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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).”
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“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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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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“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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“and reassessments found that mRNA changes explained 87 to 92% of corresponding protein changes [84, 85], as found in mouse dendritic cells responding to lipopolysaccharide [86].”
BackgroundA community effort to identify and correct mislabeled samples in proteogenomic studies
Yoo, S., Shi, Z., Wen, B., Kho, S. J., Pan, R., Feng, H., Chen, H., Carlsson, A., Edén, P., Ma, W., et al. (2021) · Patterns · doi:10.1016/j.patter.2021.100245 · Cited by 15
“For the two non-cancerous proteogenomic datasets, Battle et al. 22 and Chick et al., 23 we downloaded their published data as instructed in their publications.”
Identification and validation of common molecular targets of hydroxytyrosol
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“Another mis-sense variant, rs7285167, in APOL2 has been shown to be weakly associated with all-cause ESRD (26); additionally, it has been identified in a APOL2 protein-specific quantitative trait loci study as strongly associated with protein levels (27).”
BackgroundBenchmarking macaque brain gene expression for horizontal and vertical translation
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“Buffering of mRNA variation against protein levels can occur at multiple levels, including intra- and interindividual genomic variation (Battle et al., 2015; Liu et al., 2016).”
BackgroundAstonishing advances in mouse genetic tools for biomedical research
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Mendelian randomization study of interleukin (IL)-1 family and lung cancer
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“However, there are relatively limited studies focused on the level of translation, although protein is arguably more closely related to the phenotype 12–15 .”
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ROSE: A Deep Learning Based Framework for Predicting Ribosome StallingPreprint
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“We first used two datasets downloaded from GWIPS-viz [35], including a human dataset of lymphoblas-toid cell lines (LCLs) (denoted by Battle15) [56] and a yeast dataset of S. cerevisiae (denoted by Pop14) [25] to train our deep learning model and evaluate its prediction performance.”
MethodThe functional impact of rare variation across the regulatory cascade
Li, T., Ferraro, N. M., Strober, B. J., Aguet, F., Kasela, S., Arvanitis, M., Ni, B., Wiel, L., Hershberg, E. A., Ardlie, K., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100401 · Cited by 12
“4,5 6 In particular, the plasma proteome contains proteins generated from many different cell types, leading to its regular use as a source for biomarker discovery 6,7 ; therefore, understanding how rare ge-netic variation impacts protein abundance in samples such as plasma may help identify…”
Self-citationSingle-cell epigenomics and proteomics methods integrated in multiomics
Bi, H., & Weng, X. (2024) · Fundamental Research · doi:10.1016/j.fmre.2023.11.014 · Cited by 12
“However, transcripts and proteins are not necessarily related due to changes in gene expression regulation [97] .”
Mutation impact on mRNA versus protein expression across human cancers
Liu, Y., Elmas, A., & Huang, K. (2024) · GigaScience · doi:10.1093/gigascience/giae113 · Cited by 13
“In the first method adopted from Battle et al. [ 4 ], we compared the following 2 nested linear models using the LRT with the “anova” function in R: where g is the genotype, r represents RNA level, and p is the protein le v el.”
InfluentialThe Hypoxic Response Expression as a Survival Biomarkers in Treatment-Naive Advanced Breast Cancer
Gunawan, I., Hatta, M., Benyamin, A. F., & Islam, A. A. (2020) · Asian Pacific Journal of Cancer Prevention · doi:10.31557/apjcp.2020.21.3.629 · Cited by 12
“However, not all amplified genes are translated into an elevated protein expression (Battle et al., 2015).”
BackgroundAnalytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications
Jiang, W. M., Jones, J. C., Shankavaram, U. T., Sproull, M. T., Camphausen, K. A., & Krauze, A. V. (2022) · Cancers · doi:10.3390/cancers14092227 · Cited by 12
Spiny projection neurons exhibit transcriptional signatures within subregions of the dorsal striatum
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“38 Indeed, regional intrastriatal protein expression has not been intensively examined largely due to the lack of reagents such as antibodies or in situ assays, so the GO analyses will require further exploration at the physiologic level.”
Systems Genetics as a Tool to Identify Master Genetic Regulators in Complex Disease
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“[32], it seems that this “detrimental” impact on cell growth is being attenuated or buffered by a large reduction of protein production [30, 32, 33].”
BackgroundAnalyzing Ribosome Remodeling in Health and Disease
Petelski, A. A., & Slavov, N. (2020) · PROTEOMICS · doi:10.1002/pmic.202000039 · Cited by 13
“While suggestive evidence for ribosome remodeling has originated from indirect methods (such as measuring transcripts coding for RPs [50, 51]), such data remain inconclusive because RP synthesis and degradation are extensively regulated [48, 49, 52, 53].”
BackgroundZhao, L., Mühleisen, T. W., Pelzer, D. I., Burger, B., Beins, E. C., Forstner, A. J., Herms, S., Hoffmann, P., Amunts, K., Palomero‐Gallagher, N., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120095 · Cited by 11
“34 expression levels and receptor densities are mediated and modified by multiple posttranslational specific-regional factors (Battle et al., 2015).”
BackgroundResultHoushmand, M., Zeinali, V., Hosseini, A., Seifi, A., Danaei, B., & Kamfar, S. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1203019 · Cited by 11
“This can be caused by several factors affecting FGF21 production like mRNA degradation, translation, and protein degradation (Battle et al., 2015; Bayoumi et al., 2021).”
