Direct and maternal influences on the early growth, fattening performance, and carcass traits of pigs

Livestock Production Science 88 (2004) 199 – 212 www.elsevier.com/locate/livprodsci

Direct and maternal influences on the early growth, fattening performance, and carcass traits of pigs F.X. Solanes, K. Grandinson, L. Rydhmer, S. Stern, K. Andersson, N. Lundeheim * Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Funbo-Lo¨vsta Research Station, S-755 97 Uppsala, Sweden Received 26 February 2003; received in revised form 27 November 2003; accepted 2 December 2003

Abstract This study investigates how direct (h2) genetic effects, maternal (m2) genetic effects, and litter environmental (c2) effects influence the early growth, fattening performance, and carcass traits of pigs. This study provides new genetic knowledge about the relationship between early and later growth. Individual piglet weight records at birth, at 3 weeks, at weaning, and at 9 weeks were available for about 20,000 Yorkshire piglets. Performance records; average daily gain between birth and 90 kg live weight (DG0 – 90), between 25 and 90 kg live weight (DG25 – 90), and ultrasonic backfat thickness (at 90 kg live weight) were available for about 4000 pigs. The carcass records available for about 3000 pigs were linear backfat thickness, carcass length, and percentage of meat and bone in ham. Univariate and bivariate analyses were used to estimate (co)variance components. Piglet weight before weaning was influenced more by c2 and m2 than by h2. The m2 for individual piglet weight decreases with age from 0.18 to 0.09 between birth and 9 weeks, and h2 increases from 0.07 to 0.12. No significant m2 were present for DG0 – 90 and DG25 – 90. Estimation of m2 and the correlation between direct and maternal genetic effects depend on data structure and pedigree relationships. Low direct genetic correlations between piglet weight and growth during the fattening period were found (
0.00 to 0.37), which indicates together with the different origin (direct or maternal) of the genetic control for these traits the possibility to treat them separately in a breeding evaluation program. D 2003 Elsevier B.V. All rights reserved. Keywords: Heritability; Genetic variation; Pig weight; Backfat; Swine

1. Introduction Because growth rate is an important trait in pig production, it is included in almost all breeding

* Corresponding author. Tel.: +46-18-67-4542; fax: +46-18-674501. E-mail address: [email protected] (N. Lundeheim). 0301-6226/$ - see front matter D 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.livprodsci.2003.12.002

evaluations. Phenotypic measurements have traditionally included post-weaning growth (age at an ideal market weight or rate of gain from around 25 kg to market weight) and live animal backfat thickness. Breeding programs require accurate knowledge of genetic parameters for all traits incorporated in the breeding objective and selection index. Over the years, many studies have presented genetic parameters for performance traits (Clutter

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and Brascamp, 1998). Others have studied traits recorded on piglets (Hermesch et al., 2001; Kaufmann et al., 2000; Roehe, 1999). However, very few studies analyse pre-weaning and post-weaning growth together. During the last decades, litter size has been included in many breeding programs. The current genetic progress in litter size at birth increases the need for a broader breeding program, including piglet survival and growth (Rydhmer, 2000). Furthermore, modern piglet production characterised by larger sow units providing lower degree of supervision, and the increase in outdoor pig production over the last years, makes the sow more responsible for rearing her piglets. For livestock species, offspring are weaned at different ages: dairy cattle are weaned at birth; pigs are weaned between 3 and 5 weeks; sheep and beef cattle are weaned even later. Therefore, maternal influences are expected to differ between species. These maternal effects on growth are any maternal contributions that affect the phenotypic expression of offspring, excluding direct additive genes. The intrauterine environment, the mother’s milk production, and the mother’s care-taking are all maternal components that may be genetically and environmentally determined. Direct additive genetic effects, maternal additive genetic effects, permanent environmental effects, and litter environmental effects influence piglet weight. Maternal and litter environmental effects have been the main random effects for most analysed piglet traits (Hermesch et al., 2001; Kaufmann et al., 2000; Roehe, 1999). Robison’s (1972, 1981) literature review of maternal and direct effects concludes that maternal effects account for a significant portion of the variance also for traits that are manifested relatively late in life such as 140-day weight, carcass backfat, ovulation rate, and litter size. Bryner et al. (1992) found that in purebred Yorkshire boars tested between 36 and 104 kg a significant maternal genetic effect on average daily gain and backfat accounts for 23% and 11% of the variance, respectively. This study provides more genetic knowledge about the sow’s ability to produce fast growing piglets and the piglets’ growth rate during the pre-weaning and post-weaning period. In addition, this study examines the relationship between early growth, later growth, and carcass traits.

2. Material and methods 2.1. Data Data from Yorkshire piglets born at Funbo-Lo¨vsta Research Station (the Swedish University of Agricultural Sciences) from 1983 to 2001 were used as a base for the analyses. Since 1983, various research projects at the research station have focused on animal husbandry, animal nutrition, and animal genetics. From 1983 to 1989, the herd was used in a selection experiment to increase lean tissue growth rate in the interval 25 –90 kg in two lines, one line with a diet of low dietary protein content and one with a diet of high protein content (Stern et al., 1993, 1994). During 1990, the last generation of the selection experiment was evaluated on a commercial diet (Stern et al., 1995a). Later, animals from both selection lines were tested on either a high or a low dietary protein level diet to study the level of genotype-protein interaction (Stern et al., 1995b). Finally, the two selection lines were compared, fed either according to the most common commercial feeding system in Sweden, in which the same feed was provided throughout the entire growth span, or fed a two-phase feeding system, during 1991 (Stern and Johansen, 1996) and 1997 (Stern and Simonsson, 1999). The data included 25,715 animals with 25,500 piglets with records from 2309 litters from 1204 Yorkshire sows that were bred with 342 boars giving an average of 21 (S.D. 14.5) piglets per dam and 73 (S.D. 85.4) piglets per sire. All traits analysed were derived from individual observations on the animals. Piglets had individual weights recorded at birth (IPBW), at 3 weeks (IP3WW) with an age range between 18 and 24 days, at weaning (IPWW) with an age range between 31 and 46 days, and at 9 weeks (IP9WW) with an age range between 59 and 67 days. Birth weight was recorded within 24 h after birth. Stillborn piglets were weighed and included in the analyses if they were fully developed. Cross fostering of piglets was not done. Piglets were offered creep feed between the second and third week after birth. A proportion of the piglets (4177) born in 691 litters were used in the performance studies, raised until around 100 kg. One hundred and fifty sires and 642 dams had offspring tested during fattening period, in average about 6.5 (S.D. 2.6) and 28 (S.D. 18.7) pigs

