Genetic Architecture of Body Weight Variation and GWAS-Informed Genomic Selection in Formulated Diet-Adapted Mandarin Fish (Siniperca chuatsi)
- 1Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences & Beijing Key Laboratory of Fisheries Biotechnology, Beijing 100068, China
- 2State Key Laboratory of Biocontrol and School of Life Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Provincial Key Laboratory of Aquatic Economic Animals and Guangdong Provincial Engineering Technology Research Center for Healthy Breeding of Important Economic Fish, Sun Yat-Sen University, Guangzhou 510275, China
Abstract
Large variation in body weight poses a major challenge to the development and industrialization of formulated-feed aquaculture in Siniperca chuatsi. Breeding varieties that combine formulated-diet adaptation, rapid growth, and uniform body weight is therefore important for improving production efficiency. However, the genetic architecture of body weight and optimal genomic selection (GS) strategies remain poorly understood. Here, whole-genome resequencing was performed on 522 formulated-diet-adapted individuals. Genome-wide association study (GWAS) identified 21 significant loci, with candidate genes associated with lipid metabolism, endocrine regulation, and neuroendocrine signaling. Genomic prediction was evaluated using genome-wide single-nucleotide polymorphisms (SNPs) and GWAS-prioritized SNP panels with Bayesian and machine-learning models. Prediction accuracy increased with marker number and generally plateaued at approximately 30,000 SNPs. Random forest using GWAS-prioritized markers achieved the highest accuracy, improving prediction accuracy by 4.8% over genome-wide genomic best linear unbiased prediction (GBLUP) while using fewer markers. These findings demonstrate the potential of GWAS-informed marker prioritization to reduce marker requirements and improve GS efficiency for body weight in formulated-diet-adapted S. chuatsi.
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BibTeX
@article{geng2026genetic,
title = {{Genetic Architecture of Body Weight Variation and GWAS-Informed Genomic Selection in Formulated Diet-Adapted Mandarin Fish (Siniperca chuatsi)}},
author = {Chenfan Geng and Hailiang Song and Yinglan Xia and Xin Zhang and Shuisheng Li and Shuang Liu and Jinmeng Teng and Yong Zhang and Hongxia Hu},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = oct,
doi = {10.3390/ijms27219433},
url = {https://www.mdpi.com/1422-0067/27/21/9433},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Genetic Architecture of Body Weight Variation and GWAS-Informed Genomic Selection in Formulated Diet-Adapted Mandarin Fish (Siniperca chuatsi)
AU - Chenfan Geng
AU - Hailiang Song
AU - Yinglan Xia
AU - Xin Zhang
AU - Shuisheng Li
AU - Shuang Liu
AU - Jinmeng Teng
AU - Yong Zhang
AU - Hongxia Hu
PY - 2026
DA - 2026/10/22
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27219433
UR - https://www.mdpi.com/1422-0067/27/21/9433
AB - Large variation in body weight poses a major challenge to the development and industrialization of formulated-feed aquaculture in Siniperca chuatsi. Breeding varieties that combine formulated-diet adaptation, rapid growth, and uniform body weight is therefore important for improving production efficiency. However, the genetic architecture of body weight and optimal genomic selection (GS) strategies remain poorly understood. Here, whole-genome resequencing was performed on 522 formulated-diet-adapted individuals. Genome-wide association study (GWAS) identified 21 significant loci, with candidate genes associated with lipid metabolism, endocrine regulation, and neuroendocrine signaling. Genomic prediction was evaluated using genome-wide single-nucleotide polymorphisms (SNPs) and GWAS-prioritized SNP panels with Bayesian and machine-learning models. Prediction accuracy increased with marker number and generally plateaued at approximately 30,000 SNPs. Random forest using GWAS-prioritized markers achieved the highest accuracy, improving prediction accuracy by 4.8% over genome-wide genomic best linear unbiased prediction (GBLUP) while using fewer markers. These findings demonstrate the potential of GWAS-informed marker prioritization to reduce marker requirements and improve GS efficiency for body weight in formulated-diet-adapted S. chuatsi.
SN - 1422-0067
ER -