J. Anim. Sci. 2006. 84:E166-E171
© 2006 American Society of Animal Science
Selection for sow longevity1,2
T. Serenius3 and
K. J. Stalder
Department of Animal Science, Iowa State University, Ames 50011
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Abstract
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Sow longevity plays an important role in economically efficient piglet production because sow longevity is related to the number of piglets produced during its productive lifetime; however, selection for sow longevity is not commonly practiced in any pig breeding program. There is relatively little scientific literature concerning the genetic parameters (genetic variation and genetic correlations) or methods available for breeding value estimation for effective selection for sow longevity. This paper summarizes the current knowledge about the genetics of sow longevity and discusses the available breeding value estimation methods for sow longevity traits. The studies in the literature clearly indicate that sow longevity is a complex trait, and even the definition of sow longevity is variable depending on the researcher and research objective. In general, the measures and analyses of sow longevity can be divided into 1) continuous traits (e.g., productive lifetime) analyzed with proportional hazard models; and 2) more simple binary traits such as stayability until some predetermined fixed parity. Most studies have concluded that sufficient genetic variation exists for effective selection on sow longevity, and heritability estimates have ranged between 0.02 and 0.25. Moreover, sow longevity has shown to be genetically associated with prolificacy and leg conformation traits. Variable results from previous research have led to a lack of consensus among swine breeders concerning the valid methodology of estimating breeding values for longevity traits. One can not deny the superiority of survival analysis in the modeling approach of longevity data; however, multiple-trait analyses are not possible using currently available survival analysis software. Less sophisticated approaches have the advantage of evaluating multiple traits simultaneously, and thus, can use the genetic associations between sow longevity and other traits. Additional research is needed to identify the most efficient selection methods for sow longevity. Future research needs to concentrate on multiple trait analysis of sow longevity traits. Moreover, because longevity is a fitness trait, the nonadditive genetic effects (e.g., dominance) may play important role in the inheritance of sow longevity. Currently, not a single estimate for dominance variance of sow longevity could be identified from the scientific literature.
Key Words: survival analysis • swine
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INTRODUCTION
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Sow longevity plays an important role in piglet production for several reasons. First, length of productive life is directly related to the number of piglets produced during a sow’s productive lifetime. Second, the risk of introducing diseases to the sow facility increases with high replacement rates due to the introduction of an increased number of replacement gilts. Third, it is not ethically acceptable from an animal welfare perspective to base pork meat production from sows that are not capable of handling the physiological stress of piglet production over numerous parities. Thus, both management practices of commercial producers and breeding programs should focus on improving sow longevity.
Reliable breeding value estimation is a base for efficient selection for improving all economically important swine production traits; however, currently, no consensus exists about proper methods to estimate breeding values for sow longevity. Alternatives for breeding value estimation of sow longevity can roughly be divided into two types. The first type uses survival analysis, whereas the second type uses different modifications of linear model analysis.
Heritability and genetic correlation estimates are needed to determine which of these two approaches may be most efficient when estimating breeding values for sow longevity. Naturally, high genetic association between sow longevity and other economically important traits in a pig breeding program highlights the importance of multiple-trait evaluation and vice versa. Moreover, based on knowledge about genetic parameters, it is possible to estimate changes in sow longevity due to selection for other economically important traits, such as carcass quality (percentage of lean meat and backfat thickness). Thus, the objective of current paper is to review the current knowledge concerning the genetics of sow longevity, to discuss breeding value estimation methodology for sow longevity, and to recommend needed future genetic-based research on sow longevity.
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DIFFERENT ALTERNATIVES FOR BREEDING VALUE ESTIMATION OF SOW LONGEVITY
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As mentioned above, alternatives for breeding value estimation for sow longevity traits can roughly be divided into two groups: survival analysis, and different modifications of linear model analysis. Survival analysis is based on proportional hazard models, in which baseline hazard is most commonly assumed to follow a Weibull distribution (Ducrocq and Sölkner, 1998
;Yazdi et al., 2000a,b;Serenius and Stalder, 2004). Breeding values, and similarly other effects in a statistical model, are generally presented with a hazard rate indicating proportional risk of sows being culled at any given time. In practice, proportional risks of being culled may be difficult to interpret; however, the hazard function [h(t)] and survival function [S(t)] are linked with each other, and thus, survival function can be defined from hazard function (Ducrocq, 2001):S(t) = exp[–H(t)], whereH(t) is cumulative hazard function
h(u)du. In that case, estimated breeding values are presented in the units of original time scale, and for example, median survival time appears at the point whenS(t) = 0.5. In the animal breeding literature, a Fortran package called the Survival Kit (Ducrocq and Sölkner, 2001) is often used in longevity analysis of many species.
