Quantitative inheritance studies in sugar-cane. I. Estimation of variance components

1971 ◽  
Vol 22 (1) ◽  
pp. 93 ◽  
Author(s):  
DM Hogarth
Keyword(s):  
Factorial Design ◽  
Variance Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
The Other ◽  
Quantitative Inheritance ◽  
Genetic Determination ◽  
Nested Design ◽  
Dominance Genetic Variance ◽  
Estimation Of Variance

Two experiments in quantitative genetics were conducted, one based on a nested design in lattice squares and the other on a factorial design in a balanced lattice. Lattice designs were found to be suitable for genetic experiments if a large number of crosses was involved, but posed some problems in partitioning the sum of squares for treatments. The factorial design was considered preferable to the nested design, although neither design permitted estimation of epistatic variances which, therefore, were assumed to be negligible. Additive genetic variance was found to be more important than dominance genetic variance for most characters. However, most estimates of genetic variance lacked precision in spite of the use of large, precise experiments, which illustrated the difficulty in obtaining estimates of variance components with adequate precision. The validity of assumptions made for these analyses is discussed. The effect of competition was studied and estimates of heritability and degree of genetic determination were determined.

Quantitative inheritance studies in sugar-cane. III.* The effect of competition and violation of genetic assumptions on estimation of genetic variance components

1977 ◽  
Vol 28 (2) ◽  
pp. 257 ◽  
Author(s):  
DM Hogarth
Keyword(s):  
Maternal Effects ◽  
Quantitative Genetics ◽  
Sugar Cane ◽  
Variance Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Quantitative Inheritance ◽  
Genetic Determination ◽  
Statistical Precision ◽  
Dominance Genetic Variance

Several assumptions underlying the theory of quantitative genetics may not be valid for sugar-cane. The assumption of no epistasis was studied by comparing independent estimates of genetic variance components based on different genetic assumptions.Sugar content was measured with excellent statistical precision, and independent estimates of genetic variance components for this character agreed very well, which indicated that violation of genetic assumptions had little effect on estimation. For other characters, agreement was not as good, and there was evidence to show that epistatic variance was important for weight per stalk. For all characters, maternal effects were negligible. Additive genetic variance was more important than dominance genetic variance for all characters except yield of cane, for which the two variances were equally important. Inter-plot competition was unimportant, but several characters, notably yield of cane, exhibited substantial within-plot competition. This type of competition did not affect estimation of genetic variances, but has important implications for selection. Estimates of heritability and degree of genetic determination were determined for each character studied. _____________________ *Part II, Aust. J. Agric. Res., 22: 103-9 (1971).

scholarly journals Estimation of variance components for bodyweight of grasscutters using expectation maximization algorithm of restricted maximum likelihood

2020 ◽  
Vol 44 (5) ◽  
pp. 5-8
Author(s):  
I. Udeh
Keyword(s):  
Body Weight ◽  
Maximum Likelihood ◽  
Expectation Maximization ◽  
Variance Components ◽  
Genetic Variance ◽  
Phenotypic Variability ◽  
Additive Genetic Variance ◽  
Expectation Maximization Algorithm ◽  
Estimation Of Variance ◽  
Selection Of

The objective of this study was to estimate the variance components and heritability of bodyweight of grasscutters at 4, 6 and 8 months of age using EM algorithm of REML procedures. The data used for the study were obtained from the bodyweight records of 20 grasscutters from four families at 4, 6 and 8 months of age. The heritability of bodyweight of grasscutters at 4, 6 and 8 months of age were 0.14, 0.10 and 0.12 respectively. This implies that about 10 – 14 % of the phenotypic variability of body weight in this grasscutter population was accounted by additive genetic variance while environmental and gene combination variance made a larger contribution. The implication is that selection of grasscutters in this population should not be based on the information on the animals alone but also information fromits relatives.

