Optimizing genotype imputation pipeline for low-coverage whole genome sequencing data in spotted sea bass and its application in genomic prediction

IF 3.7 2区 农林科学 Q1 FISHERIES
Chong Zhang , Yonghang Zhang , Pengyu Li , Cong Liu , Lingyu Wang , Yani Dong , Donglei Sun , Xin Qi , Haishen Wen , Kaiqiang Zhang , Shaosen Yang , Yun Li
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引用次数: 0

Abstract

Genotype imputation following low-coverage whole genome sequencing (lcWGS) data offers a cost-effective approach for genotyping large populations, with significant potential to accelerate genomic selection in breeding programs. For spotted sea bass (Lateolabrax maculatus), genetic improvement is urgently required due to the degeneration of genetic traits and long generation intervals. However, the high costs associated with high-coverage WGS (hcWGS) for large populations have delayed breeding progress. To address this gap, the present study conducted a comprehensive evaluation of genotype imputation for lcWGS data down-sampled from 1107 individuals across four hcWGS datasets and aimed to develop an efficient imputation pipeline utilizing lcWGS data for spotted sea bass. Initially, 100data dataset was selected to preliminary assess the performance of various imputation pipelines. BEAGLE was excluded due to its lower accuracy and redundant computational requirements, while STITCH and GLIMPSE2 were retained for subsequent analyses. The effects of reference and target data on GLIMPSE2 imputation were then evaluated, identifying the optimal strategy for constructing the reference panel prioritizes population genetic diversity over sample size to maximizes imputation accuracy. It also highlighted the critical role of population structure, genetic relatedness and linkage disequilibrium (LD) level between reference and target data for imputation accuracy. Additionally, the imputation accuracy of STITCH and GLIMPSE2 was compared across three datasets, with GLIMPSE2 imputation using the optimal reference panel emerging as the most effective imputation pipeline for spotted sea bass. Finally, we demonstrated that lcWGS data combined with GLIMPSE2 imputation achieves predictive accuracy comparable to hcWGS data in genomic prediction. Our study presents an optimized workflow to impute lcWGS data in spotted sea bass and establishes the first publicly available reference panel with the highest known genetic diversity. This resource lays a crucial foundation for future genomic selection and breeding programs and serves as a valuable reference for genotype imputation in other aquaculture species.
黑鲈低覆盖全基因组测序数据基因型插补管道优化及其在基因组预测中的应用
低覆盖率全基因组测序(lcWGS)数据后的基因型插入为大规模种群的基因分型提供了一种经济有效的方法,具有加速育种计划中基因组选择的巨大潜力。斑点黑鲈(Lateolabrax maculatus)遗传性状退化、世代间隔长,迫切需要进行遗传改良。然而,大种群高覆盖WGS (hcWGS)相关的高成本延迟了育种进展。为了解决这一问题,本研究对来自4个hcWGS数据集的1107个个体的lcWGS数据进行了全面评估,旨在建立一个利用斑点海鲈鱼lcWGS数据的高效输入管道。首先,选取100个数据集,初步评估各种插补管道的性能。BEAGLE由于其较低的精度和冗余的计算要求而被排除,而STITCH和GLIMPSE2保留用于后续分析。然后评估参考和目标数据对GLIMPSE2插入的影响,确定构建参考面板的最佳策略,优先考虑群体遗传多样性而不是样本量,以最大限度地提高插入精度。种群结构、遗传亲缘性和参考数据与目标数据之间的连锁不平衡(LD)水平对估算精度的影响至关重要。此外,在三个数据集上比较了STITCH和GLIMPSE2的输入精度,其中使用最佳参考面板的GLIMPSE2输入成为斑点海鲈鱼最有效的输入管道。最后,我们证明了lcWGS数据与GLIMPSE2 imputation相结合在基因组预测方面达到了与hcWGS数据相当的预测精度。我们的研究提出了一个优化的工作流程,用于在斑点海鲈鱼中输入lcWGS数据,并建立了第一个公开可用的遗传多样性最高的参考小组。该资源为未来的基因组选择和育种计划奠定了重要基础,并为其他水产养殖物种的基因型植入提供了有价值的参考。
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来源期刊
Aquaculture Reports
Aquaculture Reports Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
5.90
自引率
8.10%
发文量
469
审稿时长
77 days
期刊介绍: Aquaculture Reports will publish original research papers and reviews documenting outstanding science with a regional context and focus, answering the need for high quality information on novel species, systems and regions in emerging areas of aquaculture research and development, such as integrated multi-trophic aquaculture, urban aquaculture, ornamental, unfed aquaculture, offshore aquaculture and others. Papers having industry research as priority and encompassing product development research or current industry practice are encouraged.
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