Selection index based on the relative importance of traits and possibilities in breeding popcorn

Estimating optimum and base selection indices in plant and animal breeding programs by development new and simple SAS and R codes

Selection of desirable genotypes or progenies is perhaps the most important practical method in plant and animal breeding programs. The selection index method is the most useful method to choose superior genotypes based on using simultaneous several traits. The optimum and base selection indices are the two indicators that are most used in plant and animal breeding. In this paper, a simple and practical code was developed for the analysis of optimum, base, and Pesek and Baker selection indices. Four different criteria were used to evaluate the selection index, and the phenotypic and genotypic variance-covariance matrix of traits was obtained based on statistical or genetical design. Moreover, an index that was more efficient on these coefficients was used for the breeding program. The results showed that simultaneous selection for the important traits desired by the breeder through economic values such as heritability, genetic, or phenotypic correlation is the most effective method for selecting the best genotypes. Therefore, the best progeny or genotype can be selected to use in breeding programs. This program provides detailed information on selection indices of segregation and natural populations involving any number of individuals or genotypes. These codes are much easier and simpler than other programs and provide more information than other programs. This code is easy to execute in both R and SAS programs.

Genomic prediction models for traits differing in heritability for soybean, rice, and maize

BACKGROUND

Genomic selection is a powerful tool in plant breeding. By building a prediction model using a training set with markers and phenotypes, genomic estimated breeding values (GEBVs) can be used as predictions of breeding values in a target set with only genotype data. There is, however, limited information on how prediction accuracy of genomic prediction can be optimized. The objective of this study was to evaluate the performance of 11 genomic prediction models across species in terms of prediction accuracy for two traits with different heritabilities using several subsets of markers and training population proportions. Species studied were maize (Zea mays, L.), soybean (Glycine max, L.), and rice (Oryza sativa, L.), which vary in linkage disequilibrium (LD) decay rates and have contrasting genetic architectures.

RESULTS

Correlations between observed and predicted GEBVs were determined via cross validation for three training-to-testing proportions (90:10, 70:30, and 50:50). Maize, which has the shortest extent of LD, showed the highest prediction accuracy. Amongst all the models tested, Bayes B performed better than or equal to all other models for each trait in all the three crops. Traits with higher broad-sense and narrow-sense heritabilities were associated with higher prediction accuracy. When subsets of markers were selected based on LD, the accuracy was similar to that observed from the complete set of markers. However, prediction accuracies were significantly improved when using a subset of total markers that were significant at P ≤ 0.05 or P ≤ 0.10. As expected, exclusion of QTL-associated markers in the model reduced prediction accuracy. Prediction accuracy varied among different training population proportions.

CONCLUSIONS

We conclude that prediction accuracy for genomic selection can be improved by using the Bayes B model with a subset of significant markers and by selecting the training population based on narrow sense heritability.