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High accuracy of predicting hybrid performance of Fusarium head blight resistance by mid-parent values in wheat.

Thomas MiedanerAlbert W SchulthessManje GowdaJochen C ReifC Friedrich H Longin
Published in: TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik (2016)
Mid-parent values of Fusarium head blight (FHB) resistance tested across several locations are a good predictor of hybrid performance caused by a preponderance of additive gene action in wheat. Hybrid breeding is intensively discussed as one solution to boost yield and yield stability including an enhanced biotic stress resistance. Our objectives were to investigate (1) the heterosis for Fusarium head blight (FHB) resistance, (2) the importance of general (GCA) vs. specific combining ability (SCA) for FHB resistance, and (3) the possibility to predict the FHB resistance of the hybrids by the parental means. We re-analyzed phenotypic data of a large population comprising 1604 hybrids and their 120 female and 15 male parental lines evaluated in inoculation trials across seven environments. Mid-parent heterosis of FHB severity averaged -9%, with a range from -36 to +35%. Mean better parent heterosis was 2% and 78 of the hybrids significantly (P < 0.05) outperformed the best commercial check variety included in our study. FHB resistance was not correlated with grain yield in healthy status for lines (r = 0.01) and hybrids (r = 0.09, P < 0.01). While a preponderance of GCA variance (P < 0.01) was found, SCA variance was not significantly different from zero. Accuracy to predict hybrid performance of FHB severity based on mid-parent values and on GCA effects was high (r = 0.70 and 0.86, respectively; P < 0.01). Similarly, line per se performance and GCA effects were significantly correlated (r = 0.77; P < 0.01). The substantial level of mid-parent heterosis in the desired direction of decreased susceptibility and the negligible better parent heterosis suggest that hybrids are an attractive alternative variety type to improve FHB resistance.
Keyphrases
  • transcription factor
  • gene expression
  • genome wide
  • dna methylation
  • machine learning
  • big data