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* Department of Animal Science and
and
Department of Statistics, Iowa State University, Ames 50011
2 Correspondence:
109 Kildee Hall (phone: 515-294-6728; fax: 515-294-5698; E-mail:
tjbaas{at}iastate.edu).
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Abstract |
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 Top Abstract IntroductionMaterials and MethodsResults and DiscussionImplicationsLiterature Cited |
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Key Words: Genetic Correlations • Growth • Leanness • Litter Traits • Pigs
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResults and DiscussionImplicationsLiterature Cited |
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Numerous researchers have investigated the genetic correlations between lean growth and litter traits in different populations. Clutter and Brascamp (1998) reviewed these genetic parameters and concluded that, in general, estimates have been plagued by insufficient precision. Because they are population specific, use of parameter estimates from the literature could be very misleading. Genetic correlations between lean growth and litter traits currently recommended by the National Swine Improvement Federation (NSIF, 1997) for genetic evaluation programs are based on results from the literature and are not breed specific. Therefore, the objective of this study was to estimate breed-specific genetic correlations between lean growth and litter traits for the U.S. Yorkshire, Duroc, Hampshire, and Landrace populations.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResults and DiscussionImplicationsLiterature Cited |
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Then, lean growth rate per day (LGR) was calculated by dividing by DAYS.
Litter data included pedigree information for each sow, CG, parity of the sow, farrowing and weighing dates, number born alive (NBA), number after transfer (NAT), number weaned (NW), and litter weight at 21 d (L21WT). Number weaned included the actual number of pigs weaned by each sow but excluded pigs transferred to other sows. Contemporary groups were defined by the breeders as a group of sows that were bred, gestated, and farrowed in a group under the same management and environmental conditions. Litter traits were adjusted according to breed-specific procedures developed by Culbertson et al. (1997) from data for these breeds from 1985 to 1996. Adjustments for NBA included parity and age at farrowing. Litter weight at 21 d was adjusted for parity, age at farrowing, age at weighing, and NAT. Number weaned was adjusted for parity and NAT.
In both data sets, single-sire CG records were removed, as were records from sires not connected across CG and sires not mated to more than one dam. Numbers of records and CG, and litters represented by breed in the lean growth data set, along with means and SD for LGR, DAYS, BF, and LMA, can be found in Chen et al. (2002a). Number of records, animals, sires, dams, service sires, and contemporary groups represented by breed in the litter data, along with means and SD for NBA, L21WT, and NW can be found in Chen et al. (2002b).
Statistical Analysis
Bivariate REML analyses were conducted to estimate genetic correlations between lean growth and litter traits using REMLF90 (Misztal, 2000). Each analysis contained one lean growth trait and one litter trait. Different fixed and random effects, along with the environmental correlation between traits, were considered in two models. Models used for bivariate analyses were as follows:
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where y1, y2 = observations for lean growth and litter traits, b1, b2 = fixed effects of contemporary group and sex for lean growth traits and fixed effects of contemporary group for litter traits, a1, a2 = random additive genetic effects of animals, c = common litter effects, ss = service sire effects, pe = permanent environmental effects, X1, X2 = incidence matrices relating to fixed effects, Z1, Z2 = incidence matrices relating to animal effects, S = incidence matrix relating to common litter effects, W1, W2 = incidence matrices relating to service sire and permanent environmental effects, e1, e2 = residual effects for lean growth and litter traits. It was assumed that the covariances between additive, common litter, service sire, permanent environmental, and residual effects were zero and that levels of each were independently distributed with variances 
and 
and covariance 
a12 for animal effects, 
for common litter effects, 
for service sire effects, 
for permanent environmental effects, and 
and 
and covariance e12 for residual effects. Standard errors of genetic correlations were estimated using formulas by Falconer (1989). In the REML analysis, the convergence criterion was set to 10-8 for all analyses.
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Results and Discussion |
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TopAbstractIntroductionMaterials and MethodsResults and DiscussionImplicationsLiterature Cited |
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and 2) in this series (Chen et al., 2002a, b). Estimates of covariances between lean growth and litter traits are in Table 3. The Landrace had the highest absolute value of genetic covariances between BF, LMA, and LGR with NBA; between BF and L21WT; and between DAYS and BF with NW. The Duroc breed had the lowest absolute value of genetic covariances between LMA and LGR with NBA; between DAYS, BF and LGR with L21WT; and between DAYS and NW. In general, the genetic covariances were consistent among the four breeds.
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. In general, estimates of genetic correlations for the maternal breeds of Yorkshire and Landrace were very similar, and differed only slightly for the terminal breeds of Duroc and Hampshire. Genetic correlations between DAYS and litter traits were low in magnitude. Estimates of the genetic correlation of DAYS with NBA are -0.041, 0.051, -0.072, and -0.061 for the Yorkshire, Duroc, Hampshire, and Landrace breeds, respectively. Crump et al. (1997) reported an estimate of 0.084 for the genetic correlation between ADG and NBA in British Landrace pigs. Short et al. (1994) reported positive values for three of the four estimates of the genetic correlation between ADG and total number born in two dam lines from two farms (0.04, 0.05, 0.23, and -0.15). The estimates from this study are also lower than the value of -0.20 recommended by NSIF (1997).