F.X. Solanes et al. / Livestock Production Science 88 (2004) 199–212

per dam and sire, respectively. The performance test period started when the pig reached 25 kg and finished after the ultrasonic test, which was conducted when the pig weighed around 90 kg. The starting day for the performance test was estimated using interpolations from two weights—before and after 25 kg—to calculate the exact age at which the pig would reach 25 kg. Ultrasonically measured backfat thickness (BF90) on the right and the left side, about 8 cm from the middle of the back at the last rib, was available for 3548 pigs. The day after slaughter the chilled carcasses were partially dissected using the jointing method developed for Swedish purposes by Andersson (1980). Carcass data collected included the following measurements: backfat thickness (BFSL)—defined as the linear measurement of the fat thickness at the last rib approximately 8 cm from the middle of the back; carcass length (CL) (measured between the anterior edge of os pubis and the bottom of the first cervical vertebra (atlas)); and percent of meat and bone in ham (MBH). The general procedure for editing growth and slaughter data was to blank the value of the trait if the weight at ultrasonically measurement was less than 80 kg and the live weight before slaughter was less than 90 kg. In addition, if the carcass weight was less than 65 kg, slaughter data was blanked. Observations on individual weights were used to derive the overall daily gain between birth and ultrasonic test (DG0 – 90) and the daily gain between 25 kg and ultrasonic test (DG25 – 90) measurements. Because the pigs were tested and slaughtered at different live weights, corrections of the calculated daily gain in the growing period and the recorded BF90 and BFSL were made to make all pigs tested at the same adjusted weight—90 and 105 kg live weight. Slaughter weight was calculated multiplying the carcass weight by 1.4 (inverse of slaughter yield). We used formulas from the Swedish breeding organisation (Quality Genetics, S-244 82 Ka¨vlinge, Sweden) to calculate these adjustments: DG0
90c ¼ ðWUT
ððWUT
90Þ
IPBWÞÞ  1000=ðAUT
ðWUT
90Þ  0:8Þ

BF90c ¼ BF90
ðWUT
90Þ  0:14 BFSLc ¼ BFSL
ððCW  1:4Þ
105Þ  0:14 where DG0 – 90c, daily gain between birth weight and ultrasonic test adjusted to 90 kg (g/day); DG25 – 90c, daily gain between 25 kg and ultrasonic test adjusted to 90 kg (g/day); WUT, weight at ultrasonic test (kg); IPBW, individual piglet birth weight (kg); AUT, age at ultrasonic test (days); A25, age at 25 kg (days); BF90c, backfat adjusted to 90 kg (mm); BF90, ultrasonically measured backfat around 90 kg (mm); BFSLc, carcass backfat adjusted to 105 kg (mm); BFSL, linear measurement of carcass backfat at slaughter (mm); CW, carcass weight (kg). 2.2. Models and statistical analyses Univariate and bivariate mixed linear animal models were used to estimate variance components for the different traits, and covariances between traits. For our piglet data, different models including combinations of direct and maternal genetic effects and litter (due to common litter environment) and permanent (due to dams) environment effects were analysed and evaluated. Analyses of fattening performance and carcass traits were initially done with more complex models, including combinations of litter environmental effects, maternal genetic effects and direct genetic effects, and the genetic correlation between direct and maternal effects (ram) in the model. For simplicity, these combinations are not shown in the tables but are presented in the results. Two models were used in the final analyses. The first model used for piglet data (IPBW, IP3WW, IPWW, and IP9WW) included three random effects: litter environmental effects, maternal genetic effects, and direct genetic effects. The second model was applied for growth traits (DG0 – 90c and DG25 – 90c), ultrasonically measured backfat (BF90c), and carcass traits (BFSLc, CL and MBH). The only random effect included was the direct genetic effects. Litter environmental and maternal genetic effects were excluded from this model. The models are presented in Table 1. A mixed linear animal model was used for the analyses. y ¼ Xb þ Zc c þ Zm m þ Za a þ e

DG25
90c ¼ ðWUT
ððWUT
90Þ
25ÞÞ  1000 =ððAUT
A25Þ
ðWUT
90Þ  0:8Þ

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y ¼ Xb þ Za a þ e

Model 2

Model 1

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Table 1 Structure of the models for the analysed traits, approximative levels of significance for the included effects and proportion of the variation explained by these models (R2) Factor

Typea

Traitb IPBW

IP3WW

IPWW

IP9WW

Batch Group

F F

***

***

***

***

Parity 5 cl. 2 cl.

F F

***

Sex Birth (2 cl.) 3 weeks (3 cl.) Weaning (3 cl.) 9 weeks (3 cl.)

F F F F

***

F F F F

***

Litter size Piglet data Birth (14 cl.) 3 weeks (10 cl.) Weaning (10 cl.) 9 weeks (10 cl.) Growing data 9 weeks (9 cl.) Age 3 weeks Age 6 weeks Age 9 weeks Carcass weight R2 Litter effect Maternal effect Direct effect

***

***

DG25 – 90c

BF90c

BFSLc

CL

MBH

***

***

***

***

***

***

***

***

***

***

**

***

***

**

***

***

0.55

0.48

0.45

0.35

*** 0.29

*** 0.38

U

U

U

U

U

U

*** **

*** *** ***

*** *** ***

F C C C C R A A

DG0 – 90c

*** *** *** 0.14 U U U

0.22 U U U

0.31 U U U

0.38 U U U

***P < 0.1%; **P < 1%. a Types of factors: F, fixed factor; C, linear covariable; R, random factor; A, random factor with relationship matrix. b Traits: IPBW, individual piglet birth weight; IP3WW, individual piglet 3 weeks weight; IPWW, individual piglet weaning weight; IP9WW, individual piglet 9 weeks weight; DG0 – 90c, daily gain between birth and ultrasonically measurement adjusted to 90 kg; DG25 – 90c, daily gain between 25 kg and ultrasonically measurement adjusted to 90 kg; BF90c, backfat adjusted to 90 kg; BFSLc, Carcass backfat at slaughter adjusted to 105 kg; CL, carcass length; MBH, percent of meat and bone in ham.

where y is the observations on piglet weight, growth, backfat, or carcass data; X and Zi are incidence matrices relating the observations to the fixed and random effects, respectively; b is a vector of fixed effects; c is a vector of random litter environmental effects; m is a vector of maternal genetic effects; a is a vector of direct genetic effects of the pig; and e is a vector of random residual effects. Piglet weights were analysed using bivariate analyses together with fattening performance and

carcass traits to estimate the genetic correlations between these traits. 2 4

y1 y2

3

2

5¼ X 4

b1 b2

3

2

5þ Zc4

c1 0

3

2

5þ Zm4

m1 0

3

2

5þ Za4

a1 a2

32 5þ4

e1

3 5

e2

where y1 is the observation on piglet weight; y2 is the observation on growth, backfat or carcass; X and Zi are incidence matrices relating the observa-

