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J. Anim. Sci. 2005. 83:499-506
© 2005 American Society of Animal Science

ANIMAL GENETICS

Threshold-linear estimation of genetic parameters for farrowing mortality, litter size, and test performance of Large White sows

J. Arango*,1,2, I. Misztal*, S. Tsuruta*, M. Culbertson{dagger} and W. Herring{dagger}

* Department of Animal and Dairy Science, the University of Georgia, Athens 30602-2771; and and {dagger} Smithfield Premium Genetics, Roanoke Rapids, NC 27870


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results and Discussion
 Implications
 Literature Cited
 
Up to 109,447 records of 49,656 Large White sows were used to evaluate the genetic relationship between number of pigs born dead (BD) and number born alive (BA) in first and later parities. Performance data (n = 30,832) for ultrasound backfat (BF) at the end of the test and days to reach 113.5 kg (AD) were used to estimate their relationships with BD and BA at first parity in a four-trait threshold-linear analysis (TL). Effects were year-farm, contemporary group (CG: farm-farrowing year-farrowing month) and animal additive genetic. At first parity, estimates of heritability were 0.09, 0.09, 0.37, and 0.31 for BA, BD, AD, and BF, respectively. The estimate of genetic correlation between BD and litter size was –0.04 (BD-BA). Corresponding values with test traits were both –0.14 (BD-AD, BD-BF). Estimates of genetic correlation between BA and performance traits were 0.08 (BA-AD) and 0.05 (BA-BF). The two test traits were moderately negatively correlated (–0.22). For later parities, a six-trait (BD, BA in three parities) TL model was implemented. The estimates of additive genetic variances and heritability increased with parity for BD and BA. Estimates of heritabilities were: 0.09, 0.10, and 0.11 for BD, and 0.09, 0.12, and 0.12 for BA in parities one to three, respectively. Estimates of genetic correlations between different parities were high (0.91 to 0.96) for BD, and slightly lower (0.74 to 0.95) for BA. Genetic correlations between BD and BA were low and positive (0.02 to 0.17) for BA in Parities 1 and 2, but negative (–0.04 to –0.10) for BA in Parity 3. Selection for increased litter size should have little effect on farrowing piglet mortality. Intense selection for faster growth and increased leanness should increase farrowing piglet mortality of first-parity sows. A repeatability model with a simple correction for the heterogeneity of variances over parities could be implemented to select against farrowing mortality. The genetic components of perinatal piglet mortality are independent of the ones for litter size in the first parity, and they show an undesirable, but not strong, genetic association in second parity.

Key Words: Backfat • Farrowing Mortality • Genetic Correlations • Growth • Litter Size


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results and Discussion
 Implications
 Literature Cited
 
Litter size is the most important trait to define sow productivity and, despite its low heritability, an increased emphasis on it was expected in selection programs (Ollivier, 1998Go). An antagonistic relationship between litter size and farrowing survival has been found (Leenhouwers et al., 1999Go; Lay et al., 2001Go; Knol et al., 2002Go), in part associated with crowded and prolonged farrowing. Surviving pigs from large litters tend to grow slower than those from small litters (Hogberg and Rydhmer, 2000Go), which indicates that survival should be considered when selecting for litter size to ensure greater productivity. On the other hand, farrowing survival tends to decrease in later parities, which seems associated with larger litters, prolonged farrowing and decreasing uterus quality (Leenhouwers et al., 1999Go; Knol et al., 2002Go; Damgaard et al., 2003Go). Selection of maternal lines should include piglet survival due to the genetic correlation among components of reproduction (litter size) and growth (lean growth) with survivability in pigs (Johnson et al., 1999Go; Rydhmer, 2000Go). Therefore, it is important to know the genetic determination of piglet mortality and its relationship with the primary traits for which selection is practiced to incorporate survival ability in the breeding program.

Most research has treated piglet mortality as a continuous trait, thereby ignoring its categorical nature. The threshold model takes into account the asymmetry and extreme incidence of some categories, as in the case of number of piglets born dead. There is little research regarding the use of multiple-trait threshold-linear (TL) models for litter size and piglet mortality across parities.

