A multi-trait repeatability animal model under restricted maximum likelihood (REML)
and Bayesian methods was used to estimate genetic parameters of
milk, fat, and protein yields in Tunisian Holstein cows. The estimates of
heritability for milk, fat, and protein yields from the REML procedure were
0.21
In the past decades, dairy cattle management in Tunisia has been oriented toward increased milk yield. Importation of cattle from developed countries (USA, the Netherlands, and Germany) has been the main element for genetic improvement of the Tunisian dairy cattle population. Rekik et al. (2003) and Hammami et al. (2007) reported that 60 % of all inseminations of dairy cows in Tunisia used Holstein semen. In recent years, more attention has been placed on milk quality traits in breeding programmes. Estimates of genetic parameters for milk yield in dairy cows are abundant in the literature (Ben Gara et al., 2006, 2012; Hammami et al., 2008a, 2009a). However, investigation of genetic parameters of milk components (quality traits) and the relationships between milk yield and quality traits is lacking. Multivariate models are of fundamental importance in applied and theoretical quantitative genetics (Gianola and Sorensen, 2004). Precise estimation of genetic parameters is typically difficult in multiple-trait models due to a large number of genetic parameters and to insufficient statistical information (Rekaya et al., 2003). In animal breeding, two major methods were particularly applied, restricted maximum likelihood (REML) and Bayesian methods. REML has emerged as the method of choice in animal breeding for variance component estimation (Neumaier and Groeneveld, 1997). In recent years, Bayesian methods were broadly used to solve many of the difficulties faced by conventional statistical methods and extend the applicability of statistics on animal breeding data. Furthermore, Markov chain Monte Carlo (MCMC) has an important impact in applied statistics, especially from a Bayesian perspective for the estimation of genetic parameters in the linear mixed effect model (Sorensen and Gianola, 2002; Hallander et al., 2010). The aim of this research was to use a multi-trait repeatability model to estimate genetic parameters for milk, fat, and protein yields in the Tunisian Holstein cattle with REML and Bayesian approaches.
Data were provided by the Tunisian Genetic Improvement Center, Livestock and Pasture Office, Tunis. Original data from the official milk recording database included 242 096 completed lactation records of parities 1 to 6 on Holstein cows from 1997 through 2014. The number of test-day (TD) records for milk, fat, and protein yields were not equal. Fat and protein yields were missing in some TD records due to technical reasons. Only records that included milk, fat, and protein yields were retained. Lactations having the date of first test > 50 days from parturition and/or average interval between successive tests > 50 days were excluded. Lactations were extended to 305 days for cows milked to or beyond this point. Cows without pedigree information were discarded and cows aged < 20 or > 40 months at first calving were deleted. After editing for unreasonable production to avoid possible erroneous data for daily milk yield (< 3 and > 60), fat content (< 1.5 and > 5 %) and protein percentage (< 1 and > 5 %), a total of 113 492 records remained. These records were of 54 105 cows sired by 3517 Holstein bulls. The pedigree file included the animal's identification number, the sire, the dam, and the date of birth and the herd of origin for each animal. Descriptive statistics for the edited data set used in the analysis are shown in Table 1.
For the analyses a multi-trait repeatability model was used. The model
equation is
Characteristics of data used in the analysis.
* Contemporary group was defined as (herd–year–season) and had to have at least five observations.
For the REML procedure, convergence of the iterative process was declared
when the relative differences of consecutive parameters were lower than
10
Estimates of additive genetic variances (
*
Summary of marginal distributions of the variance components for 305 days of milk, fat, and protein yields. HPD indicates high posterior density region.
*
Genetic (above diagonal) and permanent environmental (below diagonal) correlations (SD in brackets) and heritabilities (diagonal) for 305 days of milk, fat, and protein yields by Bayesian analysis.
Genetic (above diagonal) and permanent environmental (below diagonal) correlations (SD in brackets) and heritabilities (diagonal) for 305 days of milk, fat, and protein yields by REML analysis.
