Genomic Prediction of Arsenic Tolerance and Grain Yield in Rice: Contribution of Trait-Specific Markers and Multi-Environment Models
Genomic Prediction of Arsenic Tolerance and Grain Yield in Rice: Contribution of Trait-Specific Markers and Multi-Environment Models
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DOI：
10.1016/j.rsci.2021.04.006
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发表时间：
2021-05-10
期刊：
影响因子：
4.8
通讯作者：
Price, Adam H.
中科院分区：
文献类型：
--
作者：
Ahmadi, Nourollah;Cao, Tuong-Vi;Price, Adam H.
许多水稻种植区受到高浓度砷（As）的影响。防止As吸收和/或积累的水稻品种可以减轻As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上级基因型。我们探讨了预测能力（PA）的基因组预测与单环境模型，占或不为性状特异性标记，多环境模型，多性状和多环境模型，使用基因型（1600 K SNP）和表型（粮食As含量，粮食产量和开花天数）数据的孟加拉和阿萨姆邦的澳大利亚面板。在基线单环境模型下，籽粒产量和籽粒砷含量的PA分别达到0.707和0.654;三种预测方法（Bayesian Lasso，基因组最佳线性无偏预测和再生核希尔伯特空间）被认为表现类似，并且基于连锁不平衡的标记选择允许将SNP的数量减少到17 K，而不会对基因组预测的PA产生负面影响。单环境模型在基因组关系矩阵中对性状特异性标记给予不同的权重，优于基线模型高达32%。多环境模型，占基因型x环境的相互作用，多性状和多环境模型优于基线模型分别高达47%和61%。在多性状多环境模型中，贝叶斯多输出回归堆积函数的预测能力最高（0.831粮食砷），计算时间的效率要高得多。这些研究结果铺平了道路，在涉及孟加拉和阿萨姆邦澳大利亚小组成员的双亲杂交后代的抗砷性育种。基因组预测也可应用于多环境下其它复杂性状的育种。
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 x 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.