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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- Findingown data
A substantial fraction of regulatory genetic variants influence gene expression at all levels from mRNA to steady-state protein abundance.
“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.”
Quote in the paperSubstantial claim - Also stated in: § Abstract · boosted
- Findingown data
A class of cis QTLs affect protein abundance with little or no effect on mRNA or ribosome levels.
“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.”
Quote in the paperSubstantial claim - Findingown data
eQTLs tend to have reduced effect sizes on protein levels.
“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).”
Quote in the paperSubstantial claim - Also stated in: § Abstract · hedged
- Findingown data
eQTLs showed the lowest overlap with pQTLs (35%).
“In contrast, eQTLs showed the lowest overlap with pQTLs (35%), as expected (Fig. 1B).”
Quote in the paperSubstantial claim - Findingown data
GEUVADIS eQTLs had reduced mean effect size in protein data compared to RNA-seq data (P=6.7e-3) or ribosome data (P=5.6e-3).
“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).”
Quote in the paperSubstantial claim
- Findingown data
Among 4,322 genes quantified for all three phenotypes, 54% of genes with pQTLs also have a significant rQTL and/or eQTL.
“Among the 4,322 genes quantified for all three phenotypes, 54% of the genes with pQTLs also have a significant rQTL and/or eQTL.”
Quote in the paperSubstantial claim - Findingown data
Transcript and protein expression levels are the least correlated among the processes studied.
“We found that transcript and protein expression levels – which are the furthest removed processes studied here - are the least correlated (Fig. S9, S10).”
Quote in the paperSubstantial claim - Findingown data
Most (90%) genetic variants associated with ribosome occupancy are also associated with transcript levels.
“In particular, most (90%) genetic variants associated with ribosome occupancy are also associated with transcript levels (Fig. S13).”
Quote in the paperSubstantial claim - Findingown data
For psQTLs, low ribosome effect sizes are observed.
“Yet, for psQTLs, low ribosome effect sizes are observed.”
Quote in the paperSubstantial claim - Findingown data
146 protein-specific QTLs (psQTLs) were identified at FDR 10% using protein levels with RNA as covariate.
“Considering protein levels, with RNA levels as a covariate, we identified 146 protein-specific QTLs (psQTLs) at FDR = 10% (Fig. 2A).”
Quote in the paperSubstantial claim - Findingown data
For QTLs with discordant effects between transcript and protein, ribosome data usually tracked with RNA levels.
“Thus, for QTLs with discordant effects between transcript and protein, the ribosome data usually tracked with levels of RNA.”
Quote in the paperSubstantial claim - Findingown data
Effect sizes for ribosomal occupancy are similar to esQTL effect sizes.
“We found that the effect sizes for ribosomal occupancy are similar to the esQTL effect sizes (Fig. 2B).”
Quote in the paperSubstantial claim - Findingown data
Non-synonymous SNPs near acetylation sites showed nominal enrichment for psQTLs.
“Investigating additional annotations (Table 2, Fig. S21), we found that non-synonymous SNPs near acetylation sites showed nominal enrichment for psQTLs.”
Quote in the paperNot a substantial claim - Findingown data
A subset of variants affect protein levels but not mRNA.
“In addition to the observation of generally attenuated effect sizes in pQTLs compared with eQTLs, we identified a subset of variants that appear to affect levels of proteins but not mRNA, and hence are candidates to affect post-transcriptional gene regulation.”
Quote in the paperSubstantial claim - Findingown data
76 expression-specific QTLs (esQTLs) were identified using interaction model at FDR 10%.
“We also used the interaction model to identify 76 expression-specific QTLs (esQTLs, interaction model, LRT; FDR = 10%).”
Quote in the paperSubstantial claim - Findingown data
psQTLs are further enriched for non-synonymous sites compared to all exonic SNPs.
“In addition, psQTLs are further enriched for non-synonymous sites (compared to all exonic SNPs, Table 2).”
Quote in the paperNot a substantial claim - Findingown data
SNPs within transcribed regions (exonic and UTR) are enriched for more significant psQTL effects compared to intergenic or intronic SNPs.
“SNPs within transcribed regions (exonic and UTR) are enriched for more significant psQTL effects, compared to intergenic or intronic SNPs, even within the narrow 20 kb windows tested (Fig. S18, S19, S20).”
