Estimation of Direct and Service Sire Genetic Parameters for Reproductive Traits in Yorkshire

Direct and service sire genetic parameters for total number of pigs born (TNB), number of pigs born alive (NBA), total pig weight at birth (TWB), and average pig birth weight (ABW) were estimated by DF-REML under a multiple trait animal model. Data on 3,078 litters of the Yorkshire from Jan, 1975 to Dec, 1998 at National Livestock Research Institute were obtained. The animal model included fixed contemporary group effects and random additive direct, service sire, and residual effects. Additive genetic relationships among animals were included. A separate relationship matrix for service sires and their sire was also included. Additive direct heritability estimates for TNB, NBA, TWB, and ABW were 0.19, 0.18, 0.25 and 0.39, respectively. Service sire heritability estimates for TNB, NBA, TWB, and ABW were 0.02, 0.01, 0.02 and 0.01, respectively. The genetic and phenotypic correlations of TNB with NBA estimated in this study were 0.81 and 0.81, respectively, and the genetic and phenotypic correlations of TNB with TWB estimated were 0.82 and 0.76, respectively. Results indicate that service sires account for 1 to 2% of the total variation for TNB, NBA, TWB, and ABW. Further investigation is needed to determine whether the service sire effect is primarily genetic or environmental. (Asian-Aust. J. Anim. Sci. 2002. Vol 15, No. 9 : 1232-1236)


INTRODUCTION
Genetic evaluation has been conducted on reproductive traits, number born alive (NBA), as an aid to accelerate genetic improvement.This improvement requires knowledge on the heritability for the trait of interest.Variance component estimates are also needed for additional random effects that may be included in the genetic prediction model.These estimates are specific for a given population over a defined period of time (See et al., 1993).Both Mabry et al. (1988) and Feng (1991) reported that the service sire accounted for 3% of the total variation in NBA, and Buytels and Long (1991) found the service sire to be a significant effect, accounting for 1% of the total variation.Strang (1970) reported a significant effect due to the service sire on NBA that accounted for 0.3% of the total variation.With a reported range of 0.3 to 3% of the total variation in NBA, it seems that the service sire has a very small but significant effect on NBA in swine.Woodward et al. (1993) reported that the correlations between the 918 true and predicted sire breeding values was considerably lower for the animal model without a service sire effect than when it was included (0.53 vs 0.74, respectively).However, the difference was cut in half (0.66 vs 0.77) when only sire with greater than five daughter records were included.The high accuracy of the animal model with a random service sire effect indicates that the proposed model adequately accounts for the variation found in records for number of pigs born alive.See et al. (1993) also reported that service sires were found account for 1 to 2% of the total variation for NBA.Genetic effects influencing NBA seemed to be small in these data sets, but selection for increased NBA was effective.Therefore, genetic effects of service sire genetic parameters on NBA are needed in selection program.The purpose of this study was to estimate direct and service sire genetic parameters for four litter sizes and litter weight traits in Yorkshires by a multiple trait animal model.

Data
Reproductive traits were observed of the 3,078 litters of 2,101 sows farrowing between Jan, 1975 to Dec, 1998 at National Livestock Research Institute, RDA in Korea.The total number of animals in the pedigree file was 32,286.The traits included in simultaneous analysis were total number of pigs born (TNB), number of pigs born alive (NBA), total pig weight at birth (TWB), and average pig birth weight (ABW).
Data that were greater than three standard deviations from the mean were eliminated.The sow records with service sire record within three standard deviations from the mean were used.Number of records, year of birth, year of farrowing, season of farrowing, month of farrowing, and parity are presented in Table 1.The number of service sire and sow by year of farrowing are presented in Table 2.

Statistical model
Although NBA is measured on the litter of a sow, it is considered a measurement of the sow's reproductive ability.The sow's influence on the performance of her offspring is not limited to direct genetic effects, but also includes maternal and permanent environmental effects.Ignoring maternal effects in a selection program for sow productivity could actually prevent genetic progress because of a negative relationship between direct and maternal effects (Southwood and Kennedy, 1990).However, there was very little change in rank when sire and gilt breeding values were estimated with direct-only and direct and maternal effects models (Southwood and Kennedy, 1991).Because of the difficulty in separating maternal effects on NBA from direct genetic effects on uterine environment, ovulation rate, embryo survival, and other reproductive functions, separate direct and maternal effects were not fit for NBA.The model used for estimation of direct and service sire effects' genetic parameters, in matrix notation, is Y=Xb+Zu+Ws+e …………………………………… [1] where y is the vector of observations, b is a vector of fixed contemporary group effects, u is a vector of random additive direct a genetic effects, s is a vector of random service sire effects, and e is a vector of random residual effects particular to each litter.The matrices X, Z, and W are all incidence matrices relating litter records to the expectations of the random effects and are assumed to be zero.The variance-covariance matrix is as follows: It is assumed that  The variance of vector is as follows The best linear unbiased estimator (BLUE) of estimable functions of b and the BLUP of u and s in equation [1] is obtained by solving the following mixed-model equations (MME).

