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

生物 赤霉素 数量性状位点 玉米赤霉 加倍单倍体 基因型 最佳线性无偏预测 镰刀菌 遗传多样性 兽医学 遗传学 农学 园艺 选择(遗传算法) 人口 基因 医学 社会学 人口学 人工智能 计算机科学
作者
David Sewordor Gaikpa,Bettina Kessel,Thomas Presterl,Milena Ouzunova,Ana L. Galiano-Carneiro,Manfred Mayer,Albrecht E. Melchinger,Chris‐Carolin Schön,Thomas Miedaner
出处
期刊:Theoretical and Applied Genetics [Springer Science+Business Media]
卷期号:134 (3): 793-805 被引量:18
标识
DOI:10.1007/s00122-020-03731-9
摘要

High genetic variation in two European maize landraces can be harnessed to improve Gibberella ear rot resistance by integrated genomic tools. 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 ([Formula: see text]) correlations between GER severity and three agronomic traits were low (r = - 0.27 to 0.20; [Formula: see text]= - 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 ([Formula: see text]) of weighted GS (wRR-BLUP) were higher than [Formula: see text] 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 [Formula: see text] that could be improved by using fixed marker effects in the GS model.

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