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Multivariate analysis of litter size for multiple parities with production traits in pigs: II. Response to selection for litter size and correlated response to production traits¹

Area de Producció Animal, Centre UdL-IRTA, Rovira Roure 177, 25198 Lleida, Spain

² Correspondence:
phone: 34-973-702576; fax: 34-973-238301; E-mail:
joseluis.noguera{at}irta.es.

	Abstract

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Litter size and production trait responses to experimental selection for increased litter size in a Landrace pig population are reported. The numbers of sows and litters available for the first cycle of selection were 3,034 and 961, respectively. Selection was carried out using a BLUP repeatability animal model for number of piglets born alive (NBA). The experiment included one selection and one control line, each with three nonoverlapping generations. The selection line (H) consisted of the 160 sows with the highest breeding values and one boar from each of 25 full-sib families with the highest breeding values. The control line (C) consisted of 160 sows and 25 boars randomly chosen. The two subsequent generations in each line were obtained by random selection. A Bayesian analysis of genetic response using a multivariate model was carried out by Gibbs sampler. Marginal posterior distributions were obtained for direct response in NBA, and for correlated response in weight (WT), and backfat thickness (BT) at 175 d of age. The posterior means and posterior standard deviation (PSD) for direct genetic response of NBA ranged from 0.32 (PSD 0.08) in the first parity to 0.64 (PSD 0.08) in the fourth. The posterior means for correlated genetic response in WT and BT were -0.66 kg (PSD 0.36) and 0.20 mm (PSD 0.10), respectively. For WT and BT, the 95% highest posterior density regions (HPD) contain zero-correlated genetic response. Marginal posterior distributions of selection differentials were investigated. The posterior means for standardized selection differentials for NBA in different parities ranged from 0.70 (PSD 0.12) to 0.94 (PSD 0.06) in females for line H, from 0.22 (PSD 0.19) to 0.34 (PSD 0.10) in males for line H, and from 0.08 (PSD 0.08) to 0.13 (PSD 0.07) in females for line C. All available males were used in line C. Results from this experiment showed that selection for increased litter size is effective. Responses to selection were heterogeneous across parities, suggesting that litter size in each parity may have a different genetic background. No correlated genetic response to growth and backfat thickness was observed.

Key Words: Bayesian Theory • Pigs • Prolificacy • Selection Responses

	Introduction

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Prolificacy is one of the most important traits for the genetic improvement of swine. However, the improvement of prolificacy is generally considered a difficult task in spite of optimistic theoretical predictions (Avalos and Smith, 1987). The long-term selection experiments for directly increasing litter size by means of conventional selection have not, in general, been successful (Rothschild and Bidanel, 1998). The main causes of small or no response have been discussed. One is the low heritability of litter size (Rothschild and Bidanel, 1998). Another, is the difficulty of achieving intense selection in practice (Bolet et al., 1989).

Two alternative approaches have been proposed for increasing response to selection for litter size. One is the "hyperprolific" selection scheme, based on the application of a high selection intensity over a large population of females evaluated with an individual selection index (Legault and Gruand, 1976), and the other is the use of a family selection index in the genetic evaluation of selection candidates (Avalos and Smith, 1987).

Another alternative is to combine both approaches, the use of BLUP (utilizing all family information) together with intense selection. The aim of the present paper is to report on the results of a large-scale selection experiment to increase litter size in a Landrace pig population. The experiment was carried out using both family information and high selection intensity. The first step was to evaluate the efficiency in choosing individuals for selected and control lines using standardized selection differentials. The second step was to quantify the success of the selection experiment by estimating direct genetic responses for number of piglets born alive. Finally, correlated selection responses for production traits (weight and backfat thickness) were estimated.

	Materials and Methods

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Design of the Experiment
The selection experiment was started in 1993 at the IRTA experimental farm (Nova Genètica) in Solsona (Lleida, Spain). At the beginning of the experiment, 3,034 Landrace sows belonging to a private company were available. The animals were distributed among five farms. Two farms were used for selection, with approximately 650 sows and 25 boars, and three farms were used as multipliers. In this population, selection had been based on an index composed of growth rate and backfat thickness, but not directly for litter size. In multiplier herds, Landrace sows were crossed with Large White boars. In all cases, reproduction was carried out by artificial insemination, and the sows were mated twice to the same boar.

