Estimation of genetic parameters for micro-environmental sensitivities of production traits in Holstein cows using two-step method

2020 ◽  
Vol 60 (6) ◽  
pp. 752
Author(s):  
Jamshid Ehsaninia ◽  
Navid Ghavi Hossein-Zadeh ◽  
Abdol Ahad Shadparvar
Keyword(s):  
Milk Fat ◽  
Genetic Parameters ◽  
Environmental Changes ◽  
Holstein Cows ◽  
Residual Variance ◽  
Production Traits ◽  
Step Method ◽  
Animal Products ◽  
Estimate Variance ◽  
Genetic Coefficient

Context The request for more uniform animal products, which is motivated chiefly by economic reasons, has enhanced the interest in decreasing variability of characters via selection. In the present dairy operation, breeding dairy cows which have strong resistance against environmental changes for main traits is very important. Aims The aim of this study was to estimate genetic parameters for heterogeneity of residual variance in milk yield and composition of Iranian Holstein cows. Methods The dataset included 305-day production records of cows which were provided by the Animal Breeding Center and Promotion of Animal Products of Iran between 1983 and 2014. In two-step method, univariate analyses were conducted to estimate variance components for 305-day production traits. Then, genetic variability of residual variances was estimated. Key results Estimates of heritability for micro-environmental sensitivities of milk, fat and protein yields in the first three lactations of Holstein cows were low and equal to 0.043, 0.028 and 0.039; 0.031, 0.019 and 0.024; 0.027, 0.016 and 0.019 respectively. Considerable genetic coefficient of variations of residual variance for above mentioned traits (0.261, 0.247 and 0.218; 0.221, 0.204 and 0.194; 0.219, 0.199 and 0.178 respectively) indicated significant additive genetic variation for micro-environmental sensitivities. Conclusions The results of this study indicate that micro-environmental sensitivities were present for milk production traits of Iranian Holsteins. High genetic coefficient of variation for micro-environmental sensitivities indicated the possibility of reducing environmental variation and increase in uniformity via selection. Implications Reduction of environmental sensitivities would increase the predicted performance of animals and decreased corresponding threats for dairy farmers.

scholarly journals Genetic parameters for production traits in primiparous Holstein cows estimated by random regression models

2011 ◽  
Vol 40 (1) ◽  
pp. 85-94 ◽  
Author(s):  
Igor de Oliveira Biassus ◽  
Jaime Araújo Cobuci ◽  
Claudio Napolis Costa ◽  
Paulo Roberto Nogara Rorato ◽  
José Braccini Neto ◽  
...  
Keyword(s):  
Milk Fat ◽  
Legendre Polynomials ◽  
Regression Models ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Random Regression ◽  
Holstein Cows ◽  
Lactation Period ◽  
Production Traits ◽  
Residual Variances

The objective of this study was to estimate genetic parameters for milk, fat and protein yields of Holstein cows using 56,508; 35,091 and 8,326 test-day milk records from 7,015, 4,476 and 1,114 cows, calves of 359, 246 and 90 bulls, respectively. The additive genetic and permanent environmental effects were estimated using REML. Random regression models with Legendre polynomials from order 3 to 6 were used. Residual variances were considered homogeneous over the lactation period. The estimates of variance components showed similar trends, with an increase of the polynomial order for each trait. The heritability estimates ranged from 0.14 to 0.31; 0.03 to 0.21 and 0.09 to 0.33 for milk, fat and protein yield, respectively. Genetic correlations among milk, fat and protein yields ranged from 0.02 to 1.00; 0.34 to 1.00 and 0.42 to 1.00, respectively. Models with higher order Legendre polynomials are the best suited to adjust test-day data for the three production traits studied.

scholarly journals Genetic parameters for test-day model with random regressions for production traits of Czech Holstein cattle

2011 ◽  
Vol 50 (No. 4) ◽  
pp. 142-154 ◽  
Author(s):  
L. Zavadilová ◽  
J. Jamrozik ◽  
Schaeffer LR
Keyword(s):  
Milk Fat ◽  
Sampling Method ◽  
Random Regression ◽  
Model Parameters ◽  
Residual Variance ◽  
Posterior Distributions ◽  
Production Traits ◽  
Multiple Trait ◽  
Holstein Breed ◽  
Random Regression Model

