Investigating maternal effects on production traits in Duroc pigs using animal and sire models.

J. Anim. Breed. Genet. ISSN 0931-2668

ORIGINAL ARTICLE

Investigating maternal effects on production traits in Duroc pigs using animal and sire models B. Dube1, S.D. Mulugeta2 & K. Dzama1 1 Department of Animal Sciences, Stellenbosch University, Matielend, South Africa 2 Animal Science Programme, North West University, Mmabatho, South Africa

Summary

Keywords Genetic selection; performance testing; pigs; variance components. Correspondence B. Dube, Department of Animal Sciences, Stellenbosch University, Private Bag X2, Matieland 7602, South Africa. Tel: +27 72 698 3463; Fax: +27 86 541 3332; E-mail: [email protected]; [email protected] Received: 27 May 2013; accepted: 2 December 2013

Variance components for production traits were estimated using different models to evaluate maternal effects. Data analysed were records from the South African pig performance testing scheme on 22 224 pigs from 18 herds, tested between 1990 and 2008. The traits analysed were backfat thickness (BFAT), test period weight gain (TPG), lifetime weight gain (LTG), test period feed conversion ratio (FCR) and age at slaughter (AGES). Data analyses were performed by REML procedures in ASREML, where random effects were successively fitted into animal and sire models to produce different models. The first animal model had one random effect, the direct genetic effects, while the additional random effects were maternal genetic and maternal permanent environmental effects. In the sire model, the random effects fitted were sire and maternal grand sire effects. The best model considered the covariance between direct and maternal genetic effects or between sire and maternal grand sire effects. Fitting maternal genetic effects into the animal model reduced total additive variance, while the total additive variance increased when maternal grand sire effects were fitted into the sire model. The correlations between direct and maternal genetic effects were all negative, indicating antagonism between these effects, hence the need to consider both effects in selection programmes. Direct genetic correlations were higher than other correlations, except for maternal genetic correlations of FCR with TPG, LTG and AGES. There has been direct genetic improvement and almost constant maternal ability in production traits as shown by trends for estimated (EBVs) and maternal breeding values (MBVs), while phenotypic trends were similar to those for EBVs. These results suggest that maternal genetic effects should be included in selection programmes for these production traits. Therefore, the animal–maternal model may be the most appropriate model to use when estimating genetic parameters for production traits in this population.

Introduction Growth traits constitute production traits and can be viewed as transitional traits linking feed provided to the animal to the final product, which is meat. By the same token, weight is an indication of how much © 2014 Blackwell Verlag GmbH

• J. Anim. Breed. Genet. 131 (2014) 279–293

muscle tissue an animal has, thus indicating the amount of meat to expect at slaughter. Feed provides the raw materials to produce the animal tissue and constitutes the highest proportion of production costs (Hoque et al. 2009). An efficient animal is expected to grow faster for a given amount feed consumed and doi:10.1111/jbg.12078

Maternal effects in pigs

reach the targeted weight in the shortest time possible. In this regard, fast growth, high feed efficiency and reaching slaughter weight early satisfy the producer, but they, however, do not address the needs of the consumer. Consumer satisfaction is achieved by producing leaner carcasses, and producers are also remunerated based on the carcass leanness (Dube et al. 2013). Thus, pig characteristics that are positive for profitability are high growth rate, low feed conversion ratio and low carcass fatness. Ordinarily, producers have consequently been improving the leanness of carcasses by selecting for reduced backfat thickness (Suzuki et al. 2005). The pig industry should therefore improve production traits to address the needs of the producer and consumer to ensure productivity and sustainability. Genetic improvement of these production traits relies on the proper adjustment of fixed effects. Some of the fixed effects in the literature known to affect production traits are age of dam, age at the beginning of test, herd of origin, year and season, sex and litter size (Ilatsia et al. 2008; Dube et al. 2011; Jafari et al. 2012). Genetic selection is the conventional way of ensuring that superiority in performance is transferred from one generation to the next, thus making genetic improvement cumulative and permanent. Production traits in pigs are controlled by two routes of gene expression, the direct animal effect and the maternal effect of the dam. Maternal effects represent the dam’s milk production, mothering ability and intrauterine circumstances, influenced by either genetic factors or environmental factors, or both genetic and environmental factors (Maniatis & Pollott 2002). Likewise, maternal effects constitute a sizeable source of variation in traits in young mammals (Maniatis & Pollott 2002). Moreover, maternal effect might also affect postweaning growth as a carry-over effect from weaning weight (Meyer et al. 1993). Overall, estimations of direct and maternal genetic parameters are prerequisites for implementing sound breeding programmes to improve economically important traits (Hoque et al. 2008). Ignoring maternal effects resulted in the overestimation of direct heritability in goats (Barazandeh et al. 2011), which may cause an upward bias in predicted responses to selection. This may be partly attributed to the negative correlation between direct and maternal genetic effects that have an effect of slowing down genetic progress in traits where maternal effects are important (Eler et al. 1994). Improvement of maternal response, in addition to direct response, can lead to greater overall response. Being that, overall response can be estimated by total herita280

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bility, which is the regression of an animal’s total genotype (direct and maternal) on its phenotype. A more direct method to estimate overall genetic response may be the use of total additive variance, which takes into account direct additive and maternal genetic variance, and the direct–maternal genetic covariance. Therefore, to properly implement genetic selection for production traits, breeding programmes should also consider maternal genetic effects. This can be achieved by using either an animal–maternal model or a sire–maternal grand sire model. Implementing genetic selection requires the estimation of variance components, to successfully improve growth performance and feed utilization and reduce carcass fatness. Similarly, evaluation of the direct genetic, maternal genetic and phenotypic improvements that have been achieved should also be part of any breeding programme, as it informs decision-making. Estimation of maternal effects has not been performed in South African (SA) Duroc pigs, and the direct genetic, maternal genetic and phenotypic performances have not been evaluated in this population. The objectives of this study were thus to: (i) evaluate the maternal effects and estimate the relationship between direct and maternal effects on production traits; and (ii) compute direct genetic, maternal genetic and phenotypic trends for production traits in SA Duroc pigs.

Materials and methods Animal management

Duroc pigs participating in Phase B of the SA Pig Performance Testing Scheme were brought to one of the three testing centres, Irene, Elsenburg and Cedara, over the period 1990 to 2008. Phase B performance testing is the on-station testing of animals selected from herds across the country, where artificial insemination was used for mating, and brought to the central test station. In this phase, every year each member submitted at least 44 pigs (50% boars and 50% gilts) for testing to the nearest testing station. These pigs represented a minimum of five herd sires per breed or line, or 50% of the herd sires per breed or line. On arrival, the pigs were treated for internal and external parasites and quarantined under the supervision of the responsible state veterinarian, were individually penned on solid concrete floors and fed until they commenced testing at 27 kg. During the test period, animals were individually housed and fed ad libitum using individual self-feeders and water was also available ad libitum © 2014 Blackwell Verlag GmbH



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from nipples. The feed provided to the pigs was compiled as shown in Table 1.

821 sires, 2772 dams and 237 HYS in the data for the other traits. Table 2 contains the summary statistics for the data.

