New accuracy estimators for genomic selection with application in a cassava (Manihot esculenta) breeding program.

Genomic selection is the main force driving applied breeding programs and accuracy is the main measure for evaluating its efficiency. The traditional estimator (TE) of experimental accuracy is not fully adequate. This study proposes and evaluates the performance and efficiency of two new accuracy estimators, called regularized estimator (RE) and hybrid estimator (HE), which were applied to a practical cassava breeding program and also to simulated data. The simulation study considered two individual narrow sense heritability levels and two genetic architectures for traits. TE, RE, and HE were compared under four validation procedures: without validation (WV), independent validation, ten-fold validation through jacknife allowing different markers, and with the same markers selected in each cycle. RE presented accuracies closer to the parametric ones and less biased and more precise ones than TE. HE proved to be very effective in the WV procedure. The estimators were applied to five traits evaluated in a cassava experiment, including 358 clones genotyped for 390 SNPs. Accuracies ranged from 0.67 to 1.12 with TE and from 0.22 to 0.51 with RE. These results indicated that TE overestimated the accuracy and led to one accuracy estimate (1.12) higher than one, which is outside of the parameter space. Use of RE turned the accuracy into the parameter space. Cassava breeding programs can be more realistically implemented using the new estimators proposed in this study, providing less risky practical inferences.

The contribution of dominance to phenotype prediction in a pine breeding and simulated population.

Pedigrees and dense marker panels have been used to predict the genetic merit of individuals in plant and animal breeding, accounting primarily for the contribution of additive effects. However, nonadditive effects may also affect trait variation in many breeding systems, particularly when specific combining ability is explored. Here we used models with different priors, and including additive-only and additive plus dominance effects, to predict polygenic (height) and oligogenic (fusiform rust resistance) traits in a structured breeding population of loblolly pine (Pinus taeda L.). Models were largely similar in predictive ability, and the inclusion of dominance only improved modestly the predictions for tree height. Next, we simulated a genetically similar population to assess the ability of predicting polygenic and oligogenic traits controlled by different levels of dominance. The simulation showed an overall decrease in the accuracy of total genomic predictions as dominance increases, regardless of the method used for prediction. Thus, dominance effects may not be accounted for as effectively in prediction models compared with traits controlled by additive alleles only. When the ratio of dominance to total phenotypic variance reached 0.2, the additive-dominance prediction models were significantly better than the additive-only models. However, in the prediction of the subsequent progeny population, this accuracy increase was only observed for the oligogenic trait.

Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.).

Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression-best linear unbiased prediction (RR-BLUP), (ii) Bayes A, (iii) Bayes Cπ, and (iv) Bayesian LASSO are presented. In addition, a modified RR-BLUP (RR-BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cπ, Bayes A, and RR-BLUB B had higher predictive ability than RR-BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR-BLUP is the assumption of equal contribution of all markers to the observed variation. However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models.

Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments.

• Genomic selection is increasingly considered vital to accelerate genetic improvement. However, it is unknown how accurate genomic selection prediction models remain when used across environments and ages. This knowledge is critical for breeders to apply this strategy in genetic improvement. • Here, we evaluated the utility of genomic selection in a Pinus taeda population of c. 800 individuals clonally replicated and grown on four sites, and genotyped for 4825 single-nucleotide polymorphism (SNP) markers. Prediction models were estimated for diameter and height at multiple ages using genomic random regression best linear unbiased predictor (BLUP). • Accuracies of prediction models ranged from 0.65 to 0.75 for diameter, and 0.63 to 0.74 for height. The selection efficiency per unit time was estimated as 53-112% higher using genomic selection compared with phenotypic selection, assuming a reduction of 50% in the breeding cycle. Accuracies remained high across environments as long as they were used within the same breeding zone. However, models generated at early ages did not perform well to predict phenotypes at age 6 yr. • These results demonstrate the feasibility and remarkable gain that can be achieved by incorporating genomic selection in breeding programs, as long as models are used at the relevant selection age and within the breeding zone in which they were estimated.