Our aim was to estimate variance components and genetic parameters for farrowing mortality and its relationship with litter size and test traits using a TL model on first-parity sows. Another objective was to estimate the genetic relationship of farrowing mortality and litter size across parities.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results and Discussion
 Implications
 Literature Cited
 
Data Origin
Field data were provided by Smithfield Premium Genetics Group (Roanoke Rapids, NC) from 22 pure-line Large White farms located in North Carolina, Oklahoma, Texas, Utah, Virginia, and Mexico. Litter records were available on number of piglets born alive (BA) and number of piglets born dead (BD) from sows born between 1992 and 2003. Additional off-test performance data were available for females with litter records. Performance traits were measured with off-test target ages of 140 or 170 d depending on the specific farm’s production protocol. Traits included weight and ultrasound backfat (BF) at the 10th and last ribs (Aloka model 500V real-time ultrasound, Corometrics Medical Systems, Wallingford, CT). Growth was expressed as days of age to reach 113.5 kg (AD) as determined by NSIF (1997)Go.

An initial dataset containing 130,559 records spanning up to four parities with first farrowing age between 265 and 519 d, and number piglets born alive between four and 22 was chosen. A first-parity data subset contained 48,487 records. Records discarded included 1,033 (2.13%) litter records with missing BD, and those from small contemporary groups (≤5). After edits, there were 47,454 litter and 30,832 test performance records from 967 and 760 contemporary groups, respectively.

A second data subset consisted of 109,447 records from up to three litters and 20 farms after editing, discarding records due to the same reasons mentioned above plus: 1) data from two farms with a small number of records poorly distributed across years or parities, and 2) records from sows born in 2003 that did not have the opportunity to perform in more than one parity. A preliminary analysis including four parities showed poor mixing properties for the last parity, due to a small number of records in that parity; therefore, only three parities were used in this study. They were grouped into 950, 912, and 850 contemporary groups (farm-year of farrowing-month of farrowing) in Parities 1 to 3, respectively. The pedigree file had 52,347 animals including 2,691 parents without records.

Statistical Analyses
Multiple-Trait Threshold-Linear Analysis of First-Parity Records.
Analyses were done with a four-trait model (BD-BA-AD-BF). Number of piglets born dead showed a strong asymmetrical distribution (i.e., approximately 52.1% of the litters had no BD piglets, 23.9 and 12.5% had one or two, whereas 11.5% of them had three or more). Therefore, BD was treated as a categorical trait with four categories, as listed above. Models for all traits (with BD considered on its underlying scale) included the nongenetic effects of contemporary group (farm-farrowing year-farrowing month for litter traits, and batch-farm-barn for test traits), the animal’s direct additive genetic effect, and the residual. Additionally, the model for backfat also incorporated the linear and quadratic regression on weight. The equation for the multiple trait linear model was


[1]

where y was the vector of number of piglets born alive, underlying values of piglets born dead, adjusted days to 113.5 kg and backfat; i represented different traits (i = 4); cgij was the contemporary group j for trait i; aik was the additive genetic effects for animal k and trait I; and e was the vector of residual effects.

Analyses were carried out using THRGIBBSF90 (Misztal et al., 2002Go), a program to estimate (co)variance components and genetic parameters as well as solutions for fixed and random effects in threshold animal mixed models, which allows for any combination of categorical and continuous traits (Lee et al., 2002Go). Post Gibbs analyses were done using POSTGIBBSF90, a program developed by S. Tsuruta (Misztal et al., 2002Go).

The analysis was run as a single chain of 200,000 cycles with a burn-in period of the first 20,000, at which a stationary stage was confirmed by graphical inspection, tracing plots of the sampled values vs. iterations (Kass et al., 1998Go). Every 20th sample was stored thereafter, for a total of 9,000 samples kept to compute posterior means, standard deviations, and credible regions. Point estimates of parameters were calculated as the posterior mean of the respective variance components. Starting values for (co)variance components were obtained from preliminary analysis using a linear model implemented with restricted maximum likelihood, as proposed by A. Gelman (Kass et al., 1998Go).