Data

The data used for the study were obtained from the Integrated Recording and Genetic Information Systems (INTERGIS) of SA, while pedigree information was obtained from the Duroc breed society. The original data, which consisted of 23 231 pigs, were first edited by removing records greater or less than three standard deviations from the mean. Contemporary groups with fewer than five animals and/or fewer than two sires were removed to ensure connectedness in the data. Contemporary groups were created by concatenating herd of birth, year and season of arrival at the testing station (HYS). The two seasons of testing considered were summer (October–March) and winter (April–September). These pigs were performancetested between 1990 and 2008. The final data set for FCR consisted of 4862 animals from 15 herds, whereas the data for the other traits had 22 224 animals from 18 herds. In the data for FCR, there were 507 sires, 1454 dams and 170 HYS, and there were

Content

Yellow maize meal (%) Wheaten bran (%) Fish meal (%) Soya bean oil cake meal (%) Mono-calcium phosphate (%) Limestone powder (%) Salt (%) Molasses (%) Vitamins and minerals premix (%) Synthetic Lysine (%) Chemical composition* CP (%) Lysine (%) Fibre (%) Ca (%) P (%) Biotin (g/kg) Manganese (g/kg) Copper (g/kg) Zinc (g/kg) Iron (g/kg) Iodine (g/kg) Energy (DE) (MJ/kg) Fat (%)

67.00 10.44 12.50 5.46 0.23 0.37 1.00 3.00 0.15 0.15

18.0 1.1 6.0 0.80 0.70 0.13 52 10 120 171 0.2 14 4.0

*Figures on air dry basis.

© 2014 Blackwell Verlag GmbH

The production traits were divided into three groups, namely fatness, test period and lifetime traits. Backfat thickness (BFAT), which was taken using a Vet Backfat Scanner (FatScan) probe, 6.5 cm from the midline between the second and third last rib just before slaughter, was the fatness trait analysed. Test period weight gain (TPG) and feed conversion ratio (FCR) comprised the test period traits, while the lifetime traits were lifetime weight gain and age at slaughter (AGES). Test period weight gain was the average daily weight gain during the test period from 27 to 86 kg live weight, and the lifetime weight gain was the average daily weight gain from birth to 86 kg. Birth weight considered was average birth weight computed from litter weight, as there was no measurement of individual birth weights. Feed conversion ratio was calculated as kg of feed consumed to gain 1 kg of body weight during the test period. Statistical analysis

Table 1 Composition of the diet Items

Traits


Preliminary analyses were performed using the Mixed Procedure of SAS (SAS 2009) to determine the significance of the fixed effects, and the conditional F-tests were implemented in the form of the ANOVA method. In the final analyses, the fixed effects included comprised of sex and the HYS effects, and on the other hand, age of dam was included as a covariate. Information on litter size and individual birth weight was not available and therefore not included in the fixed effects. Age at the beginning of the test was included

Table 2 Summary statistics for the traits and covariates included in the analyses Traits

N

Mean

Min

Max

SD

BFAT (mm) TPG (g/day) LTG (g/day) FCR AGES (days) AGEB (days) AGED (days)

22 224 22 224 22 224 4862 22 224 22 224 22 224

11.8 891.5 623.9 2.26 143.2 74.0 547.8

5 613 192 1.27 112 50 283

30 1902 1024 3.73 182 102 1441

2.6 113 57 0.30 12.0 8.5 86

BFAT: backfat thickness; TPG: test period weight gain; LTG: lifetime weight gain; FCR: test period feed conversion ratio; AGES: age at slaughter; AGEB: age of animal at the start of the test; AGED: age of dam.

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as a covariate for fatness and test period traits. Table 3 shows the fixed effects fitted in the final analyses for each trait. There were fewer records for FCR because feed intake was only measured in a few animals due to the limited number of individual self-feeders. The data were subjected to analyses using an animal and then a sire model. In the animal model, random effects were successively fitted to construct six models (Models 1a to 6a). The random effects fitted into this model were direct animal genetic, maternal genetic and maternal permanent environmental effects. There was also successive fitting of random effects into the sire model, where the effects were sire and maternal grand sire effects to construct Models 1b to 3b. In these models, the covariances between direct and maternal genetic effects or between the sire and maternal grand sire effects were either computed or set to zero. Univariate analyses and then bivariate analyses were conducted using each model to estimate the variance and covariance components. The analyses were performed using REML procedures in ASREML (Gilmour et al. 2009), and the models used were the following: y = Xb + ZDaD + e y = Xb + ZDaD + ZMaM + e y = Xb + ZDaD + ZMaM + e y = Xb + ZDaD + ZCaC + e y = Xb + ZDaD + ZMaM + ZCaC + e y = Xb + ZDaD + ZMaM + ZCaC + e y = Xb + ZSaS + e y = Xb + ZSaS + ZMGSaMGS + e y = Xb + ZSaS + ZMGSaMGS + e

Cov(A, M) = 0

Model 1a Model 2a Model 3a Model 4a Model 5a

CovðA; MÞ ¼ ArADM

Model 6a

Cov(S, MGS) = 0 CovðS; MGSÞ ¼ ArAS;MGS

Model 1b Model 2b Model 3b

Cov(A, M) = 0 CovðA; MÞ ¼ ArADM

grand sire (aMGS) and residual effects (e). Incidence matrices X, ZD, ZM, ZC, ZS and ZMGS related fixed, direct genetic, maternal genetic, maternal permanent environmental, sire and maternal grand sire effects, respectively, to the observations. The covariance between direct and maternal genetic effects was rAM, while the covariance between sire and maternal grand sire effects was rS,MGS and A was the numerator relationship matrix. Log-likelihood values (log L) for the different models were compared using the test statistic D = 2 (log Lf log Ls), where log Lf is log L for a more complete model and log Ls is log L for a simpler model. A chi-square distribution with number of degrees of freedom equal to the difference in number of parameters (random effects) fitted for the two models was used to determine the associated significance level. Level of significance was set at p < 0.05, and comparisons generally involved progressively more complex models that differed by one degree of freedom (Kushwaha et al. 2009). The variance–covariance structures for the animal– maternal (A-M) and sire–maternal grand sire (SMGS) models were the following: 2 3 2 3 e In r2e 0 0 0 6u 7 6 0 0 7 Ar2A ArAM 6 A7 6 7 V6 7¼6 7 4 uM 5 4 0 0 5 ArAM Ar2A uC

0

2

3 2 uS Ar2S 6 7 6 V4 uMGS 5 ¼ 4 ArS;MGS e

where y was vector of observations, b is a vector of fixed effects, random effects consisted of direct additive genetic (aD), maternal genetic (aM), maternal permanent environmental (aC), sire (aS), maternal Table 3 Fixed effects fitted in the genetic analyses of production traits (insert under Statistical analyses)

BFAT TPG LTG FCR AGES

HYS

SEX

AGED

AGEB

9 9 9 9 9

9 9 9 9 9

9 9 9 9 9

9 9

9: significant factor; HYS: herd of birth, year and season of arrival at the testing station contemporary group; SEX: the sex of the animal; AGEB: age of animal at the start of the test; AGED: age of dam; BFAT: backfat thickness; TPG: test period weight gain; LTG: lifetime weight gain; FCR: test period feed conversion ratio; AGES: age at slaughter.