F.X. Solanes et al. / Livestock Production Science 88 (2004) 199–212

tions to the fixed and random effects, respectively; bi is a vector of fixed effects; ci is a vector of random litter environmental effects; mi is a vector of maternal genetic effects; ai is a vector of direct genetic effects of the animal; and ei is a vector of random residual effects. The random effects were assumed to follow a normal distribution with mean zero and (co)variance structure: 2 3 2 2 3 c Irc 0 0 0 6 7 6 7 6 7 6 7 2 6m7 60 Arm Arma 0 7 6 7 6 7 7 6 7 Var6 6 7¼6 7 2 6 a 7 60 Aram Ara 0 7 6 7 6 7 4 5 4 5 2 e 0 0 0 Ire where c is a vector of random litter environmental effects; m is a vector of maternal genetic effects; a is a vector of direct genetic effects; e is a vector of random residual effects; I is an identity matrix; A is the relationship matrix; rc2 is the environmental litter effects variance; rm2 is the additive genetic variance for maternal effects; ra2 is the additive genetic variance for direct effects; ram is the covariance between direct and maternal additive genetic effects; and re2 is the residual variance. Tests of which fixed effects should be included in the model were performed using the SAS procedure GLM (SAS Institute, 1996). Genetic analyses were performed using an AI-REML algorithm (Jensen et al., 1997) in the DMU package (Madsen and Jensen, 2000). Table 1 shows the relevant fixed effects for the traits studied as well as the corresponding approximative levels of significance and proportion of the variation explained by these effects (R2). Fixed effects were the same for all piglet weights: batch, parity, sex (female, male or castrate), and litter size at weighing. The batch effect was pre-planned contemporary groups of farrowings in the selection experiment. After the selection experiment, batch operation was not practised in the herd, and piglet production was performed continuously. For piglets born after 1992, batch was defined as a combination of season-year. Birth parity was included in the model with five levels for the piglet data corresponding to the first three

203

parities, the fourth and fifth parity grouped together, and parities higher than the fifth. Birth litter size was included with 14 classes ( V 5, 6, 7, . . . 17, z 18 piglets) for birth weight and 10 ( V 5, 6, 7, . . . 13, z 14 piglets) for the other piglet weights. The age at weighing was fitted as a linear regression for all the piglet weights in the model. The fixed effects for DG25 – 90c were group (batch nested within feed type given during the growth period), sex, and litter size at 9 weeks (nine classes: V 5, 6, 7, . . . 12, z 13 piglets). For DG0 – 90c also the fixed effect of parity was included with two levels corresponding to the first parity and to all higher parities grouped together. Sex and group were the fixed effects for BF90c, BFSL, CL, and MBH. Carcass weight was fitted as a regression for CL and MBH (Table 1). The phenotypic variances for piglet weights were defined as rp2 = rc2 + rm2 + ra2 + ram + re2 and for fattening performance and carcass data as rp2 = ra2 + re2. The litter environmental effects (c2), the maternal heritabilities (m2), and the direct heritabilities (h2) were calculated as rc2/rp2, rm2 /rp2, and ra2/rp2, respectively. The standard errors (S.E.) for the estimated heritabilities and for the correlations were obtained using Taylor series approximations. Estimates at least two times higher than their corresponding S.E. were assumed to be significantly different from zero.

3. Results Table 2 shows the descriptive statistics of the data for the 10 traits included in the study. Mean parity number was 2.2 (S.D. 1.6, range 1 – 16 parities). The average number of total piglets born in each of the 2309 litters was 11.9 (S.D. 2.7, range 1 – 22 piglets) and at 9 weeks average litter size was 9.1 (S.D. 2.8, range 0 –18 piglets). The mean daily gain (g/day) between birth and weaning was 224 (S.D. 59), and between birth and 9 weeks it was 246 (S.D. 67). Mean age at 25 kg, ultrasonic measurement, and slaughter were 83.6 (S.D. 9.4), 168.5 (S.D. 17.1), and 187.0 days (S.D. 18.3), respectively. The mean weight 1 day before slaughter was 103.9 kg (S.D. 5.4) and the mean carcass weight was 76.0 kg (S.D. 4.5). The phenotypic correlation between BF90c and BFSLc was 0.81.

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Table 2 Characteristics of the data set used in the study Trait

n

IPBW (kg)a IP3WW (kg) IPWW (kg) IP9WW (kg) DG0 – 90c (g/day) DG25 – 90c (g/day) BF90c (mm) BFSLc (mm) CL (cm) MBH (%)

25,429 1.44 0.35 0.10 20,911 6.13 1.42 0.80 19,340 10.0 2.5 2.0 19,805 16.9 4.3 3.0 4456 533.8 53.2 376.3 4106 807.0 107.9 496.2 3548 14.2 3.4 5.6 2740 17.7 5.6 4.5 2743 100.0 3.2 89.4 2868 78.4 3.8 60.1

a

Mean

S.D.

Minimum Maximum 2.70 11.70 21.2 34.0 710.5 1148.1 28.3 39.0 118.8 91.7

For abbreviations see Table 1.

Fixed effects estimates from DMU package (Madsen and Jensen, 2000) showed that piglets born in the third parity were the heaviest ones at birth, but at 3 weeks and later piglets born in the second parity were the heaviest and had faster growth rates compared with piglets from other parities. Males were heavier (45 F 4 g) than females at birth, but at 3 weeks, castrated piglets were heavier than both intact males and females. At weaning and at 9 weeks, intact males were heavier than females and castrated piglets. Litter size influenced piglet growth. Litter size below nine piglets had progressively heavier weights as compared to litter sizes of more than nine piglets. For our piglet data, preliminary analyses showed that the best model (maximum log likelihood) that fit to the data was a model with three random effects: litter environmental effects, genetic maternal effects, and genetic direct effects. The permanent environmental maternal effects were confounded with the litter environmental effects; 41.5% of the sows had only one litter. Therefore, the permanent environmental maternal effects were not considered in the model applied for estimating (co)variance components for piglet data (Model 1). Preliminary analyses of fattening performance and carcass traits were done including litter environmental effects, maternal genetic and direct genetic random effects, accounting for a genetic correlation between direct and maternal effect (ram) in the model. The estimates F S.E. of c2, m2, h2, and ram for the overall growth (DG0 – 90c) were 0.07 F 0.02, 0.05 F 0.03, 0.16 F 0.06, and 0.39 F 0.41, respectively. For growth during fattening period (DG25 – 90c), the estimated c2, m2, h2, and ram were 0.07 F 0.02, 0.007 F

0.03, 0.13 F 0.05, and 0.99 F 2.41, respectively. Maternal effects were not significant for these growth traits. For DG25 – 90c, a high positive direct maternal genetic correlation was estimated, possibly as a result of the very small estimated maternal effects. For BF90c the estimates of c2, m2, h2, and ram were 0.03 F 0.01, 0.08 F 0.03, 0.77 F 0.10, and
0.72 F 0.11, respectively. The presence of maternal genetic effects in the model resulted in a high negative direct maternal correlation that overestimated the direct heritability as compared to values in other studies. Anyway, maternal genetic effects could be estimated in larger data sets with a better structure and explain some of the phenotypic variation. For BFSLc, the estimates of c2, m2, h2 and ram were 0.05 F 0.02, 0.07 F 0.04, 0.86 F 0.14 and
0.93 F 0.18, respectively. The estimated values for CL for c2, m2, h2 and ram were 0.03 F 0.01, 0.12 F 0.05, 0.90 F 0.16 and
0.67 F 0.11 and for MBH were 0.02 F 0.01, 0.17 F 0.05, 1.12 F 0.17 and
0.88 F 0.06, respectively. High negative direct-maternal correlations, high direct variances, and small residual variances were found for carcass traits. We concluded that these heritabilities were overestimated. For those three traits it was difficult to estimate the maternal effect because of the data structure. Neither dams nor grand-dams had carcass data, and the number of halfsibs with such data was also small. This structure makes the estimation of maternal effects and the correlation between direct and maternal genetic effects more fragile. In those preliminary analyses of fattening performance and carcass traits, when the maternal genetic effects were excluded from the model, the estimates of c2 were small and in most cases not significant (0.002 F 0.01 – 0.08 F 0.04). Furthermore, inclusion of c2 did not improve the maximum log likelihood. Pigs from the same litters were split in different pens during the fattening period according to sex and weight at 9 weeks, which could explain these low litter environmental effects. As a consequence, a model with only direct genetic effects was considered for traits expressed late in a pig’s life (Model 2). Table 3 summarises the estimated variance components from univariate analyses. Piglet weights until 9 weeks of age are influenced mainly by litter environmental and maternal genetic effects. The maternal heritability for individual piglet weight decreases with

