Genetic evaluation of Holstein Friesian sires for daughter condition-score changes using a random regression model

1999 ◽  
Vol 68 (3) ◽  
pp. 467-475 ◽  
Author(s):  
H. E. Jonest ◽  
I. M. S. White ◽  
S. Brotherstone
Keyword(s):  
Genetic Analysis ◽  
Regression Model ◽  
Genetic Correlations ◽  
Random Regression ◽  
Stage Of Lactation ◽  
Random Regression Model ◽  
Holstein Friesian ◽  
Condition Score ◽  
Estimated Breeding Values ◽  
Stage 1

AbstractIn dairy cattle type classification schemes, heifers are condition scored (CS) only once during their first lactation. Although genetic analysis of condition-score changes is not possible using an animal model, the data can be analysed as repeated observations on the sire.CS records for 100 078 Holstein Friesian heifers, the progeny of 797 sires, were available. Sires differed in the shape of the regression of mean daughter CS on stage of lactation at both the phenotypic and genetic level. Genetic analysis was carried out using a random regression model (RRM) which can account for differences between sires in the shape of the CS curves. CS curves for individual sires were modelled using a cubic polynomial.Heritability estimates for CS at each stage of lactation generally increased through the lactation from 0·20 in stage 2 (days in milk 31 to 60) to 0·28 in later lactation stages. Genetic correlations between CS at different stages were generally high (0·80), with the exception of correlations with stage 1 (days in milk 1 to 30) which decreased to 0·63 with stages 6 and 7, suggesting that CS at stage 1 is under different biological control from CS at other stages of the lactation. Using RRM, sire estimated breeding values (EBVs) for CS at each stage of the lactation were estimated. Sire rankings on EBV at each stage were seen to change through early, mid and later lactation stages.

scholarly journals Random Regression Model for Genetic Analysis Milk Traits Holstein Friesian Cow

2019 ◽  
Vol 3 (10) ◽  
pp. 205-216
Author(s):  
Snežana Trivunović ◽  
Miroslav Plavšić ◽  
Denis Kučević
Keyword(s):  
Genetic Analysis ◽  
Regression Model ◽  
Random Regression ◽  
Milk Traits ◽  
Random Regression Model ◽  
Holstein Friesian

Genetic trends and genetic correlations between 305-day milk yield, persistency and somatic cell score of Holstein cows in Brazil using random regression model

2019 ◽  
Vol 59 (2) ◽  
pp. 207 ◽  
Author(s):  
A. Haiduck Padilha ◽  
E. P. M. Alfonzo ◽  
D. S. Daltro ◽  
H. A. L. Torres ◽  
J. Braccini Neto ◽  
...  
Keyword(s):  
Regression Model ◽  
Somatic Cell ◽  
Genetic Correlation ◽  
Milk Yield ◽  
Genetic Correlations ◽  
Somatic Cell Score ◽  
Random Regression ◽  
Random Regression Model ◽  
The Difference ◽  
Estimated Breeding Values

The objective was to estimate genetic correlations for persistency, milk yield and somatic cell score (SCS) in Holstein cattle in Brazil. A dataset with 190389 records of test-day milk and of test-day SCS from 21824 cows was used. Two-trait random regression model with a fourth order Legendre polynomial was used. Persistency (PS) was defined as the difference between estimated breeding values (EBV) along different days in milk using two formulae: and PS2=(EBV290–EBV90). Larger values for PS2 or lower ones for PS1 indicate higher persistency. Heritability was 0.24 for 305-day milk yield, 0.14 for SCS up to 305 days, 0.15 for PS1 and 0.14 for PS2. Genetic correlation between 305-day milk yield and SCS up to 305 days was –0.47. Genetic correlation of 305-day milk yield with PS1 and PS2 was –0.32 and 0.30, respectively. Genetic correlation of SCS up to 305 days was 0.25 with PS1 and –0.20 with PS2. The additive genetic correlations between milk yield, SCS and persistency showed that selection for higher persistency or for low somatic cell score will increase 305-day milk yield.

