Data from: Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials

Breeding for drought tolerance is a challenging task that requires costly, extensive and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here we evaluated the accuracy of genomic selection of additive (A) against additive+dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multi-environment trials. Phenotypic data of five drought-tolerance traits were measured in 308 hybrids in eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids’ genotypes were inferred based on their parents’ genotypes (inbred lines) using single nucleotide polymorphism data obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Results showed differences in the predictive accuracy between A and AD models for the five traits under consideration in both water conditions. For grain yield (GY), the AD model doubled the predictive accuracy in comparison to the A model. FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive- and dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Prediction performance of untested hybrids using GS that benefit from borrowing information from correlated trials increased 40% and 9% for A and AD models, respectively. These results highlighted the importance of multi-environment trial analysis with GS that incorporate dominance effects into genomic predictions of GY in maize single-cross hybrids.抗旱育种是一项极具挑战性的工作，其开展需要耗费高昂成本，并依赖大规模且精准的表型鉴定。基因组选择（Genomic Selection, GS）可用于提升玉米（Zea mays L.）抗旱育种项目中的选择效率与遗传增益。本研究在多环境试验框架下，对比评估了加性（A）模型与加性-显性联合（AD）模型的基因组选择精度，以预测未经测试的玉米单交杂交种的抗旱性能。本研究在巴西的两个地点、两年间共设置8个试验，其中水分胁迫（WS）与正常供水（WW）环境各占4个，共测定了308个玉米杂交种的5个抗旱相关性状的表型数据。研究人员通过测序分型（genotyping-by-sequencing, GBS）获得单核苷酸多态性（single nucleotide polymorphism, SNP）数据，并基于杂交种亲本（自交系）的基因型推断得到各杂交种的基因型。本研究采用基因组最佳线性无偏预测（genomic best linear unbiased prediction, GBLUP）方法，通过拟合因子分析（factor analytic, FA）乘法混合模型开展基因组选择分析。结果显示，在两种供水条件下，针对本研究关注的5个性状，A模型与AD模型的预测精度均存在显著差异。就籽粒产量（grain yield, GY）而言，AD模型的预测精度较A模型提升了一倍。FA分析框架可用于解析加性效应与显性效应在不同环境下的稳定性，以及加性×环境互作与显性×环境互作，该方法在亲本与杂交种选择领域具有良好的应用潜力。借助相关试验间的信息共享，基因组选择模型对未测试杂交种的预测性能得到提升：A模型与AD模型分别提升了40%与9%。上述结果表明，在玉米单交杂交种籽粒产量的基因组预测中，将显性效应纳入基因组选择模型并开展多环境试验分析，具有重要的研究价值。