with an lncRNA origin encode unique human brain developmental functionality
An, N. A., Zhang, J., Mo, F., Luan, X., Tian, L., Shen, Q. S., Li, X., Li, C., Zhou, F., Zhang, B., et al. (2023) · Nature Ecology & Evolution · doi:10.1038/s41559-022-01925-6 · Cited by 83
Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays
Kremling, K. A. G., Diepenbrock, C., Gore, M. A., Buckler, E. S., & Bandillo, N. B. (2019) · G3 Genes Genomes Genetics · doi:10.1534/g3.119.400549 · Cited by 80
Human adaptation to high altitude: a review of convergence between genomic and proteomic signatures
Sharma, V., Varshney, R. K., & Sethy, N. K. (2022) · Human Genomics · doi:10.1186/s40246-022-00395-y · Cited by 78
“Such information will provide new insight into associations between genetic variants and protein abundance, providing insights undetectable by mRNA associations [6].”
BackgroundExpression quantitative trait locus analysis for translational medicine
Gibson, G. C., Powell, J. E., & Marigorta, U. M. (2015) · Genome Medicine · doi:10.1186/s13073-015-0186-7 · Cited by 77
Analysis of Ribosome Stalling and Translation Elongation Dynamics by Deep Learning
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Alternative polyadenylation mediates genetic regulation of gene expression
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Dynamic evolution of regulatory element ensembles in primate CD4+ T cells
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Evolution of Gene Regulation in Humans
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“In a steady state within a cell system, the stable protein concentration is the result of the balance between protein synthesis and degradation (Dephoure et al, 2014; Battle et al, 2015; Liu et al, 2016).”
Background
Metadata
- Authors
- Battle, A., Khan, Z., Wang, S. H., Mitrano, A., Ford, M. J., Pritchard, J. K., & Gilad, Y.
- Affiliations
- Howard Hughes Medical Institute (US)
- University of Chicago (US)
- MS Bioworks, LLC, 3950 Varsity Drive, Ann Arbor, MI 48108, USA.
- Corresponding author
- Battle, Alexis
- Khan, Zia
- Wang, Sidney H.
- Pritchard, Jonathan K.
- Gilad, Yoav
- Venue
- Science
- Published
- 2015-02-06
- Volume
- 347
- Issue
- 6222
- Pages
- 664-667
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC4507520
- OpenAlex
- W2044286001
- Cited by
- 471
- References
- 44
- Citations by year
- 2026: 29 · 2025: 34 · 2024: 49 · 2023: 39 · 2022: 45 · 2021: 42 · 2020: 40 · 2019: 36 · 2018: 35 · 2017: 43 · 2016: 46 · 2015: 33
- FWCI
- 26.64
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (GM077959, HG007036, MH084703)
- Howard Hughes Medical Institute
- 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 › Molecular Biology › RNA Research and Splicing
- Keywords
- expression quantitative trait loci, ribosome occupancy, protein abundance, genetic variants, transcript expression, post-translational regulation, regulatory variation
- MeSH terms
- Cell Line, Exons, Gene Expression Regulation, Humans, Phenotype, Ribosomes, RNA, Messenger, Transcription, Genetic, Protein Biosynthesis, Genetic Variation, 5' Flanking Region, 3' Flanking Region, Quantitative Trait Loci
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A second model labelled each claim on 2026-10-05 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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 ›
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