We assembled the 9.8 Gbp genome of western redcedar (WRC, Thuja plicata), an ecologically and eco... more We assembled the 9.8 Gbp genome of western redcedar (WRC, Thuja plicata), an ecologically and economically important conifer species of the Cupressaceae. The genome assembly, derived from a uniquely inbred tree produced through five generations of self-fertilization (selfing), was determined to be 86% complete by BUSCO analysis - one of the most complete genome assemblies for a conifer. Population genomic analysis revealed WRC to be one of the most genetically depauperate wild plant species, with an effective population size of approximately 300 and no significant genetic differentiation across its geographic range. Nucleotide diversity, π, is low for a continuous tree species, with many loci exhibiting zero diversity, and the ratio of π at zero- to four-fold degenerate sites is relatively high (~ 0.33), suggestive of weak purifying selection. Using an array of genetic lines derived from up to five generations of selfing, we explored the relationship between genetic diversity and ma...
Background: Genomic selection (GS) in forestry can substantially reduce the length of breeding cy... more Background: Genomic selection (GS) in forestry can substantially reduce the length of breeding cycle and increase gain per unit time through early selection and greater selection intensity, particularly for traits of low heritability and late expression. Affordable next-generation sequencing technologies made it possible to genotype large numbers of trees at a reasonable cost. Results: Genotyping-by-sequencing was used to genotype 1,126 Interior spruce trees representing 25 open-pollinated families planted over three sites in British Columbia, Canada. Four imputation algorithms were compared (mean value (MI), singular value decomposition (SVD), expectation maximization (EM), and a newly derived, family-based k-nearest neighbor (kNN-Fam)). Trees were phenotyped for several yield and wood attributes. Single- and multi-site GS prediction models were developed using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) and the Generalized Ridge Regression (GRR) to test different assumption about trait architecture. Finally, using PCA, multi-trait GS prediction models were developed. The EM and kNN-Fam imputation methods were superior for 30 and 60% missing data, respectively. The RR-BLUP GS prediction model produced better accuracies than the GRR indicating that the genetic architecture for these traits is complex. GS prediction accuracies for multi-site were high and better than those of single-sites while multi-site predictability produced the lowest accuracies reflecting type-b genetic correlations and deemed unreliable. The incorporation of genomic information in quantitative genetics analyses produced more realistic heritability estimates as half-sib pedigree tended to inflate the additive genetic variance and subsequently both heritability and gain estimates. Principle component scores as representatives of multi-trait GS prediction models produced surprising results where negatively correlated traits could be concurrently selected for using PCA2 and PCA3. Conclusions: The application of GS to open-pollinated family testing, the simplest form of tree improvement evaluation methods, was proven to be effective. Prediction accuracies obtained for all traits greatly support the integration of GS in tree breeding. While the within-site GS prediction accuracies were high, the results clearly indicate that single-site GS models ability to predict other sites are unreliable supporting the utilization of multi-site approach. Principle component scores provided an opportunity for the concurrent selection of traits with different phenotypic optima
<b>Abstract</b><br/>Background: Genomic selection (GS) in forestry can substant... more <b>Abstract</b><br/>Background: Genomic selection (GS) in forestry can substantially reduce the length of breeding cycle and increase gain per unit time through early selection and greater selection intensity, particularly for traits of low heritability and late expression. Affordable next-generation sequencing technologies made it possible to genotype large numbers of trees at a reasonable cost. Results: Genotyping-by-sequencing was used to genotype 1,126 Interior spruce trees representing 25 open-pollinated families planted over three sites in British Columbia, Canada. Four imputation algorithms were compared (mean value (MI), singular value decomposition (SVD), expectation maximization (EM), and a newly derived, family-based k-nearest neighbor (kNN-Fam)). Trees were phenotyped for several yield and wood attributes. Single- and multi-site GS prediction models were developed using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) and the Generalized Ridge Regression (GRR) to test different assumption about trait architecture. Finally, using PCA, multi-trait GS prediction models were developed. The EM and kNN-Fam imputation methods were superior for 30 and 60% missing data, respectively. The RR-BLUP GS prediction model produced better accuracies than the GRR indicating that the genetic architecture for these traits is complex. GS prediction accuracies for multi-site were high and better than those of single-sites while multi-site predictability produced the lowest accuracies reflecting type-b genetic correlations and deemed unreliable. The incorporation of genomic information in quantitative genetics analyses produced more realistic heritability estimates as half-sib pedigree tended to inflate the additive genetic variance and subsequently both heritability and gain estimates. Principle component scores as representatives of multi-trait GS prediction models produced surprising results where negatively correlated traits could be concurrently selected for using PCA2 and PCA3. Conclusio [...]
