Integrating GWAS and Random Forest to Develop a Robust 5-SNP Panel for Genetic Sex Identification in Pacific White Shrimp (Penaeus vannamei)
- 1State Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China
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.
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BibTeX
@article{liu2026integrating,
title = {{Integrating GWAS and Random Forest to Develop a Robust 5-SNP Panel for Genetic Sex Identification in Pacific White Shrimp (Penaeus vannamei)}},
author = {Junyu Liu and Yan Xia and Pengcheng Li and Mianyu Liu and Xianhong Meng and Kun Luo and Juan Sui and Ping Dai and Qiang Fu and Jian Tan and Jiawang Cao and Baolong Chen and Xupeng Li and Jie Kong and Sheng Luan},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = oct,
doi = {10.3390/ijms27219409},
url = {https://www.mdpi.com/1422-0067/27/21/9409},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrating GWAS and Random Forest to Develop a Robust 5-SNP Panel for Genetic Sex Identification in Pacific White Shrimp (Penaeus vannamei)
AU - Junyu Liu
AU - Yan Xia
AU - Pengcheng Li
AU - Mianyu Liu
AU - Xianhong Meng
AU - Kun Luo
AU - Juan Sui
AU - Ping Dai
AU - Qiang Fu
AU - Jian Tan
AU - Jiawang Cao
AU - Baolong Chen
AU - Xupeng Li
AU - Jie Kong
AU - Sheng Luan
PY - 2026
DA - 2026/10/22
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27219409
UR - https://www.mdpi.com/1422-0067/27/21/9409
AB - 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.
SN - 1422-0067
ER -