Perennial ryegrass (Lolium perenne L.) is one of the most widely grown forage grasses in temperate agriculture. In order to maintain and increase its usage as forage in livestock agriculture, there is a continued need for improvement in biomass yield, quality, disease resistance and seed yield. Genetic gain for traits such as biomass yield has been relatively modest. This has been attributed to its long breeding cycle, and the necessity to use population based breeding methods. Thanks to recent advances in genotyping techniques there is increasing interest in genomic selection from which genomically estimated breeding values (GEBV) are derived. In this paper we compare the classical RRBLUP model with state-of-the-art machine learning (ML) techniques that should yield themselves easily to use in GS and demonstrate their application to predicting quantitative traits in a breeding population of L. perenne. Prediction accuracies varied from 0 to 0.59 depending on trait, prediction model and composition of the training population. The BLUP model produced the highest prediction accuracies for most traits and training populations. Forage quality traits had the highest accuracies compared to yield related traits. There appeared to be no clear pattern to the effect of the training population composition on the prediction accuracies. The heritability of the forage quality traits was generally higher than for the yield related traits, and could partly explain the difference in accuracy. Some population structure was evident in the breeding populations, and probably contributed to the varying effects of training population on the predictions. The average linkage disequilibrium (LD) between adjacent markers ranged from 0.121 to 0.215. Higher marker density and larger training population closely related with the test population are likely to improve the prediction accuracy.

The grass family (Poaceae), the fourth largest family of flowering plants, encompasses the most economically important cereal, forage and energy crops, and exhibits a unique gametophytic self-incompatibility (SI) mechanism that is controlled by at least two multi-allelic and independent loci, S and Z. Despite intense research efforts over the last six decades, the genes underlying S and Z remain uncharacterised.

We describe a candidate gene approach for associating SNPs with variation in flowering time and water-soluble carbohydrate (WSC) content and other quality traits in the temperate forage grass species Lolium perenne. Three analysis methods were used, which took the significant population structure into account. First, a linear mixed model was used enabling a structured association analysis to be incorporated with the nine populations identified in the structure analysis as random variables. Second, a within-population analysis of variance was performed. Third, a tree-scanning method was used, in which haplotype trees were associated with phenotypes on the basis of inferred haplotypes. Analysis of variance within populations identified several associations between WSC, nitrogen (N), and dry matter digestibility with allelic variants within an alkaline invertase candidate gene LpcAI. These associations were only detected in material harvested in one of the two years. By contrast, consistent associations between the L. perenne homolog (LpHD1) of the rice photoperiod control gene HD1 and flowering time were identified. One SNP, in the immediate upstream region of the LpHD1 coding sequence (C-4443-A), was significant in the linear mixed model. Within-population analysis of variance and tree-scanning analysis confirmed and extended this result to the 2118 polymorphisms in some of the populations. The merits of the tree-scanning method are compared to the single SNP analysis. The potential usefulness of the 4443 SNP in marker-assisted selection is currently being evaluated in test crosses of genotypes from this work with turf-grass varieties.

The Arabidopsis (Arabidopsis thaliana) FLOWERING LOCUS T (FT) gene and its orthologs in other plant species (e.g. rice [Oryza sativa] OsFTL2/Hd3a) have an established role in the photoperiodic induction of flowering response. The genomic and phenotypic variations associated with the perennial ryegrass (Lolium perenne) ortholog of FT, designated LpFT3, was assessed in a diverse collection of nine European germplasm populations, which together constituted an association panel of 864 plants. Sequencing and genotyping of a series of amplicons derived from the nine populations, containing the complete exon and intron sequences as well as 5′ and 3′ noncoding sequences of LpFT3, identified a total of seven haplotypes. Genotyping assays designed to detect the genomic variation showed that three haplotypes were present in approximately equal proportions and represented 84% of the total, with a fourth representing a further 11%. Of the three major haplotypes, two were predicted to code for identical protein products and the third contained two amino acid substitutions. Association analysis using either a mixed model with a relationship matrix to correct for population structure and relatedness or structured association with further correction using genomic control indicated significant associations between LpFT3 and variation in flowering time. These associations were corroborated in a validation population segregating for the same major alleles. The most “diagnostic” region of genomic variation was situated 5′ of the coding sequence. Analysis of this region identified that the interhaplotype variation was closely associated with sequence motifs that were apparently conserved in the 5′ region of orthologs of LpFT3 from other plant species. These may represent cis-regulatory elements involved in influencing the expression of this gene.