<b>Abstract</b><br/>Genomic selection (GS) potentially offers an unparalleled a... more <b>Abstract</b><br/>Genomic selection (GS) potentially offers an unparalleled advantage over traditional pedigree-based selection (TS) methods by reducing the time commitment required to carry out a single cycle of tree improvement. This quality is particularly appealing to tree breeders, where lengthy improvement cycles are the norm. We explored the prospect of implementing GS for interior spruce (Picea engelmannii × glauca) utilizing a genotyped population of 769 trees belonging to 25 open-pollinated families. A series of repeated tree height measurements through ages 3–40 years permitted the testing of GS methods temporally. The genotyping-by-sequencing (GBS) platform was used for single nucleotide polymorphism (SNP) discovery in conjunction with three unordered imputation methods applied to a data set with 60% missing information. Further, three diverse GS models were evaluated based on predictive accuracy (PA), and their marker effects. Moderate levels of PA (0.31–0.55) were observed and were of sufficient capacity to deliver improved selection response over TS. Additionally, PA varied substantially through time accordingly with spatial competition among trees. As expected, temporal PA was well correlated with age-age genetic correlation (r=0.99), and decreased substantially with increasing difference in age between the training and validation populations (0.04–0.47). Moreover, our imputation comparisons indicate that k-nearest neighbor and singular value decomposition yielded a greater number of SNPs and gave higher predictive accuracies than imputing with the mean. Furthermore, the ridge regression (rrBLUP) and BayesCπ (BCπ) models both yielded equal, and better PA than the generalized ridge regression heteroscedastic effect model for the traits evaluated.
Genomic selection (GS) potentially offers an unparalleled advantage over traditional pedigree-bas... more Genomic selection (GS) potentially offers an unparalleled advantage over traditional pedigree-based selection (TS) methods by reducing the time commitment required to carry out a single cycle of tree improvement. This quality is particularly appealing to tree breeders, where lengthy improvement cycles are the norm. We explored the prospect of implementing GS for interior spruce (Picea engelmannii × glauca) utilizing a genotyped population of 769 trees belonging to 25 open-pollinated families. A series of repeated tree height measurements through ages 3–40 years permitted the testing of GS methods temporally. The genotyping-by-sequencing (GBS) platform was used for single nucleotide polymorphism (SNP) discovery in conjunction with three unordered imputation methods applied to a data set with 60% missing information. Further, three diverse GS models were evaluated based on predictive accuracy (PA), and their marker effects. Moderate levels of PA (0.31–0.55) were observed and were of sufficient capacity to deliver improved selection response over TS. Additionally, PA varied substantially through time accordingly with spatial competition among trees. As expected, temporal PA was well correlated with age-age genetic correlation (r=0.99), and decreased substantially with increasing difference in age between the training and validation populations (0.04–0.47). Moreover, our imputation comparisons indicate that k-nearest neighbor and singular value decomposition yielded a greater number of SNPs and gave higher predictive accuracies than imputing with the mean. Furthermore, the ridge regression (rrBLUP) and BayesCπ (BCπ) models both yielded equal, and better PA than the generalized ridge regression heteroscedastic effect model for the traits evaluated
<p>Severe drought events are affecting forests around the world, even in temperate climates... more <p>Severe drought events are affecting forests around the world, even in temperate climates. A viable climate change adaptation strategy may involve planting forests with trees more resilient to drought. The majority of the 300 million seedlings planted annually in western Canada are genetically-selected trees derived from tree breeding programs. Since tree breeding populations supply the seed that is deployed on the landscape, it is important to closely examine the degree of genetic control of drought resilience in these populations &#8211; yet methods for evaluating drought responses in mature experimental trials are limited. We evaluated the potential to use tree rings to infer genetic adaptation to drought. Specifically, we used annual growth increments to evaluate the genetic component behind variation in drought resilience. We also quantified potential genetic trade-offs between drought resilience and growth in long-term progeny trials. We worked with two economically and ecologically valuable sympatric conifers, coastal Douglas-fir (<em>Pseudotsuga menziesii </em>var. <em>menziesii</em>) and western redcedar (<em>Thuja plicata</em>). Annual growth increment and tree height data were obtained from 1980 coastal Douglas-fir trees (93 polycross families on two well-replicated sites at age 19) and 1520 western redcedar trees (26 polycross families on three well-replicated sites at age 18). All trees showed substantial reduction in growth under drought, but there was clear variability in the longer-term response of families within each breeding population. The heritability (h<sup>2</sup>) of such drought resilience, or proportion of this variation explained by genetics, was high for Douglas-fir (h<sup>2</sup> = 0.26, SE = 0.07) and moderate for redcedar (h<sup>2</sup> = 0.13, SE = 0.04). Preliminary genetic correlations between tree height and drought resilience were also positive for both species (Douglas-fir: r<sub>g</sub> = 0.77, SE = 0.18; redcedar: r<sub>g</sub> = 0.62, SE = 0.17). Families that were both high-yielding and drought resilient could also be identified. Since growth response to drought is a variable and heritable trait, these traits are therefore under the control of the tree breeder. Moreover, the positive genetic correlations between tree height and an adaptive growth response to drought suggest that historic selection for tree height did not compromise drought resilience of planted seedlings. Tree rings appear to be an effective tool to screen these populations for drought resilience, which will help ensure that planted trees will remain healthy and productive under climate change.</p>
The genetic gain of spruce (Picea spp.) breeding programs is impeded by long recurrent selection ... more The genetic gain of spruce (Picea spp.) breeding programs is impeded by long recurrent selection cycles stemming from biological constraints such as late expression of traits, weak juvenile mature correlations, and late onset of sexual maturity. Genomic selection (GS) is capable of addressing these barriers to improving the rate of genetic gain via early prediction of phenotypes using dense genetic marker arrays. Results from GS studies focused on spruce species in Canada thus far have produced encouraging results to capture additional genetic gain for wood quality, growth, and insect resistance traits either through the re-analysis of existing progeny trials with genomic information or via prediction of phenotypes for untested candidate trees. With the continual improvement of phenotyping technologies and spruce genomic resources, we expect the capability of GS to capture genetic gain to greatly exceed that of traditional pedigree-based selection methods in the future.
Conifers are prime candidates for genomic selection (GS) due to their long breeding cycles. Previ... more Conifers are prime candidates for genomic selection (GS) due to their long breeding cycles. Previous studies have shown much reduced prediction accuracies (PA) of breeding values in unobserved environments, which may impede its adoption. The impact of explicit environmental heterogeneity modeling including genotype-by-environment (G×E) interaction effects using environmental covariates (EC) in a reaction-norm genomic prediction model was tested using single-step GBLUP (ssGBLUP). A three-generation coastal Douglas-fir experimental population with 14 genetic trials (n = 13,615) permitted estimation of intra- and inter-generation PA in unobserved environments using SNPs derived from exome capture. Intra- and inter-generation PAs ranged from 0.447-0.640 and 0.317-0.538, respectively. The inclusion of ECs in the prediction models explained up to 23% of the phenotypic variation for the fully specified model and resulted in the best model fit. Modeling G×E effects in the training populatio...
Molecular breeding : new strategies in plant improvement, 2018
The advantages of open-pollinated (OP) family testing over controlled crossing (i.e., structured ... more The advantages of open-pollinated (OP) family testing over controlled crossing (i.e., structured pedigree) are the potential to screen and rank a large number of parents and offspring with minimal cost and efforts; however, the method produces inflated genetic parameters as the actual sibling relatedness within OP families rarely meets the half-sib relatedness assumption. Here, we demonstrate the unsurpassed utility of OP testing after shifting the analytical mode from pedigree- (ABLUP) to genomic-based (GBLUP) relationship using phenotypic tree height (HT) and wood density (WD) and genotypic (30k SNPs) data for 1126 38-year-old Interior spruce ( (Moench) Voss x Parry ex Engelm.) trees, representing 25 OP families, growing on three sites in Interior British Columbia, Canada. The use of the genomic realized relationship permitted genetic variance decomposition to additive, dominance, and epistatic genetic variances, and their interactions with the environment, producing more accurate...
