HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Serenius, T. Articles by Mäki-Tanila, A. Search for Related Content PubMed PubMed Citation Articles by Serenius, T. Articles by Mäki-Tanila, A.

Genetic associations of prolificacy with performance, carcass, meat quality, and leg conformation traits in the Finnish Landrace and Large White pig populations

MTT Agrifood Research Finland, Animal Production Research, 31600 Jokioinen, Finland

	Abstract

Top Abstract Introduction Material and Methods Results Discussion Implications Literature Cited

 
The objective of this study was to estimate genetic associations of prolificacy traits with other traits under selection in the Finnish Landrace and Large White populations. The prolificacy traits evaluated were total number of piglets born, number of stillborn piglets, piglet mortality during suckling, age at first farrowing, and first farrowing interval. Genetic correlations were estimated with two performance traits (ADG and feed:gain ratio), with two carcass traits (lean percent and fat percent), with four meat quality traits (pH and L* values in longissimus dorsi and semimembranosus muscles), and with two leg conformation traits (overall leg action and buck-kneed forelegs). The data contained prolificacy information on 12,525 and 10,511 sows in the Finnish litter recording scheme and station testing records on 10,372 and 9,838 pigs in Landrace and Large White breeds, respectively. The genetic correlations were estimated by the restricted maximum likelihood method. The most substantial correlations were found between age at first farrowing and lean percent (0.19 in Landrace and 0.27 in Large White), and fat percent (–0.26 in Landrace and –0.18 in Large White), and between number of stillborn piglets and ADG (–0.38 in Landrace and –0.25 in Large White) and feed:gain (0.27 in Landrace and 0.12 in Large White). The correlations are indicative of the benefits of superior growth for piglets already at birth. Similarly, the correlations indicate that age at first farrowing is increasing owing to selection for carcass lean content. There was also clear favorable correlation between performance traits and piglet mortality from birth to weaning in Large White (r_g was –0.43 between piglet mortality and ADG, and 0.42 between piglet mortality and feed:gain), but not in Landrace (corresponding correlations were 0.26 and –0.22). There was a general tendency that prolificacy traits were favorably correlated with performance traits, and unfavorably with carcass lean and fat percents, whereas there were no clear associations between prolificacy and meat quality or leg conformation. In conclusion, accuracy of estimated breeding values may be improved by accounting for genetic associations between prolificacy, carcass, and performance traits in a multitrait analysis.

Key Words: Carcass • Genetic Correlations • Leg Conformation • Performance • Pig • Prolificacy

	Introduction

Top Abstract Introduction Material and Methods Results Discussion Implications Literature Cited

 
During recent years, considerable effort has been given to determining the best ways to select for prolificacy in swine. Most of the studies have concluded that, in addition to selection for litter size, piglet survival should be accounted for in prolificacy selection (e.g., Knol, 2001). In Finland, the prolificacy index has been updated on the basis of our earlier results (Serenius et al., 2003, 2004). In the updated prolificacy index, selection is for high total number of piglets born, for low piglet mortality (number of stillborn piglets, and piglet loss during suckling), for low age at first farrowing, and for short farrowing interval. For further development, it is important to have a good understanding about genetic correlations between prolificacy and other important traits of pig production. The knowledge about genetic correlations is valuable for two reasons. First, accuracy of estimated breeding values might be improved by accounting for all correlations available in a multivariate BLUP analysis. Second, to develop an optimal total merit index, it is necessary to know genetic correlations between the traits.

In the literature, no clear consensus exists concerning genetic correlations between prolificacy and other important traits of pig production. For example, Rothschild and Bidanel (1998) pointed out that few genetic correlation estimates between reproductive and meat quality traits are available in the literature. In addition, estimates of genetic correlations between prolificacy and leg conformation traits were not found. Moreover, the correlations between prolificacy and carcass or performance traits seem to vary between populations (Rydhmer et al., 1995; Hermesch et al., 2000; Zhang et al., 2000). Therefore, the objective of the current study was to estimate genetic correlations of prolificacy with performance, carcass, meat quality, and leg conformation traits in the Finnish pig breeding populations.

