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    This study introduces a new statistical framework to estimate genetic correlations between diseases using electronic health records (EHRs). The findings reveal shared genetic links between mental health and metabolic conditions, advancing our understanding of complex diseases.

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    Area of Science:

    • Genetics
    • Biostatistics
    • Computational Biology

    Background:

    • Electronic health records (EHRs) linked with familial data enable large-scale genetic studies.
    • Existing methods for heritability and genetic correlation struggle with complex family structures, diverse phenotypes, and scalability.
    • Investigating shared genetic influences is crucial for understanding complex diseases.

    Purpose of the Study:

    • To develop a robust statistical framework for jointly estimating heritability and genetic correlation in EHR-based family studies.
    • To address limitations of existing methods regarding familial correlation, phenotype heterogeneity, and computational efficiency.
    • To identify shared genetic etiologies between different types of complex phenotypes.

    Main Methods:

    • Utilized multi-level latent variable models to decompose phenotypic covariance into genetic and environmental components.
    • Incorporated both within- and between-family variations to capture complex familial structures.
    • Developed iterative algorithms based on generalized estimating equations (GEE) for robust estimation.

    Main Results:

    • Simulation studies confirmed the consistency and validity of the proposed estimators across various settings.
    • Applied the framework to real-world EHR data from a large urban health system.
    • Identified significant genetic correlations between mental health conditions and endocrine/metabolic phenotypes.

    Conclusions:

    • The developed framework offers a scalable and rigorous approach for coheritability analysis in high-dimensional EHR data.
    • The findings support hypotheses of shared genetic etiology between mental health and metabolic conditions.
    • This work facilitates the identification of shared genetic influences within complex disease networks.