Inclusion of weaning management group as a random effect in the genetic evaluation of postweaning traits in Nellore cattle

The objective of this study was to evaluate the impact of including weaning management group (WMG) as an uncorrelated random effect in the genetic evaluation of postweaning traits. Data from 186,507 Nellore animals' sons to 1,734 sires and 75,016 cows were analyzed. Three single-trait models were studied. These models included the contemporary group (CG) as a fixed effect and age of animal at measurement and age of dam at calving as covariates, in addition to the direct additive breeding value as random effect. The CGs for postweaning traits varied between models which included or not WMG as part of the concatenation of fixed effects. In the model in which WMG was not part of the CG, the trait was included as an uncorrelated random effect. The results suggest that although no significant effects were observed on genetic parameter estimates or animal ranking, the inclusion of WMG as an uncorrelated random effect increased the number of observations per CG and contributed to maintaining animals in the analysis that would be discarded because they were in a CG with a small number of observations. This model could therefore be recommended for the genetic evaluation of this Nellore population.     

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

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

Random regression models on Legendre polynomials to estimate genetic parameters for weights from birth to adult age in Canchim cattle*

The objective of this work was to estimate covariance functions for direct and maternal genetic effects, animal and maternal permanent environmental effects, and subsequently, to derive relevant genetic parameters for growth traits in Canchim cattle. Data comprised 49 011 weight records on 2435 females from birth to adult age. The model of analysis included fixed effects of contemporary groups (year and month of birth and at weighing) and age of dam as quadratic covariable. Mean trends were taken into account by a cubic regression on orthogonal polynomials of animal age. Residual variances were allowed to vary and were modelled by a step function with 1, 4 or 11 classes based on animal’s age. The model fitting four classes of residual variances was the best. A total of 12 random regression models from second to seventh order were used to model direct and maternal genetic effects, animal and maternal permanent environmental effects. The model with direct and maternal genetic effects, animal and maternal permanent environmental effects fitted by quadric, cubic, quintic and linear Legendre polynomials, respectively, was the most adequate to describe the covariance structure of the data. Estimates of direct and maternal heritability obtained by multi‐trait (seven traits) and random regression models were very similar. Selection for higher weight at any age, especially after weaning, will produce an increase in mature cow weight. The possibility to modify the growth curve in Canchim cattle to obtain animals with rapid growth at early ages and moderate to low mature cow weight is limited.     

Correlated responses of pre- and postweaning growth and backfat thickness to six generations of selection for ovulation rate or prenatal survival in French Large White pigs

Correlated effects of selection for components of litter size on growth and backfat thickness were estimated using data from 3 pig lines derived from the same base population of Large White. Two lines were selected for 6 generations on either high ovulation rate at puberty (OR) or high prenatal survival corrected for ovulation rate in the first 2 parities (PS). The third line was an unselected control (C). Genetic parameters for individual piglet BW at birth (IWB); at 3 wk of age (IW3W); and at weaning (IWW); ADG from birth to weaning (ADGBW), from weaning to 10 wk of age (ADGPW), and from 25 to 90 kg of BW (ADGT); and age (AGET) and average backfat thickness (ABT) at 90 kg of BW were estimated using REML methodology applied to a multivariate animal model. In addition to fixed effects, the model included the common environment of birth litter, as well as direct and maternal additive genetic effects as random effects. Genetic trends were estimated by computing differences between OR or PS and C lines at each generation using both least squares (LS) and mixed model (MM) methodology. Average genetic trends for direct and maternal effects were computed by regressing line differences on generation number. Estimates of direct and maternal heritabilities were, respectively, 0.10, 0.12, 0.20, 0.24, and 0.41, and 0.17, 0.33, 0.32, 0.41, and 0.21 (SE = 0.03 to 0.04) for IWB, IW3W, IWW, ADGBW, and ADGPW. Genetic correlations between direct and maternal effects were moderately negative for IWB (-0.21 +/- 0.18), but larger for the 4 other traits (-0.59 to -0.74). Maternal effects were nonsignificant and were removed from the final analyses of ADGT, AGET, and ABT. Direct heritability estimates were 0.34, 0.46, and 0.21 (SE = 0.03 to 0.05) for ADGT, AGET, and ABT, respectively. Direct and maternal genetic correlations of OR with performance traits were nonsignificant, with the exception of maternal correlations with IWB (-0.28 +/- 0.13) and ADGPW (0.23 +/- 0.11) and direct correlation with AGET (-0.23 +/- 0.09). Prenatal survival also had low direct but moderate to strong maternal genetic correlations (-0.34 to -0.65) with performance traits. The only significant genetic trends were a negative maternal trend for IBW in the OR line and favorable direct trends for postweaning growth (ADGT and AGET) in both lines. Selection for components of litter size has limited effects on growth and backfat thickness, although it slightly reduces birth weight and improves postweaning growth.     