282

0

0

0 ArS;MGS Ar2MGS 0

IC r2C 0

3

7 0 5 In r2e

where I is an identity matrix. The (co)variance components obtained were used to calculate genetic parameters for the traits. Means of estimated breeding values (EBVs), maternal breeding values (MBVs) and least square means from the A-M model were computed within year and used to plot the respective trends. Total additive genetic variance (r2T ) was computed as follows: r2T ¼ r2A þ 2rAM þ r2M Estimates of direct heritability (h2A ), maternal heritability (h2M ) and maternal permanent environmental effects (C2) were calculated as ratios of estimates of r2A , r2M and r2C , respectively, to the phenotypic variance. The direct–maternal genetic correlation (rAM) was computed as the ratio of the estimates of rAM to the product of the square roots of estimates of r2A and r2M . Estimation of r2A , rAM and r2M from a S-MGS © 2014 Blackwell Verlag GmbH



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model was respectively computed as follows (Eaglen et al. 2012). r2A ¼ 4r2S rAM ¼ 4rS;MGS 2rS r2M ¼ 4r2MGS þ r2S 4rS;MGS

Results and discussion Model analyses

The log-likelihoods for the different models used are given in Table 4, where the best models within the AM and S-MGS models are in bold. These results show that Model 3a was the best model for all the traits from an A-M model, and similarly, Model 3b was the best model for all the traits from an S-MGS model. In these models, direct and maternal genetic effects were fitted and the covariance between direct and maternal genetic effects could be computed. A model containing direct and maternal genetic effects should therefore predict more precisely future progeny performance than a model that contains only direct genetic effects (Lykins et al. 2000). For the A-M model, the second best model was Model 6a, which in addition to Model 3a, fitted maternal permanent environmental effects. Model 6a also underlined the suitability of a model that contains direct and maternal genetic effects and also accounts for the covariance between the direct and maternal genetic effects. Jafaroghli et al. (2010), however, recommended models for growth traits in sheep that consisted of direct and maternal effects, without the covariance between direct and maternal genetic effects. In contrast, a model that consisted of direct genetic and permanent environmental effects was recommended for growth traits in Iranian sheep (Jafari et al.

2012). In addition, the low contribution made by maternal permanent environmental effects on these traits is revealed in Models 4a to 6a, which indicate the effects of considering maternal permanent environmental effects. Fitting maternal genetic, permanent environmental or maternal grand sire effects generally improved the models for most traits. Direct genetic variations decreased after maternal genetic, permanent environmental or maternal grand sire effects were fitted. Such model improvements and decrease in direct genetic variances have been reported (Jafaroghli et al. 2010; Jafari et al. (2012). The decrease caused by fitting maternal permanent environmental effects was higher than that caused by fitting maternal genetic effects when the direct–maternal genetic covariance was ignored in all the traits. This is consistent with the observations made by Jafaroghli et al. (2010). Without the direct–maternal genetic covariance, a better model was produced when direct genetic and maternal permanent environmental effects were fitted (Model 5a) than when direct and maternal genetic effects were fitted (Model 2a). Models 5a and 6a evaluated the effect of fitting maternal genetic effects together with maternal permanent environmental effects. When the direct– maternal genetic covariance is ignored, the direct genetic, maternal genetic and permanent environmental variances were lower than when there was covariance between direct and maternal genetic effects. Accounting for the direct–maternal genetic covariance produced a better model in all traits than when this covariance was ignored. Similar trends for direct genetic effects and opposite trends for maternal genetic and permanent environmental effects were obtained by Jafaroghli et al. (2010). Fitting both maternal genetic and permanent environmental effects did not produce significant results for FCR,

Table 4 Log-likelihoods for the different models

Model 1a Model 2a Model 3a Model 4a Model 5a Model 6a Model 1b Model 2b Model 3b

BFAT

TPG

LTG

FCR

AGES

22075.5 22085.1 20242.8 22028.3 22026.0 20577.4 6558.11 6543.47 6541.06

106209 106114 105255 106082 106079 106059 5220.20 5208.86 5205.05

83988.7 83903.1 83110.5 83873.7 83870.0 83862.6 1641.55 1622.87 1621.37

4614.47 4619.75 4753.66 4626.55

57574.6 57438.0 57212.3 57402.5 57396.3 57372.1 4274.25 4267.23 4266.87

5104.17 5115.04 5116.66

*Bold values are the highest log-likelihoods indicating the best model for a given trait within the A-M or S-MGS models. BFAT: backfat thickness; TPG: test period weight gain; LTG: lifetime weight gain; FCR: test period feed conversion ratio; AGES: age at slaughter.



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probably due to the fewer records analysed. Therefore, the most appropriate way to estimate genetic parameters for these production traits in this population would be to include maternal genetic effects in the model, and also consider the covariance between direct and maternal genetic effects. Variance components and heritability estimates

The estimates of variance components for the traits are presented in Table 5. The highest direct additive genetic variances were obtained using Models 1a and 1b, which ignored the maternal genetic, permanent environmental or maternal grand sire effects. Consequently, the direct heritability estimates were highest when the genetic effects were not partitioned into direct and maternal in the A-M model. Such an effect was less pronounced in the S-MGS model, where similar estimates were obtained in some traits when the

covariance between sire and maternal grand sire effects was considered. Heritability estimates from AM models were higher than those from the S-MGS models. This was different from the observation by Eaglen & Bijma (2009), where heritability estimates from an analysis of calving ease data using an A-M model were similar to those obtained using an S-MGS model in Holstein–Friesian cattle. Total additive variance in Table 6 gives the holistic measure of the genetic variance affecting farrowing and accounts for both the maternal and direct sources of variance. Its importance is that when a farmer makes decisions about a maternally effected trait, the population performances change in response to its direct and maternal breeding value. Estimates of total additive variance show how maternal variance and direct–maternal genetic covariance contribute to the total genetic variance. Bijma et al. (2007) described total additive variance as representing the total addi-

Table 5 Estimates of variance components for direct genetic (r2a ), maternal genetic (r2m ) and maternal permanent environmental (r2c ) BFAT Model 1a r2a Model 2a r2a r2m Model 3a r2a r2m ram Model 4a r2a r2c r2e Model 5a r2a r2m r2c Model 6a r2a r2m ram r2c Model 1b r2a Model 2b r2a r2m Model 3b r2a r2m ram

