基因组和表型预测在苹果育种中的应用评价

来源: Michaela Jung et al 发布日期: 2025-03-03 访问量: 11

导读	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相关性以及使用的验证方法等因素。RADseq等替代基因分型方法和近红外光谱的补充数据可以帮助提高基因组预测的成本效益标签: 基因组选择，表型选择，苹果×家苹果，数量性状，苹果REFPOP

Evaluation of genomic and phenomic prediction for application in apple breeding
Michaela Jung^1,2*, Marius Hodel¹, Andrea Knauf^1,2, Daniela Kupper^1,2, Markus Neuditschko³,
Simone Bühlmann-Schütz¹, Bruno Studer², Andrea Patocchi^1† , Giovanni AL Broggini²

Abstract：
Background：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.

背景：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相关性以及使用的验证方法等因素。RADseq等替代基因分型方法和近红外光谱的补充数据可以帮助提高基因组预测的成本效益。然而，这些因素和替代方法对实验人群之外的预测能力的影响仍有待研究。在这项研究中，我们评估了137种不同因素和替代方法的预测情景，提供了苹果育种中实施基因组选择的建议。

Results： 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.

结果：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以将11个研究性状的平均预测能力提高0.08。该研究强调了将一个家族排除在交叉验证之外的有用性，反映了基因组预测在新家族中的应用，尽管与10倍的交叉验证相比，它将跨性状的平均预测能力降低了0.24倍。跨性状的相似平均预测能力表明，估算的RADseq数据可能是SNP阵列数据集的合适基因分型替代品。使用近红外光谱数据进行表型预测的最佳情景显示，与传统的基因组预测相比，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。

Conclusions： 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.

结论：利用与目标育种材料相关的种质资源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案，而表型预测是不切实际的。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，最终导致了质量提高的育种材料的开发。

Keywords： Genomic selection, Phenomic selection, Malus × domestica, Quantitative traits, Apple REFPOP

关键词：基因组选择，表型选择，苹果×家苹果，数量性状，苹果REFPOP

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Phenotyping

Eleven traits (floral emergence, harvest date, flower- ing intensity, total fruit weight, number of fruits, single fruit weight, titratable acidity, soluble solids content, fruit firmness, red over color, and russet frequency) were scored in the apple REFPOP for up to five years durin 2018–2022. For the AZZ material, the phenotyping of the eleven traits was done during two years in 2021–2022 and, when available, historical data were retrieved for 2018–2020. In the apple REFPOP, all traits were esti- mated from individual trees, i.e., genotype replicates. To assess titratable acidity, soluble solids content, fruit firm-ness, and russet frequency, a random sample of 5–20 fruits was drawn for each tree. For the AZZ material, the traits floral emergence, harvest date, flowering intensity, total fruit weight, number of fruits and single fruit weight were scored for individual trees, and the traits titratable acidity, soluble solids content, fruit firmness, red over color, and russet frequency were measured from pooled samples of 5–20 fruits across the available trees of each genotype.

2018年至2022年，苹果REFPOP对11个性状（出花、收获日期、开花强度、总果重、果实数量、单果重量、可滴定酸度、可溶性固形物含量、果实硬度、红色过色和赤褐色频率）进行了长达五年的评分。对于AZZ材料，在2021-2022年的两年内完成了11个性状的表型分析，并在可用的情况下检索了2018-2020年的历史数据。在苹果REFPOP中，所有性状都是从单株树木中估计出来的，即基因型复制。为了评估可滴定酸度、可溶性固形物含量、果实硬度和赤褐色频率，每棵树随机抽取5-20个果实样本。对于AZZ材料，对每棵树的出花、收获日期、开花强度、总果实重量、果实数量和单果重量等性状进行评分，并从每种基因型可用树木的5-20个果实的混合样本中测量可滴定酸度、可溶性固体含量、果实硬度、红色过色和赤褐色频率等性状。

Floral emergence was estimated in Julian days as the date when the first 10% of flowers opened. Flowering intensity was scored on a nine-grade scale as the per-centage of existing flowers from the maximum possible number of flowers. Fruits were harvested on harvest date, when at least 50% of the fruit on a tree was fully mature, which was determined in Julian days based on fruit rip-ening estimated by expert knowledge. Total fruit weight per tree was measured in kilograms (kg) and all fruits were counted to assess the number of fruits. Single fruit weight in grams (g) was calculated as the ratio of the total fruit weight to the number of fruits. Titratable acidity (g/kg), soluble solids content (°Brix) and fruit firmness (g/cm2) were measured within one week after the harvestdate using an automated laboratory Pimprenelle (Setop,France). Red over color was the percentage of red fruit skin assessed on a six-grade scale. Russet frequenc was the proportion of fruits with russet skin in the fruit sample. Total fruit weight, number of fruits, single fruit weight, red over color, and russet frequency were evalu-ated at harvest. Additional details about the assessment of the eleven traits can be found in Jung et al.
在朱利安天，花的出现被估计为前10%的花开放的日期。开花强度按9级评分，即现有花朵占最大可能花朵数量的百分比。果实在收获日期收获，此时树上至少有50%的果实完全成熟，这是根据专家知识估计的果实撕裂在朱利安天内确定的。每棵树的总果实重量以千克（kg）为单位进行测量，并对所有果实进行计数以评估果实数量。单果重量以克（g）为单位，计算为总果实重量与果实数量的比值。在收获后一周内，使用自动化实验室Pimprenelle（Setop,France）测量了可滴定酸度（g/kg）、可溶性固形物含量（°Brix）和果实硬度（g/cm2）。红色超过颜色是指根据六级量表评估的红色水果皮的百分比。赤褐色频率是指果实样本中赤褐色果皮的果实比例。在收获时评估了果实总重量、果实数量、单果重量、红色过色和赤褐色频率。关于这十一个特征的评估的更多细节可以在Jung等人的研究中找到。

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