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

Author:

Guo Zifeng12ORCID,Zou Cheng1,Liu Xiaogang1,Wang Shanhong1,Li Wen-Xue1,Jeffers Dan34,Fan Xingming4,Xu Mingliang2,Xu Yunbi1356

Affiliation:

1. Institute of Crop Science/International Maize and Wheat Improvement Center China, Chinese Academy of Agricultural Sciences, Beijing 100081, China

2. National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China

3. International Maize and Wheat Improvement Center, El Batan, Texcoco, CP 56130, México

4. Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650200, China

5. International Maize and Wheat Improvement Center China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China

6. International Maize and Wheat Improvement Center China Tropical Maize Research Center, Foshan University, Foshan 528231, China

Abstract

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 FER resistance, 2 of which (1_226233417 on chromosome 1 and 10_14501044 on chromosome 10) were associated at threshold of 2.65 × 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.

Publisher

Scientific Societies

Subject

Plant Science,Agronomy and Crop Science

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