Genomic prediction of crown rust resistance in Lolium perenne

peer-reviewedBackground Genomic selection (GS) can accelerate genetic gains in breeding programmes by reducing the time it takes to complete a cycle of selection. Puccinia coronata f. sp lolli (crown rust) is one of the most widespread diseases of perennial ryegrass and can lead to reductions in yield, persistency and nutritional value. Here, we used a large perennial ryegrass population to assess the accuracy of using genome wide markers to predict crown rust resistance and to investigate the factors affecting predictive ability. Results Using these data, predictive ability for crown rust resistance in the complete population reached a maximum of 0.52. Much of the predictive ability resulted from the ability of markers to capture genetic relationships among families within the training set, and reducing the marker density had little impact on predictive ability. Using permutation based variable importance measure and genome wide association studies (GWAS) to identify and rank markers enabled the identification of a small subset of SNPs that could achieve predictive abilities close to those achieved using the complete marker set. Conclusion Using a GWAS to identify and rank markers enabled a small panel of markers to be identified that could achieve higher predictive ability than the same number of randomly selected markers, and predictive abilities close to those achieved with the entire marker set. This was particularly evident in a sub-population characterised by having on-average higher genome-wide linkage disequilibirum (LD). Higher predictive abilities with selected markers over random markers suggests they are in LD with QTL. Accuracy due to genetic relationships will decay rapidly over generations whereas accuracy due to LD will persist, which is advantageous for practical breeding applications.This work received funding from the Irish Department of Agriculture Food and the Marine DAFM (RSF 11/S/109) and Teagasc core funding. SKA is supported by a Teagasc PhD Walsh Fellowship. SLB has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 658031

A theoretical and practical analysis of the optimum breeding system for perennial ryegrass

peer-reviewedThe goal of plant breeding is to effectively and efficiently select for the best phenotypes leading to the development of improved cultivars. The objectives for this review are to describe and critically evaluate breeding methods appropriate to the improvement of perennial ryegrass (Lolium perenne L.) in a long-term breeding programme. The optimum breeding system is dependent on the traits for improvement, and the available physical and human resources. Forage dry matter yield, persistency, disease resistance, nutritional value and seed yield are considered among the most important traits for improvement. Careful consideration should be given to the expression of the trait under the management regime imposed in the breeding programme and under real-world sward conditions in the target sowing region. Recurrent selection programmes for intrapopulation improvement are most appropriate for breeding perennial ryegrass. Three distinct types of recurrent selection may be implemented: (i) phenotypic recurrent selection, (ii) genotypic recurrent selection and (iii) marker-assisted selection. Genotypic recurrent selection will be a necessary part of the breeding system if forage yield is a trait for improvement. Genotypic recurrent selection may be practiced using full-sib or half-sib families, each with their own advantages and disadvantages. Phenotypic recurrent selection in tandem (i.e., within-family selection) or in succession with genotypic recurrent selection should be used to improve traits that have a high-correlation between performance from spaced plants and from sward plots. Genome-wide selection represents the most interesting and exciting potential application of marker-assisted selection, although it remains to be seen how beneficial it will be in practice