{
  "abstract": "In the recent growing interest of AI research toward biology, genetic association studies of AI- derived phenotypes from high-content modalities such as images emerges as a powerful means for biological discovery. However, such AI-phenotyping methods still lacks a good optimization target and an efficient evaluation framework. The number of discovered loci was the major criterion for evaluating the quality of AI-derived phenotypes. However, the time and computational resources required for running the GWAS and subsequent loci-clumping are substantial, limiting the rapid development and iteration of deep learning representation algorithms. Here we present a 1000x faster and lightweight framework, sum-h2, than traditional GWAS framework for evaluating genetic discovery through total heritability. We revisit tr(P^(-1) G), previously proposed in the context of evolutionary studies, as a measure of multi-phenotype heritability. We showed that sum-h2, the sum of heritability over phenotypic Principal Components (PCs), is equivalent to the linear transformation-invariant tr(P^(-1) G), through both theoretical proof and simulation studies. Moreover, sum-h2 can be estimated rapidly with minimal information loss over a relatedness-enriched sample, while preserving the relative ranking of endophenotypes by GWAS loci counts. Based on selected UKB data and sum-h2, we set up a Genetic Discovery Arena, enabling rapid and fair comparisons for the development and optimization of deep learning-derived phenotyping methods.",
  "abstract_shown": true,
  "affiliations": [
    "Yale University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "T. Xia"
    },
    {
      "name": "Z. Xie"
    },
    {
      "name": "X. Zhao"
    },
    {
      "name": "S. M. S. Islam"
    },
    {
      "name": "A. Naseri"
    },
    {
      "name": "Z. Fan"
    },
    {
      "name": "H. Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "D. Zhi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fqvsa18yx0hhx.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.24.753959",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.753959v1.full",
  "id": "fqvsa18yx0hhx",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.753959v1",
  "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": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1311|Genetic Associations and Epidemiology"
    ]
  ],
  "openalex": "W7214793048",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.753959v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fqvsa18yx0hhx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:17Z",
      "native_id": "10.64898/2026.09.24.753959",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.24.753959v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Sum-h2, enabling genetic discovery for deep learning-derived phenotypes through a fast evaluation framework and arena of performance",
  "title_text": "Sum-h2, enabling genetic discovery for deep learning-derived phenotypes through a fast evaluation framework and arena of performance",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:17Z",
  "url": "https://research.draconitic.com/paper/fqvsa18yx0hhx",
  "venue": "bioRxiv",
  "versions": []
}