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ANIMAL GENETICS |
* Department of Animal and Dairy Science, University of Georgia, Athens 30602; and
and
Smithfield Premium Genetics Group, Rose Hill, NC 28458
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Abstract |
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 Top Abstract INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Key Words: crossbreds • genetic correlation • genotype x environment interaction • pig • plasticity • variance component
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INTRODUCTION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Crossbreds are usually raised in environments of lower quality than those of purebreds concerning hygiene status and space per pig. These factors affect feed intake and growth (Schinckel et al., 1999
; Wolter et al., 2002). Subsequently, low rpc may be due to not only purely genetic factors but also the genotype x environment interaction.
Genotypes differ in their plasticity, e.g., levels of adaptation to specific environments (Via et al., 1995; Merks et al., 2005). A highly plastic line would perform well in an optimal environment but possibly poorly in environments that are less than optimal. Less plastic lines would perform similarly across many environments, although their performance may be less than that of high-plasticity lines in an optimal environment. Highly plastic lines would be desirable in locations in which production settings for purebreds and crossbreds are similar, e.g., in Northern Europe. A less plastic line could be more productive in locations with large environmental changes, e.g., large farms in the United States.
An approximate indication of plasticity is provided by rpc. The value of this correlation for traits of interest can also be used in making decisions regarding appropriate selection strategies, i.e., pure-line selection, reciprocal recurrent selection, or a combination of both (Wei and van der Steen, 1991; Wei et al., 1991; Wei and van der Werf, 1994). Selection for plasticity is possible (Scheiner, 2002).
The goal of this study was to estimate rpc for growth, fatness, and muscling between 2 Duroc sire lines and their corresponding terminal crossbreds.
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MATERIALS AND METHODS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Nucleus animals were produced on 4 farrow-to-finish farms. On 2 of them (50% of the data), P1 and P2 were equally represented. On farm 3, only P1 animals were produced; on farm 4, 95% of P1 and 5% of P2 animals were kept. There was no occurrence of porcine reproductive and respiratory syndrome virus, Actinobacillus pleuropneumonia, or Mycoplasma hyopneumoniae on these farms for the duration of this project. During this study, these nucleus farms provided 25% more finishing floor space to the pigs than their commercial counterparts.
Commercial crossbreds were raised on two, 1,200-sow farrow-to-finish farms. The crossbreds C1 and C2 were represented on both farms at 58 and 42%, respectively. These farms were known to be affected by porcine reproductive and respiratory syndrome virus, A. pleuro-pneumonia, and M. hyopneumoniae, as confirmed by serology and diagnostic testing. Pig space was typical of commercial production settings.
Typical and similar corn-soybean diets were used at all sites. Feeding was ad libitum for all animals. Measurements on pureline animals were taken at an average age of 172 d. These included live BW (WGT), ultrasound backfat (BF) at the 10th rib, and ultrasound muscle depth (MD; Aloka model 500V real-time ultrasound, Corometrics Medical Systems, Wallingford, CT). Weight per day of age (WDA) was calculated as the ratio between weight and age at measurement.
Crossbred animals were slaughtered at an average age of 196 d. Carcass measurements by Fat-O-Meater (SFK Technology, A/S, Herlev, Denmark) included carcass weight (WGT), BF, and MD. Carcass WDA was calculated as carcass weight divided by age at slaughter.
Only the pedigreed data from P1, P2, C1, and C2 were considered in the analyses (Table 1). There were no pedigree data on the LR x LW crossbred dams. The pedigree file contained 27,171 and 9,802 animals for P1 and P2, respectively, of which 1,519 and 1,513 parents were without records. The number of sires and dams for each genetic group is presented in Table 2.
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where P indicates the Duroc line; C indicates the corresponding crossbred line; y = a vector of observations; and ß = a vector of fixed effects including the contemporary group (farm-finisher barn-year-week) and sex; weight and age at measurement were included as covariables. For WGT as an analyzed trait, weight was omitted as a covariable, and for WDA, weight and age were omitted as covariables. Additionally, aP = a vector of additive genetic effects of the animal for P; sC = a vector of additive genetic effects of the sire for C; and d = a vector of dam effects composed of dam additive genetic effects (daC) and dam environmental effects (deC). Further, l and e = vectors of birth litter and residual effects, respectively, and X, Z, U, and W = the appropriate incidence matrices. The expectations of a, s, d, l, and e were assumed to be
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Assuming that the differences between breeds were negligible and that the environments and traits were the same in both P and C, the following applies:
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where 
= the Mendelian sampling. Because is not part of the model for the crossbreds, it becomes part of the residual. The variances were assumed to be
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where A = the numerator relationship matrix and I = appropriate identity matrices of appropriate dimensions m, n, q, r, and t. No maternal effects were included in the model based on preliminary analyses. Heritabilities in crossbreds were calculated assuming the additive variance was 4x the sire variance. Analyses were performed using AIREMLF90 (Misztal et al., 2002).
