Pégard Marie, Segura Vincent, Muñoz Facundo, Bastien Catherine, Jorge Véronique, Sanchez Leopoldo
BioForA, INRA, ONF, Orléans, France.
AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
Front Plant Sci. 2020 Oct 28;11:581954. doi: 10.3389/fpls.2020.581954. eCollection 2020.
Forest trees like poplar are particular in many ways compared to other domesticated species. They have long juvenile phases, ongoing crop-wild gene flow, extensive outcrossing, and slow growth. All these particularities tend to make the conduction of breeding programs and evaluation stages costly both in time and resources. Perennials like trees are therefore good candidates for the implementation of genomic selection (GS) which is a good way to accelerate the breeding process, by unchaining selection from phenotypic evaluation without affecting precision. In this study, we tried to compare GS to pedigree-based traditional evaluation, and evaluated under which conditions genomic evaluation outperforms classical pedigree evaluation. Several conditions were evaluated as the constitution of the training population by cross-validation, the implementation of multi-trait, single trait, additive and non-additive models with different estimation methods (G-BLUP or weighted G-BLUP). Finally, the impact of the marker densification was tested through four marker density sets. The population under study corresponds to a pedigree of 24 parents and 1,011 offspring, structured into 35 full-sib families. Four evaluation batches were planted in the same location and seven traits were evaluated on 1 and 2 years old trees. The quality of prediction was reported by the accuracy, the Spearman rank correlation and prediction bias and tested with a cross-validation and an independent individual test set. Our results show that genomic evaluation performance could be comparable to the already well-optimized pedigree-based evaluation under certain conditions. Genomic evaluation appeared to be advantageous when using an independent test set and a set of less precise phenotypes. Genome-based methods showed advantages over pedigree counterparts when ranking candidates at the within-family levels, for most of the families. Our study also showed that looking at ranking criteria as Spearman rank correlation can reveal benefits to genomic selection hidden by biased predictions.
与其他驯化物种相比，杨树等林木在许多方面都很特殊。它们的幼年期很长，存在持续的作物-野生基因流动，异交广泛，生长缓慢。所有这些特殊性往往使得育种计划和评估阶段在时间和资源方面都成本高昂。因此，像树木这样的多年生植物是实施基因组选择（GS）的理想候选对象，基因组选择是加速育种过程的好方法，它无需进行表型评估即可进行选择，同时又不影响准确性。在本研究中，我们试图将基因组选择与基于系谱的传统评估进行比较，并评估在哪些条件下基因组评估优于经典的系谱评估。评估了几个条件，如通过交叉验证构建训练群体、采用不同估计方法（G-BLUP或加权G-BLUP）实施多性状、单性状、加性和非加性模型。最后，通过四个标记密度集测试了标记密度增加的影响。所研究的群体对应一个由24个亲本和1011个后代组成的系谱，分为35个全同胞家系。四个评估批次种植在同一地点，对1年生和2年生树木评估了七个性状。预测质量通过准确性、斯皮尔曼等级相关性和预测偏差来报告，并通过交叉验证和独立个体测试集进行测试。我们的结果表明，在某些条件下，基因组评估性能可能与已经优化得很好的基于系谱的评估相当。当使用独立测试集和一组不太精确的表型时，基因组评估似乎具有优势。对于大多数家系而言，基于基因组的方法在家族内部对候选个体进行排名时，比基于系谱的方法更具优势。我们的研究还表明，将斯皮尔曼等级相关性等排名标准纳入考量，可以揭示因预测偏差而被隐藏的基因组选择的优势。