Survival analysis is considered theoretically superior in longevity analysis (e.g.,Caraviello et al., 2004;Serenius, 2004). This approach properly accounts for censored observations, nonnormal distribution, and model time-dependent effects. However, only single-trait analysis is possible when using the Survival Kit. Linear model analysis, instead, is capable of implementing multiple-trait models. These models are based either on analysis of length of productive life (VanRaden and Klaaskate, 1993;Guo et al., 2001;Caraviello et al., 2004;Serenius and Stalder, 2004) or stayability, which is recorded as a binary variable indicating whether an animal has reached some fixed time period; for example, a sow reaching some fixed parity (Tholen et al., 1996a,b;Boettcher et al., 1999;López-Serrano et al., 2000).Veerkamp et al. (1999) andMeuwissen et al. (2002) extended stayability models to incorporate repeated records, in which the animal is determined to be alive or dead at any given time. The repeated records generated can then be analyzed using repeatability or random regression models. In linear model analysis, breeding values are naturally interpreted in the units of original variable.
Based on the available scientific literature, it is very difficult to conclude what is the best way to estimate breeding values for longevity. In dairy cattle, breeding values from survival analysis and single-trait linear model analysis accounting for the same amount of information have been very closely (0.90 to 0.94) correlated (Vollema and Groen, 1998;Boettcher et al., 1999). However, correlations have been somewhat lower (0.65 to 0.71) in the comparison of breeding values from survival analysis when accounting for censoring and linear model analysis using only complete records (Vollema and Groen, 1998). Moreover,Serenius and Stalder (2004) compared sow longevity breeding values from survival analysis and 7-trait linear model analysis with only complete records. They found relatively low correlations (0.40 to 0.72), indicating that considerable re-ranking of breeding values existed between these two methods; however, it should be remembered that the correlation itself is not indicating which method is better when predicting the longevity of progeny. More research is needed to compare predictive ability of breeding values of sow longevity from multiple-trait linear model analysis and survival analysis.
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GENETIC VARIATION
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Heritability estimates presented in the literature indicate that it is possible to select for sow longevity (Table1); however, the magnitude of heritability estimates vary between different sow longevity trait definitions and among populations studied. Sow longevity heritability estimates from survival analysis have ranged between 0.11 and 0.31 (Yazdi et al., 2000a,b;Serenius and Stalder, 2004) and between 0.02 and 0.11 in a linear model analysis of stayability (Tholen et al., 1996a,b;López-Serrano et al., 2000). Moreover,Serenius and Stalder (2004) analyzed length of productive life with survival analysis and a linear model. They found that linear model estimates of heritability (0.05 to 0.10) were clearly lower than those obtained from survival analysis (0.16 to 0.19).
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Table 1. Heritability estimates (h2) and genetic correlations with sow longevity (rg) for stayability, length of productive life (LPL), and some traits associated with sow longevity1
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Guo et al. (2001) studied the effect of different censoring rates on the heritability estimates of lifetime prolificacy and length of productive life by using censored records in linear model analysis. Heritability estimates seemed to decrease with increasing censoring rate. For example, the estimated heritabilities with a 35% censoring rate were 0.16 and 0.15 for lifetime prolificacy and length of productive life, respectively. The corresponding heritabilities with only a 15.5% censoring rate were 0.25 and 0.22. Their results also showed that lifetime prolificacy is slightly less heritable (h2 ranged from 0.17 to 0.25) compared with length of productive life (h2 ranged from 0.16 to 0.34), which agrees with the results obtained bySerenius and Stalder (2004).Serenius and Stalder (2004) also showed that these two traits are genetically very highly correlated (rg > 0.95). Thus, genetic gain in length of productive life through selection will result in direct genetic gain lifetime prolificacy and vice versa.
Commercial pork production in most operations is based on using crossbred sows as parental females; however, commercial producers and swine geneticists should keep in mind that the goal of breeding for sow longevity is to genetically improve the length of productive life for both purebred and crossbred sows. That goal raises questions concerning the nonadditive genetic variation and the proper crossbred model that should be implemented when estimating breeding values for sow longevity. Currently, no estimates of nonadditive effects of sow longevity are available in the scientific literature; however,Lutaaya et al. (2001) found a moderate proportion of dominance genetic variance for ADG, whereas the dominance variation for backfat thickness was close to zero. Thus, their research indicated that nonadditive genetic effects should be accounted for in the breeding value estimation of some economically important pork production traits. Because sow longevity is a so-called fitness trait, one may expect dominance to play a significant role in its inheritance (Falconer and Mackay, 1996), and this is supported by relatively high proportions of dominance variance (0.19 to 0.52) estimated for length of productive life and lifetime production of dairy cattle (Fuerst and Sölkner, 1994). Further research is needed to determine the existence of nonadditive genetic variation for sow longevity or some similarly defined trait.