We assembled the 9.8 Gbp genome of western redcedar (WRC, Thuja plicata), an ecologically and eco... more We assembled the 9.8 Gbp genome of western redcedar (WRC, Thuja plicata), an ecologically and economically important conifer species of the Cupressaceae. The genome assembly, derived from a uniquely inbred tree produced through five generations of self-fertilization (selfing), was determined to be 86% complete by BUSCO analysis - one of the most complete genome assemblies for a conifer. Population genomic analysis revealed WRC to be one of the most genetically depauperate wild plant species, with an effective population size of approximately 300 and no significant genetic differentiation across its geographic range. Nucleotide diversity, π, is low for a continuous tree species, with many loci exhibiting zero diversity, and the ratio of π at zero- to four-fold degenerate sites is relatively high (~ 0.33), suggestive of weak purifying selection. Using an array of genetic lines derived from up to five generations of selfing, we explored the relationship between genetic diversity and ma...
Background: Genomic selection (GS) in forestry can substantially reduce the length of breeding cy... more Background: Genomic selection (GS) in forestry can substantially reduce the length of breeding cycle and increase gain per unit time through early selection and greater selection intensity, particularly for traits of low heritability and late expression. Affordable next-generation sequencing technologies made it possible to genotype large numbers of trees at a reasonable cost. Results: Genotyping-by-sequencing was used to genotype 1,126 Interior spruce trees representing 25 open-pollinated families planted over three sites in British Columbia, Canada. Four imputation algorithms were compared (mean value (MI), singular value decomposition (SVD), expectation maximization (EM), and a newly derived, family-based k-nearest neighbor (kNN-Fam)). Trees were phenotyped for several yield and wood attributes. Single- and multi-site GS prediction models were developed using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) and the Generalized Ridge Regression (GRR) to test different assumption about trait architecture. Finally, using PCA, multi-trait GS prediction models were developed. The EM and kNN-Fam imputation methods were superior for 30 and 60% missing data, respectively. The RR-BLUP GS prediction model produced better accuracies than the GRR indicating that the genetic architecture for these traits is complex. GS prediction accuracies for multi-site were high and better than those of single-sites while multi-site predictability produced the lowest accuracies reflecting type-b genetic correlations and deemed unreliable. The incorporation of genomic information in quantitative genetics analyses produced more realistic heritability estimates as half-sib pedigree tended to inflate the additive genetic variance and subsequently both heritability and gain estimates. Principle component scores as representatives of multi-trait GS prediction models produced surprising results where negatively correlated traits could be concurrently selected for using PCA2 and PCA3. Conclusions: The application of GS to open-pollinated family testing, the simplest form of tree improvement evaluation methods, was proven to be effective. Prediction accuracies obtained for all traits greatly support the integration of GS in tree breeding. While the within-site GS prediction accuracies were high, the results clearly indicate that single-site GS models ability to predict other sites are unreliable supporting the utilization of multi-site approach. Principle component scores provided an opportunity for the concurrent selection of traits with different phenotypic optima
<b>Abstract</b><br/>Background: Genomic selection (GS) in forestry can substant... more <b>Abstract</b><br/>Background: Genomic selection (GS) in forestry can substantially reduce the length of breeding cycle and increase gain per unit time through early selection and greater selection intensity, particularly for traits of low heritability and late expression. Affordable next-generation sequencing technologies made it possible to genotype large numbers of trees at a reasonable cost. Results: Genotyping-by-sequencing was used to genotype 1,126 Interior spruce trees representing 25 open-pollinated families planted over three sites in British Columbia, Canada. Four imputation algorithms were compared (mean value (MI), singular value decomposition (SVD), expectation maximization (EM), and a newly derived, family-based k-nearest neighbor (kNN-Fam)). Trees were phenotyped for several yield and wood attributes. Single- and multi-site GS prediction models were developed using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) and the Generalized Ridge Regression (GRR) to test different assumption about trait architecture. Finally, using PCA, multi-trait GS prediction models were developed. The EM and kNN-Fam imputation methods were superior for 30 and 60% missing data, respectively. The RR-BLUP GS prediction model produced better accuracies than the GRR indicating that the genetic architecture for these traits is complex. GS prediction accuracies for multi-site were high and better than those of single-sites while multi-site predictability produced the lowest accuracies reflecting type-b genetic correlations and deemed unreliable. The incorporation of genomic information in quantitative genetics analyses produced more realistic heritability estimates as half-sib pedigree tended to inflate the additive genetic variance and subsequently both heritability and gain estimates. Principle component scores as representatives of multi-trait GS prediction models produced surprising results where negatively correlated traits could be concurrently selected for using PCA2 and PCA3. Conclusio [...]
