The DOI matches its Crossref record; 43 of the 44 references it cites were found in a registry or an index; none of the 30 sources its claims cite is checked for support yet.
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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“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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“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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“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
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“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
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“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
- Not checked yetcites [9] Laurent JM Protein abundances are more conserved than mRNA abundances across diverse taxa Proteomics 10 4209 2010 21089048 PMC3113407 source
- Not checked yetcites [10] Schrimpf SP Comparative Functional Analysis of the Caenorhabditis elegans and Drosophila melanogaster Proteomes PLoS Biol 7 e1000048 2009 PMC2650730 19260763 source
- Not checked yetcites [11] Stadler M Fire A Conserved translatome remodeling in nematode species executing a shared developmental transition PLoS genetics 9 e1003739 2013 24098135 PMC3789828 source
- Not checked yetcites [12] Artieri CG Fraser HB Evolution at two levels of gene expression in yeast Genome research 24 411 3 2014 24318729 PMC3941106 source
- Not checked yetcites [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
- Not checked yetcites [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
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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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“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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“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
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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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Additional file 3 of Multi-omics analysis identifies drivers of protein phosphorylation
Zhang, T., Keele, G. R., Gyuricza, I. G., Vincent, M. J., Brunton, C., Bell, T. A., Hock, P., Shaw, G. D., Munger, S. C., de Villena, F. P., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22617587
Additional file 4 of Multi-omics analysis identifies drivers of protein phosphorylation
Zhang, T., Keele, G. R., Gyuricza, I. G., Vincent, M. J., Brunton, C., Bell, T. A., Hock, P., Shaw, G. D., Munger, S. C., de Villena, F. P., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22617590
Additional file 6 of Multi-omics analysis identifies drivers of protein phosphorylation
Zhang, T., Keele, G. R., Gyuricza, I. G., Vincent, M. J., Brunton, C., Bell, T. A., Hock, P., Shaw, G. D., Munger, S. C., de Villena, F. P., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22617596
Wang, S., Wu, H., Zhao, Y., Wang, L., Guan, X., & Zhao, T. (2023) · Figshare · doi:10.6084/m9.figshare.23730856
Self-citationWang, S., Wu, H., Zhao, Y., Wang, L., Guan, X., & Zhao, T. (2023) · Figshare · doi:10.6084/m9.figshare.23730850
Self-citationAdditional file 5 of Multi-omics analysis identifies drivers of protein phosphorylation
Zhang, T., Keele, G. R., Gyuricza, I. G., Vincent, M. J., Brunton, C., Bell, T. A., Hock, P., Shaw, G. D., Munger, S. C., de Villena, F. P., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22617593
Wang, S., Wu, H., Zhao, Y., Wang, L., Guan, X., & Zhao, T. (2023) · Figshare · doi:10.6084/m9.figshare.23730862
Self-citationWang, S., Wu, H., Zhao, Y., Wang, L., Guan, X., & Zhao, T. (2023) · Figshare · doi:10.6084/m9.figshare.23730853
Self-citationWang, S., Wu, H., Zhao, Y., Wang, L., Guan, X., & Zhao, T. (2023) · Figshare · doi:10.6084/m9.figshare.23730859
Self-citationAdditional file 2 of Multi-omics analysis identifies drivers of protein phosphorylation
Zhang, T., Keele, G. R., Gyuricza, I. G., Vincent, M. J., Brunton, C., Bell, T. A., Hock, P., Shaw, G. D., Munger, S. C., de Villena, F. P., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22617584
Wang, S., Wu, H., Zhao, Y., Wang, L., Guan, X., & Zhao, T. (2023) · Figshare · doi:10.6084/m9.figshare.23730847
Self-citationGeneral Characteristics and Properties of Organic Life
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Gene expression of tight junctions in foreskin is not affected by HIV pre-exposure prophylaxis
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“Although mRNA abundance is not always predictive of protein expression, ribosome-attached translating mRNA, as was used here, is a better reflection of protein expression than total mRNA (Battle et al., 2015).”