TPG

LTG

FCR

AGES

1.55 0.08

2569 139

302 17

0.018 0.002

31.4 17

1.22 0.09 0.21 0.02

1723 150 737 71

194 18 84 9

0.011 0.002 0.004 0.001

20.1 1.8 10.6 0.9

1.17 0.10 0.22 0.04 0.15 0.05

1792 177 932 102 374 114

211 21 86 11 32 13

0.009 0.002 0.006 0.003 0.003 0.002

27.7 2.6 16.9 1.5 9.5 1.8

1.20 0.09 0.16 0.02 1.79 0.05

1632 142 554 46 4136 84

184 10 65 6 570 0.11

0.010 0.002 0.004 0.001 0.035 0.001

19.1 1.7 7.5 0.6 49.3 1.0

1.17 0.09 0.05 0.02 0.13 0.02

1064 143 119 59 473 56

180 17 15 7 55 7

1.22 0.09 0.17 0.11

0.10 0.03 0.05 0.02

1945 253 420 547

191 83 103 59

215 29 42 62

18.8 1.7 1.8 0.8 6.3 0.7

23 10 12 7

27.1 5.2 8.9 7.6

2.62 1.24 1.52 0.74

2.06 0.31

1702 303

568 91

0.015 0.003

17.8 3.6

1.60 0.15 1.07 0.09

3006 293 2233 205

761 72 528 48

0.082 0.009 0.066 0.007

40.3 3.8 30.0 2.7

1.61 0.15 0.74 0.11 0.48 0.10

3039 295 1721 237 987 214

766 72 427 57 278 52

0.083 0.009 0.059 0.009 0.035 0.007

40.4 3.8 25.3 3.3 15.4 2.9

BFAT: backfat thickness; TPG: test period weight gain; LTG: lifetime weight gain; FCR: test period feed conversion ratio; AGES: age at slaughter.

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Table 6 Estimates of total additive genetic and environmental variance obtained using different models BFAT

TPG

Model 1a r2T r2e

1.55 0.08 1.69 0.05

2569 139 3937 84

Model 2a r2T r2e

1.43 0.08 1.80 0.05

Model 3a r2T r2e

LTG

FCR

AGES

302 17 543 11

0.018 0.002 0.032 0.002

31.4 17 46.8 1.0

2461 143 4133 87

278 17 571 11

0.015 0.002 0.035 0.002

30.7 1.7 49.2 1.0

1.09 0.07 1.47 0.05

1976 141 3760 97

234 17 522 12

0.009 0.003 0.033 0.002

25.6 1.8 44.3 1.4

Model 4a r2T r2e

1.20 0.09 1.79 0.05

1632 142 4136 84

184 10 570 0.11

0.010 0.002 0.035 0.001

19.1 1.7 49.3 1.0

Model 5a r2T r2e

1.23 0.09 1.80 0.05

1723 174 4148 85

195 18 572 11

20.6 1.8 49.3 1.0

Model 6a r2T r2e

0.97 0.07 1.50 0.05

1357 147 3974 105

160 18 554 13

14.5 1.7 45.1 1.39

Model 1b r2T r2e

2.06 0.31 3.88 0.08

1702 303 5139 104

568 91 1288 26

0.015 0.003 0.045 0.001

17.8 3.6 77.3 1.6

Model 2b r2T r2e

3.20 0.42 3.76 0.08

3032 449 4991 105

988 132 1242 26

0.024 0.004 0.044 0.001

27.6 5.0 76.2 1.6

Model 3b r2T r2e

1.79 0.34 3.77 0.08

1919 391 4995 105

527 104 1243 26

0.014 0.003 0.044 0.001

12.0 3.6 76.2 1.6

r2T : total additive variance; r2e : environmental variance; BFAT: backfat thickness; TPG: test period weight gain; LTG: lifetime weight gain; FCR: test period feed conversion ratio; AGES: age at slaughter.

tive value of an individual. However, it does not represent the impact of that individual on the population mean because this impact will depend on the time period and the frequency of expression of the direct and maternal effects in the population within that period (Eaglen et al. 2012). The total additive variance decreased from Models 1a to 6a and from Models 1b to 3b, which may suggest that failure to partition genetic variance and also ignore the direct–maternal genetic correlation may overestimate the heritability of a trait. Higher estimates were obtained in the S-MGS model compared to the A-M model for BFAT and LTG. For the test period traits, total additive variance from an A-M model was higher than that from an S-MGS model before maternal genetic effects were fitted. Consistent with © 2014 Blackwell Verlag GmbH


the findings of Eaglen et al. (2012), higher residual variances were obtained using the S-MGS models compared with A-M models. This can be attributed to the residual variance of the S-MGS model that contains the default environmental variance plus the Mendelian sampling term and the remaining additive variance terms (Eaglen et al. 2012). Direct heritability estimates (Table 7) obtained using an A-M model ranged from 0.35 0.04 for FCR to 0.48 0.02 for BFAT, whereas those obtained using an S-MGS model were in the range 0.19 0.03 to 0.35 0.03 for AGES and BFAT, respectively. Higher estimates for daily gain and backfat thickness (0.51 0.04 and 0.72 0.04, respectively) were obtained by Hoque et al. (2008) using a model similar to Model 1a in Japanese Duroc pigs. Ilatsia et al. 285


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Table 7 Direct heritability estimates (h2a ), maternal heritability (h2m ), correlations between direct and maternal genetic effects (ram), permanent environmental effects (c2) and total heritability (h2T ) BFAT

TPG

LTG

FCR

AGES

Model 1a h2a

0.48 0.02

0.39 0.02

0.36 0.02

0.35 0.04

0.40 0.02

Model 2a h2a h2m

0.38 0.03 0.06 0.01

0.26 0.02 0.11 0.01

0.23 0.02 0.10 0.01

0.22 0.05 0.07 0.02

0.25 0.02 0.13 0.01

Model 3a h2a h2m ram

0.43 0.03 0.08 0.01 0.29 0.08

0.29 0.03 0.15 0.02 0.29 0.07

0.27 0.03 0.11 0.01 0.24 0.08

0.20 0.05 0.13 0.06 0.40 0.30

0.35 0.06 0.21 0.02 0.44 0.05

Model 4a h2a c2

0.38 0.02 0.05 0.01

0.26 0.02 0.09 0.01

0.22 0.02 0.08 0.01

0.21 0.04 0.08 0.02

0.25 0.02 0.10 0.01

Model 5a h2a h2m c2

0.37 0.02 0.02 0.01 0.04 0.01

0.25 0.02 0.02 0.01 0.07 0.01

0.22 0.02 0.02 0.01 0.07 0.01

Model 6a h2a h2m ram c2

0.44 0.03 0.52 0.04

0.03 0.01 0.11 0.01

0.31 0.04 0.60 0.09

0.03 0.01 0.10 0.01

0.26 0.03 0.53 0.08

0.25 0.01 0.02 0.01 0.08 0.01

0.03 0.01 0.11 0.01

0.36 0.07 0.75 0.10

0.03 0.02 0.07 0.01

Model 1b h2a

0.35 0.03

0.25 0.03

0.31 0.03

0.25 0.04

0.19 0.03

Model 2b h2a h2m

0.28 0.03 0.18 0.02

0.21 0.03 0.16 0.03

0.26 0.03 0.19 0.02

0.20 0.03 0.15 0.03

0.17 0.03 0.10 0.02

Model 3b h2a h2m ram

0.32 0.04 0.12 0.04 0.37 0.15

0.25 0.04 0.09 0.03 0.24 0.18

0.30 0.04 0.15 0.04 0.42 0.13

0.23 0.04 0.10 0.04 0.33 0.18

0.19 0.03 0.09 0.03 0.60 0.15


(2008) observed a higher estimate (0.46 0.10) from a study using a model similar to Model 5a on Kenyan Large White pigs. Fitting the maternal genetic, permanent environmental or maternal grand sire effects reduced the direct heritability, which was consistently higher than the maternal heritability. Previous studies also reported reduced direct heritability estimates after fitting maternal effects (Hoque et al. 2008; Kushwaha et al. 2009). This may indicate an overestimation of the direct additive genetic variance when maternal genetic effects are ignored, causing an upward bias of selection responses (Bahreini-Behzadi et al. 2007). The direct and maternal heritability esti286

