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ANIMAL GENETICS |
Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061
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Abstract |
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 Top Abstract INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONIMPLICATIONSLITERATURE CITED |
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Key Words: ewe productivity • genetic correlation • heritability • Katahdin • sheep
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INTRODUCTION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONIMPLICATIONSLITERATURE CITED |
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). Thus, ewe productivity, defined as the total weight of lamb weaned by a ewe, is one of the most important economic traits for the US sheep industry and has been proposed as a biologically optimum index for improving overall flock productivity (Snowder, 2002). Total weight of lamb weaned per ewe exposed to breeding provides an overall indication of ewe fertility, prolificacy, maternal performance, and rearing ability as well as lamb survival and growth (Falconer and Mackay, 1996). Ewe productivity has a small genetic component (Safari and Fogarty, 2003), but selection for improvement of this trait appears possible (Ercanbrack and Knight, 1998), and ewe productivity has been identified as a target trait for genetic improvement by the US National Sheep Improvement Program (Notter, 1998).
The Katahdin is a composite sheep breed developed in Maine from crosses between hair- and wool-type breeds (Wildeus, 1997). Genetic improvement in total weight of lamb weaned is important to Katahdin producers because sale of meat is the primary source of income. Estimation of genetic parameters is essential for implementation of a genetic improvement program.
The objective of this study was to estimate (co)variance components for ewe productivity in Katahdin sheep, defined here as the total weight of lamb weaned per ewe lambing, and its component ewe traits, namely, number of lambs born, number of lambs weaned, and average weight of lambs weaned. Survival to weaning and weight at weaning were also analyzed as traits of the lamb because they provide insight into the genetic control of ewe productivity. In addition, weaning weight of the ewe as a lamb was analyzed with subsequent measures of number of lambs born and weaned in bivariate analyses to ascertain relationships between early growth and later prolificacy.
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MATERIALS AND METHODS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONIMPLICATIONSLITERATURE CITED |
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). Katahdin sheep have been evaluated by the NSIP since 2000, with regular recording of birth, weaning, and postweaning weights, birth dates, management codes (e.g., creep feeding policy, location codes, etc.), weigh dates, and lamb survival codes. For this study, 4 ewe traits were identified for analysis: the composite trait of ewe productivity, measured as the total weight of lamb weaned per ewe lambing (TW); the component traits of the number of lambs weaned (NW) and average lamb weaning weight (AW); and the associated trait of number of lambs born (NB). Ideally, ewe productivity would be measured as the total weight of lamb weaned per ewe exposed. However, with the current data recording procedures in NSIP flocks, ewes that did not conceive were not always clearly identified on the NSIP data sheets.
Total litter weaning weight reflects the combined effects of reproduction and preweaning growth and was calculated as the sum of the weaning weights of all lambs in the litter. For calculation of TW, lamb weaning weights were corrected to 60 d of age and adjusted for the effects of sex (to a ewe lamb basis) using multiplicative adjustment factors derived from Katahdin NSIP records (Notter and Kuehn, 2003). However, the weaning weights were not adjusted for the effects of type of birth and rearing, to allow expression of the anticipated negative relationship between number of lambs weaned and average lamb weaning weight. Lamb weights were likewise not adjusted for ewe age before calculation of TW, but ewe age effects were included in the analytical model for this trait. Ewes that lambed but lost their entire litter were given a value of zero for TW.
Number born measures the prolificacy of the ewe, whereas NW combines the effects of prolificacy and lamb survival. Average lamb weaning weight is indicative of preweaning growth associated with direct and maternal genetic effects. For calculation of AW, weaning weights were corrected to 60 d of age and adjusted for effects of lamb sex, ewe age, type of birth and rearing, and ewe age by type of birth and rearing interactions to better reflect the true genetic potential of the ewe for lamb growth. The basis for adjustment was a ewe lamb that was born and raised as a single and reared by a 3-yr-old ewe. These adjusted weaning weights were then averaged for each litter to derive AW. For ewes that lambed and lost their entire litter, the value of AW was assumed to be missing because no weaning weights were recorded.
