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

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    We developed pseudotime-dependent Transcriptome-wide association studies (pt-TWAS) to analyze gene effects on disease risk across continuous cell development. This method enhances statistical power and pinpoints causal cell stages for diseases like B-cell acute lymphoblastic leukemia.

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    Area of Science:

    • Genetics and Genomics
    • Computational Biology
    • Systems Biology

    Background:

    • Transcriptome-wide association studies (TWAS) integrate gene expression and GWAS data for disease risk interpretation.
    • Bulk-tissue expression data offers tissue-level insights, but single-cell data allows for finer cellular granularity.
    • Existing single-cell methods may miss continuous cellular processes and misidentify causal cell stages.

    Purpose of the Study:

    • To develop a novel TWAS framework, pseudotime-dependent TWAS (pt-TWAS), for dissecting gene-disease associations at a finer cell-stage resolution.
    • To capture continuous dynamic changes in gene expression along cell developmental paths.
    • To improve statistical power and identify causal cell stages by modeling gene expression as a continuous function of pseudotime.

    Main Methods:

    • Developed the pt-TWAS framework modeling gene expression as a continuous function of pseudotime.
    • Leveraged expression quantitative trait loci (eQTL) information across cell stages to boost statistical power.
    • Constructed and visualized simultaneous confidence bands for gene effect curves to identify causal cell stages.

    Main Results:

    • pt-TWAS successfully replicated known risk genes in B-cell acute lymphoblastic leukemia (ALL).
    • The method pinpointed specific cell stages associated with gene effects in ALL.
    • pt-TWAS demonstrated statistical advantages over methods analyzing discrete cell types or stages.

    Conclusions:

    • pt-TWAS offers a powerful framework for fine-resolution analysis of gene-disease associations using single-cell data.
    • The method accurately identifies causal cell stages by capturing continuous cellular processes.
    • pt-TWAS enhances the understanding of genetic contributions to diseases at a cellular level.