Institute of Quantitative Genetics and Genomics of Plants (QGGP), Heinrich-Heine-University, Universitätsstraße 1, 40225, Düsseldorf, Germany.
SaKa Pflanzenzucht GmbH & Co. KG, Eichenallee 9, 24340, Windeby, Germany.
Theor Appl Genet. 2024 Mar 6;137(3):70. doi: 10.1007/s00122-024-04567-3.
Predictive breeding approaches, like phenomic or genomic selection, have the potential to increase the selection gain for potato breeding programs which are characterized by very large numbers of entries in early stages and the availability of very few tubers per entry in these stages. The objectives of this study were to (i) explore the capabilities of phenomic prediction based on drone-derived multispectral reflectance data in potato breeding by testing different prediction scenarios on a diverse panel of tetraploid potato material from all market segments and considering a broad range of traits, (ii) compare the performance of phenomic and genomic predictions, and (iii) assess the predictive power of mixed relationship matrices utilizing weighted SNP array and multispectral reflectance data. Predictive abilities of phenomic prediction scenarios varied greatly within a range of - 0.15 and 0.88 and were strongly dependent on the environment, predicted trait, and considered prediction scenario. We observed high predictive abilities with phenomic prediction for yield (0.45), maturity (0.88), foliage development (0.73), and emergence (0.73), while all other traits achieved higher predictive ability with genomic compared to phenomic prediction. When a mixed relationship matrix was used for prediction, higher predictive abilities were observed for 20 out of 22 traits, showcasing that phenomic and genomic data contained complementary information. We see the main application of phenomic selection in potato breeding programs to allow for the use of the principle of predictive breeding in the pot seedling or single hill stage where genotyping is not recommended due to high costs.
预测性育种方法,如表型或基因组选择,有可能增加马铃薯育种计划的选择增益,这些计划的特点是早期有大量的条目,每个条目的块茎数量很少。本研究的目的是:(i)通过在来自所有市场部分的四倍体马铃薯材料的多样化面板上测试不同的预测方案,探索基于无人机衍生的多光谱反射率数据的表型预测在马铃薯育种中的能力,并考虑广泛的性状;(ii)比较表型和基因组预测的性能;(iii)评估利用加权 SNP 阵列和多光谱反射率数据的混合关系矩阵的预测能力。表型预测方案的预测能力在 -0.15 到 0.88 之间变化很大,并且强烈依赖于环境、预测性状和考虑的预测方案。我们观察到,产量(0.45)、成熟度(0.88)、叶片发育(0.73)和出苗(0.73)等性状的表型预测能力较高,而其他所有性状的基因组预测能力均高于表型预测能力。当使用混合关系矩阵进行预测时,22 个性状中有 20 个表现出更高的预测能力,这表明表型和基因组数据包含互补信息。我们认为表型选择在马铃薯育种计划中的主要应用是允许在钵苗或单株阶段使用预测性育种原则,由于成本高,不建议在该阶段进行基因分型。