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Correlated responses for litter traits to six generations of selection for ovulation rate or prenatal survival in French Large White pigs

A. Rosendo^*,1, T. Druet^*, J. Gogué, L. Canario^* and J. P. Bidanel^*,2

^* INRA, UR337 Station de Génétique quantitative et appliquée F-78350 Jouy-en-Josas; and INRA, UE332 Domaine de Galle, F-18520 Avord

	Abstract

Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Effects of selection for reproductive traits were estimated using data from 3 pig lines derived from the same Large White population base. Two lines were selected for 6 generations on high ovulation rate at puberty (OR line) or high prenatal survival corrected for ovulation rate in the first 2 parities (PS line). The third line was an unselected control line. Genetic parameters for age and BW at puberty (AP and WP); number of piglets born alive, weaned, and nurtured (NBA, NW, and NN, respectively); proportions of stillbirth (PSB) and survival from birth to weaning (PSW); litter and average piglet BW at birth (LWB and AWB), at 21 d (LW21 and AW21), and at weaning (LWW and AWW) were estimated using REML methodology. Heritability estimates were 0.38 ± 0.03, 0.46 ± 0.03, 0.16 ± 0.01, 0.08 ± 0.01, 0.09 ± 0.01, 0.04 ± 0.01, 0.04 ± 0.02, 0.19 ± 0.02, 0.10 ± 0.02, 0.10 ± 0.02, 0.36 ± 0.02, 0.27 ± 0.01, and 0.24 ± 0.01 for AP, WP, NBA, PSB, NW, NN, PSW, LWB, LW21, LWW, AWB, AW21, and AWW, respectively. The measures of litter size showed strong genetic correlations (r_a 0.95) and had antagonistic relations with PSB (r_a = –0.59 to –0.75) and average piglet BW (r_a = –0.19 to –0.46). They also had strong positive genetic correlations with prenatal survival (r_a = 0.67 to 0.78) and moderate ones with ovulation rate (r_a = 0.36 to 0.42). Correlations of litter size with PSW were negative at birth but positive at weaning. The OR and PS lines were negatively related to PSW and average piglet BW. Puberty traits had positive genetic correlations with OR and negative ones with PS. Genetic trends were estimated by computing differences between OR or PS and control lines at each generation using least squares and mixed model methodologies. Average genetic trends were computed by regressing line differences on generation number. Significant (P < 0.05) average genetic trends were obtained in OR and PS lines for AP (respectively, 2.1 ± 0.9 and 3.2 ± 1.0 d/generation) and WP (respectively, 2.0 ± 0.5 and 1.8 ± 0.5 d/generation) and in the PS line for NBA (0.22 ± 0.10 piglet/generation). Tendencies (P < 0.10) were also observed for LWB (0.21 ± 0.12 kg/generation) and AWW (–0.25 ± 0.14 kg/generation) in the PS line. Selection on components of litter size can be used to improve litter size at birth, but result in undesirable trends for preweaning survival.

Key Words: genetic parameter • litter size • ovulation rate • piglet survival • pig • prenatal survival

	INTRODUCTION

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Prolificacy is the most important reproductive trait in the genetic improvement of commercial pigs (Blasco et al., 1996; Holl and Robison, 2003; Hamann et al., 2004). Most reproductive traits exhibit low heritability, resulting in a slow response to genetic selection. Direct selection for increased litter size in pure line pig populations has often been reported with an inconsistent response (Bolet et al., 1989; Holl and Robison, 2003) but was successful in large populations as a result of applying high selection intensities in the so-called hyperprolific breeding schemes (Bidanel et al., 1994; Sorensen et al., 2000; Noguera et al., 2002). For instance, a genetic gain of almost 3 piglets has been obtained over the last 15 yr in the French Large White breed (Tribout et al., 2003).

However, efficiently selecting for litter size remains a difficult task in small populations. Several authors have proposed to use more heritable, indirect criteria to improve the efficiency of selection for litter size [e.g., uterine capacity (Bennett and Leymaster, 1989), placental efficiency (Ford, 1997; Wilson et al., 1999), or components of litter size (Johnson et al., 1984)]. Johnson et al. (1999) successfully increased litter size after 10 generations of selection for an index combining ovulation rate and embryonic survival.