In calculating NW, AW, and TW, ewes received no credit for lambs that were artificially reared or fostered. Records of weaning weights from litters in which individual lambs did not have a valid 60-d weaning weight (i.e., a weaning weight taken between 30 and 90 d of age) and were not known to have died (i.e., did not have a survival code clearly indicating lamb death) were not used and resulted in missing values for AW and TW for the ewes. Also, for AW and TW, occasional records from ewes with lambs born in the same litter but subsequently raised in different weaning weight management groups were excluded from the analysis. Weaning weights from such litters could not be averaged or summed over the entire litter without disregarding potential lamb management differences. For analysis of ewe performance, only data from ewes lambing from 2001 through 2004 were used; older records were available but not used because of lack of confidence in information on lamb survival and therefore the actual number of lambs weaned in those data. However, to utilize all genetic links between flocks, all available pedigree information was used.
Contemporary groups were formed for TW, NB, and NW based on flock, year of birth, time of birth in the lambing season (using 45-d lambing date windows), and percentage Katahdin breeding group. Only records from animals with at least 75% Katahdin breeding were used. Animals were categorized as 75 to 86%, or greater than 86%, Katahdin breeding. Ewes were assigned to contemporary groups for AW based on flock, year of birth, date of weaning of the lamb (using 7-d weaning date windows), weaning weight management code, and percentage Katahdin breeding. Lamb weaning weight contemporary groups were not used to form TW contemporary groups because ewes that lambed but did not wean any lambs (i.e., those with TW = 0) could often not be assigned unambiguously to a lamb weaning weight contemporary group. Overall, the data were from a total of 1,549 ewes representing 235 sires. The number of daughters per sire ranged from 1 to 19, with an average of 6.59; 50 sires had at least 10 daughters with records.
In addition to the ewe traits, 60-d weaning weight (WW) and lamb survival (LS) were analyzed as traits of the lamb because these traits influence ewe productivity. These analyses also allowed consideration of the relative importance of direct and maternal genetic effects on WW and LS. Before analysis, lamb weaning weights were corrected to 60 d of age and adjusted for the effects of lamb sex, ewe age, type of birth and rearing, and ewe age by type of birth and rearing interaction. Lambs were assigned to weaning weight contemporary groups as described for AW. Lamb survival was measured from birth to weaning; lambs that survived to weaning were given a score of 1, and lambs that did not survive were given a score of 0. Only data from lambs whose fate was definitively known were utilized; data from lambs that did not have a valid survival code or a valid weaning weight were excluded from the analysis. Contemporary groups for lamb survival were assigned as described for TW, NB, and NW. The final numbers of records after editing, means and SD for lamb and ewe traits, and numbers of contemporary groups are given in Table 1.
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Ewe Traits.
Initial estimates of heritability were obtained from univariate analyses. Bi- and trivariate analyses were then performed to evaluate the interrelationships among traits. The model for all ewe traits included fixed contemporary group effects and random animal (ewe) and ewe permanent environmental effects. The model for NB, NW, and TW also included a fixed effect of ewe age. The model for AW and TW also included a random service sire effect, but genetic relationships among service sires were not considered. Tests of significance for random effects in single-trait models were performed using likelihood ratio tests after deleting each random term from the model.
Lamb Traits.
Models for WW and LS included fixed effects of contemporary group and random additive direct, additive maternal, ewe permanent environmental, and litter effects. Weaning weight was analyzed with and without inclusion of a covariance between direct and maternal genetic effects. The model for LS also included effects of lamb sex, ewe age, and type of birth and was analyzed with and without inclusion of birth weight as a covariate to determine if genetic effects on lamb survival could be accounted for by differences in lamb birth weight. Tests of significance for random terms and the direct-maternal genetic covariance were performed using likelihood ratio tests after deleting each random effect or covariance from the model.
Lamb and Ewe Traits.
Bivariate analyses to estimate relationships between WW of the ewe as a lamb and subsequent measures of NB and NW were also performed. Terms included in the model were as described above for each trait. Additionally, covariances were included between animal genetic effects on NB or NW (denoted by ra) and animal and maternal genetic effects on WW (denoted by rm). The MTDFREML software did not permit direct estimation of covariances between residual environmental effects on WW and subsequent ewe permanent environmental effects on NB or NW. However, environmental conditions prevalent during weaning could affect later expression of NB or NW. To accommodate such an environmental covariance, residual effects on WW were modeled as an animal permanent environmental effect, and covariances between animal permanent environmental effects on WW and NB or NW (denoted by re) were included in the model (Rao and Notter, 2000). These bivariate analyses were repeated with and without inclusion of an animal-maternal genetic covariance for WW. Because animal permanent environmental effects were fit for WW, which is a nonrepeated trait, residual variances for WW in these analyses were correspondingly fixed at zero to yield an equivalent model to that used for univariate analysis of WW. Covariance between animal and maternal genetic effects on WW and animal genetic effects on NB or NW were tested for significance using likelihood ratio tests.