Genetic parameters for growth traits for a composite terminal sire breed of sheep.

Records of 9,055 lambs from a composite population originating from crossing Columbia rams to Hampshire x Suffolk ewes at the U.S. Meat Animal Research Center were used to estimate genetic parameters among growth traits. Traits analyzed were weights at birth (BWT), weaning (7 wk, WWT), 19 mo (W19), and 31 mo (W31) and postweaning ADG from 9 to 18 or 19 wk of age. The ADG was also divided into daily gain of males (DGM) and daily gain of females (DGF). These two traits were analyzed with W19 and with W31 in three-trait analyses. (Co)variance components were estimated with REML for an animal model that included fixed effects of sex, age of dam, type of birth or rearing, and contemporary group. Random effects were direct and maternal genetic of animal and dam with genetic covariance, maternal permanent environmental, and random residual. Estimates of direct heritability were .09, .09, .35, .44, .19, .16, and .23 for BWT, WWT, W19, W31, ADG, DGM, and DGF, respectively. Estimates of maternal permanent environmental variance as a proportion of phenotypic variance were .09, .12, .03, .03, .03, .06, and .02, respectively. Estimates of maternal heritability were .17 and .09 for BWT and WWT and .01 to .03 for other traits. Estimates of genetic correlations were large among W19, W31, and ADG (.69 to .97), small between BWT and W31 or ADG, and moderate for other pairs of traits (.32 to .45). The estimate of genetic correlation between DGM and DGF was .94, and the correlation between maternal permanent environmental effects for these traits was .56. For the three-trait analyses, the genetic correlations of DGM and DGF with W19 were .69 and .82 and with W31 were .67 and .67, respectively. Results show that models for genetic evaluation for BWT and WWT should include maternal genetic effects. Estimates of genetic correlations show that selection for ADG in either sex can be from records of either sex (DGM or DGF) and that selection for daily gain will result in increases in mature weight but that BWT is not correlated with weight at 31 mo.     

Influence of population structure on estimates of direct and maternal parameters.

The estimation of (co)variance components for multiple traits with maternal genetic effects was found to be influenced by population structure. Two traits in a closed breeding herd with random mating were simulated over nine generations. Population structures were simulated on the basis of different proportions of dams not having performance records (0, 0.1, 0.5, 0.8 and 0.9): three genetic correlations (-0.5, 0.0 and +0.5) between direct and maternal effects and three genetic correlations (0, 0.3 and 0.8) between two traits. Three ratios of direct to maternal genetic variances, (1:3, 1:1, 3:1), were also considered. Variance components were estimated by restricted maximum likelihood. The proportion of dams without records had an effect on the SE of direct-maternal covariance estimates when the proportion was 0.8 or 0.9 and the true correlation between direct and maternal effects was negative. The ratio of direct to maternal genetic variances influenced the SE of the (co)variance estimates more than the proportion of dams with missing records. The correlation between two traits did not have an effect on the SE of the estimates. The proportion of dams without records and the correlation between direct and maternal effects had the strongest effects on bias of estimates. The largest biases were obtained when the proportion of dams without records was high, the correlation between direct and maternal effects was positive, and the direct variance was greater than the maternal variance, as would be the situation for most growth traits in livestock. Total bias in all parameter estimates for two traits was large in the same situations. Poor population structure can affect both bias and SE of estimates of the direct-maternal genetic correlation, and can explain some of the large negative estimates often obtained.     

