Estimation of variance components and genetic parameters for lactation persistency indices in crossbred cattle using Bayesian and REML methods

Meta Gene ◽  
2020 ◽  
Vol 26 ◽  
pp. 100780
Author(s):  
Ajoy Mandal ◽  
Hasan Baneh ◽  
Subrata Koloi ◽  
Champak Bhakat
Keyword(s):  
Variance Components ◽  
Genetic Parameters ◽  
Crossbred Cattle ◽  
Estimation Of Variance

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).

scholarly journals Estimation of variance components of milk, fat, and protein yields of Tunisian Holstein dairy cattle using Bayesian and REML methods

2016 ◽  
Vol 59 (2) ◽  
pp. 243-248 ◽  
Author(s):  
Hafedh Ben Zaabza ◽  
Abderrahmen Ben Gara ◽  
Hedi Hammami ◽  
Mohamed Amine Ferchichi ◽  
Boulbaba Rekik
Keyword(s):  
Dairy Cattle ◽  
Bayesian Methods ◽  
Milk Fat ◽  
Variance Components ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Bayesian Procedure ◽  
Milk Traits ◽  
Residual Variances ◽  
Estimation Of Variance

Abstract. A multi-trait repeatability animal model under restricted maximum likelihood (REML) and Bayesian methods was used to estimate genetic parameters of milk, fat, and protein yields in Tunisian Holstein cows. The estimates of heritability for milk, fat, and protein yields from the REML procedure were 0.21 ± 0.05, 0.159 ± 0.04, and 0.158 ± 0.04, respectively. The corresponding results from the Bayesian procedure were 0.273 ± 0.02, 0.198 ± 0.01, and 0.187 ± 0.01. Heritability estimates tended to be larger via the Bayesian than those obtained by the REML method. Genetic and permanent environmental variances estimated by REML were smaller than those obtained by the Bayesian analysis. Inversely, REML estimates of the residual variances were larger than Bayesian estimates. Genetic and permanent correlation estimates were on the other hand comparable by both REML and Bayesian methods with permanent environmental being larger than genetic correlations. Results from this study confirm previous reports on genetic parameters for milk traits in Tunisian Holsteins and suggest that a multi-trait approach can be an alternative for implementing a routine genetic evaluation of the Tunisian dairy cattle population.

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 Estimating genetic parameters in natural populations using the ‘animal model’

2004 ◽  
Vol 359 (1446) ◽  
pp. 873-890 ◽  
Author(s):  
Loeske E. B. Kruuk
Keyword(s):  
Variance Components ◽  
Quantitative Traits ◽  
Genetic Basis ◽  
Genetic Parameters ◽  
Natural Populations ◽  
Environmental Variation ◽  
Natural Environments ◽  
Evolutionary Patterns ◽  
Powerful Means ◽  
Estimation Of Variance

Estimating the genetic basis of quantitative traits can be tricky for wild populations in natural environments, as environmental variation frequently obscures the underlying evolutionary patterns. I review the recent application of restricted maximum–likelihood ‘animal models’ to multigenerational data from natural populations, and show how the estimation of variance components and prediction of breeding values using these methods offer a powerful means of tackling the potentially confounding effects of environmental variation, as well as generating a wealth of new areas of investigation.

scholarly journals Estimating Genetic Parameters using a Random Regression Model

2008 ◽  
pp. 53-55
Author(s):  
Szilárd Márkus ◽  
Eva Němcová ◽  
István Fazekas ◽  
István Komlósi
Keyword(s):  
Regression Model ◽  
Variance Components ◽  
Genetic Parameters ◽  
Random Regression ◽  
Environmental Variance ◽  
Data Set ◽  
Computational Costs ◽  
Random Regression Model ◽  
Holstein Friesian ◽  
Estimation Of Variance

One of the most important part of the genetic evaluation using a random regression model is the estimation of variance components. This is the topic of many papers because the large computational costs. We can use restricted maximum likelihood (REML), Gibbs sampling and ℜ method for the estimation of genetic parameters. The variance components are necessary to calculate the heritabilities and repeatabilities.The aim of our paper is to estimate the variance components using a random regression repeatability model from test day data set of Hungarian Holstein-Friesian dairy cows and to analyse the change of additive genetic and permanent environmental variance, heritability and repeatability over lactation.

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.

scholarly journals Analysis of experiments in square lattice with emphasis on variance components. i. Individual analysis

1999 ◽  
Vol 34 (10) ◽  
pp. 1811-1822 ◽  
Author(s):  
Heyder Diniz Silva ◽  
Adair José Regazzi ◽  
Cosme Damião Cruz ◽  
José Marcelo Soriano Viana
Keyword(s):  
Variance Components ◽  
Variance Component ◽  
Genetic Parameters ◽  
Square Lattice ◽  
Mean Square ◽  
Adjusted Means ◽  
The Mean ◽  
Negative Estimate ◽  
Estimation Of Variance

This paper focused on four alternatives of analysis of experiments in square lattice as far as the estimation of variance components and some genetic parameters are concerned: 1) intra-block analysis with adjusted treatment and blocks within unadjusted repetitions; 2) lattice analysis as complete randomized blocks; 3) intrablock analysis with unadjusted treatment and blocks within adjusted repetitions; 4) lattice analysis as complete randomized blocks, by utilizing the adjusted means of treatments, obtained from the analysis with recovery of interblock information, having as mean square of the error the mean effective variance of this same analysis with recovery of inter-block information. For the four alternatives of analysis, the estimators and estimates were obtained for the variance components and heritability coefficients. The classification of material was also studied. The present study suggests that for each experiment and depending of the objectives of the analysis, one should observe which alternative of analysis is preferable, mainly in cases where a negative estimate is obtained for the variance component due to effects of blocks within adjusted repetitions.

MAXIMUM LIKELIHOOD ESTIMATION OF GENETIC PARAMETERS IN CANADIENNE, BROWN SWISS, AND MILKING SHORTHORN

1978 ◽  
Vol 58 (2) ◽  
pp. 271-276 ◽  
Author(s):  
W. D. SZKOTNICKI ◽  
A. K. W. TONG ◽  
K. M. KROTCH ◽  
M. A. SHARABY ◽  
L. P. JOHNSON ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Variance Components ◽  
Genetic Parameters ◽  
Mixed Model ◽  
Likelihood Estimation ◽  
Brown Swiss ◽  
Fat Yield ◽  
Estimation Of Variance ◽  
Estimation Of Genetic Parameters

The application of maximum likelihood estimation of variance components to all lactation milk and fat records of three dairy breeds was accomplished with relative computational ease. Discussion of some of the practical problems of maximum likelihood estimation for the mixed model with sires and cows nested within sire is presented. Further study of a few of these problems is warranted. The estimated heritabilities for milk and fat yields were.27 for all three breeds, and repeatabilities were.50, except for Milking Shorthorn where repeatability estimates were.53 and.52 for milk and fat yield, respectively.

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