Single-trial and across-trial spatial analyses using autoregressive error structures were conducted for growth traits based on 1,146 data sets from 275 Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] progeny trials in 45 first-generation breeding zones in the US Pacific Northwest. The breeding zones encompassed a wide range of latitude, longitude, and elevation. Efficiency of using spatial analysis in reducing variation due to site heterogeneity, estimating genetic parameters, and increasing prediction accuracy was compared among different experimental designs, traits, assessment ages, and tree spacings. More than 97% of the data sets showed significant model improvement with spatial analysis, and height showed more improvement than diameter or volume. Spatial analysis on average removed 14~34% of residual variance due to spatial heterogeneity, which resulted in an up to 20% increase in accuracy of breeding value prediction. The coefficient of variation decreased substantially due to spatial adjustment. Rank correlation between predicted gains before and after spatial analysis was about 0.96, and spatial analysis had little effect on the average predicted gain of the top 20% of parents. We did not observe substantial geographic trends in improvements due to spatial adjustment. Across-site spatial analysis had almost no effect on genotype-by-environment interaction but tended to increase among-trial heterogeneity of residual variance. Two different methods for across-trial spatial analysis were compared and discussed.
Realized gains for coastal Douglas-fir (Pseudotsuga menziesii var. menziesii) were evaluated using data collected from 15-year-old trees from five field trials planted in large block plots in the northern Oregon Cascades. Three populations with different genetic levels (elitehigh predicted gain; intermediate -moderate predicted gain; and unimproved -wild seedlot) were compared at two planting spacings (1.8 x 1.8 m and 3.6 x 3.6 m).The realized gains at age 15 averaged over both the elite and intermediate progeny were 17.2 % for stand volume per hectare, 3.5 % for mean height, and 4.3 % for diameter, compared to predicted genetic gains of 16.0 % for volume, 5.4 % for height, and 6.4 % for diameter. Realized and predicted gains correlated well at the family level, with an average correlation coefficient close to 0.80. The improved populations also had higher survival rate and lower stem sinuosity than the unimproved population. Strong genetic level x planting spacing interaction effects were revealed for the growth traits at age 15 using mixed model analyses. Realized gains for stand per-hectare volume and mean growth rate were at least twice as large in the elite population as in the intermediate population at the close spacing. By contrast, both populations performed similarly at the wide spacing. This indicates that the selected genetic materials responded differently to the changes of competitive environment, and realized gain trials should closely mimic operational plantations in order to provide valid estimates of realized gains. Realized gains in per-hectare volume varied greatly among test sites. No significant genetic level x site interactions were found for any traits.
We have developed a new complementary model of gene interaction between diploid host and haploid pathogen by allowing for arbitrary levels of dominance in the host. This model enables us to assess the effects of overdominance, incomplete dominance, and underdominance on the equilibrium frequencies of resistance and virulence genes and on the stability of equilibria. Our model reduces to a gene-for-gene model when complete dominance of resistance is assumed. Computer simulations show that our model has two new features. First, when there is overdominance or underdominance of resistance, the internal equilibrium points exist even when there is no cost of unnecessary virulence or when there is a cost of necessary virulence at the balance between cost of unnecessary virulence and effectiveness of resistance. Second, the occurrence of stable resistance and virulence polymorphism is strongly dependent on the level of dominance. These two features suggest the need for caution when using the gene-for-gene model, especially in the presence of overdominance or underdominance. Our model is particularly suitable for studying the coevolutionary dynamics between hybrid populations and their pathogens in natural pathosystems.
Using computer simulation, we evaluated the impact of using first-generation information to increase selection efficiency in a second-generation breeding program. Selection efficiency was compared in terms of increase in rank correlation between estimated and true breeding values (i.e., ranking accuracy), reduction in coefficient of variation of correlation coefficients (i.e., ranking reliability), and increase in realized gain, with best linear unbiased prediction (BLUP). The test populations were generated with varying parameters: selection strategy (forward vs backward selection of parents); number of parents (24∼96); number of crosses per parent (1∼8); heritability (0.05∼0.35); ratio of dominance to additive variance (0∼3); ratio of additive-by-site to additive variance (0∼3); and ratio of dominance-by-site to additive variance (0∼3). The two selection strategies gave distinct results. When parents of the second-generation crosses had been selected via backward selection, adding first-generation information markedly increased selection efficiency. Conversely, when parents had been selected via forward selection, first-generation information provided little increase in efficiency. The amount of increase depended more on heritabilities in both generations and less on dominance and genotype-by-environment effects. Including first-generation information helped more when there were many parents and few crosses per parent in the second generation. Only in the case of extremely low first-generation heritabilities was there no benefit to adding first-generation information in terms of improved ranking reliability and accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.