Yet, the ability to apply indirect selection to components of prolificacy as compared with direct selection on litter size critically depends on the genetic parameters of components of litter size (Pérez-Enciso et al., 1996). A selection experiment was carried out at INRA to estimate the genetic parameters of components of litter size in the French Large White breed.

The main objective of this study was to estimate genetic parameters for litter traits and their direct and correlated responses to selection for ovulation rate at puberty (OR) or prenatal survival (PS).

	MATERIALS AND METHODS

Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Animals and Experimental Design
Animal care followed the general guidelines outlined in the European welfare regulations (directive 91/630/EC).

The experiment took place at the INRA experimental herd of Galle (Avord, France). The base generation constituted the progeny of 42 sows from another INRA experimental herd (Saint-Gilles, France), inseminated with semen from 24 boars present in French artificial insemination centers. Males and females from this base generation were randomly allocated on a within-litter basis to 3 contemporary lines. Two lines were then selected for ovulation rate at puberty (OR line) or prenatal survival in the first 2 parities corrected for ovulation rate (PS line). The third line was kept as an unselected control line. At each generation, approximately 50 gilts and 6 to 8 boars from the first litters were kept for breeding in each line. Boars were chosen on a within-sire family basis in the 3 lines. Gilts were randomly chosen on a within-dam family basis in the control line and selected on a population basis in the 2 other lines. A mating plan was established to minimize inbreeding at each generation. Females produced 2 litters and were then replaced by the next generation of gilts. More details on the selection process are given in Rosendo et al. (2007).

The sow herd was managed under a batch farrowing system. At each generation, females were distributed into 7 farrowing batches. These farrowing batches then became postweaning and fattening contemporary groups of their progeny. Females were inseminated twice at a 24-h interval. Seven gilts from each line were introduced in each farrowing batch. All females that did not conceive at first mating joined the subsequent farrowing batch, where they had the opportunity to be mated once more. Hence, some variation in the number of females per line x batch combination could exist, but each batch included females from the 3 lines. Litters were born in individual farrowing crates. When necessary, some piglets were moved to another crate within the first 48 h after farrowing and raised by a foster dam. Piglets were weaned at 4 wk of age.

Age at puberty (AP), defined as age at first estrus, and BW at puberty (WP) were recorded when the gilt stood immobile to back pressure in the presence of a boar for the first time. Estrus detection began on a daily basis at 150 d of age and continued until almost all females reached puberty. Ovulation rate at puberty (ORP), defined as the number of corpora lutea visible on the ovaries, was measured in females under general anesthesia by laparoscopy between 10 and 15 d after the first estrus. Females kept for breeding were then mated at 11 mo of age on average after a synchronization treatment with a progestagen. Ovulation rate after fertilization (ORF) was measured in the 2 parities as described above between 10 and 15 d after mating.

Litter records were available on the number of piglets born alive (NBA) and the number of stillborn piglets (NSB), defined as the number of fully formed piglets found dead at birth or at the first litter examination after birth. The total number of piglets born (TNB) was calculated as (TNB = NBA + NSB). Prenatal survival (PS) was computed as: PS = 100 x (TNB/ORF). Crossfostering was practiced on a within-line basis within the first 48 h after birth and was recorded for each piglet. This practice led us to define 2 litter traits at weaning [i.e., the number of piglets weaned (NW), which refers to the number of piglets weaned from the same genetic dam, and the number of piglets nurtured (NN), which refers to the number of piglets raised by the same foster dam). The NW and NN were analyzed as traits of the genetic and foster dams, respectively.

The proportions of stillborn piglets (PSB) and of piglets surviving up to weaning (PSW) were computed as PSB = 100 x (NSB/TNB) and PSW = 100 x (NW/NBA). Survival records from fostered piglets were assigned to the birth mother. Litter weight at birth (LWB), at 21 d (LW21), and at weaning (LWW) were calculated as the sum of the individual BW of all pigs born alive and nursed by the sow at birth, at 21 d, and at weaning, respectively. Average individual BW (AWB) at birth and at 21 d (AW21) and weaning (AWW) were calculated as AWB = (LWB/NBA); AW21 = (LW21/N21), where N21 = number of piglets at 21 d of age; and AWW = (LWW/NW).

The selection criterion in the OR line was ovulation rate at puberty. Gilts were selected on their own performance, boars on the performance of their dam. The selection criterion in the PS line was the average prenatal survival over the first 2 parities, corrected for ovulation rate [CPS = 0.5(CPS₁+CPS₂), with CPS_i = PS_i + 0.018ORF_i, where i = 1,2 denotes parity number (Blasco et al., 1998; Rosendo et al., 2007)]. Animals were selected for 6 generations. A seventh generation was produced without selection.