Genetic evaluation for number of piglets born alive (NBA) was performed using a repeatability animal model (h² = 0.06, r = 0.13) that included herd-year-season and parity as fixed effects. Breeding values and permanent environmental effects were considered random. The breed of the service boar (Landrace vs Large White) was taken into account implicitly by the herd-year-season effect. Genetic parameters were previously estimated by Restricted Maximum Likelihood using all previously available data.

Once the 3,034 sows were evaluated, two genetic lines of 160 sows and 25 boars were established from this population (Figure 1). One selected (H) and one control line (C) were established. From the 3,034 female candidates evaluated, the control line C was established by choosing 160 females at random. The 25 Landrace boars currently used in the population were also used in line C (M_C). After this, the 160 sows with the highest breeding value for NBA were used to make the H line. Boars in line H (M_H) were selected from the 25 litters (one male per litter) with the highest breeding values for NBA from the 961 Landrace litters available in the selection herds. All these animals in both lines constituted the generation G0. Sows from line H and C of G0 had an average of 3.09 and 2.31 parities, respectively.

View larger version (29K): [in this window] [in a new window]   Figure 1. Design of the selection experiment.

Response to selection for NBA in the experiment was estimated with the offspring of G0 in both lines, H and C. In line H, 204 daughters and 24 sons were chosen randomly among the offspring of the mating between 117 sows and 24 boars (one son per boar) to constitute G1. In line C, 152 daughters and 23 sons were chosen randomly among the offspring of the mating between 123 sows and 23 boars (one son per boar) to constitute G1. Generation G1 in lines H and C were kept under the same intensive conditions. Gilts were mated at around 9 mo of age. No culling based on litter size was performed, and sows were kept in the herd until they were culled for other reasons (diseases, leg problems, etc.). A random sample of the G1 offspring from each line, H and C, was recorded for weight (WT) and backfat thickness (BF) at around 175 d of age. A new generation (G2) was formed for both lines to add information that is mostly captured in G1 parents. This was decided because of the limited capacity of the avalaible facilities, which made it impossible to increase the size of the experiment at G1. Males and females were selected randomly with the restriction that one son was kept from each boar. The same management as previous generations was followed. A detailed description of the selection experiment data used in this study is given in Tables 1 and 2.

The analysis of the experiment was performed using a multivariate Bayesian analysis. Mixed-model techniques do not provide exact inference about genetic change when variances are unknown. In contrast, Bayesian inference, by means of Markov Chain Monte Carlo methods, provides a full description of selection response through its marginal posterior distribution (Sorensen et al., 1994).

Model of Analysis
NBA was analyzed for the first six parities; considering each parity as a different trait. The following multivariate model was assumed:

where

y_ijk

observation ijk for NBA from each of first six parities,

HYS_i

fixed effect of herd-year-seasoni(i = 1 to 226),

AFj

fixed effect of age of sow at farrowingj (j = 1 to 10),

u_k

breeding value of animal k

e_ijk

random residual term.

Four classes were considered for each herd and year: December to February, March to May, June to August, and September to November summing up to 226 levels per parity. Age of farrowing ranged between 280 and 530 d for the first parity, 433 to 684 d for the second parity, 544 to 846 d for the third, 693 to 990 d for the fourth, 883 to 1,140 d for the fifth, and 975 to 1,288 d for the sixth. In each parity 10 balanced classes were considered, with approximately 10% of the total of farrowings per class.

For WT and BT, the model of analysis was:

where

y_ijkl

observation ijkl for weight and backfat thickness,

S_i

fixed effect of sex i (i = male or female),

B_j

fixed effect of batch j (j = 1 to 270),

_i

sex-related covariate i with age of recording (AG_ijkl) corrected at 175 d of age,

c_k

random effect of birth litter k,

u_l

breeding value of animal l,

e_ijkl

random residual term.