Importance of genotype by environment interaction on genetic analysis of milk yield in Iranian Holstein cows using a random regression model

2019 ◽  
Vol 59 (8) ◽  
pp. 1438
Author(s):  
Y. Fazel ◽  
A. Esmailizadeh ◽  
M. Momen ◽  
M. Asadi Fozi
Keyword(s):  
Genetic Analysis ◽  
Regression Model ◽  
Milk Yield ◽  
Genetic Parameter ◽  
Genotype By Environment Interaction ◽  
Random Regression ◽  
Holstein Cows ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Random Regression Model

Changes in the relative performance of genotypes (sires) across different environments, which are referred to as genotype–environment interactions, play an important role in dairy production systems, especially in countries that rely on imported genetic material. Importance of genotype by environment interaction on genetic analysis of milk yield was investigated in Holstein cows by using random regression model. In total, 68945 milk test-day records of first, second and third lactations of 8515 animals that originated from 100 sires and 7743 dams in 34 herds, collected by the Iranian animal breeding centre during 2007–2009, were used. The different sires were considered as different genotypes, while factors such as herd size, herd milk average (HMA), herd protein average and herd fat average were used as criteria to define the different environments. The inclusion of the environmental descriptor improved not only the log-likelihood of the model, but also the Bayesian information criterion. The results showed that defining the environment on the basis of HMA affected genetic parameter estimations more than did the other environmental descriptors. The heritability of milk yield during lactating days reduced when sire × HMA was fitted to the model as an additional random effect, while the genetic and phenotypic correlations between lactating months increased. Therefore, ignoring this interaction term can lead to the biased genetic-parameter estimates, reduced selection accuracy and, thus, different ranking of the bulls in different environments.

scholarly journals Genetic and permanent environmental variations in daily milk yield and milk flow rates in Hungarian Holstein Friesian

2007 ◽  
Vol 50 (6) ◽  
pp. 535-548
Author(s):  
A. A. Amin
Keyword(s):  
Milk Yield ◽  
Genetic Correlations ◽  
Random Regression ◽  
Lactation Performance ◽  
Flow Rates ◽  
Breeding Values ◽  
Milk Flow ◽  
Holstein Friesian ◽  
Estimated Breeding Values ◽  
Daily Milk Yield

Abstract. Random regression animal model was applied for analyzing the relationships between test-day milk yields (DY), and milk flow rate (FR). The current study involved 169,491 sample test-day records of Hungarian Holstein- Friesian cows. A quadratic random regression was applied for declaring additive genetic variances in all studied traits during biweekly observations across the first three parities. Estimates of heritability for test-day milk yield and udder milk flow rates ranged from 0.09 to 0.58 and from 0.02 to 0.50, respectively through 42 milk-weeks (Wk). The highest heritability estimates occurred during the end of trajectory for both traits. In general DY tended to be more heritable than FR across lactation except during the first few weeks of lactation. Performance of DY was less affected by environmental variation than FR, while both values were moderate to high (0.63 to 0.75). Correlations among measurements showed that additive correlations (Ra) of 4WkFR with the reminder part of lactation were high during early and late lactation. Also 24WkFR was more genetically correlated with next measures and reached Ra = 0.94. Whereas 42WkFR was high additively correlated with other biweekly measurements and ranged from 0.53 to 0.99. Performance of early and late DY was negative additively correlated and ranged from −0.03 to −0.53. Heritability of DY within levels of FR ranged from 0.09 to 0.33 within very slow and medium milk flow, respectively. Correlations among both traits increased linearly toward lactation end. DY during 24Week and 42Week of lactation accounted the highest additive correlations with FR across lactation. Estimated breeding values for DY and FR increased in different rates with progressing lactation. These results may indicate that individual selection results would be favorably achieved during the late part of lactation. More details about estimates of breeding values, estimates of permanent environmental and additive genetic correlations for all traits were tabulated.

