scholarly journals Varianzkomponentenschätzung unter Berücksichtigung von Dominanz an simulierten Reinzuchtlinien

2004 ◽  
Vol 47 (4) ◽  
pp. 387-395
Author(s):  
M. Wensch-Dorendorf ◽  
N. Mielenz ◽  
E. Groeneveld ◽  
M. Kovac ◽  
L. Schüler
Keyword(s):  
Animal Model ◽  
Variance Components ◽  
Random Mating ◽  
Phenotypic Variance ◽  
Dominance Relationship ◽  
Gene Model ◽  
Dominance Model ◽  
Dominance Variance ◽  
Simulation Based ◽  
Estimation Of Variance

Abstract. Title of the paper: Estimation of variance components under dominance with simulated purebred lines A stochastic simulation based on a gene model was used to investigate the estimation of variance with dominance and additive animal models. For a heritability in broad sense of 0.5 three ratios of dominance variance (5, 10 and 25%) on the phenotypic variance were investigated under different degrees of dominance. No additionally biased estimations of the variance components as consequence of different dominance degrees were found. By using the dominance model for random mating as well as for selection the differences between true parameters and estimation values were small for all dominance degrees and ratios of dominance variance. Small, but significantly, differences can be explained by the change of the allele frequencies over the generations due to the influence of selection. By using the additive animal model, that ignores the dominance relationship, for high ratios of the dominance variance (25% or greater) important biased estimations of the variances were observed. For dominance ratios of 5% no significantly overestimation of the additive variances with the reduced model were found under selection and random mating.

scholarly journals Estimation of variance components, genetic parameters and genetic trends for litter size of swines

2010 ◽  
Vol 39 (10) ◽  
pp. 2155-2159 ◽  
Author(s):  
Leandro Barbosa ◽  
Paulo Sávio Lopes ◽  
Adair José Regazzi ◽  
Robledo de Almeida Torres ◽  
Mário Luiz Santana Júnior ◽  
...  
Keyword(s):  
Litter Size ◽  
Variance Components ◽  
Genetic Parameters ◽  
Random Effect ◽  
Additive Model ◽  
Fixed Effect ◽  
Likelihood Method ◽  
Phenotypic Variance ◽  
Estimation Of Variance ◽  
Genetic Trends

Records of Large White breed animals were used to estimate variance components, genetic parameters and trends for the character total number of born piglets (TNBP) as measure of litter size. For obtaining variance components and genetic parameters, it was used the Restricted Maximum Likelihood Method using MTDFREML software. Two mixed models (additive and repeatability) were evaluated. The additive model contained fixed effect of the contemporary group and the following random effects: direct additive genetic and residual effect for the first parturition. Repeatability model had the same effects of the additive model plus parturition order fixed effect and non-correlated animal permanent environment random effect for the second, third and forth parturition. Direct additive heritability estimates for TNBP were 0.15 and 0.20 for the additive and repeatability models, respectively. The estimate of the ration among variance of the non-correlated effect of animal permanent environment effect and the phenotypic variance, expressed as total variance proportion (c2) was 0.09. The estimates of yearly genetic trends obtained in the additive and repeatability models have similar behaviors (0.02 piglets/sow/year).

What contributes to differences in phenotypic variation between generations?

2009 ◽  
Vol 2009 ◽  
pp. 200-200
Author(s):  
A Wolc ◽  
I White ◽  
M Lisowski ◽  
W G Hill
Keyword(s):  
Animal Model ◽  
Environmental Effects ◽  
Environmental Conditions ◽  
Phenotypic Variation ◽  
Variance Components ◽  
Genetic Variance ◽  
Phenotypic Variance ◽  
Base Population ◽  
Substantial Heterogeneity ◽  
Over Time

Under the animal model genetic variance is estimated in the base population taking into account inbreeding and is otherwise assumed to remain unchanged over generations. In practice, phenotypic variation differs randomly or systematically over time. Intuitively, such changes would be attributed mostly to environmental effects, and so lower heritability would be expected when variation is inflated. Studies in dairy cattle show contradictory results (e.g. Boldman and Freeman, 1990). Laying hens are kept under environmental conditions intended to be constant, but show substantial heterogeneity in phenotypic variance (VP) over generations. The aim was to investigate how variance components change.

scholarly journals Genetic evaluation of eggshell color based on additive and dominance models in laying hens

2020 ◽  
Vol 33 (8) ◽  
pp. 1217-1223
Author(s):  
Jun Guo ◽  
Kehua Wang ◽  
Liang Qu ◽  
Taocun Dou ◽  
Meng Ma ◽  
...  
Keyword(s):  
Animal Model ◽  
Egg Production ◽  
Genetic Correlations ◽  
Phenotypic Variance ◽  
Dominance Model ◽  
Shell Color ◽  
Inbreeding Coefficients ◽  
L Value ◽  
Eggshell Color

