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Genetic analysis of ewe productivity traits in Makooei sheep
Small Ruminant Research 107 (2012) 105–110
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Genetic analysis of ewe productivity traits in Makooei sheep Hossein Mohammadi ∗ , Mohammad Moradi Shahrbabak, Hossein Moradi Shahrbabak Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
a r t i c l e
i n f o
Article history: Received 25 August 2011 Received in revised form 16 April 2012 Accepted 18 April 2012 Available online 10 May 2012 Keywords: Genetic correlation Heritability Sheep
a b s t r a c t This study reports genetic and environmental (co)variance components for productivity traits of Makooei sheep, using between 2647 and 3418 records obtained from 1429 ewes. Data were collected during 1996–2009 on the Breeding Station of Makooei sheep, located in Makoo in the west-Azerbaijan province of Iran. Genetic parameters were estimated for three basic and six composite traits. The basic traits were conception rate (CR), number of lambs born (NLB) and number of lambs alive at weaning (NLAW). The composite traits were number of lambs born per ewe exposed (NLBEE), number of lambs weaned per ewe exposed (NLWEE), total litter weight at birth per ewe lambed (TLBW), total litter weight at weaning per ewe lambed (TLWW), total litter weight at birth per ewe exposed (TLBWEE) and total litter weight at weaning per ewe exposed (TLWWEE). Genetic parameters were estimated with univariate and bivariate models using restricted maximum likelihood (REML) procedures. Random effects were explored by fitting additive direct genetic effects, permanent environmental effects due to the animal as well as service sire effects in different models for ewe productivity. The most appropriate model for each trait was determined based on likelihood ratio tests and Akaike’s Information Criterion (AIC). Direct heritability estimates for CR, NLB, NLAW, NLBEE, NLWEE, TLBW, TLWW, TLBWEE and TLWWEE were 0.05 ± 0.02, 0.11 ± 0.01, 0.06 ± 0.01, 0.08 ± 0.02, 0.04 ± 0.02, 0.17 ± 0.03, 0.12 ± 0.02, 0.13 ± 0.02 and 0.10 ± 0.02, respectively. The estimate for the permanent environmental variance due to the animal ranged from 0.03 ± 0.02 for CR to 0.12 ± 0.01 for NLAW, whereas service sire effects ranged from 0.02 ± 0.01 for TLWW to 0.05 ± 0.01 for TLBW. Genetic correlation estimates among studied traits ranged from −0.13 for CR with TLWW to 0.97 for NLAW with NLWEE. Phenotypic correlation estimates were generally smaller in magnitude than genetic correlations. Service sire effects were found to be important only for litter weight traits. These estimates of genetic parameters may provide a basis for deriving selection indexes for reproductive traits. © 2012 Elsevier B.V. All rights reserved.
1. Introduction The Makooei sheep is one of the Iranian fattailed, medium-sized breeds. They are distributed in the mountainous areas of the country, especially in the westAzerbaijan province. They are valuable primarily for meat and also for their wool and milk. The wool produced is
∗ Corresponding author. Tel.: +98 261 224 80 82; fax: +98 261 224 6752. E-mail address: [email protected] (H. Mohammadi). 0921-4488/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.smallrumres.2012.04.019
coarse and usually used for carpet weaving (Tavakolian, 1999). Today, there are about 2,700,000 animals of Makooei sheep in the west-Azerbaijan. Due to the large population of this breed there is an increasing interest in its genetic improvement. The greatest part of the income in sheep farming is derived from lamb production. Efficiency of lamb production is controlled by reproduction, mothering ability and milk production of the ewe, as well as growth rate and survival of the lamb (Rao and Notter, 2000). Improvement in number or total weight of lamb weaned per ewe, as a key
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target, could partly be attained by increasing the number and weight of lambs produced per ewe within a specific year (Duguma et al., 2002). As reproductive traits have been identified as main determinants of profitability of sheep enterprises (Ekiz et al., 2005), improvement of these traits leads to more efficient lamb production. Improvement of ewe productivity and growth of lambs increases meat production. Such improvement needs a working knowledge of genetic and environmental factors contributing to the expression of each trait. The genetic characterization of the native breeds is required for conservation considerations and for establishing efficient selection and breeding schemes (Matika et al., 2003). Within breed selection of native breeds is an appropriate approach for genetic improvement in the traditional, low-input production systems of small ruminants in the tropics (Kosgey et al., 2006). Safari et al. (2005) showed that designing efficient selection and breeding strategies for genetic improvement and appropriate genetic evaluation of local breeds requires accurate estimation of genetic parameters. Previously, researchers identified increasing number of lambs at birth or weaning as the single most useful criterion to improve reproductive efficiency or net reproductive rate. Recently, the trait of interest has been total weight of lamb(s) weaned per ewe joined or lambing (Fogarty, 1995; Cloete et al., 2004). Estimates of genetic parameters for reproductive characteristics of different sheep breeds have been published by several authors (van Wyk et al., 2003; Ekiz et al., 2005; Hanford et al., 2006; Afolayan et al., 2008; Mokhtari et al., 2010; Rashidi et al., 2011). Estimation of genetic parameters for reproductive traits using an animal model to plan optimum designs for selection programs for this breed is scarce. Hence, reliable estimates of genetic parameters are needed to establish an efficient selection program for ewe productivity. Thus, the objective of this study was to estimate heritability and genetic correlations of reproductive traits for Makooei sheep using an animal model, that are necessary to develop efficient selection programs for the improvement of reproduction.
2. Materials and methods 2.1. Data and management The data set and pedigree information used in this research were reproductive traits of Makooei ewes collected from 1996 to 2009 on the Breeding Station of Makooei sheep, located in Makoo in the westAzerbaijan province of Iran. In general, the flock is managed under a semi-migratory system. Ewes are raised in an annual breeding cycle starting in August. The animals (males and females) were first mated at approximately 18 months of age. Ewes were kept in the flock for a maximum of 5 parities (approximately until the age of 7 years old). In order to avoid inbreeding, rams were allocated rotationally to each group of the ewes in different years. There is one breeding season in August–October. Hand mating was applied once a year. Ewes were assigned randomly to the rams and consequently, lambing commences in mid-January and continues until April. All lambs are identified at birth and birth weights, as well as sex, birth type and pedigree information are recorded. During the suckling period, lambs are fed with their mothers’ milk and also provided with dry alfalfa hay from 3 weeks of age. Lambs are weaned at approximately 100 days of age. The flock was mainly kept on pastures of low quality and quantity, but supplemental feeding was provided especially
around mating and during winter. The supplemental feed was included 2 kg alfalfa, 0.9 kg wheat straw and 0.4 kg barley per head per day. 2.2. Studied traits The traits analyzed can be assigned to two main categories: basic and composite traits (Table 1). Basic traits were conception rate (CR, with measure of 1 or 0, that is whether a ewe exposed to a ram did or did not lamb), total number of lambs born (NLB, the number of lambs born per ewe per lambing), number of lambs alive at weaning (NLAW). Conception rate is binary, but it is presumably based on continuous variation on the underlying liability scale that is expressed when a certain threshold is reached. Based on observation from basic traits, composite traits were derived. The composite traits with discrete numerical observation were number of lambs born per ewe exposed (NLBEE = CR × NLB) and number of lambs weaned per ewe exposed (NLWEE = CR × NLAW). Composite traits with continuous expression were total litter weight at birth per ewe lambing (TLBW), total litter weight at weaning per ewe lambing (TLWW), total litter weight at birth per ewe exposed (TLBWEE = CR × TLBW) and total litter weight at weaning per ewe exposed (TLWWEE = CR × TLWW). 2.3. Statistical analysis Important fixed effects for all traits were identified from preliminary analysis using the GLM procedure of SAS (SAS Institute, 2002). The fixed effects included in the final statistical model were lambing year in 14 classes (1996–2009) and ewe age at lambing in 6 classes (2–7 years old). Lamb age at weaning (in days) was fitted as a covariate for TLWW and TLWWEE, while lambing date was fitted as a covariate for NLB and NLAW. The interaction between lambing year and ewe age was not significant and was hence not considered in the final model. Pre adjustment of birth and weaning weights for the effect of lamb sex before calculation of total litter weight at birth and weaning for specific parity in individual ewes is of biological importance (van Wyk et al., 2003). Thus, TLWW and TLWWEE were pre-adjusted for lamb sex by means of multiplicative adjustment factors. The adjustment factors were determined using least squares analysis, then records of birth and weaning weight were adjusted accordingly. These adjusted records were used to calculate TLBW, TLWW, TLBWEE and TLWWEE. The variance components for the investigated traits were estimated by restricted maximum likelihood (REML), using the ASReml computer program (Gilmour et al., 2006). First, the following univariate models were fitted to each trait. Model 1 y = Xb + Z a a + e Model 2 y = Xb + Z a a + Z s s + e Model 3 y = Xb + Z a a + Wpe + e Model 4 y = Xb + Z a a + Z s s + Wpe + e where y is a vector of records on the respective traits; b, a, s, pe and e are vectors of fixed, direct additive genetic, service sire, permanent environmental related to repeated records of ewes and residual effects, respectively. Also, X, Za , Zs and W stand for design matrices associating the corresponding effects with elements of y. It was assumed that additive genetic effects, service sire effects, permanent environmental effects related to repeated records of ewes and residual effects to be normally distributed with mean 0 and variances are A 2a , I s 2s , I d 2pe and I n 2e , respectively. Also 2a , 2s , 2pe , and 2e are direct additive genetic variance, service sire variance, permanent environmental variance related to repeated records of the ewes and residual variance, respectively. A is the additive numerator relationship matrix, Is , Id and In are identity matrices with order equal to the number of sires, ewes and records, respectively. The importance of random effects was tested using a likelihood ratio test. A large number of the ewes’ dams were unknown limiting the study of maternal genetic and animal permanent environmental effects. In order to estimate the direct genetic and phenotypic correlations bivariate analyses applying the most appropriate model as in univariate analysis, were carried out. The fixed effects included in the bivariate animal models were those in univariate analyses. In order to determine the most appropriate
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Table 1 Number of records, mean, standard error (S.D.) and coefficient of variation (C.V.) for reproductive traits of Makooei ewes. Traita
No. of records
No. of ewes
No. of sires of the ewes
Mean
S.D.
