AUTHOR=Skøt Leif , Nay Michelle M. , Grieder Christoph , Frey Lea A. , Pégard Marie , Öhlund Linda , Amdahl Helga , Radovic Jasmina , Jaluvka Libor , Palmé Anna , Ruttink Tom , Lloyd David , Howarth Catherine J. , Kölliker Roland 

TITLE=Including marker x environment interactions improves genomic prediction in red clover (Trifolium pratense L.)

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1407609

DOI=10.3389/fpls.2024.1407609

ISSN=1664-462X

ABSTRACT=Genomic prediction has mostly been used in single environment contexts, largely ignoring genotype x environment interaction, which greatly affects the performance of plants. However, in the last decade, prediction models including marker x environment (MxE) interaction have been developed. We evaluated the potential of genomic prediction in red clover (Trifolium pratense L.) using field trial data from five European locations, obtained in the Horizon 2020 EUCLEG project. Three models were compared: (1) single environment (SingleEnv), (2) across environment (AcrossEnv), (3) marker x environment interaction (MxE). Annual dry matter yield (DMDMY) yield gave the highest predictive ability (PA). Joint analyses of DMYdata from years 1 and 2 from each location varied from 0.87 in (Britain, DM1 (year 1) and Switzerland in year 1,, DM1) to 0.48 in Norway(Norway, DM1). Overall, crude protein (CP) was predicted poorly. PAs for date of floweringflowering time (DOF), however, ranged from 0.87 to 0.67 for Britain and SwitzerlandGBR and CHE, respectively. Across the three traits, the MxE model performed best and the AcrossEnv worst, demonstrating that including marker x environment effects can improve genomic prediction in red clover. Leaving out accessions from specific regions or from specific breeders' material in the cross validation tended to reduce PA, but the magnitude of reduction depended on trait, region and breeders' material, indicating that population structure contributed to the high PAs observed for DMDMY and DOF. Testing the genomic estimated breeding values on new phenotypic data from Sweden showed that DMDMY yield training data from BritainGBR gave high PAs in both years (0.43-0.76), while DMDMY yield training data from Switzerland gave high PAs only for year 1 (0.70-0.87). The genomic predictions we report here underline the importance of population structure and the potential benefits of incorporating MxE interaction in multienvironment trials and could have perspectives for identifying markers with effects that are stable across environments, and markers with environment-specific effects.