Quote in the paperSubstantial claim - Findingown data
68 psQTLs with significantly larger effects in protein than mRNA were identified using interaction model at FDR 10%.
“Using an alternative approach, an interaction model, we identified 68 psQTLs with significantly larger effects in protein than mRNA (LRT; FDR = 10%).”
Quote in the paperSubstantial claim - Premisecitation
There is evidence consistent with buffering of protein expression with respect to variation introduced at the transcript level.
“and provided evidence consistent with buffering of protein expression with respect to variation introduced at the transcript level.”
Quote in the paperSubstantial claim0 of 5 sources checked- cites [9] Laurent JM Protein abundances are more conserved than mRNA abundances across diverse taxa Proteomics 10 4209 2010 21089048 PMC3113407 source
Not checked in the free run.
- cites [10] Schrimpf SP Comparative Functional Analysis of the Caenorhabditis elegans and Drosophila melanogaster Proteomes PLoS Biol 7 e1000048 2009 PMC2650730 19260763 source
Not checked in the free run.
- cites [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 in the free run.
- cites [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 in the free run.
- cites [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 in the free run.
- Interpretationown data
These cis QTLs may arise from differences in post-translational regulation.
“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.”
Quote in the paperSubstantial claim - Interpretationown data
The potential impact of eQTLs on downstream phenotypes is often attenuated or buffered.
“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.”
Quote in the paperSubstantial claim - Premisecitation
Protein abundances are more direct determinants of cellular functions than transcript levels.
“Yet protein abundances are more direct determinants of cellular functions (4)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4] Vogel C Marcotte EM Insights into the regulation of protein abundance from proteomic and transcriptomic analyses Nature reviews Genetics 13 227 4 2012 PMC3654667 22411467 source
Not checked in the free run.
- Premisecitation
Studies in model organisms have shown that variation in mRNA and protein expression levels are often uncorrelated.
“Studies in model organisms have shown that variation in mRNA and protein expression levels are often uncorrelated (5-8).”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [5] Ghazalpour A Comparative analysis of proteome and transcriptome variation in mouse PLoS genetics 7 e1001393 6 2011 21695224 PMC3111477 source
Not checked in the free run.
- cites [6] Foss EJ Genetic basis of proteome variation in yeast Nature genetics 39 1369 11 2007 17952072 source
Not checked in the free run.
- cites [7] Picotti P A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis Nature 494 266 2 14 2013 23334424 PMC3951219 source
Not checked in the free run.
- cites [8] Albert FW Treusch S Shockley AH Bloom JS Kruglyak L Genetics of single-cell protein abundance variation in large yeast populations Nature 506 494 2 27 2014 24402228 PMC4285441 source
Not checked in the free run.
- Study design and conductown data
High correlations between measurements of distinct peptide groups from the same protein confirmed precise quantification of inter-individual variation in protein levels.
“The high correlations (Spearman's rho 0.7-0.9; R2 of 0.3-0.7; depending on the sample) confirmed that we are able to precisely quantify inter-individual variation in protein levels (Fig. S5).”
Quote in the paperSubstantial claim - Premisecitation
Ribosome profiling is an effective way to measure changes in translational regulation using sequencing.
“Ribosome profiling is an effective way to measure changes in translational regulation using sequencing (11).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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 in the free run.
- Study design and conductown data
The reduction in effect size in protein data is robust to potential technical confounders including iBAQ intensity level and transcript model complexity.
“The reduction in effect size observed in protein data is robust with respect to potential technical confounders, including iBAQ intensity level and transcript model complexity (Fig. S15).”
Quote in the paperSubstantial claim - Study design and conductnone given
Mean effect sizes calculated from replication of GEUVADIS eQTLs are expected to be unbiased with respect to technical or biological variance.
“Mean effect sizes calculated in this way are expected to be unbiased with respect to either technical or biological variance.”
Quote in the paperNot a substantial claim - Premisecitation
There are few reports of QTLs associated with protein levels (pQTLs) in humans.
“Yet, in contrast to comparative work, there are few reports of QTLs associated with protein levels (pQTLs) in humans (15-17).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [15] Wu L Variation and genetic control of protein abundance in humans Nature 499 79 2013 23676674 PMC3789121 source
Not checked in the free run.
