Muvunyi Blaise Pascal, Zou Wenli, Zhan Junhui, He Sang, Ye Guoyou
CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
Rice Breeding Innovations Platform, International Rice Research Institute, Los Baños, Philippines.
Front Genet. 2022 Jun 22;13:883853. doi: 10.3389/fgene.2022.883853. eCollection 2022.
Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of trait-associated SNPs in MT models. MT models were not statistically significantly ( 0.05) superior to ST model under CV1, where no phenotypic information was available for the accessions in the test set. After including phenotypes from auxiliary traits in both training and test sets (MT-CV2) or simply in the test set (MT-CV3), MT models significantly ( < 0.05) outperformed ST model for all the traits. The highest increases in the predictive ability of MT models relative to ST models were 11.1% (Mn), 11.5 (Cd), 33.3% (Fe), 95.2% (Cu) and 126% (Zn). Accounting for the local epistatic effects using a haplotype-based model further improved the predictive ability of MT models by 4.6% (Cu), 3.8% (Zn), and 3.5% (Cd) relative to MT models with only additive effects. The predictive ability of the haplotype-based model was not improved after optimizing the marker panel by only considering the markers associated with the traits. This study first assessed the local epistatic effects and marker optimization strategies in the MT genomic prediction framework and then illustrated the power of the MT model in predicting trace element traits in rice for the effective use of genetic resources to improve the nutritional quality of rice grain.
多性状（MT）基因组预测模型使育种者能够通过利用来自非目标或辅助性状的可用信息，节省表型分析资源并提高未观察到的目标性状的预测准确性。我们的研究使用来自亚洲国家的250份水稻种质进行了评估，这些种质针对锌（Zn）、铁（Fe）、铜（Cu）、锰（Mn）和镉（Cd）的籽粒含量进行了基因分型和表型分析。通过以下方式评估了MT模型与传统单性状（ST）模型相比的预测性能：1）应用不同的交叉验证策略（CV1、CV2和CV3），推断不同的表型模式和预算；2）在MT模型中考虑局部上位效应以及主要加性效应；3）在MT模型中使用由性状相关单核苷酸多态性（SNP）组成的选择性标记面板。在CV1下，测试集中的种质没有表型信息，MT模型在统计学上并不显著优于ST模型（P>0.05）。在训练集和测试集中都包含辅助性状的表型（MT-CV2）或仅在测试集中包含辅助性状的表型（MT-CV3）之后，MT模型在所有性状上均显著优于ST模型（P<0.05）。MT模型相对于ST模型预测能力的最高提升分别为11.1%（Mn）、11.5%（Cd）、33.3%（Fe）、95.2%（Cu）和126%（Zn）。与仅具有加性效应的MT模型相比，使用基于单倍型的模型考虑局部上位效应进一步将MT模型的预测能力提高了4.6%（Cu）、3.8%（Zn）和3.5%（Cd）。仅考虑与性状相关的标记优化标记面板后，基于单倍型的模型的预测能力并未提高。本研究首次在MT基因组预测框架中评估了局部上位效应和标记优化策略，然后阐明了MT模型在预测水稻微量元素性状方面的能力，以便有效利用遗传资源提高稻米的营养品质。