Estimation of additive and dominance genetic variance components for female fertility traits in Iranian Holstein cows

2018 ◽  
Vol 156 (4) ◽  
pp. 565-569
Author(s):  
H. Ghiasi ◽  
R. Abdollahi-Arpanahi ◽  
M. Razmkabir ◽  
M. Khaldari ◽  
R. Taherkhani
Keyword(s):  
Dairy Cows ◽  
Variance Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Genetic Effects ◽  
Information Criteria ◽  
Phenotypic Variance ◽  
Dominance Variance ◽  
Dominance Genetic Variance ◽  
Estimated Heritability

AbstractThe aim of the current study was to estimate additive and dominance genetic variance components for days from calving to first service (DFS), a number of services to conception (NSC) and days open (DO). Data consisted of 25 518 fertility records from first parity dairy cows collected from 15 large Holstein herds of Iran. To estimate the variance components, two models, one including only additive genetic effects and another fitting both additive and dominance genetic effects together, were used. The additive and dominance relationship matrices were constructed using pedigree data. The estimated heritability for DFS, NSC and DO were 0.068, 0.035 and 0.067, respectively. The differences between estimated heritability using the additive genetic and additive-dominance genetic models were negligible regardless of the trait under study. The estimated dominance variance was larger than the estimated additive genetic variance. The ratio of dominance variance to phenotypic variance was 0.260, 0.231 and 0.196 for DFS, NSC and DO, respectively. Akaike's information criteria indicated that the model fitting both additive and dominance genetic effects is the best model for analysing DFS, NSC and DO. Spearman's rank correlations between the predicted breeding values (BV) from additive and additive-dominance models were high (0.99). Therefore, ranking of the animals based on predicted BVs was the same in both models. The results of the current study confirmed the importance of taking dominance variance into account in the genetic evaluation of dairy cows.

Comparison of F2 and F∞ diallel analyses in barley

Genome ◽  
1988 ◽  
Vol 30 (6) ◽  
pp. 865-869 ◽  
Author(s):  
T. M. Choo ◽  
E. Reinbergs ◽  
P. Y. Jui
Keyword(s):  
Hordeum Vulgare ◽  
Grain Yield ◽  
Plant Height ◽  
Variance Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Heading Date ◽  
Diallel Analysis ◽  
Hordeum Vulgare L ◽  
Hill Plots

A study was conducted in barley (Hordeum vulgare L.) to compare the relative magnitudes of heterosis to additive × additive epistasis and to compare F2 and F∞, diallel analyses. Both F2 and F∞, progenies were derived from 7 × 7 diallel crosses. Progenies and their parents were evaluated for grain yield, heading date, plant height, and the number of spikes per hill in hill plots with five replications at Elora (Ontario) in 1978. Results suggested that additive × additive epistasis were present for these traits and its magnitude was similar to that of heterosis estimated in F2. Both F2 and F∞ analyses detected the presence of epistasis. Both analyses provided similar estimates of the additive genetic variance for heading date and the number of spikes per hill, but the F2 analysis provided higher estimates than the F∞ analysis for grain yield and plant height. The estimate for grain yield and plant height obtained from the F2 analysis could be biased upward because of the invalid assumption of no epistasis. Estimates of other genetic variance components from the F2 analysis could be biased also. The F∞ diallel analysis not only provided estimates of additive × additive genetic variance for the four traits, it also allowed detection of nonindependent gene distribution in the parents for three of the four traits. Therefore, the limitations of the F2 diallel analysis in the presence of epistasis were apparent in the study. The F2 diallel analysis, however, could be used to detect dominance and maternal effects and thus to complement the F∞ diallel analysisKey words: barley, Hordeum vulgare, diallels, haploids, epistasis, heterosis.

scholarly journals Variance Component Analysis of Parthenocarpic Character in a Processing Cucumber (Cucumis sativus L.) population

HortScience ◽  
2004 ◽  
Vol 39 (4) ◽  
pp. 881A-881
Author(s):  
Zhanyong Sun* ◽  
Richard L. Lower ◽  
Jack E. Staub
Keyword(s):  
Cucumis Sativus ◽  
Variance Component ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Component Analysis ◽  
Variance Component Analysis ◽  
Individual Plant ◽  
Normal Leaf ◽  
Dominance Genetic Variance ◽  
Plant Basis

