scholarly journals Definition of subgroups for fixed regression in the test-day animal model for milk production ofHolsteincattle in theCzech Republic

2011 ◽  
Vol 50 (No. 1) ◽  
pp. 7-13 ◽  
Author(s):  
L. Zavadilová ◽  
E. Němcová ◽  
J. Přibyl ◽  
J. Wolf
Keyword(s):  
Animal Model ◽  
Milk Production ◽  
Legendre Polynomials ◽  
Environmental Effect ◽  
Random Regression ◽  
Breeding Values ◽  
Regression Test ◽  
Days Open ◽  
Calving Season ◽  
Definition Of

The investigation was based on roughly 3.9, 2.7 and 1.7 million test-day records from first, second and third lactation, respectively, sampled from 596 200 Czech Holstein cows between the years 1991 and 2002. Breeding values were estimated from multi-lactation random-regression test-day models which contained the fixed effect of herd-test day, fixed regression on days in milk and random regressions on the animal level and the permanent environmental effect. Third degree Legendre polynomials (with four coefficients) were used for both the fixed and random regressions. The models differed in fixed regression. In Analysis I, 96 subclasses were defined according to age at calving, season and year of calving within lactation. In Analysis II, days open were additionally included as a grouping factor resulting in 480 subclasses. Rank correlations over 0.98 between both analyses were observed for breeding values for sires. Grouping according to Analysis I was recommended.  

scholarly journals Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

2014 ◽  
Vol 49 (5) ◽  
pp. 372-383 ◽  
Author(s):  
Maria Gabriela Campolina Diniz Peixoto ◽  
Daniel Jordan de Abreu Santos ◽  
Rusbel Raul Aspilcueta Borquis ◽  
Frank Ângelo Tomita Bruneli ◽  
João Cláudio do Carmo Panetto ◽  
...  
Keyword(s):  
Milk Production ◽  
Legendre Polynomial ◽  
Legendre Polynomials ◽  
Regression Models ◽  
Environmental Effect ◽  
Genetic Parameters ◽  
Genetic Effect ◽  
Random Regression ◽  
Additive Genetic Effect ◽  
Fifth Order

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

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.;

scholarly journals Cluster analysis of breeding values for milk yield and lactation persistency in Guzerá cattle

2016 ◽  
Vol 46 (7) ◽  
pp. 1281-1288 ◽  
Author(s):  
Diego Augusto Campos da Cruz ◽  
Rodrigo Pelicioni Savegnago ◽  
Annaíza Braga Bignardi Santana ◽  
Maria Gabriela Campolina Diniz Peixoto ◽  
Frank Angelo Tomita Bruneli ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Animal Model ◽  
Milk Production ◽  
Random Effects ◽  
Milk Yield ◽  
Legendre Polynomials ◽  
Hierarchical Cluster ◽  
Production Level ◽  
Random Regression ◽  
Hierarchical Cluster Analyses

ABSTRACT: The aim of this study was to explore the pattern of genetic lactation curves of Guzerá cattle using cluster analysis. Test-day milk yields of 5,274 first-lactation Guzerá cows were recorded in a progeny test. A total of 34,193 monthly records were analyzed with a random regression animal model using Legendre polynomials to fit additive genetic and permanent environmental random effects and mean trends. Hierarchical and non-hierarchical cluster analyses were performed based on the EBVs for monthly test-day milk yield, peak yield, lactation persistency, and partial cumulative and 305-day yields. The heritability estimates for test-day milk yields ranged from 0.24 to 0.52. Cluster analysis identified animals in the population that belong to different groups according to milk production level and lactation persistency.

scholarly journals Comparison of different orders of Legendre polynomials in random regression model for estimation of genetic parameters and breeding values of milk yield in the Chinese Holstein population

2019 ◽  
Author(s):  
Jianbin Li ◽  
Hongding Gao ◽  
Per Madsen ◽  
Wenhao Liu ◽  
Peng Bao ◽  
...  
Keyword(s):  
Milk Yield ◽  
Legendre Polynomials ◽  
Random Regression ◽  
Residual Variance ◽  
Full Data ◽  
Data Set ◽  
Breeding Values ◽  
Chinese Holstein ◽  
Regression Test ◽  
Holstein Population