C.V. (%)
CR NLB NLAW NLBEE NLWEE TLBW TLWW TLWBEE TLWWEE
3418 3190 3190 3418 3418 2746 2647 3395 3372
1429 1304 1304 1429 1429 1387 1387 1387 1387
206 197 197 206 206 188 188 188 188
0.93 1.16 0.98 1.07 0.91 4.29 26.44 3.95 24.78
0.26 0.39 0.47 0.41 0.49 1.68 8.43 1.91 9.86
27.95 33.62 48.05 38.31 53.84 39.16 31.88 48.36 39.79
a CR: conception rate; NLB: number of lambs born per ewe lambing; NLAW: number of lambs alive at weaning; NLBEE: number of lambs born per ewe exposed; NLWEE: number of lambs weaned per ewe exposed; TLBW: total litter weight at birth; TLWW: total litter weight at weaning; TLWBEE: total litter weight at birth per ewe exposed; TLWWEE: total litter weight at weaning per ewe exposed; S.D.: standard deviation; C.V.: coefficient of variation.
model for estimating (co)variance components for each traits Akaike’s information criterion (AIC) was used as follows (Akaike, 1974): AICi = −2 log Li + 2pi where log Li is the maximized log likelihood of model i at convergence and pi is the number of parameters obtained from each model; and model with the lowest AIC was chosen as the most appropriate model.
3. Results and discussion 3.1. Fixed effects The effects of year and age of ewe were significant (P < 0.01) for CR, NLB, NLAW, NLBEE and NLWEE. Ewes mated in 1997 and 1999 had lower performance than those mated in other years. The poorest performance was recorded in two-year-old ewes. There was a tendency for the productivity of ewes to improve with age, generally reaching a maximum between four and seven years of age. The effect of production years on TLBW, TLWW, TLBWEE and TLWWEE were significant (P < 0.05), and the lowest performances for these traits were recorded in production period from 1997 to 2000. Traits that included weaning weight (TLWW and TLWWEE) were significantly affected by lamb age at weaning (P < 0.01). The significant influence of lambing year on reproductive traits in the present study can be explained by variation in climatic conditions and nutritional quality over the years. Significant effects of year on reproductive traits of different sheep breeds have been reported by several authors (Bromley et al., 2001; Ekiz et al., 2005; Vatankhah et al., 2008). There was a general tendency for TLBW, TLWW, TLBWEE and TLWWEE to improve with an increase in ewe age. Differences in maternal effects, nursing and maternal behavior of ewe at different ages are reasons for the significant effects of ewe age on lamb live weight traits used in the calculation of TLBW, TLWW, TLBWEE and TLWWEE. Significant effects of ewe age on reproductive traits of sheep have been reported in literature (Rosati et al., 2002; Ekiz et al., 2005; Ceyhan et al., 2009; Rashidi et al., 2011). Contrary to this study, Mokhtari et al. (2010) reported that NLB and NLAW of Kermani sheep have not been significantly influenced by ewe age at lambing. Because of the changes in climatic conditions and nutritional quality over the years, the significant effect of lambing year on the studied traits was expected.
3.2. (Co) variance components and genetic parameters All traits were analyzed using a linear model. CR and NLB and NLAW have a discrete distribution, which imply that a threshold model (Gianola and Foulley, 1983) should theoretically be preferred. However, assumption of a continuous distribution for these traits is justified for genetic evaluation and for estimates of genetic correlations with continuous traits (Kadarmideen et al., 2003). Threshold and linear models showed very little differences in genetic parameters for CR (Weller and Ron, 1992) and NLB as well as NLAW (Matos et al., 1997). The most appropriate models were Model 3 (including direct additive genetic effects as well as permanent environmental effects related to animals) for CR, NLB, NLAW, NLBEE, NLWEE and Model 4 (including direct additive genetic effects, permanent environmental effects related to animals and service sire effects) for TLBW, TLWW, TLBWEE and TLWWEE. These results were consistent with those of Van Vleck et al. (2003). Estimates of variance components, heritability, permanent environmental variance to phenotypic variance and service sire variance to phenotypic variance for traits are presented in Table 2. The heritability estimate for conception rate was 0.05 ± 0.02 in this study. Coefficient of heritability of this trait in Australian Merino sheep were 0.045 ± 0.005 has been reported Safari et al. (2007a), which is consistent with the results of this study. Low estimates of heritability of conception rate can be due to the importance of environmental factors on the variability of this trait and it was attributed to the discontinuous nature of the data (Matika et al., 2003; Maxa et al., 2007). Although conception rate is of high economic importance, genetic improvement may be reduced owing to low levels of additive variation. Heritability for number of lambs born per ewe lambing was estimated at 0.11 ± 0.01. In two separate reports of heritability weighted mean number of lambs born per ewe lambing to the 0.13 ± 0.01 and 0.10 ± 0.01 reported (Fogarty, 1995; Safari et al., 2005), while Hanford et al. (2006) reported a heritability estimate of 0.11 ± 0.02 for this trait, which is consistent with the present results. According to Ekiz et al. (2005) and van Wyk et al. (2003) heritability estimates for NLB amounted to respectively 0.053 for Turkish merino and 0.059 for Dormers. The heritability estimate for number of lambs born per ewe exposed
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Table 2 Estimates of variance components, genetic and phenotypic parameters from single-trait analysis for reproductive traits of Makooei ewes. Traita
2a
2pe
2s
2e
2p
h2d ± S.E.
