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，其实施需投入高昂成本，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.）抗旱育种项目的选择效率与遗传增益。本研究针对多环境试验场景下未测玉米单交组合的抗旱性表现预测任务，对比了加性（A）与加显混合（AD）两种基因组选择模型的预测精度。研究于巴西两地、两年内开展了8个试验，分别设置水分胁迫（Water-Stressed, WS）与正常供水（Well-Watered, WW）两种处理，对308个玉米单交组合的5个抗旱相关性状进行了表型数据采集。本研究基于测序分型（Genotyping-by-Sequencing）技术获取的单核苷酸多态性（Single Nucleotide Polymorphism, SNP）数据，通过杂交组合亲本（自交系）的基因型信息推断得到各单交组合的基因型。基因组选择分析采用基因组最佳线性无偏预测（Genomic Best Linear Unbiased Prediction, GBLUP）方法，结合因子分析（Factor Analytic, FA）乘法混合模型开展。结果表明，在两种水分处理条件下，针对本次研究考察的5个性状，A模型与AD模型的预测精度均存在显著差异。以籽粒产量（Grain Yield, GY）为例，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与显性效应在各环境中的稳定性，以及加性×环境、显性×环境互作效应，该方法在亲本与杂交组合选育中具备良好的应用价值。通过借用相关试验的信息开展基因组选择预测，未测杂交组合的预测性能在A模型与AD模型中分别提升了40%与9%。本研究结果凸显了在玉米单交组合籽粒产量的基因组预测中，整合显性效应的多环境试验基因组选择分析的重要性。