<b>Abstract</b><br/>Genomic selection (GS) potentially offers an unparalleled a... more <b>Abstract</b><br/>Genomic selection (GS) potentially offers an unparalleled advantage over traditional pedigree-based selection (TS) methods by reducing the time commitment required to carry out a single cycle of tree improvement. This quality is particularly appealing to tree breeders, where lengthy improvement cycles are the norm. We explored the prospect of implementing GS for interior spruce (Picea engelmannii × glauca) utilizing a genotyped population of 769 trees belonging to 25 open-pollinated families. A series of repeated tree height measurements through ages 3–40 years permitted the testing of GS methods temporally. The genotyping-by-sequencing (GBS) platform was used for single nucleotide polymorphism (SNP) discovery in conjunction with three unordered imputation methods applied to a data set with 60% missing information. Further, three diverse GS models were evaluated based on predictive accuracy (PA), and their marker effects. Moderate levels of PA (0.31–0.55) were observed and were of sufficient capacity to deliver improved selection response over TS. Additionally, PA varied substantially through time accordingly with spatial competition among trees. As expected, temporal PA was well correlated with age-age genetic correlation (r=0.99), and decreased substantially with increasing difference in age between the training and validation populations (0.04–0.47). Moreover, our imputation comparisons indicate that k-nearest neighbor and singular value decomposition yielded a greater number of SNPs and gave higher predictive accuracies than imputing with the mean. Furthermore, the ridge regression (rrBLUP) and BayesCπ (BCπ) models both yielded equal, and better PA than the generalized ridge regression heteroscedastic effect model for the traits evaluated.
Genomic selection (GS) potentially offers an unparalleled advantage over traditional pedigree-bas... more Genomic selection (GS) potentially offers an unparalleled advantage over traditional pedigree-based selection (TS) methods by reducing the time commitment required to carry out a single cycle of tree improvement. This quality is particularly appealing to tree breeders, where lengthy improvement cycles are the norm. We explored the prospect of implementing GS for interior spruce (Picea engelmannii × glauca) utilizing a genotyped population of 769 trees belonging to 25 open-pollinated families. A series of repeated tree height measurements through ages 3–40 years permitted the testing of GS methods temporally. The genotyping-by-sequencing (GBS) platform was used for single nucleotide polymorphism (SNP) discovery in conjunction with three unordered imputation methods applied to a data set with 60% missing information. Further, three diverse GS models were evaluated based on predictive accuracy (PA), and their marker effects. Moderate levels of PA (0.31–0.55) were observed and were of sufficient capacity to deliver improved selection response over TS. Additionally, PA varied substantially through time accordingly with spatial competition among trees. As expected, temporal PA was well correlated with age-age genetic correlation (r=0.99), and decreased substantially with increasing difference in age between the training and validation populations (0.04–0.47). Moreover, our imputation comparisons indicate that k-nearest neighbor and singular value decomposition yielded a greater number of SNPs and gave higher predictive accuracies than imputing with the mean. Furthermore, the ridge regression (rrBLUP) and BayesCπ (BCπ) models both yielded equal, and better PA than the generalized ridge regression heteroscedastic effect model for the traits evaluated
<p>Severe drought events are affecting forests around the world, even in temperate climates... more <p>Severe drought events are affecting forests around the world, even in temperate climates. A viable climate change adaptation strategy may involve planting forests with trees more resilient to drought. The majority of the 300 million seedlings planted annually in western Canada are genetically-selected trees derived from tree breeding programs. Since tree breeding populations supply the seed that is deployed on the landscape, it is important to closely examine the degree of genetic control of drought resilience in these populations &#8211; yet methods for evaluating drought responses in mature experimental trials are limited. We evaluated the potential to use tree rings to infer genetic adaptation to drought. Specifically, we used annual growth increments to evaluate the genetic component behind variation in drought resilience. We also quantified potential genetic trade-offs between drought resilience and growth in long-term progeny trials. We worked with two economically and ecologically valuable sympatric conifers, coastal Douglas-fir (<em>Pseudotsuga menziesii </em>var. <em>menziesii</em>) and western