Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome

Eleonora Porcu (), Marie C. Sadler, Kaido Lepik, Chiara Auwerx, Andrew R. Wood, Antoine Weihs, Maroun S. Bou Sleiman, Diogo M. Ribeiro, Stefania Bandinelli, Toshiko Tanaka, Matthias Nauck, Uwe Völker, Olivier Delaneau, Andres Metspalu, Alexander Teumer, Timothy Frayling, Federico A. Santoni, Alexandre Reymond and Zoltán Kutalik
Additional contact information
Eleonora Porcu: University of Lausanne
Marie C. Sadler: Swiss Institute of Bioinformatics
Kaido Lepik: University of Tartu
Chiara Auwerx: University of Lausanne
Andrew R. Wood: University of Exeter
Antoine Weihs: University Medicine Greifswald
Maroun S. Bou Sleiman: Ecole Polytechnique Fédérale de Lausanne
Diogo M. Ribeiro: Swiss Institute of Bioinformatics
Stefania Bandinelli: Local Health Unit Toscana Centro
Toshiko Tanaka: Clinical Res Branch, National Institute of Aging
Matthias Nauck: University Medicine Greifswald
Uwe Völker: partner site Greifswald
Olivier Delaneau: Swiss Institute of Bioinformatics
Andres Metspalu: University of Tartu
Alexander Teumer: partner site Greifswald
Timothy Frayling: University of Exeter
Federico A. Santoni: Lausanne University Hospital
Alexandre Reymond: University of Lausanne
Zoltán Kutalik: Swiss Institute of Bioinformatics

Nature Communications, 2021, vol. 12, issue 1, 1-9

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 (rBMI = 0.11, PBMI = 2.0 × 10−51 and rTG = 0.13, PTG = 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.

Date: 2021
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DOI: 10.1038/s41467-021-25805-y

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