Genomic Selection in Plant Breeding: A Comparison of Models

Abstract

Simulation and empirical studies of genomic selection (GS) show accuracies sufficient to generate rapid genetic gains. However, with the increased popularity of GS approaches, numerous models have been proposed and no comparative analysis is available to identify the most promising ones. Using eight wheat {Triti-cum aestivum L.), barley {Hordeum vulgäre L.), Arabidopsis thaliana (L.) Heynh., and maize {Zea mays L.) datasets, the predictive ability of currently available GS models along with several machine learning methods was evaluated by comparing accuracies, the genomic estimated breeding values (GEBVs), and the marker effects for each model. While a similar level of accuracy was observed for many models, the level of overfitting varied widely as did the computation time and the distribution of marker effect estimates. Our comparisons suggested that GS in plant breeding programs could be based on a reduced set of models such as the Bayesian Lasso, weighted Bayesian shrinkage regression (wBSR, a fast version of BayesB), and random forest (RF) (a machine learning method that could capture nonadditive effects). Linear combinations of different models were tested as well as bagging and boosting methods, but they did not improve accuracy. This study also showed large differences in accuracy between subpopulations within a dataset that could not always be explained by differences in phenotypic variance and size. The broad diversity of empirical datasets tested here adds evidence that GS could increase genetic gain per unit of time and cost.

Potential and limits to unravel the genetic architecture and predict the variation of Fusarium head blight resistance in European winter wheat (Triticum aestivum L.)

“…Previous genome-wide association mapping studies suggested presence of epistatic effects for FHB resistance . Thus, it is tempting to speculate that RKHSR outperforms RR-BLUP and Bayes-Cπ as observed in several previous genomic selection studies (Crossa et al, , 2011de los Campos et al, 2010;Heslot et al, 2012). In contrast to this expectation, however, no significant differences among the accuracies of the three applied genomic selection models were observed (Table 3).…”

Genome-enabled methods for predicting litter size in pigs: a comparison

“…It has been shown that RKHS can outperform linear models in predictive ability in chickens (Gonzá lez-Recio et al, 2008) and plants Gonzá lez-Camacho et al, 2012). Heslot et al (2012) compared the predictive performance of different linear and non-linear GS models for several traits in plants and found that, overall, linear and non-linear models performed similarly. RKHS seemingly gave the best predictive ability: for example, in 16 out of their 18 comparisons RKHS outperformed Bayes Cp (Habier et al, 2011).…”

Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat

“…The estimated marker effects are then applied to predict the breeding value of nonphenotyped individuals based on their molecular marker profiles. The great potential of genomic selection for complex traits has been demonstrated in several experimental studies in plant and animal breeding populations (Bernardo, 2008;Heffner et al, 2009;Heslot et al, 2012;Massman et al, 2013).…”

“…One crucial challenge in genomic selection is to choose the appropriate biometrical model (Heffner et al, 2009;Heslot et al, 2012). The relative performance of biometrical models is expected to depend on the genetic architecture of the traits under scrutiny.…”