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<p>The growing application of genome-wide selection and genome-wide association studies (GWAS) based on single-nucleotide polymorphisms (SNPs) has necessitated the design of cost-effective strategies. High-depth sequencing, while accurate, remains prohibitively expensive. Genotype phasing and imputation technologies provide a viable solution by elevating low-depth sequencing data to high-depth levels with remarkable accuracy. <i>Sebastes schlegelii</i>, a marine species with great economic value, holds significant potential for in-depth genetic research and breeding programs. This study sampled four <i>S. schlegelii</i> populations — Rongcheng-cultured, Rongcheng-wild, Wendeng-cultured, and Yantai-cultured–sampled from the coast of the Shandong Province, China. High-depth genome resequencing in 160 individuals identified &gt;7000000 high-quality SNPs. We assessed the hidden Markov chain model-based tools, phasing accuracies of SHAPEIT2, Eagle2, and Beagle5, and the imputation performances of Beagle5 and GLIMPSE2 across varying sequencing depths and chromosomes. The results indicated comparable phasing accuracies among these tools, with SHAPEIT2 being slightly superior, while GLIMPSE2 consistently outperformed Beagle5 in imputation accuracy. Consequently, an optimal strategy combining SHAPEIT2 for phasing and GLIMPSE2 for imputation was established, achieving an accuracy of about 93% at 1× sequencing depth. Validation using imputed data from an additional 80 individuals sequenced at 1× depth, together with the originally-selected 160 individuals, was down-sampled to 1× using the seqtk tool and imputed. The data were consistent with high-depth (10×) results obtained during population structure analysis. These affirmed the reliability of this cost-effective approach for SNP-based studies in <i>S. schlegelii</i>. The methods presented in this research markedly reduced the costs associated with genomic selection in <i>S. schlegelii</i>, paving the way for accelerating breeding programs to enhance economically valuable traits in this species.</p>

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Optimization of Genotype Phasing and Imputation Strategies for Cost-Effective Genomics in Sebastes schlegelii

  • 期刊论文

  • 发表日期：

    2026年5月22日

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  • Zeyu Liu,
  • Ziyang Luo,
  • Weihao Song,
  • Yi Li,
  • Xinlu Yue,
  • Ruiyan Yang,
  • Fengyan Zhang,
  • Xiangyu Gao,
  • Zongcheng Song,
  • Jie Qi,
  • Quanqi Zhang,
  • Yan He

摘要

The growing application of genome-wide selection and genome-wide association studies (GWAS) based on single-nucleotide polymorphisms (SNPs) has necessitated the design of cost-effective strategies. High-depth sequencing, while accurate, remains prohibitively expensive. Genotype phasing and imputation technologies provide a viable solution by elevating low-depth sequencing data to high-depth levels with remarkable accuracy. Sebastes schlegelii, a marine species with great economic value, holds significant potential for in-depth genetic research and breeding programs. This study sampled four S. schlegelii populations — Rongcheng-cultured, Rongcheng-wild, Wendeng-cultured, and Yantai-cultured–sampled from the coast of the Shandong Province, China. High-depth genome resequencing in 160 individuals identified >7000000 high-quality SNPs. We assessed the hidden Markov chain model-based tools, phasing accuracies of SHAPEIT2, Eagle2, and Beagle5, and the imputation performances of Beagle5 and GLIMPSE2 across varying sequencing depths and chromosomes. The results indicated comparable phasing accuracies among these tools, with SHAPEIT2 being slightly superior, while GLIMPSE2 consistently outperformed Beagle5 in imputation accuracy. Consequently, an optimal strategy combining SHAPEIT2 for phasing and GLIMPSE2 for imputation was established, achieving an accuracy of about 93% at 1× sequencing depth. Validation using imputed data from an additional 80 individuals sequenced at 1× depth, together with the originally-selected 160 individuals, was down-sampled to 1× using the seqtk tool and imputed. The data were consistent with high-depth (10×) results obtained during population structure analysis. These affirmed the reliability of this cost-effective approach for SNP-based studies in S. schlegelii. The methods presented in this research markedly reduced the costs associated with genomic selection in S. schlegelii, paving the way for accelerating breeding programs to enhance economically valuable traits in this species.