"Quasi-REML" correlation estimates between production and health traits in the presence of selection and confounding: a simulation study

Performance of the "quasi-REML" method for estimating correlations between a continuous trait and a categorical trait, and between two categorical traits, was studied with Monte Carlo simulations. Three continuous, correlated traits were simulated for identical populations and three scenarios with either no selection, selection for one moderately heritable trait (Trait 1, h2 = .25), and selection for the same trait plus confounding between sires and management groups. The "true" environmental correlations between Traits 2 (h2 = .10) and 3 (h2 = .05) were always of the same absolute size (.20), but further data scenarios were generated by setting the sign of environmental correlation to either positive or negative. Observations for Traits 2 and 3 were then reassigned to binomial categories to simulate health or reproductive traits with incidences of 15 and 5%, respectively. Genetic correlations (r(g12), r(g13), and r(g23) and environmental correlations (r(e12), r(e13), and r(e23)) were estimated for the underlying continuous scale (REML) and the visible categorical scales ("quasi-REML") with linear multiple-trait sire and animal models. Contrary to theory, practically all "quasi-REML" genetic correlations were underestimated to some extent with the sire and animal models. Selection inflated this negative bias for sire model estimates, and the sign of r(e23) noticeably affected r(g23) estimates for the animal model, with greater bias and SD for estimates when the "true" r(e23) was positive. Transformed "quasi-REML" environmental correlations between a continuous and a categorical trait were estimated with good efficiency and little bias, and corresponding correlations between two categorical traits were systematically overestimated. Confounding between sires and contemporary groups negatively affected all correlation estimates on the underlying and the visible scales, especially for sire model "quasi-REML" estimates of genetic correlation. Selection, data structure, and the (co)variance structure influences how well correlations involving categorical traits are estimated with "quasi-REML" methods.     

Analyses of growth curves of nellore cattle by multiple-trait and random regression models

The purpose of this study was to compare estimates of genetic parameters for sequential growth of beef cattle using two models and two data sets. Growth curves of Nellore cattle were analyzed using body weights measured at ages 1 (birth weight) to 733 d. Two data samples were created, one with 71,867 records sampled from all herds (MISS), and the other with 74,601 records sampled from herds with no missing traits (NMISS). Records preadjusted to a fixed age were analyzed by a multiple-trait model (MTM), which included the effects of contemporary group, age of dam class, additive direct, additive maternal, and maternal permanent environment. Analyses were by REML, with five traits at a time. The random regression model (RRM) included the effects of age of animal, contemporary group, age of dam class, additive direct, additive maternal, permanent environment, and maternal permanent environment. All effects were modeled as cubic Legendre polynomials. These analyses were also by REML. Shapes of estimates of variances by MTM were mostly similar for both data sets for all except late ages, where estimates for MISS were less regular, and for birth weight with MISS. Genetic correlations among ages for the direct and maternal effects were less smooth with MISS. Genetic correlations between direct and maternal effects were more negative for NMISS, where few sires were maternal grandsires. Parameter estimates with RRM were similar to MTM cept that estimates of variances showed more artifacts for MISS; the estimates of additive direct-maternal correlations were more negative with both data sets and approached -1.0 for some ages with NMISS. When parameters of a growth model obtained by used for genetic evaluation, these parameters should be examined for consistency with parameters from MTM and prior information, and adjustments may be required to eliminate artifacts.     

Genetic relationships between length of productive life and lifetime production efficiency in a commercial swine herd in Northern Thailand

