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

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.

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

Revista Brasileira de Zootecnia ◽

10.1590/s1516-35982011000100012 ◽

2011 ◽

Vol 40 (1) ◽

pp. 85-94 ◽

Cited By ~ 6

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.

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

Biotechnology in Animal Husbandry ◽

10.2298/bah1402261m ◽

2014 ◽

Vol 30 (2) ◽

pp. 261-279 ◽

Cited By ~ 1

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.

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

Ciência Rural ◽

10.1590/0103-8478cr20150473 ◽

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.

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

Canadian Journal of Animal Science ◽

10.1139/cjas-2015-0125 ◽

2016 ◽

Vol 96 (3) ◽

pp. 410-415 ◽

Cited By ~ 1

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.

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

Archives Animal Breeding ◽

10.7482/0003-9438-56-027 ◽

2013 ◽

Vol 56 (1) ◽

pp. 276-284 ◽

Cited By ~ 1

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.

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Components of (co)variance of the days open in Siboney dairy cows

Arquivo Brasileiro de Medicina Veterinária e Zootecnia ◽

10.1590/s0102-09352010000100019 ◽

2010 ◽

Vol 62 (1) ◽

pp. 136-143 ◽

Cited By ~ 2

Author(s):

D. González-Peña ◽

J.L. Espinoza-Villavicencio ◽

D. Guerra ◽

A. Palacios ◽

J.C. Évora ◽

...

Keyword(s):

Dairy Cows ◽

Legendre Polynomials ◽

Regression Models ◽

Genetic Correlations ◽

Random Regression ◽

Residual Variance ◽

Parity Number ◽

Days Open ◽

Univariate Model

The records of 63,406 calvings of Siboney dairy cows (5/8 Holstein 3/8 Cuban Zebu) were used to estimate the components of covariance of the days open (DO). Five models were used: of repeatability; univariate; bivariate; of random regression with Legendre polynomials and the parity number as predicting variable; and a model of random regression with Legendre polynomials and heterogeneity of the residual variance. The heritability obtained with the univariate model was 0.09 in the first calving and decreased to 0.05 in the fifth. A higher estimate of heritability (0.12) was obtained with the repeatability model. When the model of random regression with heterogeneity of the residual variance was used, the heritability was higher than the values estimated with the previous models. The genetic correlations among the DO in different calvings, estimated with the models of random regression with and without heterogeneity of the residual variance, were close to 1.0. It is concluded that the estimates of heritability increased with the use of the random regression models. The genetic correlations among the DO of different calvings indicated that in the first three, the DO are regulated for the most part by the same genes.

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Estimates of direct and maternal genetic parameters for weight traits and backfat thickness in a multibreed population of beef cattle

Canadian Journal of Animal Science ◽

10.4141/a99-014 ◽

1999 ◽

Vol 79 (4) ◽

pp. 433-439 ◽

Cited By ~ 11

Author(s):

J. J. Tosh ◽

R. A. Kemp ◽

D. R. Ward

Keyword(s):

Beef Cattle ◽

Maternal Effects ◽

Variance Components ◽

Genetic Parameters ◽

Genetic Correlations ◽

Backfat Thickness ◽

Residual Effects ◽

Parameter Estimates ◽

Ratio Test ◽

Environmental Correlations

Variance components were estimated for weight at birth, weaning, and 365 d of age, and yearling ultrasonic backfat thickness in a multibreed population of beef cattle. Data (n = 5880) were available on F1 animals and various crosses leading up to, and including, a composite breed (7/16 British, 1/4 Charolais, 1/4 Simmental, and 1/16 Limousin). Single- and two-trait analyses were conducted using an animal model and derivative-free REML procedures. Covariates representing fractional contributions of each of the four purebred groups and average heterotic effects, both direct and maternal, accounted for breed composition. Direct and maternal genetic, maternal permanent environmental, and residual effects were the random variables. Only minor differences existed between parameter estimates obtained from the various analyses. Mean direct heritabilities were 0.51, 0.33, 0.48, and 0.37 for weight at birth, weaning, and 365-d, and backfat, respectively, which, though well within the range of published estimates, seemed higher than average, indicating diversity in the founding purebreds. Mean maternal heritabilites were 0.09, 0.13, and 0.08 for the three consecutive weights. A likelihood ratio test showed maternal heritability and(or) the direct-maternal correlation was important (P < 0.001) for 365-d weight. Averaged across weights, the direct-maternal correlation was 0.07, lacking evidence of genetic antagonism. No age of dam or random maternal effects on backfat were apparent. Phenotypic correlations between weight at birth and weaning and weight at weaning and 365-d were 0.46 and 0.76, respectively. Though moderate between birth and weaning weight, maternal genetic and permanent environmental correlations between weaning and 365-d weight approached unity, demonstrating maternal carryover effects. Phenotypic and genetic correlations of 0.19 and –0.13, respectively, implied a limited association between 365-d weight and backfat, which possibly would have been stronger had fat been measured later in life. Estimates of variance components suitable for general use can be obtained from data from multibreed animals by considering breed and heterotic effects. Key words: Beef cattle, crossbreds, genetic parameters, heritability, maternal effects

