Performance of genomic prediction within and across generations in maritime pine.
Performance of genomic prediction within and across generations in maritime pine.
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海松世代内和世代间基因组预测的表现。
DOI：
10.1186/s12864-016-2879-8
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发表时间：
2016-08-11
期刊：
影响因子：
4.4
通讯作者：
Bouffier L
中科院分区：
文献类型：
--
作者：
Bartholomé J;Van Heerwaarden J;Isik F;Boury C;Vidal M;Plomion C;Bouffier L
基因组选择是缩短林木育种周期的有效途径。因此，评估后代的表现和GS模型的预测精度在几代人是一个关键问题。利用模拟数据，首先选择了一个估计有效近交群体大小（状态数）为25的海洋松（Pinuspinaster）参考群体。该参考人群（n = 818）覆盖了三代（G 0，G1和G2），并使用4436个单核苷酸多态性（SNP）标记进行基因分型。我们评估了校正集和验证集之间的相关性对预测准确性的影响，以及基于后代表现的验证。基于谱系的（最佳线性无偏预测，ABLUP）和基于标记的（基因组BLUP和贝叶斯LASSO）模型被用来预测三个不同性状的育种值：周长，高度和茎直。平均而言，ABLUP模型优于基因组预测模型，预测精度的最大差异为0.12，这取决于性状和验证方法。在预测准确度的平均差异为0.17，发现验证方法之间的相关性不同。在校准集中包括祖细胞将预测准确度的这种差异降低到0.03。当仅将来自G 0和G1代的基因型用于校准集并且将来自G2的基因型用于验证集（后代验证）时，预测精度范围为0.70至0.85。这项研究表明，预测模型对亲本种群的训练，可以预测后代的遗传价值与高精度：一个令人鼓舞的结果GS在海洋松育种计划的实施。本文的在线版本（doi：10.1186/s12864-016-2879-8）包含补充材料，可供授权用户使用。
Genomic selection (GS) is a promising approach for decreasing breeding cycle length in forest trees. Assessment of progeny performance and of the prediction accuracy of GS models over generations is therefore a key issue. A reference population of maritime pine (Pinus pinaster) with an estimated effective inbreeding population size (status number) of 25 was first selected with simulated data. This reference population (n = 818) covered three generations (G0, G1 and G2) and was genotyped with 4436 single-nucleotide polymorphism (SNP) markers. We evaluated the effects on prediction accuracy of both the relatedness between the calibration and validation sets and validation on the basis of progeny performance. Pedigree-based (best linear unbiased prediction, ABLUP) and marker-based (genomic BLUP and Bayesian LASSO) models were used to predict breeding values for three different traits: circumference, height and stem straightness. On average, the ABLUP model outperformed genomic prediction models, with a maximum difference in prediction accuracies of 0.12, depending on the trait and the validation method. A mean difference in prediction accuracy of 0.17 was found between validation methods differing in terms of relatedness. Including the progenitors in the calibration set reduced this difference in prediction accuracy to 0.03. When only genotypes from the G0 and G1 generations were used in the calibration set and genotypes from G2 were used in the validation set (progeny validation), prediction accuracies ranged from 0.70 to 0.85. This study suggests that the training of prediction models on parental populations can predict the genetic merit of the progeny with high accuracy: an encouraging result for the implementation of GS in the maritime pine breeding program. The online version of this article (doi:10.1186/s12864-016-2879-8) contains supplementary material, which is available to authorized users.