	Material and Methods

Top Abstract Introduction Material and Methods Results Discussion Implications Literature Cited

 
Data

Two data sets were merged and then used to estimate genetic correlations of prolificacy with performance, carcass, meat quality, and leg conformation traits (Table 1). All the data were obtained from the Finnish Animal Breeding Association (FABA). Prolificacy data were from the litter recording scheme and included information on five traits: total number of piglets born (TNB), number of stillborn piglets (NSB), piglet mortality during suckling (PM), age at first farrowing (AFF, days), and first farrowing interval (FFI, days). Records from purebred Landrace (LR) and Large White (LW) first-parity litters born between 1991 and 2001 were used. For a more detailed description of prolificacy traits, see Serenius et al. (2004). Because of computational reasons, records only from farms with more than 80 litters per year were included.

The final LR data set contained 12,525 sows with prolificacy records and 10,372 pigs with station test records (Table 1). The corresponding numbers were 10,511 and 9,838 for LW. Because the records used were only from purebred litters, only about half the sows had FFI records. The station test groups descended from 720 LR and 665 LW sires, and from 3,555 LR and 3,390 LW dams. Approximately half the sires (354 LR and 336 LW) of station test groups had at least one daughter in the prolificacy data, and similarly approximately half the dams (1,226 LR and 1,693 LW) of the test groups had their own record in the prolificacy data. Pedigrees were traced back to 1980 and resulted in 34,089 LR and 29,641 LW pigs in the pedigree.

Statistical Analysis

Genetic correlations between the prolificacy and other economically important traits of pig meat production were analyzed using restricted maximum likelihood by the DMU package (Madsen and Jensen, 2002). All analyses were carried out with trivariate models, where AFF was always included (i.e., the effect of AFF on other prolificacy traits was accounted through correlations). The statistical model applied to the prolificacy traits was

where y_ijklm is an observation of a sow trait, hy_i is the fixed effect of herd and year combination (i = 1 to 343 for LR and i = 1 to 282 for LW), month_j is the fixed effect of farrowing month (j = 1 to 12), AI_k is the fixed effect of mating type (artificial insemination or natural mating), a_l is the additive genetic effect of an animal (the sow), and e_ijklm is the residual effect. In addition to these effects, the fixed effect of weaning age (1 = under 3 wk; 2 = 3 to 4 wk; 3 = 4 to 5 wk; 4 = 5 to 6 wk; 5 = 6 to 7 wk; and 6 = over 7 wk) was included in the statistical model for PM and FFI. Moreover, the linear regression of litter size (TNB) was included in the statistical model for NSB and PM; that is because an unfavorable phenotypic correlation exists between TNB and piglet survival (Serenius, et al., 2004). The effect of mating type was not included in the statistical models for AFF and FFI.

Statistical models for: 1) performance and carcass, 2) meat quality, and 3) leg conformation traits were identical to those used in the breeding value estimation in Finland:

where ssy_i is the fixed effect of station, season, and year combination (i = 1 to 311 for LR and i = 1 to 282 for LW), age_j is the fixed effect of age of an animal at the beginning of test period (1 = under 66 d; 2= 66 to 72 d; 3 = 73 to 78 d; 4 = 79 to 84 d; and 5 =over 84 d), and sex_k is the fixed effect of sex of an animal (female, castrate, boar), time_l is the fixed effect of time from slaughtering to measuring of a trait (days), and l_m is the effect of litter. The effects of animal, litter, and residual were assumed random with zero means and variances A G₀, I L₀, and I R₀, where I is identity matrix; A is additive genetic relationship matrix between animals; and L₀, G₀, and R₀ are (co)variance matrices of the traits for litter, additive genetic, and residual effects, respectively. Because all females were slaughtered at the end of the station testing period, a pig had either a prolificacy or a station testing record in the data. For this reason, residual (and phenotypic) correlations between prolificacy and other traits studied were not estimable.

