Genome-wide prediction in a hybrid maize population adapted

发布时间：2021-03-02 23:57

　　Genome-wide prediction is a promising approach to boost selection gain in hybrid breeding.Our main objective was to evaluate the potential and limits of genome-wide prediction to identify superior hybrid combinations adapted to Northwest China. A total of 490 hybrids derived from crosses among 119 inbred lines from the Shaan A and Shaan B heterotic pattern were used for genome-wide prediction of ten agronomic traits. We tested eight different statistical prediction models considering additive（A）... 

【文章来源】：The Crop Journal. 2020,8(05)

【文章页数】：13 页

【文章目录】：
1. Introduction
2. Materials and methods
    2.1. Plant materials and field trials
    2.2. Phenotypic data analyses
    2.3. Genotyping
    2.4. Analyses of population structure
    2.5. Genome-wide prediction
        2.5.1. Additive model
        2.5.2. Additive and non-additive model
        2.5.3. Additive and dominance models including trait-specific SNPs
        2.5.4. Selection differential to select superior hybrids
3. Results
    3.1. Extensive phenotypic variation observed in field trials
    3.2. Genetically distinct subpopulations
    3.3. Performance of genome-wide prediction models based on additive effects
    3.4. Adding dominance and epistatic effects to the model with additive effects did not improve the prediction ability
    3.5. The potential of trait-specific SNPs in genome-wide prediction
    3.6. Genome-wide selection is more effective than conventional selection
    3.7. Additional 34 excellent single-cross combinations were identified
4. Discussion
    4.1. Hybrid maize breeding based on the two divergent heterotic groups Shaan A and B
    4.2. The prediction models only marginally influenced the prediction abilities
    4.3. Modelling of non-additive effects did not improve the prediction ability for most traits
    4.4. Trait-specific SNPs can drive an increase in predictive ability
    4.5. Implications of genomic selection for hybrid maize breeding
5. Conclusions



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