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Additive genetic and permanent environmental correlation between different parts of lactation in moderate and cold regions

Czech Journal of Animal Science ◽

10.17221/254/2020-cjas ◽

2021 ◽

Vol 66 (No. 4) ◽

pp. 112-121

Author(s):

Farzad Atrian Afiani ◽

Sahereh Joezy-Shekalgorabi ◽

Mehdi Amin-Afshar ◽

Ali-Asghar Sadeghi ◽

Just Jensen

Keyword(s):

Genetic Parameters ◽

Early Stage ◽

Genotype By Environment Interaction ◽

Cold Climate ◽

Random Regression ◽

Likelihood Method ◽

Holstein Cows ◽

Environment Interaction ◽

Environmental Variance ◽

Moderate Climate

The objective of this study was to investigate genotype by environment interaction as well as genetic parameters for somatic cell score (SCS) in first lactation Holstein cows in Iran. Data were collected by the National Animal Breeding Centre of Iran during 2003 to 2018. Data consisted of 1 031 885 SCS test-day records of 145 817 first lactation Holstein cows. Records were classified into the cold and moderate climate on data of synoptic stations. Variance components and genetic parameters were estimated using a random regression model and restricted maximum likelihood method. The analyses were performed using the AI-REML algorithm of the DMU package. The mean SCS was 1.859 (1.598) and 1.823 (1.522) for the cold and moderate climate, respectively. Genetic variance of SCS was lower than the corresponding permanent environmental variance. The permanent environmental variance in the moderate climate was higher than that of the cold climate during lactation. The highest values of heritability were observed in the early stage of lactation. However, the estimates of heritability during the lactation curve were low. The estimates of heritability for the entire 305-day lactation were higher in the cold climate than those in the moderate climate. Genetic correlation between the cold and moderate climate ranged from 0.25 to 0.81. The results indicated the existence of genotype × environment interaction and hence the need for different breeding program for SCS in the studied climates.

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Adjustment of Lactation Curves of Holstein Cows from Herds of Minas Gerais, Brazil

Journal of Agricultural Science ◽

10.5539/jas.v10n2p1 ◽

2018 ◽

Vol 10 (2) ◽

pp. 1

Author(s):

Jairo Azevedo Junior ◽

Tarcisio De Moraes Gonçalves ◽

José Camisão De Souza ◽

Mary Ana Petersen Rodriguez ◽

Cláudio Nápolis Costa ◽

...

Keyword(s):

Legendre Polynomial ◽

Regression Models ◽

Minas Gerais ◽

Random Regression ◽

Holstein Cows ◽

Residual Variance ◽

Environmental Correlations ◽

Production Trend ◽

Residual Variances ◽

Selection Of

Random regression models (RRM) differ in terms of the functions used to describe the shape of lactation curves. The aim was to compare random regression models under different functions to describe the lactation curves from Holstein cows in herds of the state of Minas Gerais. A database of 28,118 production records was analyzed using the test-day records of 4,230 first parity cows from five herds. The Wilmink, Ali & Schaeffer and Legendre polynomial (orders 4, 5 and 6) functions were adjusted in RRM to model the mean production trend (fixed) and genetic and permanent environmental (random) effects. The residual variances were assumed to be constant throughout lactation. Analyses were performed using the AIREMLF90 program. Except for the model with the polynomial function of order 5, all models converged. The Wilmink function showed lower values for criteria based on the -2log (L), AIC and BIC. The model with the Legendre polynomial of order 6 showed lower residual variance. Heritability estimates were similar between functions, ranging from 0.07 to 0.18 and were higher from 215 days of lactation. From 155 days of lactation, genetic and permanent environmental correlations between successive controls are of high magnitude. The Wilmink function is the most suitable for the study of milk yields from primiparous Holstein cows. The selection of animals is possible from 155 days of lactation on. Permanent environmental effects have greater influence on the milk production at the end of lactation of primiparous cows and should be considered since they are important and may be cumulative throughout lactation.

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Estimating Genetic Parameters using a Random Regression Model

Acta Agraria Debreceniensis ◽

10.34101/actaagrar/31/3006 ◽

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.

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