scholarly journals Estimation of breeding value for bodyweight of grasscutters

2021 ◽  
Vol 48 (2) ◽  
pp. 1-5
Author(s):  
I. Udeh
Keyword(s):  
Animal Model ◽  
Breeding Program ◽  
Breeding Value ◽  
Breeding Values ◽  
Genetic Merit ◽  
Phenotypic Information ◽  
Poids Corporel ◽  
Estimated Breeding Value ◽  
Estimated Breeding Values

Animal breeders are interested in the genetic worth or total genetic merit of an animal for a given trait. The value of an animal in a breeding program for a particular trait is called the breeding value. The aim of this study was to predict the breeding values for bodyweight of grasscutters at 4, 6 and 8 months of age using univariate animal model. Four families of grasscutters with five grasscutters per family were used for the study. Families 3 and 4 had higher bodyweight at 4 and 6 months compared with families 1 and 2. Family 4 had the highest bodyweight at 8 month and family 2 had the least. The estimated breeding values (EBV) for bodyweight of grasscutters ranged from -0.06kg to 0.45kg at 4 month, -0.05kg to 0.45 kg at 6 month and -0.04kg to 0.55kg at 8 month. The reliability of the EBV (%) ranged from 51.00 to 62.50, 22.25 to 43.81 and 25.84 to 49.00 at 4, 6 and 8 months of age respectively. This implies that the correlations between estimated breeding value and true genetic merit were medium to high in magnitude. The reliability of the EBV could be improved further through collecting more phenotypic information on the animal and its relatives and by improving the heritability of the trait.     Les éleveurs s'intéressent à la valeur génétique ou au mérite génétique total d'un animal pour un trait donné. La valeur d'un animal dans un programme d'élevage pour un trait particulier est appelée valeur de reproduction. Le but de cette étude était de prédire les valeurs de reproduction du poids corporel des coupe-herbes à l'âge de 4, 6 et 8 mois à l'aide d'un modèle animal univarié. Quatre familles de coupe-herbes avec cinq coupes-herbes par famille ont été utilisées pour l'étude. Les familles 3 et 4 avaient un poids corporel plus élevé à 4 et 6 mois comparativement aux familles 1 et 2. Famille 4 avait le poids corporel le plus élevé à 8 mois et la famille 2 avait le moins. Les valeurs de reproduction estimées (le 'EBV') pour le poids corporel des coupe-herbes allaient de -0.06 kg à 0,45 kg à 4 mois, -0.05 kg à 0.45 kg à 6 mois et -0.04 kg à 0.55 kg à 8 mois. La fiabilité de l'EBV (%) 51.00 à 62.50, 22.25 à 43.81 et 25.84 à 49.00 à 4, 6 et 8 mois respectivement. Cela implique que les corrélations entre la valeur de reproduction estimée et le véritable mérite génétique étaient de taille moyenne à élevée. La fiabilité de l'EBV pourrait être encore améliorée en recueillant plus d'informations phénotypique sur l'animal et ses parents et en améliorant l'hérabilité du trait.

Repeatability and bias of estimated breeding values for dairy bulls and bull dams calculated from animal model evaluations

1993 ◽  
Vol 57 (2) ◽  
pp. 175-182 ◽  
Author(s):  
P. Uimari ◽  
E. A. Mäntysaari
Keyword(s):  
Animal Model ◽  
Dairy Cow ◽  
Relative Weight ◽  
Breeding Value ◽  
Potential Bias ◽  
Data Set ◽  
Breeding Values ◽  
Dairy Bulls ◽  
Estimated Breeding Values ◽  
Genetic Standard Deviation

AbstractAn animal model and an approximative method for calculating repeatabilities of estimated breeding values are used in Finnish dairy cow evaluation. Changes in estimated breeding values over time as daughters accumulate were studied. Special emphasis was given to the accuracy and potential bias in the pedigree indices of young sires. The data set used was the same as in the national evaluation and the traits investigated were protein yield and somatic cell count. The average repeatability in evaluation of bulls without daughters was 0·37. The empirical repeatability defined as a squared correlation between the pedigree index and the final sire proof was only 0·15. The reduction in the repeatability was attributed to the selection on pedigree index. The upward bias observed in pedigree indices was 5 kg (approx. 0·3 of genetic standard deviation). The bias was caused by the overestimation of bull dams' breeding value. Also the proofs of bull sires increased after the second crop of daughters. The correlation between the evaluations of the same sire calculated from two separate equal size daughter groups was 0·91 when the bull had 10 to 50 daughters and 0·87 with over 100 daughters. This illustrates how the relative weight of the pedigree decreases while more progeny information is accumulated in the evaluation.

