Summary: Here we report the draft genome sequence of perennial ryegrass (Lolium perenne), an economically important forage and turf grass species that is widely cultivated in temperate regions worldwide. It is classified along with wheat, barley, oats and Brachypodium distachyon in the Pooideae sub‐family of the grass family (Poaceae). Transcriptome data was used to identify 28 455 gene models, and we utilized macro‐co‐linearity between perennial ryegrass and barley, and synteny within the grass family, to establish a synteny‐based linear gene order. The gametophytic self‐incompatibility mechanism enables the pistil of a plant to reject self‐pollen and therefore promote out‐crossing. We have used the sequence assembly to characterize transcriptional changes in the stigma during pollination with both compatible and incompatible pollen. Characterization of the pollen transcriptome identified homologs to pollen allergens from a range of species, many of which were expressed to very high levels in mature pollen grains, and are potentially involved in the self‐incompatibility mechanism. The genome sequence provides a valuable resource for future breeding efforts based on genomic prediction, and will accelerate the development of new varieties for more productive grasslands.

Genotyping-by-Sequencing (GBS) is an excellent tool for characterising genetic variation between plant genomes. To date, its use has been reported only for genotyping of single individuals. However, there are many applications where resolving allele frequencies within populations on a genome-wide scale would be very powerful, examples include the breeding of outbreeding species, varietal protection in outbreeding species, monitoring changes in population allele frequencies. This motivated us to test the potential to use GBS to evaluate allele frequencies within populations. Perennial ryegrass is an outbreeding species, and breeding programs are based upon selection on populations. We tested two restriction enzymes for their efficiency in complexity reduction of the perennial ryegrass genome. The resulting profiles have been termed Genome Wide Allele Frequency Fingerprints (GWAFFs), and we have shown how these fingerprints can be used to distinguish between plant populations. Even at current costs and throughput, using sequencing to directly evaluate populations on a genome-wide scale is viable. GWAFFs should find many applications, from varietal development in outbreeding species right through to playing a role in protecting plant breeders’ rights.

Key message Genomic prediction models for starch content and chipping quality show promising results, suggesting that genomic selection is a feasible breeding strategy in tetraploid potato. Abstract Genomic selection uses genome-wide molecular markers to predict performance of individuals and allows selections in the absence of direct phenotyping. It is regarded as a useful tool to accelerate genetic gain in breeding programs, and is becoming increasingly viable for crops as genotyping costs continue to fall. In this study, we have generated genomic prediction models for starch content and chipping quality in tetraploid potato to facilitate varietal development. Chipping quality was evaluated as the colour of a potato chip after frying following cold induced sweetening. We used genotyping-by-sequencing to genotype 762 offspring, derived from a population generated from biparental crosses of 18 tetraploid parents. Additionally, 74 breeding clones were genotyped, representing a test panel for model validation. We generated genomic prediction models from 171,859 single-nucleotide polymorphisms to calculate genomic estimated breeding values. Cross-validated prediction correlations of 0.56 and 0.73 were obtained within the training population for starch content and chipping quality, respectively, while correlations were lower when predicting performance in the test panel, at 0.30–0.31 and 0.42–0.43, respectively. Predictions in the test panel were slightly improved when including representatives from the test panel in the training population but worsened when preceded by marker selection. Our results suggest that genomic prediction is feasible, however, the extremely high allelic diversity of tetraploid potato necessitates large training populations to efficiently capture the genetic diversity of elite potato germplasm and enable accurate prediction across the entire spectrum of elite potatoes. Nonetheless, our results demonstrate that GS is a promising breeding strategy for tetraploid potato.

Genomic selection (GS) has become a commonly used technology in animal breeding. In crops, it is expected to significantly improve the genetic gains per unit of time. So far, its implementation in plant breeding has been mainly investigated in species farmed as homogeneous varieties. Concerning crops farmed in family pools, only a few theoretical studies are currently available. Here, we test the opportunity to implement GS in breeding of perennial ryegrass, using real data from a forage breeding program. Heading date was chosen as a model trait, due to its high heritability and ease of assessment. Genome Wide Association analysis was performed to uncover the genetic architecture of the trait. Then, Genomic Prediction (GP) models were tested and prediction accuracy was compared to the one obtained in traditional Marker Assisted Selection (MAS) methods. Several markers were significantly associated with heading date, some locating within or proximal to genes with a well-established role in floral regulation. GP models gave very high accuracies, which were significantly better than those obtained through traditional MAS. Accuracies were higher when predictions were made from related families and from larger training populations, whereas predicting from unrelated families caused the variance of the estimated breeding values to be biased downwards. We have demonstrated that there are good perspectives for GS implementation in perennial ryegrass breeding, and that problems resulting from low linkage disequilibrium (LD) can be reduced by the presence of structure and related families in the breeding population. While comprehensive Genome Wide Association analysis is difficult in species with extremely low LD, we did identify variants proximal to genes with a known role in flowering time (e.g. CONSTANS and Phytochrome C).

Whole-genome sequences established for model and major crop species constitute a key resource for advanced genomic research. For outbreeding forage and turf grass species like ryegrasses (Lolium spp.), such resources have yet to be developed. Here, we present a model of the perennial ryegrass (Lolium perenne) genome on the basis of conserved synteny to barley (Hordeum vulgare) and the model grass genome Brachypodium (Brachypodium distachyon) as well as rice (Oryza sativa) and sorghum (Sorghum bicolor). A transcriptome-based genetic linkage map of perennial ryegrass served as a scaffold to establish the chromosomal arrangement of syntenic genes from model grass species. This scaffold revealed a high degree of synteny and macrocollinearity and was then utilized to anchor a collection of perennial ryegrass genes in silico to their predicted genome positions. This resulted in the unambiguous assignment of 3,315 out of 8,876 previously unmapped genes to the respective chromosomes. In total, the GenomeZipper incorporates 4,035 conserved grass gene loci, which were used for the first genome-wide sequence divergence analysis between perennial ryegrass, barley, Brachypodium, rice, and sorghum. The perennial ryegrass GenomeZipper is an ordered, information-rich genome scaffold, facilitating map-based cloning and genome assembly in perennial ryegrass and closely related Poaceae species. It also represents a milestone in describing synteny between perennial ryegrass and fully sequenced model grass genomes, thereby increasing our understanding of genome organization and evolution in the most important temperate forage and turf grass species.

Summary: Metabolic profiling was carried out in the forage grass Lolium perenne L. (perennial ryegrass) to uncover mechanisms involved in the plants response to water stress. When leaf and root materials from two genotypes, with a contrasting water stress response, were analysed by GC–MS, a clear difference in the metabolic profiles of the leaf tissue under water stress was observed. Differences were principally due to a reduction in fatty acid levels in the more susceptible Cashel genotype and an increase in sugars and compatible solutes in the more tolerant PI 462336 genotype. Sugars with a significant increase included: raffinose, trehalose, glucose, fructose and maltose. Increasing the ability of perennial ryegrass to accumulate these sugars in response to a water deficit may lead to more tolerant varieties. The metabolomics approach was combined with a transcriptomics approach in the water stress tolerant genotype PI 462336, which has identified perennial ryegrass genes regulated under water stress.