CR NLB NLAW NLBEE NLWEE TLBW TLWW TLBWEE TLWWEE
0.924 0.604 1.65 1.529 0.549 4.73 5.887 2.724 6.271
0.634 0.265 3.172 1.368 0.871 2.431 1.945 0.833 3.052
– – – – – 1.451 1.083 0.619 2.274
14.57 4.665 22.368 10.458 9.753 18.523 38.449 15.476 50.03
16.13 5.334 26.19 18.845 11.173 27.135 47.364 19.652 61.627
0.05 0.11 0.06 0.08 0.04 0.17 0.12 0.13 0.10
± ± ± ± ± ± ± ± ±
0.02 0.01 0.01 0.02 0.02 0.03 0.02 0.02 0.02
pe2 ± S.E. 0.03 0.04 0.12 0.09 0.07 0.08 0.04 0.04 0.04
± ± ± ± ± ± ± ± ±
0.02 0.01 0.01 0.03 0.01 0.02 0.01 0.02 0.02
s2 ± S.E. – – – – – 0.05 ± 0.02 ± 0.03 ± 0.03 ±
0.01 0.01 0.01 0.01
2a : direct variance genetic; 2pe : permanent environmental variance; 2s : service sire variance; 2e : residual variance; 2p : phenotypic variance; h2d : direct heritability; pe2 : ratio of permanent environmental variance on phenotypic variance; s2 : ratio of service sire variance to phenotypic variance; S.E.: standard error. a For traits abbreviations see footnote of Table 1.
was 0.08 ± 0.02. This estimate is lower than for number of lambs born per ewe lambing, but consistent with the weighted mean derived from literature values for this trait (Fogarty, 1995; Safari et al., 2005). Because the number from lambs born per ewe exposed is composed from conception rate and number of lambs born per ewe lambing, and the heritability for conception rate was low, it is reasonable to expect that this would impact on the heritability of the composite trait. The heritability estimates for number of lambs alive at weaning per ewe lambing (0.06 ± 0.01) and per ewe exposed (0.04 ± 0.02) were higher than those in literature (Fogarty, 1995; Safari et al., 2005). Ekiz et al. (2005) and van Wyk et al. (2003) reported heritability estimates for this trait as respectively 0.043 for Turkish merino and 0.026 for Dormers. The estimate of direct heritability for number of lambs weaned per ewe exposed was lower than for number of lambs born per ewe lambing probably because the loss of lambs from birth to weaning is more related to environmental effects and to genotypes of lambs than to the genotypes of the ewes. Total litter weight at birth per ewe lambing measures the capacity of the ewes to produce lambs weight at birth without considering the number of lambs born. Observations of the trait are continuous and can be considered approximately normally distributed although skewed to the right. The estimate of heritability for total litter weight at birth per ewe lambing was 0.17 ± 0.03, which is consistent with a pooled estimate, reported by Fogarty (1995) and the weighted mean reported by Safari et al. (2005). This large estimate of heritability seems to offer the possibility to select for total litter weight at birth per ewe lambing. Selection for productivity can also be applied through total litter weight at birth per ewe lambing because of the high genetic correlation estimates that total litter weight at birth per ewe lambing has with other reproduction traits (Table 3). Selection intensity could be larger if out-of-season breeding were successful; in fact generation interval might be reduced for observation of total litter weight at birth per ewe lambing that is obtained at birth. Therefore, genetic trends can be larger when generation intervals are reduced. The estimate of heritability for total litter weight at weaning per ewe lambing (0.12 ± 0.02) is similar to that
reported by Vanimisetti et al. (2007) in Katahdin breed (0.12). Cloete et al. (2004) studied lambs weaned per ewe joined, which corresponded closer with TLWWEE in the present study. Total litter weight at birth per ewe exposed is the combination of CR, NLB and mean litter weight per lamb born (LMWLB). The trait measures the ability of the ewe to produce lamb weight at birth after exposure to the ram. The estimation of heritability for total litter weight at birth per ewe exposed was 0.13 ± 0.02, which is similar to results in other breeds (Rosati et al., 2002). Total litter weight at weaning per ewe exposed expresses the ability of the ewe to produce lamb weight at weaning given exposure to the ram and is a combination of CR, NLAW and litter mean weight per lamb weaned (LMWLW). The range observed was large, and had a lower heritability than Total litter weight born per ewe exposed (0.10 ± 0.02). The trait has a low heritability estimate, possibly partly due to the unusual distribution of the trait and partly due to the number of possible environmental effects. Total litter weight at birth per ewe exposed could be considered for selection purpose because it measures a total productivity of the ewe for lamb-meat production for a breeding year. Permanent environmental heritability estimates for the investigated traits were low, and ranged from 0.03 for CR to 0.12 for NLAW. Estimates of variance due to permanent environmental effects were generally higher for composite traits than for basic traits except for NLW. The estimated fraction of variance due to permanent environmental effects of the ewe for NLW and NLWEE was higher than direct heritability while the estimated direct heritability was lower than the estimated fraction of variance due to permanent environmental effects of ewe for the other traits. Estimated values for permanent environmental of reproductive traits in the present study were consistent with those of Vatankhah et al. (2008) and Rashidi et al. (2011). Higher estimates have also been reported (Rosati et al., 2002; Safari et al., 2005). Inclusion of service sire effects together with direct additive genetic effects and permanent environmental effects related to repeated records of ewe significantly
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Table 3 Estimation of genetic (above diagonal) and phenotypic (below diagonal) correlations among productivity ewe in Makooei sheep (standard errors in parentheses). Traitsa
CR
NLB
NLAW
NLBEE
NLWEE
TLBW
TLWW
TLBWEE
TLWWEE
CR NLB NLAW NLBEE NLWEE TLBW TLWW TLWBEE TLWWEE
– 0.05 (0.02) 0.11 (0.03) 0.09 (0.01) 0.06 (0.07) 0.10 (0.03) 0.04 (0.02) 0.03 (0.02) 0.01 (0.03)
−0.02 (−0.01) – 0.32 (0.10) 0.57 (0.12) 0.22 (0.06) 0.32 (0.08) 0.27 (0.11) 0.29 (0.12) 0.16 (0.02)
−0.04 (0.02) 0.68 (0.11) – 0.27 (0.02) 0.33 (0.10) 0.19 (0.02) 0.35 (0.06) 0.18 (0.05) 0.14 (0.07)
0.19 (0.02) 0.94 (0.21) 0.60 (0.12) – 0.35 (0.04) 0.28 (0.06) 0.19 (0.02) 0.17 (0.01) 0.25 (0.03)
0.13 (0.06) 0.55 (0.14) 0.97 (0.25) 0.66 (0.11) – 0.19 (0.03) 0.38 (0.06) 0.16 (0.14) 0.31 (0.12)
−0.09 (0.05) 0.89 (0.11) 0.76 (0.17) 0.59 (0.22) 0.49 (0.24) – 0.46 (0.07) 0.53 (0.11) 0.24 (0.05)
−0.13 (0.03) 0.31 (0.08) 0.87 (0.19) 0.24 (0.16) 0.59 (0.10) 0.78 (0.12) – 0.37 (0.09) 0.46 (0.012)
−0.06 (0.02) 0.84 (0.10) 0.53 (0.10) 0.48 (0.19) 0.31 (0.12) 0.94 (0.14) 0.81 (0.05) – 0.33 (0.10)
−0.09 (0.05) 0.22 (0.06) 0.65 (0.11) 0.17 (0.03) 0.46 (0.25) 0.58 (0.14) 0.92 (0.06) 0.44 (0.13) –
a
For traits abbreviations see footnote of Table 1.
affected Log likelihoods. Thus, it seems that service sire had a significant influence on the studied composite traits of Makooei sheep. These estimates were lower than the corresponding direct heritability estimates. Estimates of service sire were near zero and varied from 0.02 for TLWW to 0.05 for TLBW. The estimates are in accordance with other research reports (Ceyhan et al., 2009; Zhang et al., 2009).