The incorporation of genes for parthenocarpy (production of fruit without fertilization) has potential for increasing yield in pickling cucumber (Cucumis sativus L.). The inheritance of parthenocarpy in cucumber is not well understood, and thus a genetic analysis was performed on F3 cross-progeny resulting from a mating between the processing cucumber inbred line 2A (P1, gynoecious, parthenocarpic, indeterminate, normal leaf) and Gy8 (P2, gynoecious, non-parthenocarpic, indeterminate, normal leaf). A variance component analysis was performed to fruit yield data collected at two locations (designated E-block and G-block) at Hancock, WI in 2000. The relative importance of additive genetic variance compared to dominance genetic variance changed across environments. The additive genetic variance was 0.5 and 4.3 times of dominance genetic variance in E-block and G-block, respectively. The estimated environmental variance accounted for ≈90% of the total phenotypic variance on an individual plant basis in both locations. Narrow-sense heritability estimated on an individual plant basis ranged from 0.04 (E-block) to 0.12 (G-block). Broad-sense heritability estimated on an individual plant basis ranged from 0.12 (E-block) to 0.15 (G-block). The minimum number of effective factors controlling parthenocarpy was estimated to range between 5 (G-block) to 13 (E-block). These results suggest that the response to direct selection of individual plants for improving parthenocarpy character will likely be slow and difficult. Experiment procedures that minimize the effect of environment on the expression of parthenocarpy will likely maximize the likelihood of gain from selection.

scholarly journals Genomic Prediction and the Practical Breeding of 12 Quantitative-Inherited Traits in Cucumber (Cucumis sativus L.)

2021 ◽  
Vol 12 ◽  
Author(s):  
Ce Liu ◽  
Xiaoxiao Liu ◽  
Yike Han ◽  
Xi'ao Wang ◽  
Yuanyuan Ding ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Complex Traits ◽  
Variance Components ◽  
Genetic Variance ◽  
Cross Validation ◽  
Additive Genetic Variance ◽  
Inbred Lines ◽  
Cucumis Sativus L ◽  
The Cross ◽  
Two Populations

Genomic prediction is an effective way for predicting complex traits, and it is becoming more essential in horticultural crop breeding. In this study, we applied genomic prediction in the breeding of cucumber plants. Eighty-one cucumber inbred lines were genotyped and 16,662 markers were identified to represent the genetic background of cucumber. Two populations, namely, diallel cross population and North Carolina II population, having 268 combinations in total were constructed from 81 inbred lines. Twelve cucumber commercial traits of these two populations in autumn 2018, spring 2019, and spring 2020 were collected for model training. General combining ability (GCA) models under five-fold cross-validation and cross-population validation were applied to model validation. Finally, the GCA performance of 81 inbred lines was estimated. Our results showed that the predictive ability for 12 traits ranged from 0.38 to 0.95 under the cross-validation strategy and ranged from −0.38 to 0.88 under the cross-population strategy. Besides, GCA models containing non-additive effects had significantly better performance than the pure additive GCA model for most of the investigated traits. Furthermore, there were a relatively higher proportion of additive-by-additive genetic variance components estimated by the full GCA model, especially for lower heritability traits, but the proportion of dominant genetic variance components was relatively small and stable. Our findings concluded that a genomic prediction protocol based on the GCA model theoretical framework can be applied to cucumber breeding, and it can also provide a reference for the single-cross breeding system of other crops.

