Northern corn leaf blight (NCLB), the most devastating leaf pathogen in maize (Zea mays L.), is caused by the heterothallic ascomycete Setosphaeria turcica. The pathogen population shows an extremely high genetic diversity in tropical and subtropical regions. Varietal resistance is the most efficient technique to control NCLB. Host resistance can be qualitative based on race-specific Ht genes or quantitative controlled by many genes with small effects. Quantitative resistance is moderately to highly effective and should be more durable combatting all races of the pathogen. Quantitative resistance must, however, be analyzed in many environments (= location × year combinations) to select stable resistances. In the tropical and subtropical environments, quantitative resistance is the preferred option to manage NCLB epidemics. Resistance level can be increased in practical breeding programs by several recurrent selection cycles based on disease severity rating and/or by genomic selection. This review aims to address two important aspects of the NCLB pathosystem: the genetics of the fungus S. turcica and the modes of inheritance of the host plant maize, including successful breeding strategies regarding NCLB resistance. Both drivers of this pathosystem, pathogen, and host, must be taken into account to result in more durable resistance.
Key message
High genetic variation in two European maize landraces can be harnessed to improve Gibberella ear rot resistance by integrated genomic tools.
Abstract
Fusarium graminearum (Fg) causes Gibberella ear rot (GER) in maize leading to yield reduction and contamination of grains with several mycotoxins. This study aimed to elucidate the molecular basis of GER resistance among 500 doubled haploid lines derived from two European maize landraces, “Kemater Landmais Gelb” (KE) and “Petkuser Ferdinand Rot” (PE). The two landraces were analyzed individually using genome-wide association studies and genomic selection (GS). The lines were genotyped with a 600-k maize array and phenotyped for GER severity, days to silking, plant height, and seed-set in four environments using artificial infection with a highly aggressive Fg isolate. High genotypic variances and broad-sense heritabilities were found for all traits. Genotype-environment interaction was important throughout. The phenotypic (r) and genotypic ($${r}_{g}$$
r
g
) correlations between GER severity and three agronomic traits were low (r = − 0.27 to 0.20; $${r}_{g}\hspace{0.17em}$$
r
g
= − 0.32 to 0.22). For GER severity, eight QTLs were detected in KE jointly explaining 34% of the genetic variance. In PE, no significant QTLs for GER severity were detected. No common QTLs were found between GER severity and the three agronomic traits. The mean prediction accuracies ($$\rho $$
ρ
) of weighted GS (wRR-BLUP) were higher than $$\rho $$
ρ
of marker-assisted selection (MAS) and unweighted GS (RR-BLUP) for GER severity. Using KE as the training set and PE as the validation set resulted in very low $$\rho $$
ρ
that could be improved by using fixed marker effects in the GS model.
Maize production is on risk by Gibberella ear rot (GER) caused by Fusarium graminearum. This is one of the most important ear rot diseases in temperate zones as it leads to yield losses and production of harmful mycotoxins. We investigated, for the first time, the potential use of Brazilian tropical maize to increase resistance levels to GER in temperate European flint germplasm by analyzing six interconnected biparental populations. We assessed GER symptoms in Brazil and in Europe in up to six environments (= location × year combinations) during the growing seasons of 2018 and 2019. We conducted multi-parent QTL and biparental QTL mapping, and identified four QTLs with additive gene action, each explaining 5.4 to 21.8% of the total genotypic variance for GER resistance. Among them, QTL q1 was stable across test environments, populations, and between inbred lines and testcrosses. The accuracies of genomic prediction ranged from 0.50 to 0.59 depending on the resistance donor and prediction model. Jointly, our study reveals the potential use of Brazilian resistance sources to increase GER resistance levels by genomics-assisted breeding.
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