Multiple-Trait Threshold-Linear Analysis Across Parities.
Data exploration revealed an increase in piglet farrowing mortality (BD) with increasing parity, especially after the first litter (i.e., there was, on average, an extra piglet born dead in Parity 6 or later, than in Parity 1 or 2). There was an interest to determine whether piglet mortality in different parities is the same genetic trait. Therefore, a six-trait threshold-linear model was implemented to analyze farrowing piglet mortality and litter size simultaneously in parities one to three. The model included three categorical (BD) traits in first (BD1), second (BD2), and third (BD3) parity, and the corresponding three linear (BA) traits (BA1, BA2, and BA3). The nongenetic effects of farm, year and contemporary group, and the genetic animal’s additive effect were included in the final model.

(Co)variance components were estimated simultaneously for the TL combination of traits using the same procedure as described for first-parity records. This time, two chains were run using different sets of starting values to check for the length of the burn-in period (Garcia-Cortes et al., 1998Go). Thereafter, a single chain of 150,000 rounds was run, using a conservative burn-in period of the first 15,000. Every 10th sample was stored, and a total of 13,500 were kept to compute posterior estimates of variance components.


   Results and Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results and Discussion
Implications
 Literature Cited
 
Mean of Litter and Performance Traits
First-parity litters had, on average, 11.4 total piglets born, of which 10.5 were born alive and 0.96 were dead and "wet" behind the sow. On average, litters had 7.9% of their piglets born dead (Table 1Go). These values compared well with studies for litter traits in dual-purpose breeds reviewed by Rothschild and Bidanel (1998)Go. Moeller et al. (2004)Go reported means of 11.1, 10.0, and 0.77 for total born, born alive, and stillbirth, respectively, of first-parity litters from the National Pork Producers Council Maternal Line Genetic Evaluation Program. In the present study, first parity Large White sows averaged 180 d of age when they reached the standard 113.5 kg BW, and ended the performance test with 1.2 cm backfat. Litter size tended to increase with parity from 10.4 in the first litter to 11.5 in the third one (Table 1Go). These values are, on average, superior to the ones reviewed for litter traits in different parities of dual-purpose breeds (Rothschild and Bidanel, 1998Go) and also larger than the means (9.89 to 10.04) reported by Moeller et al. (2004)Go in the first three parities of six commercial lines derived primarily from Large White and Landrace. Farrowing mortality increased steadily with parity and represented approximately 10% of the piglets born alive in early parities. Other studies have reported increased farrowing piglet mortality in latter parities, which have been associated with larger litters, prolonged farrowing and decreasing uterus quality (Leenhouwers et al., 1999Go; Lay et al. 2001Go; Knol et al., 2002Go; Damgaard et al., 2003Go). An association between increased farrowing mortality and increased litter size has been reported in these and other studies (Robinson and Quinton, 2002Go).


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  		Table 1. Number of records, unadjusted means, and standard deviations for litter and test traits at first parity, and litter size and farrowing mortality in different parities
 
Multiple-Trait Threshold-Linear Analysis of First-Parity Records
Variance component and parameter estimates are shown in Table 2Go. Histograms of posterior distributions of the additive genetic variances for all the traits were quasi-normal (figure not shown), showing good agreement between the mode and the mean. Estimates of heritability were low for litter traits, and moderate for test traits, which agreed with previous literature reports. In a comprehensive literature review, Rothschild and Bidanel (1998)Go summarized mean heritability of 0.09 (96 studies) for number of piglets born alive. For the same trait, Rydhmer (2000)Go reported a median heritability of 0.1. For first-parity sows, Haley et al. (1988)Go calculated a weighted mean heritability of 0.084 for number born alive, similar to the 0.09 found here. Our results also agree with the breed-specific estimates reported for Yorkshire, Duroc, Hampshire and Land-race (0.10, 0.09, 0.08, and 0.08, respectively) by Chen et al. (2003aGo, b)Go in the Swine Testing and Genetic Evaluation System (STAGES).


