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    Area of Science:

    • Genetics and Genomics
    • Computational Biology
    • Neuroscience

    Background:

    • Understanding genetic architecture of complex traits requires accounting for heterogeneous genetic effects.
    • Current methods for modeling genetic heterogeneity often rely on pre-specified variables, limiting scope.
    • Unobserved or complex factors driving genetic effects remain challenging to capture with existing approaches.

    Purpose of the Study:

    • To develop a novel Bayesian analytical paradigm, MOCHA (Multi-Omics Clustering for Heterogeneous Association), for identifying latent population subgroups with distinct genetic effects.
    • To enable direct identification of genetic heterogeneity from multi-omics data without requiring a priori variable specification.
    • To advance personalized management strategies by revealing complex genetic trait architectures.

    Main Methods:

    • Proposed MOCHA, a Bayesian analytical paradigm utilizing multi-omics data.
    • Designed MOCHA to identify latent population subgroups and cluster-specific genetic effects without pre-specified variables.
    • Validated MOCHA through extensive simulations and application to the IMAGEN study's genomic and transcriptomic data.

    Main Results:

    • MOCHA accurately identified underlying clustering structures in simulations.
    • The method demonstrated superior performance in identifying and ranking features with cluster-specific effects.
    • Application to IMAGEN data revealed two distinct neurodevelopmental clusters associated with adolescent inhibitory control.

    Conclusions:

    • MOCHA provides a powerful, flexible approach to uncovering genetic heterogeneity in complex traits.
    • The identified neurodevelopmental clusters offer novel insights into brain plasticity mechanisms.
    • MOCHA demonstrates practical utility and interpretability for multi-omics data analysis in complex trait research.