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RESULTS AND DISCUSSION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONIMPLICATIONSLITERATURE CITED |
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. All 4 traits had heritability estimates that were moderately low but significantly different from zero. For NB and NW, only negligible amounts of variation were accounted for by ewe permanent environmental effects. These measures of prolificacy are expected to be under similar genetic control unless genetic differences exist in lamb survival. Effects of ewe permanent environment on AW were also significant, but effects of the service sire were not significant. Ewe permanent environmental effects accounted for a small amount of variation in TW, but the service sire of the ewe did not explain any of the phenotypic variation in TW and was excluded from subsequent bivariate models.
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and 4, respectively. Heritability estimates and proportions of variation due to ewe permanent environment and service sire effects in multivariate analyses were similar to those obtained from univariate analyses, except that the proportion of variation explained by ewe permanent environmental effects was somewhat higher for NB and NW in multivariate analyses. This result could have arisen because of the small variation in NB and NW and was associated with large negative estimates of the ewe permanent environmental correlation.
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and Safari et al. (2005). The absence of service sire effects on TW was consistent with findings of Bromley et al. (2001) and Rosati et al. (2002) who reported that the service sire component accounted for 0 to 3% of phenotypic variance. The only heritability estimate of AW using an animal model that we found in the literature was 0.15 (Rosati et al., 2002). Although few literature estimates of genetic parameters are available for this trait, approximations can be made using direct and maternal variance component estimates for individual lamb weaning weight and are discussed later in this paper.
Correlations Among Ewe Traits
Correlations among ewe traits from bi- and trivariate analyses are given in Tables 3
and 4, respectively. Genetic correlations obtained from these analyses were similar. The genetic correlation of TW with NB averaged 0.30, whereas correlation of TW with NW and AW exceeded 0.88 and 0.72, respectively. Number of lambs weaned had a genetic correlation of 0.70 to 0.75 with NB and a genetic correlation of approximately 0.50 with AW. However, NB and AW were genetically independent.
Permanent environmental correlations among ewe traits varied more than genetic correlation among analyses. Permanent environmental correlations were generally negative between NB and NW, small between NB and TW, moderate to high for AW with NB and NW, and high for TW with AW and NW. The high ewe permanent environmental correlations observed between some of the traits could be artificially inflated by the small ewe permanent environment variance components associated with NB, NW, and TW, which make denominators of correlation terms extremely small. Residual and phenotypic correlations (Tables 3 and 4) among traits were generally moderate to high and positive except for those between AW and reproductive traits such as NB and NW, which were small and negative.
Genetic correlations among the component and composite ewe traits do not give any indication of antagonistic relationships. Genetic correlations among NB, NW, and TW from the current data are within the ranges reported by Safari and Fogarty (2003). As with heritability estimates of AW, few literature estimates of genetic correlations involving AW are available. Rosati et al. (2002) reported a genetic correlation of 0.05 between AW and NB, 0.02 between AW and NW, and –0.07 between AW and TW. Our estimates of genetic correlation between AW and NW or TW are much higher than those of Rosati et al. (2002). A high genetic correlation between TW and AW is expected because TW is a product of AW and NW. However, genetic correlations between growth and reproductive traits seem to vary considerably across populations (Safari and Fogarty, 2003; Safari et al., 2005).
Heritabilities and Other Variance Proportions for Lamb Traits
Estimates of variance components from univariate analyses of survival and weaning weight in lambs are given in Table 5. There was no evidence of direct or maternal genetic effects or ewe permanent environmental effects on lamb survival, but there were significant effects due to litter. No additive-maternal covariance was estimated for lamb survival because of the small components of variance for these genetic effects. The regression of lamb survival on birth weight was 0.031 ± 0.003 kg–1, indicating, as expected, that heavier lambs had a better chance of survival. However, inclusion of birth weight in the model for lamb survival had essentially no effect on estimates of genetic parameters.
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; Safari et al., 2005). Somewhat higher estimates for direct heritability of 0.14 and maternal heritability of 0.11 for lamb survival were reported by Everett-Hincks et al. (2005). In our study, lamb survival was analyzed as a 0/1 trait, with no transformation to the logit or probit scales. Morris et al. (2000) reported that direct and maternal heritabilities were only slightly higher when survival data were transformed, and Amer and Jopson (2003) reported that transformation of survival data using logit or probit functions did not greatly affect heritability estimates. In several studies reported by Safari and Fogarty (2003), analysis of the same data with or without transformation led to similar heritability estimates. In any case, our results suggest that there is not much genetic variation in lamb survival, although full-sib littermates appear to share a common risk of death. Selection to improve lamb survival may therefore not be fruitful in this situation, but opportunity exists for improvement by means of environmental manipulation, perhaps through more intensive management, especially in the case of triplets.
