Association mapping and genomic prediction for ear rot disease caused byFusarium verticillioidesin a tropical maize germplasm

Abstract

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.

Genetic dissection of maize disease resistance and its applications in molecular breeding

“…The average accuracy ranges from 0.46 to 0.86 for the MLN disease severity and 0.46 to 0.87 for the MLN area under disease progress curve (Sitonik et al 2019). GS also showed moderate-to-high accuracy in predicting Fusarium ear rot resistance, in which the maximum prediction accuracy was 0.46 for Fusarium ear rot and 0.67 for fumonisin (Liu et al 2020b;Kuki et al 2020;Holland et al 2020). The prediction accuracy could be greatly elevated if using improved training population.…”

Association mapping and pathway analysis of ear rot disease caused by Aspergillus flavus in a panel of tropical maize germplasm

“…The panel had been previously genotyped by sequencing and reported in Kuki et al (2020) to generate the SNP data set used in the current analysis. Principal components analysis and a kinship coefficient estimation matrix (K) from Kuki et al (2020) was used to quantify population structure and to create a matrix of covariates for use in the mixed linear model GWAS analysis. Genome-wide association analysis was run using the least squares means obtained for the combined analysis of the field data, which were AER ratings scores, as input phenotypes.…”

Introduction to the special Crop Science issue: Celebrating the International Year of Plant Health

“…Kuki et al. (2020) examined resistance to fusarium ear rot caused by Fusarium verticillioides , a disease that impacts grain quality and yield of maize. They used GWAS and genomic prediction in a large collection of tropical maize lines grown in southern Brazil, and found associations with certain aspects of resistance.…”