F.X. Solanes et al. / Livestock Production Science 88 (2004) 199–212

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Table 3 2 Variance components due to litter environmental (rc2), maternal genetic effects (rm ), direct genetic effects (r2a), covariance between direct and maternal effects (ram) and random residual (re2), ratio of litter environmental variance to total variance (c2), maternal (m2) and direct heritabilities (h2) and genetic correlation between direct and maternal effects (ram), estimated with univariate analyses, and range of maternal and direct heritabilities estimated using bivariate analyzes Trait

Variance components

IPBWa IP3WWb IPWWb IP9WWb DG0 – 90c DG25 – 90c BF90c BFSLc CLc MBHc

Range in bivariate model

rc

rm

ra

ram

2re

c

m

h

ram

m

h

0.009 0.33 0.93 2.68 – – – – – –

0.020 0.28 0.82 1.10 – – – – – –

0.007 0.043 0.26 1.53 615.4 2145.1 3.37 11.98 5.79 6.28

0.00007 0.024
0.05
0.18 – – – – – –

0.072 1.02 2.67 7.32 811.6 4504.9 2.95 6.86 2.16 3.04

0.080.007 0.190.01 0.210.01 0.210.01 – – – – – –

0.180.02 0.170.02 0.180.03 0.090.02 – – – – – –

0.070.02 0.030.02 0.060.02 0.120.03 0.430.04 0.320.04 0.530.04 0.640.06 0.730.06 0.670.05

0.0060.15 0.220.30
0.110.19
0.140.17 – – – – – –

0.17 – 0.19 0.14 – 0.18 0.14 – 0.18 0.06 – 0.09 – – – – – –

0.06 – 0.08 0.02 – 0.04 0.05 – 0.08 0.12 – 0.15 0.41 – 0.44 0.31 – 0.33 0.53 – 0.54 0.62 – 0.66 0.72 – 0.73 0.66 – 0.68

S.E. are given as subscripts. a For abbreviations see Table 1. b Age at weighing fitted as a regression. c Carcass weight fitted as a regression.

age from 0.18 to 0.09 between birth and 9 weeks. Direct heritability, however, increases from 0.07 to 0.12. Until weaning, the effect of mother’s genes on piglet weights is more important than a piglet’s own genes; however, after weaning the piglet’s genes influence weight more than the mother’s genes. Genetic correlations between direct and maternal genetic effects were low and negative except for IPBW and IP3WW (Table 3) and in no case significant. Moderate to large heritability values were obtained for fattening performance and carcass traits, ranging between 0.32 and 0.73. Similar genetic parameters were found when using bivariate analyses. Table 4 shows estimates of genetic correlations between traits of the young and older pigs. S.E. of

genetic correlations ranged between almost 0 and 0.46; therefore, many of the genetic correlations were estimated with a high degree of uncertainty. The residual correlations, however, were estimated with smaller S.E. (results not shown). Concerning the direct genetic correlations, weaning weight was highly positively correlated with DG0 – 90c. Weight at 9 weeks had highly positive correlation with DG0 – 90c and moderate positive correlation with DG25 – 90c. Piglet weights before weaning were negatively correlated with BF90c and BFSLc and positively correlated with CL and MBH, but after weaning IP9WW was positively correlated with the backfat measurements and negatively with CL and MBH. There were favourable correlations between

Table 4 Estimates of maternal-direct and direct genetic correlations between piglet weights, fattening, and carcass traits a

IPBW

IP3WW IPWW IP9WW

Maternal Direct Maternal Direct Maternal Direct Maternal Direct

DG0 – 90c

DG25 – 90c

BF90c

BFSLc

CL

MBH

0.200.09 0.390.16 0.310.10 0.640.23 0.480.10 0.800.13 0.380.13 0.950.07

0.300.10
0.00010.19 0.270.12 0.0090.32 0.300.12 0.350.24 0.150.17 0.370.18


0.130.08
0.250.16 0.020.10
0.640.38 0.070.10
0.340.21
0.140.14 0.080.15


0.180.10
0.260.18 0.040.12
0.830.46 0.160.12
0.590.26 0.020.17 0.180.17

0.060.10 0.380.17
0.050.12 0.670.31 0.050.13 0.130.23 0.040.18
0.030.19

0.320.09 0.290.16 0.070.12 0.440.24 0.040.12 0.330.20 0.200.16
0.150.16

S.E. are given as subscripts. a For abbreviations see Table 1.

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maternal genetic effects for piglet weights and direct genetic effects for daily gains and carcass traits, but in some cases they were unfavourable to direct genetic effects for backfat measurements.

4. Discussion 4.1. Models Most of the performance and carcass data came from a selection experiment (Stern et al., 1993, 1994), so to take into account this selection process required us to consider all the information on which selection was based (Im et al., 1989). This selection was based on the trait Lean Tissue Growth Rate in the interval 25– 90 kg, which was calculated from eight ultrasonic measurements and growth from 25 to 90 kg. We used univariate and bivariate analyses to estimate the genetic parameters and the genetic correlations between the traits. Univariate and bivariate analyses produced similar results; also when one of the traits on which selection was based (BF or DG25 – 90) was present in the analyses. Zhang et al. (2000a) found a tendency towards lower estimates of both direct and maternal components of genetic variance in univariate analyses of production traits compared to multivariate analyses; however, we did not have similar results. The range in estimates from bivariate analyses was small and close to those obtained from the univariate analyses (Table 3). Hermesch et al. (2001) compared models with a number of random effects to estimate (co)variance components for individual piglet weights. They concluded that the model that best fit the data (criterion was maximum log likelihood) was the model with three random effects: litter effects, maternal genetic effects, and direct genetic effects. In beef cattle (Meyer, 1992b) and in sheep (Edriss et al., 2002; Maniatis and Pollott, 2002b), the different authors showed that the best model for parameter estimation for early weights was an animal model including permanent maternal environmental effects, maternal genetic effects, direct genetic effects and a genetic correlation between the direct and maternal effects. In a simulation study, Cle´ment et al. (2001) showed that a reduced model led to biases arising from confusion between different variance components. Furthermore,