Efficiency of part lactation test day records for genetic evaluations using fixed and random regression models

2002 ◽  
Vol 74 (2) ◽  
pp. 189-197 ◽  
Author(s):  
R. A. Mrode ◽  
G. J. T. Swanson ◽  
C. M. Lindberg
Keyword(s):  
Regression Model ◽  
Milk Yield ◽  
Regression Models ◽  
Rank Correlation ◽  
Random Regression ◽  
Moderate Correlation ◽  
Data Set ◽  
Random Regression Model ◽  
Holstein Friesian ◽  
Genetic Evaluations

AbstractThe efficiency of part lactation test day (TD) records in first parity for the genetic evaluation of bulls and cows using a random regression model (RRM) and a fixed regression model (FRM) was studied, modelling the random and fixed lactation curves by Legendre polynomials. The data set consisted of 9 242 783 TD records for first lactation milk yield of 1 134 042 Holstein Friesian heifers. The efficiency of both models with part lactation TD records was examined by comparing predicted transmitting abilities (PTAs) for 305-day milk yield for 114 bulls and their 4697 daughters, from analyses where the maximum number of TD records of these daughters was restricted to the initial 2, 4 or 6 TDs with those estimated from 10 TDs. The correlations of PTAs estimated from 2, 4 or 6 TDs with those from 10 TDs computed for cows and bulls within each model were very similar. A rank correlation of 0·91 (0·92 FRM) was obtained for cows between PTAs based on 2 TDs and those from 10 TDs. The correlation increased to 0·96 with 4 TDs and 0·98 with 6 TDs. For bulls, correlations between PTAs estimated from 4 or 6 TDs with those estimated from 10 TDs were high at 0·98 and 0·99 respectively. With 2 TDs, the correlation was 0·95. The average under-prediction of PTAs with 2, 4 or 6 TDs relative to 10 TDs was generally higher and more variable with a FRM compared with a RRM for highly persistent cows and bulls. A similar trend was observed for mean over-prediction of PTAs, except for the initial predictions based on 2 TDs when the RRM gave a higher mean over-prediction for bulls and their daughters with low persistency but high initial TD records. The range of over and under-predictions were large (up to 200 kg milk) for some bulls when only 2 TDs were included in both models. A moderate correlation of 0·64 was obtained between persistency evaluations estimated from 10 TDs with those estimated from 2 TDs. The correlation increased to 0·71 with 4 TDs included and 0·85 with 6 TDs. The moderately high correlation between 6 TDs and 10 TDs of 0·85 was unexpected given the high correlation of 0·99 between PTAs for yield estimated from 6TDs with those estimated from 10 TDs.

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 Estimation of genetic parameters for test-day milk yield in Girolando cows using a random regression model

2021 ◽  
Vol 73 (1) ◽  
pp. 18-24
Author(s):  
E.P.B. Santos ◽  
G.L. Feltes ◽  
R. Negri ◽  
J.A. Cobuci ◽  
M.V.G.B. Silva
Keyword(s):  
Regression Model ◽  
Milk Yield ◽  
Legendre Polynomials ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Random Regression ◽  
Third Order ◽  
Good Tool ◽  
Components Of Variance ◽  
Random Regression Model

ABSTRACT The objective of this study was to estimate the components of variance and genetic parameters of test-day milk yield in first lactation Girolando cows, using a random regression model. A total of 126,892 test-day milk yield (TDMY) records of 15,351 first-parity Holstein, Gyr, and Girolando breed cows were used, obtained from the Associação Brasileira dos Criadores de Girolando. To estimate the components of (co) variance, the additive genetic functions and permanent environmental covariance were estimated by random regression in three functions: Wilmink, Legendre Polynomials (third order) and Linear spline Polynomials (three knots). The Legendre polynomial function showed better fit quality. The genetic and permanent environment variances for TDMY ranged from 2.67 to 5.14 and from 9.31 to 12.04, respectively. Heritability estimates gradually increased from the beginning (0.13) to mid-lactation (0.19). The genetic correlations between the days of the control ranged from 0.37 to 1.00. The correlations of permanent environment followed the same trend as genetic correlations. The use of Legendre polynomials via random regression model can be considered as a good tool for estimating genetic parameters for test-day milk yield records.

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