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  		Table 2. Estimates of (co)variance components and genetic parameters for litter and test traits of first parity Large White sowsa
 
An estimate of heritability for number of born alive by Roehe (1999)Go was 0.08. Noguera et al. (2002)Go reported a mean heritability of 0.06 for number of piglets born alive in the first parity, lower than the one presented here. Hamann et al. (2004)Go reported low estimates of maternal (0.15) and paternal (0.05) heritabilities for number of piglets born alive for first parity German Landrace sows. Corresponding estimates for later parities (2 through 10) were even lower (0.11 and 0.03, respectively).

The heritability of the number of piglets born dead was 50% greater with the threshold model (0.09) than in preliminary analysis treating it as a linear trait (0.06) to reach the level of the estimates for litter size. Increased estimates of heritability in the threshold polychotomous scale with respect to the linear scale was described by Gianola (1979)Go. With categorical traits, estimated heritability is a function of incidences in the linear model but is not in the threshold model. Consequently, breeding values can be predicted more accurately when applying the threshold model for farrowing mortality especially when incidences change across farms and years.

Another study that reported estimates of heritability for piglet mortality (Grandinson et al., 2002Go) using the threshold model reported larger estimates with the threshold model than with the linear model. That study, however, estimated variance components at the piglet level; therefore, the categorical variable was binary (i.e., 0 or 1). In that study, heritability was estimated for stillbirth, crushing, total piglet mortality (the sum of the previous two), and birth weight. Estimates of heritability (based on sow component) with the threshold-linear model were larger for all traits, especially for the mortality traits, which were 0.03, 0.04, and 0.01 with the linear model, whereas they were 0.05, 0.15, and 0.06 with the TL model (Grandinson et al., 2002Go).

Most estimates of heritability for farrowing mortality or its complement piglet survival using linear models are lower than the values obtained here, ranging from 0.00 to 0.05 (Hanenberg et al., 2001Go; Grandinson et al., 2002Go; Knol et al., 2002Go). Earlier estimates of heritability for piglet survival also have been low, with a mean of 0.05 from 16 studies summarized by Rothschild and Bidanel (1998)Go. In an experiment to increase litter size (Johnson et al., 1999Go), a greater heritability (0.17) for number of stillborn pigs was found. Damgaard et al. (2003)Go reported an estimate of 0.13 for the proportion of stillborn piglets for Swedish Yorkshire sows. Values in the present study agree with estimates with transformed data using Snell scoring, which assumed a logistic distribution for records from Yorkshire sows (Robinson and Quinton, 2002Go).

Leenhouwers et al. (2003)Go described the application of selection for piglet farrowing survival based on breeding values obtained from a linear repeatability model, which treated still birth as a binary (0, 1) trait. They reported that the number of stillbirths significantly decreased (P < 0.01) with increasing breeding values for piglet survival. They preferred selection for the sow’s component because it does not affect litter size, while it does decrease the incidence of stillbirths. Such a result is encouraging, especially because with the linear model that they used (Knol et al., 2002Go), estimates of heritability for farrowing survival (<0.05) were lower than the estimates obtained here with a threshold model (0.09). Therefore, one could expect to predict breeding values more accurately and to obtain a better response to selection, when applying the latter analytical method to predict breeding values for farrowing mortality.

Estimates of heritability for performance-test traits of future sows were intermediate (0.3) for growth rate and backfat thickness of purebred Yorkshire sows maintained in nucleus herds of the Swedish pig-litter-recording scheme (Rydhmer et al., 1995Go), and for back-fat (0.35) in a Spanish Landrace line selected for litter size and backfat (Noguera et al., 2002Go). These results agree with estimates in the present study. Chen et al. (2003a)Go reported breed-specific estimates of heritability for backfat (0.47 to 0.50) and days to 113.5 kg (0.36 to 0.43) for Yorkshire, Duroc, Hampshire, and Landrace in the National Swine Registry (STAGES). A wide range of values have been reported depending on the population, breed, feeding regimen, and method of estimation, spanning from 0.0 to 0.74 in 16 and eight studies of pigs with restricted and ad libitum (average 0.49) or semi-ad libitum (average 0.31) access to feed, respectively (Clutter and Brascamp, 1998Go). Our heritability estimate for backfat is within the range of the 10% quantile (0.25) and the median (0.44) summarized from 38 literature sources by Peskovicová et al. (2002)Go.