Objective: Eggshells with a uniform color and intensity are important for egg production because many consumers assess the quality of an egg according to the shell color. In the present study, we evaluated the influence of dominant effects on the variations in eggshell color after 32 weeks in a crossbred population.Methods: This study was conducted using 7,878 eggshell records from 2,626 hens. Heritability was estimated using a univariate animal model, which included inbreeding coefficients as a fixed effect and animal additive genetic, dominant genetic, and residuals as random effects. Genetic correlations were obtained using a bivariate animal model. The optimal diagnostic criteria identified in this study were: L* value (lightness) using a dominance model, and a* (redness), and b* (yellowness) value using an additive model.Results: The estimated heritabilities were 0.65 for shell lightness, 0.42 for redness, and 0.60 for yellowness. The dominance heritability was 0.23 for lightness. The estimated genetic correlations were 0.61 between lightness and redness, –0.84 between lightness and yellowness, and –0.39 between redness and yellowness.Conclusion: These results indicate that dominant genetic effects could help to explain the phenotypic variance in eggshell color, especially based on data from blue-shelled chickens. Considering the dominant genetic variation identified for shell color, this variation should be employed to produce blue eggs for commercial purposes using a planned mating system.

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.

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.

Estimation of variance components for body weight of grasscutters (Thryonomys swinderianus) using a repeatability model

2020 ◽  
Vol 47 (2) ◽  
pp. 33-36
Author(s):  
I. Udeh
Keyword(s):  
Animal Model ◽  
Body Weight ◽  
Variance Components ◽  
Genetic Parameters ◽  
Growth Potential ◽  
Likelihood Method ◽  
Mass Selection ◽  
Thryonomys Swinderianus ◽  
Estimate Variance ◽  
Estimation Of Variance

Genetic parameters for growth and other economically important traits of grasscutters are scant in literature. Therefore, the aim of this study was to estimate variance components,heritability and repeatability of body weight of grasscutters using restricted maxim um likelihood method in a repeatability animal model. Sixteen grasscutter families were used for the study. Each family was made up of one male and four females. Each grasscutter has four repeated records giving a total of 320. The pedigree consisted of 80 animals, progenies of 16 sires and 16 dams. Fixed factors included in the model were family and sex. The WOMBAT program was used for the analysis. The heritability of body weight of grasscutters ranged from 0.23±0.04 to 0.68±0.10, thus implying that mass selection will be appropriate for this population. The repeatability estimates ranged from 0.82±0.08 to 0.93±0.11. It can be concluded that the number of body weight records was a good indicator of the animal's growth potential and that mass selection will be reliable.

scholarly journals Schätzung von Varianzkomponenten mit Gruppenmittelwerten am Beispiel von Legehennen, gehalten in Gruppenkäfigen

2003 ◽  
Vol 46 (5) ◽  
pp. 491-498
Author(s):  
N. Mielenz ◽  
V. Nurgiartiningsih ◽  
M. Schmutz ◽  
L. Schüler
Keyword(s):  
Animal Model ◽  
Variance Components ◽  
Genetic Parameters ◽  
Genetic Relationships ◽  
Information Matrix ◽  
Egg Weight ◽  
Complex Relationships ◽  
Estimate Variance ◽  
Additive Genetic Effects ◽  
Estimation Of Variance

Abstract. Title of the paper: Estimation of variance components from group mean records of laying hens housed in group cages Two models are presented to estimate variance components if only group mean records are available. The first model accounts for additive genetic relationships and full-sib group effects (SIMIANER and GJERDE, 1991) and the second model contains the additive genetic effects of all animals from one cage by using modified design matrices. Estimates of the genetic parameters were obtained by the MIVQUE-method (RAO, 1971; LAMOTTE, 1973). The variances of the estimated heritabilities were derived from the information matrix. Estimations from individual records and from average records (cage average) were compared in a small application on laying hen data. The analysed trait was single egg weight measured on hens housed in group cages. It could be shown: If cage variance is negligible, than for the estimation of the heritabilities full-sib data can be used successfully. The application of the modified animal model is suggested, because this model can take into account more complex relationships between the animals of one cage.

ESTIMATING HERITABILITY OF FAMILIES DERIVED FROM SINGLE PLANTS AT AN ADVANCED GENERATION OF SELF-FERTILIZING SPECIES: DEVELOPING GENERAL FORMULAS AND ESTIMATING SPRING WHEAT TRAITS

1998 ◽  
Vol 46 (3) ◽  
pp. 209-212
Author(s):  
Alex Beharav ◽  
Moshe Pinthus J. ◽  
Avigdor Cahaner
Keyword(s):  
Spring Wheat ◽  
Variance Components ◽  
Genetic Variance ◽  
Heading Date ◽  
Phenotypic Variance ◽  
Additive Variance ◽  
Dominance Variance ◽  
Total Genetic Variance ◽  
Two Populations ◽  
Advanced Generation

Genetic expectations of total genetic variance, and between-family and within-family variance components were developed for any given generation (Fn) derived from single selfed plants of an earlier generation (Fk). A formula to estimate the heritability (h2) in any desired generation (Fn) was developed on the basis of these expectations. This formula estimates the value of the genetic variance from the phenotypic variance adjusted to the F2 generation. Heritability estimates of culm length, heading date, and mean grain weight from two populations of F6 families, each derived from a single F5 plant, were computed using this formula, and a formula which estimates the value of the genetic variance from the phenotypic variance in the Fn generation (“Fn estimates”). The FN h2 estimates at F6 were always higher than those adjusted to F2 variance, due to the increase in additive variance and the reduction in dominance variance.

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