{
  "abstract": "The Pacific white shrimp (Penaeus vannamei) exhibits pronounced sexual dimorphism in growth, making early and accurate sex identification crucial for targeted monitoring of sexual development and in-depth research into monosex aquaculture. However, morphological sex identification is unreliable at early developmental stages, and single-marker molecular assays are prone to diagnostic failures caused by genotyping errors or missing data. In this study, we developed a multi-SNP framework for sex prediction by integrating genome-wide association studies (GWAS) with Random Forest (RF) modeling. Analyzing 1244 individuals with a 55K SNP array, we identified 18 genome-wide significant SNPs and 10 candidate genes, including SRRM1 and SPSB3, that may be associated with sexual differentiation. Subsequent multi-marker comparative analyses demonstrated that although the top-performing single marker yielded high accuracy, simulated genotyping errors and missing data caused significant drops in accuracy and elevated no-call rates, whereas multi-SNP panels exhibited substantially improved robustness. A stable performance plateau was reached when expanding the panel to 5 SNPs. The model achieved 100% classification accuracy in the two mixed-sex independent populations, ZJ and HN, and correctly identified all females in the all-female MK cohort. Overall, this study provides a robust multi-SNP tool for sex identification in P. vannamei and highlights the practical utility of machine learning in aquaculture.",
  "abstract_shown": true,
  "affiliations": [
    "State Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 15,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Junyu Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yan Xia"
    },
    {
      "affs": [
        0
      ],
      "name": "Pengcheng Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Mianyu Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Xianhong Meng"
    },
    {
      "affs": [
        0
      ],
      "name": "Kun Luo"
    },
    {
      "affs": [
        0
      ],
      "name": "Juan Sui"
    },
    {
      "affs": [
        0
      ],
      "name": "Ping Dai"
    },
    {
      "affs": [
        0
      ],
      "name": "Qiang Fu"
    },
    {
      "affs": [
        0
      ],
      "name": "Jian Tan"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiawang Cao"
    },
    {
      "affs": [
        0
      ],
      "name": "Baolong Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Xupeng Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Jie Kong"
    },
    {
      "affs": [
        0
      ],
      "name": "Sheng Luan",
      "orcid": "0000-0002-5220-1870"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c3y9v22rh4z3p.bib",
  "cited_by": 0,
  "doi": "10.3390/ijms27219409",
  "fulltext_url": null,
  "id": "c3y9v22rh4z3p",
  "issns": [
    "1422-0067"
  ],
  "landing_url": "https://www.mdpi.com/1422-0067/27/21/9409",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "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": "W7223282983",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/c3y9v22rh4z3p.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:57Z",
      "native_id": "10.3390/ijms27219409",
      "source": "crossref",
      "url": "https://www.mdpi.com/1422-0067/27/21/9409"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1311|Genetic and phenotypic traits in livestock",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Integrating GWAS and Random Forest to Develop a Robust 5-SNP Panel for Genetic Sex Identification in Pacific White Shrimp (Penaeus vannamei)",
  "title_text": "Integrating GWAS and Random Forest to Develop a Robust 5-SNP Panel for Genetic Sex Identification in Pacific White Shrimp (Penaeus vannamei)",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:57Z",
  "url": "https://research.draconitic.com/paper/c3y9v22rh4z3p",
  "venue": "International Journal of Molecular Sciences",
  "versions": []
}