Genome-wide association analysis of ear rot resistance caused by Fusarium verticillioides in maize

Genome-wide association analysis for fumonisin content in maize kernels

“…These novel QTLs for fumonisin content would be at bins 3.02, 5.02, 7.05 and 8.07. Genomic regions significantly associated with FER in previous GWAS did not overlap, in general with QTL supporting intervals for kernel contamination with fumonisins, excepting particular regions in bins 3.08, 4.05, 7.05, and 9.03 [19][20][21][22][23][24][25]. Therefore, These QTL could be especially useful to reduce kernel contamination with fumonisins without significantly interfering in mycelia development and growth and, consequently, in the known beneficial endophytic behavior of Fusarium verticillioides.…”

“…In this context, genome-wide association study (GWAS) using inbred line panels appears as an effective alternative to this step-by-step approach for detection of genes involved in resistance to maize kernel contamination with fumonisin. GWAS has been extensively used for detecting associations between molecular markers and resistance to FER or to seedling infection [19][20][21][22][23][24][25][26]. Novel maize loci significantly associated with improved resistance to FER were identified, each locus explaining a small proportion of phenotypic variability.…”

Genomics of Maize Resistance to Fusarium Ear Rot and Fumonisin Contamination

“…Four genes associated to significant SNPs encode defense-related proteins, including a gibberellin 2-oxidase4, a glucosyltransferase, a Ras-related protein RHN1, and an anthranilate phosphoribosyltransferase. Similarly, de Jong et al [ 67 ] showed that significant DarT-seq markers explained 37–51% of the phenotypic variability in each environment, but few coincidences between markers significantly associated with the percentage of ears with rot symptoms in joint and individual analyses, pointing out large QTL × environment interaction effects. These authors also reported few co-localizations with QTL reported in previous studies but that could be consequences of the different inbred panels, marker distribution, and disease-related traits used.…”

“…These authors also reported few co-localizations with QTL reported in previous studies but that could be consequences of the different inbred panels, marker distribution, and disease-related traits used. de Jong et al [ 67 ] performed GWAS analyses with the percentage of ears per plot presenting rot symptoms instead of using a visual score to assess ear damage extension in a representative sample of ears, as it has been commonly done in most studies. The percentage of ears presenting symptoms is not appropriate to accurately estimate damage spread, because no differentiation is made between ears with few kernels presenting rot symptoms and ears totally rotten.…”

Genetic Dissection of Resistance to Gray Leaf Spot by Combining Genome-Wide Association, Linkage Mapping, and Genomic Prediction in Tropical Maize Germplasm

“…Quantitative trait loci (QTLs) underlying resistance to several diseases in maize have been identified in the last two decades (Welz and Geiger, 2000;Shi et al, 2014;Xu et al, 2014;de Jong et al, 2018;He et al, 2018;Sitonik et al, 2019;Du et al, 2020;Lv et al, 2020). Despite the substantial number of QTLs reported, majority of them had huge confidence intervals, which represented large segments of chromosomes.…”