Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations

Abstract

Though epistasis has long been postulated to have a critical role in genetic regulation of important pathways as well as provide a major source of variation in the process of speciation, the importance of epistasis for genomic selection in the context of plant breeding is still being debated. In this paper, we report the results on the prediction of genetic values with epistatic effects for 280 accessions in the Nebraska Wheat Breeding Program using adaptive mixed least absolute shrinkage and selection operator (LASSO). The development of adaptive mixed LASSO, originally designed for association mapping, for the context of genomic selection is reported. The results show that adaptive mixed LASSO can be successfully applied to the prediction of genetic values while incorporating both marker main effects and epistatic effects. Especially, the prediction accuracy is substantially improved by the inclusion of two-locus epistatic effects (more than onefold in some cases as measured by cross-validation correlation coefficient), which is observed for multiple traits and planting locations. This points to significant potential in using non-additive genetic effects for genomic selection in crop breeding practices.

Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications

“…However, with progeny models, the prediction accuracy regarding the breeding value increased from G_A H to G_A H +D H . Our study generated new findings to supplement previously published results based on experimental data that had diverse conclusions, for example, in plants, Dudley andJohnson (2009), Hu et al (2011) and Wang et al (2012) found that including marker interactions substantially increased the prediction accuracy, but Lorenzana and Bernardo (2009) and Muñoz et al (2014) came to opposite conclusions. With animals, Su et al (2012) and Sun et al (2014) showed that including non-additive effects improved the prediction.…”

Goals and hurdles for a successful implementation of genomic selection in breeding programme for selected annual and perennial crops

“…Modelling of epistatic effects should provide more accurate results and the estimation of non-additive effects in genetic predictions is needed (Wang et al, 2012). Implementation of GS in commercial wheat breeding programmes needs further investigation before it can be fully employed Crossa, Beyene et al, 2013;Heffner, Jannink, Iwata et al, 2011;Rutkoski et al, 2012).…”

Modeling Epistasis in Genomic Selection

“…Genomic selection approaches based on additive and dominance effects have been successfully applied to predict complex traits in human (Yang et al 2010), animal (Hayes et al 2009, and plant populations (Jannink et al 2010;Zhao et al 2015). Moreover, several genomic selection approaches have been developed to model both main and epistatic effects (Xu 2007;Cai et al 2011;Wittenburg et al 2011;Wang et al 2012). While in some studies prediction accuracies increased (Hu et al 2011), in others modeling epistasis adversely affected prediction accuracies (Lorenzana and Bernardo 2009).…”