Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model

Abstract

We report a genomic selection (GS) study of growth and wood quality traits in an outbred F hybrid Eucalyptus population (n=768) using high-density single-nucleotide polymorphism (SNP) genotyping. Going beyond previous reports in forest trees, models were developed for different selection targets, namely, families, individuals within families and individuals across the entire population using a genomic model including dominance. To provide a more breeder-intelligible assessment of the performance of GS we calculated the expected response as the percentage gain over the population average expected genetic value (EGV) for different proportions of genomically selected individuals, using a rigorous cross-validation (CV) scheme that removed relatedness between training and validation sets. Predictive abilities (PAs) were 0.40-0.57 for individual selection and 0.56-0.75 for family selection. PAs under an additive+dominance model improved predictions by 5 to 14% for growth depending on the selection target, but no improvement was seen for wood traits. The good performance of GS with no relatedness in CV suggested that our average SNP density (~25 kb) captured some short-range linkage disequilibrium. Truncation GS successfully selected individuals with an average EGV significantly higher than the population average. Response to GS on a per year basis was ~100% more efficient than by phenotypic selection and more so with higher selection intensities. These results contribute further experimental data supporting the positive prospects of GS in forest trees. Because generation times are long, traits are complex and costs of DNA genotyping are plummeting, genomic prediction has good perspectives of adoption in tree breeding practice.

Integration of Dominance and Marker×Environment Interactions into Maize Genomic Prediction Models

“…Our findings are also supported by goodness-of-fit values since dominance models are more parsimonious than the additive model. Accordingly, other studies have shown similar empirical results [Resende et al, 2017, Dias et al, 2018. Despite the importance of dominance effect in grain yield, additivity explained a large portion of variance in grain moisture, suggesting that both traits have different genetic architectures.…”

“…This source of genetic variation was neglected for different reasons, including the lack of informative pedigrees, computational complexities related to estimation of dominance effects [Vitezica et al, 2013], the thought that most genetic variance is additive [Hill, 2010] or can be captured with additive parameterizations [Huang and Mackay, 2016], and the fact that even when non-additive effects are included in the models they are not easily partitioned from additive effects [Muñoz et al, 2014]. Nonetheless, recent inclusion of non-additive effects have demonstrated increased prediction accuracies in some traits in animal and plant breeding [Technow et al, 2014, de Almeida Filho et al, 2016, dos Santos et al, 2016, Resende et al, 2017, Dias et al, 2018 In addition to the source of genetic variability controlling a trait of interest, a second relevant issue to plant breeders is how to manage the challenges of genotype-by-environment (G×E) interaction. G×E interactions are expressed as changes in the relative performance of genotypes across environments, which can affect the genotype ranking.…”

Genomic basis of European ash tree resistance to ash dieback fungus

“…The levels of accuracy which our GP reached are high, and comparable to those that are used to inform selections in crop [46][47][48][49][50] , tree 12,51 and livestock breeding programmes 52,53 . Thus, our results have the potential to increase the speed at which we can successfully breed ash dieback resistant trees.…”

Genomic basis of European ash tree resistance to ash dieback fungus

“…The levels of accuracy which our GP reached are high, and comparable to those that are used to inform selections in crop 46–50 , tree 12,51 and livestock breeding programmes 52,53 . Thus, our results have the potential to increase the speed at which we can successfully breed ash dieback resistant trees.…”