The genetic correlation of number of piglets born alive with number of piglets born dead was low (–0.04). Genetic correlations between litter size and test traits were low. There was a negative genetic correlation between sows’ performance traits and number of piglets born dead in their first parity, being –0.14 and –0.14 for age to reach 113.5 kg and backfat, respectively. Results indicate that there is no evidence of a strong antagonistic genetic relationship of litter size with farrowing mortality or test performance.

An antagonistic relationship that exists between the sow’s growth and backfat performance and survival of their first litter piglets is not of important magnitude (i.e., genetic correlations –0.14) and, therefore, probably is not causing important increases in piglet mortality over the short term. The negative sign of these correlations, however, is consistent with previous research indicating that selection of pigs for production traits, such as decreased backfat, may decrease sow performance in the long term (Rauw et al., 1999Go; Lay et al., 2001Go). Rydhmer et al. (1995)Go reported a low and non-significant genetic relationship between backfat of future sows with litter size at first (–0.011 ± 0.10) and second parities (–0.06 ± 0.11). The correlation between backfat thickness and growth was positive (0.44), which would correspond to the negative correlation (–0.22) with age to 113.5 kg in the present study. Noguera et al. (2002)Go reported a low genetic correlation (–0.05) between number born alive and backfat in the first parity of a Spanish Landrace line. Corresponding estimates in later parities were also low (–0.14 to 0.14). Chen et al. (2003a)Go also reported low estimates of genetic correlation between number born alive and days to 113.5 kg (–0.04 to 0.05) and number born alive and backfat (0.18 to 0.20) in Yorkshire, Duroc, Hampshire, and Landrace from the National Swine Registry (STAGES). Our estimated genetic correlation between BA and BF (0.05) agrees with the median (0.06) from 23 studies summarized by Peskovicová et al. (2002)Go. There are no literature reports on the genetic relationship between sow performance on test and piglet survival.

Shifting selection emphasis to produce leaner sows may produce a long-term increase in piglet mortality at birth from first-parity sows. Lay et al. (2001)Go indicated that selection for decreased fatness results in decreased feed intake, which could result in sows consuming less food than needed to meet the requirements of a larger litter. Leenhouwers et al. (2002)Go reported that piglets with high genetic merit for survival had higher body fat percent at the end of gestation, which possibly contributed to better thermal insulation and prevented heat loss. They also reported a resemblance between litters with high breeding values for piglet survival and genetically obese pigs such as Meishan. Knol (2003)Go reported a moderate (0.5) genetic correlation between piglet survival and fatness, and stated that intense selection for leanness will decrease survival.

Multiple-Trait Threshold-Linear Analysis Across Parities
Posterior means of (co)variance components and parameter estimates are shown in Table 3Go. Distribution of additive genetic variances for number of BA and BD are quasi-normal (figure not shown), especially in the first two parities. The additive genetic variance increased with parity in a similar proportion for both traits. The estimates of heritability also increased with parity from 0.09 to 0.11 for piglet mortality and from 0.09 to 0.12 for litter size. Such heterogeneity suggests some systematic trend for the expression of additive genetic effects for litter traits in the progress of the sow’s reproductive life. Literature estimates for litter size by parity were summarized by Haley et al. (1988)Go, who calculated weighted means of 0.084, 0.087, and 0.094 in parities one to three from eight studies, and also found an increase of heritability with parity number. Peskovicová et al. (2002)Go summarized median heritability values of 0.11 and 0.10 for the same trait in first and second or later parities, from 25 and 5 literature reports, respectively. Hermesch et al. (2000)Go reported similar estimates of heritability (0.08 to 0.09) in Parities 1 to 3 for Australian Large White and Landrace sows. Hanenberg et al. (2001)Go reported values of 0.06, 0.04, and 0.08 from multivariate analyses from the first three parities of Dutch Landrace sows. Estimates were larger when they analyzed pooled data from Parities 2 to 6 (0.089) separately from first litter records (0.084) using a repeatability model. Estimates of heritability in the first three parities (0.064 to 0.090) of a Spanish Landrace line were slightly smaller than in the present study, but followed the same systematic increasing trend, which continued up to the sixth parity (0.146) (Noguera et al., 2002Go). In a recent study, Hamann et al. (2004)Go reported estimates of direct heritability of 0.149 and 0.107 for first and second or later (up to 10) parities of German Landrace sows.


