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ADELLE: A global testing method for Trans-eQTL mapping.

Takintayo Akinbiyi, Mary Sara McPeek, Mark Abney

    Biorxiv : the Preprint Server for Biology
    |March 11, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Discovering distant genetic regulators (trans-eQTLs) is challenging. ADELLE, a new statistical framework using summary statistics, significantly improves the detection of these crucial gene expression regulators.

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    Area of Science:

    • Genetics
    • Bioinformatics
    • Systems Biology

    Background:

    • Understanding gene expression regulation is complex.
    • Cis-eQTLs are easily found near genes, but trans-eQTLs distant from genes are harder to identify.
    • Trans-eQTLs contribute significantly to heritability but are under-discovered due to statistical challenges.

    Purpose of the Study:

    • To develop a powerful statistical method for identifying trans-eQTLs.
    • To overcome limitations in detecting SNPs associated with multiple gene expression levels.

    Main Methods:

    • Proposed ADELLE, a statistical framework using only summary statistics.
    • Designed ADELLE to be sensitive to SNPs affecting multiple gene expression traits.
    • Evaluated ADELLE against six other methods using simulations and a mouse dataset.

    Main Results:

    • ADELLE demonstrated superior power in detecting SNPs associated with 0.1%-2% of 10,000 traits compared to existing methods.
    • ADELLE identified significant trans-eQTLs in mouse data that were missed by standard analyses.
    • The framework is particularly effective for SNPs associated with multiple gene expression levels.

    Conclusions:

    • ADELLE is a powerful and effective tool for uncovering trans regulators of genetic expression.
    • The method advances the discovery of complex genetic regulatory mechanisms.
    • ADELLE offers a significant improvement for trans-eQTL mapping using summary statistics.