scholarly journals Genetic trend for jumping performance in Czech warm-blooded horses

2004 ◽  
Vol 52 (1) ◽  
pp. 97-102
Author(s):  
Iva Jiskrová
Keyword(s):  
Animal Model ◽  
Regression Analysis ◽  
Regression Coefficient ◽  
Breeding Value ◽  
Positive Trend ◽  
Genetic Trend ◽  
Breeding Values ◽  
Jumping Performance ◽  
The Mean ◽  
Estimated Breeding Value

Data on the jumping performance of horses in the period of 1991 - 2002 were analysed. The data included 252781 starts of 10671 horses in 10911 jumping competitions. The performance was characterised on the basis of the obtained bad points (penalties) of the competing horses. The BLUP Animal model was used to estimate the breeding value of the sport horses; the genetic trend in the jumping performance of the Czech warm-blooded horse was assessed on the basis of these results. Regression analysis and calculations of the mean breeding values based on the year of birth were used to determine the dependence of the estimated breeding value on the year of birth. The jumping performance of the population of the Czech warm-blooded horse shows a positive trend. The regression coefficient of –0.1337 shows the genetic trend in the population, which means an increasing jumping performance of the horses expressed in reduced earnings of bad points by 0.1337 in dependence on the year of birth.

scholarly journals 45 A permutation test for validation of genomic estimated breeding values

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 8-9
Author(s):  
Zahra Karimi ◽  
Brian Sullivan ◽  
Mohsen Jafarikia
Keyword(s):  
Permutation Test ◽  
Breeding Value ◽  
Small Data ◽  
Data Sets ◽  
Type I ◽  
Future Performance ◽  
Small Data Sets ◽  
Estimated Breeding Value ◽  
Estimated Breeding Values ◽  
Top 40

Abstract Previous studies have shown that the accuracy of Genomic Estimated Breeding Value (GEBV) as a predictor of future performance is higher than the traditional Estimated Breeding Value (EBV). The purpose of this study was to estimate the potential advantage of selection on GEBV for litter size (LS) compared to selection on EBV in the Canadian swine dam line breeds. The study included 236 Landrace and 210 Yorkshire gilts born in 2017 which had their first farrowing after 2017. GEBV and EBV for LS were calculated with data that was available at the end of 2017 (GEBV2017 and EBV2017, respectively). De-regressed EBV for LS in July 2019 (dEBV2019) was used as an adjusted phenotype. The average dEBV2019 for the top 40% of sows based on GEBV2017 was compared to the average dEBV2019 for the top 40% of sows based on EBV2017. The standard error of the estimated difference for each breed was estimated by comparing the average dEBV2019 for repeated random samples of two sets of 40% of the gilts. In comparison to the top 40% ranked based on EBV2017, ranking based on GEBV2017 resulted in an extra 0.45 (±0.29) and 0.37 (±0.25) piglets born per litter in Landrace and Yorkshire replacement gilts, respectively. The estimated Type I errors of the GEBV2017 gain over EBV2017 were 6% and 7% in Landrace and Yorkshire, respectively. Considering selection of both replacement boars and replacement gilts using GEBV instead of EBV can translate into increased annual genetic gain of 0.3 extra piglets per litter, which would more than double the rate of gain observed from typical EBV based selection. The permutation test for validation used in this study appears effective with relatively small data sets and could be applied to other traits, other species and other prediction methods.

