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Association mapping and genomic prediction for ear rot disease caused by Fusarium verticillioides in a tropical maize germplasm
Author(s) -
Kuki Maurício Carlos,
Pinto Ronald José Barth,
Bertagna Filipe Augusto Bengosi,
Tessmann Dauri José,
Teixeira do Amaral Antônio,
Scapim Carlos Alberto,
Holland James Brendan
Publication year - 2020
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.1002/csc2.20272
Subject(s) - biology , germplasm , single nucleotide polymorphism , fusarium , quantitative trait locus , genetic architecture , association mapping , snp , trait , genetics , candidate gene , genotype , inbred strain , gene , agronomy , computer science , programming language
Fusarium ear rot (FER), caused by Fusarium verticillioides (Sacc.) Nirenberg, is one of the major ear diseases that affect both yield and grain quality in maize ( Zea mays L.), especially in tropical environments. Fusarium genetic resistance is a complex trait, controlled by several small‐effect genes and strongly influenced by the environment. We applied a comprehensive genome‐wide association study and genomic prediction for ear rot and starburst symptoms, using 291,633 high‐quality single nucleotide polymorphism (SNPs) markers in 320 tropical maize inbred lines, in two distinct locations in Brazil's southern region. Three SNPs were significantly associated with starburst symptoms, each associated with 6–8% of the phenotypic variance, and with gene models that have expression levels in ears, pericarp, and kernels, corresponding to disease infection period and suggesting some role in FER resistance. No significant SNP was associated with FER, which is an indication that the genetic architecture for this trait is highly polygenic, with potentially many variants having small effects that are not detectable in the association mapping analysis. We observed genomic prediction accuracies ranging from 0.34 to 0.4 for FER and starburst, respectively. Further research is required to validate these significant SNPs and their relationship to genes affecting FER resistance, and also to improve genomic prediction accuracies across different genetic backgrounds.