3.3. Correlation estimates Estimates of genetic and phenotypic correlations are shown in Table 3. The estimates of genetic correlations of CR with other traits were negative in sign, small in magnitude, and that the 95% confidence interval would include zero for all estimates with the exception of two (TLWW and TLBWEE), which is consistent with another report (Vatankhah et al., 2008) but much lower than weighted means in the literature review of Safari et al. (2005) and for Australian merino sheep Safari et al. (2007b). The low genetic correlation observed is partly due to the measurement method of our traits. As CR is a binary trait (0 or 1), NLB has a value only if fertility is 1 (success of lambing). Also, ewes not able to lamb by 2 years of age and to successive possible lambing times were culled which could influence the estimate of heritability and genetic correlations. The low estimation of genetic variances for CR and other reproductive traits can an important factor for the low heritability and genetic correlation between traits. Genetic and phenotypic correlation estimates between NLB and NLAW were positive and under medium to high which generally agree with the results of Safari and Fogarty (2003). Selection for each trait will cause an increase in the number of lambs at weaning. Genetic and phenotypic correlation estimates between NLB and NLAW with NLBEE and NLWEE were positive and high which generally agree with the values estimated by others (Hanford et al., 2002; Cloete et al., 2004). NLB and NLAW were positive and medium to high genetic correlations with TLBW and TLWW in terms of genetic effects. Vatankhah et al. (2008) and Mokhtari et al. (2010) reported the similar results. NLB or NLAW were positively and strongly correlated with TLBW and TLWW in terms of genetic effects. The low heritability of NLB or NLAW and the high genetic correlation estimates for NLB
and NLAW with TLBW and TLWW suggests that indirect selection for NLB or NLAW may be useful through selection for TLBW and/or TLWW. The estimate of the genetic correlation between TLBW and TLWW was high (0.78) implying that genes responsible for heavier weight of lambs at birth through number and weight of lamb may also influence milk production and thus mothering ability of the ewes from birth to weaning. Therefore, the high positive genetic correlation between TLBW and TLWW suggests that productivity can also be selected based on TLWW. Results of this study along with a companion study (Ceyhan et al., 2009) also provide the necessary parameters to determine whether selection directed at the composite traits described in this study is comparably as effective in improvement as selection on an index of the component traits (Smith, 1967). Although selection for improved ewe productivity is possible, caution must be practiced when predicting genetic merit for animals in different environments because of the possibility of a genotype × environment interaction. Caution is especially warranted for a trait like ewe productivity because of the potential for different management practices for twin 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). However, selection for increased ewe productivity within a single production system or environment is likely to result in animals that are optimally adapted to that production system or environment (Snowder et al., 2002).
4. Conclusions Heritability estimates were quite small for almost all traits. Only for litter weight traits larger heritability estimates were found. For some traits, such as CR, the small estimates may also be due to binomial measurement. Heritability estimates may also be influenced by other factors not considered in the model used, although the high phenotypic variance (e.g. coefficient of variation of 53% for NLWEE) means that high selection differentials can be achieved in effective breeding programs and response to selection predicted. Genetic change does not only depend on the heritability of traits, but also on the observed
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phenotypic variation. Therefore, improvement of nongenetic factors in the flocks such as ewe nutrition before mating and late pregnancy can lead to the improvement of these characteristics. There were significant permanent environmental effects related to repeated records of ewes. The influence of service sire considered as an uncorrelated random effect on basic ewe productivity was negligible for all traits even for litter weight traits. Genetic correlations of CR with other binomially distributed reproduction traits are generally negative in sign and mostly not significant. Genetic correlations among other reproduction traits were positive and ranged from moderate to very high. Selection based on such traits may lead to correlated genetic responses in the total weaning weight per ewe exposed. References Afolayan, R., Fogarty, N., Gilmour, A., Ingham, V., Gaunt, G., Cummins, L., 2008. Reproductive performance and genetic parameters in first cross ewes from different maternal genotypes. J. Anim. Sci. 86, 804–814. Akaike, H., 1974. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723. Bromley, C., Van Vleck, L., Snowder, G., 2001. Genetic correlations for litter weight weaned with growth, prolificacy, and wool traits in Columbia, Polypay, Rambouillet, and Targhee sheep. J. Anim. Sci. 79, 339–346. Ceyhan, A., Sezenler, T., Erdogan, I., 2009. The estimation of variance components for prolificacy and growth traits of Sakiz sheep. Livest. Sci. 122, 68–72. Cloete, S., Gilmour, A., Olivier, J., van Wyk, J., 2004. Genetic and phenotypic trends and parameters in reproduction, greasy fleece weight and liveweight in Merino lines divergently selected for multiple rearing ability. Aust. J. Exp. Agric. 44, 745–754. Duguma, G., Schoeman, S., Cloete, S., Jordaan, G., 2002. Genetic and environmental parameters for ewe productivity in Merinos. S. Afr. J. Anim. Sci. 32, 154–159. Ekiz, B., Ozcan, M., Yilmaz, A., Ceyhan, A., 2005. Estimates of phenotypic and genetic parameters for ewe prolificacy traits of Turkish Merino (Karacabey Merino) Sheep. Turk. J. Vet. Anim. Sci. 29, 557–564. Fogarty, N., 1995. Genetic parameters for live weight, fat and muscle measurements, wool production and reproduction in sheep: a review. Anim. Breed. Abstr. 63, 101–143. Gianola, D., Foulley, J., 1983. Sire evaluation for ordered categorical data with a threshold model. Genet. Sel. Evol. 15, 201–224. Gilmour, A., Gogel, B., Cullis, B., Thompson, R., 2006. ASReml User Guide Release 2.0. VSN International Ltd, Hempstead, UK. Hanford, K.J., Van Vleck, L.D., Snowder, G., 2002. Estimates of genetic parameters and genetic change for reproduction, weight, and wool characteristics of Columbia sheep. J. Anim. Sci. 80, 3086–8098. Hanford, K.J., Van Vleck, L.D., Snowder, G., 2006. Estimates of genetic parameters and genetic trend for reproduction, weight, and wool characteristics of Polypay sheep. Livest. Sci. 102, 72–82. Head, W., Hatfield, P., Fitzgerald, J., Hallford, D., Petersen, M., Stellflug, J., 1995. Effects of lifetime selection for kilograms of lamb weaned per ewe on ewe milk production, ewe and lamb feed intake and body weight change. Sheep Goat Res. J. 11, 78–83. Kadarmideen, H.N., Thompson, R., Coffey, M.P., Kossaibati, M.A., 2003. Genetic parameters and evaluations from single-and multiple-trait
analysis of dairy cow fertility and milk production. Livest. Prod. Sci. 81, 183–195. Kosgey, I., Baker, R., Udo, H., Van Arendonk, J., 2006. Successes and failures of small ruminant breeding programmes in the tropics: a review. Small Rumin. Res. 61, 13–28. Matika, O., van Wyk, J., Erasmus, G., Baker, R., 2003. Genetic parameter estimates in Sabi sheep. Livest. Prod. Sci. 79, 17–28. Matos, C., Thomas, D., Gianola, D., Tempelman, R., Young, L., 1997. Genetic analysis of discrete reproductive traits in sheep using linear and nonlinear models: I. Estimation of genetic parameters. J. Anim. Sci. 75, 76–87. Maxa, J., Norberg, E., Berg, P., Pedersen, J., 2007. Genetic parameters for growth traits and litter size in Danish Texel, Shropshire, Oxford Down and Suffolk. Small Rumin. Res. 68, 312–317. Mokhtari, M., Rashidi, A., Esmailizadeh, A., 2010. Estimates of phenotypic and genetic parameters for reproductive traits in Kermani sheep. Small Rumin. Res. 88, 27–31. Rao, S., Notter, D., 2000. Genetic analysis of litter size in Targhee, Suffolk, and Polypay sheep. J. Anim. Sci. 78, 2113–2120. Rashidi, A., Mokhtari, M., Esmailizadeh, A., Asadi Fozi, M., 2011. Genetic analysis of ewe productivity traits in Moghani sheep. Small Rumin. Res. 96, 11–15. Rosati, A., Mousa, E., Van Vleck, L., Young, L., 2002. Genetic parameters of reproductive traits in sheep. Small Rumin. Res. 43, 65–74. Safari, A., Fogarty, N.M., 2003. Genetic Parameters for Sheep Production Traits: Estimates from the Literature. NSW Agriculture, Orange, Australia. Safari, E., Fogarty, N., Gilmour, A., 2005. A review of genetic parameter estimates for wool, growth, meat and reproduction traits in sheep. Livest. Prod. Sci. 92, 271–289. Safari, E., Fogarty, N., Gilmour, A., Atkins, K., Mortimer, S., Swan, A., Brien, F., Greeff, J., Van Der Werf, J., 2007a. Across population genetic parameters for wool, growth, and reproduction traits in Australian Merino sheep. 2. Estimates of heritability and variance components. Aust. J. Agric. Res. 58, 177–184. Safari, E., Fogarty, N., Gilmour, A., Atkins, K., Mortimer, S., Swan, A., Brien, F., Greeff, J., Van Der Werf, J., 2007b. Genetic correlations among and between wool, growth and reproduction traits in Merino sheep. J. Anim. Breed. Genet. 124, 65–72. SAS Institute, 2002. SAS User’s Guide Version 9.1: Statistics. SAS Institute Inc., Cary, NC. Smith, C., 1967. A note on the improvement of a trait by selecting on its components. Anim. Prod. 9, 127–130. Snowder, G.D., Stellflug, J.N., Van Vleck, L.D., 2002. Heritability and repeatability of sexual performance of rams. J. Anim. Sci. 80, 1508–1511. Tavakolian, J., 1999. The Genetic Resources of Native Farm Animals of Iran. Animal Science Research Institute of Iran (in Persian). Van Vleck, L.D., Snowder, G., Hanford, K.J., 2003. Models with cytoplasmic effects for birth, weaning, and fleece weights, and litter size at birth for a population of Targhee sheep. J. Anim. Sci. 81, 61–67. van Wyk, J., Fair, M., Cloete, S., 2003. Revised models and genetic parameter estimates for production and reproduction traits in the Elsenburg Dormer sheep stud. S. Afr. J. Anim. Sci. 33, 213–222. Vanimisetti, H., Notter, D., Kuehn, L., 2007. Genetic (co)variance components for ewe productivity traits in Katahdin sheep. J. Anim. Sci. 85, 60–68. Vatankhah, M., Talebi, M., Edriss, M., 2008. Estimation of genetic parameters for reproductive traits in Lori-Bakhtiari sheep. Small Rumin. Res. 74, 216–220. Weller, J., Ron, M., 1992. Genetic analysis of fertility traits in Israeli Holsteins by linear and threshold models. J. Dairy Sci. 75, 2541–2548. Zhang, C.Y., Chen, S.L., Li, X., Xu, D.Q., Zhang, Y., Yang, L.G., 2009. Genetic and phenotypic parameter estimates for reproduction traits in the Boer dam. Livest. Sci. 125, 60–65.