A method for reducing inbreeding with Best Linear Unbiased Prediction

1993 ◽  
Vol 1993 ◽  
pp. 33-33
Author(s):  
B Grundy ◽  
WG Hill
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Breeding Value ◽  
Best Linear Unbiased Predictor ◽  
Linear Unbiased Prediction ◽  
Genetic Merit ◽  
Selection Decisions ◽  
Best Linear Unbiased ◽  
Estimated Breeding Value ◽  
Commercial Environment ◽  
Unbiased Prediction

An optimum way of selecting animals is through a prediction of their genetic merit (estimated breeding value, EBV), which can be achieved using a best linear unbiased predictor (BLUP) (Henderson, 1975). Selection decisions in a commercial environment, however, are rarely made solely on genetic merit but also on additional factors, an important example of which is to limit the accumulation of inbreeding. Comparison of rates of inbreeding under BLUP for a range of hentabilities highlights a trend of increasing inbreeding with decreasing heritability. It is therefore proposed that selection using a heritability which is artificially raised would yield lower rates of inbreeding than would otherwise be the case.

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.

scholarly journals Estimation of genetic parameters and breeding values for litter size in the first three parity of landrace sows

2016 ◽  
Vol 32 (3) ◽  
pp. 261-269 ◽  
Author(s):  
Dragomir Lukac ◽  
Vitomir Vidovic ◽  
Teodora Vasiljevic ◽  
Oliver Stankovic
Keyword(s):  
Animal Model ◽  
Genetic Parameters ◽  
Multivariate Analyses ◽  
Reproductive Traits ◽  
Phenotypic Variance ◽  
Breeding Values ◽  
The Third ◽  
Reproduction Traits ◽  
Genetic And Phenotypic Correlations ◽  
Estimated Breeding Values

The aim of this study was to estimate the genetic parameters and breeding values for reproduction traits of Landrace sows in the first three parities by Animal model. Records of 2238 first parity; 2125 second parity and 1872 third parity Landrace sows farrowing between 2007 and 2012 were included in the analysis. The traits included in the analyses were total pigs born (TB), number of pigs born alive (NBA), number of pigs weaned (NW) and litter weaning weight (LW) in the parities. The genetic parameters were estimated using a multivariate analyses Animal model using REML procedure. Estimates of heritability for TB were 0.03, 0.05 and 0.18, for NBA were 0.04, 0.02 and 0.17, for NW were 0.08, 0.08 and 0.01 and for LW were 0.09, 0.11 and 0.03 for parities 1 to 3. Genetic and phenotypic variance were increased from the first to the third parity. Between the majorities of studied reproductive traits were the recorded positive genetic and phenotypic correlations, except between LW and other analyzed properties where they recorded a high correlation negative in third parity. Means of estimated breeding values of reproductive traits from first parity to third parity was indecreased.

scholarly journals Comparison of BLUP and Bayesian methods for different sizes of training population in genomic selection

2020 ◽  
Vol 44 (5) ◽  
pp. 994-1002
Author(s):  
Samet Hasan ABACI ◽  
Hasan ÖNDER
Keyword(s):  
Genomic Selection ◽  
Milk Yield ◽  
Predictive Ability ◽  
Breeding Value ◽  
Training Population ◽  
Breeding Values ◽  
Value Prediction ◽  
The Usa ◽  
Population Sizes ◽  
Estimated Breeding Values

This study aims to compare the accuracy of pedigree-based and genomic-based breeding value prediction for different training population sizes. In this study, Bayes (A, B, C, Cpi) and GBLUP methods for genomic selection and BLUP method for pedigree-based selection were used. Genomic and pedigree-based breeding values were estimated for partial milk yield (158 days) of Holstein cows (400 individuals) from a private enterprise in the USA. For this aim, populations were created for indirect breeding value estimates as training (322–360) and test (78–40) populations. In animals genotyped with a 54k SNP, the marker file was encoded as –10, 0, and 10 for AA, AB, and BB marker genotypes, respectively. Bayes and GBLUP methods were performed using GenSel 4.55 software. A total of 50,000 iterations were used, with the first 5000 excluded as the burn-in. Pedigree-based breeding values were estimated by REML using MTDFREML software employing an animal model. Correlations between partial milk yield and estimated breeding values were used to assess the predictive ability for methods. Bayes B method gave the highest accuracy for the indirect estimate of breeding value.

scholarly journals Weaning results of beef Hungarian Fleckvieh calves 2. Genetic parameters, breeding values