redcedar (<em>Thuja plicata</em>). Annual growth increment and tree height data were obtained from 1980 coastal Douglas-fir trees (93 polycross families on two well-replicated sites at age 19) and 1520 western redcedar trees (26 polycross families on three well-replicated sites at age 18). All trees showed substantial reduction in growth under drought, but there was clear variability in the longer-term response of families within each breeding population. The heritability (h<sup>2</sup>) of such drought resilience, or proportion of this variation explained by genetics, was high for Douglas-fir (h<sup>2</sup> = 0.26, SE = 0.07) and moderate for redcedar (h<sup>2</sup> = 0.13, SE = 0.04). Preliminary genetic correlations between tree height and drought resilience were also positive for both species (Douglas-fir: r<sub>g</sub> = 0.77, SE = 0.18; redcedar: r<sub>g</sub> = 0.62, SE = 0.17). Families that were both high-yielding and drought resilient could also be identified. Since growth response to drought is a variable and heritable trait, these traits are therefore under the control of the tree breeder. Moreover, the positive genetic correlations between tree height and an adaptive growth response to drought suggest that historic selection for tree height did not compromise drought resilience of planted seedlings. Tree rings appear to be an effective tool to screen these populations for drought resilience, which will help ensure that planted trees will remain healthy and productive under climate change.</p>
The genetic gain of spruce (Picea spp.) breeding programs is impeded by long recurrent selection ... more The genetic gain of spruce (Picea spp.) breeding programs is impeded by long recurrent selection cycles stemming from biological constraints such as late expression of traits, weak juvenile mature correlations, and late onset of sexual maturity. Genomic selection (GS) is capable of addressing these barriers to improving the rate of genetic gain via early prediction of phenotypes using dense genetic marker arrays. Results from GS studies focused on spruce species in Canada thus far have produced encouraging results to capture additional genetic gain for wood quality, growth, and insect resistance traits either through the re-analysis of existing progeny trials with genomic information or via prediction of phenotypes for untested candidate trees. With the continual improvement of phenotyping technologies and spruce genomic resources, we expect the capability of GS to capture genetic gain to greatly exceed that of traditional pedigree-based selection methods in the future.
Conifers are prime candidates for genomic selection (GS) due to their long breeding cycles. Previ... more Conifers are prime candidates for genomic selection (GS) due to their long breeding cycles. Previous studies have shown much reduced prediction accuracies (PA) of breeding values in unobserved environments, which may impede its adoption. The impact of explicit environmental heterogeneity modeling including genotype-by-environment (G×E) interaction effects using environmental covariates (EC) in a reaction-norm genomic prediction model was tested using single-step GBLUP (ssGBLUP). A three-generation coastal Douglas-fir experimental population with 14 genetic trials (n = 13,615) permitted estimation of intra- and inter-generation PA in unobserved environments using SNPs derived from exome capture. Intra- and inter-generation PAs ranged from 0.447-0.640 and 0.317-0.538, respectively. The inclusion of ECs in the prediction models explained up to 23% of the phenotypic variation for the fully specified model and resulted in the best model fit. Modeling G×E effects in the training populatio...
Molecular breeding : new strategies in plant improvement, 2018
The advantages of open-pollinated (OP) family testing over controlled crossing (i.e., structured ... more The advantages of open-pollinated (OP) family testing over controlled crossing (i.e., structured pedigree) are the potential to screen and rank a large number of parents and offspring with minimal cost and efforts; however, the method produces inflated genetic parameters as the actual sibling relatedness within OP families rarely meets the half-sib relatedness assumption. Here, we demonstrate the unsurpassed utility of OP testing after shifting the analytical mode from pedigree- (ABLUP) to genomic-based (GBLUP) relationship using phenotypic tree height (HT) and wood density (WD) and genotypic (30k SNPs) data for 1126 38-year-old Interior spruce ( (Moench) Voss x Parry ex Engelm.) trees, representing 25 OP families, growing on three sites in Interior British Columbia, Canada. The use of the genomic realized relationship permitted genetic variance decomposition to additive, dominance, and epistatic genetic variances, and their interactions with the environment, producing more accurate...
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Papers by Omnia Gamal El-Dien