Genetic parameters and trends for length of productive life (LPL), lifetime number of piglets born alive per year (LBAY), lifetime number of piglets weaned per year (LPWY), lifetime litter birth weight per year (LBWY) and lifetime litter weaning weight per year (LWWY) were estimated using phenotypic records of 3085 sows collected from 1989 to 2013 in a commercial swine farm in Northern Thailand. The five‐trait animal model included the fixed effects of first farrowing year‐season, breed group and age at first farrowing. Random effects were animal and residual. Heritability estimates ranged from 0.04 ± 0.02 for LBWY to 0.17 ± 0.04 for LPL. Genetic correlations ranged from 0.66 ± 0.14 between LPL and LBAY to 0.95 ± 0.02 between LPWY and LWWY. Spearman rank correlations among estimated breeding values for LPL and lifetime production efficiency traits tended to be higher for boars than for sows. Sire genetic trends were negative and significant for all traits, except for LPWY. Dam genetic trends were positive and significant for all traits. Sow genetic trends were mostly positive and significant only for LPWY and LBWY. Improvement of LPL and lifetime production efficiency traits will require these traits to be included in the selection indexes used to choose replacement boars and gilts in this population.     

Testicular traits as selection criteria for young Nellore bulls

The objective of this study was to evaluate the possible use of biometric testicular traits as selection criteria for young Nellore bulls using Bayesian inference to estimate heritability coefficients and genetic correlations. Multitrait analysis was performed including 17,211 records of scrotal circumference obtained during andrological assessment (SCAND) and 15,313 records of testicular volume and shape. In addition, 50,809 records of scrotal circumference at 18 mo (SC18), used as an anchor trait, were analyzed. The (co)variance components and breeding values were estimated by Gibbs sampling using the Gibbs2F90 program under an animal model that included contemporary groups as fixed effects, age of the animal as a linear covariate, and direct additive genetic effects as random effects. Heritabilities of 0.42, 0.43, 0.31, 0.20, 0.04, 0.16, 0.15, and 0.10 were obtained for SC18, SCAND, testicular volume, testicular shape, minor defects, major defects, total defects, and satisfactory andrological evaluation, respectively. The genetic correlations between SC18 and the other traits were 0.84 (SCAND), 0.75 (testicular shape), 0.44 (testicular volume), -0.23 (minor defects), -0.16 (major defects), -0.24 (total defects), and 0.56 (satisfactory andrological evaluation). Genetic correlations of 0.94 and 0.52 were obtained between SCAND and testicular volume and shape, respectively, and of 0.52 between testicular volume and testicular shape. In addition to favorable genetic parameter estimates, SC18 was found to be the most advantageous testicular trait due to its easy measurement before andrological assessment of the animals, even though the utilization of biometric testicular traits as selection criteria was also found to be possible. In conclusion, SC18 and biometric testicular traits can be adopted as a selection criterion to improve the fertility of young Nellore bulls.     

Genetics and non-genetics parameters of body weight for post-weaning traits in Raini Cashmere goats

Genetic parameters were estimated for 6-month weight (W6), 9-month weight (W9), 12-month weight (W12), average daily gain from birth to 6 months old (ADG6), and Kleiber ratio at 6 months (KL6) traits using 6,442 records obtained from a Raini Cashmere goat flock. The parameters were estimated using the restricted maximum likelihood procedure and applying four animal models excluding or including maternal additive genetic and permanent environmental effects. Heritability estimates for W6, W9, W12, ADG6, and KL6, under the most appropriate model were 0.028, 0.26, 0.29, 0.02, and 0.25, respectively. The estimates of genetic and phenotypic correlations among W6, W9, W12, and ADG6 were high and ranged from 0.73 to 0.99. The estimates of genetic and phenotypic correlations among KL6 and others traits were negative and low. Thus, these estimates of genetic parameters may provide a basis for deriving selection indices for postweaning growth traits also low genetic correlation between growth traits with KL6, it is possible to increase efficiency in Raini kids by multitrait selection.     

