{
  "abstract": "IntroductionPolygenic risk scores (PRS) may improve Alzheimers disease (AD) risk prediction before symptom onset, yet choosing an appropriate model can be challenging.\n\nMethodsUsing the standardized GenoPred pipeline, 1,752 PRS models (9 algorithms; 584 configurations; 3 genome-wide association studies) were evaluated and stratified by genetic ancestry and APOE diplotype. PRS models were evaluated using 11,200 clinical or autopsy-confirmed AD cases and 19,321 controls age [≥]65 from the Alzheimers Disease Sequencing Project Release 5.\n\nResultsPRS results were not consistent across methodologies (Spearmans {rho}: -0.49 to 1), with >95% of individuals having PRS in both the top and bottom risk deciles. Top-performing PRS were effective at stratifying AD risk across ancestries (AFR: P=2.04x10- 26; AMR: P=4.57x10-21; EAS: P=6.21x10-40; EUR: P=7.90x10-187).\n\nDiscussionPRS parameters should be optimized for each ancestry. Contradictory signals across methodologies underscore the need for carefully choosing suitable PRS methods and fine-tuning algorithmic parameters to ensure accuracy and consistency.\n\nData AvailabilityAccess to the ADSP is controlled by The National Institute on Aging Genetics of Alzheimers Disease (NIAGADS). All scripts used to analyze the data are freely available at https://github.com/jmillerlab/prs_comparisons.",
  "abstract_shown": true,
  "affiliations": [
    "University of Kentucky"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "E. L. Ward"
    },
    {
      "name": "P. T. Nelson"
    },
    {
      "name": "Y. Katsumata"
    },
    {
      "name": "D. W. Fardo"
    },
    {
      "name": "G. A. Jicha"
    },
    {
      "name": "Alzheimer's Disease Neuroimaging Initiative"
    },
    {
      "affs": [
        0
      ],
      "name": "J. B. Miller"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/062w47e908d0n.bib",
  "cited_by": 1,
  "doi": "10.64898/2026.09.02.26362060",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.02.26362060v1.full",
  "id": "062w47e908d0n",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.02.26362060v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "genetic and genomic medicine"
    ],
    [
      "openalex",
      "2737|Alzheimer's disease research and treatments"
    ]
  ],
  "openalex": "W7211963702",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.02.26362060v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/062w47e908d0n.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:57Z",
      "native_id": "10.64898/2026.09.02.26362060",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.02.26362060v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:genetic and genomic medicine",
      "subfield": "human-genetics"
    }
  ],
  "title": "Alzheimer's Polygenic Risk Scores Are Not Interchangeable: Evidence from 1,752 Models",
  "title_text": "Alzheimer's Polygenic Risk Scores Are Not Interchangeable: Evidence from 1,752 Models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:57Z",
  "url": "https://research.draconitic.com/paper/062w47e908d0n",
  "venue": "medRxiv",
  "versions": []
}