Heterotic quantitative trait loci analysis and genomic prediction of seedling biomass-related traits in maize triple testcross populations

文献类型： 外文期刊

第一作者： Zhang, Tifu

作者： Zhang, Tifu;Liang, Shuaiqiang;Lin, Feng;Lu, Haiyan;Zhao, Han;Jiang, Lu;Ruan, Long;Qian, Yiliang;Dai, Huixue

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关键词： Seedling biomass-related traits; Heterotic quantitative trait loci; Genomic prediction; Triple testcross; Maize

期刊名称：PLANT METHODS （ 影响因子：4.993； 五年影响因子：5.312 ）

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年卷期： 2021 年 17 卷 1 期

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收录情况： SCI

摘要： Background Heterosis has been widely used in maize breeding. However, we know little about the heterotic quantitative trait loci and their roles in genomic prediction. In this study, we sought to identify heterotic quantitative trait loci for seedling biomass-related traits using triple testcross design and compare their prediction accuracies by fitting molecular markers and heterotic quantitative trait loci. Results A triple testcross population comprised of 366 genotypes was constructed by crossing each of 122 intermated B73 x Mo17 genotypes with B73, Mo17, and B73 x Mo17. The mid-parent heterosis of seedling biomass-related traits involved in leaf length, leaf width, leaf area, and seedling dry weight displayed a large range, from less than 50 to similar to 150%. Relationships between heterosis of seedling biomass-related traits showed congruency with that between performances. Based on a linkage map comprised of 1631 markers, 14 augmented additive, two augmented dominance, and three dominance x additive epistatic quantitative trait loci for heterosis of seedling biomass-related traits were identified, with each individually explaining 4.1-20.5% of the phenotypic variation. All modes of gene action, i.e., additive, partially dominant, dominant, and overdominant modes were observed. In addition, ten additive x additive and six dominance x dominance epistatic interactions were identified. By implementing the general and special combining ability model, we found that prediction accuracy ranged from 0.29 for leaf length to 0.56 for leaf width. Different number of marker analysis showed that similar to 800 markers almost capture the largest prediction accuracies. When incorporating the heterotic quantitative trait loci into the model, we did not find the significant change of prediction accuracy, with only leaf length showing the marginal improvement by 1.7%. Conclusions Our results demonstrated that the triple testcross design is suitable for detecting heterotic quantitative trait loci and evaluating the prediction accuracy. Seedling leaf width can be used as the representative trait for seedling prediction. The heterotic quantitative trait loci are not necessary for genomic prediction of seedling biomass-related traits.

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