Estimation of genetic parameters for average daily gain using models with competition effects

Components of variance for ADG with models including competition effects were estimated from data provided by the Pig Improvement Company on 11,235 pigs from 4 selected lines of swine. Fifteen pigs with average age of 71 d were randomly assigned to a pen by line and sex and taken off test after approximately 89 d (off-test BW ranged from 61 to 158 kg). Models included fixed effects of line, sex, and contemporary group and initial test age as a covariate, with random direct genetic, competition (genetic and environmental), pen, litter, and residual effects. With the full model, variances attributable to direct, direct-competition, genetic competition, and litter (co)variance components could be partitioned; genetic competition variance was small but statistically significantly different from zero. Variances attributable to environmental competition, pen, and residual effects could not be partitioned, but combinations of these environmental variances were estimable. Variances could be partitioned with either pen effects or environmental competition effects in the model. Environmental competition effects seemed to be the source of variance associated with pens. With pen as a fixed effect and without environmental competition effects in the model, genetic components of variance could not be partitioned, but combinations of genetic (co)variances were estimable. With both pen and environmental competition effects ignored, estimates of direct-competition and genetic competition (co)variance components were greatly inflated. With competition (genetic and environmental) effects ignored, the estimate of pen variance increased by 39%, with little change in estimates of direct genetic or residual variance. When both pen and competition (genetic and environmental) effects were dropped from the model, variance attributable to direct genetic effects was inflated. Estimates of variance attributable to competition effects were small in this study. Including environmental competition effects as permanent environmental effects in the model did not change estimates of genetic (co)variances. We concluded that including either pen effects or environmental competition effects as random effects in the model avoids bias in estimates of genetic variances but that including pen effects is much easier.     

Effect of competition on gain in feedlot bulls from Hereford selection lines

This study examined competition effects on ADG in the feedlot of 1,882 Hereford bulls representing 8 birth years from a selection experiment. Each year, 8 feedlot pens were used to feed bulls in groups, with 2 pens nested within each of the 4 selection lines. Gains were recorded for up to 8 periods of 28 d. Models for analyses included pen effects (fixed or random), fixed effects such as year and line, and random direct genetic, competition genetic (and in some analyses competition environmental), and environmental effects. Each pen mate as a competitor affected the records of all others in the pen. All lines traced to common foundation animals, so the numerator relationships among and within pens were the bases for separating direct and competition genetic effects and pen effects. For this population and pen conditions (average of 30 bulls per pen), the major results were 1) competition genetic effects seemed present for the first 28-d period but not for the following 7 periods; 2) models with pens considered as fixed effects could not separate variances and covariance due to direct and competition genetic effects; 3) models without competition effects had large estimates of the variance component due to pen effects for gain through 8 periods; and 4) models with genetic and environmental competition effects accounted for nearly all of the variance traditionally attributed to pen effects (even though estimates of the competition variance component were small, the estimates of pen variance were near zero).     

Direct and associative effects for androstenone and genetic correlations with backfat and growth in entire male pigs

In the pig industry, male piglets are surgically castrated early in life to prevent boar taint. Boar taint is mainly caused by androstenone and skatole. Androstenone is a pheromone that can be released from the salivary glands when the boar is sexually aroused. Boars are housed in groups and as a consequence boars can influence and be influenced by the phenotype of other boars by (non-)heritable social interactions. The influence of these social interactions on androstenone is not well understood. The objective of this study is to investigate whether androstenone concentrations are affected by (non-)heritable social interactions and estimate their genetic correlation with growth rate and backfat. The dataset contained 6,245 boars, of which 4,455 had androstenone observations (68%). The average number of animals per pen was 7 and boars were housed in 899 unique pen-groups (boars within a single pen) and 344 unique compartment-groups (boars within a unique 'room' within a barn during time). Four models including different random effects, were compared for androstenone. Direct genetic, associative (also known as social genetic or indirect genetic effects), group, compartment, common environment and residual effects were included as random effects in the full model (M3). Including random pen and compartment effects (non-heritable social effects) significantly improved the model (M2) compared with including only direct, common environment and residual as random effects (M1, P < 0.001), and including associative effects even more (M3, P < 0.001). The sum of the direct and associative variance components determines the total genetic variance of the trait. The associative effect explained 11.7% of the total genetic variance. Backfat thickness was analysed using M2 and growth using M3. The genetic correlation between backfat (direct genetic variance) and total genetic variance for androstenone was close to 0. Backfat and the direct and associative effects for androstenone had genetic correlations of 0.14 ± 0.08 and -0.25 ± 0.18, respectively. The genetic correlation between total genetic variances for growth rate and androstenone was 0.33 ± 0.18. The genetic correlation between direct effects was 0.11 ± 0.09 and between associative effects was 0.42 ± 0.31. The genetic correlations and current selection towards lower backfat and greater growth rate suggest that no major change in androstenone is expected when breeding goals are not changed. For selection against boar taint and therefore also against androstenone , results recommend that at least the social environment of the boars should be considered.     