	Results

Top Abstract Introduction Material and Methods Results Discussion Implications Literature Cited

 
Estimated genetic correlations of the prolificacy traits with the performance, carcass, meat quality, and leg conformation traits are presented in Tables 2 and 3. The correlation estimates varied from low to moderate, and had high standard errors. High standard errors are most probably due to low heritabilities of the studied prolificacy traits (Table 1); however, both favorable and unfavorable associations between prolificacy and the other traits studied were found. Approximately, half (28 in Landrace and 27 in Large White) of the 50 estimates were favorable and the other half unfavorable.

Genetic associations of TNB and FFI with performance traits differed between breeds (Tables 2 and 3). The genetic correlations estimated were moderate and favorable between FFI and the performance traits in LR (–0.35 with ADG and 0.32 with feed:gain), whereas they were low and unfavorable in LW (0.02 and –0.14). The correlations between TNB and the performance traits were favorable in LR and unfavorable in LW. The correlation estimates were close to zero between AFF and the performance traits in both breeds.

Nearly all (8 of 10 estimates in both breeds) of the estimated correlations between the prolificacy and carcass (fat and lean percents) traits were unfavorable (Tables 2 and 3). The only favorable correlations were between PM and the carcass traits; the absolute values of the favorable estimates ranged from 0.07 to 0.15. The estimated correlations between prolificacy and carcass lean and fat percents had the same sign in both breeds.

Approximately half (11/20 in Landrace, Table 2, and 13/20 in Large White, Table 3) of the signs of the estimated correlations between the prolificacy and meat quality traits were favorable. There were also some breed differences in the correlation estimates. In addition, the estimates had high standard errors. Associations between the meat quality and prolificacy traits were not strong. Although there were no clear associations between the prolificacy and meat quality traits, some single correlation estimates were higher than their standard errors, with absolute values of the estimates ranging from 0.01 to 0.75. Based on these moderate estimates and general tendency of signs of the estimates, it seems that meat quality may be favorably correlated with AFF and PM, whereas the correlation between meat quality and TNB seems unfavorable.

Six of 10 correlation estimates between the prolificacy and leg conformation traits were favorable in both breeds (Tables 2 and 3); however, the magnitude of these estimates differed between the breeds, and they had high standard errors. The correlations between overall leg action and FFI (–0.41 in Landrace and –0.27 in Large White) were the only estimates that were higher than their standard errors for both breeds. The extremely high correlations between FFI and buck-kneed forelegs in Large White (0.99), and between FFI and lean percent in Landrace (0.95) cannot be considered reliable.

	Discussion

Top Abstract Introduction Material and Methods Results Discussion Implications Literature Cited

 
The objective of the current study was to estimate genetic correlations of different prolificacy traits with the other important traits in the Finnish pig breeding program. The prolificacy traits were treated as a trait of a sow. Heritability estimates were also obtained. In general, the estimated heritabilities were moderate for performance, carcass quality, and meat quality traits, and low for the other traits studied. These heritability estimates were in agreement with our earlier results. For further discussion about the heritabilities of the traits studied, see Serenius et al. (2001, 2004).

In general, prolificacy traits tended to be favorably correlated with performance traits and unfavorably correlated with carcass traits, whereas no clear association with meat quality or leg scores was found. Genetic correlations between the two performance traits (ADG and feed:gain) and, similarly, between two carcass traits (fat and lean percents) were highly negative. Therefore, the genetic associations of ADG and feed:gain with the prolificacy traits and, similarly, the genetic associations of fat percent and lean percent with the prolificacy traits are very similar, but with opposite signs.