- cites [16] Hause RJ Identification and Validation of Genetic Variants that Influence Transcription Factor and Cell Signaling Protein Levels American journal of human genetics 95 194 8 7 2014 25087611 PMC4129400 source
Not checked in the free run.
- cites [17] Garge N Identification of quantitative trait loci underlying proteome variation in human lymphoblastoid cells Molecular & cellular proteomics: MCP 9 1383 7 2010 20179311 PMC2938086 source
Not checked in the free run.
- Interpretationown data
The majority of genetic variants affecting transcript levels also alter ribosomal occupancy, typically with similar magnitude.
“We thus conclude that the majority of genetic variants affecting transcript levels also alter ribosomal occupancy, typically with a similar magnitude of effect.”
Quote in the paperSubstantial claim - Interpretationcitation
The observation that many eQTLs are not associated with protein differences is consistent with protein levels diverging less than transcript levels across species.
“Our observation that many SNPs identified as eQTLs are not associated with differences in protein levels is consistent with the notion that, across species, protein levels diverge less than transcript levels (12-17).”
Quote in the paperSubstantial claim0 of 6 sources checked- cites [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 in the free run.
- cites [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 in the free run.
- cites [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
Not checked in the free run.
- cites [15] Wu L Variation and genetic control of protein abundance in humans Nature 499 79 2013 23676674 PMC3789121 source
Not checked in the free run.
- cites [16] Hause RJ Identification and Validation of Genetic Variants that Influence Transcription Factor and Cell Signaling Protein Levels American journal of human genetics 95 194 8 7 2014 25087611 PMC4129400 source
Not checked in the free run.
- cites [17] Garge N Identification of quantitative trait loci underlying proteome variation in human lymphoblastoid cells Molecular & cellular proteomics: MCP 9 1383 7 2010 20179311 PMC2938086 source
Not checked in the free run.
- Study design and conductown data
Protein measurements were likely not subject to ratio compression.
“The median ratios measured from these peptides matched the expected values closely, indicating that our protein measurements were likely not subject to ratio compression (Figs. S7, S8).”
Quote in the paperSubstantial claim - Study design and conductown data
Identification of psQTLs is generally robust to choice of technology for transcript expression.
“The identification of psQTLs is generally robust to the choice of technology used to characterize transcript expression (Fig S17).”
Quote in the paperNot a substantial claim - Interpretationown data
This attenuation is not evident at the stage of translation.
“Our analysis indicates that this attenuation is not evident at the stage of translation.”
Quote in the paperSubstantial claim - Interpretationown data
Overall phenotypic similarity between ribosome occupancy and protein abundance is high.
“While the overall phenotypic similarity between ribosome occupancy and protein abundance is high, cis-regulatory genetic effects on ribosome occupancy appear to be more strongly shared with mRNA than with protein.”
Quote in the paperSubstantial claim - Interpretationown data
There are effects with specific impact on particular expression phenotypes.
“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.”
Quote in the paperSubstantial claim - Interpretationown data
These variants are candidates to affect post-transcriptional gene regulation.
“In addition to the observation of generally attenuated effect sizes in pQTLs compared with eQTLs, we identified a subset of variants that appear to affect levels of proteins but not mRNA, and hence are candidates to affect post-transcriptional gene regulation.”
Quote in the paperSubstantial claim - Interpretationown data
cis-regulatory genetic effects on ribosome occupancy appear more strongly shared with mRNA than with protein.
“While the overall phenotypic similarity between ribosome occupancy and protein abundance is high, cis-regulatory genetic effects on ribosome occupancy appear to be more strongly shared with mRNA than with protein.”
Quote in the paperSubstantial claim
- May combine two claims. 6
- Classified as probably not a substantial claim. 3
May combine two claims.
Findingown dataAt FDR 10%, 2,355 eQTLs, 939 rQTLs, and 278 pQTLs were detected.
“At an FDR of 10% we detected 2,355 eQTLs, 939 rQTLs, and 278 pQTLs (Table 1, Fig. S11).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAverage effect sizes for RNA-seq and ribosome data are not significantly different and are highly correlated across tested eQTLs (Pearson c=0.79, P<10-96).
“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).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationProtein expression evolves under greater evolutionary constraint than transcript levels.
