Parental‐progeny‐based best linear unbiased prediction for determining maize single‐cross performance and resistance to Pythium root and stalk rot

Root and stalk rot (RSR) of maize (Zea mays L.) plants, caused by soil‐borne disease pathogens of the genus Pythium, can get worse in global warming. It has been known that the resistance of F1 hybrids often disaccords with those of their parental inbreds, which makes it difficult to develop resista...

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Veröffentlicht in:Grassland science 2022-07, Vol.68 (3), p.226-232
Hauptverfasser: Mitsuhashi, Shohei, Tamaki, Hiroyuki
Format: Artikel
Sprache:eng
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Zusammenfassung:Root and stalk rot (RSR) of maize (Zea mays L.) plants, caused by soil‐borne disease pathogens of the genus Pythium, can get worse in global warming. It has been known that the resistance of F1 hybrids often disaccords with those of their parental inbreds, which makes it difficult to develop resistant hybrids effectively. Best linear unbiased prediction (BLUP) is a standard mixed model equation, which is fitted for predicting hybrid performance by the parental inbreds of maize. The objective of this study was to evaluate simple parental‐progeny‐based BLUP in predicting single‐cross performance and to determine the importance of general combining ability of the resistance to Pythium RSR. The performance prediction of the parental inbreds from BLUP was consistent with empirical knowledge and was determined mostly useful, despite not using a coefficient of coancestry. Correlation coefficients between breeding values from BLUP and actual field data for hybrids, across different experiments from 2018 to 2019, were relatively high (R = 0.854 and 0.703, respectively). These results indicate the potential of the parental‐progeny‐based BLUP for maize single‐cross performance. This is the first report in predicting the resistance to this disease with BLUP, and the findings can be applied to routine breeding programs as well as to genome‐wide molecular polymorphism data to contribute to the future breeding programs.
ISSN:1744-6961
1744-697X
DOI:10.1111/grs.12358