Adding genetically distant individuals to training populations reduces genomic prediction accuracy in Barley

One of the most important factors affecting genomic prediction accuracy appears to be training population (TP) composition. The objective of this study was to evaluate the effect of genomic relationship on genomic prediction accuracy and determine if adding increasingly unrelated individuals to a TP can reduce prediction accuracy. To accomplish this, a population of barley (Hordeum vulgare L.) lines from the University of Minnesota (lines denoted as MN) and North Dakota State University (lines denoted as ND) breeding programs were used for model training. Predictions were validated using two independent sets of progenies derived from MN x MN crosses and ND x ND crosses. Predictive ability sharply decreased with decreasing relationship between the TP and validation population (VP). More importantly, it was observed that adding increasingly unrelated individuals to the TP can actually reduce predictive ability compared with smaller TPs consisting of highly related individuals only. Reported results are possibly conditional on the relatively low marker density (342 single nucleotide polymorphisms [SNPs]) used. Nevertheless, these findings suggest plant breeding programs desiring to use genomic selection could benefit from focusing on good phenotyping of smaller TPs closely related to the selection candidates rather than developing large and diverse TPs.