Genomic prediction ability for yield-related traits in German winter barley elite material

Abstract

Genomic prediction was evaluated in German winter barley breeding lines. In this material, prediction ability is strongly influenced by population structure and main determinant of prediction ability is the close genetic relatedness of the breeding material. To ensure breeding progress under changing environmental conditions the implementation and evaluation of new breeding methods is of crucial importance. Modern breeding approaches like genomic selection may significantly accelerate breeding progress. We assessed the potential of genomic prediction in a training population of 750 genotypes, consisting of multiple six-rowed winter barley (Hordeum vulgare L.) elite material families and old cultivars, which reflect the breeding history of barley in Germany. Crosses of parents selected from the training set were used to create a set of double-haploid families consisting of 750 genotypes. Those were used to confirm prediction ability estimates based on a cross-validation with the training set material using 11 different genomic prediction models. Population structure was inferred with dimensionality reduction methods like discriminant analysis of principle components and the influence of population structure on prediction ability was investigated. In addition to the size of the training set, marker density is of crucial importance for genomic prediction. We used genome-wide linkage disequilibrium and persistence of linkage phase as indicators to estimate that 11,203 evenly spaced markers are required to capture all QTL effects. Although a 9k SNP array does not contain a sufficient number of polymorphic markers for long-term genomic selection, we obtained fairly high prediction accuracies ranging from 0.31 to 0.71 for the traits earing, hectoliter weight, spikes per square meter, thousand kernel weight and yield and show that they result from the close genetic relatedness of the material. Our work contributes to designing long-term genetic prediction programs for barley breeding.

Exploring the Realm of Possibilities: Trying to Predict Promising Crosses and Successful Offspring through Genomic Mating in Barley

“…[50] obtained accuracies ranging from 0.40 to 0.83. Similarly, Thorwarth et al [51] found that the prediction accuracy differed substantially between traits and that yield and thousand-kernel weight and heading date were among those showing the highest accuracy (0.74 to 0.83) very similar to results obtained in the current study. Finally, for DON content, Lorenz et al [52] reported an accuracy of 0.37 (without correction for heritability), somewhat lower but similar to the value obtained here (0.5).…”

“…Similar results were found by Sallam and Smith [10], with GS accuracies ranging from 0.19 to 0.43 for yield and from 0.25 to 0.75 for DON concentration (prior to correcting for heritability). Thorwarth et al[51] evaluated the accuracy of prediction within families generated by intercrossing parents from old and modern cultivars. Similar to our results, they found that TKW showed the highest (0.71) prediction accuracy, while GYD exhibited the lowest value (0.31).…”

Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat

“…These results indicate that GS will not be advantageous over pedigree-based selections in the EYT stage, given the family structure in these nurseries, highlighting the importance of implementing GS at the appropriate stage in the breeding program, where there is sufficient Mendelian sampling variation between the sibs. Nevertheless, predictions in populations with full-sibs in the training population compared to populations with only one progeny per cross, resulted in 24–321% increase in accuracies, clearly implying that genetic relatedness between the training and validation populations is crucial for good predictions as also reported in previous studies (Habier et al 2010; Clark et al 2012; Pszczola et al 2012; Thorwarth et al 2017). …”

Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.)

“…Barley is the fourth most important cereal crop worldwide with an annual production of more than 143 million tons and is mainly used for animal feed and malting (FAOSTAT, 2019). Therefore, barley breeding efforts have focused mainly on grain yield and malting quality traits (Thorwarth et al, 2017). However, the collection of phenotypic data such as malting quality traits in large-scale breeding programs is expensive and time-consuming.…”