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Small Ruminant Research journal homepage: www.elsevier.com/locate/smallrumres
Genetic analysis of ewe productivity traits in Makooei sheep Hossein Mohammadi ∗ , Mohammad Moradi Shahrbabak, Hossein Moradi Shahrbabak Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
a r t i c l e
i n f o
Article history: Received 25 August 2011 Received in revised form 16 April 2012 Accepted 18 April 2012 Available online 10 May 2012 Keywords: Genetic correlation Heritability Sheep
a b s t r a c t This study reports genetic and environmental (co)variance components for productivity traits of Makooei sheep, using between 2647 and 3418 records obtained from 1429 ewes. Data were collected during 1996–2009 on the Breeding Station of Makooei sheep, located in Makoo in the west-Azerbaijan province of Iran. Genetic parameters were estimated for three basic and six composite traits. The basic traits were conception rate (CR), number of lambs born (NLB) and number of lambs alive at weaning (NLAW). The composite traits were number of lambs born per ewe exposed (NLBEE), number of lambs weaned per ewe exposed (NLWEE), total litter weight at birth per ewe lambed (TLBW), total litter weight at weaning per ewe lambed (TLWW), total litter weight at birth per ewe exposed (TLBWEE) and total litter weight at weaning per ewe exposed (TLWWEE). Genetic parameters were estimated with univariate and bivariate models using restricted maximum likelihood (REML) procedures. Random effects were explored by fitting additive direct genetic effects, permanent environmental effects due to the animal as well as service sire effects in different models for ewe productivity. The most appropriate model for each trait was determined based on likelihood ratio tests and Akaike’s Information Criterion (AIC). Direct heritability estimates for CR, NLB, NLAW, NLBEE, NLWEE, TLBW, TLWW, TLBWEE and TLWWEE were 0.05 ± 0.02, 0.11 ± 0.01, 0.06 ± 0.01, 0.08 ± 0.02, 0.04 ± 0.02, 0.17 ± 0.03, 0.12 ± 0.02, 0.13 ± 0.02 and 0.10 ± 0.02, respectively. The estimate for the permanent environmental variance due to the animal ranged from 0.03 ± 0.02 for CR to 0.12 ± 0.01 for NLAW, whereas service sire effects ranged from 0.02 ± 0.01 for TLWW to 0.05 ± 0.01 for TLBW. Genetic correlation estimates among studied traits ranged from −0.13 for CR with TLWW to 0.97 for NLAW with NLWEE. Phenotypic correlation estimates were generally smaller in magnitude than genetic correlations. Service sire effects were found to be important only for litter weight traits. These estimates of genetic parameters may provide a basis for deriving selection indexes for reproductive traits. © 2012 Elsevier B.V. All rights reserved.
1. Introduction The Makooei sheep is one of the Iranian fattailed, medium-sized breeds. They are distributed in the mountainous areas of the country, especially in the westAzerbaijan province. They are valuable primarily for meat and also for their wool and milk. The wool produced is
∗ Corresponding author. Tel.: +98 261 224 80 82; fax: +98 261 224 6752. E-mail address: [email protected] (H. Mohammadi). 0921-4488/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.smallrumres.2012.04.019
coarse and usually used for carpet weaving (Tavakolian, 1999). Today, there are about 2,700,000 animals of Makooei sheep in the west-Azerbaijan. Due to the large population of this breed there is an increasing interest in its genetic improvement. The greatest part of the income in sheep farming is derived from lamb production. Efficiency of lamb production is controlled by reproduction, mothering ability and milk production of the ewe, as well as growth rate and survival of the lamb (Rao and Notter, 2000). Improvement in number or total weight of lamb weaned per ewe, as a key
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target, could partly be attained by increasing the number and weight of lambs produced per ewe within a specific year (Duguma et al., 2002). As reproductive traits have been identified as main determinants of profitability of sheep enterprises (Ekiz et al., 2005), improvement of these traits leads to more efficient lamb production. Improvement of ewe productivity and growth of lambs increases meat production. Such improvement needs a working knowledge of genetic and environmental factors contributing to the expression of each trait. The genetic characterization of the native breeds is required for conservation considerations and for establishing efficient selection and breeding schemes (Matika et al., 2003). Within breed selection of native breeds is an appropriate approach for genetic improvement in the traditional, low-input production systems of small ruminants in the tropics (Kosgey et al., 2006). Safari et al. (2005) showed that designing efficient selection and breeding strategies for genetic improvement and appropriate genetic evaluation of local breeds requires accurate estimation of genetic parameters. Previously, researchers identified increasing number of lambs at birth or weaning as the single most useful criterion to improve reproductive efficiency or net reproductive rate. Recently, the trait of interest has been total weight of lamb(s) weaned per ewe joined or lambing (Fogarty, 1995; Cloete et al., 2004). Estimates of genetic parameters for reproductive characteristics of different sheep breeds have been published by several authors (van Wyk et al., 2003; Ekiz et al., 2005; Hanford et al., 2006; Afolayan et al., 2008; Mokhtari et al., 2010; Rashidi et al., 2011). Estimation of genetic parameters for reproductive traits using an animal model to plan optimum designs for selection programs for this breed is scarce. Hence, reliable estimates of genetic parameters are needed to establish an efficient selection program for ewe productivity. Thus, the objective of this study was to estimate heritability and genetic correlations of reproductive traits for Makooei sheep using an animal model, that are necessary to develop efficient selection programs for the improvement of reproduction.
