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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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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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“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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InfluentialMulti-omics analysis unravels chemical roadmap and genetic basis for peach fruit aroma improvement
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Vitamin D Modulates Expression of the Airway Smooth Muscle Transcriptome in Fatal Asthma
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Evolutionary Divergence of Gene and Protein Expression in the Brains of Humans and Chimpanzees
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An Overview of Matrix Metalloproteinase 9 Polymorphism and Gastric Cancer Risk
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“A substantial fraction of regulatory genetic variants influence gene expression at all levels from mRNA to steady-state protein abundance (Battle et al., 2015).”
BackgroundNeuroinflammation — using big data to inform clinical practice
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Post-translational buffering leads to convergent protein expression levels between primates
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“For evaluating inter-species divergence in protein translation, we performed ribosome profiling experiments (in one batch) for four human, four chimpanzee, and four rhesus macaque LCLs (Additional file 1: Table S1).”
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Massively Parallel CRISPR‐Based Genetic Perturbation Screening at Single‐Cell Resolution
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“Since proteins are the primary effectors of cellular processes, pQTLs establish a more direct link to disease phenotypes than expression quantitative trait loci (eQTLs), which primarily influence mRNA levels [186, 187].”
Metabolomics-driven understanding of genotype-phenotype relations in model organisms
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Living in an adaptive world: Genomic dissection of the genus Homo and its immune response
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“Indeed, it has been shown that while a substantial fraction of genetic variants influence gene expression at all levels, from mRNA to steady-state protein abundance, the effects of eQTL are attenuated at the protein level, and some pQTL have little or no effect at the mRNA level suggesting that they affect posttranscr…”
BackgroundIdentification of TBX15 as an adipose master trans regulator of abdominal obesity genes
Pan, D. Z., Miao, Z., Comenho, C., Rajkumar, S., Koka, A., Lee, S. H., Alvarez, M., Kamińska, D., Ko, A., Sinsheimer, J. S., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00939-2 · Cited by 40
“protein-QTLs (p-QTLs) [92], gene expression levels of TBX15 may not correlate strongly with protein levels, thus possibly diluting many of the genetic and expression level signals.”
BackgroundPhysiological Functions of Intracellular Protein Degradation
McShane, E., & Selbach, M. (2022) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-120420-091943 · Cited by 41
Global variation in gene expression and the value of diverse sampling
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Regulation of protein abundance in genetically diverse mouse populations
Keele, G. R., Zhang, T., Pham, D. T., Vincent, M. J., Bell, T. A., Hock, P., Shaw, G. D., Paulo, J. A., Munger, S. C., de Villena, F. P., et al. (2021) · Cell Genomics · doi:10.1016/j.xgen.2021.100003 · Cited by 38
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Self-citationLong-read sequencing for 29 immune cell subsets reveals disease-linked isoforms
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“Genotypes Genotypes from the phase 3 variant set called by the 1000 Genomes Project (1000 Genomes Project Consortium 2012) were downloaded from here: ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20130502 We restricted our analysis of ribosome profiling data from Battle et al (Battle et al. 2015) to the 62 samp…”
BackgroundMethodInfluentialEvaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression
Li, B., Verma, S. S., Veturi, Y., Verma, A., Bradford, Y., Haas, D. W., & Ritchie, M. D. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0041 · Cited by 36
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“It is a common phenomenon that mRNA levels do not correlate with protein levels [40][41].”
BackgroundGyuricza, I. G., Chick, J. M., Keele, G. R., Deighan, A. G., Munger, S. C., Korstanje, R., Gygi, S. P., & Churchill, G. A. (2022) · Genome Research · doi:10.1101/gr.275672.121 · Cited by 36
“The additive effects of genetic variation on transcripts and proteins have been widely documented (Hause et al. 2014; Battle et al. 2015; Chick et al. 2016; Albert et al. 2018; Yao et al. 2018) and are not discussed here, but we have made the mapping results available for others to explore.”
Increasing proteomics throughput
Slavov, N. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00881-z · Cited by 34
RNA Sequencing in Disease Diagnosis
Smail, C., & Montgomery, S. B. (2024) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-021623-121812 · Cited by 35
“…a disease-associated transcriptome signature may not always provide immediate insights into effective therapies as RNA signatures can have high interindividual variability and inaccuracy with respect to transcript isoform annotation, and may even be buffered at the protein-coding level (5).”
Gene expression and protein abundance: Just how associated are these molecular traits?
Samih, A., Ferreira, M. A. D. M., & Nikoloski, Z. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108720 · Cited by 35
The translational landscape of ground state pluripotency
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Human Knockout Carriers: Dead, Diseased, Healthy, or Improved?
Narasimhan, V. M., Xue, Y., & Tyler‐Smith, C. (2016) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2016.02.006 · Cited by 35
“Surprisingly, even if genetic variation triggers NMD and there is degradation of the RNA, the reduction in RNA levels may not reduce the protein level [11].”
BackgroundA pooling-based approach to mapping genetic variants associated with DNA methylationPreprint
Kaplow, I. M., MacIsaac, J. L., Mah, S. M., McEwen, L. M., Kobor, M. S., & Fraser, H. B. (2015) · Genome Research · doi:10.1101/gr.183749.114 · Cited by 38
“This suggests a buffering or dilution of mQTL effects at transcriptional and post-transcriptional levels, consistent with recent models for the attenuated effects of eQTLs on protein levels (Battle et al. 2014).”
