Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments

Abstract

Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F2-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F2-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.

Applications of Population Genetics to Animal Breeding, from Wright, Fisher and Lush to Genomic Prediction

“…Theory/simulation studies (e.g., Jannink et al 2010), and also experimental trials, have been undertaken. Results to date for maize correspond to those in livestock, in that predictions are better within families (i.e., specific F2 of initial cross) than across families (0.72 vs. 0.47 for grain yield; Albrecht et al 2011), and in another data set predictions of cross performance had essentially zero accuracy (Windhausen et al 2012). Wimmer et al (2013) provide analyses in three species and find that methods involving marker selection (in contrast to BLUP employing ridge regression) can be unreliable unless data sets are large.…”

Genomic Predictability of Interconnected Biparental Maize Populations

“…However, with more investigations in plant breeding applications, new challenges emerge, mainly as the result of the greater possibilities of genetic manipulation and reproduction modes in plants compared to animals. Genomic predictions within diverse populations (Crossa et al 2010;Riedelsheimer et al 2012a,b;Windhausen et al 2012) largely overlap with the scenarios in animal breeding. Predicting crossbred performance in animals also has some similarities with the prediction of maize hybrids from fully homozygous inbred lines drawn from genetically distant heterotic pools (Technow et al 2012).…”

Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize

“…Genomic prediction of hybrid performance came into focus recently, with studies exploring its prospects in maize (Maenhout et al 2010;Massman et al 2013), sunflower , and wheat . However, the low number of markers or the low number of parental lines and phenotyped hybrids used in these studies allowed only preliminary inferences about the prospects of genomic prediction in commercial hybrid breeding programs of ordinary size.Optimal composition of training sets is crucial for successful application of genomic prediction (Rincent et al 2012;Windhausen et al 2012). For hybrid prediction, a critical question is how many hybrids per inbred line, i.e., crosses with lines from the opposite heterotic group, should be included in the training set.…”

“…Optimal composition of training sets is crucial for successful application of genomic prediction (Rincent et al 2012;Windhausen et al 2012). For hybrid prediction, a critical question is how many hybrids per inbred line, i.e., crosses with lines from the opposite heterotic group, should be included in the training set.…”