Based on only purebred information, the breeding values of the sires used on the commercial farms can be predicted as follows:
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where uc = the EBV of the crossbred animal; up = the EBV of the purebred animal; 
2c = the additive genetic variance of the crossbred population; 2p = the additive genetic variance of the purebred population; and rpc = the genetic correlation between purebreds and cross-breds.
The growth traits in purebreds and crossbreds were different, i.e., WGT in the purebreds and carcass weights in crossbreds. Following Johnson et al. (2004) and Schinckel et al. (2001), we assumed that the carcasses were 75% of WGT and that the SD of the carcasses were 83% of the SD of live BW. Subsequently, variances at the WGT level were assumed to be 1.45 of those measured at the carcass level, and the prediction formulas above need to be multiplied by 1.2.
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RESULTS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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. On average, commercial animals were 24 to 26 d older at measurement than the nucleus animals. Assuming 75% dressing percentage (Boggs et al., 2006), the average carcass weight of C1 and C2 adjusted to WGT would be 119 kg, which is on the same level as P2 (120.3 kg). Thus, adjusted WDA was lower in the crossbreds than in the purebreds. However, compared with the pure-breds, the crossbreds exhibited more BF and increased loin depth.
Backfat
Variance components for BF are presented in Table 3. The additive genetic variance of C1 (0.89), which is a sire variance, was about 25% of the additive variance of P1 (2.95). One-quarter would be expected with the same parental breeds and the same environment for purebreds and the crossbreds. The dam variance (0.95) was similar to the sire variance, suggesting that it is mostly genetic rather than environmental. The birth litter variances for P1 (0.45) and C1 (0.62) were similar in amount and accounted for 5 and 7% of the total variances, respectively. The heritability estimate of the purebred line P1 (0.46) was about 50% greater than that of the crossbreds C1 (0.32). This difference can mainly be explained by the approximately 3-times-greater residual variance in C1 (8.76). The residual in the model for crossbreds contained the Mendelian sampling. Assuming that the variance of the Mendelian sampling was 2 x sire variance, the part of the residual variance in C1 that can be compared with that in P1 was 6.98, which was still more than twice that in P1.
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The genetic correlations between purebreds and crossbreds were rather high for both lines (rP1,C1: 0.83; rP2,C2: 0.89) in spite of different environments (nucleus and commercial farms) and similar but not identical traits (ultrasound measurement on live pigs and optical measurement on carcasses). This means that the selection for BF in the purebred lines is likely to be effective on the commercial level.
Muscle Depth
The heritability estimates were lower for MD (Table 4) than for backfat. The additive genetic variances were greatest in the Duroc 1 group, in which the sire variance in C1 (1.61) was about 25% of the additive genetic variance of P1 (6.16). In C1, the dam variance (2.16) was 34% greater than the sire variance, whereas the birth litter variance (0.59) accounted for only 2% of the total variance. In P1, the birth litter variance (0.92) was nearly double that in C1. The residual variance in P1 (13.09) was about double the additive genetic variance. In C1, the residual variance (29.91) was more than double that in P1. As the dam variance in this group was also part of the phenotypic variance, the heritability estimate (0.19) was much lower.
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Weight at Measurement
Variances for crossbreds were adjusted to live BW. The heritability of P1 (0.31) was about twice the value estimated for C1 (0.16; Table 5). The additive genetic sire variance (5.7) was about one-sixth of the additive genetic variance in P1 (34.4). The residual variance in C1 (115.3) was about twice the residual variance of P1 (63.7). The dam variance in C1 (7.0) was at approximately the same level as the sire variance. Birth litter variances in both groups accounted for 11 to 12% of the total variance and were of greater magnitude compared with BF and muscle depth.
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In the Duroc 2 group, the heritability estimates of P2 (0.21) and C2 (0.18) were roughly at the same level. The sire variance in C2 (6.7) was only about 25% of the additive genetic variance in P2 (26.6) but greater than that in C1-P1. The C2 dam variance (4.5) was relatively low, whereas the birth litter variances were similar to those for P1. The residual variance in C2 (117.0) was about 40% greater than that in P2 (84.9).