BackgroundResultGenetic Control of Alternative Splicing and its Distinct Role in Colorectal Cancer Mechanisms
Zhang, M., Chen, C., Lu, Z., Cai, Y., Li, Y., Zhang, F., Liu, Y., Chen, S., Zhang, H., Yang, S., et al. (2023) · Gastroenterology · doi:10.1053/j.gastro.2023.07.019 · Cited by 33
Discordant skeletal muscle gene and protein responses to exercise
Bishop, D. J., Hoffman, N. J., Taylor, D. F., Saner, N. J., Lee, M. J.-C., & Hawley, J. A. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2023.08.005 · Cited by 36
Extensive allele‐specific translational regulation in hybrid mice
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“Indeed, a recent study, based on their genome-wide eQTL, rQTL and pQTL mapping data, suggested that protein degradation might play a more important role in maintaining robust protein cellular abundance during evolution (Battle et al , 2015).”
InfluentialArtificial intelligence used in genome analysis studies
D’Agaro, E. (2018) · The EuroBiotech Journal · doi:10.2478/ebtj-2018-0012 · Cited by 33
Systems-Wide Approaches in Induced Pluripotent Stem Cell Models
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“…2020), as previous work has found pQTL effect size to be positively correlated with eQTL effect size for variants ascertained through eQTL in human (Battle et al., 2015; Li et al., 2016), and that colocalized eQTL and pQTL signal leads to higher observed RNA-protein correlations in mice (Chick et…”
BackgroundEngelken, J., Espadas, G., Mancuso, F. M., Bonet, N., Scherr, A.-L., Jímenez-Álvarez, V., Codina-Solà, M., Medina-Stacey, D., Spataro, N., Stoneking, M., et al. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msv267 · Cited by 31
“Although the use of these approaches to evaluate genome-wide SNP–protein associations (trans-effects) is still limited due to required number of samples, a few studies have successfully performed pQTL experiments for a restricted number of SNP–protein associations (Lourdusamy et al. 2012; Johansson et al. 2013; Wu et…”
BackgroundShameer, K., Tripathi, L. P., Kalari, K. R., Dudley, J. T., & Sowdhamini, R. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv084 · Cited by 33
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“Conversely, there are also genes that are more variable at the protein level than at the mRNA levels (Battle et al., 2015; Chick et al., 2016; Khan et al., 2013).”
BackgroundSelf-citationLiu, M., Degner, J. F., Georgantas, R. W., Nader, A. M., Mostafa, N. M., Teixeira, H. D., Williams, D., Kirsner, R. S., Nichols, A. J., Davis, J. W., et al. (2019) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2019.06.152 · Cited by 30
A spectrum of explainable and interpretable machine learning approaches for genomic studies
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“(a) Depiction of a black box neural network architecture with genotypes as inputs and phenotypes as output (Battle et al., 2015; Bell et al., 2011; Corsello et al., 2020; Rahman et al., 2019; Rakitsch & Stegle, 2016; Xiong et al., 2015).”
Wang, G., Zhao, J., Lin, Y., Liu, T., Zhao, Y., & Zhao, H. (2025) · Nature Communications · doi:10.1038/s41467-025-60333-z · Cited by 29
“…assays and its coding gene expression in scRNA-seq data, show weaker relationships which are often not robust enough to reliably guide integration, as mRNA levels do not always correlate with protein abundance due to post-transcriptional regulation, degradation, and protein modi fi cations 14 – 17 .”
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Trans -pQTL study identifies immune crosstalk between Parkinson and Alzheimer loci
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“Of interest, these trans associations were not observed at the mRNA level, highlighting the importance of protein-level QTL studies, which can capture effects of posttranslational regulation.(22) PTK2B, a member of the focal adhesion kinase family, is a protein tyrosine kinase that is rapidly activated by a number of…”
BackgroundGage, J. L., Mali, S., McLoughlin, F., Khaipho-Burch, M., Monier, B., Bailey-Serres, J. N., Vierstra, R. D., & Buckler, E. S. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2112516119 · Cited by 30
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) · Genome biology · doi:10.1186/s13059-023-02892-2 · Cited by 28
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“In general, gene expression levels at mRNA level do not necessarily correlate well with those at protein level [27, 28] which are subject to multiple layers of regulation [27–29].”
The case for too little melatonin signalling in increased diabetes risk
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RPS15 mutations rewire RNA translation in chronic lymphocytic leukemia
Ntoufa, S., Gerousi, M., Laidou, S., Psomopoulos, F. E., Tsiolas, G., Moysiadis, T., Papakonstantinou, N., Mansouri, L., Anagnostopoulos, Α., Stavrogianni, N., et al. (2021) · Blood Advances · doi:10.1182/bloodadvances.2020001717 · Cited by 27
Guidelines for clinical interpretation of variant pathogenicity using RNA phenotypes
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“In addition, the effects of variants leading to aberrant RNA phenotypes, such as aberrant under-expression, can be compensated on the protein level by protein buffering mechanisms (Battle et al., 2015; Ishikawa et al., 2017; Vogel & Marcotte, 2012).”
BackgroundKim, S.-H., Yang, S., Lim, K., Ko, E., Jang, H., Kang, M., Suh, P., & Joo, J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2011250118 · Cited by 27
“Therefore, we reasoned that genome-wide association study (GWAS)-derived deep learning would be an effective model system for predicting AD and its progression.”
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Genome-Wide Expression Quantitative Trait Loci Analysis Using Mixed Models
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“Expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTL) provide insight into the genetic basis of transcriptional variation and protein abundance, respectively [22,23].”
BackgroundOppong, A. E., Coelewij, L., Robertson, G., Martin‐Gutierrez, L., Waddington, K. E., Dönnes, P., Nytrová, P., Farrell, R. A., Pineda‐Torra, I., & Jury, E. C. (2024) · iScience · doi:10.1016/j.isci.2024.109225 · Cited by 26
“57 The analysis platform selected can also influence the reproducibility of the omic data obtained.”
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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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