Dataset Information

Parental selection of hybrid breeding based on maternal and paternal inheritance of traits in rapeseed (Brassica napus L.).

ABSTRACT: Parental selection is crucial for hybrid breeding, but the methods available for such a selection are not very effective. In this study, a 6×6 incomplete diallel cross was designed using 12 rapeseed germplasms, and a total of 36 hybrids together with their parental lines were planted in 4 environments. Four yield-related traits and seed oil content (OC) were evaluated. Genetic distance (GD) was estimated with 359 simple sequence repeats (SSRs) markers. Heterosis levels, general combining ability (GCA) and specific combining ability (SCA) were evaluated. GD was found to have a significant correlation with better-parent heterosis (BPH) of thousand seed weight (TSW), SCA of seeds per silique (SS), TSW, and seed yield per plant (SY), while SCA showed a statistically significant correlation with heterosis levels of all traits at 1% significance level. Statistically significant correlations were also observed between GCA of maternal or paternal parents and heterosis levels of different traits except for SS. Interestingly, maternal (TSW, SS, and OC) and paternal (siliques per plant (SP) and SY) inheritance of traits was detected using contribution ratio of maternal and paternal GCA variance as well as correlations between GCA and heterosis levels. Phenotype and heterosis levels of all the traits except TSW of hybrids were significantly correlated with the average performance of parents. The correlations between SS and SP, SP and OC, and SY and OC were statistically significant in hybrids but not in parents. Potential applications of parental selection in hybrid breeding were discussed.

SUBMITTER: Xing N

PROVIDER: S-EPMC4111582 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Parental selection of hybrid breeding based on maternal and paternal inheritance of traits in rapeseed (Brassica napus L.).

Xing Nailin N Fan Chuchuan C Zhou Yongming Y

PloS one 20140725 7

Parental selection is crucial for hybrid breeding, but the methods available for such a selection are not very effective. In this study, a 6×6 incomplete diallel cross was designed using 12 rapeseed germplasms, and a total of 36 hybrids together with their parental lines were planted in 4 environments. Four yield-related traits and seed oil content (OC) were evaluated. Genetic distance (GD) was estimated with 359 simple sequence repeats (SSRs) markers. Heterosis levels, general combining ability ...[more]

PMID: 25061995

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Project description:BackgroundStudying the relationships between rapeseed seed yield (SY) and its yield-related traits can assist rapeseed breeders in the efficient indirect selection of high-yielding varieties. However, since the conventional and linear methods cannot interpret the complicated relations between SY and other traits, employing advanced machine learning algorithms is inevitable. Our main goal was to find the best combination of machine learning algorithms and feature selection methods to maximize the efficiency of indirect selection for rapeseed SY.ResultsTo achieve that, twenty-five regression-based machine learning algorithms and six feature selection methods were employed. SY and yield-related data from twenty rapeseed genotypes were collected from field experiments over a period of 2 years (2019-2021). Root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2) were used to evaluate the performance of the algorithms. The best performance with all fifteen measured traits as inputs was achieved by the Nu-support vector regression algorithm with quadratic polynomial kernel function (R2 = 0.860, RMSE = 0.266, MAE = 0.210). The multilayer perceptron neural network algorithm with identity activation function (MLPNN-Identity) using three traits obtained from stepwise and backward selection methods appeared to be the most efficient combination of algorithms and feature selection methods (R2 = 0.843, RMSE = 0.283, MAE = 0.224). Feature selection suggested that the set of pods per plant and days to physiological maturity along with plant height or first pod height from the ground are the most influential traits in predicting rapeseed SY.ConclusionThe results of this study showed that MLPNN-Identity along with stepwise and backward selection methods can provide a robust combination to accurately predict the SY using fewer traits and therefore help optimize and accelerate SY breeding programs of rapeseed.

Dataset Information

Parental selection of hybrid breeding based on maternal and paternal inheritance of traits in rapeseed (Brassica napus L.).

Publications

Parental selection of hybrid breeding based on maternal and paternal inheritance of traits in rapeseed (Brassica napus L.).

Similar Datasets

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