Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 34561431.
- Also identified by DOI 10.1038/s41467-021-25805-y and PMC identifier 8463674.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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<sub>BMI </sub>= 0.11, P<sub>BMI </sub>= 2.0 × 10<sup>-51</sup> and r<sub>TG </sub>= 0.13, P<sub>TG </sub>= 1.1 × 10<sup>-68</sup>), 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.
Medical subject headings
- Algorithms
- Gene Expression Profiling
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Polymorphism, Single Nucleotide
- Transcriptome