Investigating pig survival in different production phases using genomic models

Pig survival is an economically important trait with relevant social welfare implications, thus standing out as an important selection criterion for the current pig farming system. We aimed to estimate (co)variance components for survival in different production phases in a crossbred pig population as well as to investigate the benefit of including genomic information through single-step genomic best linear unbiased prediction (ssGBLUP) on the prediction accuracy of survival traits compared with results from traditional BLUP. Individual survival records on, at most, 64,894 crossbred piglets were evaluated under two multi-trait threshold models. The first model included farrowing, lactation, and combined postweaning survival, whereas the second model included nursery and finishing survival. Direct and maternal breeding values were estimated using BLUP and ssGBLUP methods. Furthermore, prediction accuracy, bias, and dispersion were accessed using the linear regression validation method. Direct heritability estimates for survival in all studied phases were low (from 0.02 to 0.08). Survival in preweaning phases (farrowing and lactation) was controlled by the dam and piglet additive genetic effects, although the maternal side was more important. Postweaning phases (nursery, finishing, and the combination of both) showed the same or higher direct heritabilities compared with preweaning phases. The genetic correlations between survival traits within preweaning and postweaning phases were favorable and strong, but correlations between preweaning and postweaning phases were moderate. The prediction accuracy of survival traits was low, although it increased by including genomic information through ssGBLUP compared with the prediction accuracy from BLUP. Direct and maternal breeding values were similarly accurate with BLUP, but direct breeding values benefited more from genomic information. Overall, a slight increase in bias was observed when genomic information was included, whereas dispersion of breeding values was greatly reduced. Combined postweaning survival presented higher direct heritability than in the preweaning phases and the highest prediction accuracy among all evaluated production phases, therefore standing out as a candidate trait for improving survival. Survival is a complex trait with low heritability; however, important genetic gains can still be obtained, especially under a genomic prediction framework.     

Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs

It is of interest to evaluate crossbred pigs for hot carcass weight (HCW) and birth weight (BW); however, obtaining a HCW record is dependent on livability (LIV) and retained tag (RT). The purpose of this study is to analyze how HCW evaluations are affected when herd removal and missing identification are included in the model and examine if accounting for the reasons for missing traits improves the accuracy of predicting breeding values. Pedigree information was available for 1,965,077 purebred and crossbred animals. Records for 503,716 commercial three-way crossbred terminal animals from 2014 to 2019 were provided by Smithfield Premium Genetics. Two pedigree-based models were compared; model 1 (M1) was a threshold-linear model with all four traits (BW, HCW, RT, and LIV), and model 2 (M2) was a linear model including only BW and HCW. The fixed effects used in the model were contemporary group, sex, age at harvest (for HCW only), and dam parity. The random effects included direct additive genetic and random litter effects. Accuracy, dispersion, bias, and Pearson correlations were estimated using the linear regression method. The heritabilities were 0.11, 0.07, 0.02, and 0.04 for BW, HCW, RT, and LIV, respectively, with standard errors less than 0.01. No difference was observed in heritabilities or accuracies for BW and HCW between M1 and M2. Accuracies were 0.33, 0.37, 0.19, and 0.23 for BW, HCW, RT, and LIV, respectively. The genetic correlation between BW and RT was 0.34 ± 0.03, and between BW and LIV was 0.56 ± 0.03. Similarly, the genetic correlation between HCW and RT was 0.26 ± 0.04, and between HCW and LIV was 0.09 ± 0.05, respectively. The positive and moderate genetic correlations between BW and other traits imply a heavier BW resulted in a higher probability of surviving to harvest. Genetic correlations between HCW and other traits were lower due to the large quantity of missing records. Despite the heritable and correlated aspects of RT and LIV, results imply no major differences between M1 and M2; hence, it is unnecessary to include these traits in classical models for BW and HCW.     