The genetic correlation between C2 and P2 was 0.80, substantially greater than that between P1 and C1. This indicates that there might be a difference in gene frequency between the 2 Duroc populations, implying that nonadditive effects may act in a different way. Purebred selection for weight is likely to be more efficient with P2.
WDA
The estimates for WDA are provided in Table 6. Variances for crossbreds were adjusted to live BW. The difference between the additive genetic variance of P1 (1178) and the sire variance of C1 (152) was relatively larger than for WGT. However, the heritability estimates were similar (P1: 0.32; C1: 0.16). The proportions of dam (C1: 4.5%) and birth litter variances (P1: 10.9%; C1: 11.5%) were comparable to those for WGT. The genetic correlation between P1 and C1 was 0.60, which was close to the estimate for WGT. The pattern of the estimates for P2-C2 was similar to those for WGT.
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). These weights varied between 0.43 and 0.92 with traits and lines. The greatest values were for BF in both lines (BF1: 0.91; BF2: 0.92), followed by MD (MD1: 0.80; MD2: 0.72). The lowest values were for WGT (0.43) and WDA (0.43) in Duroc line 1. For line 2, the weights were somewhat greater (WGT2: 0.80; WDA2: 0.70). The deviations from the genetic correlations were due to the greater (WGT, WDA) or lower (BF) additive genetic variance in the purebreds compared with the crossbreds.
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DISCUSSION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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; van Wijk et al., 2005). Birth litter variances were proportionately smaller for BF and MD compared with those for WGT and WDA, in which preweaning growth is part of the trait. In BF, these litter environmental effects seemed to be of greater importance than in MD. Because dam x sire interactions were included in the birth litter variance, this variance also represented the upper bound of the parental dominance variance, which was 25% of the dominance variance.
Although P1 and P2 were the same breed and shared the same environmental conditions, their variances differed; P2 generally showed 18 to 97% greater residual variances and, with the exception of BF, 25 to 30% lower additive variances than did P1, leading to lower heritability estimates. Heritability estimates for commercial crossbred pigs were similar for both lines. However, the estimates were up to 50% lower compared with those for the corresponding purebreds. Other studies have reported lower heritabilities for BF and ADG in field data compared with station data (Bidanel and Ducos, 1996; Wolf et al., 2001; Csato et al., 2002; Peskovicova et al., 2002).
Genetic Correlations
The strongest genetic correlations between nucleus purebred data and corresponding commercial crossbred data were for BF in both lines (P1-C1:0.83; P2-C2: 0.89), followed by MD (P1-C1: 0.78; P2-C2: 0.80). For WGT and WDA, the purebred-crossbred genetic correlations between P2 and C2 (0.80 and 0.79) were larger than those between P1 and C1 (0.53 and 0.60). These values also included genotype x environment interactions, because the Duroc nucleus animals were raised under a management with decreased pathogen loads and more pig space compared with the commercial crossbred pigs. Additionally, the traits measured were not identical. Although live animals were measured in the purebred lines, the traits in the commercial pigs were measured on warm carcasses. Divergent results between station and field data were reported. Under Czech and Slovak conditions, the genetic correlations for BF range from 0.72 to 0.84 (Wolf et al., 2001; Peskovicova et al., 2002), whereas the estimates for both French LW and LR are 0.91 (Bidanel and Ducos, 1996). In Hungary, the genetic correlations vary from 0.12 to 0.64 according to the point of measurement (Csato et al., 2002). Wolf et al. (2001) and Peskovicova et al. (2002) reported correlations of approximately 0.5 for ADG.
Genetic correlations among similar traits in the literature do not differ much from unity under ad libitum feeding conditions. For BF, Newcom et al. (2005) reported a genetic correlation of 0.98 between live animal BF and carcass backfat. Tholen et al. (1998) presented similar results for daily gain when comparing live with carcass weight. Nguyen and McPhee (2005), however, reported estimates of 0.81 for BF and 0.86 for daily gain for pigs under restricted feeding. In this study, the pigs were raised on a few large farms under an ad libitum feeding regimen. Thus, feeding differences did not affect the correlations. Estimates of genetic correlations between BW at 160 and 190 d of age were 0.983 (Huisman et al., 2002). These ages are comparable to those of the P and C groups.
The purebred-crossbred genetic correlations obtained for BF in this study were at the upper end of values found in the literature. These varied from 0.32 to 0.98, according to breed combination (Brandt and Täubert, 1998; Merks and Hanenberg, 1998; Lutaaya et al., 2001). The results for WGT and WDA were at the lower end of literature values for WGT and daily gain. Although Merks and Hanenberg (1998) reported only high estimates of 0.90 and 1.00 for 3 different breeds involved, the results of Brandt and Täubert (1998), Lutaaya et al. (2001), and Fischer et al. (2002) were more divergent. They ranged from 0.47 to 0.99, involving 2 to 3 breeds.
