4.8 Article

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

Journal

NATURE COMMUNICATIONS
Volume 12, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41467-021-25805-y

Keywords

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Funding

  1. Swiss National Science Foundation [310030-189147, 32473B-166450, 32003B-173092, 31003A_182632]
  2. Horizon2020 Twinning projects [692145]
  3. Federal Ministry of Education and Research [01ZZ9603, 01ZZ0103, 01ZZ0403, 03IS2061A]
  4. Ministry of Cultural Affairs
  5. Social Ministry of the Federal State of Mecklenburg-West Pomerania
  6. Ecole Polytechnique Federale de Lausanne (EPFL), European Research Council [ERC-AdG-787702]
  7. Swiss National Science Foundation (SNSF) [310030B160318]

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Identification of gene expression changes between healthy and diseased individuals can reveal mechanistic insights and biomarkers. The bi-directional transcriptome-wide Mendelian Randomization approach was proposed to assess causal effects between gene expression and complex traits. Comparing transcript levels between healthy and diseased individuals allows for the identification of differentially expressed genes that may be causes, consequences or mere correlates of the disease under scrutiny.
Identification of gene expression changes between healthy and diseased individuals can reveal mechanistic insights and biomarkers. Here, the authors propose a bi-directional transcriptome-wide Mendelian Randomization approach to assess causal effects between gene expression and complex traits. 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 x 10(-51) and r(TG )= 0.13, P-TG = 1.1 x 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.

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