Choice of statistical model for estimating genetic parameters using restricted maximum likelihood in swine

Summary Restricted maximum likelihood (REML) was used to determine the choice of statistical model, additive genetic maternal and common litter effects and consequences of ignoring these effects on estimates of variance–covariance components under random and phenotypic selection in swine using computer simulation. Two closed herds of different size and two traits, (i) pre‐weaning average daily gain and (ii) litter size at birth, were considered. Three levels of additive direct and maternal genetic correlations (r_dm) were assumed to each trait. Four mixed models (denoted as GRM1 through GRM4) were used to generate data sets. Model GRM1 included only additive direct genetic effects, GRM2 included only additive direct genetic and common litter effects, GRM3 included only additive direct and maternal genetic effects and GRM4 included all the random effects. Four mixed animal models (defined as EPM1 through EPM4) were defined for estimating genetic parameters similar to GRM. Data from each GRM were fitted with EPM1 through EPM4. The largest biased estimates of additive genetic variance were obtained when EPM1 was fitted to data generated assuming the presence of either additive maternal genetic, common litter effects or a combination thereof. The bias of estimated additive direct genetic variance (VA_d) increased and those of recidual variance (VE) decreased with an increase in level of r_dm when GRM3 was used. EPM1, EPM2 and EPM3 resulted in biased estimation of the direct genetic variances. EPM4 was the most accurate in each GRM. Phenotypic selection substantially increased bias of estimated additive direct genetic effect and its mean square error in trait 1, but decreased those in trait 2 when ignored in the statistical model. For trait 2, estimates under phenotypic selection were more biased than those under random selection. It was concluded that statistical models for estimating variance components should include all random effects considered to avoid bias.     

Variance and covariance estimates for weaning weight of Senepol cattle.

Variance and covariance components were estimated for weaning weight from Senepol field data for use in the reduced animal model for a maternally influenced trait. The 4,634 weaning records were used to evaluate 113 sires and 1,406 dams on the island of St. Croix. Estimates of direct additive genetic variance (sigma 2A), maternal additive genetic variance (sigma 2M), covariance between direct and maternal additive genetic effects (sigma AM), permanent maternal environmental variance (sigma 2PE), and residual variance (sigma 2 epsilon) were calculated by equating variances estimated from a sire-dam model and a sire-maternal grandsire model, with and without the inverse of the numerator relationship matrix (A-1), to their expectations. Estimates were sigma 2A, 139.05 and 138.14 kg2; sigma 2M, 307.04 and 288.90 kg2; sigma AM, -117.57 and -103.76 kg2; sigma 2PE, -258.35 and -243.40 kg2; and sigma 2 epsilon, 588.18 and 577.72 kg2 with and without A-1, respectively. Heritability estimates for direct additive (h2A) were .211 and .210 with and without A-1, respectively. Heritability estimates for maternal additive (h2M) were .47 and .44 with and without A-1, respectively. Correlations between direct and maternal (IAM) effects were -.57 and -.52 with and without A-1, respectively.     

