Towards a sweetpotato genomic-enabled breeding: optimizing two-stage analysis of multi-environment augmented trials

Key message Using the full weight matrix and deregressed pedigree-based best linear unbiased predictions in second
stage models lead to selections and genomic predictions closer to those obtained using a single-stage model.
Abstract In multi-environment genomic selection, although single-stage (SS) models are generally more efficient (no loss
of information), there are contexts where they are difficult to fit, making two-stage models the most practical alternative. An
example is the evaluation of early-stage observational trials (OTs) of sweetpotato breeding, where several clones are tested
in unreplicated trials. In this study, 1,138 clones derived from partial diallels within two gene pools had their storage root
yield evaluated across six OTs. Using this scenario, we compared the selection and prediction performances of models under
different two-stage strategies against the SS benchmark. We also tested whether pool-specific genomic prediction models
offered advantages over models trained with the complete dataset. Given the lack of replication in OTs, we hypothesized that
deregressed best linear unbiased predictions (dBLUPs) or pedigree-based dBLUPs (dABLUPs) would work more appropri
ately as inputs for second-stage models than best linear unbiased estimates (BLUEs). These comparisons were conducted
within weighted models using either a diagonal weight matrix or the full weight matrix. For selection, differences among
second-stage models were minor, with a slight advantage for those using dABLUPs as entries, combined with the full weight
matrix. For prediction, however, the choice of weighting scheme had a greater impact on performance than the choice of
entry. Using the complete dataset, differences between entries were marginal, but for pool-specific predictions, dABLUPs
provided the best performance. Overall, if adopting a two-stage strategy for the analysis of augmented trials, we recommend
using dABLUPs together with the full weight matrix.