New Method Identifies Dynamic Genetic Effects on Gene Expression Along Cell Development Trajectories in Lung Disease
Researchers have developed a novel computational framework called dynamic eQTL analysis that maps how genetic regulatory effects on gene expression change continuously along single-cell developmental trajectories, applying it to idiopathic pulmonary fibrosis (IPF). The method was tested on lung tissue single-cell RNA sequencing data from 114 individuals and integrated with GWAS summary statistics from three IPF studies. The approach reveals that genetic causal effects on disease are not static but shift dynamically during cell differentiation, offering a more nuanced view of how genes contribute to lung disease.
A new statistical framework introduced in a bioRxiv preprint models gene expression as a continuous function of pseudotime along inferred single-cell trajectories, enabling the detection of 'dynamic eQTLs' — genetic variants whose regulatory effects on gene expression change across cellular developmental states rather than remaining constant. The method addresses key limitations of existing pseudo-bulk approaches, which aggregate cells by type and ignore intra-individual cell-to-cell variability. To handle the sparsity and high variability characteristic of single-cell RNA sequencing data, the authors employ an empirical likelihood-based, non-parametric inference approach. Significant dynamic eQTLs are then used as instrumental variables in a Mendelian randomization-style framework to infer causal relationships between gene expression and complex traits such as IPF. Applied to 114 human lung tissue samples (66 IPF cases, 48 controls), the method identified pseudotime-dependent causal effects along the AT2–transitional AT2–AT1 cell trajectory, a pathway central to lung tissue repair and regeneration. Additionally, a causal mediation analysis component determines whether genes act on disease directly or indirectly by influencing cell fate decisions, with 30 genes found to have mediated effects through cell fate transitions.
What's missing
As a preprint, this work has not yet undergone peer review, so the validity of the methods and findings has not been independently assessed. The study is limited to a single disease (IPF) and one cell trajectory, leaving generalizability to other diseases or tissue types undemonstrated. The sample size of 114 individuals is relatively modest for genetic studies, which may limit statistical power and replication. The causal inference framework relies on instrumental variable assumptions (e.g., exclusion restriction) that cannot be fully verified from observational data alone.
What different sources said
- bioRxivCenter
Uncovering Pseudotime-Varying Genetic Causal Effects Along Single-Cell Trajectories for Pulmonary Disease Trait
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