Genetic factors contributing to variation in littersize in British Large White gilts

The number of ova released (ovulation rate) by 516 Large White gilts born between 1986 and 1989 was recorded. The weight of the gilt at birth, weaning and time of ovulation rate measurement and her number of teats were also recorded. Parrowing data (number born alive and litter weight at birth) corresponding to the ovulation rate were recorded from 382 of the gilts, enabling calculation of prenatal survival (number born alive/ovulation rate). The data were analysed using univariate and multivariate restricted maximum likelihood (REML) techniques with an individual animal model. The additive genetic direct and maternal components of variance and the common family and residual environmental components of variance and the additive genetic and residual environmental covariances between traits were estimated. The univariate REML analyses showed that the additive genetic direct component was a significant source of variation for gilt weight at birth and weaning, teat number, ovulation rate on the left hand side, total ovulation rate and litter weight at birth. Common family environmental effects were significant sources of variation for gilt weights and teat number. The multivariate REML analyses indicated that the genetic correlations between total ovulation rate and ovulation rate from the left and right ovaries were close to unity, with an estimate of the heritability of total ovulation rate of 0.37±0.09. In the data from gilts that farrowed, the heritabilities of ovulation rate, number born alive and prenatal survival were 0.30±0.10, 0.09±0.06 and 0.00±0.00, respectively. The genetic correlation between ovulation rate and litter size was close to unity, suggesting that genetic variation in ovulation rate explains virtually all of the genetic variation in number born alive in the population of Large White gilts understudy.     

On identifiability of (co)variance components in animal models with competition effects

There is an increased interest in estimating the (co)variance components of additive animal models with direct and competition effects (AMC). However, some attempts to estimate the dispersion parameters in different animal species faced problems of convergence or inaccurate estimates when pen effects entered the model. We argue that the problem relates to lack of identifiability of the (co)variance components in some AMC. The check for identifiability of the dispersion parameters in mixed models with linear (co)variance structure requires that all the eigenvalues of the restricted maximum likelyhood information matrix ( I ( θ )) be positive. We show, by way of simple numerical examples, that the singularity of I ( θ ) is due to confounding between fixed pen effects and the additive competition effects (SBVs). It is also observed that setting pen effects as random does not always remedy the collinearity with SBVs. An alternative AMC is presented in which the incidence matrix of the SBVs can be written as a function of the ‘intensity of competition’ (IC) among animals in the same pen. Examples are presented in which the ICs are related to time. The distribution of families of full and half sibs across pens also plays a role in the identifiability and asymptotic variances of the (co)variance components.     

A genetic analysis of feed conversion efficiency and associated traits in swine

Performance records were analyzed for 1,869 purebred Duroc and Yorkshire gilts tested in littermate groups of two to four pigs in 703 pens. Traits studied were average daily gain during a standard test period from 56 d of age to 90.7 kg (ADG), average daily feed consumption during the test period (ADF), average backfat thickness (ABF) measured from polaroid photos of ultrasonic scans at 90.7 kg and efficiency of feed conversion for the whole test period (feed conversion efficiency, FCE). The main objectives were: 1) to estimate pertinent genetic and phenotypic parameters and 2) to discuss applications of the findings to the swine industry in the United States. A balanced 16% crude protein diet in pelleted form was provided ad libitum during the entire test period. Nested analyses of variance were computed with both the individual and pen mean records. Sire components of variance and covariance, adjusted for level of inbreeding, were used to estimate heritabilities and genetic and phenotypic variances, covariances and correlations. Heritabilities estimated from individual records were .098 for ADG and .423 for ABF. Estimates from pen means were .105 for ADF and .061 for FCE. Genetic correlations of FCE with ADG, ADF and ABF were estimated as -.520, -.520 and .694, respectively, while phenotypic correlations for the same traits were -.240, .570 and .212, respectively, all from pen means. Genetic and phenotypic correlations of ADG with ABF from individual records were .176 and .254, respectively. Implications for swine testing programs were discussed, including a proposal that would include FCE indirectly in an index with ADG and ABF, based on genetic covariances of FCE with ADG and ABF, thereby removing the need to measure FCE directly.     