Genetic and environmental parameters for steer ultrasound and carcass traits

Carcass measurements for weight, longissimus muscle area, 12-13th-rib fat thickness, and marbling score, as well as for live animal measurements of weight at the time of ultrasound, ultrasound longissimus muscle area, ultrasound 12-13th-rib fat thickness, and ultrasound-predicted percentage ether extract were taken on 2,855 Angus steers. The average ages for steers at the time of ultrasound and at slaughter were 391 and 443 d, respectively. Genetic and environmental parameters were estimated for all eight traits in a multivariate animal model. In addition to a random animal effect, the model included a fixed effect for contemporary group and a covariate for measurement age. Heritabilities for carcass weight, carcass longissimus muscle area, carcass fat thickness, carcass marbling score, ultrasound weight, ultrasound longissimus muscle area, ultrasound fat thickness, and ultrasound-predicted percentage ether extract were 0.48, 0.45, 0.35, 0.42, 0.55, 0.29, 0.39, and 0.51, respectively. Genetic correlations between carcass and ultrasound longissimus muscle area, carcass and ultrasound fat thickness, carcass marbling score and ultrasound-predicted percentage ether extract, and carcass and ultrasound weight were 0.69, 0.82, 0.90, and 0.96, respectively. Additional estimates were derived from a six-trait multivariate animal model, which included all traits except those pertaining to weight. This model included a random animal effect, a fixed effect for contemporary group, as well as covariates for both measurement age and weight. Heritabilities for carcass longissimus muscle area, carcass fat thickness, carcass marbling score, ultrasound longissimus muscle area, ultrasound fat thickness, and ultrasound-predicted percentage ether extract were 0.36, 0.39, 0.40, 0.17, 0.38, and 0.49, respectively. Genetic correlations between carcass and ultrasound longissimus muscle area, carcass and ultrasound fat thickness, and carcass marbling and ultrasound-predicted percentage ether extract were 0.58, 0.86, and 0.94, respectively. The high, positive genetic correlations between carcass and the corresponding real-time ultrasound traits indicate that real-time ultrasound imaging is an alternative to carcass data collection in carcass progeny testing programs.     

Fertility traits in spring-calving Aberdeen Angus cattle. 1. Model development and genetic parameters

Calving records (n = 6,763) obtained from first, second, and third parities of 3,442 spring-calving, Uruguayan Aberdeen Angus cows were used to estimate heritabilities and genetic correlations for the linear trait calving day (CD) and the binary trait calving success (CS), using models that considered CD and CS at 3 calving opportunities as separate traits. Three approaches were defined to handle the CD observations on animals that failed to calve: 1) the cows were assigned a penalty value of 21 d beyond the last observed CD record within contemporary group (PEN); 2) the censored CD values were randomly obtained from a truncated normal distribution (CEN); and 3) the CD records were treated as missing, and the parameters were estimated in a joint threshold-linear analysis including CS traits (TLMISS). The models included the effects of contemporary group (herd x year of calving x mating management), age at calving (3 levels), physiological status at mating (nonlactating or lactating), animal additive genetic effects, and residual. Estimates of heritability for CD traits in the PEN and CEN data sets ranged from 0.20 to 0.31, with greater values in the first calving opportunity. Genetic correlations were positive and medium to high in magnitude, 0.57 to 0.59 in the PEN data set and 0.38 to 0.91 in the CEN data set. In the TLMISS data set, heritabilities ranged from 0.19 to 0.23 for CD and 0.37 to 0.42 for CS. Genetic correlations between CD traits varied between 0.82 and 0.88; between CS traits, genetic correlations varied between 0.56 and 0.80. Negative (genetically favorable), medium to high genetic correlations (-0.54 to -0.91) were estimated between CD and CS traits, suggesting that CD could be used as an indicator trait for CS. Data recording must improve in quality for practical applications in genetic evaluation for fertility traits.     

Genetic correlations between performance at different racing distances in Thoroughbreds

The aim of the present study was to estimate genetic correlations between time at different racing distances in Thoroughbred racehorses based on data provided by Turftotal Ltda. The traits evaluated were times in seconds for distances of 1000, 1100, 1200, 1300, 1400, 1500 and 1600 m, with a total of 32,145 races and 238,890 time records being analyzed. The (co)variance components necessary to obtain the genetic correlations were estimated using the MTGSAM program in a two-trait animal model. The model used for analysis of the data involved animal and permanent environmental random effects, and race, sex, age and post position at start as fixed effects. All genetic correlations were positive and ranged from medium (0.54) to high (0.93).     