A diallel cross in black spruce

Genome ◽  
1987 ◽  
Vol 29 (1) ◽  
pp. 180-186 ◽  
Author(s):  
T. J. B. Boyle
Keyword(s):  
Combining Ability ◽  
Variance Components ◽  
Genetic Variance ◽  
Black Spruce ◽  
Diallel Cross ◽  
Specific Combining Ability ◽  
Environment Interaction ◽  
Forest Genetics ◽  
Data Set ◽  
Dominance Genetic Variance

A complete 7 × 7 diallel of black spruce (Picea mariana (Mill.) B.S.P.), without selfs, planted in three locations, was measured for height growth at several ages. Analysis using Griffing's method 3, model II, demonstrated that general combining ability (GCA) was the dominant genetic component of variation, although specific combining ability (SCA) appeared to be proportionately increasing in importance with age. When data from all locations were combined, the GCA × environment interaction proved to be highly significant. If the trend of increasing proportional importance of SCA continues, existing improvement strategies exploiting only GCA may need to be radically altered. Greater genetic gain would result from crosses among a few clones of high specific combining ability. Whatever approach is used, it appears likely that genotypes will have to be carefully matched to sites. Imbalance in the data set appeared to invalidate F-tests. As a result of heterozygosity in the parents and the likely presence of epistasis and linkage disequilibrium, the interpretation of GCA and SCA variance components in terms of additive and dominance genetic variance cannot be made. Key words: diallel cross, combining ability, black spruce, forest genetics.

scholarly journals Estimation of Additive and Dominance Genetic Effects on Body Weight, Carcass and Ham Quality Traits in Heavy Pigs

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 481
Author(s):  
Valentina Bonfatti ◽  
Roberta Rostellato ◽  
Paolo Carnier
Keyword(s):  
Variance Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Model Fitting ◽  
Genetic Effects ◽  
Phenotypic Variance ◽  
Genetic Response ◽  
Dominance Variance ◽  
Dry Cured Ham ◽  
Genetic Evaluations

Neglecting dominance effects in genetic evaluations may overestimate the predicted genetic response achievable by a breeding program. Additive and dominance genetic effects were estimated by pedigree-based models for growth, carcass, fresh ham and dry-cured ham seasoning traits in 13,295 crossbred heavy pigs. Variance components estimated by models including litter effects, dominance effects, or both, were compared. Across traits, dominance variance contributed up to 26% of the phenotypic variance and was, on average, 22% of the additive genetic variance. The inclusion of litter, dominance, or both these effects in models reduced the estimated heritability by 9% on average. Confounding was observed among litter, additive genetic and dominance effects. Model fitting improved for models including either the litter or dominance effects, but it did not benefit from the inclusion of both. For 15 traits, model fitting slightly improved when dominance effects were included in place of litter effects, but no effects on animal ranking and accuracy of breeding values were detected. Accounting for litter effects in the models for genetic evaluations would be sufficient to prevent the overestimation of the genetic variance while ensuring computational efficiency.

scholarly journals Bedeutung der Varianzkomponentenschätzung für die Zucht von landwirtschaftlichen Nutztieren – eine Übersicht

2000 ◽  
Vol 43 (5) ◽  
pp. 523-534
Author(s):  
R. Röhel ◽  
J. Krieter ◽  
R. Preisinger
Keyword(s):  
Variance Components ◽  
Genetic Relationships ◽  
Additive Genetic Variance ◽  
Period Change ◽  
Farm Animals ◽  
Selection Strategy ◽  
Sampling Variance ◽  
Breeding Values ◽  
Variance Components Estimation ◽  
Estimation Of Variance

Abstract. Title of the paper: The importance of variance components estimation in breeding of farm animals – a review The present paper showed the importance of variance components estimation in animal breeding. Beside the use of variance components for estimation of breeding values, the components have a high importance on further breeding aspects, such as indication of selection limits, optimisation of test period, change of Performance during growth, and determination of the best selection traits. Maternal and non-additive genetic variance components can be estimated and their high influence on choice of the optimal selection strategy are explained. Standard errors of crossbreeding parameters are influenced by genetic relationships and are only unbiased when using all genetic variances and covariances among animals. Genotype-environmental-interaction and heterogeneous variances, which result in high reduction in selection response, can be obtained in a variance components estimation. The high value of Bayesian methods in order to describe the sampling variance of variance components and to account for the Standard error of estimation of variance components in the estimation of breeding values is explained.

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