Statistical Analyses
Thirteen traits were analyzed (i.e., AP, WP, NBA, NW, NN, PSB, PSW, LWB, LW21, LWW, AWB, AW21, and AWW). All records were considered a trait of the gilt. Table 1 shows descriptive statistics for the traits studied.

Mixed Model Analyses.
Variance components were first estimated using REML methodology (Patterson and Thompson, 1971) applied to a multivariate animal model. Depending on the trait, the selected models (Table 2) were derived from the following base model:

where y represents the vector of observations; X, Z_a, W_c, and W_p are known incidence matrices relating observations to fixed and random effects; ß represents the vector of fixed effects [i.e., generation, line, parity, and farrowing batch (contemporary group)]; a is the vector of random additive genetics effects; c is the vector of random common environmental effects of dams born in the same litter; p is the vector of random permanent environmental effects of the dams; and e is the vector of random residual effects.

The following means and (co)variance structures were assumed across random effects in the model:

where is the additive genetic variance; A is the additive relationship matrix; , , and are the common environmental, permanent environmental, and residual variances, respectively, with order equal to the number of traits in the analyses; and I are identity matrices of appropriate dimensions. The analyses were performed using VCE (Neumaier and Groeneveld, 1998) and ASREML (Gilmour et al., 2002) computer packages. The BLUP EBV were then computed as back-solutions from REML analyses.

Genetic trends were estimated from LS and mixed model (MM) analyses. Least squares estimates of the response to selection were computed as the differences between the average performance of the animals in selected lines and the average performance in the control line at each generation. Mixed model estimates of genetic trends were obtained by averaging EBV of animals for each line x generation combination. The LS and MM average genetic trends were then estimated. The LS estimates were obtained by regressing line differences on generation number; MM estimates were computed by regressing BLUP-EBV on generation number within each line. Because no selection occurred in the seventh generation, it was grouped with generation 6 to compute the coefficients of regression on generation number. Approximate SE of LS estimates of within-generation and average responses (regression coefficients on generation number) to selection were computed, accounting for drift and measurement error variances (Hill, 1972). The variance of MM estimates of within-generation differences and of regression coefficients on generation number was computed from the animal by animal part of the inverse of the coefficient matrix from the MM equations at convergence for variance components, as described by, for example, Johnson et al. (1999).

	RESULTS

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Genetic Parameters
The REML estimates of genetic parameters are shown in Table 3. Puberty traits (AP and WP) showed the highest heritability values (>0.30). Traits related to average individual weights of piglets had moderate heritabilities. Among litter weight traits, the heritability of LWB was almost twice that of LW21 and LWW, although they were all lower than 0.20. Low heritabilities were also estimated for traits associated with litter size, particularly at weaning, as well as for PSB and PSW (0.08 and 0.04, respectively). Common litter effects, which include a large part of dominance effects, were moderate for AP and WP, although lower than the corresponding heritability values. For all other traits, common litter effects were low, but higher than the heritability estimates for PSW and NN. Permanent environmental effects were also low and nonsignificant, except for NBA and average individual BW traits.

View larger version (13K): [in this window] [in a new window]   Figure 1. Difference (selected – control line) in phenotypic least squares (LS) and average breeding value (BLUP-AM) for age at puberty (AP) plotted by generation between the line selected for ovulation rate at puberty (a) or prenatal survival (b) and the unselected control line. **P < 0.01; ***P < 0.001.

View larger version (10K): [in this window] [in a new window]   Figure 2. Difference (selected – control line) in phenotypic least squares (LS) and average breeding value (BLUP-AM) for BW at puberty (WP) plotted by generation between the line selected for ovulation rate at puberty (a) or prenatal survival (b) and the unselected control line. *P < 0.05; **P < 0.01; ***P < 0.001.

View larger version (7K): [in this window] [in a new window]   Figure 3. Difference (selected – control line) in phenotypic least squares (LS) and average breeding value (BLUP-AM) for number born alive (NBA) plotted by generation between the line selected for prenatal survival and the unselected control line. *P < 0.05; **P < 0.01; ***P < 0.001.