Bayesian Analysis
A multivariate implementation of the above animal models was performed for the whole Landrace pig population, as described in Noguera et al. (2002). Joint posterior distributions of data and parameters were constructed by multiplying the multivariate normal likelihood of observed and missing data by the prior distributions. These prior distributions were multivariate normal for breeding values and permanent environmental effects, flat for systematic effect, and inverted Wishart for (co)variance components. A Gibbs sampling algorithm (Geman and Geman, 1984; Gelfand and Smith, 1990) with a data augmentation step (Tanner and Wong, 1987) was carried out. Convergence was checked using the algorithms of Raftery and Lewis (1992) and García-Cortés et al. (1998). Effective sample size was computed following the algorithm of Geyer (1992). Estimates for genetic parameters were reported in a companion paper (Noguera et al., 2002).

Standardized Selection Differentials
The standardized selection differentials were calculated as a posteriori measures of the efficiency of the selection process. They were calculated from the difference between breeding values of selected individuals in G0 and the breeding values of the base population, and its magnitude is expressed in terms of genetic standard deviations.

From the Gibbs sampling output at every iteration, the following statistics were calculated for NBA for the i-th parity:

where _hm(i) and _hf(i) are the means of breeding values of males and females in G0 of line H weighted by the number of offspring of every individual in G1,_pm(i) and _pf(i) are the means of breeding values of males and females that were candidates for selection in generation G0,_cf(i) is the weighted mean of breeding values of females for G0 of line C, and _u(i) is the genetic standard deviation calculated following Sorensen et al. (2001). From that, S_hm(i) and S_hf(i) are random samples from the posterior marginal distribution of the selection differentials for males and females producing progeny in line H in the i-th parity. Moreover, S_cf(i) is a random sample from the posterior marginal distribution of the selection differential for females in the creation of line C in the i-th parity. However, all available males (25) at the moment of creation of lines were used in line C. Thus, the selection differential for males in the i-th parity (S_cm(i)) in line C should be close to zero, except for unequal contributions of males to the next generation caused by chance.

From the samples of selection differentials, density estimation techniques (Silverman, 1986) were used to calculate the posterior marginal densities.

Response to Selection
From every iteration of Gibbs sampling, the following statistics were computed for NBA in each parity:

where _h1is the posterior mean of breeding values of individuals of G1 in line H for the i-th parity,_c1(i) is the posterior mean of breeding values of individuals of G1 in line C for the i-th parity, andR(i) is a random sample of the marginal posterior distributions of response to selection for that parity.

For correlated responses in WT and BT, the following statistics were also computed:

where

_h1(WT) and_h1(BT) are the posterior means of breeding values of individuals of G1 in line H for WT and BT, respectively;_c1(WT) and _c1(BT) are the posterior means of breeding values of G1 individuals in line C for WT and BT, respectively. Thus,R(WT) and R (BT) are random samples of the marginal posterior distributions of response to selection for WT and BT, respectively.

From the samples of R, density estimation techniques (Silverman, 1986) were used to calculate the posterior marginal densities.

	Results

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Analysis of Convergence of the Gibbs Sampler
The number of iterations discarded (10,000) was greater than every value proposed by the procedures of Raftery and Lewis (1992) and García-Cortés et al. (1998). The number of iterations to discard based on their procedures ranged from 48 to 2,958 with the first procedure and from 210 to 8,629 with the second.

The effective sample size computed with the procedure of Geyer (1992) ranged from 40.25 to 4,231.5. Posterior means and standard deviations were directly estimated from the mean and standard deviation of the samples.

Phenotypic Data
Phenotypic means and standard deviations for NBA for the different parities and generation in each line are presented in Table 1. Line H showed values higher than line C in all parities and generations except of the sixth parity of G1 and the fifth parity of G2. Ages at farrowing were very similar for both lines in each generation. Phenotypic means and their standard deviations for WT and BT in the G1 are presented in Table 2. Average weight and backfat thickness were very similar in both lines.

Standardized Selection Differentials
Marginal posterior means and posterior standard deviations (PSD) of standardized selection differentials for G0 in all parities are presented in Table 3. Posterior means of standardized selection differentials for females in line H ranged from 0.70 (PSD 0.12) in the sixth parity to 0.94 (PSD 0.06) in the fifth. For males in the line H, posterior means ranged from 0.22 (PSD 0.19) in the sixth parity to 0.34 (PSD 0.10) in the fourth. In all cases except the sixth parity in males, highest posterior density regions (HPD) of 95% did not include zero.

