Development and application of a low-density 10 K liquid SNP array for genetic improvement in large yellow croaker (Larimichthys crocea)

Developing cost-effective and scalable genotyping platforms is essential for expanding germplasm identification and genomic breeding in aquaculture. However, the high cost of genotyping remains a major constraint on the large-scale application of these approaches in large yellow croaker. To address this limitation, we developed a low-density 10 K liquid SNP array, “NingXin-IV”, based on the previously established “NingXin-III” 55 K array. Representative SNPs were selected from haplotype blocks and supplemented with trait-associated loci. The array was further evaluated for germplasm identification, pedigree analysis, sex identification, and genomic selection. The 10 K panel showed excellent genotyping performance, with detection rate and genotype concordance both exceeding 99% and SNPs evenly distributed across the 24 chromosomes, indicating excellent genome-wide representativeness. Population genetic analyses showed that the panel effectively distinguished different populations. Machine learning models based on the 10 K dataset achieved an accuracy of > 0.99 for germplasm identification, with results highly consistent with the 55 K panel. In addition, the array achieved 100% accuracy in parentage assignment, pedigree reconstruction, and sex prediction. After genotype imputation, the 10 K panel retained genomic prediction performance close to that of the 55 K panel, although the absolute predictive ability of both datasets was moderate. Imputation accuracy ranged from 0.817 to 0.964, with an 86.21%–87.93% overlap with the 55 K panel among the top 10% of GEBV-ranked individuals. Overall, “NingXin-IV” provides an efficient and cost-effective tool for germplasm evaluation, parentage verification, breeding strain management, and large-scale genomic selection breeding in large yellow croaker, and serves as a useful reference for the development of low-density genotyping platforms in other aquaculture species.

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Journal
BMC Genomics
Published
2026-09-25
DOI
https://doi.org/10.1186/s12864-026-13387-2
Primary Topic
Genetic diversity and population structure
Type
article
Field-Weighted Citation Impact
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article

Development and application of a low-density 10 K liquid SNP array for genetic improvement in large yellow croaker (Larimichthys crocea)

Junjia Zeng, Qiaozhen Ke, Fei Pu, Jiaying Wang et al.
BMC Genomics
Genetic diversity and population structure
article

Development and application of a low-density 10 K liquid SNP array for genetic improvement in large yellow croaker (Larimichthys crocea)

Junjia Zeng, Qiaozhen Ke, Fei Pu, Jiaying Wang, Peng Xu, Ji Zhao, Pengxin Jiang, Ning Li, Tao Zhou, Huasong Weng, Yin Li
article en

Abstract

Developing cost-effective and scalable genotyping platforms is essential for expanding germplasm identification and genomic breeding in aquaculture. However, the high cost of genotyping remains a major constraint on the large-scale application of these approaches in large yellow croaker. To address this limitation, we developed a low-density 10 K liquid SNP array, “NingXin-IV”, based on the previously established “NingXin-III” 55 K array. Representative SNPs were selected from haplotype blocks and supplemented with trait-associated loci. The array was further evaluated for germplasm identification, pedigree analysis, sex identification, and genomic selection. The 10 K panel showed excellent genotyping performance, with detection rate and genotype concordance both exceeding 99% and SNPs evenly distributed across the 24 chromosomes, indicating excellent genome-wide representativeness. Population genetic analyses showed that the panel effectively distinguished different populations. Machine learning models based on the 10 K dataset achieved an accuracy of > 0.99 for germplasm identification, with results highly consistent with the 55 K panel. In addition, the array achieved 100% accuracy in parentage assignment, pedigree reconstruction, and sex prediction. After genotype imputation, the 10 K panel retained genomic prediction performance close to that of the 55 K panel, although the absolute predictive ability of both datasets was moderate. Imputation accuracy ranged from 0.817 to 0.964, with an 86.21%–87.93% overlap with the 55 K panel among the top 10% of GEBV-ranked individuals. Overall, “NingXin-IV” provides an efficient and cost-effective tool for germplasm evaluation, parentage verification, breeding strain management, and large-scale genomic selection breeding in large yellow croaker, and serves as a useful reference for the development of low-density genotyping platforms in other aquaculture species.

BMC Genomics
Jimei University (CN), Xiamen University (CN), Zhejiang Institute of Freshwater Fisheries (CN), Ningde Normal University (CN), Shanghai Ocean University (CN), Chinese Academy of Fishery Sciences (CN)
National Science Fund for Distinguished Young Scholars
Openalex Percentile: Top 12%
Genetic diversity and population structure
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