Effect of including relationships in the estimation of genetic parameters of beef calves.

Variances and covariances for birth weight, gain from birth to weaning (ADG), and 205-d weight were obtained from a sire-dam model and a sire-maternal grandsire model for a herd of Angus and a herd of Hereford cattle. Estimates of direct additive genetic variance (sigma 2A), maternal additive genetic variance (sigma 2M), covariance between direct and maternal additive genetic effects (sigma AM), permanent environmental variance (sigma 2PE), and residual variance (sigma 2e) were obtained both with and without the inverse of the numerator relationship matrix (A-1) included. Estimates of heritability for direct genetic effects (h2A), maternal genetic effects (h2M), and the correlation between direct and maternal effects (rAM) for birth weight were .37, .18, and -.01 in Angus and .53, .23, and -.19 in Herefords, respectively, for the analyses without A-1. For the analyses with A-1, estimates of h2A, h2M, and rAM were .42, .22, and -.12 for Angus and .58, .22, and -.13 for Herefords, respectively. Estimates of h2A, h2M, and rAM for ADG were .43, .15, and -.44 in Angus and .52, .38, and -.03 in Herefords, respectively, without A-1. With A-1, estimates of h2A, h2M, and rAM were .57, .15, and -.32 for Angus and .58, .39, and -.05 for Herefords, respectively. Estimates of h2A, h2M, and rAM for 205-d weight were .49, .15, and -.46 for Angus and .58, .43, and -.06 for Herefords, respectively, without A-1. With A-1, estimates of h2A, h2M, and rAM were .63, .16, and -.36 for Angus and .66, .43, and -.08 for Herefords, respectively. Estimates of h2A were higher with A-1 than without A-1, but estimates of h2M were similar. Using variances and covariances obtained from analyses including A-1 generally gave higher estimates of direct breeding values than using variances and covariances obtained from analyses not including A-1. Both Pearson product-moment and Spearman rank correlations were high (.99) between estimates of breeding values from the two analyses, although some changes in rank did occur.     

Estimation of direct and maternal genetic variance for litter size in Canadian Yorkshire and Landrace swine using an animal model

Estimates of additive direct heritability (h2a) for traits such as litter size may be biased by maternal effects. The size of these effects was estimated using a derivative-free restricted maximum likelihood procedure under an animal model. First-parity records from Yorkshire (Y) and Landrace (L) gilts were obtained from the Quebec Record of Performance sow productivity program for 21,127 litters born between 1977 and 1987. Direct (sigma 2a) and maternal (sigma 2m) additive genetic variances, their covariance (sigma am) and error variance (sigma 2e) were estimated for total numbers born (NOBN), born alive (NOBA) and weaned (NOWN). Analysis of purebred Y and crossbred litters indicated that estimates of sigma 2a were of similar magnitude for all traits, with h2a ranging from .06 to .13. Except for L litters, estimates of sigma 2m were relatively low for NOBN and NOBA, and increased in size for NOWN, with h2m ranging from 0 to .08. Also, estimates of sigma am were negative, except for NOBN and NOBA with crossbred litters, and became increasingly negative for NOWN. Results from purebred L litters indicated there was a stronger negative correlation between direct and maternal genetic effects for NOBN and NOBA than for NOWN.     

Unexpected estimates of variance components with a true model containing genetic competition effects

