Abstract
Genomic selection study (GS) focusing on nonadditive genetic effects of dominance and the first order of epistatic effects, in a full-sib family population of 695 Scots pine (Pinus sylvestris L.) trees, was undertaken for growth and wood quality traits, using 6,344 single nucleotide polymorphism markers (SNPs) generated by genotyping-by-sequencing (GBS). Genomic marker-based relationship matrices offer more effective modeling of nonadditive genetic effects than pedigree-based models, thus increasing the knowledge on the relevance of dominance and epistatic variation in forest tree breeding. Genomic marker-based models were compared with pedigree-based models showing a considerable dominance and epistatic variation for growth traits. Nonadditive genetic variation of epistatic nature (additive × additive) was detected for growth traits, wood density (DEN), and modulus of elasticity (MOEd) representing between 2.27 and 34.5% of the total phenotypic variance. Including dominance variance in pedigree-based Best Linear Unbiased Prediction (PBLUP) and epistatic variance in genomic-based Best Linear Unbiased Prediction (GBLUP) resulted in decreased narrow-sense heritability and increased broad-sense heritability for growth traits, DEN and MOEd. Higher genetic gains were reached with early GS based on total genetic values, than with conventional pedigree selection for a selection intensity of 1%. This study indicates that nonadditive genetic variance may have a significant role in the variation of selection traits of Scots pine, thus clonal deployment could be an attractive alternative for the species. Additionally, confidence in the role of nonadditive genetic effects in this breeding program should be pursued in the future, using GS.
Highlights
Additive genetic variance is the main source of the variation contributing to selection response in breeding programs (Hem et al, 2021)
The highest goodness-of-fit was between total genetic values from the genomic-based Best Linear Unbiased Prediction (GBLUP)-ADE model and adjusted phenotypes for all traits except Ht1, which had its highest goodness-of-fit for the total genetic values estimated from pedigree-based Best Linear Unbiased Prediction (PBLUP)-AD (Table 2)
GBLUP-ADE revealed the highest standard error of the predictions (SEPs) for all traits evaluated in the current study with the only exception of Ht1 for which the highest SEPs were observed with PBLUPAD model
Summary
Additive genetic variance is the main source of the variation contributing to selection response in breeding programs (Hem et al, 2021). Narrow-sense heritability, as a measurement of the proportion of additive genetic variation over the phenotypic variation, contributes to selection efficiency in tree breeding programs in terms of accuracy of breeding values (White et al, 2007). Many advanced forest tree breeding programs use clonal and/or full-sib family tests in their selection strategies, where the resemblance between related individuals consists of additive genetic variation and nonadditive genetic variation (i.e., dominance and epistatic effects) (Falconer and Mackay, 1996; Lynch and Walsh, 1998). (loblolly pine) (Baltunis et al, 2007), but contributed to fusiform rust (Isik et al, 2003), whereas dominance variance was considerable in the volume variation at age six (Isik et al, 2003) and somewhat large on early growth in clonal and seedling full-sib populations (Baltunis et al, 2007) Epistatic effects did not have a significant effect on growth in Pinus taeda L. (loblolly pine) (Baltunis et al, 2007), but contributed to fusiform rust (Isik et al, 2003), whereas dominance variance was considerable in the volume variation at age six (Isik et al, 2003) and somewhat large on early growth in clonal and seedling full-sib populations (Baltunis et al, 2007)
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