{
  "abstract": "Background/objectives\n\nGenomic selection (GS) can accelerate crop improvement, but its application in breeding programs is often constrained by the cost of high-density genotyping. This study evaluated genomic prediction using 4647 targeted sequencing-derived SNPs in 829 rice accessions and investigated the effects of prediction model selection, population structure, training population optimization, and GWAS-assisted marker selection on predictive performance.Methods\n\nSeventeen GS models were evaluated for eight agronomic traits using repeated five-fold cross-validation, cross-population prediction, STPGA-based training population optimization, and GWAS-derived marker subsets. Resaults: Predictive performance varied substantially among traits and models. For grain length (GL), EnsembleGS achieved the highest predictive performance among the 17 models (r = 0.838), while RKHS showed the highest performance for grain number per panicle (r = 0.698), RFR for heading date (r = 0.854), and EnsembleGS for length-to-width ratio (r = 0.844). Cross-population prediction generally resulted in lower predictive performance than within-population cross-validation, with substantial variation among transfer directions. STPGA retained substantial predictive performance with reduced training population sizes; for GL, the highest performance reached r = 0.800 with a 400-individual STPGA-selected training population. GWAS-guided marker prioritization also retained substantial predictive information after marker reduction. For GL, the highest performance reached r = 0.789 and 0.802 using the GWAS-Top500 and GWAS-Top1000 subsets, respectively, compared with 0.751 and 0.775 for the corresponding Random500 and Random1000 subsets. For length-to-width ratio, the corresponding values were r = 0.764 and 0.800 for GWAS-Top500 and GWAS-Top1000, compared with 0.727 and 0.783 for the random subsets.Conclusions\n\nIntegrating targeted sequencing with trait- and model-aware prediction, optimized training population design, and GWAS-guided marker prioritization can retain substantial genomic prediction performance while reducing marker and training population requirements, providing a potentially cost-effective framework for genomic selection in rice breeding.",
  "abstract_shown": true,
  "affiliations": [
    "Fujian Key Laboratory of Agricultural Genetic Engineering, Institute of Biotechnology, Fujian Academy of Agricultural Sciences, Fuzhou 350003, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zaijie Chen",
      "orcid": "0009-0007-7261-2005"
    },
    {
      "affs": [
        0
      ],
      "name": "Leibo Pan"
    },
    {
      "affs": [
        0
      ],
      "name": "Yue Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Min Lin"
    },
    {
      "affs": [
        0
      ],
      "name": "Yan Lin"
    },
    {
      "affs": [
        0
      ],
      "name": "Mingji Wu"
    },
    {
      "affs": [
        0
      ],
      "name": "Huaqing Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Taijiao Hu"
    },
    {
      "affs": [
        0
      ],
      "name": "Shaohua Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Yana Song"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b7f6e0zvsb7fh.bib",
  "cited_by": 0,
  "doi": "10.3390/genes17101240",
  "fulltext_url": "https://europepmc.org/articles/PMC13704531",
  "id": "b7f6e0zvsb7fh",
  "issns": [
    "2073-4425"
  ],
  "landing_url": "https://europepmc.org/article/MED/42938356",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1311|Genetic and phenotypic traits in livestock"
    ]
  ],
  "openalex": "W7221033731",
  "pdf_url": "https://europepmc.org/articles/PMC13704531?pdf=render",
  "pmcid": "PMC13704531",
  "pmid": "42938356",
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/b7f6e0zvsb7fh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:24Z",
      "native_id": "MED:42938356",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42938356"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1311|Genetic and phenotypic traits in livestock",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Genomic Selection in Rice Using Targeted Sequencing: Model Evaluation and Training Population Optimization for Breeding Applications.",
  "title_text": "Genomic Selection in Rice Using Targeted Sequencing: Model Evaluation and Training Population Optimization for Breeding Applications.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:24Z",
  "url": "https://research.draconitic.com/paper/b7f6e0zvsb7fh",
  "venue": "Genes",
  "versions": []
}