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Association mapping of resistance to tar spot complex in maize

文献类型： 外文期刊

作者： Yan, Shufeng 1 ; Loladze, Alexander 2 ; Wang, Nan 3 ; Sun, Shuku 4 ; Chilvers, Martin, I 5 ; Olsen, Michael 6 ; Burgueno, Juan 2 ; Petroli, Cesar Daniel 2 ; Molnar, Terry 2 ; San Vicente, Felix 2 ; Zhang, Xuecai 2 ; Boddupalli, Maruthi Prasanna 6 ;

作者机构： 1.Henan Acad Agr Sci HAAS, Cereal Crops Res Inst, Zhengzhou, Peoples R China

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

3.Chinese Acad Agr Sci CAAS, Inst Crop Sci, Beijing, Peoples R China

4.Qiqihar Fuer Agron Inc, Qiqihar, Peoples R China

5.Michigan State Univ, Dept Plant Soil & Microbial Sci, E Lansing, MI 48824 USA

6.Kenya World Agroforestry Ctr ICRAF, Int Maize & Wheat Improvement Ctr CIMMYT, Nairobi, Kenya

关键词： disease resistance; genome-wide association study; GWAS; maize; QTL; quantitative trait loci

期刊名称：PLANT BREEDING （ 影响因子：2.536； 五年影响因子：2.306 ）

ISSN： 0179-9541

年卷期： 2022 年 141 卷 6 期

页码：

收录情况： SCI

摘要： Tar spot complex (TSC) is a major fungal disease of maize in Mexico and several Central and South American countries. The causal agents of the disease are Phyllachora maydis and Monographella maydis. Yield losses of up to 58% associated with this disease have been reported previously. The majority of commercial maize germplasm in the United States is considered highly susceptible to TSC. In order to accelerate the disease resistance improvement process, genes for resistance to TSC and molecular markers linked to these genes, need to be identified and characterized. Here, we used an association mapping technique to detect quantitative trait loci (QTLs) for TSC resistance using genome-wide association study (GWAS)-based genotyping-by-sequencing (GBS) and Diversity Arrays Technology Sequencing (DArTseq) single-nucleotide polymorphisms. An association mapping panel of 228 CIMMYT maize lines was used for the GWAS. One major QTL explaining 27.66% of the phenotypic variance and four minor QTLs explaining 10.18%, 12.72%, 10.14% and 13.38% of the phenotypic variance, respectively, were identified by the GBS-based association mapping. Two QTLs were identified by DArTseq-based association mapping explaining 1.07% and 18.29% of the phenotypic variance, respectively. These QTLs are expected to facilitate subsequent maize improvement using marker-assisted selection.

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