Complex Genetic System Involved in Fusarium Ear Rot Resistance in Maize as Revealed by GWAS, Bulked Sample Analysis, and Genomic Prediction

文献类型： 外文期刊

第一作者： Guo, Zifeng

作者： Guo, Zifeng;Zou, Cheng;Liu, Xiaogang;Wang, Shanhong;Li, Wen-Xue;Xu, Yunbi;Guo, Zifeng;Xu, Mingliang;Jeffers, Dan;Xu, Yunbi;Jeffers, Dan;Fan, Xingming;Xu, Yunbi;Xu, Yunbi

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关键词： bulked sample analysis; Fusarium verticillioides; genome-wide association study; genomic selection; maize

期刊名称：PLANT DISEASE （ 影响因子：4.438； 五年影响因子：4.7 ）

ISSN： 0191-2917

年卷期： 2020 年 104 卷 6 期

页码：

收录情况： SCI

摘要： Fusarium ear rot (FER) caused by Fusarium verticillioides is one of the most prevalent maize diseases in China and worldwide. Resistance to FER is a complex trait controlled by multiple genes highly affected by environment. In this paper, genome-wide association study (GWAS), bulked sample analysis (BSA), and genomic prediction were performed for understanding FER resistance using 509 diverse inbred lines, which were genotyped by 37,801 high-quality single-nucleotide polymorphisms (SNPs). Ear rot evaluation was performed using artificial inoculation in four environments in China: Xinxiang, Henan, and Shunyi, Beijing, during 2017 and 2018. Significant phenotypic and genetic variation for FER severity was observed, and FER resistance was significantly correlated among the four environments with a generalized heritability of 0.78. GWAS identified 23 SNPs that were associated with PER resistance, 2 of which (1_226233417 on chromosome 1 and 10_14501044 on chromosome 10) were associated at threshold of 2.65 x 10(-7) [-log(0.01/37,801)]. Using BSA, resistance quantitative trait loci were identified on chromosomes 3, 4, 7, 9, and 10 at the 90% confidence level and on chromosomes 3 and 10 at the 95% confidence level. A key region, bin 10.03, was detected by both GWAS and BSA. Genomic prediction for FER resistance showed that the prediction accuracy by trait-related markers was higher than that by randomly selected markers under different levels of marker density. Marker-assisted selection using genomic prediction could be an efficient strategy for genetic improvement for complex traits like FER resistance.

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