de Verdal Hugues, Baertschi Cédric, Frouin Julien, Quintero Constanza, Ospina Yolima, Alvarez Maria Fernanda, Cao Tuong-Vi, Bartholomé Jérôme, Grenier Cécile
CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398, Montpellier, France.
Rice (N Y). 2023 Sep 27;16(1):43. doi: 10.1186/s12284-023-00661-0.
Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S plants were all genotyped and advanced by selfing and bulk seed harvest to the S, S, and S generations. The PCT27 was then divided into two sets. The S and S progenies for PCT27A and the S progenies for PCT27B were phenotyped in two locations: Santa Rosa the target selection location, within the upland rice growing area, and Palmira, the surrogate location, far from the upland rice growing area but easier for experimentation. While the calibration used either one of the two sets phenotyped in one or two locations, the validation population was only the PCT27B phenotyped in Santa Rosa. Five scenarios of genomic prediction and 24 models were performed and compared. Training the prediction model with the PCT27B phenotyped in Santa Rosa resulted in predictive abilities ranging from 0.19 for grain zinc concentration to 0.30 for grain yield. Expanding the training set with the inclusion of the PCT27A resulted in greater predictive abilities for all traits but grain yield, with increases from 5% for plant height to 61% for grain zinc concentration. Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27). For plant height, the model assuming a single G × E variance provided higher accuracy (0.28). The gain in predictive ability for grain yield was the greatest (0.25) when environment-specific variance deviation effect for G × E was considered. While the best scenario was specific to each trait, the results indicated that the gain in predictive ability provided by the multi-location and multi-generation calibration was low. Yet, this approach could lead to increased selection intensity, acceleration of the breeding cycle, and a sizable economic advantage for the program.
基因组选择是一种在轮回选择育种方案中提高遗传增益的有效育种方法。在穿梭育种背景下，整合多代和多地点信息可显著改进基因组预测模型。国际农业研究磋商组织（Cirad）-国际热带农业中心（CIAT）的旱稻育种计划采用轮回基因组选择，并试图优化该方案以增加遗传增益，同时减少表型鉴定工作。我们使用了一个合成群体（PCT27），其中S株植物均进行了基因分型，并通过自交和混合收获种子推进到S、S和S代。然后将PCT27分为两组。PCT27A的S和S后代以及PCT27B的S后代在两个地点进行了表型鉴定：位于旱稻种植区内的目标选择地点圣罗莎，以及远离旱稻种植区但便于试验的替代地点帕尔米拉。在校准过程中，使用了在一个或两个地点进行表型鉴定的两组中的任意一组，而验证群体仅为在圣罗莎进行表型鉴定的PCT27B。进行并比较了5种基因组预测方案和24种模型。用在圣罗莎进行表型鉴定的PCT27B训练预测模型，预测能力范围从籽粒锌浓度的0.19到籽粒产量的0.30。将PCT27A纳入训练集可提高所有性状（除籽粒产量外）的预测能力，株高提高5%，籽粒锌浓度提高61%。当模型假设开花期（0.38）和籽粒锌浓度（0.27）不存在基因型与环境互作（G×E）时，用在两个地点进行表型鉴定的PCT27B构建的模型预测准确性更高。对于株高，假设单一G×E方差的模型准确性更高（0.28）。当考虑G×E的环境特异性方差偏差效应时，籽粒产量的预测能力提升最大（0.25）。虽然最佳方案因性状而异，但结果表明多地点和多代校准带来的预测能力提升较低。然而，这种方法可能会提高选择强度，加速育种周期，并为该计划带来可观的经济优势。
