{
  "abstract": "Polygenic risk scores (PRS) assume additive SNP effects, yet genetic risk also arises from interactions between loci and environmental factors that contribute to broad-sense heritability. We developed an extended PRS (ePRS) framework for type 2 diabetes (T2D) that incorporates locus-by-locus non-additive effects beyond those captured by additive single-locus PRS or linkage disequilibrium (LD) tagging. These were modelled as interaction-informed cumulative burden (G+G; summed allele counts), epistasis-screened (GxG; allele count products), and gene-environment effects derived from cardiometabolic variables in electronic health records. Across 235,000 UK Biobank participants, five complementary ePRS models captured largely non-overlapping high-risk individuals, suggesting that a key to individual risk predictions comprises the inclusion of multiple interaction-driven biological components rather than a single signal. A composite score was predictive over traditional additive PRS and predictive of T2D among individuals whose clinical symptoms fall within normal ranges.",
  "abstract_shown": true,
  "affiliations": [
    "Victoria University of Wellington"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "K. Multerer"
    },
    {
      "name": "P. Atkinson"
    },
    {
      "name": "B. Nguyen"
    },
    {
      "name": "L. Woods"
    },
    {
      "name": "Y. Tanigawa"
    },
    {
      "name": "M. Kellis"
    },
    {
      "name": "A. Munkacsi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/386m7gk50j50g.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.12.26360291",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.08.12.26360291v2.full",
  "id": "386m7gk50j50g",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.08.12.26360291v2",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "genetic and genomic medicine"
    ],
    [
      "openalex",
      "1311|Genetic Associations and Epidemiology"
    ]
  ],
  "openalex": "W7203465809",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.08.12.26360291v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/386m7gk50j50g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:54Z",
      "native_id": "10.64898/2026.08.12.26360291",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.08.12.26360291v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:genetic and genomic medicine",
      "subfield": "human-genetics"
    }
  ],
  "title": "Gene-gene and Gene-Environment Interactions Improve Genetic Risk Predictions for Complex Diseases",
  "title_text": "Gene-gene and Gene-Environment Interactions Improve Genetic Risk Predictions for Complex Diseases",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:54Z",
  "url": "https://research.draconitic.com/paper/386m7gk50j50g",
  "venue": "medRxiv",
  "versions": []
}