Genomic Prediction Can Provide Precise Estimates of the Genotypic Value of Barley Lines Evaluated in Unreplicated Trials.

Genomic prediction has been established in breeding programs to predict the genotypic values of selection candidates without phenotypic data. First results in wheat showed that genomic predictions can also prove useful to select among material for which phenotypic data are available. In such a scenario, the selection candidates are evaluated with low intensity in the field. Genome-wide effects are estimated from the field data and are then used to predict the genotypic values of the selection candidates. The objectives of our simulation study were to investigate the correlations r(y, g) between genomic predictions y and genotypic values g and to compare these with the correlations r(p, g) between phenotypic values p and genotypic values g. We used data from a yield trial of 250 barley lines to estimate variance components and genome-wide effects. These parameters were used as basis for simulations. The simulations included multiple crossing schemes, population sizes, and varying sizes of the components of the masking variance. The genotypic values g of the selection candidates were obtained by genetic simulations, the phenotypic values p by simulating evaluation in the field, and the genomic predictions y by RR-BLUP effect estimation from the phenotypic values. The correlations r(y, g) were greater than the correlations r(p, g) for all investigated scenarios. We conclude that using genomic predictions for selection among candidates tested with low intensity in the field can proof useful for increasing the efficiency of barley breeding programs.

Prediction of Means and Variances of Crosses With Genome-Wide Marker Effects in Barley.

Background: The expected genetic variance is an important criterion for the selection of crossing partners which will produce superior combinations of genotypes in their progeny. The advent of molecular markers has opened up new vistas for obtaining precise predictors for the genetic variance of a cross, but fast prediction methods that allow plant breeders to select crossing partners based on already available data from their breeding programs without complicated calculations or simulation of breeding populations are still lacking. The main objective of the present study was to demonstrate the practical applicability of an analytical approach for the selection of superior cross combinations with experimental data from a barley breeding program. We used genome-wide marker effects to predict the yield means and genetic variances of 14 DH families resulting from crosses of four donor lines with five registered elite varieties with the genotypic information of the parental lines. For the validation of the predicted parameters, the analytical approach was extended by the masking variance as a major component of phenotypic variance. The predicted parameters were used to fit normal distribution curves of the phenotypic values and to conduct an Anderson-Darling goodness-of-fit test for the observed phenotypic data of the 14 DH families from the field trial. Results: There was no evidence that the observed phenotypic values deviated from the predicted phenotypic normal distributions in 13 out of 14 crosses. The correlations between the observed and the predicted means and the observed and predicted variances were r = 0.95 and r = 0.34, respectively. After removing two crosses with downward outliers in the phenotypic data, the correlation between the observed and predicted variances increased to r = 0.76. A ranking of the 14 crosses based on the sum of predicted mean and genetic variance identified the 50% best crosses from the field trial correctly. Conclusions: We conclude that the prediction accuracy of the presented approach is sufficiently high to identify superior crosses even with limited phenotypic data. We therefore expect that the analytical approach based on genome-wide marker effects is applicable in a wide range of breeding programs.

Genome-Wide Prediction of the Performance of Three-Way Hybrids in Barley.

Predicting the grain yield performance of three-way hybrids is challenging. Three-way crosses are relevant for hybrid breeding in barley ( L.) and maize ( L.) adapted to East Africa. The main goal of our study was to implement and evaluate genome-wide prediction approaches of the performance of three-way hybrids using data of single-cross hybrids for a scenario in which parental lines of the three-way hybrids originate from three genetically distinct subpopulations. We extended the ridge regression best linear unbiased prediction (RRBLUP) and devised a genomic selection model allowing for subpopulation-specific marker effects (GSA-RRBLUP: general and subpopulation-specific additive RRBLUP). Using an empirical barley data set, we showed that applying GSA-RRBLUP tripled the prediction ability of three-way hybrids from 0.095 to 0.308 compared with RRBLUP, modeling one additive effect for all three subpopulations. The experimental findings were further substantiated with computer simulations. Our results emphasize the potential of GSA-RRBLUP to improve genome-wide hybrid prediction of three-way hybrids for scenarios of genetically diverse parental populations. Because of the advantages of the GSA-RRBLUP model in dealing with hybrids from different parental populations, it may also be a promising approach to boost the prediction ability for hybrid breeding programs based on genetically diverse heterotic groups.

Genomic Prediction of Barley Hybrid Performance.

