杨树杂交子代苗期动态生长性状的全基因组选择OA
Genomic Selection for Dynamic Growth Traits during the Seedling Stage of Poplar Hybrid Population
[目的]优化杨树苗期动态生长性状的全基因组选择,为提高预测准确度和实现优良子代早期选择提供参考.[方法]以母本'南林 895'杨和父本'京兴 1号'杨的 400株杂交F1 子代为材料,分别在 4-9月每月测定1次地径和株高,并采用全基因组重测序获取基因型数据.利用GBLUP、BayesA、BayesC、支持向量回归、梯度提升、随机森林方法,评估不同月份表型数据对全基因组选择模型预测准确度的影响.在 12月生长季结束时,测定最终的地径和株高,以验证各全基因组选择模型对最终表型值的预测准确度.[结果]杂交群体的地径和株高均值随月份增加逐渐上升,并在 9月达到最高值,变异系数范围分别为 0.23～0.34和 0.18～0.54,广泛的遗传变异表明具有较高的选择潜力,狭义遗传力估算范围分别为 0.42～0.47和 0.39～0.62.GBLUP、BayesA、BayesC和支持向量回归模型在所有月份对地径和株高的预测准确度高于梯度提升和随机森林模型,其中地径和株高的预测准确率最高的月份分别在 6月和 9月.利用 12月最终生长数据对不同全基因组选择模型预测准确率进行评估,6、7、8、9月构建的全基因组选择模型在地径和株高上的预测准确率显著高于 4月和 5月,其中,9月份表型构建的 BayesA模型对地径和株高的预测准确率较高,因此选择该模型对 400个杂交子代的育种值进行预测和筛选.根据 12月表型观测值和全基因组选择预测育种值筛选出的优良基因型,有 4个杂交子代被2种方法同时选出.[结论]全基因组选择能够有效筛选出杨树苗期动态生长性状中优良子代,为杨树育种中优良子代的早期选择提供了有效方法.
[Objective]Genomic selection for optimizing dynamic growth traits during the seedling stage of poplar is crucial for improving prediction accuracy and facilitating early selection of superior offspring.[Method]A total of 400 F1 hybrid progeny derived from a cross between Nanlin 895 poplar(female parent)and Jingxing Yihao poplar(male parent)were used as materials.Ground diameter and plant height were measured monthly from April to September,and genotypic data were obtained through whole-genome resequencing.Six genomic selection models,such as GBLUP,BayesA,BayesC,support vector regression,gradient boosting,and random forest,were employed to evaluate the impact of monthly phenotypic data on the prediction accuracy of genomic selection.Additionally,final ground diameter and plant height were measured at the end of the growing season in December to validate the predictive accuracy of each genomic selection model for final phenotypic values.[Result]The mean values of ground diameter and plant height in the hybrid population increased gradually over the months,reaching their maximum in September.The coefficients of variation ranged from 0.23 to 0.34 for ground diameter and from 0.18 to 0.54 for plant height,indicating substantial genetic variation and significant selection potential.Narrow-sense heritability estimates ranged from 0.42 to 0.47 for ground diameter and from 0.39 to 0.62 for plant height.Among the genomic selection models,GBLUP,BayesA,BayesC,and support vector regression consistently had higher prediction accuracy for ground diameter and plant height across all months compared to gradient boosting and random forest.The highest prediction accuracies for ground diameter and plant height were observed in June and September,respectively.The final growth data collected in December was used to evaluate the prediction accuracy of different genomic selection models,and the results showed that models constructed with phenotypic data from June,July,August,and September had significantly higher prediction accuracies for ground diameter and plant height compared to those built with data from April and May.Among them,the BayesA model based on September phenotypes exhibited the highest prediction accuracy for the both traits and was therefore selected to predict and screen the breeding values of the 400 hybrid progenies.Based on the December phenotypic observations and the predicted breeding values through genome selection,four hybrid progenies were consistently selected by both methods for their superior genotype.[Conclusion]Genomic selection can effectively identify superior progeny for dynamic growth traits during the seedling stage of poplar,providing an efficient method for the early selection of superior progeny in poplar breeding.
Guo Chenchen;Li Qi;Li Siyuan;Wang Zemin;Chen Yingnan;Wei Suyun;Hu Jianjun
State Key Laboratory of Tree Genetics and Breeding Co-Innovation Center for Sustainable Forestry in Southern China Key Laboratory of Forest Genetics and Biotechnology of Ministry of Education College of Forestry and Grassland,Nanjing Forestry University Nanjing 210037State Key Laboratory of Tree Genetics and Breeding Co-Innovation Center for Sustainable Forestry in Southern China Key Laboratory of Forest Genetics and Biotechnology of Ministry of Education College of Forestry and Grassland,Nanjing Forestry University Nanjing 210037State Key Laboratory of Tree Genetics and Breeding Co-Innovation Center for Sustainable Forestry in Southern China Key Laboratory of Forest Genetics and Biotechnology of Ministry of Education College of Forestry and Grassland,Nanjing Forestry University Nanjing 210037Jiangsu Huanghai Agricultural Reclamation Co.,Ltd.Yancheng 224000State Key Laboratory of Tree Genetics and Breeding Co-Innovation Center for Sustainable Forestry in Southern China Key Laboratory of Forest Genetics and Biotechnology of Ministry of Education College of Forestry and Grassland,Nanjing Forestry University Nanjing 210037State Key Laboratory of Tree Genetics and Breeding Co-Innovation Center for Sustainable Forestry in Southern China Key Laboratory of Forest Genetics and Biotechnology of Ministry of Education College of Forestry and Grassland,Nanjing Forestry University Nanjing 210037State Key Laboratory of Tree Genetics and Breeding Research Institute of Forestry,Chinese Academy of Forestry Beijing 100091
农业科技
全基因组选择动态生长性状杨树预测准确度五折交叉验证
genomic selectiondynamic growth traitspoplarprediction accuracyfive-fold cross-validation
《林业科学》 2026 (1)
32-41,10
农业生物育种重大项目(2022ZD0401501)国家自然科学基金面上项目(32471900).
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