Jung Michaela, Hodel Marius, Knauf Andrea, Kupper Daniela, Neuditschko Markus, Bühlmann-Schütz Simone, Studer Bruno, Patocchi Andrea, Broggini Giovanni Al
Agroscope, Mueller-Thurgau-Strasse 29, Waedenswil, 8820, Switzerland.
Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Universitaetstrasse 2, Zurich, 8092, Switzerland.
BMC Plant Biol. 2025 Jan 24;25(1):103. doi: 10.1186/s12870-025-06104-w.
Apple breeding schemes can be improved by using genomic prediction models to forecast the performance of breeding material. The predictive ability of these models depends on factors like trait genetic architecture, training set size, relatedness of the selected material to the training set, and the validation method used. Alternative genotyping methods such as RADseq and complementary data from near-infrared spectroscopy could help improve the cost-effectiveness of genomic prediction. However, the impact of these factors and alternative approaches on predictive ability beyond experimental populations still need to be investigated. In this study, we evaluated 137 prediction scenarios varying the described factors and alternative approaches, offering recommendations for implementing genomic selection in apple breeding.
Our results show that extending the training set with germplasm related to the predicted breeding material can improve average predictive ability across eleven studied traits by up to 0.08. The study emphasizes the usefulness of leave-one-family-out cross-validation, reflecting the application of genomic prediction to a new family, although it reduced average predictive ability across traits by up to 0.24 compared to 10-fold cross-validation. Similar average predictive abilities across traits indicate that imputed RADseq data could be a suitable genotyping alternative to SNP array datasets. The best-performing scenario using near-infrared spectroscopy data for phenomic prediction showed a 0.35 decrease in average predictive ability across traits compared to conventional genomic prediction, suggesting that the tested phenomic prediction approach is impractical.
Extending the training set using germplasm related with the target breeding material is crucial to improve the predictive ability of genomic prediction in apple. RADseq is a viable alternative to SNP array genotyping, while phenomic prediction is impractical. These findings offer valuable guidance for applying genomic selection in apple breeding, ultimately leading to the development of breeding material with improved quality.
通过使用基因组预测模型来预测育种材料的性能，可以改进苹果育种方案。这些模型的预测能力取决于性状遗传结构、训练集大小、所选材料与训练集的亲缘关系以及所使用的验证方法等因素。诸如RADseq等替代基因分型方法以及来自近红外光谱的补充数据，可能有助于提高基因组预测的成本效益。然而，这些因素和替代方法对超出实验群体的预测能力的影响仍有待研究。在本研究中，我们评估了137种预测方案，这些方案改变了上述因素和替代方法，为在苹果育种中实施基因组选择提供了建议。
我们的结果表明，用与预测育种材料相关的种质扩展训练集，可使11个研究性状的平均预测能力提高多达0.08。该研究强调了留一家庭交叉验证的有用性，这反映了基因组预测在新家庭中的应用，尽管与10倍交叉验证相比，它使各性状的平均预测能力降低了多达0.24。各性状相似的平均预测能力表明，估算的RADseq数据可能是SNP阵列数据集合适的基因分型替代方法。使用近红外光谱数据进行表型预测的最佳表现方案显示，与传统基因组预测相比，各性状的平均预测能力下降了0.35，这表明所测试的表型预测方法不切实际。
使用与目标育种材料相关的种质扩展训练集对于提高苹果基因组预测的预测能力至关重要。RADseq是SNP阵列基因分型的可行替代方法，而表型预测不切实际。这些发现为在苹果育种中应用基因组选择提供了有价值的指导，最终导致培育出品质更好的育种材料。
