He Sang, Jiang Yong, Thistlethwaite Rebecca, Hayden Matthew J, Trethowan Richard, Daetwyler Hans D
Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia.
CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
Front Plant Sci. 2021 Oct 6;12:735285. doi: 10.3389/fpls.2021.735285. eCollection 2021.
Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders' main interest - response to selection - was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs.
在育种计划的早期阶段增加作物品系表型分析的环境数量可以提高选择准确性。然而，由于成本原因，这通常不可行。在我们的研究中，我们研究了一种稀疏表型分析方法，该方法并非在所有环境中对所有条目进行测试，而是利用基因组预测来预测额外环境中缺失的表型，而无需额外的表型分析费用。直接模拟了育种者的主要兴趣——对选择的响应，以评估稀疏基因组表型分析方法在小麦和水稻数据集中的有效性。稀疏表型分析是否能带来更多的选择响应取决于环境间表型的相关性。当大多数完全表型分析的环境呈负相关（小麦）或低正相关（水稻），且任何扩展环境与任何完全表型分析的环境呈高正相关时，与完全表型分析相比，稀疏表型分析方法始终显示出统计学上显著更高的选择响应。当所有环境呈正相关（小麦）或存在任何高正相关环境（小麦和水稻）时，稀疏表型分析并不能提高响应。我们的结果表明，基于基因组学的稀疏表型分析可以在作物育种计划的中期提高选择响应。