they found fitting unnecessary random effects neither yielded biased estimates nor substantial losses in the precision of estimates. Meyer (1992a) found an influence of unnecessary effects on the precision of estimated variance components. For our piglet data, in agreement with those previous results, the best model (maximum log likelihood) that fit to the data was a model with three random effects: litter environmental effects, maternal genetic effects and direct genetic effects (Model 1). High positive direct maternal genetic correlations were estimated in the preliminary analyses of fattening and carcass data, as a result of the very small and not significant maternal effects. This agrees with Zhang et al. (2000a), who showed that only maternal effects accounting for more than 8% of phenotypic variance could be detected. Thompson (1976) and Meyer (1992a) also showed that S.E. of heritability estimates could be larger when including maternal effects in the model compared to a model involving only direct effects. They also showed large S.E. for the estimated genetic correlations. It has been suggested that data structure (number of offspring per dam) and the number of dams and grand-dams with records are important determinants when estimating direct and maternal effects (Gerstmayr, 1992; Meyer, 1992a; Maniatis and Pollott, 2003). Maniatis and Pollott (2003) obtained a high negative direct maternal genetic correlation (
0.99) when the proportion of dams with performance records was small (10%) and the number of progenies per dam was only one or two. Some authors (Meyer, 1992a, b; Maniatis and Pollott, 2002a, b, 2003; Robinson, 1996) have identified strong antagonistic interactions between direct and maternal effects, together with high estimates of both direct and the maternal genetic variances. Maniatis and Pollott (2002b) studied weight, muscle, and fat depth at 5 months in lambs. They found no significant difference between the likelihood ratio tests conducted in order to determine the significance of each parameter for models considering maternal effects and/or considering a covariance between direct and maternal genetic effects. They concluded that the maternal effects appeared to be more of environmental than genetic origin for traits measured at that age. To accurately estimate (co)variance components for fattening performance and carcass traits, we had to

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remove the genetic maternal effect from the final models (Model 2). Hermesch et al. (2000a) pointed out that the size or structure of the data sets does not always allow a reliable simultaneous estimation of genetic effects and litter effects. Recently, Maniatis and Pollott (2002a) estimated genetic parameters of growth and ultrasonically measured traits of lambs considering genotype by environment interactions. Their results indicate a reduced importance of the common environmental effects with advanced age of lambs. Furthermore, they argued that the lower amount of data for live body weight at 5 months caused the lower c2 for this trait compared to c2 for weight at 8 weeks. All this led to our decision to also exclude the litter environmental effect from the model when estimating the (co)variance components of fattening performance and carcass traits (Model 2). 4.2. Heritabilities and variance components Litter environmental and maternal genetic effects were the main random effects influencing piglet weights until weaning. The direct genetic effects were of minor importance. These estimates and the increasing impact of the direct genetic effects and the corresponding decreasing impact of the maternal genetic effects with age agree with results presented by Kaufmann et al. (2000); Zhang et al. (2000a); Hermesch et al. (2001). The litter environmental effects were low for birth weight and moderate for the other piglet weights, accounting for 8% and around 20% of the phenotypic variance, respectively. Estimated genetic parameters for IPBW are consistent with literature results with a range for maternal heritabilities between 0.16 and 0.22 and direct heritabilities ranging between 0.02 and 0.08 (Hermesch et al., 2001; Kaufmann et al., 2000; Roehe, 1999). Grandinson et al. (2002) estimated maternal and direct genetic heritabilities for IPBW of 0.15 and 0.04 using birth weights from gilt litters also included in our study. At 3 weeks of age, Silio´ et al. (1994) studied several lines of Iberian pigs. They found that maternal heritability had a range of estimates from 0.16 to 0.17, and for the direct heritability they found a range from 0 to 0.08. Hermesch et al. (2001) analysed a limited data set of two generations and obtained estimates for weight at 2 weeks of 0.13 and 0.04 for maternal and

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direct heritabilities, respectively. Zhang et al. (2000a) used a Chinese – European Tiameslan composite line found a maternal heritability of 0.11 and a direct heritability of 0.03 for weight at 4 weeks. Our results agree with these findings and suggest that piglet preweaning growth is largely controlled by the dam (environmentally and genetically) rather than controlled by the piglet’s individual genes. The maternal effects on weight progressively decrease with higher weights as is shown for IP9WW, where the maternal heritability is lower than the direct heritability. This agrees with Zhang et al. (2000a) who presented maternal and direct heritabilities for 8-week piglet weight of 0.11 and 0.17, respectively. For post-weaning growth, the maternal influences were not significant. This agrees with Crump et al. (1997) and Zhang et al. (2000a) but not with Bryner et al. (1992); Johnson et al. (2002) and Chen et al. (2002). Bryner et al. (1992) estimated a moderate maternal heritability value of 0.23 for average daily gain in Yorkshire boars. These relatively large estimates of maternal genetic effects could be due to confounding between common litter effects and maternal effects since common litter effects were not included in this study. Johnson et al. (2002); Chen et al. (2002) estimated lower maternal heritabilities for average daily gain and lean growth rate, respectively. Their estimates ranged between 0.01 and 0.04 for Landrace, Yorkshire, Duroc, and Hampshire pigs. Our estimate of direct heritability for DG25 – 90c was in the medium range of literature estimates. Clutter and Brascamp (1998) summarised literature for post-weaning average daily gain with restricted feed intake, a range of heritability estimates from 0.14 to 0.76, with an average value of 0.30. Stern et al. (1994) estimated genetic parameters on partly the same data as we used. The model used in these analyses assumes that all genetic effects act additively and that no common litter or genetic maternal effects exist as in our study. Heritability estimates for growth rate from 25 to 90 kg were 0.31 F 0.08 for the high protein line and 0.37 F 0.09 for the low protein line. Knol et al. (2001) studied direct genetic and common environmental effects on growth in a commercial line. The estimates of direct heritability and common environmental effects for daily gain from birth to 29 kg were 0.17 F 0.04 and 0.16 F 0.02 and for daily gain from 29 to 78 kg were 0.19 F 0.04 and 0.10 F 0.03. They

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suggest the existence of an environmental influence of the sow’s ability to take care of the piglets and of her milk production, also during the finishing period. According to them, fattening pigs from the same litter resemble each other through their common environment as through their common genes, which agrees with Hermesch et al. (2000a) but disagrees with our results. Hermesch et al. (2000a) presented estimates for direct heritability and common environmental effects for growth rate from three to 18 weeks of age (when the animal entered the test station) of 0.10 F 0.05 and 0.20 F 0.04 for Large White. Maternal genetic effects were not possible to estimate for backfat thickness. Crump et al. (1997) obtained low and non-significant maternal heritabilities for backfat in Landrace. Zhang et al. (2000a), however, reported maternal heritability of 0.23, 0.11, and 0.07 for backfat thickness recorded as the average of six backfat measurements adjusted to 100 kg for male, female, and both sexes together, respectively. Bryner et al. (1992) presented a maternal heritability of 0.11 for backfat adjusted to 104.5 kg for Yorkshire boars. Johnson et al. (2002); Chen et al. (2002) estimated lower maternal heritabilities for backfat, ranging between 0.02 and 0.07 for Landrace, Yorkshire, Duroc, and Hampshire pigs. The presence of a maternal influence could be explained by differences in the distribution of energy during post-weaning growth as a consequence of differences in body composition at weaning caused by prenatal and/or postnatal maternal effects (Johnson, 1981). The direct heritability for backfat traits was in the higher range of the literature values presented (Clutter and Brascamp, 1998; Hermesch, 1996). However, Zhang et al. (2000a) also found high direct heritabilities ranging from 0.71 to 0.90 when using different statistical models. They suggest the presence of segregating major genes or quantitative trait loci (QTL) in their population, which could inflate heritability estimates. No maternal effects could be estimated for carcass traits in our data because the structure of the data was not suitable. Johnson (1981) reported that maternal effects were important for CL, backfat, and longissimus muscle area. Baas et al. (1992) found maternal effects on backfat measurements and longissimus muscle area but not on CL. In sheep, Na¨sholm (2002) estimated maternal heritabilities for carcass weight between 0.08 and 0.12.