Correlations with Performance and Carcass Traits

The current estimates indicate that ADG and feed:gain are favorably associated with piglet survival, and thus they are in agreement with the results presented by Zhang et al. (2000). Similarly, Knol (2001) concluded that selection for piglet survival (direct genetic effect) will increase ADG. Thus, it seems that pigs with genetically high growth potential and feed efficiency are more likely to survive until weaning than the ones with low growth potential and poor feed efficiency; however, the correlations between PM and the performance traits were unfavorable in LR. When NSB is recorded before, and PM after farrowing, it can be concluded that, in LR, high growth potential is beneficial for piglets before but not after farrowing. That raises question about the mothering ability of LR sows. For example, it is possible that the milking capability may be lower in LR than in LW sows, or there could be problems in milking of some families of LR sows. The results obtained by Lund et al. (2002) also raise the same question. They found an unfavorable genetic correlation between direct (piglet) and maternal (sow) effects in proportion of live pigs born that were alive at 3 wk in a Finnish Landrace population (–0.41 ± 0.09), whereas the corresponding correlation was close to zero in a Finnish Large White population (0.16 ± 0.50). In any case, it seems that there are some differences in genetics of PM between the LR and LW breeds. We have found between breed differences in genetic variation for other traits as well (Serenius et al., 2004).

All of the correlation estimates between the prolificacy and carcass traits had the same sign in both breeds, and nearly all the estimates were unfavorable. That tendency is in agreement with the studies by Knol (2001) and Zhang et al. (2000). This is undesirable because carcass traits are among the most economically important traits of pig meat production (e.g., Ollivier, 1998). In the literature, many studies have concluded that there is zero or only slight unfavorable correlation between litter size and performance or carcass traits (Rydhmer et al., 1995; Estany et al., 2002a; Noguera et al., 2002; Chen et al., 2003). Correlation estimates in the present study between TNB and performance, and similarly, between TNB and carcass traits seem to be slightly unfavorable, and thus are in agreement with the literature.

It is interesting to note that AFF is correlated with carcass traits (unfavorably) but not with ADG. It is plausible that sows having superior growth rate will reach their puberty at an earlier age, and, thus, the correlation between ADG and AFF should be negative (i.e., favorable). On the other hand, the observed trends in Finnish Landrace and Large White populations support our estimates. Estimated genetic trends during the last 10 yr are 110 g/d for ADG and 4% for lean meat content, whereas AFF has increased simultaneously as a correlated character (FABA, 2002). In addition, according to the review by Schinkel (1999), there are some minimum requirements for live weight and fat mass before sexual maturity can occur.

Contrary to our results, Rydhmer et al. (1995) showed that the genetic correlation between AFF and ADG was moderately or highly negative, whereas the correlation between AFF and backfat thickness was small (–0.16). However, backfat thickness was clearly higher in their population (mean = 11.9 mm) than in the current Finnish LR and LW populations (mean in 2001 was 8.4 mm; FABA, 2002). Thus, it seems that fat mass may be the limiting factor for sexual maturity in the current populations, whereas live weight may have been the limiting factor in the population studied by Rydhmer et al. (1995). Backfat thickness differences may be due to differences in feeding strategies, or simply differences in the genetic predisposition for backfat deposition of the populations evaluated in the various studies.

Correlations with Meat Quality

In general, the estimated correlations between the prolificacy and meat quality traits had high standard errors and were quite different between the breeds. For example, none of the estimates had the same magnitude and smaller standard error than the estimate itself for both breeds. However, the general tendencies of the associations of the meat quality traits with PM and AFF were favorable, whereas there might be slight unfavorable association between TNB and meat quality. In Finland, the objective is to select for higher pH and lower L* (darker meat), and, thus, the unfavorable association leads to lower pH and lighter meat, and vice versa.

Biologically it is difficult to imagine high genetic associations between the prolificacy and meat quality (pH, L*) traits. In addition, meat quality and prolificacy traits have not been found to be correlated in earlier studies (Hermesch et al., 2000; Estany et al., 2002b); however, in the long run, simultaneous selection for two uncorrelated traits leads to unfavorable correlation owing to faster fixation of favorable gene pairs than unfavorable gene pairs (Falconer and Mackay, 1996). In Finland, both TNB and meat quality have been under selection since 1990, and unfavorable correlations may therefore be expected according to that theory. Conversely, the selection for meat quality has not been very intensive during these 12 yr; it is thus unlikely to be the explanation for the slight tendencies observed in our study. Overall, the correlations between prolificacy and meat quality are very close to zero; therefore, simultaneous genetic gain for both traits is possible if at least some selection is placed on each trait in a selection index program.