Native, Spatiotemporal Profiling of the Global Human RegulomePreprint
Pino, L. K., Canzani, D., Gutierrez, A., Robbins, J. E., Mcellin, B. M., Hubbard, E., Broderick, E., Prymolenna, A. V., Tatka, L., Paez, S. J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.14.659727
Evaluating the contribution of genome 3D folding to variation in human height using machine learningPreprint
Gu, W., Gilbertson, E. N., Baranzini, S. E., Salem, R. S., & Capra, J. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.09.675195
“First, the vast majority of genetic variants influencing height appear to act through regulatory mechanisms that do not require substantial alterations to local 3D chromatin conformation, such as modulating enhancer strength 58 , splicing efficiency 59 , or mRNA stability 60 .”
Inci, E., Ozuynuk, A. S., Ekici, B., Köseoğlu, P. K., Koçak, A., & Çoban, N. (2025) · Respiratory Medicine · doi:10.1016/j.rmed.2025.108415
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Self-citationWitte, F., Ruiz‐Orera, J., Mattioli, C. C., Blachut, S., Adami, E., Schulz, J. F., Schneider-Lunitz, V., Hummel, O., Patone, G., Mücke, M. B., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14870609
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Constantinescu, A.-E., Bull, C. J., Goudswaard, L. J., Zheng, J., Elsworth, B., Timpson, N. J., Moore, S. F., Hers, I., & Vincent, E. E. (2024) · Figshare · doi:10.6084/m9.figshare.26638797
Hoskins, I., Sun, S., Coté, A. G., Roth, F. P., & Cenik, C. (2024) · Figshare · doi:10.6084/m9.figshare.26583798
Additional file 1 of Multi-omics analysis identifies drivers of protein phosphorylation
Zhang, T., Keele, G. R., Gyuricza, I. G., Vincent, M. J., Brunton, C., Bell, T. A., Hock, P., Shaw, G. D., Munger, S. C., de Villena, F. P.-M., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22617581
Hoskins, I., Sun, S., Coté, A. G., Roth, F. P., & Cenik, C. (2024) · Figshare · doi:10.6084/m9.figshare.26583795
Additional file 7 of Multi-omics analysis identifies drivers of protein phosphorylation
Zhang, T., Keele, G. R., Gyuricza, I. G., Vincent, M. J., Brunton, C., Bell, T. A., Hock, P., Shaw, G. D., Munger, S. C., de Villena, F. P.-M., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22617599
Witte, F., Ruiz‐Orera, J., Mattioli, C. C., Blachut, S., Adami, E., Schulz, J. F., Schneider-Lunitz, V., Hummel, O., Patone, G., Mücke, M. B., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14870585
Hoskins, I., Sun, S., Coté, A. G., Roth, F. P., & Cenik, C. (2024) · Figshare · doi:10.6084/m9.figshare.26583792
Constantinescu, A.-E., Bull, C. J., Goudswaard, L. J., Zheng, J., Elsworth, B., Timpson, N. J., Moore, S. F., Hers, I., & Vincent, E. E. (2024) · Figshare · doi:10.6084/m9.figshare.26638788
Constantinescu, A.-E., Bull, C. J., Goudswaard, L. J., Zheng, J., Elsworth, B., Timpson, N. J., Moore, S. F., Hers, I., & Vincent, E. E. (2024) · Figshare · doi:10.6084/m9.figshare.26638791
Ke, X., Yao, S., Wu, H., Zheng, X., Liu, T., Yang, F., Li, Y., Zheng, Z.-H., Zheng, Z.-H., & Zhu, P. (2026) · Clinical Proteomics · doi:10.1186/s12014-026-09581-9
“…genetic variation can affect protein abundance by altering the rate and stability of gene expression; however, these effects tend to be significantly reduced during the translation process, indicating that their potential effects on downstream phenotypes may be further attenuated or buffered [19].”
Multi‐Tissue Genetic Regulation of RNA Editing in Pigs
Pan, X., Gong, W., Cai, X., Teng, J., Cai, J., Zeng, H., Ayalew, W., Shen, Q., Zhong, Z., Wang, Y., et al. (2026) · Advanced Science · doi:10.1002/advs.202518238
“In addition to these, other molecular traits have been studied using the molQTL approach, such as DNA methylation (meQTL) [ 20 ], protein abundance (pQTL) [ 21 ], and RNA editing (edQTL) [ 22 ].”
