Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families.

Genomic selection has been extensively implemented in plant breeding schemes. Genomic selection incorporates dense genome-wide markers to predict the breeding values for important traits based on information from genotype and phenotype records on traits of interest in a reference population. To date, most relevant investigations have been performed using single trait genomic prediction models (STGP). However, records for several traits at once are usually documented for breeding lines in commercial breeding programs. By incorporating benefits from genetic characterizations of correlated phenotypes, multiple trait genomic prediction (MTGP) may be a useful tool for improving prediction accuracy in genetic evaluations. The objective of this study was to test whether the use of MTGP and including proper modeling of spatial effects can improve the prediction accuracy of breeding values in commercial barley and wheat breeding lines. We genotyped 1,317 spring barley and 1,325 winter wheat lines from a commercial breeding program with the Illumina 9K barley and 15K wheat SNP-chip (respectively) and phenotyped them across multiple years and locations. Results showed that the MTGP approach increased correlations between future performance and estimated breeding value of yields by 7% in barley and by 57% in wheat relative to using the STGP approach for each trait individually. Analyses combining genomic data, pedigree information, and proper modeling of spatial effects further increased the prediction accuracy by 4% in barley and 3% in wheat relative to the model using genomic relationships only. The prediction accuracy for yield in wheat and barley yield trait breeding, were improved by combining MTGP and spatial effects in the model.

Fungal diseases are a major constraint for wheat production. Effective disease resistance is essential for ensuring a high production quality and yield. One of the most severe fungal diseases of wheat is Septoria tritici blotch (STB), which influences wheat production across the world. In this study, genomewide association mapping was used to identify new chromosomal regions on the wheat genome conferring effective resistance towards STB. A winter wheat population of 164 North European varieties and breeding lines was genotyped with 15K single nucleotide polymorphism (SNP) wheat array. The varieties were evaluated for STB in field trials at three locations in Denmark and across 3 years. The association analysis revealed four quantitative trait loci, on chromosomes 1B, 2A, 5D and 7A, highly associated with STB resistance. By comparing varieties containing several quantitative trait loci (QTL) with varieties containing none of the found QTL, a significant difference was found in the mean disease score. This indicates that an effective resistance can be obtained by pyramiding several QTL.

Until now, genomic prediction (GP) in plant breeding has only used information from individuals that have been genotyped. Information from nongenotyped relatives of genotyped individuals can also be used. Single‐step GP combines marker and pedigree information into a single relationship matrix to perform GP. The objective of this study was to evaluate single‐step GP in a wheat breeding program. We compared the performance of pedigree‐based, marker‐based, and single‐step models (ABLUP, GBLUP, and HBLUP, respectively). Data consisted of 1176 genotyped (via genotyping‐by‐sequencing) and 11,131 nongenotyped wheat lines replicated in five management environments at the CIMMYT experiment station in Obregon, Mexico. Analyses involved three scenarios: (i) all lines had pedigree information but only some were genotyped, with phenotypes from one or two environments in the 2011–2012 season, (ii) all lines had genotype and pedigree information and phenotypes from four or five environments in the 2012–2013 season, and (iii) the combination of Scenarios 1 and 2. Prediction accuracies were calculated by five‐fold cross validation on plant height, maturity, heading date, and grain yield. The single‐step HBLUP outperformed GBLUP and pedigree‐based ABLUP in all cases. We conclude that the single‐step procedure combining pedigree and genomic marker data should be favored where appropriate data is available for GP in wheat breeding programs.

Genomic selection is a method to predict breeding values using genome-wide single-nucleotide polymorphism (SNP) markers. High-quality marker data are necessary for genomic selection. The aim of this study was to investigate the effect of marker-editing criteria on the accuracy of genomic predictions in the Nordic Holstein and Jersey populations. Data included 4429 Holstein and 1071 Jersey bulls. In total, 48,222 SNP for Holstein and 44,305 SNP for Jersey were polymorphic. The SNP data were edited based on (i) minor allele frequencies (MAF) with thresholds of no limit, 0.001, 0.01, 0.02, 0.05 and 0.10, (ii) deviations from Hardy-Weinberg proportions (HWP) with thresholds of no limit, chi-squared p-values of 0.001, 0.02, 0.05 and 0.10, and (iii) GenCall (GC) scores with thresholds of 0.15, 0.55, 0.60, 0.65 and 0.70. The marker data sets edited with different criteria were used for genomic prediction of protein yield, fertility and mastitis using a Bayesian variable selection and a GBLUP model. De-regressed EBV were used as response variables. The result showed little difference between prediction accuracies based on marker data sets edited with MAF and deviation from HWP. However, accuracy decreased with more stringent thresholds of GC score. According to the results of this study, it would be appropriate to edit data with restriction of MAF being between 0.01 and 0.02, a p-value of deviation from HWP being 0.05, and keeping all individual SNP genotypes having a GC score over 0.15.