mates were relatively higher when the maternal genetic effects were correlated to the direct genetic effects than when the correlation was set to zero. An exception was the direct heritability of FCR that decreased from 0.22 0.05 to 0.20 0.05 when an A-M model was used. In Model 2a, the direct heritability estimates ranged from 0.22 0.05 to 0.38 0.03, while they ranged from 0.20 0.05 to 0.43 0.03 in Model 3a, showing a general increase. Heritability estimates in Models 2b and 3b were, respectively, ranging from 0.17 0.03 to 0.28 0.03 and 0.19 0.03 to 0.32 0.04. The direct–maternal genetic correlation probably had the effect of uncov© 2014 Blackwell Verlag GmbH



B. Dube et al.

ering some genetic effects that might have remained hidden when the correlation was not considered. This may be due to some direct and maternal gene interactions, where the two set of genes may have ‘paired up’ and could only be expressed and detected together. Maternal heritability estimates, contained in Table 7, increased from the range 0.06 0.01 to 0.13 0.01 (Model 2a) to the range 0.08 0.01 to 0.21 0.02 (Model 3a). The same increasing trend was observed after fitting maternal permanent environmental effects, which, however, reduced the maternal heritability estimates. On the other hand, the S-MGS Models 2b and 3b produced comparable maternal heritability estimates that ranged from 0.10 0.02 to 0.19 0.02 and 0.09 0.03 to 0.15 0.04, respectively. The S-MGS model may not be capable of revealing the effect of some sets of genes when there are possible direct–maternal gene interactions. These maternal heritability estimates are generally low and indicate the decline in maternal effects in these traits with age. The low maternal heritability shows that the role of maternal effects diminishes during the postweaning period (Snyman et al. 1997). As the piglets grow, environmental relations between piglets’ traits and maternal abilities, such as mothering ability and milk production, gradually decrease. Therefore, postweaning traits are important for breeding and animal performance, which is an indication of the expression of genes with direct additive influences in that period. Conversely, the current results are also evidence that maternal genetic influence might sustain into the postweaning growth period or throughout the entire life, as suggested by Snyman et al. (1997). In addition, these results show that ignoring the correlation between direct and maternal genetic effects may result in an underestimation of the direct heritability of traits, thus underestimating predicted responses. This underestimation may be exacerbated by the degrading effect of maternal genetic effects on direct genetic progress due to the negative direct– maternal genetic correlation (Chen et al. 2002; Chimonyo & Dzama 2007). Maternal permanent environmental effects in Table 7 were higher than the corresponding maternal genetic effects, contrary to the report by Hoque et al. (2008). These maternal permanent environmental effects were comparable to those obtained by Hoque et al. (2008), but lower than the 0.15 reported by Johnson et al. (2002) for backfat thickness. Fitting maternal permanent environmental effects caused reductions in direct and maternal genetic variances, which resulted in the correlation between direct and © 2014 Blackwell Verlag GmbH


maternal genetic effects almost doubling. These reductions in variances may indicate some ‘intersections’ of maternal permanent environmental effects with direct and maternal genetic effects, where portions of the direct and maternal genetic effects were taken up by the maternal permanent environmental effects. Thus, if maternal permanent environmental effects are to be properly computed, these ‘intersections’ should be determined. This may be accomplished by computing the environmental dam– offspring covariance. Conversely, this may indicate that most of the maternal permanent environmental effects are included in direct and maternal genetic effects, hence the lack of improvement of the model observed when maternal permanent environmental effects were fitted. Consequently, maternal permanent environmental effects may have caused an underestimation of direct and maternal genetic effects and inflated the direct–maternal genetic correlation. Therefore, maternal permanent environmental effects may not be necessary in genetic selection programmes for these traits in this population. Correlations between direct and maternal genetic effects

The correlations between direct and maternal genetic effects obtained using A-M and S-MGS models shown in Table 7 were all negative, consistent with previous literature estimates (Chen et al. 2002; Chimonyo & Dzama 2007; Eaglen & Bijma 2009). Hoque et al. (2008) found positive correlations between direct and maternal genetic effects. These direct–maternal genetic correlations almost doubled when maternal permanent environmental effects were fitted in A-M model. The estimates ranged from 0.24 0.08 for LTG to 0.44 0.05 for AGES before maternal permanent environmental effects were fitted. Fitting maternal permanent environmental effects produced estimates that ranged from 0.52 0.11 for BFAT to 0.75 0.07 for AGES. Estimates obtained using an S-MGS model were characterized by large standard errors and were different from those obtained using an A-M model. Higher estimates of 0.37 0.15, 0.42 0.13 and 0.60 0.15 for BFAT and lifetime traits (LTG and AGES), respectively, were obtained using the S-MGS model compared with those from the A-M model (0.29 0.08, 0.24 0.08 and 0.44 0.05, respectively). For test period traits (TPG and FCR), estimates from an AM model were higher than those from an S-MGS model. Such disparities between the two models have been reported in the literature. Gutierrez et al. (1997) 287


obtained direct–maternal genetic correlations for growth traits in Asturiana de los Valles beef cattle from A-M models that were higher than those from S-MGS models. The S-MGS model produced lower estimates than an A-M model in a study conducted by Eaglen et al. (2012). The antagonism suggests that both direct and maternal genetic effects should be considered to achieve optimum genetic progress (Johnson et al. 2002). The negative correlation between the direct and maternal genetic effects may also be considered to be caused by management differences in herds as suggested by Van Vleck et al. (1996) and shown in the study by MacNeil et al. (1998). If this antagonism is a result of management differences, then in production systems where management is uniform across herds, the direct–maternal genetic correlation may be ignored. Thus, negative correlations between direct and maternal genetic effects for growth traits were considered not to be biologically possible (Maniatis & Pollott 2002), which has, however, not been proven. Including the direct–maternal genetic correlation may therefore be considered to be an adjustment for management differences across herds. From a developmental perspective, some negative correlations provide checks and balances between direct and maternal effects for growth traits (Jafari et al. 2012). This may be ideal for the SA pig industry, which is characterized by vast climatic differences in areas where pig production takes place. Therefore, the observed negative direct–maternal genetic correlations in test period traits may indicate carry-over effects of management differences in the various herds where the animals originated. For fatness and lifetime traits, this may suggest that the effects of onfarm management could not be erased by on-station treatments. Maternal genetic effects and the correlation between direct and maternal genetic effects may thus be included in selection programmes to improve the accuracy of selection. Correlations among traits