Weaning weight was moderately heritable with significant maternal, ewe permanent environmental, and litter effects (Table 5). Direct and maternal genetic effects had a small negative correlation (r = –0.38; P = 0.14). Variance components for lamb weaning weight from our data are in general agreement with literature estimates reviewed by Safari and Fogarty (2003) and summarized by Safari et al. (2005).
The direct genetic variance for average lamb weaning weight as a trait of the ewe can be approximated from lamb weaning weight variance components as the sum of maternal genetic variance, one-quarter of the direct genetic variance, and the covariance between direct and maternal effects. The phenotypic variance for AW for single, twin, and triplet litters can similarly be derived from the variance components for lamb weaning weight, giving a predicted weighted heritability for AW of 0.15, which is similar to the direct heritability estimate of 0.13 obtained for AW. Also, the service sire variance in AW is expected to be one-quarter of the direct heritability for lamb weaning weight, which is equal to 0.05 and is equal to the service sire component of AW. The proportion of phenotypic variance due to ewe permanent environment was similar for individual lamb weaning weight and AW at 0.14. The negative correlation between direct and maternal genetic effects suggests that genetic selection to improve growth is possible but that there may be a small antagonism between direct genetic effects on growth and genetic effects on milk production. This should be taken into consideration if selection is for growth alone because reduced milk production in ewes could reduce lamb survival. However, the magnitude of the genetic correlation between direct and maternal effects is sufficiently small that the antagonism could be easily managed by proper attention to these 2 components of lamb growth.
Bivariate Analyses of Lamb and Ewe Traits
Results from bivariate analyses of WW and NB or NW are given in Table 6. Heritabilities and other variance proportions for each of the traits were similar to those obtained in prior analyses. Correlations between animal and maternal genetic effects on WW and animal genetic effects on NB (ra and rm, respectively), as well as the phenotypic correlation (rp), were small and not significantly different from zero, indicating that these 2 traits are genetically independent. Correlations (re) between residual environmental effects on WW (ew2 ), which were modeled as animal permanent environmental effects for a nonrepeated trait, and animal permanent environmental effects for NB (ca–r2 ) were high and could have been artificially inflated by the small ewe permanent environment variance component associated with NB. Correlations between animal genetic effects on weaning weight of the ewe as a lamb and subsequent measures of NB are reported to range from –0.14 to 0.38 in different breeds (Rao and Notter, 2000; Ap Dewi et al., 2002; Hanford et al., 2002). Olivier et al. (2001) reported that genetic correlation between weaning weight and lifetime NB over 3 parities in South African Merino flocks ranged from 0.32 to 0.45. Safari et al. (2005) reported an average animal genetic correlation of 0.29 between weaning weight and NB. Correlations between maternal genetic effects of the ewe on weaning weight and NB range from –0.23 to 0.40 in different breeds (Rao and Notter, 2000; Ap Dewi et al., 2002; Hanford et al., 2002).
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reported correlations between animal and maternal genetic effects on weaning weight of the ewe as a lamb and NW in Columbia sheep as 0.24 and 0.66, respectively, which are both higher than our estimates. Genetic correlation between WW of the ewe as a lamb and lifetime NW over 3 parities ranged from 0.34 to 0.57 (Olivier et al., 2001). Safari et al. (2005) reported an average genetic correlation of –0.05 between weaning weight at different ages and NW.
General Discussion
The response to selection for a trait is dependent on the selection intensity, heritability, and phenotypic SD (Falconer and Mackay, 1996). If heritability is low, genetic progress can occur if reasonable variation is present in the trait, which is the case for NB, NW, and TW. Using the heritability and phenotypic SD obtained in this study and assuming that 50% of females and 5% of males are selected as parents, response to selection for TW is expected to be 1.66 kg (5.9%) per generation. Luxford and Beilharz (1990) reported a positive response to selection for increased litter weight at weaning in mice. Ercanbrack and Knight (1998) reported a 0.43 to 1.06 kg/generation genetic improvement in TW in lines of sheep selected on current lifetime average TW. Olivier et al. (2001) reported an estimated genetic improvement of 6.37 to 9.03 kg/generation when selection was based on the sum of individual TW over 3 parities. This prediction is considerably greater than 3 times the change in single parity TW reported by Ercanbrack and Knight (1998) or predicted from this study, in part because of an increase in phenotypic CV for a sum of correlated measures. The heritability of TW in that flock was also higher, ranging from 0.19 to 0.21 in the study of Olivier et al. (2001) and from 0.13 to 0.22 in a report on the same flock by Snyman et al. (1997).