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  		Table 3. Estimates of the posterior distribution of (co)variance components and parameters for farrowing mortality and litter size from six-trait analysis and first three parities of Large White sows by a threshold-linear modela
 
Only one literature report was found regarding estimates of heritability for farrowing piglet mortality in different parities (Hanenberg et al., 2001Go). However, it considered farrowing piglet mortality as a linear trait, and computed very low estimates of heritability in the first three parities (0.01 to 0.05) from multivariate analyses. Estimates with a repeatability model of Parities 2 to 6 (0.048) were also lower than the estimates reported here using a threshold model.

Posterior mean estimates of genetic correlations between BA across parities (Table 3Go) were lower between Parity 1 and 2 (0.74) and between Parity 1 and 3 (0.77) than between Parities 2 and 3 (0.95). Such results, along with the heterogeneity of variance, suggest that the genetic components of litter size at first parity are somewhat different from those in later parities. Haley et al. (1988)Go summarized the literature and obtained weighted mean genetic correlations of 0.89, 0.72, and 0.33 between first and later (up to fourth) parities. However, these estimates seem to be overestimated given that 43% of the reported values were out of the bounds of the parameter space (1.01 to 2.60), especially for the correlation between first and second parity. The authors also observed that these estimates could be biased due to selection at early parities (i.e., due to the use of analytical methods that do not take selection bias into account), and also by not restricting estimates to bounds of parameter space. Rydhmer et al. (1995)Go estimated a genetic correlation of 0.68 between number of piglets born alive in first and second parities in Swedish purebred Yorkshire sows. More recent estimates (Hermesch et al., 2000Go; Hanenberg et al., 2001Go) agree with our results, indicating a relatively low genetic correlation (<0.70) of Parity 1 with later parities. They concluded that the first parity is genetically different from later parities for number born alive in Australian Large White and Landrace (Hermesch et al., 2000Go), and for most of the seven reproduction traits considered in Dutch Landrace sows (Hanenberg et al., 2001Go). A study with a Spanish Landrace line found high genetic correlations (0.8 to 0.9) for number of piglets born alive between adjacent parities but genetic correlations tended to decrease as the number of parities increased up to six (0.5 to 0.8), and concluded that number of piglets born alive in different parities should be considered as different traits (Noguera et al., 2002Go). Similar results were reported between number of piglets born alive in first and second or later parities of Czech (r = 0.83) and Slovak (r = 0.78) sows of Landrace origin (Peskovicová et al., 2002Go). Hamann et al. (2004)Go reported a low maternal genetic correlation between number of piglets born alive in parity one and pooled Parities 2 to 10 of 0.646 (Landrace) and 0.462 (Pietrain). Our results are in agreement with other studies, and indicate that selection based only on first-parity records for litter size must be taken cautiously. The use of a multivariate model, instead of the repeatability model, should be preferred to predict breeding values for litter size. The latter would assume homogeneity of variances and perfect correlation across parities. A model discriminating between first and later (two or more) parities may be a good compromise between the repeatability vs. a more complex multivariate model.

Genetic correlations between farrowing mortality in different parities (Table 3Go) were high (>0.91), especially between adjacent parities (0.96), indicating that the genetic components for such a trait act in a similar way across parities and that a repeatability model may suffice for the prediction of breeding values for farrowing piglet survival ability. However, a simple adjustment for heterogeneity of variance would increase accuracy of breeding values for farrowing mortality, while keeping the model of prediction simple. Only one study has reported genetic correlations for piglet mortality (number of stillborn) across parities (Hanenberg et al., 2001Go). Their estimates ranged from 0.37 to 0.96 and were especially variable between first and later parities. However, that study treated piglet mortality as a continuous linear trait, and reported low estimates of heritability (0.01 to 0.05) in the first four parities.