Estimates of genetic, environmental and phenotypic correlations among the production traits obtained from Models 1a and 4a are presented in Table 8. Table 9 contains the estimates of direct genetic, maternal genetic, environmental and phenotypic correlations among the traits obtained using Models 3a and 6a. Generally, direct genetic correlations were comparatively higher than other correlations. Direct genetic correlations between the test period traits, TPG and FCR, were 054 0.05, 0.55 0.08 and 288

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0.64 0.06 from Models 1a, 3a and 4a, respectively. These moderate direct genetic correlations suggest that the traits can be adequately improved if they are both included in selection programmes. The direct genetic correlation for FCR with TPG and lifetime traits increased when the maternal genetic or maternal permanent environmental effect was fitted. Residual correlations were generally not affected by fitting the maternal permanent environmental effect. The lifetime traits (LTG and AGES) had direct genetic correlations of 0.98 0.03, 0.98 0.00, 0.99 0.00 and 0.97 0.01, obtained from Models 1a, 3a, 4a and 6a, respectively. Selecting for AGES would be expected to improve LTG; thus, there is no need to record both traits for improvement purposes. Direct genetic correlations between test period and lifetime traits from the four models ranged from 0.58 0.05 to almost unity. As expected, TPG and LTG had very high favourable direct genetic correlations, while AGES also had very high direct genetic correlations with these traits. These traits are probably controlled by similar genes, hence the very high genetic correlation. Age at slaughter can be considered as lifetime weight gain expressed differently, hence the very high genetic correlation between LTG and AGES. The direct genetic correlations between test period and lifetime traits were not affected by fitting the maternal genetic effect. Therefore, improvement of lifetime traits may be achieved by selecting for test period traits or vice versa. Low to moderate direct genetic correlations for the fatness trait (BFAT) with test period and lifetime traits were observed. A favourable direct genetic correlation between BFAT and FCR was obtained (0.26 0.07, 0.21 0.12 and 0.42 0.08) in Models 1a, 3a and 4a, respectively. Johnson et al. (1999) reported a comparable estimate of 0.40. The direct genetic correlations for BFAT with TPG and lifetime traits were, however, unfavourable. Selecting for reduced backfat thickness may result in reduced growing/finishing weight gain and delay reaching market weight. A similar observation was made by Chimonyo & Dzama (2007). Contrasting results where backfat was not correlated to average daily gain and slaughter age have been reported in pigs that were also fed ad libitum (Lo et al. 1992; Chimonyo & Dzama 2007). The different observation by Chimonyo & Dzama (2007) may be attributed to the study being conducted on the indigenous unselected Mukota pigs. Fitting the maternal genetic effect increased the direct genetic correlation of BFAT with TPG and lifetime traits. However, the direct genetic correlation between BFAT and FCR was reduced when the © 2014 Blackwell Verlag GmbH



B. Dube et al.

Table 8 Estimates of genetic (rg), environmental (re) and phenotypic (rp) correlations among the production traits from an animal–maternal model Model 1a

Model 4a re

rg

rp

rg

re

rp


0.18 0.18 0.26 0.16

TPG LTG FCR AGES

0.99 0.02 0.54 0.05 0.99 0.01

0.95 0.01 0.52 0.02 0.91 0.01

0.97 0.00 0.53 0.01 0.94 0.01

0.99 0.02 0.64 0.06 0.98 0.01

0.95 0.01 0.48 0.02 0.91 0.01

0.97 0.01 0.51 0.01 0.94 0.02

LTG FCR AGES

0.58 0.05 0.98 0.03

0.57 0.02 0.82 0.01

0.58 0.01 0.88 0.01

0.68 0.06 0.99 0.00

0.52 0.02 0.82 0.01

0.55 0.01 0.88 0.01

FCR AGES

0.82 0.03

0.70 0.01

0.75 0.01

0.88 0.03

0.67 0.01

0.74 0.01

0.04 0.04 0.07 0.04

0.14 0.17 0.07 0.10

0.02 0.02 0.03 0.02

0.16 0.18 0.15 0.12

0.01 0.01 0.02 0.01

0.14 0.17 0.42 0.11

0.04 0.04 0.08 0.04

0.14 0.17 0.10 0.08

0.02 0.02 0.03 0.02

0.14 0.17 0.21 0.07

0.01 0.01 0.01 0.11

BFAT: backfat thickness; TPG: test period weight gain; LTG: lifetime weight gain; FCR: feed conversion ratio; AGES: age at slaughter.

Table 9 Estimates of direct genetic (rg), maternal genetic (rm), environmental (re) and phenotypic (rp) correlations among production traits from an animal–maternal model Model 3a

Model 6a rm

rg

re

rp

rg


0.25 0.25 0.21 0.19

TPG LTG FCR AGES

0.99 0.01 0.55 0.08 0.99 0.01

0.98 0.01 0.86 0.09 0.97 0.01

0.95 0.00 0.51 0.02 0.90 0.01

0.97 0.01 0.53 0.01 0.94 0.01

LTG FCR AGES

0.63 0.08 0.98 0.00

0.86 0.09 0.97 0.01

0.56 0.02 0.82 0.01

0.58 0.01 0.89 0.01

FCR AGES

0.87 0.03

0.95 0.04

0.68 0.02

0.75 0.01

0.06 0.06 0.12 0.06

0.03 0.04 0.17 0.01

0.10 0.10 0.31 0.09

0.13 0.16 0.10 0.09

0.02 0.02 0.04 0.02

0.19 0.19 0.14 0.12

0.01 0.01 0.02 0.01

rm

re

rp

0.26 0.06 0.25 0.06

0.07 0.14 0.09 0.15

0.13 0.02 0.16 0.02

0.16 0.01 0.18 0.01

0.19 0.06

0.04 0.14

0.09 0.02

0.11 0.01

0.99 0.00

1.00 0.01

0.95 0.01

0.97 0.01

0.99 0.01

0.99 0.01

0.84 0.01

0.89 0.01

0.97 0.01

0.99 0.01

0.72 0.01

0.80 0.01


maternal genetic effect was fitted. On the other hand, fitting the maternal permanent environmental effect had no effect on the direct genetic correlation of BFAT with TPG and lifetime traits. The relationship between these traits may therefore be independent of the permanent environment provided by the dam. Maternal genetic correlations among the traits are shown in Table 9, where they ranged from © 2014 Blackwell Verlag GmbH


0.01 0.09 between BFAT and AGES to unity between TPG and LTG. The maternal genetic correlations of BFAT with other traits were lower than the corresponding direct genetic correlations. These maternal genetic correlations increased when the maternal permanent environmental effect was fitted, probably due to the reduced maternal genetic variance. Maternal genetic correlations for average 289


