ArticleNature communications2021
Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.
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Who cites it
80 citing papers in PubMed.
- Hybrid CNN and multi-head attention model for analyzing epigenetic mechanisms and gene expression across fungal phylogenetic distances.iScience · 2026Article
- Single-cell transcriptomics of human brain disorders.Molecular psychiatry · 2026Review
- Integrative Transcriptomics and Mendelian Randomization IdentifyCurrent issues in molecular biology · 2026Article
- An interpretable clinical-proteomic framework identifies baseline neuroinflammatory proteomic patterns associated with post-thrombectomy mortality.Journal of neuroinflammation · 2026Article
- Network-based machine learning to identify biomarkers for systemic lupus erythematosus.BMC biology · 2026Article
- Unveiling the potential regulatory functions of long non-coding RNAs in potato tuberization through integrated analysis of transcriptomics and phytohormone profiles.BMC plant biology · 2026Article
- RYBP regulates selective genomic binding of TrxG and PcG components in embryonic stem cell fate control.The EMBO journal · 2026Article
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- Single-cell multi-omic integration analysis prioritizes druggable genes and reveals cell-type-specific causal effects in glioblastomagenesis.Journal of translational medicine · 2026Article
- Proteomic aging clocks in epidemiological studies: advances, applications and prospects.Nature aging · 2026Review
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- MTG2 as a Causal and Druggable Target in Uveal Melanoma via Mitochondrial DNA Dynamics: Evidence From Functional Validation and DMSA Identification.Investigative ophthalmology & visual science · 2026Article
- The polygenic, omnigenic and stratagenic models of complex disease risk.Nature genetics · 2026Review
- Transcriptome profiling indicates varied gene responses to Pasteurella multocida mutant infections in cattle.PloS one · 2026Article
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- ACmix-Swin Deep Learning of 4-Day-OldGenes · 2025Article
- Integrating population-level and cell-based signatures for drug repositioning.Bioinformatics (Oxford, England) · 2025Article
- Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives.Journal of advanced research · 2025Review
- Transcriptome-wide root causal inference.PLoS computational biology · 2025Article
20 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Comparing transcript levels between healthy and diseased individuals allows the identification of differentially expressed genes, which may be causes, consequences or mere correlates of the disease under scrutiny. We propose a method to decompose the observational correlation between gene expression and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects (r
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