2. Materials and methods 2.1. Data and management The data set and pedigree information used in this research were reproductive traits of Makooei ewes collected from 1996 to 2009 on the Breeding Station of Makooei sheep, located in Makoo in the westAzerbaijan province of Iran. In general, the flock is managed under a semi-migratory system. Ewes are raised in an annual breeding cycle starting in August. The animals (males and females) were first mated at approximately 18 months of age. Ewes were kept in the flock for a maximum of 5 parities (approximately until the age of 7 years old). In order to avoid inbreeding, rams were allocated rotationally to each group of the ewes in different years. There is one breeding season in August–October. Hand mating was applied once a year. Ewes were assigned randomly to the rams and consequently, lambing commences in mid-January and continues until April. All lambs are identified at birth and birth weights, as well as sex, birth type and pedigree information are recorded. During the suckling period, lambs are fed with their mothers’ milk and also provided with dry alfalfa hay from 3 weeks of age. Lambs are weaned at approximately 100 days of age. The flock was mainly kept on pastures of low quality and quantity, but supplemental feeding was provided especially
around mating and during winter. The supplemental feed was included 2 kg alfalfa, 0.9 kg wheat straw and 0.4 kg barley per head per day. 2.2. Studied traits The traits analyzed can be assigned to two main categories: basic and composite traits (Table 1). Basic traits were conception rate (CR, with measure of 1 or 0, that is whether a ewe exposed to a ram did or did not lamb), total number of lambs born (NLB, the number of lambs born per ewe per lambing), number of lambs alive at weaning (NLAW). Conception rate is binary, but it is presumably based on continuous variation on the underlying liability scale that is expressed when a certain threshold is reached. Based on observation from basic traits, composite traits were derived. The composite traits with discrete numerical observation were number of lambs born per ewe exposed (NLBEE = CR × NLB) and number of lambs weaned per ewe exposed (NLWEE = CR × NLAW). Composite traits with continuous expression were total litter weight at birth per ewe lambing (TLBW), total litter weight at weaning per ewe lambing (TLWW), total litter weight at birth per ewe exposed (TLBWEE = CR × TLBW) and total litter weight at weaning per ewe exposed (TLWWEE = CR × TLWW). 2.3. Statistical analysis Important fixed effects for all traits were identified from preliminary analysis using the GLM procedure of SAS (SAS Institute, 2002). The fixed effects included in the final statistical model were lambing year in 14 classes (1996–2009) and ewe age at lambing in 6 classes (2–7 years old). Lamb age at weaning (in days) was fitted as a covariate for TLWW and TLWWEE, while lambing date was fitted as a covariate for NLB and NLAW. The interaction between lambing year and ewe age was not significant and was hence not considered in the final model. Pre adjustment of birth and weaning weights for the effect of lamb sex before calculation of total litter weight at birth and weaning for specific parity in individual ewes is of biological importance (van Wyk et al., 2003). Thus, TLWW and TLWWEE were pre-adjusted for lamb sex by means of multiplicative adjustment factors. The adjustment factors were determined using least squares analysis, then records of birth and weaning weight were adjusted accordingly. These adjusted records were used to calculate TLBW, TLWW, TLBWEE and TLWWEE. The variance components for the investigated traits were estimated by restricted maximum likelihood (REML), using the ASReml computer program (Gilmour et al., 2006). First, the following univariate models were fitted to each trait. Model 1 y = Xb + Z a a + e Model 2 y = Xb + Z a a + Z s s + e Model 3 y = Xb + Z a a + Wpe + e Model 4 y = Xb + Z a a + Z s s + Wpe + e where y is a vector of records on the respective traits; b, a, s, pe and e are vectors of fixed, direct additive genetic, service sire, permanent environmental related to repeated records of ewes and residual effects, respectively. Also, X, Za , Zs and W stand for design matrices associating the corresponding effects with elements of y. It was assumed that additive genetic effects, service sire effects, permanent environmental effects related to repeated records of ewes and residual effects to be normally distributed with mean 0 and variances are A 2a , I s 2s , I d 2pe and I n 2e , respectively. Also 2a , 2s , 2pe , and 2e are direct additive genetic variance, service sire variance, permanent environmental variance related to repeated records of the ewes and residual variance, respectively. A is the additive numerator relationship matrix, Is , Id and In are identity matrices with order equal to the number of sires, ewes and records, respectively. The importance of random effects was tested using a likelihood ratio test. A large number of the ewes’ dams were unknown limiting the study of maternal genetic and animal permanent environmental effects. In order to estimate the direct genetic and phenotypic correlations bivariate analyses applying the most appropriate model as in univariate analysis, were carried out. The fixed effects included in the bivariate animal models were those in univariate analyses. In order to determine the most appropriate
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Table 1 Number of records, mean, standard error (S.D.) and coefficient of variation (C.V.) for reproductive traits of Makooei ewes. Traita
No. of records
No. of ewes
No. of sires of the ewes
Mean
S.D.
C.V. (%)
CR NLB NLAW NLBEE NLWEE TLBW TLWW TLWBEE TLWWEE
3418 3190 3190 3418 3418 2746 2647 3395 3372
1429 1304 1304 1429 1429 1387 1387 1387 1387
206 197 197 206 206 188 188 188 188
0.93 1.16 0.98 1.07 0.91 4.29 26.44 3.95 24.78
0.26 0.39 0.47 0.41 0.49 1.68 8.43 1.91 9.86
27.95 33.62 48.05 38.31 53.84 39.16 31.88 48.36 39.79
a CR: conception rate; NLB: number of lambs born per ewe lambing; NLAW: number of lambs alive at weaning; NLBEE: number of lambs born per ewe exposed; NLWEE: number of lambs weaned per ewe exposed; TLBW: total litter weight at birth; TLWW: total litter weight at weaning; TLWBEE: total litter weight at birth per ewe exposed; TLWWEE: total litter weight at weaning per ewe exposed; S.D.: standard deviation; C.V.: coefficient of variation.
model for estimating (co)variance components for each traits Akaike’s information criterion (AIC) was used as follows (Akaike, 1974): AICi = −2 log Li + 2pi where log Li is the maximized log likelihood of model i at convergence and pi is the number of parameters obtained from each model; and model with the lowest AIC was chosen as the most appropriate model.
3. Results and discussion 3.1. Fixed effects The effects of year and age of ewe were significant (P < 0.01) for CR, NLB, NLAW, NLBEE and NLWEE. Ewes mated in 1997 and 1999 had lower performance than those mated in other years. The poorest performance was recorded in two-year-old ewes. There was a tendency for the productivity of ewes to improve with age, generally reaching a maximum between four and seven years of age. The effect of production years on TLBW, TLWW, TLBWEE and TLWWEE were significant (P < 0.05), and the lowest performances for these traits were recorded in production period from 1997 to 2000. Traits that included weaning weight (TLWW and TLWWEE) were significantly affected by lamb age at weaning (P < 0.01). The significant influence of lambing year on reproductive traits in the present study can be explained by variation in climatic conditions and nutritional quality over the years. Significant effects of year on reproductive traits of different sheep breeds have been reported by several authors (Bromley et al., 2001; Ekiz et al., 2005; Vatankhah et al., 2008). There was a general tendency for TLBW, TLWW, TLBWEE and TLWWEE to improve with an increase in ewe age. Differences in maternal effects, nursing and maternal behavior of ewe at different ages are reasons for the significant effects of ewe age on lamb live weight traits used in the calculation of TLBW, TLWW, TLBWEE and TLWWEE. Significant effects of ewe age on reproductive traits of sheep have been reported in literature (Rosati et al., 2002; Ekiz et al., 2005; Ceyhan et al., 2009; Rashidi et al., 2011). Contrary to this study, Mokhtari et al. (2010) reported that NLB and NLAW of Kermani sheep have not been significantly influenced by ewe age at lambing. Because of the changes in climatic conditions and nutritional quality over the years, the significant effect of lambing year on the studied traits was expected.