Hybrid breeding in barley ( L.) offers great opportunities to accelerate the rate of genetic improvement and to boost yield stability. A crucial requirement consists of the efficient selection of superior hybrid combinations. We used comprehensive phenotypic and genomic data from a commercial breeding program with the goal of examining the potential to predict the hybrid performances. The phenotypic data were comprised of replicated grain yield trials for 385 two-way and 408 three-way hybrids evaluated in up to 47 environments. The parental lines were genotyped using a 3k single nucleotide polymorphism (SNP) array based on an Illumina Infinium assay. We implemented ridge regression best linear unbiased prediction modeling for additive and dominance effects and evaluated the prediction ability using five-fold cross validations. The prediction ability of hybrid performances based on general combining ability (GCA) effects was moderate, amounting to 0.56 and 0.48 for two- and three-way hybrids, respectively. The potential of GCA-based hybrid prediction requires that both parental components have been evaluated in a hybrid background. This is not necessary for genomic prediction for which we also observed moderate cross-validated prediction abilities of 0.51 and 0.58 for two- and three-way hybrids, respectively. This exemplifies the potential of genomic prediction in hybrid barley. Interestingly, prediction ability using the two-way hybrids as training population and the three-way hybrids as test population or vice versa was low, presumably, because of the different genetic makeup of the parental source populations. Consequently, further research is needed to optimize genomic prediction approaches combining different source populations in barley.

AFLP-based genetic mapping of the "bud-flowering" trait in heather (Calluna vulgaris).

BACKGROUND: Calluna vulgaris is one of the most important landscaping plants produced in Germany. Its enormous economic success is due to the prolonged flower attractiveness of mutants in flower morphology, the so-called bud-bloomers. In this study, we present the first genetic linkage map of C. vulgaris in which we mapped a locus of the economically highly desired trait "flower type". RESULTS: The map was constructed in JoinMap 4.1. using 535 AFLP markers from a single mapping population. A large fraction (40%) of markers showed distorted segregation. To test the effect of segregation distortion on linkage estimation, these markers were sorted regarding their segregation ratio and added in groups to the data set. The plausibility of group formation was evaluated by comparison of the "two-way pseudo-testcross" and the "integrated" mapping approach. Furthermore, regression mapping was compared to the multipoint-likelihood algorithm. The majority of maps constructed by different combinations of these methods consisted of eight linkage groups corresponding to the chromosome number of C. vulgaris. CONCLUSIONS: All maps confirmed the independent inheritance of the most important horticultural traits "flower type", "flower colour", and "leaf colour". An AFLP marker for the most important breeding target "flower type" was identified. The presented genetic map of C. vulgaris can now serve as a basis for further molecular marker selection and map-based cloning of the candidate gene encoding the unique flower architecture of C. vulgaris bud-bloomers.

Towards a unified genetic map for diploid roses.

We have constructed the first integrated consensus map (ICM) for rose, based on the information of four diploid populations and more than 1,000 initial markers. The single population maps are linked via 59 bridge markers, on average 8.4 per linkage group (LG). The integrated map comprises 597 markers, 206 of which are sequence-based, distributed over a length of 530 cM on seven LGs. By using a larger effective population size and therefore higher marker density, the marker order in the ICM is more reliable than in the single population maps. This is supported by a more even marker distribution and a decrease in gap sizes in the consensus map as compared to the single population maps. This unified map establishes a standard nomenclature for rose LGs, and presents the location of important ornamental traits, such as self-incompatibility, black spot resistance (Rdr1), scent production and recurrent blooming. In total, the consensus map includes locations for 10 phenotypic single loci, QTLs for 7 different traits and 51 ESTs or gene-based molecular markers. This consensus map combines for the first time the information for traits with high relevance for rose variety development. It will serve as a tool for selective breeding and marker assisted selection. It will benefit future efforts of the rose community to sequence the whole rose genome and will be useful for synteny studies in the Rosaceae family and especially in the section Rosoideae.

Molecular markers from a BAC contig spanning the Rdr1 locus: a tool for marker-assisted selection in roses.

We constructed a BAC contig of about 300 kb spanning the Rdr1 locus for black spot resistance in Rosa multiflora hybrids, using a new BIBAC library from DNA of this species. From this contig, we developed broadly applicable simple sequence repeat (SSR) markers tightly linked to Rdr1, which are suitable for genetic analyses and marker-assisted selection in roses. As a source for the high molecular weight DNA, we chose the homozygous resistant R. multiflora hybrid 88/124-46. For the assembly of the BAC contig, we made use of molecular markers derived from a previously established R. rugosa contig. In order to increase the resolution for fine mapping, the size of the population was increased to 974 plants. The genomic region spanning Rdr1 is now genetically restricted to 0.2 cM, corresponding to a physical distance of about 300 kb. One single-stranded conformational polymorphism (SSCP) and one SSR marker cosegregate with the Rdr1-mediated black spot resistance, while one SSR and several cleaved amplified polymorphic sequence or SSCP markers are very tightly linked with one to three recombinants among the 974 plants. The benefits of the molecular markers developed from the R. multiflora contig for the genetic analysis of roses and the integration of rose genetic maps are discussed.