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.