Efficiency of linear selection index in predicting rice hybrid performance
Tóm tắt
Selection index (SI) theory has been applied to predict the net genetic merit, select parents for the next cycle, and maximize the selection response in plant breeding. However, up to now, SI has not been applied to predict unobserved hybrid performance. In hybrid breeding, it is impossible to test every cross, and accurate prediction can help breeders greatly reduce the experimental cost. Traditional genomic selection (GS) targets single-trait prediction, and useful information about other related traits is ignored. With the data set of 575 rice hybrids subjected to the North Carolina mating design II, this information was utilized to develop a linear SI-based GS method to predict rice hybrid for a more accurate and comprehensive selection. Cross-validation results showed that genetic information of a low-heritability target trait such as grain yield could be greatly aggregated from auxiliary traits using SI. When SI was used for directly predicting the target trait, the SI-direct prediction underperformed the traditional genomic prediction in most cases. However, when the SI-direct prediction was combined with the traditional genomic prediction using a suitable weight ω inferred from the training data set, significantly higher accuracy could be obtained. This method was called as SI-assisted prediction. It provided a promising prediction means for breeding application, which used the phenotypes of auxiliary traits only in the training data set. Additionally, it was found that SI-assisted accuracy increased as the genetic correlation between auxiliary traits and the target trait increased, and high heritability of auxiliary traits could also improve the prediction performance.
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