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A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or

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    We introduce hierarchical joint analysis of marginal summary statistics (hJAM), a novel method for genetic association studies. hJAM enhances power and accuracy by incorporating prior information, outperforming existing Mendelian randomization and transcriptome-wide association study approaches.

    Keywords:
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    Area of Science:

    • Genetics
    • Statistical Genetics
    • Bioinformatics

    Background:

    • Hierarchical modeling is valuable for integrating prior information in genetic association studies.
    • Existing methods like instrumental variable analysis and transcriptome-wide association studies (TWAS) can be viewed as specific cases of hierarchical models.
    • There is a need for methods that can jointly analyze multiple correlated genetic variants and intermediate traits.

    Purpose of the Study:

    • To extend hierarchical modeling for the joint analysis of marginal summary statistics (hJAM).
    • To incorporate prior information effectively into genetic association analyses.
    • To provide a flexible framework comparable to Mendelian randomization (MR) and TWAS but with enhanced capabilities.

    Main Methods:

    • Developed the hierarchical joint analysis of marginal summary statistics (hJAM) framework.
    • Extended previous approaches for joint analysis of marginal summary statistics.
    • Utilized estimates from association analyses of single-nucleotide polymorphisms (SNP)-intermediate or SNP-gene expression as prior information.
    • Applied hJAM to analyze multiple correlated SNPs and intermediates.

    Main Results:

    • hJAM yields unbiased estimates and maintains correct type-I error rates.
    • Simulations demonstrate increased statistical power for hJAM compared to existing MR and TWAS methods.
    • hJAM successfully estimated causal effects of body mass index and type 2 diabetes on myocardial infarction.
    • hJAM also estimated causal effects of gene expression on prostate cancer risk.

    Conclusions:

    • hJAM offers an advantageous and powerful approach for genetic association studies.
    • The method effectively integrates diverse prior information, improving causal inference.
    • hJAM demonstrates broad applicability in complex disease genetics and gene expression studies.