Table_2_Genomic Prediction Using Low Density Marker Panels in Aquaculture: Performance Across Species, Traits, and Genotyping Platforms.xlsx

Genomic selection increases the rate of genetic gain in breeding programs, which results in significant cumulative improvements in commercially important traits such as disease resistance. Genomic selection currently relies on collecting genome-wide genotype data accross a large number of individuals, which requires substantial economic investment. However, global aquaculture production predominantly occurs in small and medium sized enterprises for whom this technology can be prohibitively expensive. For genomic selection to benefit these aquaculture sectors, more cost-efficient genotyping is necessary. In this study the utility of low and medium density SNP panels (ranging from 100 to 9,000 SNPs) to accurately predict breeding values was tested and compared in four aquaculture datasets with different characteristics (species, genome size, genotyping platform, family number and size, total population size, and target trait). The traits show heritabilities between 0.19–0.49, and genomic prediction accuracies using the full density panel of 0.55–0.87. A consistent pattern of genomic prediction accuracy was observed across species with little or no accuracy reduction until SNP density was reduced below 1,000 SNPs (prediction accuracies of 0.44–0.75). Below this SNP density, heritability estimates and genomic prediction accuracies tended to be lower and more variable (93% of maximum accuracy achieved with 1,000 SNPs, 89% with 500 SNPs, and 70% with 100 SNPs). A notable drop in accuracy was observed between 200 SNP panels (0.44–0.75) and 100 SNP panels (0.39–0.66). Now that a multitude of studies have highlighted the benefits of genomic over pedigree-based prediction of breeding values in aquaculture species, the results of the current study highlight that these benefits can be achieved at lower SNP densities and at lower cost, raising the possibility of a broader application of genetic improvement in smaller and more fragmented aquaculture settings.

基因组选择技术能显著提升育种程序中遗传进展的速率，从而在诸如抗病性等商业重要性状方面实现显著的累积改进。当前，基因组选择依赖于从大量个体中收集全基因组基因型数据，这需要巨额的经济投入。然而，全球水产养殖业主要集中于小型和中型企业，对于这些企业而言，该技术可能因其高昂成本而变得难以承受。为了使基因组选择技术惠及这些水产养殖业部门，迫切需要更经济高效的基因分型方法。在本研究中，我们测试并比较了低密度和中密度单核苷酸多态性（SNP）芯片（范围从100至9,000个SNPs）在准确预测育种值方面的效用，并在具有不同特征（物种、基因组大小、基因分型平台、家系数量和规模、总人口规模和目标性状）的四个水产养殖数据集中进行了应用。这些性状的遗传力介于0.19至0.49之间，使用全密度面板进行基因组预测的准确性为0.55至0.87。在物种间观察到基因组预测准确性的稳定模式，直至SNP密度降至1,000个以下（预测准确性为0.44至0.75）。在此SNP密度以下，遗传力估计值和基因组预测准确性往往较低且变化较大（在1,000个SNPs时达到最大准确性的93%，500个SNPs时为89%，100个SNPs时为70%）。在200个SNP芯片（0.44至0.75）和100个SNP芯片（0.39至0.66）之间观察到准确性的显著下降。随着众多研究强调了基因组选择相对于系谱预测在水产养殖物种育种值预测中的优势，本研究的结果凸显了这些优势可以在较低的SNP密度和较低的成本下实现，从而为在小规模和更碎片化的水产养殖环境中更广泛地应用遗传改良提供了可能性。