Simulation of a model containing genetic competition effects was initiated to determine how well REML could untangle variances due to direct and competition genetic effects and pen effects. A two-generation data set was generated with six unrelated males that were each mated to five unrelated females to produce 300 progeny, from which 30 females (one per mating in previous generation) were mated to six unrelated males to produce 300 more progeny. Progeny were randomly assigned, six per pen, to 50 pens per generation. Parameters were V(g), V(c), C(gc), V(p), and V(e), representing direct and competition genetic variance with covariance, and pen and residual variance. Eight statistical models were used to analyze each of 400 replicates of 16 sets of parameters. Both V(g) and V(e) were fixed at 16.0. Values of C(gc) were -2.0, -1.0, 0.1, 1.0, and 2.0. Values of V(c) were 1.0 and 4.0, and values of V(p) were 0.1, 1.0, and 10.0. With the full model, average estimates resembled true parameters, except that V(p) was consistently overestimated when small (0.1 and 1.0), which in turn slightly changed other estimates. The most unexpected result was overestimation of V(p) when V(c) and Cgc were ignored. Overestimation depended on V(c) and the number of competitors in common between records in a pen. Upward bias was somewhat greater when Cg(c) was positive than when it was negative. For example, with C(gc) = 2, V(c) = 4, and V(p) = 0.1, the mean estimate of V(p) was 20.4 when C(gc) and V(c) were dropped from the model and 15.3 when C(gc) = -2.0. When V(p) was ignored, estimates of both C(gc) and V(c) increased in proportion with V(p). Also V(g) increased more with greater V(p). Another unexpected result occurred when pen was considered fixed. Empirical sampling standard errors of estimates of C(gc) and V(c) were decreased generally by factors of 2 to 30. Based on these results, we conclude a high estimate of pen variance may indicate genetic competition effects are important, and ignoring either the pen or competition effects will bias estimates of other components.     

Additive, dominance, and epistatic loss effects on preweaning weight gain of crossbred beef cattle from different Bos taurus breeds

(Co)variance components, direct and maternal breed additive, dominance, and epistatic loss effects on preweaning weight gain of beef cattle were estimated. Data were from 478,466 animals in Ontario, Canada, from 1986 to 1999, including records of both purebred and crossbred animals from Angus, Blonde d'Aquitaine, Charolais, Gelbvieh, Hereford, Limousin, Maine-Anjou, Salers, Shorthorn, and Simmental breeds. The genetic model included fixed direct and maternal breed additive, dominance, and epistatic loss effects, fixed environmental effects of age of the calf, contemporary group, and age of the dam x sex of the calf, random additive direct and maternal genetic effects, and random maternal permanent environment effects. Estimates of direct and maternal additive genetic, maternal permanent environmental and residual variances, expressed as proportions of the phenotypic variance, were 0.32, 0.20, 0.12, and 0.52, respectively. Correlation between direct and maternal additive genetic effects was -0.63. Breed ranking was similar to previous studies, but estimates showed large SE. The favorable effects of direct and maternal dominance (P < 0.05) on preweaning gain were equivalent to 1.3 and 2.3% of the phenotypic mean of purebred calves, respectively. The same features for direct and maternal epistatic loss effects were -2.2% (P < 0.05) and -0.1% (P > 0.05). The large SE of breed effects were likely due to multicollinearity among predictor variables and deficiencies in the dataset to separate direct and maternal effects and may result in a less reliable ranking of the animals for across breed comparisons. Further research to identify the causes of the instability of estimates of breed additive, dominance, and epistatic loss genetic effects, and application of alternative statistical methods is recommended.     

Estimation of additive and nonadditive genetic variances in Hereford, Gelbvieh, and Charolais by Method R.

Parameters for direct and maternal dominance were estimated in models that included non-additive genetic effects. The analyses used weaning weight records adjusted for age of dam from populations of Canadian Hereford (n = 467,814), American Gelbvieh (n = 501,552), and American Charolais (n = 314,552). Method R estimates of direct additive genetic, maternal additive genetic, permanent maternal environment, direct dominance, and maternal dominance variances as a proportion of the total variance were 23, 12, 13, 19, and 14% in Hereford; 27, 7, 10, 18, and 2% in Gelbvieh; and 34, 15, 15, 23, and 2% in Charolais. The correlations between direct and maternal additive genetic effects were -0.30, -0.23, and -0.47 in Hereford, Gelbvieh, and Charolais, respectively. The correlations between direct and maternal dominance were -0.38, -0.02, and -0.04 in Hereford, Gelbvieh, and Charolais, respectively. Estimates of inbreeding depression were -0.20, -0.18, and -0.13 kg per 1% of inbreeding for Hereford, Gelbvieh, and Charolais, respectively. Estimates of the maternal inbreeding depression were -0.01, -0.02, and -0.02 kg, respectively. The high ratio of direct dominance to additive genetic variances provided some evidence that direct dominance effects should be considered in beef cattle evaluation. However, maternal dominance effects seemed to be important only for Hereford cattle.     

