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.

scholarly journals A note on genetic parameters and accuracy of estimated breeding values in honey bees

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Evert W. Brascamp ◽  
Piter Bijma
Keyword(s):  
Honey Bees ◽  
Variance Components ◽  
Genetic Parameters ◽  
Additive Genetic Variance ◽  
Breeding Value ◽  
Phenotypic Variance ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Additive Genetic Relationship ◽  
The Relationship

Abstract Background In honey bees, observations are usually made on colonies. The phenotype of a colony is affected by the average breeding value for the worker effect of the thousands of workers in the colony (the worker group) and by the breeding value for the queen effect of the queen of the colony. Because the worker group consists of multiple individuals, interpretation of the variance components and heritabilities of phenotypes observed on the colony and of the accuracy of selection is not straightforward. The additive genetic variance among worker groups depends on the additive genetic relationship between the drone-producing queens (DPQ) that produce the drones that mate with the queen. Results Here, we clarify how the relatedness between DPQ affects phenotypic variance, heritability and accuracy of the estimated breeding values of replacement queens. Second, we use simulation to investigate the effect of assumptions about the relatedness between DPQ in the base population on estimates of genetic parameters. Relatedness between DPQ in the base generation may differ considerably between populations because of their history. Conclusions Our results show that estimates of (co)variance components and derived genetic parameters were seriously biased (25% too high or too low) when assumptions on the relationship between DPQ in the statistical analysis did not agree with reality.

Genetic variation within and between subpopulations of the Australian Merino breed

2016 ◽  
Vol 56 (1) ◽  
pp. 87 ◽  
Author(s):  
Andrew A. Swan ◽  
Daniel J. Brown ◽  
Julius H. J. van der Werf
Keyword(s):  
Genetic Variation ◽  
Variance Components ◽  
Additive Genetic Variance ◽  
Genetic Correlations ◽  
Fibre Diameter ◽  
Genetic Group ◽  
Additive Variance ◽  
Breeding Values ◽  
Genetic Groups ◽  
Australian Merino

Genetic variation within and between Australian Merino subpopulations was estimated from a large breeding nucleus in which up to 8500 progeny from over 300 sires were recorded at eight sites across Australia. Subpopulations were defined as genetic groups using the Westell–Quaas model in which base animals with unknown pedigree were allocated to groups based on their flock of origin if there were sufficient ‘expressions’ for the flock, or to one of four broad sheep-type groups otherwise (Ultra/Superfine, Fine/Fine-medium, Medium/Strong, or unknown). Linear models including genetic groups and additive genetic breeding values as random effects were used to estimate variance components for 12 traits: yearling greasy and clean fleece weight (ygfw and ycfw), yearling mean and coefficient of variation of fibre diameter (yfd and ydcv), yearling staple length and staple strength (ysl and yss), yearling fibre curvature (ycuv), yearling body wrinkle (ybdwr), post-weaning weight (pwt), muscle (pemd) and fat depth (pfat), and post-weaning worm egg count (pwec). For the majority of traits, the genetic group variance ranged from approximately equal to two times larger than the additive genetic (within group) variance. The exceptions were pfat and ydcv where the genetic group to additive variance ratios were 0.58 and 0.22, respectively, and pwec and yss where there was no variation between genetic groups. Genetic group correlations between traits were generally the same sign as corresponding additive genetic correlations, but were stronger in magnitude (either more positive or more negative). These large differences between genetic groups have long been exploited by Merino ram breeders, to the extent that the animals in the present study represent a significantly admixed population of the founding groups. The relativities observed between genetic group and additive genetic variance components in this study can be used to refine the models used to estimate breeding values for the Australian Merino industry.

scholarly journals Estimation of additive, dominance and epistatic variance components using finite locus models implemented with a single-site Gibbs and a descent graph sampler

2000 ◽  
Vol 76 (2) ◽  
pp. 187-198 ◽  
Author(s):  
F.-X. DU ◽  
I. HOESCHELE
Keyword(s):  
Variance Components ◽  
Additive Genetic Variance ◽  
Genetic Parameter ◽  
Genetic Effects ◽  
Single Site ◽  
Phenotypic Data ◽  
Variance Components Estimation ◽  
Locus Number ◽  
Variance Estimates ◽  
Finite Locus

In a previous contribution, we implemented a finite locus model (FLM) for estimating additive and dominance genetic variances via a Bayesian method and a single-site Gibbs sampler. We observed a dependency of dominance variance estimates on locus number in the analysis FLM. Here, we extended the FLM to include two-locus epistasis, and implemented the analysis with two genotype samplers (Gibbs and descent graph) and three different priors for genetic effects (uniform and variable across loci, uniform and constant across loci, and normal). Phenotypic data were simulated for two pedigrees with 6300 and 12300 individuals in closed populations, using several different, non-additive genetic models. Replications of these data were analysed with FLMs differing in the number of loci. Simulation results indicate that the dependency of non-additive genetic variance estimates on locus number persisted in all implementation strategies we investigated. However, this dependency was considerably diminished with normal priors for genetic effects as compared with uniform priors (constant or variable across loci). Descent graph sampling of genotypes modestly improved variance components estimation compared with Gibbs sampling. Moreover, a larger pedigree produced considerably better variance components estimation, suggesting this dependency might originate from data insufficiency. As the FLM represents an appealing alternative to the infinitesimal model for genetic parameter estimation and for inclusion of polygenic background variation in QTL mapping analyses, further improvements are warranted and might be achieved via improvement of the sampler or treatment of the number of loci as an unknown.

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.

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 variance components and prediction of breeding values using pooled data1

2008 ◽  
Vol 86 (11) ◽  
pp. 2845-2852 ◽  
Author(s):  
F. Biscarini ◽  
H. Bovenhuis ◽  
J. A. M. van Arendonk
Keyword(s):  
Variance Components ◽  
Breeding Values ◽  
Estimation Of Variance

scholarly journals Estimation of variance components and prediction of breeding values in rubber tree breeding using the REML/BLUP procedure

2005 ◽  
Vol 28 (2) ◽  
pp. 271-276 ◽  
Author(s):  
Renata Capistrano Moreira Furlani ◽  
Mario Luiz Teixeira de Moraes ◽  
Marcos Deon Vilela de Resende ◽  
Enes Furlani Junior ◽  
Paulo de Souza Gonçalves ◽  
...  
Keyword(s):  
Variance Components ◽  
Rubber Tree ◽  
Tree Breeding ◽  
Breeding Values ◽  
Estimation Of Variance

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.

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