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EARLY INDICATORS OF SOW LONGEVITY
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Because sow longevity information can be recorded only after the sow has been culled or has died, the selection decision for sow longevity must be carried out using information on relatives (through pedigree). Thus, traits recorded earlier in life and that are highly genetically correlated with sow longevity may improve the reliability of estimated breeding values of sow longevity. Prolificacy and leg conformation traits are considered as candidate traits for such early indicators of sow longevity (Serenius, 2004).
Prolificacy Traits
A review byStalder et al. (2004) demonstrated that reproductive failure is the predominant reason for early culling of young sows from commercial pork operations. Because the farrowing interval increases due to problems in rebreeding sows after weaning, it is not surprising that a moderate negative genetic correlation (–0.40 and –0.43) was found between farrowing interval and length of productive life in the Finnish Landrace and Large White populations (Serenius and Stalder, 2004). Similarly,Tholen et al. (1996b) reported that the genetic correlation between farrowing interval and stayability ranged between –0.24 and –0.54, andTantasuparuk et al. (2001) reported that sows having longer than a 30-d weaning-to-estrus interval had a 1.7 times greater risk of being culled than sows with a weaning-to-estrus interval lower than 4 d.
Several traits and trait definitions exist that indicate various reproductive problems, such as farrowing interval, interval from weaning to estrus or conception, and return rate. Heritability estimates for these traits were reviewed byRydhmer (2000), who pointed out that weaning-to-estrus interval had a higher heritability (0.20 to 0.30) compared with heritability estimates for the intervals from weaning to conception, weaning to farrowing, and farrowing interval (h2 = approximately 0.10). These differences may be due to very low heritability (0.00 to 0.04) of return rate (Leukkunen, 1984;Brandt and Grandjot, 1998;Hanenberg et al., 2001;Varona and Noguera, 2001;Holm et al., 2005).
Serenius et al. (2005) reported that low feed intake and high backfat loss during lactation were detrimental to future sow longevity. Thus, it seems that these two measurements are indicative of subsequent reproduction problems, which is in agreement with the high genetic and residual correlations between weaning-to-service interval and following return rate (rg = 0.93; re = 0.79) reported byHolm et al. (2005). In other words, the high correlations indicate that the sows with an extended weaning-to-estrus interval also will most likely have problems with conception. However, there is no information regarding the genetics of feed intake and backfat loss during lactation available in the literature. Thus, the association between feed intake and backfat loss during lactation with sow longevity needs further research.
Association between litter size and sow longevity seems to play a role in culling decisions. Based on analysis of field datasets, litter size is associated with sow longevity (Table1
), whereas it was statistically significantly associated with length of productive life only in 2 out of 6 lines in the study reported bySerenius et al. (2005). In the later data, culling of sows due to poor production was not allowed until the fourth parity, and, as a result, the data did not include associations due to culling decisions. Thus, based on differences among the studies inTable1, one can raise a question whether the associations between litter size and sow longevity found in studies based on field data are due to autocorrelation (i.e., by the fact that farmers are not retaining or keeping sows that produce small litters).
Leg Conformation
After reproductive failure, leg weakness is the next most common reason for involuntary culling of sows (Stalder et al., 2004). However, the genetic correlations between sow longevity and leg conformation seems to vary depending on the population that is evaluated. For example, a moderate (0.32) genetic correlation was noted between overall leg action and length of productive life in a Finnish Landrace population, whereas the corresponding correlation in Finnish Large White population was lower (0.17; (Serenius and Stalder, 2004). Similarly, the correlation between leg score and stayability ranged between 0.19 and 0.36 in the German Landrace population, but was close to zero in a German Large White population (López-Serrano et al., 2000). In Sweden, however, the associations between osteochondrosis and length of productive life were of the same magnitude (0.08 to 0.12) in Landrace and Yorkshire populations (Yazdi et al., 2000a).
Heritability estimates for leg conformation traits presented in the literature have ranged from very low (0.01) to moderate or even relatively large (approximately 0.40;Bereskin, 1979;Webb et al., 1983;Jörgensen and Vestergaard, 1990;Huang et al., 1995;Stern et al., 1995;López-Serrano et al., 2000;Serenius et al., 2001). Thus, there seems to be large variation in the heritability estimates that are dependent on the population evaluated. In addition to heritability estimates, however, the associations of different symptoms of leg weakness with sow longevity determine the traits that should be included in an overall leg index. In the other words, economic weights for leg conformation traits are driven by their association with sow longevity.
