Developing genomic selection for dry matter yield in white clover

Abstract

Genomic selection (GS) integrates DNA marker and trait data to develop a model that enables prediction of trait performance (genomic-estimated breeding values; GEBVs) based on genotype data alone. GS has been shown to improve the efficiency and effectiveness of breeding programmes, especially for complex traits such dry matter yield (DMY). DMY data were generated from a training population of 200 white clover half-sibling (HS) families assessed in multi-location field trials for two years. We generated a GS prediction model after integrating genotyping-by-sequencing marker data from parents of the HS families with the HS DMY data. We then compared two selection strategies: a conventional method where individuals were chosen randomly from the phenotypically top-ranked HS families (HS_P); and another where GEBVs were used to select the best individual from the top-ranked HS families (A_PWF_GS). The mean predicted DMY GEBVs of the selected plants, as well as the predicted response to selection, were compared with those of the base population. This study showed that, compared with conventional selection (HS_P), incorporating genomic selection (A_PW_GSHS) is predicted to double the increase in DMY and response to selection relative to the base population. Synthetic populations based on these selections have been generated and will be tested in a field trial to determine empirically the impact of genomic selection for a complex trait.