Direct Genetic Response to Selection for NBA
Marginal posterior distributions of direct genetic responses for NBA are presented in Figure 2. All distributions were nearly symmetric. Therefore, posterior means, modes, and medians were very similar (Table 4). Posterior means of direct genetic response for NBA ranged from 0.32 (PSD 0.08) in the first parity to 0.64 (PSD 0.08) in the fourth one.

View larger version (19K): [in this window] [in a new window]   Figure 2. Estimated marginal posterior distribution of direct genetic response for number of piglets born alive (NBA) in different parities on generation 1 in Line H.

View larger version (10K): [in this window] [in a new window]   Figure 3. Estimated marginal posterior distribution of correlated genetic response for weight (WT) and backfat thickness (BT) at 175 d of age on generation 1 in Line H.

	Discussion

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Our results are in agreement with Le Roy et al. (1987) and with Bolet et al. (1989), who observed a general tendency to increase selection efficiency in later parities. They are also in agreement with Tartar (1981), who showed theoretically that selection over an average of the first five parities results in greater response in later parities. Moreover, average response for the six parities (0.46) is very similar to the expected response (0.41) obtained in a simulation experiment although a repeatability animal model was assumed (Noguera et al., 1994).

The classical "hyperprolific" selection scheme (Legault and Gruand, 1976), based on high selection intensity for litter size applied to a large population of females through individual selection index, resulted in an initial increase of the female side of around one piglet (Bolet and Legault, 1982). After 20 yr of selection, accumulated response was around 1.4 piglets per litter (Bidanel et al., 1994; Herment et al., 1994), but annual genetic gain was smaller (0.07 piglets per year) because of the need for backcrossing boars from the progeny of dams with extreme prolificacy to other sows with extreme prolificacy during repeated cycles of selection.

Higher rates of genetic improvement in litter size when selection is based on information from relatives have been reported. Thus, Wang et al. (1994) reported an experiment to improve total number born per litter with a selection criteria consisting of breeding values predicted with BLUP. The rate of genetic response was about 1.6% per year. More recently, Sorensen et al. (2000) reported a direct genetic response to selection of 0.43 piglets in a large-scale selection experiment for total number of piglets born. The criterion of selection was the average predicted breeding values (BLUP) of the sire and dam of the litter that contributed piglets to the selected line. The average response in litter size in the experiment reported in this paper was in line with these previous experiments.

The calculation of standarized selection differentials allows evaluation of the efficiency of a selection process given the available variance. As observed in Table 3, selection was effective in both males and females in line H, but greater values were observed for females. Sows were selected using their own information plus their relatives’ information. However, males were selected based on information of their ancestors only. On the contrary, the HPD of 95% for selection differentials for females in line C always included zero. Although selection was performed at random, culling of individuals resulted in slight positive values of selection differentials. Selection differentials for males in line C should be around zero, because all available males in the population were used to create G1.

Correlated responses in WT and BT were not observed in our experiment and are also consistent with results of other authors (Brien, 1986; Kuhlers and Jungst, 1991; Kerr and Cameron, 1996). It is consistent with estimates of genetic correlations close to zero between litter size and WT or BT reported by Noguera et al. (2002).

The results of the scheme proposed demonstrate an important response of litter size and do not produce undesirable correlated response in production traits. Nevertheless, a disadvantage of "hyperprolific" schemes can be a genetic lag for production traits due to selection pressure on litter size (Bichard and Seidel, 1982; Bidanel et al., 1994).

	Implications

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The application of one cycle of intense selection based on mixed model estimates of breeding values for litter size increased prolificacy in pigs. Genetic response was heterogeneous across parities, suggesting that litter size in each parity may have a different genetic background. Undesirable correlated responses in weight and backfat were negligible. Overall, the results obtained confirm the effectiveness of the strategy used in the experiment to achieve a genetic lift in prolificacy in order to create a dam line with high reproductive performance.

	Footnotes

 
¹ Financial support was provided by INIA, Spain (grant 9084). We acknowledge L. Gómez Raya for his collaboration and useful comments, and Maite Arbonés for her technical assistance. The authors are indebted to the staff of Nova Genètica for cooperating in the experimental protocol, in particular to Eva Ramells and P. Borràs.

Received for publication September 7, 2001. Accepted for publication June 6, 2002.

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