Although it is better to utilize both purebred and crossbred information unless the test capacity is limited, the importance of crossbred information increases with decreasing purebred-crossbred genetic correlation (Wei and van der Werf, 1994; Bijma and van Arendonk, 1998). The simulations of Bijma and van Arendonk (1998) illustrate that the potential benefit with rpc > 0.9 is small, and a large amount of crossbred information is needed to obtain an additional response.
In this study, both the genetic correlations and the weights for the prediction of crossbred breeding values based on purebred information only would justify pure-bred selection for BF in both populations. For MD, the correlated response would be reduced by 20 (group 1) to 30% (group 2). Although the genetic correlations for WGT and WDA in the Duroc 2 group are reasonably high, the lower weights could justify the inclusion of the crossbred information. The lower rpc (0.5 to 0.6) and weights (0.36) of WGT and WDA found in the Duroc 1 group suggest the use of the crossbred data for an efficient selection in this trait.
Lower correlations in WGT and WDA compared with the P2 line indicated a greater plasticity of P1. According to Schinckel et al. (1999), "some genetic populations selected for leanness and reduced feed intakes are more sensitive to environmental stressors than some greater feed intake U.S. genetic populations." Environmental sensitivity, also called phenotypic plasticity, is a heritable, evolvable trait (de Jong and Bijma, 2002; Scheiner, 2002). Canalizing selection, i.e., the aptitude to maintain a constant phenotype in fluctuating environments, could also be exploited in animal breeding. SanCristobal-Gaudi et al. (1998) developed indices and approximate expressions of parent-offspring regressions for canalizing populations toward an economic optimum. Falconer (1990) showed that stabilizing selection is antagonistic selection in both directions (i.e., selection upwards in a bad environment and downwards in a good environment at the same time) and so is expected to decrease environmental variance. Thus, selection of sires based on crossbred performance, i.e., upward selection in the commercial environment, would be expected to reduce the plasticity in P1 for WGT and WDA. Because plasticity for important traits may be unfavorably correlated, low plasticity for the breeding goal should be desirable (Strandberg, 2005), provided there are high performances in good environments.
From an economic point of view, the inclusion of cross-bred information is not necessarily suggested. The simulation results of Mielenz et al. (2003), excluding the existence of overdominance, indicate only small extra benefits for high to moderate purebred-crossbred genetic correlation levels when including crossbred information. In addition, it is important to update the genetic parameters frequently for long-term selection (Wei and van der Werf, 1994).
There are also alternative indicators for the usefulness of the inclusion of crossbred information, such as the ratio between dominance variance and total genetic variance (Uimari and Gibson, 1998). However, the estimation of dominance variances in most cases is inaccurate due to insufficient sample sizes. Necessary sample sizes for hierarchical full- and half-sib structure were estimated by Mielenz and Schüler (2004). In the case of many sire breeds, genetic evaluation including heterotic effects may be appropriate (Wolf et al., 2006).
This study looked at only a few traits and 1 paternal breed. A more complete comprehension concerning the efficiency of purebred selection on commercial animals would also involve maternal breeds, more traits, and possibly more environments. With crosses more complicated than F1, accurate modeling may be a challenge (Lo et al., 1997).
In conclusion, the 2 Duroc lines differed consistently concerning heritability and genetic correlations between purebreds and their respective commercial cross-breds. Heritability estimates were consistently larger for traits measured in P1 and P2 compared with those for traits measured in C1 and C2. Differences in heritabilites between P1 and C1 were consistently larger compared with differences between P2 and C2. Genetic correlations ranged from 0.53 to 0.89. They were consistently lower for traits measured in P1 and C1 compared with traits measured in P2 and C2. The range of correlations between P1 and C1 was larger (0.53 to 0.83) compared with the range of correlations between P2 and C2 (0.79 to 0.89). In addition, growth traits tended to have lower correlations compared with carcass traits. These differences indicate greater environmental sensitivity (plasticity) of P1, which is a leaner line, especially in growth traits. When nucleus and commercial environments differ substantially concerning pathogen load, pig space, and other factors, selection strategies should include crossbred data from typical production environments, especially for growth traits.
1 Corresponding author: birgit{at}uga.edu
Received for publication July 25, 2006. Accepted for publication December 7, 2006.
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LITERATURE CITED |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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