B. Dube et al.

daily gain with backfat thickness and feed conversion ratio of 0.33 0.06 and 0.22 0.04, respectively, were obtained by Hoque et al. (2008). The maternal genetic correlations between TPG and lifetime traits were almost unity. This may indicate that these traits share most of their maternal genes. Feed conversion ratio had very high maternal genetic correlations with TPG, LTG and AGES (0.86 0.09, 0.86 0.09 and 0.95 0.04, respectively), which were higher than the direct genetic, environmental and phenotypic correlations. This suggests that FCR may share more maternal genes with test period and lifetime traits than it shares direct genetic and environmental effects.

thickness had some improvement from 1990 until 2008, shown by the decreasing EBVs and phenotypes. Its MBVs were almost constant during this period. For TPG, LTG and AGES, the EBVs and MBVs were almost constant from 1990 to 1997. This may have caused the decline in phenotypic performance observed during the same period. The EBVs and MBVs for FCR were oscillating from 1990 to 2008, but they exhibited the known antagonism between direct and maternal genetic effects. Despite this, FCR showed some improvement in phenotypic performance. Generally, EBVs followed trends that were contrasting those of MBVs. Van Pelt & de Jong (2011) observed similar contrasting direct and maternal breeding value trends for liveability in the Netherlands Holstein cows. This may be due to the negative correlation between the direct and maternal genetic effects observed in this study and in the literature (Chen et al. 2002; Chimonyo & Dzama 2007; Eaglen & Bijma 2009). The trends for the two sets of

Trends

Figures 1–5 show the trends for means of the EBVs and MBVs (by birth years) and phenotypic performance obtained from the A-M model. Backfat 0.50

14.00 13.50 13.00 12.50

–0.50

12.00 11.50

–1.00

11.00 –1.50

–2.00

EBV

10.50

MBV

10.00

Phenotype

Mean phenotype

Mean EBV/MBV

0.00

9.50 9.00

–2.50 2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

Figure 1 Mean EBV, MBV and phenotypic trend for BFAT.

Year of birth

80.0

980

EBV

70.0

MGE

60.0

Phenotype

960 940 920

40.0

900

30.0 880

20.0

860

10.0

840

0.0 –10.0

820

–20.0

800 2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

Year of birth

290

Phenotype

Mean EBV/MBV

50.0

Figure 2 Mean EBV, MBV and phenotypic trend for TPG.




B. Dube et al.

0.010

1.6

0.005

1.4

0.000

1.2 1.0

–0.010 0.8 –0.015 0.6

–0.020 EBV

–0.025

0.4

MBV –0.030

0.2

Phenotype

–0.035

0.0 2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

Figure 3 Mean EBV, MBV and phenotypic trend for FCR.

Phenotype

Mean EBV/MBV

–0.005

Year of birth 30.00

680

EBV MBV

25.00

660

Phenotype 20.00 Mean EBV/MBV

10.00

620

5.00

Phenotype

640

15.00

600

0.00 580

–5.00 –10.00

560 2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

Figure 4 Mean EBV, MBV and phenotypic trend for LTG.

Year of birth 4.00

155

3.00

150

2.00

1.00 140 0.00

Mean phenotype

Mean EBV/MBV

145

135 –1.00

EBV 130

MBV

–2.00

Phenotype –3.00

125 2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996


1995


1994

breeding values had different gradients, thus were not mirror images. Slopes for the EBVs were steeper than those for MBVs, which may have been a result of selection for direct genetic improvement in these traits. The negative direct–maternal genetic correla-

1993

1992

1991

1990

Figure 5 Mean EBV, MBV and phenotypic trend for AGES.

Year of birth

tion might have implied deterioration in maternal ability if selection was targeted at direct genetic improvement. However, MBVs were almost constant for most traits, suggesting there is an optimum direct genetic progress where maternal ability can at least be 291


maintained. Thus, if maternal ability is also targeted for selection, there may be room for simultaneous direct and maternal genetic improvement. The magnitudes of their respective heritabilities may also explain the different slopes of the direct and maternal genetic effects. This indicates that despite the inferred cancelling effect of maternal genetic effects on direct genetic effects, implied by the negative direct–maternal genetic correlation, the net effect may be improved phenotypic performance, evidenced by improvements in phenotypic trends. The study therefore highlights the contribution made by genetic effects to phenotypic performance. Conclusions The results show that there is a substantial direct and maternal genetic variation in this population, which when maternal effects are ignored can lead to the overestimation of the heritability for these production traits. The best model for all the traits consisted of direct and maternal genetic effects, when the covariance between them was considered. Negative correlations between direct and maternal genetic effects may suggest that both direct and maternal effects should be considered in genetic selection programmes. Also, this can be used to adjust for management differences between herds. There may have been an underestimation of maternal genetic effects when maternal permanent environment effects were fitted. Consequently, correlations between direct and maternal genetic effects might have been inflated, and therefore, maternal permanent environmental effects may not be necessary in selection programmes for these production traits. Correlations among the traits show that most traits share more direct genetic effects than other effects. However, FCR shares more maternal genetic effects than direct genetic effects with TPG and lifetime traits. The trends show that there has been genetic and phenotypic improvement in production traits, where maternal genetic effects were almost constant. Both direct and maternal genetic improvement can be achieved if selection is targeted at the direct and maternal genetic effects. These trends also revealed the antagonism that existed between direct and maternal genetic effects.

Acknowledgements The data for this research were supplied by the INTERGIS hosted by the Agricultural Research Council Livestock Business Division in Irene, Pretoria. Special

292

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thanks are to Mr. SF. Voordwind, the Technical Manager for Pig and Poultry Recording and Improvement Scheme, AR-AP.

References Bahreini-Behzadi M.R., Eftekhar-Shahroudi F., Van Vleck L.D. (2007) Estimates of genetic parameters for growth traits in Kermani sheep. J. Anim. Breed. Genet., 124, 296– 301. Barazandeh A., Molaei Moghbeli S., Vatankhah M., Mohammadabadi M. (2011) Estimating non-genetic and genetic parameters of pre-weaning growth traits in Raini Cashmere goat. Trop. Anim. Health Prod., 44, 864– 867. Bijma P., Muir W.M., Van Arendonk J.A.M. (2007) Multilevel selection 1: Quantitative genetics of inheritance and response to selection. Genetics, 175, 277–288. Chen P., Baas T.J., Mabry J.W., Dekkers J.C.M., Koehler K.J. (2002) Genetic parameters and trends for lean growth rate and its components in U.S. Yorkshire, Duroc, Hampshire, and Landrace pigs. J. Anim. Sci., 80, 2062–2070. Chimonyo M., Dzama K. (2007) Estimation of genetic parameters for growth performance and carcass traits in Mukota pigs. Animal, 1, 317–323. Dube B., Mulugeta S.D., Van der Westhuizen R.R., Dzama K. (2011) Non-genetic factors affecting growth performance and carcass characteristics of two South African pig breeds. S. Afr. J. Anim. Sci., 41, 162–176. Dube B., Mulugeta S.D., Dzama K. (2013) Integrating economic parameters into genetic selection for Large White pigs. Animal, 7, 1231–1238. Eaglen S.A.E., Bijma P. (2009) Genetic parameters of direct and maternal effects for calving ease in Dutch Holstein-Friesian cattle. J. Dairy Sci., 92, 2229–2237. Eaglen S.O.E., Coffey M.P., Woolliams J.A., Wall E. (2012) Evaluating alternate models to estimate genetic parameters of calving traits in United Kingdom Holstein-Friesian dairy cattle. Genet. Sel. Evol., 44, 23–35. Eler J.P., Ferraz J.B.S., Lobo R.B., Josakian L.A. (1994) Genetic antagonism between growth and maternal ability in Nelore cattle. Rev. Brasil. Genet., 17, 59–64. Gilmour A.R., Gogel B.J., Cullis B.R., Thompson R. (2009) ASReml User Guide Release 3.0. VSN International Ltd, Hemel Hempstead, HP1 1ES, UK. Gutierrez J.P., Canon J., Goyache F. (1997) Estimation of direct and maternal genetic parameters for preweaning traits in the Asturiana de los Valles beef cattle breed through animal and sire and models. J. Anim. Breed. Genet., 114, 261–266. Hoque M.A., Kadowaki H., Shibata T., Suzuki K. (2008) Maternal and direct genetic parameters for production traits and maternal correlations among production and




