Many rice-growing areas are affected by high concentrations of arsenic (As). Rice varieties that prevent As uptake and/or accumulation can mitigate As threats to human health. Genomic selection is known to facilitate rapid selection of superior genotypes for complex traits. We explored the predictive ability (PA) of genomic prediction with single-environment models, accounting or not for trait-specific markers, multi-environment models, and multi-trait and multi-environment models, using the genotypic (1600K SNPs) and phenotypic (grain As content, grain yield and days to flowering) data of the Bengal and Assam Aus Panel. Under the base-line single-environment model, PA of up to 0.707 and 0.654 was obtained for grain yield and grain As content, respectively; the three prediction methods (Bayesian Lasso, genomic best linear unbiased prediction and reproducing kernel Hilbert spaces) were considered to perform similarly, and marker selection based on linkage disequilibrium allowed to reduce the number of SNP to 17K, without negative effect on PA of genomic predictions. Single-environment models giving distinct weight to trait-specific markers in the genomic relationship matrix outperformed the base-line models up to 32%. Multi-environment models, accounting for genotype × environment interactions, and multi-trait and multi-environment models outperformed the base-line models by up to 47% and 61%, respectively. Among the multi-trait and multi-environment models, the Bayesian multi-output regressor stacking function obtained the highest predictive ability (0.831 for grain As) with much higher efficiency for computing time. These findings pave the way for breeding for As-tolerance in the progenies of biparental crosses involving members of the Bengal and Assam Aus Panel. Genomic prediction can also be applied to breeding for other complex traits under multiple environments.

许多水稻种植地区都受到高浓度砷（As）的影响。防止砷吸收和/或积累的水稻品种可以减轻砷对人类健康的威胁。已知基因组选择有助于快速选择复杂性状的优良基因型。我们使用基因型（1600K SNP）和表型探索了单环境模型，是否考虑性状特异性标记，多环境模型以及多性状和多环境模型的基因组预测的预测能力（PA）。 （晶粒作为含量，谷物产量和开花天数方面）的孟加拉和阿萨姆的数据奥斯控制板。在基线单环境模型下，谷物产量和As含量分别达到了0.707和0.654。三种预测方法（贝叶斯套索，基因组最佳线性无偏预测和重现内核希尔伯特空间）被认为具有相似的表现，并且基于连锁不平衡的标记选择使SNP的数量减少到17K，而对基因组预测的PA没有负面影响。在基因组关系矩阵中赋予特征特异标记独特权重的单环境模型的性能比基线模型高出32％。多环境模型，占基因型 × 环境交互作用，多特征和多环境模型分别比基准模型高出47％和61％。在多特征和多环境模型中，贝叶斯多输出回归器堆叠函数获得了最高的预测能力（谷物As为0.831），计算时间效率更高。这些结果铺平育种在涉及孟加拉和阿萨姆成员双亲杂交后代作为容忍的方式澳元面板。基因组预测也可用于多种环境下其他复杂性状的育种。