With
Genetic evaluation of reproductive traits in swine have been performed by using an animal model which included additive direct and service sire genetic effects as random effects (e.g., Woodward et al., 1993;See et al., 1993).

Parameter estimation
Derivative-free REML estimation of variance and covariance components was performed using MTDFREML (Boldman et al., 1995) with 10 -9 of simplex variance stopping criterion.Restricted maximum likelihood is generally recognized as the method most able to avoid biases due to selection (Henderson, 1986;Searle, 1989).Variance components were estimated using the solution obtained from the model, with relationships including service sire.

RESULTS AND DISCUSSION
The means and their standard errors for four traits are presented in Table 3. Variance components and the proportions of the total variation that accounted for service sire genetics are presented for four traits in Table 4. Estimates of variance on service sire components were very similar to those reported by Buytels and Long (1991) from data set collected from 26 Australian farms.Heritability, genetic correlations and ratio of contribution for four traits are presented in Table 5. Woodward et al. (1993) reported that the high accuracy of the animal model with a random service sire effect indicates that the proposed model adequately accounts for the variation found in records for number of pigs born alive.See et al. (1993) also reported that service sires were founded account for 1 to 2% of the total variation for NBA.Genetic effects influencing NBA seemed to be small in these data sets, but selection for increased NBA should be effective.
Often simultaneous selection for two or more traits is recommended to improve overall economic response.For example, in swine breeding, improvement of litter size results in reduction of piglet birth weight which in turn increases pre-and postnatal mortality and decreases early postnatal growth (Roehe, 1998).
The proportion of the total variation for four traits due to service sire in Yorkshire was TNB 0.02 (TNB), 0.01 (NBA), 0.02 (TWB), and 0.01 (ABW) and those were in close agreement with previous studies.
Both Mabry et al. (1988) and Feng (1991) reported that the service sire accounted for 3% of the total variation in NBA, and Buytels and Long (1991) found the service sire to be a significant effect, accounting for 1% of the total g 11 g 12 g 21 g 22 g 11 g 12 g 21 g 22 G -1 = and Strang (1970) also reported a significant effect due to the service sire on NBA that accounted for 0.3% of the total variation.With a reported range of 0.3 to 3% of the total variation in NBA, it seems that the service sire has a very small but significant effect on NBA in swine.Van Vleck and Johnson (1980), in a study on the effect of service sire on dairy cattle milk production, found that the service sire accounted for 1% of the variation in milk yield and that the correlation between the service sire and sire of the cow was nearly zero.However, Van Vleck and Johnson (1980) concluded that the genetic and economic implications of the service sire effect were not important probably.The service sire could influence NBA either by semen quality and quantity or by genetic effects that influence embryo development and survival.Further research is needed to investigate whether the service sire effect is largely genetic or environmental, whether a covariance exists between the service sire and the sire of sow, whether direct selection on service sire can improve NBA, and whether improvements in accuracy of breeding value prediction can be made by including this effect in the prediction model (See et al., 1993).
The NBA showed the positive genetic relationship with TNB (0.84) and TWB (0.82), but negative with ABW (-0.25) (Table 6).The NBA showed the positive phenotypic relationship with TNB (0.86) and TWB (0.81), but negative with ABW (-0.01).Especially, negative genetic correlation estimates were obtained between ABW and the other TNB and NBA.

CONCLUSION
As a conclusion, the service sire effect showed 1 to 2% heritabilities, and it could be considerable in terms of low heritabilities in reproductive traits.However, there could be various effects influencing reproductive traits, further research is needed to find out whether the service sire effect is genetic or environmental.
genetic variance for direct effects, g 22 =additive genetic variance for service sire effects, g 12 =additive genetic covariance between direct and service sire effects, 2 e σ =residual error variance.

Table 1 .
Number of animals by year of birth, year of farrowing, season of farrowing, month of farrowing and parity

Table 2 .
Number of service sire and sow by year of farrowing and farrowing frequency a Number of records including those with reproductive trait records.

Table 3 .
Least square means on the traits observed and their a TNB=Total number of born, NBA=Number of born alive.TWB=Total weight at birth, ABW=Average pig birth weight.

Table 4 .
Genetic and environmental variance for each traits by model with direct genetic and service sire effects

Table 6 .
Genetic (below diagonal) and phenotypic (above diagonal) correlations among the four traits observed TNB=Total number of born, NBA=Number of born alive.TWB=Total weight at birth, ABW=Average pig birth weight. a

Table 5 .
Heritability, genetic correlations and ratio of contribution for each traits by model with direct genetic and service sire effects ass r =Correlation between direct genetic and service sire effects.e ρ =Ratio of contribution by error.