Variance components for each trait estimated from data using the REML
procedure are shown in Table 2. Summary statistics (mean, mode, median,
standard deviation, and 95 % highest probability density interval for
genetic, residual, and permanent variances) that were estimated by Bayesian
analysis are presented in Table 3. Posterior means and standard deviations
for heritabilities, genetic, and permanent correlations between milk, fat,
and protein yields are presented in Tables 4–5. The additive genetic
variance estimates for milk, fat, and protein yields by Bayesian method were
313 070, 397.16, and 216.33 kg
Values obtained in this study for heritabilities for 305-day milk and fat
yields are comparable to those found by Carabaño et al. (1989) for
Spanish data using the REML procedure. Results obtained in this study basically
agree with those obtained by Alijani et al. (2012) in terms of the
comparison between both methods. Heritability estimates in the Iranian
Holsteins population ranged from 0.13 to 0.26, from 0.1 to 0.17, and from
0.15 to 0.21 for milk, fat, and protein yields, respectively, in the first
three lactations by REML procedure. Respective estimates obtained in the same
study using Bayesian analysis ranged from 0.19 to 0.29, from 0.17 to 0.21,
and from 0.2 to 0.25 for milk, fat, and protein yields, respectively. Values
of variance components estimated with this Bayesian method were different
from those obtained by Ben Gara et al. (2006) in the same population. In
fact, genetic variances associated with 305-day milk yields were consistently
larger than those found by Ben Gara et al. (2006). However, the magnitude of
genetic variance obtained in this study was low compared to estimates in
other dairy cattle populations (Meyer, 1984; Misztal et al., 1992; Dedkova
and Wolf, 2001). These differences were probably caused by difficulties
encountered by daughters of superior sires to express their genetic
potential under harsh climatic conditions and limited feed resources
(Hammami et al., 2008b). The permanent environmental variances were more
than 10 % of total variances for milk, fat, and protein yields, in
agreement with previous reports by Ben Gara et al. (2006). The high values
of the environmental variance would be explained by poor management
practices, feeding fluctuations during the year, and stressful climatic
conditions, which may result in an additional variation that is permanently
associated with each cow (Hammami et al., 2008a). Residual variances
estimated by the both methods were the largest components, particularly by
REML procedure. Residual variances obtained in this study had standard
deviations larger than those found by Ben Gara et al. (2006) implying
elevated heterogeneity in estimates. This result can be explained by the use
of the multi-trait model associated with a large number of genetic
parameters and hindered by lack of information (Rekaya et al., 2003). Data
in this study included records of years 2010 and 2011 where the civil unrest
had a sizeable impact on herd management, data recording (reduced herd sizes
and herds being recorded), and all activities – in particular those related to
animal breeding. Heritability estimates for milk, fat, and protein yields in
this study were also comparable with 0.25, 0.17, and 0.21 obtained in the
Tunisian Holsteins by Hammami et al. (2008b) using a TD random
regression model. The genetic correlations for all traits, milk, fat, and
protein yields were high, ranging from 0.89 to 0.95. Genetic correlations
were higher between milk and protein yields than between milk and fat yields
(0.94 vs. 0.89). Genetic correlation estimates in this study were in
accordance in terms of relations among milk traits, but obtained values were
larger than most estimates reported in other studies (Meyer et al., 1984;
Carabaño et al., 1989; Dedkova and Wolf, 2001). Nevertheless,
occasionally above 90 % genetic correlation estimates were reported in the
literature (Rekaya et al., 1999; Jakobsen et al., 2002; Hammami et al.,
2008b). Carabaño et al. (1989) in the same study found different
genetic correlations between milk and fat yields in US and Spanish Holsteins
(0.63 and 0.69 for two US samples vs. 0.94 for a Spanish sample). In
addition, fat and protein yields are derived from fat and protein
percentage, respectively. Therefore, they might be essentially fat percentage,
subject to sampling errors. Hammami et al. (2008b) iterated that the
processes of sampling and chemical analyses in stressful climatic conditions
in Tunisia could seriously affect data recording quality, especially for fat
and protein yields. High genetic correlation estimates reveal that milk,
fat, and protein yields are effectively controlled by the same genes. No
notable differences were found among genetic and permanent correlations in
terms of magnitude and sign. Consideration of different levels of production
and genotype by environment interaction in the literature have shown
that heritability differs significantly among cow populations and production
levels (Meuwissen et al., 1996; Rekaya et al., 2003; Strabel and Jamrozik,
2006; Hammami et al., 2009b). Heritability estimates were lower for
countries with low milk compared with countries with high milk production
levels (Ben Gara et al., 2006; Hammami et al., 2009a). Gengler et al. (2005)
using a TD model reported that heritability estimates for TD
milk yield were higher (0.25) for high-yield herds and lower (0.15) for low-yield herds. Furthermore, it is important to take into consideration the
constraining climatic conditions in Tunisia. In fact, maximum temperature
exceeded 32
Variance components of 305-day milk, fat, and protein yields were investigated by REML and Bayesian procedures using a multi-trait animal model. Moderate heritability estimates were found from the Tunisian data compared with those from other studies on Holstein populations probably because of the reduced additive genetic and the important environmental and residual variances observed in the Tunisian population. The large environmental variances can be explained by the Tunisian constraining conditions and the heat stress effects permanently associated with lifetime performance of the cow population. Arguably, this study suggests that results from both methods were reasonably similar to suggest both methods can be used. However, time of computing to store all observations with Bayesian procedures was greater than the corresponding REML. Genetic parameter estimates in this research might be included as integral elements in a routine genetic evaluation via a multi-trait repeatability model to consolidate ongoing breeding practices to improve the Tunisian Holstein population. Nevertheless, efforts should be made to improve data quality mainly for fat and protein daily records.
The authors are thankful to the Tunisian Livestock and Pasture Office for providing data (the access to these data is restricted). Special thanks to anonymous reviewers for improving the quality of the manuscript. Edited by: S. Maak Reviewed by: three anonymous referees