Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II

Genomic selection (GS) is more efficient than traditional phenotype-based methods in hybrid breeding. The present study investigated the predictive ability of genomic best linear unbiased prediction models for rice hybrids based on the North Carolina mating design II, in which a total of 115 inbred rice lines were crossed with 5 male sterile lines. Using 8 traits of the 575 (115 × 5) hybrids from two environments, both univariate (UV) and multivariate (MV) prediction analyses, including additive and dominance effects, were performed. Using UV models, the prediction results of cross-validation indicated that including dominance effects could improve the predictive ability for some traits in rice hybrids. Additionally, we could take advantage of GS even for a low-heritability trait, such as grain yield per plant (GY), because a modest increase in the number of top selection could generate a higher, more stable mean phenotypic value for rice hybrids. Thus this strategy was used to select s...

基因组选择（Genomic selection, GS）在杂交育种中的应用效率优于传统基于表型的选育方法。本研究基于北卡罗来纳交配设计II（North Carolina mating design II），探究水稻杂交种的基因组最佳线性无偏预测模型的预测能力：以115个水稻自交品系与5个雄性不育系杂交，共获得575（115×5）个杂交组合；随后针对该群体在两种环境下的8个性状，开展包含加性效应与显性效应的单变量（univariate, UV）及多变量（multivariate, MV）预测分析。基于单变量模型的交叉验证结果显示，引入显性效应可提升部分水稻杂交种性状的预测能力。此外，即便针对单株籽粒产量（grain yield per plant, GY）这类低遗传力性状，仍可借助基因组选择开展选育——仅需对少量顶尖单株进行选择，即可使水稻杂交种获得更高且更稳定的平均表型值。据此，本研究采用该策略开展选育……