Estimates of heritabilities for CL (0.73 F 0.06) and MBH (0.67 F 0.05) were high. In the literature, heritability values range from 0.55 to 0.60 for CL (Sellier, 1998) and from 0.36 to 0.60 for weight of the ham (Hermesch, 1996). Johansson et al. (1987), using data from the Swedish station testing, estimated high heritabilities and low common environmental effects for CL and meat and bone in ham. 4.3. Correlations Unfavourable correlations between maternal genetic effects and direct genetic effects for different growth traits are found in the literature (Bryner et al., 1992; Robison, 1981; Zhang et al., 2000a). This indicates an antagonistic relationship between the sow’s genes to provide a good genetic environment for her offspring and the genes of the offspring for growth. In beef cattle, estimated correlations between maternal genetic effects and direct genetic effects may be influenced by negative dam – offspring covariances or by additional sire or sire  year variation (Robinson, 1996). Unfavourable correlations may also be explained by a potential bias due to the fact that the environmental correlations between direct and maternal effects are assumed to be null (Meyer, 1992b; Thompson, 1976). Maniatis and Pollott (2002a) showed that accounting for sire  environmental (year  flock) interaction effects as additional random effect substantially increased the likelihood value of the model and considerably decreased the magnitude of the negative direct maternal correlation in sheep. However, for IPBW the correlation between maternal genetic and direct effects was null but was favourable for IP3WW. Grandinson et al. (2002) presented a favourable correlation (0.33) between direct and maternal effects for IPBW. They analysed data on piglets from the same data set as we did in this study, but they included only piglets from gilt litters. It has been suggested that positive associations exist between direct genetic effects for weight and early maternal effects (prior to 4 weeks), but due to interactions with certain environmental factors this correlation becomes negative after 4 weeks (Robison, 1981). Creep feed was supplied to our piglets at 3 weeks of age. Pigs suckling sows that are producing low amounts of milk are driven to consume supplementary feed at a younger age than pigs suckling high-

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producing sows, and in this case the supplementary feed is compensating for lack of milk. Such compensation would lead to a negative correlation between direct and maternal effects for later weights and growth rates. Thompson and Fraser (1986) showed that the smallest piglets tended to have an accelerating growth curve after 21 days of age, which is due to their higher intake of creep feed. Maternal genetic correlations between piglet weights and fattening growth, backfat, or carcass traits were not possible to estimate as a result of our data structure. However, some previous studies (Kelley et al., 1995; Rehfeldt et al., 1993, 2001; Robison, 1981; Simmen and Simmen, 1991) suggest that maternal effects during early gestation and/or lactation could affect the postnatal lean tissue growth rate. Kelley et al. (1995) examined if an injection of growth factors (30 Ag pST/kg body weight per day) in gilts during days 28 –40 of gestation resulted in altered neonatal and postnatal fattening performance. In neonatal (20 kg) and market-weight (100 kg) carcasses of these offspring, they found significant larger longissimus muscle cross-sectional areas, and they found a significant decrease in the 10th rib backfat at 20 kg. They concluded that it is possible that cellular mechanisms affecting hyperplasia and/or hypertrophy of muscle cells and potentially adipocytes have been altered by pST injection during early gestation. McNamara and Martin (1982) performed a selection experiment for high and low backfat for 18 generations. They proposed that genetic selection might alter the partitioning of nutrients to lean or adipose tissue early in the pig’s development, which may lead to a marked difference in early body composition. Most of the direct genetic correlations between piglet weights and growth were favourable in our study. Piglet weight before weaning compared with after weaning shows very different correlations to backfat and carcass measurements (CL and MBH). Two piglets with similar weaning weight can apparently enter the fattening period with very different experiences and possibilities. Selection experiments for increased lean growth in pigs have indicated an increase in birth weight, which agrees with our results (Kerr and Cameron, 1995; Mersmann et al., 1984; Vangen, 1972). Hermesch et al. (2000b) concluded that selection for lean meat growth increases litter weight and average birth

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weight. A breed comparison by Herpin et al. (1993) shows that piglets selected for lean growth are born heavier but seem to be less mature since they have lower proportion of body fat and protein and a lower ability to metabolise fat at birth. Knol et al. (2001) found negative correlations between birth weight and backfat, but they found positive correlations between birth weight and growth traits. They concluded that selection for heavier piglets at birth gives favourable responses in terms of higher protein deposition and leaner carcasses. Hutchens et al. (1981) show that direct genetic correlation between birth weight and weaning weight and average gain in test were positive and in agreement with our results for DG0 – 90c (0.50 and 0.49 between ADG and birth and weaning weight, respectively). Zhang et al. (2000a) presented negative direct genetic correlations between weight at 4 and at 22 weeks of age and average backfat thickness in females, but they found positive direct genetic correlations for males at 8 weeks of age. This difference between sexes was explained by the early sexual maturity and the sexual dimorphism and by the impaired feeding behaviour of males after puberty resulting in lower growth rates compared with females. However, Zhang et al. (2000b) estimated genetic trends for animals selected for 12 years on an index comprising the average of six backfat measurements and days on test. They found that genetic trends were moderately low for both direct and maternal effects for weights at 4 and 8 weeks of age. These results indicate that selection for backfat had a limited effect on early post-weaning growth rate, which agrees with our estimates for IP9WW and BF90c. Therefore, selection for lean tissue daily gain in pigs produces heavier piglets until weaning, but the improvement of 9 weeks weight will be limited. Furthermore, the moderate-low but positive correlation between piglet weights and pig growth (0.37) makes it possible to improve both growth traits separately. Estimates of genetic correlations between individual piglet weights and carcass traits are not available in the literature. Genetic correlations between piglet weights before weaning and CL were between 0.13 and 0.67, and for MBH they were between 0.29 and 0.33. However, Hermesch et al. (2000b) found favourable correlations between average litter birth weight and weight of the whole left back leg and

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weight of slash boned left back leg (rg = 0.13 –0.37). Along with the negative correlation between piglet weights before weaning and backfat, this indicates that selection for increased piglet weights before weaning could lead to reduced backfat, increased leanness, and improved carcass quality. The data was insufficient to estimate maternal effects on fattening and carcass traits. However, according to preliminary analyses, maternal impact was more important in early weight traits than late growth traits. Therefore, selection for heavier piglets as a maternal trait would have a greater impact than selection for heavier piglets as a direct trait. Since it is labour intensive to record individual piglet weights, the direct heritability is low and the correlation between maternal effects on piglet weight before weaning and direct effects for later growth is favourable, average piglet weight of the litter could be considered as a good alternative to individual piglet weights when evaluating the mother for breeding purposes. However, in order to consider the possible increase of litter weight heterogeneity when we select for average litter weight, litter mortality should also be consider in the breeding programme. Selection for late growth and backfat thickness would also improve piglet weight, both via the maternal genetic effects and the direct genetic effects.

