Optimizing whole-genomic prediction for autotetraploid blueberry breeding

Blueberry (Vaccinium spp.) is an important autopolyploid crop
with significant benefits for human health. Apart from its genetic
complexity, the feasibility of genomic prediction has been proven for
blueberry, enabling a reduction in the breeding cycle time and
increasing genetic gain. However, as for other polyploid
crops, sequencing costs still hinder the implementation
of genome-based breeding methods for blueberry. This motivated us
to evaluate the effect of training population sizes and composition, as
well as the impact of marker density and sequencing depth on phenotype
prediction for the species. For this, data from a large real breeding
population of 1 804 individuals was used. Genotypic data from 86 930
markers and three traits with different genetic architecture (fruit
firmness, fruit weight, and total yield) were evaluated. Herein, we
suggested that marker density, sequencing depth, and training population
size can be substantially reduced with no significant impact on model
accuracy. Our results can help guide decisions towards resource allocation
(e.g., genotyping and phenotyping) in order to maximize prediction
accuracy. These findings have the potential to allow for a faster and more
accurate release of varieties with a substantial reduction of
resources for the application of genomic prediction in blueberry.
We anticipate that the benefits and pipeline described in our
study can be applied to optimize genomic prediction for other
diploid and polyploid species.