Marker-assisted selection with spatial analysis of unreplicated field trials

Many studies have shown that molecular markers can improve the efficiency of the selection of quantitative traits in plant breeding provided that large population sizes are used. As a way to limit experimental costs it appears that the use of unreplicated trials may be more valuable than the use of replicated plots in one trial. In this particular context of unreplicated large trials, spatial heterogeneity within the field may reduce the efficiency of the selection. The problem of controlling spatial heterogeneity was seldom considered in the case of marker-assisted selection (MAS). Here, we propose an integrated method to predict genetic values considering simultaneously marker information and possible spatial heterogeneity. This method was applied to a population of 300 F-3 lines of maize evaluated in ii unreplicated trials for grain yield. We show that when spatial field heterogeneity is considered through appropriate statistical models the accuracy of genetic value predictions is improved and the same genetic gain can be achieved with a reduced number of trials.