2010 ◽  
Vol 53 (1) ◽  
pp. 26-36 ◽  
Author(s):  
S. Bene ◽  
I. Füller ◽  
A. Fördős ◽  
F. Szabó
Keyword(s):  
Genetic Parameters ◽  
Correlation Coefficients ◽  
Breeding Value ◽  
Phenotypic Variance ◽  
Weaning Weight ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Genetic Value ◽  
Maternal Heritability ◽  
Daily Gain

Abstract. Weaning weight, preweaning daily gain and 205-day weight of Hungarian Fleckvieh calves (n=8 929, bulls =4 539, heifers =4 390) born from 232 sires between 1980 and 2003 were examined. Variance, covariance components and heritability values and correlation coefficients were estimated. The effect of the maternal permanent environment on genetic parameters and breeding values were examined. Two animal models were used for breeding value estimation. The direct heritability (hd2) of weaning weight, preweaning daily gain and 205-day weight was between 0.37 and 0.42. The maternal heritability (hm2) of these traits was 0.06 and 0.07. The direct-maternal correlations (rdm) were medium and negative −0.52 and −0.74. Contribution of the maternal heritability and maternal permanent environment to phenotype is smaller than that of direct heritabilities (hm2+c2< hd2). The ratio of the variance of maternal permanent environment in the phenotypic variance (c2) changed from 3 to 6 %. Estimated breeding values changed whether the permanent environmental effect of dam wasn’t taken into consideration but the rank of the animals was not modified. The genetic value for weaning results of Hungarian Fleckvieh population has increased since 1997.

scholarly journals Bias in genetic evaluation using random regression test-day model

2008 ◽  
Vol 16 (2) ◽  
pp. 103 ◽  
Author(s):  
M. LIDAUER ◽  
E.A. MÄNTYSAARI
Keyword(s):  
Animal Model ◽  
Evaluation Model ◽  
Protein Yield ◽  
Random Regression ◽  
Data Sets ◽  
Heterogeneous Variance ◽  
Breeding Values ◽  
Regression Test ◽  
Estimated Breeding Values ◽  
Reduced Data

The effect of an upgraded Finnish evaluation model on bias in estimated breeding values for protein yield was investigated. Evaluations based on repeatability animal model and on random regression test-day model without and with heterogeneous variance adjustment were compared. Comparisons were based on the average difference between pedigree indices and the future estimated breeding values, based on own or on daughter performance records. This was defined as empirical bias. The pedigree indices were computed from reduced data sets where four years of the most recent data were excluded. Results showed an upward bias in the protein yield pedigree indices for Ayrshire young sires of 2.2 kg, 2.5 kg and 1.8 kg from the repeatability animal model, random regression test-day model and random regression test-day model with heterogeneous variance adjustment, respectively. Pedigree indices for daughters of young sires were upward biased, whereas pedigree indices for daughters of proven sires were slightly underestimated when heterogeneous variance was not accounted. Inclusion of test-day yields from the fourth lactation onwards increased the bias. Moving from repeatability animal model to random regression test-day model did not reduce the bias, whereas adjustment of heterogeneous variance reduced bias.;

Preselection of pigs for performance testing

1994 ◽  
Vol 1994 ◽  
pp. 16-16
Author(s):  
B. Grundy
Keyword(s):  
Animal Model ◽  
Stochastic Simulation ◽  
Performance Testing ◽  
Breeding Value ◽  
Correct Identification ◽  
Common Environment ◽  
Genetic Merit ◽  
Error Components ◽  
Animal Model Blup ◽  
Two Stages

An important aspect of animal improvement is the correct identification of those individuals with superior genetic merit. Testing to accurately assess merit, however, can be a costly component of improvement. In the Group Nucleus there is a substantial number of potential offspring to be tested even within a system where the farrowing and testing is practised continuously throughout the year. In order, therefore, to reduce costs it could be beneficial to accurately predict genetic worth prior to test on either mean parental breeding value or a trait expressed early before test.A Group Nucleus population was modelled by stochastic simulation with selection at two stages The model for the trait(s) selected comprised animal, common environment, herd-year-season and error components. A multivariate animal model BLUP routine was used for the evaluation.

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