Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass

Abstract

Key messageExploitation of data from a ryegrass breeding program has enabled rapid development and implementation of genomic selection for sward-based biomass yield with a twofold-to-threefold increase in genetic gain.AbstractGenomic selection, which uses genome-wide sequence polymorphism data and quantitative genetics techniques to predict plant performance, has large potential for the improvement in pasture plants. Major factors influencing the accuracy of genomic selection include the size of reference populations, trait heritability values and the genetic diversity of breeding populations. Global diversity of the important forage species perennial ryegrass is high and so would require a large reference population in order to achieve moderate accuracies of genomic selection. However, diversity of germplasm within a breeding program is likely to be lower. In addition, de novo construction and characterisation of reference populations are a logistically complex process. Consequently, historical phenotypic records for seasonal biomass yield and heading date over a 18-year period within a commercial perennial ryegrass breeding program have been accessed, and target populations have been characterised with a high-density transcriptome-based genotyping-by-sequencing assay. Ability to predict observed phenotypic performance in each successive year was assessed by using all synthetic populations from previous years as a reference population. Moderate and high accuracies were achieved for the two traits, respectively, consistent with broad-sense heritability values. The present study represents the first demonstration and validation of genomic selection for seasonal biomass yield within a diverse commercial breeding program across multiple years. These results, supported by previous simulation studies, demonstrate the ability to predict sward-based phenotypic performance early in the process of individual plant selection, so shortening the breeding cycle, increasing the rate of genetic gain and allowing rapid adoption in ryegrass improvement programs.Electronic supplementary materialThe online version of this article (10.1007/s00122-018-3121-7) contains supplementary material, which is available to authorized users.

Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program

“…However, the implementation of GS implies cross-validation on the evaluation of the genetic and phenotypic breeding values of individuals for accurate selection of superior genotypes [41,42]. GS, in perennial ryegrass, has currently followed the trend of evaluating the HY of sward plots of cultivars and breeding lines [43,44]. The phenotypic selection at individual plants level is required to explore the full potential of GS programs.…”

Field Spectroscopy to Determine Nutritive Value Parameters of Individual Ryegrass Plants

“…The biggest obstacle for improvement of NV is the expense and time required for analysis, which currently entails destructive harvesting and preparation for lab-based methods, demanding high human labour [13][14][15]. This presents a problem considering large amounts of analysis are required for breeding programs due to the quantitative genetics of the traits and several aspects of perennial ryegrass biology [16]. Perennial ryegrass is prone to inbreeding depression; therefore, a large breeding pool is needed to maintain the necessary genetic diversity while at the same time increasing desired alleles [17,18].…”

High-Throughput Genome-Wide Genotyping To Optimize the Use of Natural Genetic Resources in the Grassland Species Perennial Ryegrass (Lolium perenneL.)

“…In perennial ryegrass, the implementation of GP resulted in a range of predictive ability values depending on different factors such as the heritability of the trait, the size of the calibration set, the marker density and the relatedness between the calibration set and the evaluated genetic material (Fè et al 2015a;Grinberg et al 2016;Faville et al 2018;Pembleton et al 2018;Cericola et al 2018). GP has been used in this species to predict i) the phenotypes of individuals evaluated as spaced plants (Grinberg et al 2016), ii) the sward performances of progeny families, including half sib families (Faville et al 2018) and full sib families (Fè et al 2015a) and iii) the sward performances of synthetic cultivars (Pembleton et al 2018). All these studies only addressed elite genetic material from breeding programs.…”