“Comparative studies (9-13) have suggested that protein expression evolves under greater evolutionary constraint than transcript levels (14)”
Quote in the paperSubstantial claim0 of 6 sources checked- cites [9] Laurent JM Protein abundances are more conserved than mRNA abundances across diverse taxa Proteomics 10 4209 2010 21089048 PMC3113407 source
Not checked in the free run.
- cites [10] Schrimpf SP Comparative Functional Analysis of the Caenorhabditis elegans and Drosophila melanogaster Proteomes PLoS Biol 7 e1000048 2009 PMC2650730 19260763 source
Not checked in the free run.
- cites [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 in the free run.
- cites [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 in the free run.
- cites [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 in the free run.
- cites [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
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPrevious studies have evaluated the association between genetic variation and transcript expression in humans.
“Previous studies have evaluated the association between variation and transcript expression in humans (1-3).”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [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 in the free run.
- cites [2] Lappalainen T Transcriptome and genome sequencing uncovers functional variation in humans Nature 501 506 9 26 2013 24037378 PMC3918453 source
Not checked in the free run.
- cites [3] Grundberg E Mapping cis- and trans-regulatory effects across multiple tissues in twins Nature genetics 44 1084 10 2012 22941192 PMC3784328 source
Not checked in the free run.
May combine two claims.
Study design and conductown dataRibosome profiling reads are highly concentrated within coding regions and show enrichment of a 3-bp periodicity.
“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).”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThere is a scarcity of translation-specific QTLs and minimal attenuation of genetic impact between mRNA and ribosome phenotypes.
“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.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationThis enrichment possibly reflects the functional role of lysine acetylation in modulating protein degradation.
“This possibly reflects the functional role of lysine acetylation in modulating protein degradation (23).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [23] Yang XJ Seto E Lysine acetylation: codified crosstalk with other posttranslational modifications Molecular cell 31 449 8 22 2008 18722172 PMC2551738 source
Not checked in the free run.
May combine two claims.
Interpretationown dataGenetic variants involved in post-transcriptional regulation are functionally distinct from variants primarily affecting transcription, being more likely in translated regions and at non-synonymous sites.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThese results identify loci where genetic variants have specific impacts on protein levels not fully mediated by transcription or translation and may affect protein degradation rates.
“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.”
Quote in the paperSubstantial claim
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
How 5′ UTR sequences affect translation is only beginning to be understood.
“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]
In the section labeled Abstract (page unavailable), the paper says that how genetic differences affect gene expression through translation has not been fully characterized. That supports the general point that understanding is incomplete, but the cited passage does not specifically address how 5′ UTR sequences affect translation. (LLM, conf=0.83)
Experimental — accuracy not yet measuredA number of studies have shown that while levels of many proteins are strongly correlated with their transcript levels, with coincident eQTL and pQTL, the correlations for most protein-transcript pairs are modest.
“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]
Human biology is closely tied to proteins, and mass spectrometry-based proteomics has linked phenotype to protein-level biology.
“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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A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis
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Noisy Splicing Drives mRNA Isoform Diversity in Human Cells
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Self-citationPrimate Transcript and Protein Expression Levels Evolve Under Compensatory Selection Pressures
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Self-citationComparative Functional Analysis of the Caenorhabditis elegans and Drosophila melanogaster Proteomes
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Practical Issues in Imputation-Based Association Mapping
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Protein abundances are more conserved than mRNA abundances across diverse taxa
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Genetics of single-cell protein abundance variation in large yeast populations
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Evolution at two levels of gene expression in yeast
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Self-citationConserved Translatome Remodeling in Nematode Species Executing a Shared Developmental Transition
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Quantitative measurement of allele‐specific protein expression in a diploid yeast hybrid by LC‐MS
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On the Dependency of Cellular Protein Levels on mRNA Abundance
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Multi-omics approaches to disease
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An Expanded View of Complex Traits: From Polygenic to Omnigenic
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“Moreover, many trans-QTLs may act through protein networks and thus may not be detectable from RNA, though current data on trans-acting controls of proteins are very limited (Battle et al., 2015; Chick et al., 2016; Sun et al., 2017).”
BackgroundSelf-citationGenomic atlas of the human plasma proteome
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Age and Age-Related Diseases: Role of Inflammation Triggers and Cytokines
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“Proteomic methods measuring cytokine production and expression have demonstrated further layers of complexity and control in cytokine production and expression involving long coding RNAs, siRNAs, and miRNAs, which make for challenging interpretation of cytokine production and control in the inflammatory process (6).”
