Genomic Prediction of Resistance to Tar Spot Complex of Maize in Multiple Populations Using Genotyping-by-Sequencing SNPs

文献类型： 外文期刊

第一作者： Cao, Shiliang

作者： Cao, Shiliang;Hu, Guanghui;Zhang, Jianguo;Cao, Jingsheng;Cao, Shiliang;Song, Junqiao;Yuan, Yibing;Zhang, Ao;Ren, Jiaojiao;Liu, Yubo;Qu, Jingtao;Hu, Guanghui;San Vicente, Felix;Zhang, Xuecai;Song, Junqiao;Song, Junqiao;Wang, Chunping;Yuan, Yibing;Qu, Jingtao;Zhang, Ao;Liu, Yubo;Ren, Jiaojiao;Olsen, Michael;Prasanna, Boddupalli M.

作者机构：

关键词： maize; tar spot complex; genomic prediction; genomic selection; prediction accuracy; genotyping-by sequencing

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.754； 五年影响因子：6.612 ）

ISSN： 1664-462X

年卷期： 2021 年 12 卷

页码：

收录情况： SCI

摘要： Tar spot complex (TSC) is one of the most important foliar diseases in tropical maize. TSC resistance could be furtherly improved by implementing marker-assisted selection (MAS) and genomic selection (GS) individually, or by implementing them stepwise. Implementation of GS requires a profound understanding of factors affecting genomic prediction accuracy. In the present study, an association-mapping panel and three doubled haploid populations, genotyped with genotyping-by-sequencing, were used to estimate the effectiveness of GS for improving TSC resistance. When the training and prediction sets were independent, moderate-to-high prediction accuracies were achieved across populations by using the training sets with broader genetic diversity, or in pairwise populations having closer genetic relationships. A collection of inbred lines with broader genetic diversity could be used as a permanent training set for TSC improvement, which can be updated by adding more phenotyped lines having closer genetic relationships with the prediction set. The prediction accuracies estimated with a few significantly associated SNPs were moderate-to-high, and continuously increased as more significantly associated SNPs were included. It confirmed that TSC resistance could be furtherly improved by implementing GS for selecting multiple stable genomic regions simultaneously, or by implementing MAS and GS stepwise. The factors of marker density, marker quality, and heterozygosity rate of samples had minor effects on the estimation of the genomic prediction accuracy. The training set size, the genetic relationship between training and prediction sets, phenotypic and genotypic diversity of the training sets, and incorporating known trait-marker associations played more important roles in improving prediction accuracy. The result of the present study provides insight into less complex trait improvement via GS in maize.

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