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 (A_PWF_GS) for improving dry matter yield (DMY) in perennial ryegrass. In A_PWF_GS 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), HS_P (random sample from the six phenotypically-best HS), A_PWF_GS and A_PWF_GS-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 A_PWF_GS> HS_P>Base.  Averaged across all populations, A_PWF_GS increased DMY by 43% (P<0.05) compared to Base, more than twice the level of improvement achieved with conventional HS_P. Our results show the A_PWF_GS breeding approach can substantially improve selection response for a genetically complex trait from a single breeding cycle.