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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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A Pseudotime-Dependent TWAS Framework Identifies Disease Genes along Cell Developmental Paths.

Rui Cao¹, Chunlin Li², Erjia Cui¹

¹Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, 55455, USA.

|June 24, 2026

Summary

Pseudotime-dependent TWAS (pt-TWAS) models gene expression dynamically along cell development. This method enhances statistical power for identifying genes linked to disease risk at specific cell stages.

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Transcriptome-wide association studies (TWAS) integrate gene expression and genome-wide association study (GWAS) data to link genes with disease risk.
Single-cell expression data allows for finer resolution analysis, potentially revealing genetic effects missed by bulk TWAS.
Current single-cell TWAS methods often assign associations to discrete cell types, overlooking continuous cellular processes and dynamic gene effects.

Purpose of the Study:

To develop a novel framework, pseudotime-dependent TWAS (pt-TWAS), that models gene expression as a continuous function of pseudotime.
To capture dynamic gene effects along cellular developmental trajectories and improve the identification of causal cell stages for disease risk.
To enhance statistical power and accuracy compared to existing single-cell TWAS methods.

Main Methods:

Developed pt-TWAS, a computational framework modeling gene expression continuously across pseudotime.
Utilized shared genetic effects across cell stages to increase statistical power in simulations.
Constructed confidence bands for gene effect curves to identify causal cell stages.

Main Results:

pt-TWAS demonstrated higher statistical power than existing single-cell TWAS methods in extensive simulations.
The method successfully identified known risk genes for B-cell acute lymphoblastic leukemia.
pt-TWAS pinpointed specific cell stages relevant to the identified genetic mechanisms of disease risk.

Conclusions:

pt-TWAS offers a powerful approach to dissect gene-disease associations at a fine-grained, cell-stage-specific level.
The framework effectively models dynamic gene expression along developmental trajectories, overcoming limitations of discrete cell type mapping.
This method advances the understanding of genetic mechanisms underlying diseases by revealing cell-stage-specific effects.