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Genome-Wide Analysis of Tar Spot Complex Resistance in Maize Using Genotyping-by-Sequencing SNPs and Whole-Genome Prediction

文献类型： 外文期刊

作者： Cao, Shiliang 2 ; Loladze, Alexander 2 ; Yuan, Yibing 3 ; Wu, Yongsheng 4 ; Zhang, Ao 5 ; Chen, Jiafa 2 ; Huestis, Gor 1 ;

作者机构： 1.Heilongjiang Acad Agr Sci, Maize Res Inst, Harbin 150086, Heilongjiang, Peoples R China

2.CIMMYT, Int Maize & Wheat Improvement Ctr, El Baton 56237, Texcoco, Mexico

3.Sichuan Agr Univ, Maize Res Inst, Wenjiang 611130, Sichuan, Peoples R China

4.Guangxi Acad Agr Sci, Maize Res Inst, Nanning 530007, Guangxi, Peoples R China

5.Shenyang Agr Univ, Agron Coll, Shenyang 110866, Liaoning, Peoples R China

6.CIMMYT, Int Maize & Wheat Improvement Ctr, POB 1041, Nairobi 00621, Kenya

7.CIMMYT,

期刊名称：PLANT GENOME （ 影响因子：4.089； 五年影响因子：4.972 ）

ISSN： 1940-3372

年卷期： 2017 年 10 卷 2 期

页码：

收录情况： SCI

摘要： Tar spot complex (TSC) is one of the most destructive foliar diseases of maize (Zea mays L.) in tropical and subtropical areas of Central and South America, causing significant grain yield losses when weather conditions are conducive. To dissect the genetic architecture of TSC resistance in maize, association mapping, in conjunction with linkage mapping, was conducted on an association- mapping panel and three biparental doubled-haploid (DH) populations using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). Association mapping revealed four quantitative trait loci (QTL) on chromosome 2, 3, 7, and 8. All the QTL, except for the one on chromosome 3, were further validated by linkage mapping in different genetic backgrounds. Additional QTL were identified by linkage mapping alone. A major QTL located on bin 8.03 was consistently detected with the largest phenotypic explained variation: 13% in association-mapping analysis and 13.18 to 43.31% in linkage-mapping analysis. These results indicated that TSC resistance in maize was controlled by a major QTL located on bin 8.03 and several minor QTL with smaller effects on other chromosomes. Genomic prediction results showed moderate-to-high prediction accuracies in different populations using various training population sizes and marker densities. Prediction accuracy of TSC resistance was > 0.50 when half of the population was included into the training set and 500 to 1,000 SNPs were used for prediction. Information obtained from this study can be used for developing functional molecular markers for marker-assisted selection (MAS) and for implementing genomic selection (GS) to improve TSC resistance in tropical maize.

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