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    This summary is machine-generated.

    Ancestral recombination graphs (ARGs) can enhance complex trait analysis by complementing genotype imputation. A new method, ARG-RHE, efficiently uses ARGs for heritability estimation and association testing in large datasets.

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    Area of Science:

    • Genetics
    • Computational Biology
    • Statistical Genetics

    Background:

    • Genome-wide ancestral recombination graphs (ARGs) offer detailed genealogical relatedness.
    • ARGs can improve complex trait analyses by accounting for unobserved genetic variants.
    • Current ARG analyses are computationally intensive for large-scale genomic datasets.

    Purpose of the Study:

    • To introduce ARG-RHE, a computationally efficient method for analyzing ARGs.
    • To enable estimation of narrow-sense heritability and perform region-based association testing using ARGs.
    • To facilitate linear mixed-model analyses with large genomic datasets.

    Main Methods:

    • Developed ARG-RHE, leveraging sublinear time genotype-matrix products from genealogical data.
    • Employed scalable randomized algorithms for efficient variance component estimation.
    • Applied the method to UK Biobank data (337,464 individuals) for gene-trait association testing.

    Main Results:

    • Demonstrated computational efficiency, statistical power, and robustness through extensive simulations.
    • Identified associations between 21,159 genes and 52 blood-related traits.
    • Observed an 8% increase in gene-trait associations by combining ARG-based and imputation-based testing.

    Conclusions:

    • ARG-RHE provides a computationally feasible approach for large-scale genetic analyses using ARGs.
    • Inferred genome-wide genealogies effectively complement genotype imputation for complex trait studies.
    • The method accelerates heritability estimation and association testing, enhancing genetic discovery.