AbstractRandom regression test-day model has become the most commonly adopted model for routine genetic evaluations for different dairy populations, which allows accurately accounting for genetic and environmental effects at different periods during lactation. The objective of this study was to explore appropriate random regression test-day model for genetic evaluation of milk yield in Chinese Holstein population. Data included 419,567 test-day records from 54,417 cows in the first lactation. Variance components and breeding values were estimated using random regression test-day model with different order (first order to fifth order) of Legendre polynomials, and accounted for homogeneous or heterogeneous residual variance across the lactation. The goodness of fit of the models was evaluated by total residual variance (TRV) and − 2logL. Further, the predictive ability of the models was assessed by Spearman’s rank correlation between estimated breeding values for 305d milk yield (EBV305) from the full data set and reduced data set in which the records from the last calving year were masked. The results showed that random regression models using third order Legendre polynomials (LP3) with heterogeneous residual variance achieved the lower TRV and − 2logL value and the highest correlation for EBV305 between full data and reduced data. Heritability estimated by this model was 0.250 for 305d milk yield and ranged from 0.163 to 0.304 for test-day milk yield. We suggest random regression model with Legendre polynomial of order 3 and accounting for heterogeneous residual variances could be an appropriate model to be used for genetic evaluation of milk yield for Chinese Holstein population.

scholarly journals Random regression models using different functions to estimate genetic parameters for milk production in Holstein Friesians

2016 ◽  
Vol 46 (9) ◽  
pp. 1649-1655
Author(s):  
Mariana de Almeida Dornelles ◽  
Paulo Roberto Nogara Rorato ◽  
Luis Telo Lavadinho da Gama ◽  
Fernanda Cristina Breda ◽  
Carlos Bondan ◽  
...  
Keyword(s):  
Milk Production ◽  
Legendre Polynomials ◽  
Fixed Effects ◽  
Regression Models ◽  
Genetic Parameters ◽  
Random Regression ◽  
Residual Variance ◽  
Lower Estimation ◽  
The One ◽  
Residual Variances

ABSTRACT: The objective of this study was to compare the functions of Wilmink and Ali and Schaeffer with Legendre polynomials in random regression models using heterogeneous residual variances for modeling genetic parameters during the first lactation in the Holstein Friesian breed. Five thousand eight hundred and eighty biweekly records of test-day milk production were used. The models included the fixed effects of group of contemporaries and cow age at calving as covariable. Statistical criteria indicated that the WF.33_HE2, LEG.33_HE2, and LEG.55_HE4 functions best described the changes in the variances that occur throughout lactation. Heritability estimates using WF.33_HE2 and LEG.33_HE2 models were similar, ranging from 0.31 to 0.50. The LEG.55_HE4 model diverged from these models, with higher estimates at the beginning of lactation and lower estimates after the 16th fortnight. The LEG55_HE4, among the three better models indicated by the index, is the one with highest number of parameters (14 vs 34) and resulted in lower estimation of residual variance at the beginning and at the end of lactation, but overestimated heritability in the first fortnight and presented a greater difficulty to model genetic and permanent environment correlations among controls. Random regression models that used the Wilmink and Legendre polynomials functions with two residual variance classes appropriately described the genetic variation during lactation of Holstein Friesians reared in Rio Grande do Sul.

Effect of milk fat to protein ratio on genetic variance for milk yield in Thai tropical Holstein cattle

2016 ◽  
Vol 96 (3) ◽  
pp. 410-415 ◽  
Author(s):  
S. Puangdee ◽  
M. Duangjinda ◽  
W. Boonkum ◽  
S. Buaban ◽  
S. Katawatin
Keyword(s):  
Milk Fat ◽  
Variance Components ◽  
Legendre Polynomials ◽  
Bayesian Method ◽  
Genetic Variance ◽  
Genetic Correlations ◽  
Random Regression ◽  
Third Order ◽  
Protein Ratio ◽  
Regression Test

