ArticleNature communications2025
Impact of common variants on brain gene expression from RNA to protein to schizophrenia risk.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Multi-Tissue Omics Analysis Uncovers Translational Regulation Underlying Complex Traits in Pigs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Functional implications of polygenic risk for schizophrenia in human neurons.Nature communications · 2026Article
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
Genetic variants influencing gene expression have been extensively studied at the transcriptional level. How these variants affect downstream processes remains unclear. We quantitated ribosome occupancy in prefrontal cortex samples from the BrainGVEX cohort and integrated these data with transcriptomic and proteomic profiles from the same individuals. Through cis-QTL mapping, we identified genetic variants associated with transcript level (eQTLs), ribosome occupancy (rQTLs), and protein level (pQTLs). Notably, only 34% of eQTLs have their effects propagated to the protein levels, suggesting widespread post-transcriptional attenuation. Using both a gene-based approach and a variant-based approach we identified omics-specific QTLs that associated with brain disorder GWAS signals and found the majority of them to be driven predominantly by transcriptional regulation. Consistently, using a TWAS approach, we identified 74 SCZ risk genes across the three omics layers, 52 were discovered using transcriptome with 68% showing limited impact on protein expression. Our findings indicated that many disease-associated variants act through regulatory mechanisms that do not lead to an observable impact on the protein level.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.