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• W3200602313 abstract "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 BMI = 0.11, P BMI = 2.0 × 10 −51 and r TG = 0.13, P TG = 1.1 × 10 −68 ), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones." @default.
• W3200602313 created "2021-09-27" @default.
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• W3200602313 date "2021-09-24" @default.
• W3200602313 modified "2023-10-09" @default.
• W3200602313 title "Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome" @default.
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• W3200602313 doi "https://doi.org/10.1038/s41467-021-25805-y" @default.
• W3200602313 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8463674" @default.
• W3200602313 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34561431" @default.

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