No 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.

No abstract

Copy number variations (CNVs) have been involved in multiple genomic disorders but their impact on complex traits remains understudied. We called CNVs in the UK Biobank and performed genome-wide association scans (GWASs) between the copy-number of CNV-proxy probes and 57 continuous traits, revealing 131 signals spanning 47 phenotypes. Our analysis recapitulated well-known associations (1q21 and height), revealed the pleiotropy of recurrent CNVs (26 traits for 16p11.2-BP4-BP5), and suggested new gene functionalities (MARF1 in female reproduction). Forty CNV signals overlapped known GWAS loci (RHD deletion and hematological traits). Conversely, others overlapped Mendelian disorder regions, suggesting variable expressivity and a broad impact of these loci, as illustrated by signals mapping to Rotor syndrome (SLCO1B1/3), renal cysts and diabetes (HNF1B), or Charcot-Marie-Tooth (PMP22) loci. The total CNV burden negatively impacted 35 traits, leading to increased adiposity, liver/kidney damage, and decreased intelligence and physical capacity. Thirty traits remained burden-associated after correcting for CNV-GWAS signals, pointing to a polygenic CNV-architecture. The burden negatively correlated with socio-economic indicators, parental lifespan, and age (survivorship proxy), suggesting that CNVs contribute to decreased longevity. Together, our results showcase how studying CNVs can reveal new biological insights, emphasizing the critical role of this mutational class in shaping complex traits.