Genetic correlations among ewe traits were generally positive and moderate to high in magnitude, except for NB and AW, which seem to be genetically independent. Therefore, selection on any of the component traits should result in improvement of TW and vice versa. Also, selection on any of the component traits should not adversely affect other component traits. High genetic correlations between TW and either NW or AW were expected because TW is a product of AW and NW. Bromley et al. (2001) indicated that direct selection for NW should improve ewe productivity and suggested that counting the number of lambs weaned may be more practical than weighing all the lambs, especially in extensive production systems. Improvements in total weight of litter weaned through selection on weaning weight (Bradford et al., 1999) and litter size at birth and weaning (Bradford et al., 1999; Cloete et al., 2004) have been reported. Olivier et al. (2001) also reported that selection on weaning weight, litter size at birth, and litter size at weaning is expected to result in genetic improvement in total litter weight weaned. Although selection for improved ewe productivity or total weight of lamb weaned is possible, caution must be practiced when predicting genetic merit for animals in different environments because of the possibility of genotype x environment interactions. Caution is especially warranted for a trait like ewe productivity because of the potential for different management practices for triplet lambs and differences in average lamb survival across environments. Some researchers have also cautioned that differences in feed resources among environments or production systems must be considered because nutrition may become a limiting factor for the potentially most productive ewes (Head et al., 1995; Hatfield and Stellflug, 1996). However, selection for increased total weight of lamb weaned within a single production system or environment is likely to result in animals that are optimally adapted to that production system or environment (Snowder, 2002).
Several researchers have proposed direct selection for total litter weight at weaning (Luxford and Beilharz, 1990; Olivier et al., 2001; Bradford, 2002) and observed responses to such selection have been substantial (Luxford and Beilharz, 1990; Ercanbrack and Knight, 1998). However, direct genetic evaluation of TW in field data is complicated, among other reasons, because of incomplete reporting of data (for example, when weaning weights cannot be used because they are taken outside the prescribed 30- to 90-d age window or when information on lamb survival, and thus on NW, is not clearly reported) and potential difficulties in forming contemporary groups. Direct genetic evaluation of TW is relatively straightforward if ewes that lamb together are all maintained together until weaning so that all their lambs are in the same weaning weight contemporary group but becomes difficult when lambs are divided into different weaning weight contemporary groups. This situation is common in field data and in particular creates problems in assigning ewes that do not wean any lambs to an appropriate contemporary group. Contemporary groups for NB can usually be assigned in a consistent manner and could also be used for TW, but doing so will not take into account differences in preweaning management of the lambs. It is possible to additively adjust weaning weights of lambs born within a common birth contemporary group for subsequent effects of weaning weight contemporary groups before calculating TW, but this approach adds an additional step to the genetic analysis. In the current application, such an adjustment had little effect on variance component estimates for TW. The heritability estimated following adjustment for differences in lamb weaning contemporary group was 0.11, and the proportion of variance attributed to ewe permanent environment effects was 0.08, which were similar to the estimates shown in Tables 2 to 4. However, failure to account for differences in the preweaning environment could still bias estimates of lamb breeding values.
The direct evaluation of TW is further complicated by its complex distribution (Figure 1). As pointed out by Rosati et al. (2002), the distribution of TW is conditioned by the distribution of its component traits, including the categorically distributed NW, which can range from zero to 3 or 4, and weaning weight (which is assumed to be normally distributed). Usually, estimated breeding values, which are used for selection of breeding animals, are derived using linear model theory under the assumptions that data are normally distributed (Henderson, 1984). Clearly, the distribution of TW violates this assumption.
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IMPLICATIONS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONIMPLICATIONSLITERATURE CITED |
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Footnotes |
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3 Present address: US Meat Animal Research Center, Clay Center, NE 68933.
2 Corresponding author: drnotter{at}vt.edu
Received for publication April 19, 2006. Accepted for publication August 4, 2006.
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LITERATURE CITED |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONIMPLICATIONSLITERATURE CITED |
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