Effects of Different Strategies for Exploiting Genomic Selection in Perennial Ryegrass Breeding Programs

Abstract

Genomic selection (GS) is a potential pathway to accelerate genetic gain for perennial ryegrass (Lolium perenne L.). The main objectives of the present study were to investigate the level of genetic gain and accuracy by applying GS in commercial perennial ryegrass breeding programs. Different scenarios were compared to a conventional breeding program. Simulated scenarios differed in the method of selection and structure of the breeding program. Two scenarios (Phen-Y12 and Phen) for phenotypic selection and three scenarios (GS-Y12, GS and GS-SP) were considered for genomic breeding schemes. All breeding schemes were simulated for 25 cycles. The amount of genetic gain achieved was different across scenarios. Compared to phenotypic scenarios, GS scenarios resulted in substantially larger genetic gain for the simulated traits. This was mainly due to more efficient selection of plots and single plants based on genomic estimated breeding values. Also, GS allows for reduction in waiting time for the availability of the superior genetic materials from previous cycles, which led to at least a doubling or a trebling of genetic gain compared to the traditional program. Reduction in additive genetic variance levels were higher with GS scenarios than with phenotypic selection. The results demonstrated that implementation of GS in ryegrass breeding is possible and presents an opportunity to make very significant improvements in genetic gains.

Characterization and practical use of self-compatibility in outcrossing grass species

“…Recent estimates indicated an annual genetic gain for biomass yield of 0.45 % in L. perenne and from 0.27 to 0.37 % for L. multiflorum , amongst the lowest compared with other major crops ( Laidig et al , 2014 ; McDonagh et al , 2014 ), including forage Z. mays ( Taube et al ., 2020 ), where hybrid production technologies have been applied. To redress this deficiency, genomic selection technologies, developed in animal breeding, are currently being applied to recurrent selection programmes of outbreeding temperate forage grasses, most recently by Danish ( Esfandyari et al , 2020 ), French/Belgian ( Keep et al , 2020 ), New Zealand ( Arojju et al , 2020 ), Australian ( Jighly et al , 2019 ) and UK ( Grinberg et al , 2016 ) research groups and breeding companies. Four reviews of the technology applied to outcrossing grasses have been published ( Hayes et al , 2013 ; Yabe et al , 2013 ; Lin et al , 2014 ; Talukder and Saha, 2017 ).…”

Genomic prediction in family bulks using different traits and cross-validations in pine

“…Pioneer studies implementing genomic prediction in plants were performed in major crop species with traditional hybrid selection such as maize ( Combs and Bernardo 2013 ; Massman et al 2013 ) and trees ( Kumar et al 2012 ; Resende et al 2012 ), or variety selection in self-pollinating species ( Poland et al 2012 ). Genomic prediction showed to be a powerful tool to achieve higher genetic gain in plant breeding in many other species ( Crossa et al 2017 ; Lara et al 2019 ; de Bem Oliveira et al 2020 ; Esfandyari et al , 2020 ). Large commercial breeding companies have been applying genomic prediction; however, the success of the process depends strongly on the species and the breeding program scheme ( Voss-Fels et al 2019 ; Xu et al 2020 ).…”

Empirical assessment of a genomic breeding strategy in perennial ryegrass

“…Genomic selection makes the application of meaningful within-HS selection pressure for sward DMY possible on single plants, by using genomic prediction models trained using sown row or plot DMY data. Esfandyari et al (2020) showed that the ability to accurately select single plants for sward traits, by using sward trait GEBV's, substantially increased ∆G.…”

“…These relative differences corresponded closely to those from a simulation study reported by Barrett et al (2021), which showed that application of 5% among-and 5% within-HS selection pressure in A P WF GS doubled ∆G relative to HS P in a perennial ryegrass selection population. Similarly, modelling by Esfandyari et al (2020) showed that ∆G increased in GS schemes because it enabled more accurate selection of single plants for sward traits by using GEBV's. These relative differences corresponded closely to those from a simulation study reported by Barrett et al (2021), which showed that application of 5% among-and 5% within-HS selection pressure in APWFGS doubled ∆G relative to HSP in a perennial ryegrass selection population.…”