Genomic prediction for fusiform rust disease incidence in a large cloned population of Pinus taeda

Abstract

In this study, 723 Pinus taeda L. (loblolly pine) clonal varieties genotyped with 16920 SNP markers were used to evaluate genomic selection for fusiform rust disease caused by the fungus Cronartium quercuum f. sp. fusiforme. The 723 clonal varieties were from five full-sib families. They were a subset of a larger population (1831 clonal varieties), field-tested across 26 locations in the southeast US. Ridge regression, Bayes B and Bayes Cπ models were implemented to study marker-trait associations and estimate predictive ability for selection. A cross-validation scenario based on random sampling of 80% of the clonal varieties for model building had higher (0.71- 0.76) prediction accuracies of genomic estimated breeding values compared with family and within-family cross-validation scenarios. Random sampling within families for model training to predict genomic estimated breeding values of the remaining progenies within each family produced accuracies between 0.38 to 0.66. Using four families out of five for model training was not successful. The results showed the importance of genetic relatedness between the training and validation sets. Bayesian whole genome regression models detected three QTL with large effects on the disease outcome, explaining 54% of the genetic variation in the trait. The significance of QTL was validated with GWAS while accounting for the population structure and polygenic effect. The odds of disease incidence for heterozygous AB genotypes were 10.7 and 12.1 times greater than the homozygous AA genotypes for SNP11965 and SNP6347 loci, respectively. Genomic selection for fusiform rust disease incidence could be effective in P. taeda breeding. Markers with large effects could be fit as fixed covariates to increase the prediction accuracies, provided that their effects are validated further.

Preselection of QTL markers enhances accuracy of genomic selection in Norway spruce

“…In many animals and crop GP, using the whole-genome resequencing markers or SNP array with whole genome-level imputation were believed to improve the PA of GP, especially when low minor allele frequency loci in the low linkage disequilibrium with neighboring variants were included in the prediction model (Wainschtein et al , 2022). In tree species, GP is usually performed with 20k-50k SNP arrays (Cappa et al , 2022; Shalizi et al , 2021) or 100k to 200k exome capture markers or genotyping-by-sequencing (GBS) markers (Calleja-Rodriguez et al , 2020; Chen et al , 2018).…”

Genome-Wide SNP Markers Accelerate Perennial Forest Tree Breeding Rate for Disease Resistance through Marker-Assisted and Genome-Wide Selection

“…For this reason, the random selection of samples from the population and creation of training and validation populations compared to random sampling scenarios of families and within families leads to the highest prediction value (0.71–0.76) of different models. In different cross validation scenarios, the Bayesian method is preferred over the whole genome regression models in order to capture major effect QTLs involved in Fusarium rust resistance [ 102 ].…”

Increasing genomic prediction accuracy for unphenotyped full-sib families by modeling additive and dominance effects with large datasets in white spruce

“…However, the genomic predictions for new families suffer from a large decrease in accuracy due to reduced relatedness between training and validation populations (e.g., Beaulieu et al., 2014b ; Lenz et al., 2017 ; Chen et al., 2018 ), thus limiting the use of GS in this context ( Pégard et al., 2020 ). In forest trees, most previous studies did not model non-additive effects when predicting new full-sib families ( Beaulieu et al., 2014b ; Lenz et al., 2017 ; Chen et al., 2018 ), or, if they did, they still only estimated the prediction accuracy of breeding values instead of using the total genetic values ( Pégard et al., 2020 ; Shalizi et al., 2021 ; Lauer et al., 2022 ; Walker et al., 2022 ). Thus, more research is needed to determine whether the dominance deviations of offspring in new controlled crosses can be successfully predicted.…”