Faculty, Staff and Student Publications

    Language

    English

    Publication Date

    1-1-2025

    Journal

    Frontiers in Genetics

    DOI

    10.3389/fgene.2025.1507395

    PMID

    39967687

    PMCID

    PMC11832703

    PubMedCentral® Posted Date

    2-4-2025

    PubMedCentral® Full Text Version

    Post-print

    Abstract

    Introduction: Polygenic Scores (PGSs) assess cumulative genetic risk variants that contribute to the association with complex diseases like Alzheimer's Disease (AD). The PGS Catalog is a valuable repository of PGSs of various complex diseases, but it lacks standardized annotations and harmonization, making the information difficult to integrate for a specific disease.

    Methods: In this study, we curated 44 PGS datasets for AD from the PGS Catalog, categorized them into five methodological groups, and annotated 813,257 variants to nearby genes. We aligned the scores based on the "GWAS significant variants" (GWAS-SV) method with the GWAS Catalog and flagged redundant files and those with a "limited scope" due to insufficient external GWAS support. Using rank aggregation (RA), we prioritized consistently important variants and provided an R package, "PgsRankRnnotatR," to automate this process.

    Results: Of the six RA methods evaluated, "Dowdall" method was the most robust. Our refined dataset, enhanced by multiple RA options, is a valuable resource for AD researchers selecting PGSs or exploring AD-related genetic variants.

    Discussion: Our approach offers a framework for curating, harmonizing, and prioritizing PGS datasets, improving their usability for AD research. By integrating multiple RA methods and automating the process, we provide a flexible tool that enhances PGS selection and genetic variant exploration. This framework can be extended to other complex diseases or traits, facilitating broader applications in genetic risk assessment.

    Published Open-Access

    yes

    Download

    Share

    COinS
     
     

    To view the content in your browser, please download Adobe Reader or, alternately,
    you may Download the file to your hard drive.

    NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.