摘要
Fusarium ear rot(FER)is a destructive maize fungal disease worldwide.In this study,three tropical maize populations consisting of 874 inbred lines were used to perform genomewide association study(GWAS)and genomic prediction(GP)analyses of FER resistance.Broad phenotypic variation and high heritability for FER were observed,although it was highly influenced by large genotype-by-environment interactions.In the 874 inbred lines,GWAS with general linear model(GLM)identified 3034 single-nucleotide polymorphisms(SNPs)significantly associated with FER resistance at the P-value threshold of 1×10^(-5),the average phenotypic variation explained(PVE)by these associations was 3%with a range from 2.33%to 6.92%,and 49 of these associations had PVE values greater than 5%.The GWAS analysis with mixed linear model(MLM)identified 19 significantly associated SNPs at the P-value threshold of 1×10^(-4),the average PVE of these associations was 1.60%with a range from 1.39%to 2.04%.Within each of the three populations,the number of significantly associated SNPs identified by GLM and MLM ranged from 25 to 41,and from 5 to 22,respectively.Overlapping SNP associations across populations were rare.A few stable genomic regions conferring FER resistance were identified,which located in bins 3.04/05,7.02/04,9.00/01,9.04,9.06/07,and 10.03/04.The genomic regions in bins 9.00/01 and 9.04 are new.GP produced moderate accuracies with genome-wide markers,and relatively high accuracies with SNP associations detected from GWAS.Moderate prediction accuracies were observed when the training and validation sets were closely related.These results implied that FER resistance in maize is controlled by minor QTL with small effects,and highly influenced by the genetic background of the populations studied.Genomic selection(GS)by incorporating SNP associations detected from GWAS is a promising tool for improving FER resistance in maize.
基金
The authors gratefully acknowledge the financial support from the MasAgro project funded by Mexico’s Secretary of Agriculture and Rural Development(SADER),the Genomic Open-source Breeding Informatics Initiative(GOBII)(grant number OPP1093167)supported by the Bill&Melinda Gates Foundation,and the CGIAR Research Program(CRP)on maize(MAIZE)
MAIZE receives W1&W2 support from the Governments of Australia,Belgium,Canada,China,France,India,Japan,the Republic of Korea,Mexico,Netherlands,New Zealand,Norway,Sweden,Switzerland,the United Kingdom,USA,and the World Bank
The authors also thank the National Natural Science Foundation of China(grant number 31801442)
the CIMMYT–China Specialty Maize Research Center Project funded by the Shanghai Municipal Finance Bureau
the China Scholarship Council.