Correlations with Leg Conformation

In Finland, selection against leg weakness is carried out through selection for overall leg action and against buck-kneed forelegs (Serenius et al., 2001). The objective is to select indirectly for the longevity and well-being of animals. In addition, there is a belief in the field that selection for leg conformation will indirectly increase piglet survival (i.e., sows without leg problems are not crushing their piglets). One would therefore expect that the genetic correlation between leg conformation and piglet survival traits should be favorable; however, such a clear association between piglet survival and leg conformation was not supported by our study. On the other hand, the tendency was rather favorable than unfavorable because the correlation estimates between overall leg action and NSB in LR and between BK and PM in LW were favorable and higher than their standard errors. It is also possible that a high environmental correlation might exist. Phenotypic correlation may be high although genetic correlation is close to zero; however, based on our results, it seems that piglet survival is genetically more associated with performance traits than with leg conformation.

We were unable to identify previously published estimates for genetic correlations between prolificacy and leg conformation traits. In general, the genetic correlations estimated between leg conformation and prolificacy traits were quite different between the two breeds involved in this study and they had high standard errors. Because there were no strong unfavorable associations between the traits, simultaneous selection could be practiced without a decrease in genetic gain owing to unfavorable correlations. The moderate favorable correlation between overall leg action and FFI was the only estimate that had higher value than its standard error for both breeds in this study. This estimate is in agreement with the practical experience that heat symptoms are difficult to see from the sows having difficulties in standing as a result of poor legs.

	Implications

Top Abstract Introduction Material and Methods Results Discussion Implications Literature Cited

 
Current results indicated that carcass traits are unfavorably correlated and performance traits are favorably correlated with prolificacy traits. Thus, prolificacy, performance, and carcass traits should be taken into account in the same index to control the genetic changes owing to correlations. In addition, accuracy might increase by taking these correlations into account in the breeding value estimation. Prolificacy traits are not significantly associated with meat quality or leg conformation traits, and, thus, simultaneous selection could be practiced without a decrease in genetic gain owing to unfavorable correlations.

¹ Correspondence—phone: +358 3 41881; fax: +358 3 4188 3618;e-mail: timo.serenius{at}mtt.fi.

Received for publication December 18, 2003. Accepted for publication May 10, 2004.

	Literature Cited

Top Abstract Introduction Material and Methods Results Discussion Implications Literature Cited

 


Chen, P., T. J. Baas, J. W. Mabry, and K. J. Koehler. 2003. 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]

CIE. 1971. Calorimetry, official recommendations of the international commission on illumination. Pub. No. 15 (E-1.3.1), Paris, France.

Estany, J., D. Villalba, J. Tibau, J. Soler, D. Babot, and J. L. Noguera. 2002a. Correlated response to selection for litter size in pigs: I. Growth, fat deposition, and feeding behavior traits. J. Anim. Sci. 80:2556–2565.[Abstract/Free Full Text]

Estany, J., D. Villalba, J. M. Tor, D. Cubiló, and J. L. Noguera. 2002b. Correlated response to selection for litter size in pigs: II. Carcass, meat, and fat quality traits. J. Anim. Sci. 80:2566–2573.[Abstract/Free Full Text]

FABA. 2002. Kotieläinjalostuksen tilastokirja (Animal Breeding Statistics). Finn. Anim. Breed. Assoc., Vantaa, Finland.

Falconer, D. S., and T. F. C. Mackay. 1996. Introduction to Quantitative Genetics. 4th ed. Longman, Essex, U.K.

Hermesch, S., B. G. Luxford, and H.-U. Graser. 2000. Genetic parameters for lean meat yield, meat quality, reproduction and feed efficiency traits for Australian pigs. 3. Genetic parameters for reproduction traits and genetic correlations with production, carcass and meat quality traits. Livest. Prod. Sci. 65:261–270.[Medline]

Knol, E. F. 2001. Genetic aspects of piglet survival. Ph.D. Diss., Wageningen University, The Netherlands.