B. Dube et al.

feed efficiency traits in Duroc pigs. Asian-Aust. J. Anim. Sci., 21, 961–966. Hoque M.A., Kadowaki H., Shibata T., Oikawa T., Suzuki K. (2009) Genetic parameters for measures of residual feed intake and growth traits in seven generations of Duroc pigs. Livest. Sci., 121, 45–49. Ilatsia E.D., Githinji M.G., Muasya T.K., Okeno T.O., Kahi A.K. (2008) Genetic parameter estimates for growth traits of Large White pigs in Kenya. S. Afr. J. Anim. Sci., 38, 166–173. Jafari S., Hashemi A., Manafiazer G., Darvishzadeh S., Farhadian M. (2012) Genetic analysis of growth rate in Iranian Makuie sheep breed. Ital. J. Anim. Sci., 11, 98– 102. Jafaroghli M., Rashidi A., Mokhtari M.S., Shadparvara A.A. (2010) (Co)Variance components and genetic parameter estimates for growth traits in Moghani sheep. Small Rumin. Res., 91, 170–177. Johnson Z.B., Chewning J.J., Nugent R.A. III (1999) Genetic parameters for production traits and measures of residual feed intake in large white swine. J. Anim. Sci., 77, 1679–1685. Johnson Z.B., Chewning J.J., Nugent R.A. III (2002) Maternal effects on traits measured during postweaning performance test of swine from four breeds. J. Anim. Sci., 80, 1470–1477. Kushwaha B.P., Mandal A., Arora A.L., Kumar R., Kumar S., Notter D.R. (2009) Direct and maternal (co) variance components and heritability estimates for body weights in Chokla sheep. J. Anim. Breed. Genet., 126, 278–287. Lo L.L., McLaren D.G., McKeith F.K., Fernando R.L., Novakofski J. (1992) Genetic analyses of growth, realtime ultrasound, carcass, and pork quality traits in Duroc and Landrace pigs: II. Heritabilities and correlations. J. Anim. Sci., 70, 2387–2396.



Lykins L.E., Bertrand J.K., Baker J.F., Kiser T.E. (2000) Maternal birth weight breeding value as an additional factor to predict calf birth weight in beef cattle. J. Anim. Sci., 78, 21–26. MacNeil M.D., Urick J.J., Snelling W.M. (1998) Comparison of selection by independent culling levels for belowaverage birth weight and high yearling weight with mass selection for high yearling weight in line 1 Hereford cattle. J. Anim. Sci., 76, 458–467. Maniatis N., Pollott G.E. (2002) The impact of data structure on genetic (co) variance components of early growth in sheep, estimated using an animal model with natural effects. J. Anim. Sci., 81, 101–108. Meyer K., Carrick M.J., Donnelly B.J.P. (1993) Genetic parameters for growth traits of Australian beef cattle from a multibreed selection experiment. J. Anim. Sci., 71, 2614–2622. SAS (2009) SAS User’s Guide. SAS Institute Inc., Cary, NC, USA. Snyman M.A., Olivier J.J., Erasmus G.J., Van Wyk J.B. (1997) Genetic parameter estimates for total weight of lamb weaned in Afrino and Merino sheep. Livest. Prod. Sci., 48, 111–116. Suzuki K., Irie M., Kadowaki H., Shibata T., Kumagai M., Nishida A. (2005) Genetic parameter estimates of meat quality traits, average daily gain, longissimus muscle area, backfat thickness, and intramuscular fat content in Duroc pigs selected for average. J. Anim. Sci., 83, 2058–2065. Van Van M., de Jong G. (2011) Genetic evaluation for direct and maternal liveability in The Netherlands. Interbull Bull., 44, 235–239. Van Vleck L.D., Gregory K.E., Bennett G.L. (1996) Direct and maternal genetic covariances by age of dam for weaning weight. J. Anim. Sci., 74, 1801–1805.

293

Investigating long noncoding RNAs using animal models.

Genomic prediction using models with dominance and imprinting effects for backfat thickness and average daily gain in Danish Duroc pigs.

Effects of alternative lamb production systems, terminal sire breed, and maternal line on ewe productivity and its components.

Genetic analyses of growth, real-time ultrasound, carcass, and pork quality traits in Duroc and Landrace pigs: I. Breed effects.

Effect of VRTN gene polymorphisms on Duroc pig production and carcass traits, and their genetic relationships.

DECR1 and ME1 genotypes are associated with lipid composition traits in Duroc pigs.

Investigating the role of polyunsaturated fatty acids in bone development using animal models.

An embryo transfer study of reciprocal cross differences in growth and carcass traits of Duroc and Landrace pigs.

Investigating human disease using stem cell models.

Characterization of the Fecal Microbial Communities of Duroc Pigs Using 16S rRNA Gene Pyrosequencing.

Effects of exercise on brain functions in diabetic animal models.

Animal Models of Maternal Immune Activation in Depression Research.

Effects of crossbreeding and gender on the carcass traits and meat quality of korean native black pig and duroc crossbred.

Genetic parameters of calving ease using sire-maternal grandsire model in Korean Holsteins.

Prenatal maternal and possible transgenerational epigenetic effects on milk production.

Effects of a chelated copper as growth promoter on performance and carcass traits in pigs.

Genome-wide association study and accuracy of genomic prediction for teat number in Duroc pigs using genotyping-by-sequencing.

The effects of 5-hydroxytryptamine1a receptor agonist, buspirone on the gastric fundus accommodation in an animal model using guinea pigs.

Animal-to-human extrapolation using compartmental models.

Effects of iodine level on performance and thyroid traits of growing pigs.

Inferring causal relationships between reproductive and metabolic health disorders and production traits in first-lactation US Holsteins using recursive models.

Age-related effects of alcohol from adolescent, adult, and aged populations using human and animal models.

Pharmacokinetic and pharmacological effects of β-hydroxyphosphocarnitine in animal models.

Genetic analyses of growth, real-time ultrasound, carcass, and pork quality traits in Duroc and Landrace pigs: II. Heritabilities and correlations.