3.2. (Co) variance components and genetic parameters All traits were analyzed using a linear model. CR and NLB and NLAW have a discrete distribution, which imply that a threshold model (Gianola and Foulley, 1983) should theoretically be preferred. However, assumption of a continuous distribution for these traits is justified for genetic evaluation and for estimates of genetic correlations with continuous traits (Kadarmideen et al., 2003). Threshold and linear models showed very little differences in genetic parameters for CR (Weller and Ron, 1992) and NLB as well as NLAW (Matos et al., 1997). The most appropriate models were Model 3 (including direct additive genetic effects as well as permanent environmental effects related to animals) for CR, NLB, NLAW, NLBEE, NLWEE and Model 4 (including direct additive genetic effects, permanent environmental effects related to animals and service sire effects) for TLBW, TLWW, TLBWEE and TLWWEE. These results were consistent with those of Van Vleck et al. (2003). Estimates of variance components, heritability, permanent environmental variance to phenotypic variance and service sire variance to phenotypic variance for traits are presented in Table 2. The heritability estimate for conception rate was 0.05 ± 0.02 in this study. Coefficient of heritability of this trait in Australian Merino sheep were 0.045 ± 0.005 has been reported Safari et al. (2007a), which is consistent with the results of this study. Low estimates of heritability of conception rate can be due to the importance of environmental factors on the variability of this trait and it was attributed to the discontinuous nature of the data (Matika et al., 2003; Maxa et al., 2007). Although conception rate is of high economic importance, genetic improvement may be reduced owing to low levels of additive variation. Heritability for number of lambs born per ewe lambing was estimated at 0.11 ± 0.01. In two separate reports of heritability weighted mean number of lambs born per ewe lambing to the 0.13 ± 0.01 and 0.10 ± 0.01 reported (Fogarty, 1995; Safari et al., 2005), while Hanford et al. (2006) reported a heritability estimate of 0.11 ± 0.02 for this trait, which is consistent with the present results. According to Ekiz et al. (2005) and van Wyk et al. (2003) heritability estimates for NLB amounted to respectively 0.053 for Turkish merino and 0.059 for Dormers. The heritability estimate for number of lambs born per ewe exposed
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Table 2 Estimates of variance components, genetic and phenotypic parameters from single-trait analysis for reproductive traits of Makooei ewes. Traita
2a
2pe
2s
2e
2p
h2d ± S.E.
CR NLB NLAW NLBEE NLWEE TLBW TLWW TLBWEE TLWWEE
0.924 0.604 1.65 1.529 0.549 4.73 5.887 2.724 6.271
0.634 0.265 3.172 1.368 0.871 2.431 1.945 0.833 3.052
– – – – – 1.451 1.083 0.619 2.274
14.57 4.665 22.368 10.458 9.753 18.523 38.449 15.476 50.03
16.13 5.334 26.19 18.845 11.173 27.135 47.364 19.652 61.627
0.05 0.11 0.06 0.08 0.04 0.17 0.12 0.13 0.10
± ± ± ± ± ± ± ± ±
0.02 0.01 0.01 0.02 0.02 0.03 0.02 0.02 0.02
pe2 ± S.E. 0.03 0.04 0.12 0.09 0.07 0.08 0.04 0.04 0.04
± ± ± ± ± ± ± ± ±
0.02 0.01 0.01 0.03 0.01 0.02 0.01 0.02 0.02
s2 ± S.E. – – – – – 0.05 ± 0.02 ± 0.03 ± 0.03 ±
0.01 0.01 0.01 0.01
2a : direct variance genetic; 2pe : permanent environmental variance; 2s : service sire variance; 2e : residual variance; 2p : phenotypic variance; h2d : direct heritability; pe2 : ratio of permanent environmental variance on phenotypic variance; s2 : ratio of service sire variance to phenotypic variance; S.E.: standard error. a For traits abbreviations see footnote of Table 1.
was 0.08 ± 0.02. This estimate is lower than for number of lambs born per ewe lambing, but consistent with the weighted mean derived from literature values for this trait (Fogarty, 1995; Safari et al., 2005). Because the number from lambs born per ewe exposed is composed from conception rate and number of lambs born per ewe lambing, and the heritability for conception rate was low, it is reasonable to expect that this would impact on the heritability of the composite trait. The heritability estimates for number of lambs alive at weaning per ewe lambing (0.06 ± 0.01) and per ewe exposed (0.04 ± 0.02) were higher than those in literature (Fogarty, 1995; Safari et al., 2005). Ekiz et al. (2005) and van Wyk et al. (2003) reported heritability estimates for this trait as respectively 0.043 for Turkish merino and 0.026 for Dormers. The estimate of direct heritability for number of lambs weaned per ewe exposed was lower than for number of lambs born per ewe lambing probably because the loss of lambs from birth to weaning is more related to environmental effects and to genotypes of lambs than to the genotypes of the ewes. Total litter weight at birth per ewe lambing measures the capacity of the ewes to produce lambs weight at birth without considering the number of lambs born. Observations of the trait are continuous and can be considered approximately normally distributed although skewed to the right. The estimate of heritability for total litter weight at birth per ewe lambing was 0.17 ± 0.03, which is consistent with a pooled estimate, reported by Fogarty (1995) and the weighted mean reported by Safari et al. (2005). This large estimate of heritability seems to offer the possibility to select for total litter weight at birth per ewe lambing. Selection for productivity can also be applied through total litter weight at birth per ewe lambing because of the high genetic correlation estimates that total litter weight at birth per ewe lambing has with other reproduction traits (Table 3). Selection intensity could be larger if out-of-season breeding were successful; in fact generation interval might be reduced for observation of total litter weight at birth per ewe lambing that is obtained at birth. Therefore, genetic trends can be larger when generation intervals are reduced. The estimate of heritability for total litter weight at weaning per ewe lambing (0.12 ± 0.02) is similar to that
reported by Vanimisetti et al. (2007) in Katahdin breed (0.12). Cloete et al. (2004) studied lambs weaned per ewe joined, which corresponded closer with TLWWEE in the present study. Total litter weight at birth per ewe exposed is the combination of CR, NLB and mean litter weight per lamb born (LMWLB). The trait measures the ability of the ewe to produce lamb weight at birth after exposure to the ram. The estimation of heritability for total litter weight at birth per ewe exposed was 0.13 ± 0.02, which is similar to results in other breeds (Rosati et al., 2002). Total litter weight at weaning per ewe exposed expresses the ability of the ewe to produce lamb weight at weaning given exposure to the ram and is a combination of CR, NLAW and litter mean weight per lamb weaned (LMWLW). The range observed was large, and had a lower heritability than Total litter weight born per ewe exposed (0.10 ± 0.02). The trait has a low heritability estimate, possibly partly due to the unusual distribution of the trait and partly due to the number of possible environmental effects. Total litter weight at birth per ewe exposed could be considered for selection purpose because it measures a total productivity of the ewe for lamb-meat production for a breeding year. Permanent environmental heritability estimates for the investigated traits were low, and ranged from 0.03 for CR to 0.12 for NLAW. Estimates of variance due to permanent environmental effects were generally higher for composite traits than for basic traits except for NLW. The estimated fraction of variance due to permanent environmental effects of the ewe for NLW and NLWEE was higher than direct heritability while the estimated direct heritability was lower than the estimated fraction of variance due to permanent environmental effects of ewe for the other traits. Estimated values for permanent environmental of reproductive traits in the present study were consistent with those of Vatankhah et al. (2008) and Rashidi et al. (2011). Higher estimates have also been reported (Rosati et al., 2002; Safari et al., 2005). Inclusion of service sire effects together with direct additive genetic effects and permanent environmental effects related to repeated records of ewe significantly
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Table 3 Estimation of genetic (above diagonal) and phenotypic (below diagonal) correlations among productivity ewe in Makooei sheep (standard errors in parentheses). Traitsa
CR
NLB
NLAW
NLBEE
NLWEE
TLBW
TLWW
TLBWEE
TLWWEE
CR NLB NLAW NLBEE NLWEE TLBW TLWW TLWBEE TLWWEE
– 0.05 (0.02) 0.11 (0.03) 0.09 (0.01) 0.06 (0.07) 0.10 (0.03) 0.04 (0.02) 0.03 (0.02) 0.01 (0.03)
−0.02 (−0.01) – 0.32 (0.10) 0.57 (0.12) 0.22 (0.06) 0.32 (0.08) 0.27 (0.11) 0.29 (0.12) 0.16 (0.02)
−0.04 (0.02) 0.68 (0.11) – 0.27 (0.02) 0.33 (0.10) 0.19 (0.02) 0.35 (0.06) 0.18 (0.05) 0.14 (0.07)
0.19 (0.02) 0.94 (0.21) 0.60 (0.12) – 0.35 (0.04) 0.28 (0.06) 0.19 (0.02) 0.17 (0.01) 0.25 (0.03)
0.13 (0.06) 0.55 (0.14) 0.97 (0.25) 0.66 (0.11) – 0.19 (0.03) 0.38 (0.06) 0.16 (0.14) 0.31 (0.12)
−0.09 (0.05) 0.89 (0.11) 0.76 (0.17) 0.59 (0.22) 0.49 (0.24) – 0.46 (0.07) 0.53 (0.11) 0.24 (0.05)
−0.13 (0.03) 0.31 (0.08) 0.87 (0.19) 0.24 (0.16) 0.59 (0.10) 0.78 (0.12) – 0.37 (0.09) 0.46 (0.012)
−0.06 (0.02) 0.84 (0.10) 0.53 (0.10) 0.48 (0.19) 0.31 (0.12) 0.94 (0.14) 0.81 (0.05) – 0.33 (0.10)
−0.09 (0.05) 0.22 (0.06) 0.65 (0.11) 0.17 (0.03) 0.46 (0.25) 0.58 (0.14) 0.92 (0.06) 0.44 (0.13) –
a
For traits abbreviations see footnote of Table 1.