BackgroundMethodInfluentialmRNAs, proteins and the emerging principles of gene expression control
Buccitelli, C., & Selbach, M. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0258-4 · Cited by 1,243 · see citing works
The role of regulatory variation in complex traits and disease
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Multiplexed quantification of proteins and transcripts in single cells
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The Post-GWAS Era: From Association to Function
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RNA splicing is a primary link between genetic variation and disease
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Self-citationThe Translational Landscape of the Human Heart
van Heesch, S., Witte, F., Schneider-Lunitz, V., Schulz, J. F., Adami, E., Faber, A. B., Kirchner, M., Maatz, H., Blachut, S., Sandmann, C.-L., et al. (2019) · Cell · doi:10.1016/j.cell.2019.05.010 · Cited by 619 · see citing works
“Most of these variants are not associated with ribosome occupancy (Table S4), consistent with extensive buffering observed previously in a similarly-sized cohort of HapMap lymphoblastoid cell lines (Battle et al., 2015; Cenik et al., 2015).”
ResultSequential regulatory activity prediction across chromosomes with convolutional neural networks
Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., & Snoek, J. (2018) · Genome Research · doi:10.1101/gr.227819.117 · Cited by 537 · see citing works
“23 Discussion Transcriptional regulation is the primary driver of gene expression specificity across cell types and states (Waszak et al. 2015; Grubert et al. 2015; Battle et al. 2015; Li et al. 2016).”
BackgroundInfluentialTrans effects on gene expression can drive omnigenic inheritance
Liu, X., Li, Y. I., & Pritchard, J. K. (2019) · Cell · doi:10.1016/j.cell.2019.04.014 · Cited by 523 · see citing works
Self-citationHuman 5′ UTR design and variant effect prediction from a massively parallel translation assay
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Multi-Omics Profiling for Health
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“Importantly, proteomics has revealed the effect of genetic variants in conditions otherwise undetectable by RNA analysis (72).”
BackgroundDefining the consequences of genetic variation on a proteome-wide scale
Chick, J. M., Munger, S. C., Simecek, P., Huttlin, E. L., Choi, K., Gatti, D. M., Raghupathy, N., Svenson, K. L., Churchill, G. A., & Gygi, S. P. (2016) · Nature · doi:10.1038/nature18270 · Cited by 361 · see citing works
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“(C) Boxplot of translation efficiency (Battle et al., 2015) (y axis) in mRNAs with the highest and lowest 20% circularization scores.”
BackgroundWhere Are the Disease-Associated eQTLs?
Umans, B. D., Battle, A., & Gilad, Y. (2021) · Trends in Genetics · doi:10.1016/j.tig.2020.08.009 · Cited by 296 · see citing works
Self-citationKinetic Analysis of Protein Stability Reveals Age-Dependent Degradation
McShane, E., Sin, C., Zauber, H., Wells, J. N., Donnelly, N., Wang, X., Hou, J., Chen, W., Štorchová, Z., Marsh, J. A., et al. (2016) · Cell · doi:10.1016/j.cell.2016.09.015 · Cited by 291 · see citing works
“The data might also help to explain posttranslational buffering (Battle et al., 2015).”
BackgroundRibosome Profiling: Global Views of TranslationPreprint
Ingolia, N. T., Hussmann, J. A., & Weissman, J. S. (2018) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a032698 · Cited by 285 · see citing works
“It has also been applied to study gene expression variation between individual humans (Battle et al. 2015; Cenik et al. 2015).”
BackgroundUnravelling the human genome–phenome relationship using phenome-wide association studies
Bush, W. S., Oetjens, M. T., & Crawford, D. C. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.36 · Cited by 269 · see citing works
Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms
Walker, R. L., Ramaswami, G., Hartl, C., Mancuso, N., Gandal, M. J., de la Torre-Ubieta, L., Paşaniuc, B., Stein, J. L., & Geschwind, D. H. (2019) · Cell · doi:10.1016/j.cell.2019.09.021 · Cited by 262 · see citing works
Autoimmune diseases — connecting risk alleles with molecular traits of the immune system
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Tunable and reversible drug control of protein production via a self-excising degron
Chung, H. K., Jacobs, C. L., Huo, Y., Yang, J., Krumm, S. A., Plemper, R. K., Tsien, R. Y., & Lin, M. Z. (2015) · Nature Chemical Biology · doi:10.1038/nchembio.1869 · Cited by 233 · see citing works
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The roles of RNA processing in translating genotype to phenotype
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Conservation of uORF repressiveness and sequence features in mouse, human and zebrafish
Chew, G.-L., Pauli, A., & Schier, A. F. (2016) · Nature Communications · doi:10.1038/ncomms11663 · Cited by 197 · see citing works
“In humans, sequence variations in 5 0 leaders have been statistically associated with variation in gene expression 48 , while mutations at uORFs have been shown to contribute to disease 49 .”
