Genome wide IStraw90 SNP array probes

The University of Florida strawberry (Fragaria ×ananassa) breeding program
has implemented genomic  prediction (GP) as a tool for choosing
outstanding parents for crosses over the last five seasons. This has
allowed the use of some parents one year earlier than with traditional
methods, thus reducing the duration of the breeding cycle. However, as the
number of breeding cycles increases over time, greater knowledge is needed
on how multiple cycles can be used in the practical implementation of GP
in strawberry breeding. Advanced selections and cultivars totaling 1,558
unique individuals were tested in field trials for yield and fruit quality
traits over five consecutive years and genotyped for 9,908 SNP markers.
Prediction of breeding values was carried out using Bayes B models.
Independent validation was carried out using separate trials/years as
training (TRN) and testing (TST) populations. Single-trial predictive
abilities for five polygenic traits averaged 0.35, which was reduced to
0.24 when individuals common across trials were excluded, emphasizing the
importance of relatedness among training and testing populations. Training
populations including up to four previous breeding cycles increased
predictive abilities, likely due to increases in both training population
size and relatedness. Predictive ability was also strongly influenced by
heritability, but less so by changes in linkage disequilibrium and
effective population size. Genotype by year interactions were minimal. A
strategy for practical implementation of GP in strawberry breeding is
outlined that uses multiple cycles to predict parental performance and
accounts for traits not included in GP models when constructing crosses.
Given the importance of relatedness to the success of GP in strawberry,
future work could focus on the optimization of relatedness in the design
of TRN and TST populations to increase predictive ability in the
short-term without compromising long-term genetic gains.