affected Log likelihoods. Thus, it seems that service sire had a significant influence on the studied composite traits of Makooei sheep. These estimates were lower than the corresponding direct heritability estimates. Estimates of service sire were near zero and varied from 0.02 for TLWW to 0.05 for TLBW. The estimates are in accordance with other research reports (Ceyhan et al., 2009; Zhang et al., 2009).
3.3. Correlation estimates Estimates of genetic and phenotypic correlations are shown in Table 3. The estimates of genetic correlations of CR with other traits were negative in sign, small in magnitude, and that the 95% confidence interval would include zero for all estimates with the exception of two (TLWW and TLBWEE), which is consistent with another report (Vatankhah et al., 2008) but much lower than weighted means in the literature review of Safari et al. (2005) and for Australian merino sheep Safari et al. (2007b). The low genetic correlation observed is partly due to the measurement method of our traits. As CR is a binary trait (0 or 1), NLB has a value only if fertility is 1 (success of lambing). Also, ewes not able to lamb by 2 years of age and to successive possible lambing times were culled which could influence the estimate of heritability and genetic correlations. The low estimation of genetic variances for CR and other reproductive traits can an important factor for the low heritability and genetic correlation between traits. Genetic and phenotypic correlation estimates between NLB and NLAW were positive and under medium to high which generally agree with the results of Safari and Fogarty (2003). Selection for each trait will cause an increase in the number of lambs at weaning. Genetic and phenotypic correlation estimates between NLB and NLAW with NLBEE and NLWEE were positive and high which generally agree with the values estimated by others (Hanford et al., 2002; Cloete et al., 2004). NLB and NLAW were positive and medium to high genetic correlations with TLBW and TLWW in terms of genetic effects. Vatankhah et al. (2008) and Mokhtari et al. (2010) reported the similar results. NLB or NLAW were positively and strongly correlated with TLBW and TLWW in terms of genetic effects. The low heritability of NLB or NLAW and the high genetic correlation estimates for NLB
and NLAW with TLBW and TLWW suggests that indirect selection for NLB or NLAW may be useful through selection for TLBW and/or TLWW. The estimate of the genetic correlation between TLBW and TLWW was high (0.78) implying that genes responsible for heavier weight of lambs at birth through number and weight of lamb may also influence milk production and thus mothering ability of the ewes from birth to weaning. Therefore, the high positive genetic correlation between TLBW and TLWW suggests that productivity can also be selected based on TLWW. Results of this study along with a companion study (Ceyhan et al., 2009) also provide the necessary parameters to determine whether selection directed at the composite traits described in this study is comparably as effective in improvement as selection on an index of the component traits (Smith, 1967). Although selection for improved ewe productivity is possible, caution must be practiced when predicting genetic merit for animals in different environments because of the possibility of a genotype × environment interaction. Caution is especially warranted for a trait like ewe productivity because of the potential for different management practices for twin 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). However, selection for increased ewe productivity within a single production system or environment is likely to result in animals that are optimally adapted to that production system or environment (Snowder et al., 2002).
4. Conclusions Heritability estimates were quite small for almost all traits. Only for litter weight traits larger heritability estimates were found. For some traits, such as CR, the small estimates may also be due to binomial measurement. Heritability estimates may also be influenced by other factors not considered in the model used, although the high phenotypic variance (e.g. coefficient of variation of 53% for NLWEE) means that high selection differentials can be achieved in effective breeding programs and response to selection predicted. Genetic change does not only depend on the heritability of traits, but also on the observed
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phenotypic variation. Therefore, improvement of nongenetic factors in the flocks such as ewe nutrition before mating and late pregnancy can lead to the improvement of these characteristics. There were significant permanent environmental effects related to repeated records of ewes. The influence of service sire considered as an uncorrelated random effect on basic ewe productivity was negligible for all traits even for litter weight traits. Genetic correlations of CR with other binomially distributed reproduction traits are generally negative in sign and mostly not significant. Genetic correlations among other reproduction traits were positive and ranged from moderate to very high. Selection based on such traits may lead to correlated genetic responses in the total weaning weight per ewe exposed. References Afolayan, R., Fogarty, N., Gilmour, A., Ingham, V., Gaunt, G., Cummins, L., 2008. Reproductive performance and genetic parameters in first cross ewes from different maternal genotypes. J. Anim. Sci. 86, 804–814. Akaike, H., 1974. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723. Bromley, C., Van Vleck, L., Snowder, G., 2001. Genetic correlations for litter weight weaned with growth, prolificacy, and wool traits in Columbia, Polypay, Rambouillet, and Targhee sheep. J. Anim. Sci. 79, 339–346. Ceyhan, A., Sezenler, T., Erdogan, I., 2009. The estimation of variance components for prolificacy and growth traits of Sakiz sheep. Livest. Sci. 122, 68–72. Cloete, S., Gilmour, A., Olivier, J., van Wyk, J., 2004. Genetic and phenotypic trends and parameters in reproduction, greasy fleece weight and liveweight in Merino lines divergently selected for multiple rearing ability. Aust. J. Exp. Agric. 44, 745–754. Duguma, G., Schoeman, S., Cloete, S., Jordaan, G., 2002. Genetic and environmental parameters for ewe productivity in Merinos. S. Afr. J. Anim. Sci. 32, 154–159. Ekiz, B., Ozcan, M., Yilmaz, A., Ceyhan, A., 2005. Estimates of phenotypic and genetic parameters for ewe prolificacy traits of Turkish Merino (Karacabey Merino) Sheep. Turk. J. Vet. Anim. Sci. 29, 557–564. Fogarty, N., 1995. Genetic parameters for live weight, fat and muscle measurements, wool production and reproduction in sheep: a review. Anim. Breed. Abstr. 63, 101–143. Gianola, D., Foulley, J., 1983. Sire evaluation for ordered categorical data with a threshold model. Genet. Sel. Evol. 15, 201–224. Gilmour, A., Gogel, B., Cullis, B., Thompson, R., 2006. ASReml User Guide Release 2.0. VSN International Ltd, Hempstead, UK. Hanford, K.J., Van Vleck, L.D., Snowder, G., 2002. Estimates of genetic parameters and genetic change for reproduction, weight, and wool characteristics of Columbia sheep. J. Anim. Sci. 80, 3086–8098. Hanford, K.J., Van Vleck, L.D., Snowder, G., 2006. Estimates of genetic parameters and genetic trend for reproduction, weight, and wool characteristics of Polypay sheep. Livest. Sci. 102, 72–82. Head, W., Hatfield, P., Fitzgerald, J., Hallford, D., Petersen, M., Stellflug, J., 1995. Effects of lifetime selection for kilograms of lamb weaned per ewe on ewe milk production, ewe and lamb feed intake and body weight change. Sheep Goat Res. J. 11, 78–83. Kadarmideen, H.N., Thompson, R., Coffey, M.P., Kossaibati, M.A., 2003. Genetic parameters and evaluations from single-and multiple-trait
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