Madsen, P., and Jensen. J. 2000. A user’s guide to DMU. A package for analysing multivariate mixed models. Mimeo. Danish Institute of Agricultural Sciences, Tjele.

Noguera, J. L., L. Varona, D. Babot, and J. Estany. 2002. Multivariate analysis of litter size for multiple parities with production traits in pigs: I. Bayesian variance component estimation. J. Anim. Sci. 80:2540–2547.[Abstract/Free Full Text]

Ollivier, L. 1998. Genetic improvement of the pig. Pages 511–540 in the Genetics of the Pig. M. F. Rothschild and A. Ruvinsky, ed. CAB International, University Press, Cambridge, U.K.

Rothschild, M. F., and J. P. Bidanel. 1998. Biology and genetics of reproduction. Pages 313–343 in the Genetics of the Pig. M. F. Rothschild and A. Ruvinsky, ed. CAB International, University Press, Cambridge, U.K.

Rydhmer, L., N. Lundeheim, and K. Johansson. 1995. Genetic parameters for reproduction traits in sows and relations to performance-test measurements. J. Anim. Breed. Genet. 112:33–42.

Schinkel, A. P. 1999. Describing the pig. Pages 9–38 in a Quantitative Biology of the Pig. I. Kyriazakis, ed. CAB International, University Press, Cambridge, U.K.

Serenius, T., M.-L. Sevón-Aimonen, and E. A. Mäntysaari. 2001. The genetics of leg weakness in Finnish Large White and Landrace populations. Livest. Prod. Sci. 69:101–111.

Serenius, T., M.-L. Sevón-Aimonen, and E. A. Mäntysaari. 2003. Effect of service sire and validity of repeatability model in litter size and farrowing interval of Finnish Landrace and Large White populations. Livest. Prod. Sci. 81:213–222.

Serenius, T., M.-L. Sevón-Aimonen, A. Kause, E. A. Mäntysaari, and A. Mäki-Tanila. 2004. Selection potential of different prolificacy traits in the Finnish Landrace and Large White populations. Acta Agric. Scand. A Anim. Sci. 54:36–43.

Zhang, S., J.-P. Bidanel, T. Burlot, C. Legault, and J. Naveau. 2000. Genetic parameters and genetic trends in the Chinese x European Tiameslan composite pig line. I. Genetic parameters. Genet. Sel. Evol. 32:41–56.[Medline]

This article has been cited by other articles:

				M. Koivula, I. Stranden, and E. A. Mantysaari Direct and maternal genetic effects on first litter size, maturation age, and animal size in Finnish minks J Anim Sci, October 1, 2009; 87(10): 3083 - 3088. [Abstract] [Full Text] [PDF]

				X. F. de Sevilla, E. Fabrega, J. Tibau, and J. Casellas Genetic background and phenotypic characterization over two farrowings of leg conformation defects in Landrace and Large White sows J Anim Sci, May 1, 2009; 87(5): 1606 - 1612. [Abstract] [Full Text] [PDF]

				A. Rosendo, L. Canario, T. Druet, J. Gogue, and J. P. Bidanel Correlated responses of pre- and postweaning growth and backfat thickness to six generations of selection for ovulation rate or prenatal survival in French Large White pigs J Anim Sci, December 1, 2007; 85(12): 3209 - 3217. [Abstract] [Full Text] [PDF]

				N. Imboonta, L. Rydhmer, and S. Tumwasorn Genetic parameters for reproduction and production traits of Landrace sows in Thailand J Anim Sci, January 1, 2007; 85(1): 53 - 59. [Abstract] [Full Text] [PDF]

				T. Serenius and K. J. Stalder Genetics of length of productive life and lifetime prolificacy in the Finnish Landrace and Large White pig populations J Anim Sci, November 1, 2004; 82(11): 3111 - 3117. [Abstract] [Full Text] [PDF]

This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Serenius, T. Articles by Mäki-Tanila, A. Search for Related Content PubMed PubMed Citation Articles by Serenius, T. Articles by Mäki-Tanila, A.