BackgroundSuresh, H., Ganapathy, M., Abinaya, K., Sakthivelu, M., Thirumavalavan, M., Anbu, P., & Pachaiappan, R. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.138312 · Cited by 11
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“protein abundance QTL pQTL Protein expression level for post-transcriptional regulation (mass spectrometry) [50]”
BackgroundTranslatome profiling in fatal familial insomnia implicates TOR signaling in somatostatin neurons
Bauer, S., Dittrich, L., Kaczmarczyk, L., Schleif, M., Benfeitas, R., & Jackson, W. S. (2022) · Life Science Alliance · doi:10.26508/lsa.202201530 · Cited by 11
Making Biological Sense of Genetic Studies of Age-Related Macular Degeneration
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Chemerin Impact on Alternative mRNA Transcription in the Porcine Luteal Cells
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Quantification of Intracellular Proteins in Single Cells Based on Engineered Picoliter Droplets
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Prioritization of genes associated with type 2 diabetes mellitus for functional studies
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Higher‐order modular regulation of the human proteome
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Variable protein homeostasis in housekeeping and non-housekeeping pathways under mycotoxins stress
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Tejaas: reverse regression increases power for detecting trans-eQTLs
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Evolution of higher mesenchymal CD44 expression in the human lineage
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The genetic architecture of molecular traits
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Improved surfaceome coverage with a label‐free nonaffinity‐purified workflow
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A compendium of genetic variations associated with promoter usage across 49 human tissues
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“…trait loci (QTLs) 4 , such as gene expression (eQTLs) 5,6 , splicing patterns (sQTLs) 7 , DNA methylation(meQTLs) 8 ,chromatinaccessibility(caQTLs) 9 – 12 ,3 ′ UTRalternative polyadenylation (3 ′ aQTLs) 13 , N 6 -methyladenosine modi fi cation (m 6 AQTLs) 14,15 , and protein expression (pQTLs) 16 .”
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De novo network analysis reveals autism causal genes and developmental links to co-occurring traits
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“Furthermore, transcript levels alone are insuf fi cient to explain protein expression (Battle et al, 2015; Brion et al, 2020; GTEx Consortium, 2020).”
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Impact of common variants on brain gene expression from RNA to protein to schizophrenia risk
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“Thisobservationechoesearlierworkin HapMap lymphoblastoid cell lines 20 and extends the prior conclusion from in vitro cell lines to complex human tissues.”
InfluentialSelf-citationPost-Transcriptional Dynamics is Involved in Rapid Adaptation to Hypergravity in Jurkat T Cells
Vahlensieck, C., Thiel, C. S., Pöschl, D., Bradley, T. J., Krammer, S., Lauber, B. A., Polzer, J., & Ullrich, O. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.933984 · Cited by 9
“Of course, correlation between gene expression and protein content is generally modest, and not always given (Khan et al., 2013; Battle et al., 2015).”
BackgroundTranslation of genome-wide association study: from genomic signals to biological insights
Bruner, W. S., & Grant, S. F. A. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1375481 · Cited by 10
Omics Analyses Uncover Host Networks Defining Virus-Permissive and -Hostile Cellular States
Chen, H., Charles, P. D., Gu, Q., Liberatori, S., Robertson, D. L., Palmarini, M., Wilson, S. J., Mohammed, S., & Castelló, A. (2025) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2025.100966 · Cited by 9
Beyond single cells: microfluidics empowering multiomics analysis
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“Compared with transcripts, proteins have less stochastic noises, higher stability, and higher expression levels than cognate mRNA [48].”
Jackson, W. S., Bauer, S., Kaczmarczyk, L., & Magadi, S. S. (2024) · Biology · doi:10.3390/biology13020067 · Cited by 8
“Both methods face challenges with contamination from unintended cells [21].”
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GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations
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Regulatory Nucleotide Sequence Signals for Expression of the Genes Encoding Ribosomal Proteins
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“The ribosome occupancy levels of 72 individuals were produced by sequencing ribosomeprotected fragments (RPFs) of mRNA isolated using Illumina HiSeq 2500 with ARTseq Ribosome Profiling kit (Epicenter, RPHMR12126; Battle et al., 2015).”
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Post-transcriptional regulation supports the homeostatic expression of mature RNA
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Joo, J., Lim, K., Yang, S., Kim, S.-H., Cocco, L., & Suh, P. (2021) · Advances in Biological Regulation · doi:10.1016/j.jbior.2021.100833 · 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
“Previous studies have reported the association between genomic loci and protein expression levels (protein QTL: pQTL) (Battle et al., 2015; Sun et al., 2018; Wu et al., 2013); thus, by integrating the puQTL and pQTL data obtained from matched samples, we were able to discover promoter-change-associated protein express…”
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
“Additionally, splicing cis-eQTLs and protein cis-eQTLs generated in LCLs 28,29 have also been incorporated into the pipeline.”
BackgroundInfluentialDalziel, 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
Winnard, P. T., Vesuna, F., Muthukumar, S., & Raman, V. (2020) · PLoS ONE · doi:10.1371/journal.pone.0242384 · Cited by 8
Sun, Y., Duan, Y., Gao, P., Liu, C., Jin, K., Dou, S., Tang, W., Zhang, H., & Lü, J. (2025) · eLife · doi:10.7554/elife.104074.3 · Cited by 7
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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