Multi‐Tissue Omics Analysis Uncovers Translational Regulation Underlying Complex Traits in Pigs
Wang, C., Zhang, Y., Chen, C., Xu, X., Qin, S., Qi, J., Bao, Y., Li, H., Chen, R., Zheng, W., et al. (2026) · Advanced Science · doi:10.1002/advs.74200
Genetic variation shapes human mRNA translation and disease riskPreprint
Wang, S., Chen, C., Xiao, X. G., & Wang, Z. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.10.705206
Self-citationLarsen, E. C., Moon, J. E., King, O. D., & Lawrence, J. B. (2026) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2026.02.014
“16 QTLs can also affect other molecular processes such as DNA methylation (mQTLs), 17 RNA splicing, 18,19 RNA stability, 20,21 and translation rates, 22,23 which may or may not alter gene expression levels.”
Yuasa, T., Nakagawa, T., Honda, T., Nishiuchi, G., Sato, M., Tokunaga, A., Nakahara, M., Tourtas, T., Schlötzer-Schrehardt, U. M., Kruse, F., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-43789-x
“…analyses have identified widespread alterations in gene expression and splicing patterns in the FECD corneal endothelium 16,19 ; however, due to post-transcriptional regulation and other factors, the observed gene expression changes do not always correlate directly with protein abundance 20,21 .”
Proteome dynamics during establishment of California grunion (Leuresthes tenuis) cell lines
Corona, M. M., Li, J., & Kültz, D. (2026) · BMC Biology · doi:10.1186/s12915-026-02577-9
Szabłowska‐Gadomska, I., Rudziński, S., Mroczko, A., Mrozikiewicz-Rakowska, B., Cysewski, D., Gasperowicz, P., & Bocian, K. (2026) · Cells · doi:10.3390/cells15070614
“Similarly, analyses of genetic effects on expression showed that ~64% of mRNA–protein changes occur in the same direction, only slightly above the random expectation of 50% [91].”
Computational analysis and modelling of regulatory networks controlling embryonic development
Floc’hlay, S. (2020) · theses.fr (ABES) · doi:10.70675/8d0e3a18z3294z4502z870dz9c18564e6415
Species-wide survey of transcript and protein abundance variation in yeast
Teyssonnière, É. (2023) · theses.fr (ABES) · doi:10.70675/9c5f617cz3bcfz4064z9e8eza0b9f5bca082
Dissecting human population variation in single-cell responses to SARS-CoV-2
Aquino, Y. (2023) · theses.fr (ABES) · doi:10.70675/061da45czf5e5z482dzb71dz92b95c997a05
High concordance between genetic effects on mRNA and protein abundancePreprint
Dyke, K. V., Feraru, M. A., & Albert, F. W. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.26.727960
“How common are pQTLs that arise from protein-specific mechanisms such as mRNA translation [35,41] and protein degradation [42–44] that are independent of the gene’s mRNA abundance?”
InfluentialZhang, X., Teng, J., Chen, Z., & Zhang, Q. (2026) · Animals · doi:10.3390/ani16152334
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“Detailed information on the iden-ti fi ed ACPs is provided in Supplementary Data 2.”
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“However, plasma treatment positively influences chlorophyll accumulation and net photosynthetic rate in wheat grown under drought stress [70].”
Predicting risk of ischemic stroke: A transformer model using genomic data
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Machine learning and statistical methods for molecular quantitative trait loci
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“We used publicly available ribosome profiling (Ribo-seq) datasets from five organisms: E. coli , Saccharomyces cerevisiae (yeast), Drosophila melanogaster (fruit fly), Pseudomonas aeruginosa , and human [ 29– 33].”
PWAS Atlas: a comprehensive knowledgebase of proteome-wide association studies
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RNA-Adjusted TUFM Protein State Captures Mitochondrial Proteome Organization in Lung Adenocarcinoma
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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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