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). The largest absolute value of the estimate of the genetic correlation of DAYS with L21WT was 0.095 in the Hampshire breed. Estimates of the genetic correlation of DAYS with L21WT from this study are much lower than the value of 0.43 recommended by NSIF (1997). Estimates of the genetic correlation between DAYS and NW are low and smaller in magnitude than their standard errors, which supports the value of 0 recommended by NSIF (1997). These correlations may be biased due to crossfostering of piglets, which occurred in 50% of the litters in this study. Even though the correlations between DAYS and litter traits are small, the signs indicate that selection for increased gain could slightly increase litter size but decrease litter weight in most breeds.
Estimates of genetic correlations between BF and NBA and L21WT are relatively high and consistent across breeds (Table 4). Estimates of the genetic correlation of BF with NBA are 0.19, 0.18, 0.18, and 0.20 for the Yorkshire, Duroc, Hampshire, and Landrace breeds, respectively. These results agree with the value of 0.21 reported by Crump et al. (1997) in British Landrace pigs. Johansson and Kennedy (1983) also reported positive estimates of 0.13 to 0.22 for BF with NBA in Swedish Landrace and Yorkshire pigs. Löbke et al. (1986) reported positive correlations of 0.03 and 0.28 for BF with NBA for the first litter and the first three litters, respectively. However, Morris (1975) and Bereskin (1984) reported negative genetic correlations between BF and litter size. Short et al. (1994) also reported three of four estimates of the genetic correlation between BF and total number born were negative, -0.12, -0.03, -0.08, and 0.06. The estimates from this study disagree with the value of 0.00 recommended by NSIF (1997). Estimates of the genetic correlation between BF and L21WT ranged from -0.27 to -0.30 for the four breeds (Table 4). These estimates for each of the breeds are lower than the value of -0.40 recommended by NSIF (1997). Estimates of the genetic correlation between BF and NW are very low and smaller than their standard errors (Table 4), which supports the value of 0 recommended by NSIF (1997). The signs of the correlations indicate that selection for decreased BF could slightly decrease litter size but increase litter weight.
Estimates of genetic correlations between LMA and litter traits are also low in magnitude and consistent across breeds. Estimates of the genetic correlation between LMA and NBA are lower than their standard errors and averaged -0.020 across the four breeds. Estimates of the genetic correlation between LMA and L21WT are -0.054, 0.083, -0.031, and -0.017 for the Yorkshire, Duroc, Hampshire, and Landrace breeds, respectively (Table 4). Estimates of the genetic correlation between LMA and NW are very low and smaller in magnitude than their standard errors (Table 4). There are no previously reported estimates of genetic correlations between LMA and litter traits.
Nearly all of the genetic correlations for LGR with litter traits were negative. Estimates of the genetic correlation of LGR with NBA were low in magnitude and consistent across the four breeds. Estimates of the genetic correlation of LGR with L21WT were also low and averaged -0.060 across the four breeds. The genetic correlations between LGR and NW were very low and smaller than their standard errors (Table 4). These genetic correlations may be biased due to crossfostering of piglets between litters that was present in the data. Chen et al. (2001) reported negative genetic correlations of -0.18 and -0.05 for LGR with NBA and NW, respectively, and a positive correlation of 0.13 for LGR with L21WT from a selection experiment in a synthetic line of Yorkshire-Meishan pigs.
Environmental correlation estimates are very low across all traits and all breeds. This result agrees with the findings of Crump et al. (1997), who reported that environmental correlations between performance and litter traits were, in general, low.
This study was the first to provide breed-specific genetic correlation estimates between lean growth and litter traits based on a large amount of field data. These breed-specific estimates could be used for jointly evaluating lean growth and litter traits using multiple-trait analyses in genetic evaluation programs. They would be applicable when litter traits are included in the selection objective for the breeds evaluated.
Currently, the STAGES program from which these data were obtained evaluates lean growth and litter traits in two separate multiple-trait evaluations. One of the reasons for including the relationship between lean growth traits and litter traits in a maternal-line evaluation is to increase the accuracy of the genetic evaluation for litter traits. Analyzing all traits simultaneously would, however, require an increase in computer resources. This tradeoff between accuracy and the need for additional inputs would need to be considered before implementation.
In general, the genetic correlations between lean growth and litter traits are unfavorable but relatively low in magnitude. This negative relationship may, in part, help explain the relatively slow genetic progress in litter traits in these populations demonstrated by Chen et al. (2002b). It also indicates that long-term selection for LGR could possibly harm litter traits. Because the genetic correlations and heritabilities for litter traits are low, the effect of selection for lean growth traits on litter traits without accounting for these correlations would not be observed in the short term.
As selection for lean growth traits continues, it is unknown whether relationships between them in these populations will change. It is possible that genetic relationships between components of LGR and litter traits are not linear, and that correlations may change as lean growth traits reach new thresholds. Therefore, it will be necessary to evaluate these relationships periodically and incorporate them into the evaluation program.
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Implications |
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TopAbstractIntroductionMaterials and MethodsResults and DiscussionImplicationsLiterature Cited |
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Footnotes |
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Received for publication August 2, 2002. Accepted for publication March 4, 2003.
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Literature Cited |
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TopAbstractIntroductionMaterials and MethodsResults and DiscussionImplicationsLiterature Cited |
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