Empirical assessment of a genomic breeding strategy in perennial ryegrass

Abstract

In genomic selection (GS) DNA markers and trait data are integrated in a model that then predicts genomic-estimated breeding values (GEBV’s) for individuals using DNA marker information alone, improving breeding efficiency. We assessed a genomic breeding strategy (APWFGS) for improving dry matter yield (DMY) in perennial ryegrass. In APWFGS the best-performing half-sibling families (HS) are identified using phenotypic data and GS is used to select the best individuals within those HS. Four selections were made from three breeding populations: Base (random sample of plants from all HS), HSP (random sample from the six phenotypically-best HS), APWFGS and APWFGS-L (top or bottom 5% of plants, respectively, selected by GEBV from the six HS). Selected plants were polycrossed, creating 12 experimental synthetics that were evaluated as sown rows for DMY (n=7 harvests) in field trials at two locations over 18 months. In each population, mean DMY across locations and harvests showed a trend of APWFGS> HSP>Base. Averaged across all populations, APWFGS increased DMY by 43% (P<0.05) compared to Base, more than twice the level of improvement achieved with conventional HSP. Our results show the APWFGS breeding approach can substantially improve selection response for a genetically complex trait from a single breeding cycle.

Implementation of rapid cycle recurrent genomic selection for forage yield in perennial ryegrass

“…It is important to highlight that the genetic gain from C 0 to C1 is compounded by among-family selection based on field data, and at least part of the gain from C 0 to C 1 resulted from selecting plants from the best half-sib families. However, other studies have demonstrated the advantages of withinfamily genomic selection over random selection from remnant seeds (Faville et al 2022). The genetic gain from C1 to C2 was not compounded by among-family selection, and gains were 6.7% for spring yield, 3.8% for summer yield, 9.2% for autumn yield, and 6.4% for value of annual forage yield.…”

“…In fact, there have been many studies demonstrating the importance of maintaining a strong relationship between training population and selection candidates (Goddard 2009, Speed and Balding 2015, Konkolewska et al 2021. Empirical studies using genomics for within-family selection in perennial ryegrass have been reported (Faville et al 2022), although there have not been any empirical reports on using genomics for rapid cycle recurrent genomic selection. However, there are limited examples of empirical studies of rapid cycle recurrent genomic selection in other species, and encouraging results have been reported (Zhang et al 2017, Veenstra et al 2020, Dreisigacker et al 2023.…”

“…Previous studies have demonstrated the potential of genomic prediction for diploid perennial ryegrass populations (Fè et al 2015a,b;Lin et al 2016, Byrne et al 2017, Faville et al 2018, Arojju et al 2018Esfandyari et al 2020). However, there are limited empirical studies on genomic selection (Faville et al 2022) and none have evaluated rapid cycle recurrent genomic selection in perennial ryegrass.…”

Quantitative genetic analysis reveals potential to breed for improved white clover growth in symbiosis with nitrogen-fixing Rhizobium bacteria

“…Additional increases in genetic gain for a trait can be made by accessing the ¾ additive variation residing within the half-sib families. Methodologies, such as genomic selection, which has been applied in forages ( Faville et al, 2018 , 2020 , 2021 ; Ehoche et al, 2021 ), address this issue. The population in the current study could, therefore, provide a platform for testing genomic selection for improving genetic gain of symbiotic traits.…”