View larger version (7K): [in this window] [in a new window]   Figure 4. Difference (selected – control line) in phenotypic least squares (LS) and average breeding value (BLUP-AM) for litter weight at birth (LWB) plotted by generation between the line selected for prenatal survival and the unselected control line. *P < 0.05; **P < 0.01.

View larger version (8K): [in this window] [in a new window]   Figure 5. Difference (selected – control line) in phenotypic least squares (LS) and average breeding value (BLUP-AM) for average piglet BW at weaning (AWW) plotted by generation between the line selected for prenatal survival and the unselected control line. *P < 0.05; **P< 0.01.

	DISCUSSION

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Heritability of AP is close to previous estimates of Bidanel et al. (1996) in the French Large White breed and to average literature values (Lamberson et al., 1991; Rothschild and Bidanel, 1998), but is lower than the estimates recently reported by Ruiz-Flores and Johnson (2001) and Holl and Johnson (2005). Conversely, the heritability estimate obtained for WP is lower than most estimates reported in the literature (Bidanel et al., 1996; Ruiz-Flores and Johnson, 2001). Litter size traits exhibited low heritability estimates similar to average literature values (Rothschild and Bidanel, 1998; Knol et al., 2002; Mesa et al., 2005) and to the estimates obtained in the same population by Ducos and Bidanel (1996). The very low heritability values obtained for PSB and PSW are also close to the literature mean of Rothschild and Bidanel (1998) and to the estimates reported by Knol et al. (2002) and Mesa et al. (2005). Conversely, they are lower than the estimate for PSB recently reported by Canario et al. (2006b) in the French Large White population and the values reported by Johnson et al. (1999), Grandinson et al. (2002), and Damgaard et al. (2003). Heritabilities for litter weight are lower than mean estimates reported by Rothschild and Bidanel (1998) and the values obtained by Johnson et al. (1999) and Ruiz-Flores and Johnson (2001). Finally, the estimates for average piglet BW are slightly higher, particularly at birth, than the average values reported in the recent literature review of Canario (2006): 0.23, 0.24, and 0.20 for AWB, AW21, and AWW, respectively.

The positive (i.e., unfavorable) genetic correlation between age at puberty and ovulation rate is consistent in sign with the estimate obtained by Ruiz-Flores and Johnson (2001), but disagrees with the literature mean value of Rothschild and Bidanel (1998) and the previous estimate of Bidanel et al. (1996), who reported a negative genetic correlation between the 2 traits. This correlation depends on whether OR is measured at a constant physiological (same estrus number) or chronological age. A negative value is more likely to occur at a given chronological age because OR increases with estrus number. Yet, positive (Ruiz-Flores and Johnson, 2001; current study) and negative (Young et al., 1978; Bidanel et al., 1996) correlations have been reported at a given physiological age. Results may hence be population dependent and involve different physiological mechanisms. A positive value could be due to the fact that late maturing gilts may have a larger number of mature follicles or be able to more efficiently recruit follicles when ovulating for the first time and even later, as a positive genetic correlation was also obtained with ovulation rate at fertilization (0.24 ± 0.06). Conversely, a negative correlation might reflect a higher ovarian efficiency of early maturing gilts.

Contrary to ovulation rate, litter size at birth (TNB and NBA) had negative (i.e., favorable) genetic correlations with AP, in agreement with Young et al. (1978), Bidanel et al. (1996), and Ruiz-Flores and Johnson (2001). Though the average literature estimate of Rothschild and Bidanel (1998) is lowly positive, other results support the hypothesis of a favorable relationship between sexual maturity and litter size at birth. Després et al. (1992) and Ruiz-Flores and Johnson (2001) and, to some extent, Tribout et al. (2003), reported a decrease in age at puberty in pig populations selected for litter size. The negative correlation of AP with OR and the positive one with TNB mathematically resulted in a negative correlation with prenatal survival, unlike Bidanel et al. (1996), who reported a near zero correlation between AP and PS, presumably because of the negative correlation between AP and OR they obtained.

