Exploiting genetic diversity in two European maize landraces for improving Gibberella ear rot resistance using genomic tools

David Sewordor Gaikpa, Bettina Kessel, Thomas Presterl, Milena Ouzunova, Ana L. Galiano-Carneiro, Manfred Mayer, Albrecht E. Melchinger, Chris Carolin Schön, Thomas Miedaner

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

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 (rg) correlations between GER severity and three agronomic traits were low (r = − 0.27 to 0.20; rg= − 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 (ρ) of weighted GS (wRR-BLUP) were higher than 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 ρ that could be improved by using fixed marker effects in the GS model.

Original languageEnglish
Pages (from-to)793-805
Number of pages13
JournalTheoretical and Applied Genetics
Volume134
Issue number3
DOIs
StatePublished - Mar 2021

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