Genetic and environmental parameters for traits derived from the Brody growth curve and their relationships with weaning weight in Angus cattle

Direct and maternal genetic and environmental variances and covariances were estimated for weaning weight and growth and maturing traits derived from the Brody growth curve. Data consisted of field records of weight measurements of 3,044 Angus cows and 29,943 weaning weight records of both sexes. Growth traits included weights and growth rates at 365 and 550 d, respectively. Maturing traits included the age of animals when they reached 65% of mature weight, relative growth rates, and degrees of maturity at 365 and 550 d. Variance and covariance components were estimated by REML from a set of two-trait animal models including weaning weight paired with a growth or maturing trait. Weaning and cow contemporary groups were defined as fixed effects. Random effects for weaning weight included direct genetic, maternal genetic, and permanent environmental effects. For growth and maturing traits, a random direct genetic effect was included in the model. Direct heritability estimates for growth traits ranged from .46 to .52 and for maturing traits from .31 to .34. Direct genetic correlations between weaning weight and weights and growth rates at 365 and 550 d ranged from .56 to .70. Correlations of maternal weaning genetic effects with direct genetic effects on weights at 365 and 550 d were positive, but those with growth rates were negative. Between weaning weight and degrees of maturity at both 365 and 550 d, direct genetic correlation estimates were .55 and maternal genetic correlations estimates were -.05, respectively. Direct genetic correlations of weaning weight with relative growth rates and age at 65% of mature weight ranged from .04 to .06, and maternal-direct genetic correlation estimates ranged from -.50 to -.56, respectively. These estimates indicate that higher genetic capacity for milk production was related to higher body mass and degrees of maturity between 365 and 550 d of age but was negatively related to absolute and relative growth rates in that life stage.     

Genetic parameters and trends for lean growth rate and its comvonents in U.S. Yorkshire,Duroc, Hampshire,and Landrace pigs

Records on 361,300 Yorkshire, 154,833 Duroc, 99,311 Hampshire, and 71,097 Landrace pigs collected between 1985 and April of 2000 in herds on the National Swine Registry Swine Testing and Genetic Evaluation System were analyzed. Animal model and REML procedures were used to estimate random effects of animal genetic, common litter, maternal genetic, and the covariances between animal and maternal for lean growth rate (LGR), days to 113.5 kg (DAYS), backfat adjusted to 113.5 kg (BF), and loin eye area adjusted to 113.5 kg (LEA). Fixed effects of contemporary group and sex were also in the statistical model. Based on the single-trait model, estimates of heritabilities were 0.44, 0.44, 0.46, and 0.39 for LGR; 0.35, 0.40, 0.44, and 0.40 for DAYS; 0.48, 0.48, 0.49, and 0.48 for BF; and 0.33, 0.32, 0.35, and 0.31 for LEA in the Yorkshire, Duroc, Hampshire, and Landrace breeds, respectively. Estimates of maternal genetic effects were low and ranged from 0.01 to 0.05 for all traits across breeds. Estimates of common litter effects ranged from 0.07 to 0.16. A bivariate analysis was used to estimate the genetic correlations between lean growth traits. Average genetic correlations over four breeds were -0.83, -0.37, 0.44, -0.07, 0.08, and -0.37 for LGR with DAYS, BF, and LEA, DAYS with BF and LEA, and BF with LEA, respectively. Average genetic trends were 2.35 g/yr, -0.40 d/yr, -0.39 mm/yr, and 0.37 cm2/yr for LGR, DAYS, BF, and LEA, respectively. Results indicate that selection based on LGR can improve leanness and growth rate simultaneously and can be a useful biological selection criterion.     

Age of dam and sex of calf adjustments and genetic parameters for gestation length in Charolais cattle