Multiple-lactation random regression model was applied to test-day records of milk, fat and protein yields in the first three lactations of the Czech Holstein breed. Data included 9 583 cows, 89 584, 44 207 and 11 266 test-day records in the first, second and third lactation, respectively. Milk, fat and protein in the first three lactations were analysed separately and in a multiple-trait analysis. Linear model included herd-test date, fixed regressions within age-season class and two random effects: animal genetic and permanent environment modelled by regressions. Gibbs sampling method was used to generate samples from marginal posterior distributions of the model parameters. The single- and multiple-trait models provided similar results. Genetic and permanent environmental variances and heritability for particular days in milk were high at the beginning and at the end of lactation. The residual variance decreased throughout the lactation. The resulting heritability ranged from 0.13 to 0.52 and increased with parity.  

scholarly journals Estimation of genetic parameters and comparison of random regression animal and sire models of production traits in the first three lactations of Iranian Holsteins

2014 ◽  
Vol 30 (2) ◽  
pp. 261-279 ◽  
Author(s):  
A. Mohammadi ◽  
S. Alijani
Keyword(s):  
Milk Fat ◽  
Genetic Parameters ◽  
Additive Genetic Variance ◽  
Genetic Correlations ◽  
Random Regression ◽  
Lactation Period ◽  
Production Traits ◽  
Environmental Variance ◽  
In The Beginning ◽  
Residual Variances

This study was conducted to compare of random regression (RR) animal and sire models for estimation of the genetic parameters for production traits of Iranian Holstein dairy cows. For this purpose, the test day records were used belonged to first three lactations of cows and for, milk, fat and protein yields traits where, collected from 2003 to 2010, by the national breeding center of Iran. The genetic parameters were estimated using restricted maximum likelihood algorithm. To compare the model, different criterion -2logL value, AIC, BIC and RV were used for considered traits. Residual variances were considered homogeneous over the lactation period. Obtained results showed that additive genetic variance was highest in the beginning and end lactation and permanent environmental variance was highest in beginning of lactation than other lactation period. Heritabilities estimate for milk, fat and protein yields by RR animal and sire models were found to be lowest during early lactation (0.05, 0.04 and 0.07; 0.05, 0.19 and 0.13; 0.14, 0.19 and 0.15, for milk, fat and protein yields and in first, second and third lactation respectively). However, estimated heritabilities during lactation did not vary among different order Legendre polynomials, and also between RR animal and sire models. The variation in genetic correlations estimate in the RR animal and sire models was larger in the first lactation than in the second and third lactations. Thus, based on the results obtained, it can be inferred that the RR animal model is better for modeling yield traits in Iranian Holsteins.

scholarly journals Genetic parameters for female fertility and milk production traits in first-parity Czech Holstein cows

2012 ◽  
Vol 57 (No. 3) ◽  
pp. 108-114 ◽  
Author(s):  
V. Zink ◽  
J. Lassen ◽  
M. Štípková
Keyword(s):  
Milk Production ◽  
Genetic Parameters ◽  
Selection Index ◽  
Female Fertility ◽  
Holstein Cows ◽  
Production Traits ◽  
Milk Production Traits ◽  
Clear Trend ◽  
Genetic Potential ◽  
Fertility Traits

The aim of this study was to estimate genetic parameters for female fertility and production traits in first-parity Czech Holstein cows and to quantify the effect of using this information on the accuracy of a selection index in seven different scenarios. In order to estimate genetic (co)variance components, the DMU software running an AI-REML algorithm was used. The analyses were made using a series of bivariate animal models. The pedigree included 164 125 animals and it was set up using a pruned animal model design. The present study included the following female fertility traits for the first lactations: calving to the first insemination (CF), days open (DO), calving from the first to the last insemination (FL), and milk production traits: milk production (MLK), kg of fat (FAT), and kg of protein (PROT). The heritability for all the investigated fertility traits was low and close to 0. Moderate heritabilities for production traits ranging from 0.20 (MLK) to 0.23 (PROT) were estimated. The strongest unfavourable correlation was found between PROT and DO (0.49). Other estimated correlations between fertility traits and production traits were moderate, ranging from 0.26 to 0.41. The results of this study evidence that cows with the poorest genetic potential for reproductive performance are those having high genetic potential for milk production and milk components. The results also show that the number of days from calving to new pregnancy depends on the production level. Seven investigated scenarios using selection index theory show a clear trend for increasing accuracy when more fertility traits were added as well as when higher numbers of daughters with information on reproduction traits per sire were available.  

