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.

比较健康个体和患病个体之间的转录水平可以识别差异表达的基因，这些基因可能是所研究疾病的原因、后果或仅仅是相关因素。我们提出了一种方法来分解由混杂因素、正向和反向因果效应驱动的基因表达和表型之间的观察相关性。基因表达和复杂性状之间的双向因果效应是通过孟德尔随机化整合来自 GWAS 和全血 eQTL 的汇总级数据获得的。将这种方法应用于复杂特征表明前向效应的贡献可以忽略不计。例如，BMI和甘油三酯基因表达相关系数与性状与表达的因果效应密切相关（ r _BMI = 0.11， P _BMI = 2.0 × 10 ⁻⁵¹和r _TG = 0.13， P _TG = 1.1 × 10 ⁻⁶⁸ ），但无法检测到表达对特征的影响。我们的结果表明，比较患病和健康受试者的转录组的研究更容易揭示疾病诱导的基因表达变化，而不是导致疾病的基因表达变化。