The positive (i.e., antagonistic) correlation between OR and PSB is in line with the results of Johnson et al. (1999) and Ruiz-Flores and Johnson (2001), and clearly shows that increasing OR not only results in increased prenatal mortality, but also hinders farrowing survival. This increased number of stillbirths would, according to Johnson et al. (1999) at least partly be related to a decline in birth weight. A lower birth weight is clearly associated with a higher risk of stillbirth, at least on the phenotypic scale (e.g., Canario et al., 2006a). A negative genetic correlation between OR and AWB (–0.21 ± 0.06) has indeed been obtained in our study, in agreement with Johnson et al. (1999). Lighter piglets might be less mature at birth (Klemcke et al., 1993) and more sensitive to hypoxia during the farrowing process (Leenhouwers et al., 2003; Canario, 2006). Yet, the decrease in birth weight is not necessarily the unique cause of the increased number of stillbirths. Other factors, such as increased farrowing duration in larger litters, may also be involved (Johnson et al., 1999).

Conversely, PSW is almost independent of OR and negatively (i.e., unfavorably) correlated with PS. Although they did not give genetic correlations of OR and PS with PSW, Johnson et al. (1999) and Ruiz-Flores and Johnson (2001) showed that selection on a combination of OR and PS or TNB resulted in deterioration of PSW. This unfavorable trend is consistent with our genetic parameter estimates and would essentially be due to an antagonism between prenatal and birth to weaning survival. This antagonism may again be mediated through a correlated response in piglet BW. Indeed, our genetic parameter estimates tend to show that selection for high PS would result in lower AWB (r_g = –0.41), in association with greater intrauterine competition (Vallet et al., 2002). Lower AWB would then be associated with reduced PSW (r_g = 0.75).

The negative (i.e., favorable) genetic correlations between PSB and TNB disagree with most recent literature results, which show a moderate genetic antagonism (Johnson et al., 1999; Lund et al., 2002; Holm et al., 2004; Bouquet et al., 2006; Canario et al., 2006b). Though strongly correlated with TNB, NBA has a rather different genetic relationship with PSB because most genetic correlation estimates are favorable (Damgaard et al., 2003; current study) or close to zero (Arango et al., 2005; Bouquet et al., 2006; Canario et al., 2006b). They tend to show that, contrary to TNB, selection on NBA would allow increasing litter size without impairing farrowing survival.

Conversely, TNB and NBA had negative (i.e., antagonistic) relationships with PSB, in agreement with Lund et al. (2002), Damgaard et al. (2003), and Bouquet et al. (2006). Hence, selection for litter size at birth is not an optimal way to improve NW because it also hinders PSW. The number of piglets weaned, or birth to weaning survival, a priori seem to be the simplest and most obvious selection criteria for that purpose. However, it is often advocated that obtaining good quality data for these traits is not straightforward due to crossfostering, partial early weaning of piglets, or both. This practice may result in very low heritability values for these traits (0.04 for NN and PSW in this study) and biased estimates of breeding values. Several indirect criteria, such as behavioral traits or subjective grading of sow maternal ability, have been proposed to improve piglet survival, but more research is needed to assess their real value and allow them to be used on a large scale (see review by Grandinson, 2005). Birth weight, which has been shown to be a major determinant of piglet survival (e.g., Roehe and Kalm, 2000; Leenhouwers et al., 2003), has also been proposed as a potential selection criterion. Our results show strong positive correlations between PSW and AWB, but relationships of AWB with litter size at weaning (NW or NN) are negative. In fact, as emphasized by Knol et al. (2002) and Canario et al. (2006a), genetic relationships between birth weight and survival are complex, with presumably a population dependent optimal value, and increasing AWB is unlikely to result in improved preweaning survival. Conversely, within-litter homogeneity in birth weight was favorably correlated with survival (Damgaard et al., 2003; Huby et al., 2003) and is a potentially interesting criterion to improve survival, as shown in a rabbit selection experiment (Garreau et al., 2004).

In conclusion, litter size at birth can be efficiently selected for through direct selection or indirect selection on its component traits, ovulation rate and prenatal survival. However, total number born and its components have antagonistic genetic relationships with farrowing and birth to weaning survival, which may strongly impair the genetic gain obtained at birth. Selection criteria aimed at improving the number of piglets produced per sow should consequently include (an) additional trait(s) favorably related to farrowing and birth to weaning survival, which seem to have a low, or even negative, genetic correlation.

	Footnotes

 
¹ The thesis work of A. Rosendo was funded by the Consejo Nacional de Ciencia y Tecnologia (CONACyT) and the Secretaria de Educacion Publica, Programa de Becas Complemantarias, México.

² Corresponding author: jean-pierre.bidanel{at}jouy.inra.fr

Received for publication October 17, 2006. Accepted for publication March 12, 2007.

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