Introducing a sinusoidal equation to describe lactation curves for cumulative milk yield and composition in Holstein cows

2020 ◽  
Vol 87 (2) ◽  
pp. 220-225
Author(s):  
Navid Ghavi Hossein-Zadeh ◽  
Hassan Darmani Kuhi ◽  
James France ◽  
Secundino López
Keyword(s):  
Linear Model ◽  
Milk Yield ◽  
Milk Fat ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Information Criterion ◽  
Holstein Cows ◽  
Coefficient Of Determination ◽  
Production Traits ◽  
Lactation Curve

AbstractThe aim of the work reported here was to investigate the appropriateness of a sinusoidal function by applying it to model the cumulative lactation curves for milk yield and composition in primiparous Holstein cows, and to compare it with three conventional growth models (linear, Richards and Morgan). Data used in this study were 911 144 test-day records for milk, fat and protein yields, which were recorded on 834 dairy herds from 2000 to 2011 by the Animal Breeding Centre and Promotion of Animal Products of Iran. Each function was fitted to the test-day production records using appropriate procedures in SAS (PROC REG for the linear model and PROC NLIN for the Richards, Morgan and sinusoidal equations) and the parameters were estimated. The models were tested for goodness of fit using adjusted coefficient of determination $\lpar {R_{{\rm adj}}^2 } \rpar $, root mean square error (RMSE), Akaike's information criterion (AIC) and the Bayesian information criterion (BIC). $R_{{\rm adj}}^2 $ values were generally high (>0.999), implying suitable fits to the data, and showed little differences among the models for cumulative yields. The sinusoidal equation provided the lowest values of RMSE, AIC and BIC, and therefore the best fit to the lactation curve for cumulative milk, fat and protein yields. The linear model gave the poorest fit to the cumulative lactation curve for all production traits. The current results show that classical growth functions can be fitted accurately to cumulative lactation curves for production traits, but the new sinusoidal equation introduced herein, by providing best goodness of fit, can be considered a useful alternative to conventional models in dairy research.

scholarly journals Genetic parameters for test day milk yield of first lactation Holstein cows estimated by random regression using Legendre polynomials

2008 ◽  
Vol 37 (4) ◽  
pp. 602-608 ◽  
Author(s):  
Claudio Napolis Costa ◽  
Claudio Manoel Rodrigues de Melo ◽  
Irineu Umberto Packer ◽  
Ary Ferreira de Freitas ◽  
Nilson Milagres Teixeira ◽  
...  
Keyword(s):  
Variance Components ◽  
Legendre Polynomials ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Random Regression ◽  
Holstein Cows ◽  
Residual Variance ◽  
Environmental Variance ◽  
Environmental Correlations ◽  
Lp Estimates

Data comprising 263,390 test-day (TD) records of 32,448 first parity cows calving in 467 herds between 1991 and 2001 from the Brazilian Holstein Association were used to estimate genetic and permanent environmental variance components in a random regression animal model using Legendre polynomials (LP) of order three to five by REML. Residual variance was assumed to be constant in all or in some classes of lactation periods for each LP. Estimates of genetic and permanent environmental variances did not show any trend due to the increase in the LP order. Residual variance decreased as the order of LP increased when it was assumed constant, and it was highest at the beginning of lactation and relatively constant in mid lactation when assumed to vary between classes. The range for the estimates of heritability (0.27 - 0.42) was similar for all models and was higher in mid lactation. There were only slight differences between the models in both genetic and permanent environmental correlations. Genetic correlations decreased for near unity between adjacent days to values as low as 0.24 between early and late lactation. A five parameter LP to model both genetic and permanent environmental effects and assuming a homogeneous residual variance would be a parsimonious option to fit TD yields of Holstein cows in Brazil.

scholarly journals Random regression models to estimate genetic parameters for test-day milk yield and composition in Iranian buffaloes

2013 ◽  
Vol 56 (1) ◽  
pp. 276-284 ◽  
Author(s):  
M. Madad ◽  
N. Ghavi Hossein-Zadeh ◽  
A. A. Shadparvar ◽  
D. Kianzad
Keyword(s):  
Milk Yield ◽  
Milk Fat ◽  
Milk Protein ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Random Regression ◽  
Production Traits ◽  
Environmental Correlations ◽  
Protein Percentage ◽  
Heritability Estimates