5. Conclusions The genes of the sow control piglet growth until weaning more than the piglets’ own genes, but after weaning the genes of the piglets have a higher importance. Our results suggest that no significant maternal effects are present for late growth. However, estimation of maternal effects and the correlation between direct and maternal genetic effects is dependent on data structure and pedigree relationships. We suggest that breeding evaluation aiming at improving piglet growth, could be based on average weaning weight regarded as a sow trait (without including direct piglet effects in the model) together with a litter mortality trait. For the breeding evaluation of growth of fattening pigs, maternal effects could be ignored and be based on direct pig effects in the model.

Acknowledgements This study was supported by the Swedish Farmer’s Foundation for Agricultural Research (project G12100). We wish to thank the staff at the Funbo-Lo¨vsta Research Station for their help in recording data and for taking care of the animals. We also wish to thank Ulla Schmidt for organising all recordings in the database.

References Andersson, K., 1980. Studies on Crossbreeding and Carcass Evaluation in Pigs. Thesis Report 46. Uppsala: Department of Animal Breeding and Genetics, Sweden University of Agricultural Science, 126 pp. Baas, T.J., Christian, L.L., Rothschild, M.F., 1992. Heterosis and recombination effects in Hampshire and Landrace swine: II. Performance and carcass traits. J. Anim. Sci. 70, 99 – 105. Bryner, S.M., Mabry, J.W., Bertrand, J.K., Benyshek, L.L., Kriese, L.A., 1992. Estimation of direct and maternal heritability and genetic correlation for backfat and growth rate in swine using data from centrally tested Yorkshire boars. J. Anim. Sci. 70, 1755 – 1759. 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 US Yorkshire, Duroc, Hampshire, and Landrace pigs. J. Anim. Sci. 80, 2062 – 2070. Cle´ment, V., Bibe´, B., Verrier, E´., Elsen, J.-M., Manfredi, E., Bouix, E´., Hanocq, E´., 2001. Simulation analysis to test the influence of model adequacy and data structure on the estimation of genetic parameters for traits with direct and maternal effects. Genet. Sel. Evol. 33, 369 – 395. Clutter, A.C., Brascamp, E.W., 1998. Genetics of performance traits. In: Rothschild, M.F., Ruvinsky, A. (Eds.), The Genetics of the Pig. CAB International, New York, pp. 427 – 462. Crump, R.E., Haley, C.S., Thompson, R., Mercer, J., 1997. Individual animal model estimates of genetic parameters for performance test traits of male and female Landrace pigs tested in commercial nucleus herd. Anim. Sci. 65, 275 – 283. Edriss, M.A., Tahmasebi, F., Vatan-khah, M., 2002. Estimation of Variance Components for Pre-weaning Weights in Bakhtiari Sheep Breed. Proceedings of the 7th World Congress on Genetics Applied to Livestock Production CD-Rom communication number 02-09. Gerstmayr, S., 1992. Impact of the data structure on the reliability of the estimated genetic parameters in an animal model with maternal effects. J. Anim. Breed. Genet. 109, 321 – 336. Grandinson, K., Lund, M.S., Rydhmer, L., Strandberg, E., 2002. Genetic parameters for the piglet mortality traits crushing, stillbirth and total mortality, and their relation to birth weight. Acta Agric. Scand. Sect. A Anim. Sci. 52, 167 – 172. Hermesch, S., 1996. Genetic parameters for lean meat yield, meat quality, reproduction and feed efficiency traits for Australian

F.X. Solanes et al. / Livestock Production Science 88 (2004) 199–212 pigs. Dissertation. Animal Genetics and Breeding Unit and Department of Animal Science, University of New England. Hermesch, S., Luxford, B.G., Graser, H.-U., 2000a. Genetic parameters for lean meat yield, meat quality, reproduction and feed efficiency traits for Australian pigs 1. Description of traits and heritability estimates. Livest. Prod. Sci. 65, 239 – 248. Hermesch, S., Luxford, B.G., Graser, H.-U., 2000b. Genetic parameters for lean meat yield, meat quality, reproduction and feed efficiency traits for Australian pigs 3. Genetic parameters for reproduction traits and genetic correlations with production, carcase and meat quality traits. Livest. Prod. Sci. 65, 261 – 270. Hermesch, S., Luxford, B.G., Graser, H.-U., 2001. Estimation of variance components for individual piglet weights at birth and 14 days of age. Proc. Assoc. Adv. Anim. Breed. Genet. 14, 207 – 210. Herpin, P., Le Dividich, J., Amaral, N., 1993. Effect of selection for lean tissue growth and body composition and physiological state of the pig at birth. J. Anim. Sci. 71, 2645 – 2653. Hutchens, L.K., Hintz, R.L., Johnson, R.K., 1981. Genetic and phenotypic relationships between pubertal and growth characteristics of gilts. J. Anim. Sci. 53, 946 – 951. Im, S., Fernando, R.L., Gianola, D., 1989. Likelihood inferences in animal breeding under selection: a missing-data theory view point. Ge´ne´t. Se´l. Evol. 21, 399 – 414. Jensen, J., Ma¨ntysaari, E.A., Madsen, P., Thompson, R., 1997. Residual maximum likelihood estimation of (co)variance components in multivariate mixed linear models using average information. J. Indian Soc. Agric. Stat. 49, 215 – 236. Johansson, K., Andersson, K., Sigvardsson, J., 1987. Evaluation of station testing of pigs. III. Genetic parameters for carcass measurements of partially dissected pigs. Acta Agric. Scand. 37, 120 – 129. Johnson, R.K., 1981. Crossbreeding in swine: experimental results. J. Anim. Sci. 52, 906 – 923. Johnson, Z.B., Chewning, J.J., Nugent, R.A., 2002. Maternal effects on traits measured during postweaning performance test of swine from four breeds. J. Anim. Sci. 80, 1470 – 1477. Kaufmann, D., Hofer, A., Bidanel, J.P., Ku¨nzi, N., 2000. Genetic parameters for individual birth and weaning weight and for litter size of Large White pigs. J. Anim. Breed. Genet. 117, 121 – 128. Kelley, R.L., Jungst, S.B., Spencer, T.E., Owsley, W.F., Rahe, C.H., Mulvaney, D.R., 1995. Maternal treatment with somatotropin alters embryonic development and early postnatal growth of pigs. Domestic Anim. Endocrinol. 12, 83 – 94. Kerr, J.C., Cameron, N.D., 1995. Reproductive performance of pigs selected for components of efficient lean growth. Anim. Sci. 60, 281 – 290. Knol, E.F., Bergsma, R., van Arendonk, J.A.M., van der Lende, T., 2001. Genetic correlations between piglet survival, birth weight and performance traits. Doctoral thesis (Knol). Genetic Aspects of Piglet Survival. The Netherlands: IPG, Institute for Pig Genetics and Animal Breeding and Genetics Group, Wageningen University, 57 – 74. Madsen, P., Jensen, J., 2000. A User’s Guide to DMU. A Package for Analyzing Multivariate Mixed Models. National Institute of Animal Science. Research Center Foulum, Denmark. Maniatis, N., Pollott, G.E., 2002a. Genotype by environment inter-

