Data from: 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 (GBLUP) models for rice hybrids based on the North Carolina mating design II, in which a total of 115 inbred rice lines were crossed with five male sterile lines. Using eight 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 superior potential crosses between the 115 inbred lines, and those between the five male sterile lines and other genotyped varieties. In our MV research, an MV model (MV-ADV) was developed utilizing a multivariate relationship matrix constructed with auxiliary variates. Based on joint analysis with multi-trait (MT) or with multi-environment (ME), the prediction results confirmed the superiority of MV-ADV over an UV model, particularly in the MT scenario for a low-heritability target trait (such as GY), with highly correlated auxiliary traits. For a high-heritability trait (such as thousand-grain weight), MT prediction is unnecessary, and UV prediction is sufficient.

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择方法。本研究基于北卡罗来纳交配设计II（North Carolina mating design II），探究了基因组最佳线性无偏预测（Genomic Best Linear Unbiased Prediction, GBLUP）模型对水稻杂交种的预测能力。该设计中共115个水稻自交系与5个雄性不育系开展杂交。研究利用两个环境下575份（115×5）杂交种的8个性状数据，实施了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精度。此外，即便针对单株产量（grain yield per plant, GY）这类低遗传力性状，仍可借助基因组选择策略：只需适度增加优异单株的选择数量，即可为水稻杂交种获得更高且更稳定的平均表型值。基于该策略，本研究从115个自交系间，以及5个雄性不育系与其他已完成基因型鉴定的品种间，筛选出了具备优异潜力的杂交组合。在多变量分析环节，本研究构建了一种基于辅助变量构建多变量关系矩阵的多变量模型（MV-ADV）。基于多性状（multi-trait, MT）或多环境（multi-environment, ME）联合分析的预测结果证实，MV-ADV模型的性能优于单变量模型，尤其在多性状分析场景下，针对单株产量这类低遗传力目标性状且辅助性状与目标性状高度相关时，优势更为显著。而对于千粒重这类高遗传力性状，开展多性状预测并无必要，单变量预测即可满足需求。