Estimates of genetic parameters for direct and maternal effects on embryonic survival in swine.

Survival of 16,838 potential embryos was determined by counting corpora lutea and fetuses at 50 d of gestation for 1,081 litters by 225 sires. These data, coded as 1 or 0 depending on whether an ovulation was represented by a fetus, were used to estimate direct and maternal additive genetic variances and their covariance for embryonic survival. Data were from first-parity gilts of a Large White-Landrace composite population subdivided into two lines, one selected for an index of ovulation rate and embryonic survival for seven generations and a contemporary control line. Variance components were obtained by ANOVA and expectations of covariances among relatives and by derivative-free restricted maximum likelihood (DFREML) in an animal model. As a trait of the embryo, heritability of direct effects obtained with ANOVA was 3.8%, heritability of maternal effects was 1.5%, and the genetic correlation between them was -.51. After adjustment of embryonic survival for ovulation rate, lower estimates of each parameter were obtained with ANOVA. Heritability of embryonic survival as a trait of the dam was 9 to 10%. Estimates of heritability of both direct and maternal effects obtained with DFREML were less than 1% and the genetic correlation between them was -.64. When survival of embryos from only those dams with 15 or more ovulations was analyzed, heritability of maternal effects was 4.4%. Estimates of common environmental effects on embryonic survival ranged from 5 to 7%.     

Genetic and environmental influences on size of first litter in sheep, estimated by the REML method

Fixed effects of age at first litter and of season of lambing as well as variance components for additive genetic, flock × year and sire of litter effects on size of first litter were estimated for an animal model by a derivative-free REML procedure. Data of the four Swiss sheep breeds White Alpine (WAS), Brown-headed Meat (BFS), Black-Brown Mountain (SBS) and Valais Black Nose (SN) were available. Number of first litters used were 21 384, 21 607, 15 013, 12 394 and 18 110 the WAS (two data sets, WAS1, WAS2), BFS, SBS and SN, respectively. Litter size of ewes lambing the first time at 2 years of age was 0.27, 0.31, 0.41, 0.46 and 0.26 lambs larger than of ewes lambing the first time at 1 year of age. The largest increase occurred for the two breeds (BFS, SBS) with the lowest average age at first litter. The largest difference between any two lambing seasons within breed were 0.16, 0.16, 0.29, 0.22 and 0.07 lambs. Estimates of additive genetic variance of size of first litter were between 0.0269 (SN) and 0.0765 (SBS). Heritability estimates for this trait were 0.171, 0.156, 0.114, 0.225 and 0.122 for WAS1, WAS2, BFS, SBS and SN, respectively. A large flock × year component (relative to phenotypic variance) of 0.148 was found for SN, compared with estimates between 0.042 and 0.067 for the other breeds. A sire of litter component (relative to phenotypic variance) of 0.066 was found for SN, compared with estimates between 0.016 and 0.039 for the other breeds. It can be concluded that all nongenetic effects investigated should be taken into account for the estimation of additive genetic variance and breeding values for size of first litter, and that considerable variation in size of genetic and nongenetic effects exists in the sheep breeds under consideration.     

The empirical bias of estimates by restricted maximum likelihood, Bayesian method, and method R under selection for additive, maternal, and dominance models.

Bayesian analysis via Gibbs sampling, restricted maximum likelihood (REML), and Method R were used to estimate variance components for several models of simulated data. Four simulated data sets that included direct genetic effects and different combinations of maternal, permanent environmental, and dominance effects were used. Parents were selected randomly, on phenotype across or within contemporary groups, or on BLUP of genetic value. Estimates by Bayesian analysis and REML were always empirically unbiased in large data sets. Estimates by Method R were biased only with phenotypic selection across contemporary groups; estimates of the additive variance were biased upward, and all the other estimates were biased downward. No empirical bias was observed for Method R under selection within contemporary groups or in data without contemporary group effects. The bias of Method R estimates in small data sets was evaluated using a simple direct additive model. Method R gave biased estimates in small data sets in all types of selection except BLUP. In populations where the selection is based on BLUP of genetic value or where phenotypic selection is practiced mostly within contemporary groups, estimates by Method R are likely to be unbiased. In this case, Method R is an alternative to single-trait REML and Bayesian analysis for analyses of large data sets when the other methods are too expensive to apply.     