211

actions in lamb weight and carcass composition traits. Anim. Sci. 75, 3 – 14. Maniatis, N., Pollott, G.E., 2002b. Maternal effects on weight and ultrasonically measured traits of lambs in small closed Suffolk flock. Small Ruminant Res. 45, 235 – 246. Maniatis, N., Pollott, G.E., 2003. The impact of data structure on genetic (co)variance components of early growth traits in sheep, estimated using an animal model with maternal effects. J. Anim. Sci. 81, 101 – 108. McNamara, J.P., Martin, R.J., 1982. Muscle and adipose tissue lipoprotein lipase in fetal and neonatal swine as affected by genetic selection for high or low backfat. J. Anim. Sci. 55, 1057 – 1061. Mersmann, H.J., Pond, W.G., Stone, R.T., Yen, J.T., Lindvall, R.N., 1984. Factors affecting growth and survival of neonatal genetically obese and lean swine: cross fostering experiments. Growth 48, 209 – 220. Meyer, K., 1992a. Bias and sampling covariances of estimates of variance components due to maternal effects. Genet. Sel. Evol. 24, 487 – 509. Meyer, K., 1992b. Variance components due to direct and maternal effects for growth traits of Australian beef cattle. Livest. Prod. Sci. 31, 179 – 204. Na¨sholm, A., 2002. Direct and Maternal Genetic Relationships Between Lamb Live Weight and Carcass Traits in Swedish Sheep Breeds. Proceedings of the 7th World Congress on Genetics Applied to Livestock Production. CD-Rom communication number 11 – 14. Rehfeldt, C., Fiedler, I., Weikard, R., Kanitz, E., Ender, K., 1993. It is possible to increase skeletal muscle fibre number in utero. Biosci. Rep. 13, 213 – 220. Rehfeldt, C., Kuhn, G., Vanselow, J., Fu¨rbass, R., Fiedler, I., Nu¨rnberg, G., Clelland, A.K., Stickland, N.C., Ender, K., 2001. Maternal treatment with somatotropin during early gestation affects basic events of myogenesis in pigs. Cell Tissue Res. 306, 429 – 440. Robinson, D.L., 1996. Models which might explain negative correlation between direct and maternal genetic effects. Livest. Prod. Sci. 45, 111 – 122. Robison, O.W., 1972. The role of maternal effects in animal breeding: V. Maternal effects in swine. J. Anim. Sci. 35, 1303 – 1315. Robison, O.W., 1981. The influence of maternal effects on the efficiency of selection; a review. Livest. Prod. Sci. 8, 121 – 137. Roehe, R., 1999. Genetic determination of individual birth weight and its association with sow productivity traits using Bayesian analyses. J. Anim. Sci. 77, 330 – 343. Rydhmer, L., 2000. Genetics of sow reproduction, including puberty, oestrus, pregnancy, farrowing and lactation. Livest. Prod. Sci. 66, 1 – 12. SAS Institute, 1996. SAS/STATR User’s Guide, Version 6, 4th ed. SAS Institute, Cary, NC. 1686 pp. Sellier, P., 1998. Genetics of meat and carcass traits. In: Rothschild, A., Ruvinsky, A. (Eds.), The Genetics of the Pig. CAB International, New York, pp. 463 – 510. Silio´, L., Rodriguez, M.C., Toro, M.A., Rodriga´n˜ez, J., 1994. Maternal and Individual Effects on Piglet Weight. Proceedings of the 5th World Congress on Genetics Applied to Livestock

212

F.X. Solanes et al. / Livestock Production Science 88 (2004) 199–212

Production, Guelph, vol. 17. University of Guelph, Guelph, pp. 355 – 358. Simmen, F.A., Simmen, R.C.M., 1991. Mini review: peptide growth factors and proto-oncogenes in mammalian conceptus development. Biol. Reprod. 44, 1 – 5. Stern, S., Johansen, S., 1996. Effects of Phase Feeding on Growth and Carcass Traits of Pigs with Different Genetic Potential for Lean Growth. 47th EAAP Meeting. Commision on Pig Production, Lillehammer, Norway. Stern, S., Simonsson, A., 1999. Fasutfodring-ett resursbesparande sa¨tt att skona djur, ma¨nniskor och miljo¨n (Phase feeding—a way to spare animals, humans and environment. In Swedish). Swedish University of Agricultural Sciences, Jordbrukskonfernsen, Uppsala, Sweden, p. 1999. Stern, S., Johansson, K., Rydhmer, L., Andersson, K., 1993. Performance testing of pigs for lean tissue growth rate in a selection experiment with low and high protein diets: I. Experimental design and efficiency of selection. Acta Agric. Scand. Sect. A Anim. Sci. 43, 136 – 143. Stern, S., Johansson, K., Rydhmer, L., Andersson, K., 1994. Performance testing of pigs for lean tissue growth rate in a selection experiment with low and high protein diets: II. Correlated responses of lean percentage and growth rate. Acta Agric. Scand. Sect. A Anim. Sci. 44, 1 – 7.

Stern, S., Lundeheim, N., Andersson, K., 1995a. Growth and carcass traits in pigs after selection for lean tissue growth rate on low and high protein diets. Anim. Sci. 61, 341 – 346. Stern, S., Lundeheim, N., Andersson, K., 1995b. Test of Genotype – Protein Level Interaction for Growth Rate and Lean Percentage in Pigs. 46th EAAP Meeting. Commision on Pig Production, Prague, Czech Republic. Thompson, R., 1976. The estimation of maternal genetic variance. Biometrics 32, 903 – 917. Thompson, B.K., Fraser, D., 1986. Variation in piglet weights: development of within-litter variation over a 5-week lactation and effect of farrowing crate design. Can. J. Anim. Sci. 66, 361 – 372. Vangen, O., 1972. Mortality in two different lines of pigs selected for rate of gain and thickness of backfat. Acta Agric. Scand. 22, 238 – 242. Zhang, S., Bidanel, J.P., Burlot, T., Legault, C., Naveau, J., 2000a. Genetic parameters and genetic trends in the Chinese  European Tiameslan composite pig line: I. Genetic parameters. Genet. Sel. Evol. 32, 41 – 56. Zhang, S., Bidanel, J.P., Burlot, T., Legault, C., Naveau, J., 2000b. Genetic parameters and genetic trends in the Chinese  EuroEuropean Tiameslan composite pig line. II. Genetic trends. Genet. Sel. Evol. 32, 57 – 71.