Jörgensen (1996) found that buck-kneed forelegs, upright pasterns on hind legs, and swaying hindquarters were significantly associated in an unfavorable manner with sow longevity. Moreover, he found that weak pasterns on forelegs are favorably associated with sow longevity.Grindflek and Sehested (1996) also reported that upright pasterns were unfavorably associated, and that weak pasterns on forelegs were favorably associated, with sow longevity. Moreover, buck-kneed forelegs are genetically correlated with overall leg action, which is a trait that is indicative of a pig’s ability to move without any pain in the legs (Webb et al., 1983;Jörgensen and Vestergaard, 1990;Serenius et al., 2001).
In general, two types of scoring systems exist for leg conformation traits: 1) binary type of recording, indicating whether the pig has a certain problem (and the level of problem;Andersen and Hansen, 1996;de Koning, 1996;Grindflek and Sehested, 1996;Serenius et al., 2001); and 2) a linear scoring system, where all pigs are evaluated for certain leg locomotions (Lundeheim, 1996). Although both types of recording systems are used in practical breeding programs, the linear scoring system is considered the superior method for leg evaluation in swine. This method is superior because the variation in the traits is more effectively recorded in a linear scoring system compared with the binary type of scoring system (Thompson et al., 1981, 1983).
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GENETIC ASSOCIATIONS OF SOW LONGEVITY WITH MEAT PRODUCTION TRAITS
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Very high genetic improvement for production traits (e.g., ADG, G:F, percentage of carcass lean) has been obtained during last 20 yr (Boyd, 1999;FABA, 2002). During the same period, sow longevity has decreased, which raises the question as to whether there are unfavorable genetic associations between these traits. It is expected that the general tendency for these correlations is unfavorable. For example,López-Serrano et al. (2000) reported that stayability was genetically correlated in the unfavorable direction with backfat thickness (from 0.11 to 0.27), and ADG (from –0.06 to –0.32); however, the associations were not that clear in the study byTholen et al. (1996b). Their genetic correlation estimates between stayability and backfat thickness ranged from –0.03 to 0.36, and similarly genetic correlation between stayability and ADG ranged from 0.02 to –0.13.Serenius and Stalder (2004) found an unfavorable genetic correlation (0.22) between length of productive life and backfat thickness in a Finnish Large White population, whereas the same genetic correlation in a Finnish Landrace population was close to zero. Moreover, their study did not support that ADG is significantly correlated with either length of productive life or lifetime prolificacy.
The results ofStalder et al. (2005) indicate that the relationship between backfat thickness and sow longevity might be nonlinear. They grouped sows by standard deviation of backfat thickness, loin muscle area, and days from birth to 113 kg of live weight, and found that sows in lowest backfat category clearly had a lower lifetime prolificacy, whereas the sows in the highest backfat group had a greater length of productive life compared with sows in other groups. More research is needed to determine whether a threshold might exist for backfat thickness to maximize or optimize length of productive life.
Although an unfavorable association between carcass quality and sow longevity exists, it should be noted that the relationships are not extremely large, indicating that simultaneous selection for both traits is likely possible.
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CONCLUSIONS
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Heritability estimates presented in the literature indicate that sufficient genetic variation is present in most swine populations so that sow longevity may be improved through selection. Nonetheless, consensus about proper methods for breeding value estimation for sow longevity does not exist in the literature. Survival analysis is considered theoretically appropriate in the analysis of this type of data; however, multiple-trait analysis is not currently possible using available survival analysis software. Because sow longevity is genetically associated with prolificacy and carcass quality, all available information should be used in the breeding value estimation through appropriate genetic correlations. More research is needed to determine the best methodology for estimating breeding values for sow longevity. More research also is needed to determine the magnitude of nonadditive genetic variation that affects sow longevity, to estimate the genetic relationships between sow longevity and feed intake and backfat loss during lactation, to determine the symptoms of leg weakness that are associated with sow longevity, and finally to determine whether nonlinear relationships between sow longevity and production traits exist.
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Footnotes
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1 Invited review. Presented at the American Society of Animal Science Midwest Section Meeting, Des Moines, IA, March 21–23, 2005. 
2 This journal paper of the Iowa Agric. and Home Econ. Exp. Stn., Ames, IA, was supported by Hatch Act and State of Iowa funds. Additional funding and support was provided by the Academy of Finland.
3 Corresponding author: Serenius{at}iastate.edu
Received for publication April 18, 2005.
Accepted for publication August 11, 2005.
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Abstract
INTRODUCTION