Weaning weight and post-weaning gain genetic parameters and genetic trends in a Blanco Orejinegro–Romosinuano–Angus–Zebu multibreed cattle population in Colombia

Genetic parameters and genetic trends for weaning weight adjusted to 240 d of age (WW240), and weight gain from weaning to 24 mo of age (GW730) were estimated in a Colombian beef cattle population composed of Blanco Orejinegro, Romosinuano, Angus, and Zebu straightbred and crossbred animals. Calves were born and weaned in a single farm, and moved to 14 farms postweaning. Data were analyzed using a multiple trait mixed model procedures. Estimates of variance components and genetic parameters were obtained by Restricted Maximum Likelihood. The 2-trait model included the fixed effects of contemporary group (herd–year–season–sex), age of dam (WW240 only), breed direct genetic effects (as a function of breed fractions of calves), breed maternal genetic effects (as a function of breed fractions of dams; WW240 only), individual heterosis (as a function of calf heterozygosity), and maternal heterosis (as a function of dam heterozygosity; WW240 only). Random effects for WW240 were calf direct genetic, dam maternal genetic, permanent environmental maternal, and residual. Random effects for GW730 were calf direct genetic and residual. All relationships among animals were accounted for. Program AIREML was used to perform computations. Estimates of heritabilities for additive direct genetic effects were 0.20 ± 0.003 for WW240, and 0.32 ± 0.004 for GW730. Maternal heritability was 0.14 ± 0.002 for WW240. Estimates of heritability suggest that selection for preweaning and postweaning growth in this population is feasible. Low direct and maternal preweaning heritabilities suggest that nutrition and management should be improved to allow fuller expressions of calf direct growth and cow maternal ability. The genetic correlation between direct additive and maternal additive effects for WW240 was − 0.42 ± 0.009, indicating an antagonistic relationship between these effects. The correlation between additive direct genetic effects for WW240 and GW730 was almost zero (− 0.04 ± 0.009), suggesting that genes affecting growth preweaning may differ from those influencing growth postweaning. Trends were negative for direct WW240 and GW730 weighted yearly means of calves, sires, and dams from 1995 to 2006. Maternal WW240 showed near zero trends during these years. Trends for calf direct WW240 and GW730 followed sire trends closely, suggesting that more emphasis was placed on choosing sires than on dam replacements.     

Effects of social interactions on empirical responses to selection for average daily gain of boars

Effects of social interactions on responses to selection for ADG were examined with records of 9,720 boars from dam lines (1 and 2) and sire lines (3 and 4) provided by Pig Improvement Company. Each line was analyzed separately. Pens contained 15 boars. Average daily gains were measured from about 71 to 161 d of age and BW from 31 to 120 kg. Models included fixed effects of contemporary groups and initial test age as a covariate and random direct genetic (a), social genetic (c), social environmental (ce), and litter (lt) effects. Estimates of direct heritability with model 1 (the full model with a, c, ce, and lt) were 0.21, 0.28, 0.13, and 0.15 for lines 1 to 4. Estimates of heritability of social effects were near zero. Estimates of total heritable variance were 55, 52, 38, and 96% of phenotypic variance for lines 1 through 4. Empirical responses to selection with model 1 were calculated using the parameter estimates from model 1. For response of 1 genetic SD for both components (a and c), the proportions of expected total gain due to social effects (with economic weights of 1 and pen size-1 = 14) were 54, 28, 65, and 65% for the 4 lines. Genetic superiorities of the top 10% of boars were calculated for boars ranked using reduced models, but with EBV calculated using the full model (model 1). Average total breeding values (ETBV = EBV(a)+14EBV(c)) for the top 10% of boars selected with model 1 were 74.08, 94.26, 31.79, and 92.88 g for lines 1 through 4, respectively. For rankings based on model 2 (a, ce, and lt), but EBV calculated with model 1, average total breeding values for the top 10% were 68.15, 94.03, 7.33, and 84.72 g with empirical correlated responses for genetic social effects from selection for direct effects of 0.93, 1.89, -2.19, and 3.52 g for lines 1 to 4.     

