Genomic Prediction of Arsenic Tolerance and Grain Yield in Rice: Contribution of Trait-Specific Markers and Multi-Environment Models  

作　　者：Nourollah AHMADI Tuong-Vi CAO Julien FROUIN Gareth J.NORTON Adam H.PRICE 

机构地区：[1]Institute of Genetic Improvement and Adaptation of Mediterranean and Tropical Plants,French Agricultural Research and International Cooperation Organization,Montpellier F-34398,France [2]University of Montpellier,National Research Institute for Agriculture,Food and Environment,French Agricultural Research and International Cooperation Organization,Montpellier SupAgro,Montpellier 34090,France [3]School of Biological Sciences,University of Aberdeen,Aberdeen AB243UU,Scotland

出　　处：《Rice science》2021年第3期268-278,共11页水稻科学（英文版）

摘　　要：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.

关 键 词：genomic prediction model genomic selection complex trait arsenic tolerance RICE predictive ability 

分 类 号：X503.231[环境科学与工程—环境工程] S511[农业科学—作物学]