Estimates of genetic correlations between BD and BA in different parities were low (Table 3Go) and erratic but close to zero in all parities. These results indicate that there is no evidence of an antagonistic genetic relationship between farrowing mortality and litter size in the first parity; however, there is an undesirable, but not strong, genetic association with litter size in second parity. Estimates in the third parity are close to zero but in a different direction (–0.04 to –0.10). Therefore, it is difficult to reach a conclusion about the nature of that weak genetic association. The only additional literature report that shows estimates of genetic correlations between litter size and farrowing mortality in different parities considered stillbirth as a linear trait and its correlation with total number of piglets born (Hanenberg et al., 2001Go). In that study, genetic correlations were intermediate (0.29 to 0.60), and phenotypic correlations were smaller (0.23 to 0.35) than the genetic ones across six parities. Other researchers have also found an unfavorable genetic relationship between neonatal survival and the number of total born using records from a specific parity or fitting a repeatability model (Hogberg and Rydhmer, 2000Go; Knol et al., 2002Go; Robinson and Quinton, 2002Go). However, moderate negative genetic (–0.24) and phenotypic (–0.22) correlations between the proportion of stillbirth and number of born alive were also reported (Damgaard et al., 2003Go). Therefore, there is no conclusive agreement about the nature of that genetic association.


   Implications
Top
 Abstract
 Introduction
 Materials and Methods
 Results and Discussion
 Implications
Literature Cited
 
There is no apparent antagonistic genetic relationship between litter size and piglet farrowing mortality. Selection for lean, fast growth may have a long-term negative effect on piglet survival ability. A repeatability model could be implemented, with an adjustment for the increase of additive genetic variance across parities, to predict breeding values to select for piglet survival ability. Selection for that trait is encouraging due to larger estimates of heritability obtained with the threshold model than with the traditional linear model. Birth-to-slaughter mortality in suboptimal commercial conditions is greater than in nucleus herds accounting for up to 30% of the total piglets born. The genetic relationship of that trait between pure-line sows and their crossbred relatives at the commercial level should be investigated. Because of the few realized categories and the extremely skewed distribution, number of piglets born dead can be analyzed as a categorical trait.


   Footnotes
 
2 On leave from Facultad de Ciencias Veterinarias, Universidad Central de Venezuela, Apartado, 4563, Maracay 2105, Aragua, Venezuela. Back

1 Correspondence: 306 Dept. of Anim. and Dairy Sci. (phone: 706-583-0250; fax: 706-583-0274; e-mail: arangoj{at}uga.edu).

Received for publication June 25, 2004. Accepted for publication November 30, 2004.


   Literature Cited
Top
 Abstract
 Introduction
 Materials and Methods
 Results and Discussion
 Implications
 Literature Cited
 


Chen, P., T. J. Baas, J. W. Mabry, and K. J. Koehler. 2003a. Genetic correlations between lean growth and litter traits in U.S. Yorkshire, Duroc, Hampshire, and Landrace pigs. J. Anim. Sci. 81:1700–1705.[Abstract/Free Full Text]

Chen, P., T. J. Baas, J. W. Mabry, K. J. Koehler, and J. C. M. Dekkers. 2003b. Genetic parameters and trends for litter traits in U. S. Yorkshire, Duroc, Hampshire, and Landrace. J. Anim. Sci. 81:46–53.[Abstract/Free Full Text]

Clutter, A. C., and E. W. Brascamp. 1998. Genetics of performance traits. Pages 427–462 in The Genetics of the Pig. M. F. Rothschild, and A. Ruvinsky, ed. CAB Int., Oxford, U.K.

Damgaard, L. H., L. Rydhmer, P. Lovendahl, and K. Grandinson. 2003. Genetic parameters for within-litter variation in piglet birth weight and change in within-litter variation during suckling. J. Anim. Sci. 81:604–610.[Abstract/Free Full Text]

Garcia-Cortes, L. A., M. Rico, and E. Groeneveld. 1998. Using coupling with the Gibbs sampler to assess convergence in animal models. J. Anim. Sci. 76:441–447.[Abstract/Free Full Text]

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