To estimate adjustment factors and genetic parameters for gestation length (GES), AI and calving date records (n = 40,356) were extracted from the Canadian Charolais Association field database. The average time from AI to calving date was 285.2 d (SD = 4.49 d) and ranged from 274 to 296 d. Fixed effects were sex of calf, age of dam (2, 3, 4, 5 to 10, > or = 11 yr), and gestation contemporary group (year of birth x herd of origin). Variance components were estimated using REML and 4 animal models (n = 84,332) containing from 0 to 3 random maternal effects. Model 1 (M1) contained only direct genetic effects. Model 2 (M2) was G1 plus maternal genetic effects with the direct x maternal genetic covariance constrained to zero, and model 3 (M3) was G2 without the covariance constraint. Model 4 (M4) extended G3 to include a random maternal permanent environmental effect. Direct heritability estimates were high and similar among all models (0.61 to 0.64), and maternal heritability estimates were low, ranging from 0.01 (M2) to 0.09 (M3). Likelihood ratio tests and parameter estimates suggested that M4 was the most appropriate (P < 0.05) model. With M4, phenotypic variance (18.35 d2) was partitioned into direct and maternal genetic, and maternal permanent environmental components (hd2 = 0.64 +/- 0.04, hm2 = 0.07 +/- 0.01, r(d,m) = -0.37 +/- 0.06, and c2 = 0.03 +/- 0.01, respectively). Linear contrasts were used to estimate that bull calves gestated 1.26 d longer (P < 0.02) than heifers, and adjustments to a mature equivalent (5 to 10 yr old) age of dam were 1.49 (P < 0.01), 0.56 (P < 0.01), 0.33 (P < 0.01), and -0.24 (P < 0.14) d for GES records of calves born to 2-, 3-, 4-, and > or = 11-yr-old cows, respectively. Bivariate animal models were used to estimate genetic parameters for GES with birth and adjusted 205-d weaning weights, and postweaning gain. Direct GES was positively correlated with direct birth weight (BWT; 0.34 +/- 0.04) but negatively correlated with maternal BWT (-0.20 +/- 0.07). Maternal GES had a low, negative genetic correlation with direct BWT (-0.15 +/- 0.05) but a high and positive genetic correlation with maternal BWT (0.62 +/- 0.07). Generally, GES had near-zero genetic correlations with direct and maternal weaning weights. Results suggest that important genetic associations exist for GES with BWT, but genetic correlations with weaning weight and postweaning gain were less important.     

Genetic evaluation of carcass traits in Simmental-sired cattle at different slaughter end points

Our objectives were to estimate genetic parameters for carcass traits and evaluate the influence of slaughter end point on estimated breeding values (BV). Data provided by the American Simmental Association were divided into three sets: 1) 9,604 records of hot carcass weight (CW) and percentage retail cuts (PRC), 2) 6,429 records of CW, PRC, and marbling score (MS), and 3) 1,780 records of CW, PRC, MS, fat thickness (FT), and longissimus muscle area (LMA). Weaning weights (WW) from animals with carcass data and from their weaning contemporaries were used. Data were analyzed with a multiple-trait animal model and REML procedures to estimate genetic parameters and BV on an age-, CW-, MS-, or FT-constant basis. The model for carcass traits included fixed contemporary group and covariates for breed, heterozygosity, and slaughter end point and random additive direct genetic and residual effects. Weaning weight was preadjusted for founder effects, direct and maternal heterosis, age of dam, and age of calf. The model for WW included fixed contemporary group and random additive direct genetic, maternal genetic, maternal permanent environment, and residual effects. Heritabilities from data set 1 were 0.34 for CW and 0.25 for PRC on an age-constant basis and 0.25 for PRC on a CW end point. Heritabilities for data set 2 were 0.35, 0.24, and 0.36 for CW, PRC, and MS, respectively, on an age-constant basis. Data set 2 heritabilities were 0.25 for PRC and 0.34 for MS on a CW-constant basis and 0.33 for CW and 0.25 for PRC at a constant MS end point. Heritabilities on an age-constant basis for data set 3 were as follows: CW, 0.32; PRC, 0.09; MS, 0.12; FT, 0.10; and LMA, 0.26. Heritability estimates for data set 3 on a CW-, MS-, and FT-constant basis were similar to those on an age-constant basis. Heritabilities were 0.12 for PRC, 0.12 for MS, 0.14 for FT, and 0.22 for LMA on a CW-constant basis; 0.30 for CW, 0.09 for PRC, 0.10 for FT, and 0.28 for LMA at a constant MS end point; and 0.33, 0.17, 0.13, and 0.29 for CW, PRC, MS, LMA on a FT-constant basis. Genetic correlations among traits varied across groups and end points but suggested that it should be possible to select for improved lean yield without sacrificing quality grade. Correlations were calculated among BV computed at different end points. Adjustment to various end points resulted in some changes in BV and reranking of sires, especially for PRC; however, the number of records available had a larger influence than slaughter end point.