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Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions.

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    We developed multivariate generalized multifactor dimensionality reduction (GMDR) to analyze gene interactions for complex diseases. This method uses correlated phenotypes for more stable and powerful genetic association analysis.

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    Area of Science:

    • Genetics and Genomics
    • Statistical Genetics
    • Bioinformatics

    Background:

    • Genome-wide association studies (GWAS) face challenges in detecting gene-gene and gene-environment interactions for complex diseases.
    • Multifactor dimensionality reduction (MDR) and its extension, generalized MDR (GMDR), are methods for interaction analysis.
    • GMDR accommodates covariates and handles both dichotomous and continuous phenotypes.

    Purpose of the Study:

    • To extend GMDR for analyzing multivariate phenotypes, aiming to increase power for detecting gene-gene interactions.
    • To develop a multivariate GMDR method that jointly analyzes correlated phenotypes.

    Main Methods:

    • Proposed multivariate GMDR, an extension of GMDR for multivariate phenotypes.
    • Constructed generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models.
    • Applied the method to blood pressure (SBP, DBP) and hypertension status data from the Korean Association Resource study.

    Main Results:

    • Identified significant SNP combinations associated with high blood pressure and hypertension risk using univariate and multivariate GMDR.
    • Multivariate GMDR analysis showed more stable balanced accuracy (BA) with smaller standard deviations compared to univariate analysis.
    • The study compared multivariate GMDR with univariate GMDR for both blood pressure and hypertension phenotypes.

    Conclusions:

    • Developed and validated the multivariate GMDR method using a GEE approach.
    • Multivariate GMDR is effective for analyzing correlated multiple phenotypes in genetic association studies.