Genetic analysis of longitudinal measurements of performance traits in selection lines for residual feed intake in Yorkshire swine

A 5-generation selection experiment in Yorkshire pigs for feed efficiency consists of a line selected for low residual feed intake (LRFI) and a random control line (CTRL). The objectives of this study were to use random regression models to estimate genetic parameters for daily feed intake (DFI), BW, backfat (BF), and loin muscle area (LMA) along the growth trajectory and to evaluate the effect of LRFI selection on genetic curves for DFI and BW. An additional objective was to compare random regression models using polynomials (RRP) and spline functions (RRS). Data from approximately 3 to 8 mo of age on 586 boars and 495 gilts across 5 generations were used. The average number of measurements was 85, 14, 5, and 5 for DFI, BW, BF, and LMA. The RRP models for these 4 traits were fitted with pen × on-test group as a fixed effect, second-order Legendre polynomials of age as fixed curves for each generation, and random curves for additive genetic and permanent environmental effects. Different residual variances were used for the first and second halves of the test period. The RRS models were fitted with the same fixed effects and residual variance structure as the RRP models and included genetic and permanent environmental random effects for both splines and linear Legendre polynomials of age. The RRP model was used for further analysis because the RRS model had erratic estimates of phenotypic variance and heritability, despite having a smaller Bayesian information criterion than the RRP model. From 91 to 210 d of age, estimates of heritability from the RRP model ranged from 0.10 to 0.37 for boars and 0.14 to 0.26 for gilts for DFI, from 0.39 to 0.58 for boars and 0.55 to 0.61 for gilts for BW, from 0.48 to 0.61 for boars and 0.61 to 0.79 for gilts for BF, and from 0.46 to 0.55 for boars and 0.63 to 0.81 for gilts for LMA. In generation 5, LRFI pigs had lower average genetic curves than CTRL pigs for DFI and BW, especially toward the end of the test period; estimated line differences (CTRL-LRFI) for DFI were 0.04 kg/d for boars and 0.12 kg/d for gilts at 105 d and 0.20 kg/d for boars and 0.24 kg/d for gilts at 195 d. Line differences for BW were 0.17 kg for boars and 0.69 kg for gilts at 105 d and 3.49 kg for boars and 8.96 kg for gilts at 195 d. In conclusion, selection for LRFI has resulted in a lower feed intake curve and a lower BW curve toward maturity.     

Genetic parameters for individual birth and weaning weight and for litter size of Large White pigs

Data from a French experimental herd recorded between 1990 and 1997 were used to estimate genetic parameters for individual birth and weaning weight, as well as litter size of Large White pigs using restricted maximum likelihood (REML) methodology applied to a multivariate animal model. In addition to fixed effects the model included random common environment of litter, direct and maternal additive genetic effects. The data consisted of 1928 litters including individual weight observations from 18 151 animals for birth weight and from 15 360 animals for weaning weight with 5% of animals transferred to a nurse. Estimates of direct and maternal heritability and proportion of the common environmental variance for birth weight were 0.02, 0.21 and 0.11, respectively. The corresponding values for weaning weight were 0.08, 0.16 and 0.23 and for litter size 0.22, 0.02 and 0.06, respectively. The direct and the maternal genetic correlations between birth and weaning weight were positive (0.59 and 0.76). Weak positive (negative) genetic correlations between direct effects on weight traits and maternal effects on birth weight (weaning weight) were found. Negative correlations were found between direct genetic effect for litter size and maternal genetic effects on all three traits. The negative relationship between litter size and individual weight requires a combined selection for litter size and weight.