Kriaridou Christina, Tsairidou Smaragda, Houston Ross D, Robledo Diego
The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, United Kingdom.
Front Genet. 2020 Feb 27;11:124. doi: 10.3389/fgene.2020.00124. eCollection 2020.
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.
基因组选择提高了育种计划中的遗传增益率，这导致了诸如抗病性等商业上重要性状的显著累积改善。基因组选择目前依赖于收集大量个体的全基因组基因型数据，这需要大量的经济投入。然而，全球水产养殖生产主要发生在中小企业中，对它们来说这项技术可能成本过高。为了使基因组选择惠及这些水产养殖部门，更具成本效益的基因分型是必要的。在本研究中，测试并比较了低密度和中密度SNP面板（100至9000个SNP）在四个具有不同特征（物种、基因组大小、基因分型平台、家系数量和大小、总群体大小以及目标性状）的水产养殖数据集中准确预测育种值的效用。这些性状的遗传力在0.19 - 0.49之间，使用全密度面板的基因组预测准确性在0.55 - 0.87之间。在各物种中观察到了一致的基因组预测准确性模式，在SNP密度降低到1000个SNP以下之前，准确性几乎没有降低（预测准确性为0.44 - 0.75）。低于这个SNP密度，遗传力估计值和基因组预测准确性往往较低且更具变异性（1000个SNP时达到最大准确性的93%，500个SNP时为89%，100个SNP时为70%）。在200个SNP面板（0.44 - 0.75）和100个SNP面板（0.39 - 0.66）之间观察到准确性显著下降。鉴于众多研究强调了在水产养殖物种中基于基因组的育种值预测优于基于系谱的预测，当前研究结果表明，在较低的SNP密度和较低成本下也能实现这些益处，这增加了在规模较小且更为分散的水产养殖环境中更广泛应用遗传改良的可能性。