Inter-individual variability and genetic influences on cytokine responses to bacteria and fungi
Li, Y., Oosting, M., Deelen, P., Ricaño-Ponce, I., Smeekens, S. P., Jaeger, M., Matzaraki, V., Swertz, M. A., Xavier, R. J., Franke, L., et al. (2016) · Nature Medicine · doi:10.1038/nm.4139 · Cited by 189 · see citing works
Gene Regulation and Speciation
Mack, K. L., & Nachman, M. W. (2016) · Trends in Genetics · doi:10.1016/j.tig.2016.11.003 · Cited by 192 · see citing works
“The regulation of gene expression is a complex process that may be modulated at many stages, including transcription, translation, and post-translation [117].”
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DNA methylation in lung cells is associated with asthma endotypes and genetic risk
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Self-citationGenetics of trans-regulatory variation in gene expression
Albert, F. W., Bloom, J. S., Siegel, J. J., Day, L., & Kruglyak, L. (2018) · eLife · doi:10.7554/elife.35471 · Cited by 180 · see citing works
Tensor decomposition for multiple-tissue gene expression experiments
Hore, V., Viñuela, A., Buil, A., Knight, J. C., McCarthy, M. I., Small, K. S., & Marchini, J. (2016) · Nature Genetics · doi:10.1038/ng.3624 · Cited by 178 · see citing works
Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction
Genshaft, A. S., Li, S., Gallant, C. J., Darmanis, S., Prakadan, S. M., Ziegler, C. G. K., Lundberg, M., Fredriksson, S., Hong, J., Regev, A., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-1045-6 · Cited by 175 · see citing works
“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 The eGTEx Project
Stranger, B., Brigham, L., Hasz, R., Hunter, M., Johns, C., Johnson, M. D., Kopen, G., Leinweber, W. F., Lonsdale, J., McDonald, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3969 · Cited by 189 · see citing works
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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
Li, J. J., & Biggin, M. D. (2015) · Science · doi:10.1126/science.aaa8332 · Cited by 164 · see citing works
The ribosome-engaged landscape of alternative splicing
Weatheritt, R. J., Sterne-Weiler, T., & Blencowe, B. J. (2016) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3317 · Cited by 162 · see citing works
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Reduced changes in protein compared to mRNA levels across non-proliferating tissues
Perl, K., Ushakov, K., Pozniak, Y., Barnea, O. Y., Bhonker, Y., Shivatzki, S., Geiger, T., Avraham, K. B., & Ron Shamir, A. (2017) · BMC Genomics · doi:10.1186/s12864-017-3683-9 · Cited by 168 · see citing works
“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 155 · see citing works
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 148 · see citing works
The role of alternative splicing in adaptation and evolution
Verta, J., & Jacobs, A. (2021) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2021.11.010 · Cited by 148 · see citing works
Methods, Tools and Current Perspectives in Proteogenomics
Ruggles, K. V., Krug, K., Wang, X., Clauser, K. R., Wang, J., Payne, S., Fenyö, D., Zhang, B., & Mani, D. R. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.mr117.000024 · Cited by 148 · see citing works
Quantifying the regulatory effect size of cis -acting genetic variation using allelic fold changePreprint
Mohammadi, P., Castel, S. E., Brown, A. A., & Lappalainen, T. (2017) · Genome Research · doi:10.1101/gr.216747.116 · Cited by 146 · see citing works
Insights into the mechanisms of eukaryotic translation gained with ribosome profiling
Andreev, D. E., O’Connor, P. B. F., Loughran, G., Dmitriev, S. E., Baranov, P. V., & Shatsky, I. N. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1190 · Cited by 145 · see citing works
“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 144 · see citing works
“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 144 · see citing works
Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling
Raj, A., Wang, S. H., Shim, H., Harpak, A., Li, Y., Engelmann, B. W., Stephens, M., Gilad, Y., & Pritchard, J. K. (2016) · eLife · doi:10.7554/elife.13328 · Cited by 142 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
Alterations of the translation apparatus during aging and stress response
Gonskikh, Y., & Polacek, N. (2017) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2017.04.003 · Cited by 140 · see citing works
“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 139 · see citing works
“, [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.”
