{
  "abstract": "Adult height serves as a comprehensive biological proxy for early-life conditions and long-term well-being. Despite the well-established genetic basis of height, current research predominantly employs single-trait or timepoint-specific analyses, thereby limiting the systemic elucidation of the shared genetic architecture spanning from developmental initiation to structural maturation. We applied Genomic Structural Equation Modeling (Genomic SEM) to integrate large-scale GWAS summary statistics for five phenotypes: birth weight, IGF-1 levels, bone mineral density, childhood height, and adult height. Post-GWAS analyses included fine-mapping, TWAS with FOCUS, pathway enrichment, cell-type specificity, and spatial transcriptomic mapping. A single latent growth trait demonstrated excellent model fit (CFI = 0.996, SRMR = 0.028). Multivariate GWAS identified 2,120 independent lead variants, with fine-mapping prioritizing high-confidence causal variants at loci including ACAN, IHH, and ADAMTS17. Pathway enrichment converged on endochondral ossification and GH/IGF-1 signaling. Cell-type analysis revealed significant enrichment in mesenchymal lineages, while spatial mapping localized signals predominantly to the cartilage primordium. This study provides a unified genetic framework connecting early-life initiation, molecular mediation, and structural development, offering novel insights into the systemic developmental principles governing human height.",
  "abstract_shown": true,
  "affiliations": [
    "Longhua District Central Hospital of Shenzhen",
    "Department of Genetics and Endocrinology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jiajun Chang"
    },
    {
      "affs": [
        0
      ],
      "name": "Juan Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Ziying Wu"
    },
    {
      "affs": [
        1
      ],
      "name": "Aijing Xu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8503e9wx0xbtw.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-63047-4",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-63047-4",
  "id": "8503e9wx0xbtw",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-63047-4",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1311|Genetic Associations and Epidemiology"
    ]
  ],
  "openalex": "W7170385671",
  "pdf_url": "https://www.nature.com/articles/s41598-026-63047-4_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/8503e9wx0xbtw.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T14:21:19Z",
      "native_id": "0a61d59d9db14f3d9f1a06a93cf65a16",
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      "url": "https://doi.org/10.1038/s41598-026-63047-4"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1311|Genetic Associations and Epidemiology",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Genomic structural equation modeling reveals a common genetic dimension contributing to variation in growth‑related traits across multiple developmental stages",
  "title_text": "Genomic structural equation modeling reveals a common genetic dimension contributing to variation in growth‑related traits across multiple developmental stages",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T14:20:46Z",
  "url": "https://research.draconitic.com/paper/8503e9wx0xbtw",
  "venue": "Scientific Reports",
  "versions": []
}