Abstract
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 1 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.
Highlights
The development of high throughput genotyping and new methods for analyzing genome-wide molecular data are revolutionizing crop improvement
Genomic Selection Over Multiple Cycles values (BV) based on DNA marker data alone for a testing population (TST). This methodology requires that the genome has been covered by a sufficiently dense panel of markers, that moderate to high linkage disequilibrium (LD) exists between marker loci and the underlying quantitative trait loci and that there is some degree of relatedness between the TRN and TST populations (Meuwissen et al, 2001)
The increase in predictive ability of early marketable yield (EMY) and Total marketable yield (TMY) from cycle 2 to cycle 3 seems to be associated with an increase in heritability, from the TRN to the TST population, that was not present in other traits
Summary
The development of high throughput genotyping and new methods for analyzing genome-wide molecular data are revolutionizing crop improvement. Genomic Selection Over Multiple Cycles values (BV) based on DNA marker data alone for a testing population (TST). This methodology requires that the genome has been covered by a sufficiently dense panel of markers, that moderate to high linkage disequilibrium (LD) exists between marker loci and the underlying quantitative trait loci and that there is some degree of relatedness between the TRN and TST populations (Meuwissen et al, 2001). GP methods will capture both LD and relatedness among individuals in the TRN and TST populations for predictions (Habier et al, 2007; Albrecht et al, 2014). Understanding the relative impacts of LD and relatedness in a breeding program may be helpful, since LD has greater potential to persist across populations and generations (Hayes et al, 2009)
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