Zheng, Z., Huang, D., Wang, J., Zhao, K., Zhou, Y., Guo, Z., Zhai, S., Xu, H., Cui, H. S., Yao, H., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz888 · Cited by 138 · see citing works
Multiple repressive mechanisms in the hippocampus during memory formation
Cho, J., Yu, N., Choi, J., Sim, S.-E., Kang, S. J., Kwak, C., Lee, S., Kim, J., Choi, D. I., Kim, V. N., et al. (2015) · Science · doi:10.1126/science.aac7368 · Cited by 135 · see citing works
Imaging mRNA In Vivo, from Birth to Death
Tutucci, E., Livingston, N. M., Singer, R. H., & Wu, B. (2018) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-070317-033037 · Cited by 133 · see citing works
Dumitriu, A., Golji, J., Labadorf, A. T., Gao, B., Beach, T. G., Myers, R. H., Longo, K. A., & Latourelle, J. C. (2015) · BMC Medical Genomics · doi:10.1186/s12920-016-0164-y · Cited by 129 · see citing works
“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
Mudge, J. M., & Harrow, J. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.119 · Cited by 122 · see citing works
Targeted m6A Reader Proteins To Study Epitranscriptomic Regulation of Single RNAs
Rauch, S., He, C., & Dickinson, B. C. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.8b05012 · Cited by 122 · see citing works
Widespread Post-transcriptional Attenuation of Genomic Copy-Number Variation in Cancer
Gonçalves, E., Fragoulis, A., García‐Alonso, L., Cramer, T., Sáez-Rodríguez, J., & Beltrão, P. (2017) · Cell Systems · doi:10.1016/j.cels.2017.08.013 · Cited by 113 · see citing works
“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 · see citing works
The Werner syndrome RECQ helicase targets G4 DNA in human cells to modulate transcription
Tang, W., Robles, A. I., Beyer, R. P., Gray, L. T., Nguyen, G. H., Oshima, J., Maizels, N., Harris, C. C., & Monnat, R. J. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw079 · Cited by 109 · see citing works
An Atlas of Protein-Protein Interactions Across Mouse Tissues
Skinnider, M. A., Scott, N. E., Prudova, A., Kerr, C. H., Stoynov, N., Stacey, R. G., Chan, Q. W. T., Rattray, D., Gsponer, J. A., & Foster, L. J. (2021) · Cell · doi:10.1016/j.cell.2021.06.003 · Cited by 107 · see citing works
Genetic control of the human brain proteome
Robins, C., Wingo, A. P., Fan, W., Duong, D. M., Meigs, J., Gerasimov, E. S., Dammer, E. B., Cutler, D. J., Jager, P. L. D., Bennett, D. A., et al. (2019) · doi:10.1101/816652 · Cited by 104 · see citing works
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 103 · see citing works
“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 102 · see citing works
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 · see citing works
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 94 · see citing works
“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 90 · see citing works
“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
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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 86 · see citing works
Pierce, B. L., Lin, T., Argos, M., Demanelis, K., Jasmine, F., Rakibuz‐Zaman, M., Sarwar, G., Islam, T., Shahriar, H., Islam, T., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03209-9 · Cited by 86 · see citing works
Integrated omics dissection of proteome dynamics during cardiac remodeling
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Genetic mapping uncovers cis-regulatory landscape of RNA editing
Ramaswami, G., Deng, P., Zhang, R., Carbone, M. A., Mackay, T. F. C., & Li, J. B. (2015) · Nature Communications · doi:10.1038/ncomms9194 · Cited by 83 · see citing works
“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 82 · see citing works
De novo genes with an lncRNA origin encode unique human brain developmental functionality
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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 79 · see citing works
Human adaptation to high altitude: a review of convergence between genomic and proteomic signatures
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“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
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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
- 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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