The objective of this research was to investigate the optimum fat to protein ratio (FPR) in Thai tropical Holstein dairy cattle. First parity data consisting of 20 492 milk yields (MY) records for 24 891 cows for the period 2001 and 2011, were used in the analysis. The analysis used a random regression test-day animal model of third-order Legendre polynomials through the creation of a covariance function based on different FPRs. Variance components were estimated using the Bayesian method via the Gibbs sampling. The estimated heritability of MY in relation to FPR ranged from 0.19 to 0.27 with the pattern being similar to the genetic variances. Genetic correlations of MY at different FPRs were high at consecutive FPRs and then declined to negative in response to greater differences in FPR. Based on the results, it is concluded that the optimum FPR is in the range of 0.9 to 1.9, corresponding to the genetically controlled energy balance for MY in tropical Holsteins.

scholarly journals Reliability of breeding values between random regression and 305-day lactation models

2016 ◽  
Vol 51 (11) ◽  
pp. 1848-1856
Author(s):  
Alessandro Haiduck Padilha ◽  
◽  
Jaime Araujo Cobuci ◽  
Darlene dos Santos Daltro ◽  
José Braccini Neto
Keyword(s):  
Legendre Polynomials ◽  
Regression Models ◽  
Protein Yield ◽  
Random Regression ◽  
Holstein Cattle ◽  
Average Increase ◽  
Data Set ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Genetic Evaluations

Abstract The objective of this work was to verify the gain in reliability of estimated breeding values (EBVs), when random regression models are applied instead of conventional 305-day lactation models, using fat and protein yield records of Brazilian Holstein cattle for future genetic evaluations. Data set contained 262,426 test-day fat and protein yield records, and 30,228 fat and protein lactation records at 305 days from first lactation. Single trait random regression models using Legendre polynomials and single trait lactation models were applied. Heritability for 305-day yield from lactation models was 0.24 (fat) and 0.17 (protein), and from random regression models was 0.20 (fat) and 0.21 (protein). Spearman correlations of EBVs, between lactation models and random regression models, for 305-day yield, ranged from 0.86 to 0.97 and 0.86 to 0.98 (bulls), and from 0.80 to 0.89 and 0.81 to 0.86 (cows), for fat and protein, respectively. Average increase in reliability of EBVs for 305-day yield of bulls ranged from 2 to 16% (fat) and from 4 to 26% (protein), and average reliability of cows ranged from 24 to 38% (fat and protein), which is higher than in the lactation models. Random regression models using Legendre polynomials will improve genetic evaluations of Brazilian Holstein cattle due to the reliability increase of EBVs, in comparison with 305-day lactation models.

scholarly journals Development of genetic models for estimation of racing performances in German thoroughbreds

2004 ◽  
Vol 47 (6) ◽  
pp. 505-516
Author(s):  
A.-E. Bugislaus ◽  
R. Roehe ◽  
H. Uphaus ◽  
E. Kalm
Keyword(s):  
Animal Model ◽  
Linear Regression ◽  
Statistical Models ◽  
Regression Models ◽  
Genetic Variance ◽  
Fixed Effect ◽  
Random Regression ◽  
Square Root ◽  
Breeding Values ◽  
High Performing

Abstract. The objective of this study was to develop new statistical models for genetic estimation of racing performances in German thoroughbreds. Analysed performance traits were "square root of rank at finish", "square root of distance to first placed horse in a race" and "log of earnings". These traits were found to be influenced by the carried weight, which was determined by the horse's earlier performance. Therefore, new traits were developed based on random regression models, which were independent from the carried weights. Heritabilities were first estimated for these created traits "new rank at finish" (h2 = 0.101) and "new distance to first placed horse in a race" (h2 = 0.142) by using two univariate animal models. When considering a linear regression of carried weights as fixed effect in the statistical model, heritabilities for "square root of rank at finish" (h2 = 0.086) and "square root of distance to first placed horse in a race" (h2 = 0.124) decreased. Breeding values of “new rank at finish” and "new distance to first placed horse in a race" were compared with breeding values of "square root of rank at finish" and "square root of distance to first placed horse in a race", in which carried weight was considered as fixed regression in the model. These two different models were compared by two criteria. Breeding values were overestimated for low performing thoroughbreds and underestimated for high performing horses when considering a linear regression of carried weights as fixed effect in the model. Statistical models considering new created traits ("new rank at finish" and "new distance to first placed horse in a race") which were independent of carried weights, showed better suitability for genetic estimation. Due to high genetic correlation with other traits and showing highest genetic variance a univariate animal model for the trait “new distance to first placed horse in a race” was recommended for genetic estimation.

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