Abstract. The objective of this study was to estimate genetic parameters for milk yield and milk percentages of fat and protein in Iranian buffaloes. A total of 9,278 test-day production records obtained from 1,501 first lactation buffaloes on 414 herds in Iran between 1993 and 2009 were used for the analysis. Genetic parameters for productive traits were estimated using random regression test-day models. Regression curves were modeled using Legendre polynomials (LPs). Heritability estimates were low to moderate for milk production traits and ranged from 0.09 to 0.33 for milk yield, 0.01 to 0.27 for milk protein percentage and 0.03 to 0.24 for milk fat percentage, respectively. Genetic correlations ranged from −0.24 to 1 for milk yield between different days in milk over the lactation. Genetic correlations of milk yield at different days in milk were often higher than permanent environmental correlations. Genetic correlations for milk protein percentage ranged from −0.89 to 1 between different days in milk. Also, genetic correlations for milk percentage of fat ranged from −0.60 to 1 between different days in milk. The highest estimates of genetic and permanent environmental correlations for milk traits were observed at adjacent test-days. Ignoring heritability estimates for milk yield and milk protein percentage in the first and final days of lactation, these estimates were higher in the 120 days of lactation. Test-day milk yield heritability estimates were moderate in the course of the lactation, suggesting that this trait could be applied as selection criteria in Iranian milking buffaloes.

scholarly journals Estimation of Genetic Parameters for Some Reproductive Disorders and Its Relationship with Production Traits in Iranian Holstein Cows

2018 ◽  
Vol 9 (19) ◽  
pp. 71-75
Author(s):  
Sheida Varkoohi ◽  
Saeed Khosravi ◽  
Saheb Forotanifar ◽  
Mohammad bagher Sayadnejad ◽  
◽  
...  
Keyword(s):  
Genetic Parameters ◽  
Holstein Cows ◽  
Production Traits ◽  
Reproductive Disorders ◽  
Estimation Of Genetic Parameters

Genetic parameters of milk cholesterol content in Holstein cattle

2018 ◽  
Vol 98 (4) ◽  
pp. 714-722 ◽  
Author(s):  
Duy N. Do ◽  
Allison Fleming ◽  
Flavio S. Schenkel ◽  
Filippo Miglior ◽  
Xin Zhao ◽  
...  
Keyword(s):  
Milk Fat ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Cholesterol Content ◽  
Production Traits ◽  
Fat Percentage ◽  
Bivariate Models ◽  
Estimate Heritability ◽  
Fat Yield ◽  
Using Data

This study aimed to estimate heritability for milk cholesterol (CHL) and genetic correlations between milk CHL and other production traits (test-day milk, fat, and protein yields, fat and protein percentages, and somatic cell score). Milk CHL content was determined by gas chromatography and expressed as mg of CHL in 100 g of fat (CHL_fat) or in 100 mg of milk (CHL_milk). Univariate models were used to estimate variances and heritability, whereas bivariate models were used to compute correlations using data from 1793 cows. The average concentrations (standard deviation) of CHL_fat and CHL_milk were 275.63 (75) mg and 11.16 (3.63) mg, respectively. Milk CHL content was significantly affected by days in milk and herd (P < 0.05), but not by parity, regardless of the scale of expression. Heritability estimates for CHL_fat and CHL_milk were 0.06 ± 0.04 and 0.17 ± 0.06, respectively. Phenotypic and genetic correlations between CHL_fat and CHL_milk were 0.82 and 0.44 ± 0.24, respectively. CHL_fat had nonsignificant genetic correlations with all production traits, whereas CHL_milk had significant (P < 0.05) genetic correlations with milk yield (−0.47), fat yield (0.51), protein percentage (0.56), and fat percentage (0.88). This is the first study to estimate genetic parameters for milk CHL content. Further studies are required to assess the possibility of genetically selecting cows with lower milk CHL content.

scholarly journals Genetic Parameters for Linear Type Traits and Milk, Fat, and Protein Production in Holstein Cows in Brazil

2015 ◽  
Vol 28 (4) ◽  
pp. 476-484 ◽  
Author(s):  
Rafael Viegas Campos ◽  
Jaime Araujo Cobuci ◽  
Elisandra Lurdes Kern ◽  
Cláudio Napolis Costa ◽  
Concepta Margaret McManus
Keyword(s):  
Milk Fat ◽  
Protein Production ◽  
Genetic Parameters ◽  
Holstein Cows ◽  
Linear Type ◽  
Type Traits
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