Evaluation of animal models to explore the influence of maternal genetic and maternal permanent environment effect on reproductive performance of Jersey crossbred cattle

The present study was conducted on 1,002 reproductive records of 430 Jersey crossbred cattle, descended from 57 sires and 198 dams, maintained at the Eastern Regional Station of ICAR-National Dairy Research Institute, Kalyani, Nadia, West Bengal, India to investigate the influence of direct genetic, maternal genetic and maternal permanent environmental effect on three most important reproductive traits viz., number of service per conception (NSPC), days open (DO) and calving interval (CI) of Jersey crossbred cattle. Six single-trait animal models (including or excluding maternal genetic or permanent environmental effects) were fitted to analyse these traits, and the best model was chosen after testing the significant increase in the log-likelihood values when additional parameters were added in the model. Direct heritability estimates for NSPC, DO and CI from the best model were 0.10, 0.14 and 0.20, respectively. The maternal permanent environmental (c²) effects on reproductive traits accounted for almost negligible fraction of the total phenotypic variance in this study. The maternal genetic effects (m²) also contributed very little (0%–3%) to the total phenotypic variance except for CI where it was important and accounted for 20% of phenotypic variance. A significantly large negative genetic correlation was observed between direct and maternal genetic effects for all traits, suggesting the presence of antagonistic relationship between dam's direct additive component and daughter's additive genetic component. Results suggest that both direct and maternal effects were important only for CI but not for other traits. Therefore, both direct additive effects and maternal genetic effect need to be considered for improving this trait by selection.     

Restricted maximum likelihood estimation of additive genetic variance when selected base animals are considered fixed.

A method to estimate genetic parameters with a model that considers selected base animals as fixed was investigated. The model estimates genetic variance as a conditional variance based on the Mendelian sampling of gametes from the base parents. In a simulation study, 20 sires were selected and each was mated to 20 dams to create 400 animals for the next generation. Selection was for five generations, but only animals of Generations 4 and 5 were assumed to have performance records and known parents. Simulated values for additive genetic and residual variance were 10. Estimated genetic variance was 8.58 when base animals were assumed random and 6.03 when they were assumed fixed. Residual variance was overestimated in the latter case. When males of Generation 4 were not selected to have progeny, estimated genetic variance was 9.91. It was concluded that estimates for genetic parameters in a model with base animals assumed as fixed were not biased by selection of base animals, but a new bias was introduced if descendants of fixed base animals were selected. Estimation of genetic variance from dairy records of daughters of AI test bulls gave differences of up to 8% when the model removed bias from selected base animals.     

Estimation of genetic parameters and response to selection for a continuous trait subject to culling before testing

The consequences of assuming a zero environmental covariance between a binary trait 'test-status' and a continuous trait on the estimates of genetic parameters by restricted maximum likelihood and Gibbs sampling and on response from genetic selection when the true environmental covariance deviates from zero were studied. Data were simulated for two traits (one that culling was based on and a continuous trait) using the following true parameters, on the underlying scale: h2 = 0.4; r(A) = 0.5; r(E) = 0.5, 0.0 or -0.5. The selection on the continuous trait was applied to five subsequent generations where 25 sires and 500 dams produced 1500 offspring per generation. Mass selection was applied in the analysis of the effect on estimation of genetic parameters. Estimated breeding values were used in the study of the effect of genetic selection on response and accuracy. The culling frequency was either 0.5 or 0.8 within each generation. Each of 10 replicates included 7500 records on 'test-status' and 9600 animals in the pedigree file. Results from bivariate analysis showed unbiased estimates of variance components and genetic parameters when true r(E) = 0.0. For r(E) = 0.5, variance components (13-19% bias) and especially (50-80%) were underestimated for the continuous trait, while heritability estimates were unbiased. For r(E) = -0.5, heritability estimates of test-status were unbiased, while genetic variance and heritability of the continuous trait together with were overestimated (25-50%). The bias was larger for the higher culling frequency. Culling always reduced genetic progress from selection, but the genetic progress was found to be robust to the use of wrong parameter values of the true environmental correlation between test-status and the continuous trait. Use of a bivariate linear-linear model reduced bias in genetic evaluations, when data were subject to culling.     

Genetic control of the environmental variance for birth weight in seven generations of a divergent selection experiment in mice

Data from seven generations of a divergent selection experiment designed for environmental variability of birth weight were analysed to estimate genetic parameters and to explore signs of selection response. A total of 10 783 birth weight records from 638 females and 1127 litters in combination with 10 007 pedigree records were used. Each record of birth weight was assigned to the mother of the pup in a heteroscedastic model, and after seven generations of selection, evidence of success in the selection process was shown. A Bayesian analysis showed that success of the selection process started from the first generation for birth weight and from the second generation for its environmental variability. Genetic parameters were estimated across generations. However, only from the third generation onwards were the records useful to consider the results to be reliable. The results showed a consistent positive and low genetic correlation between the birth weight trait and its environmental variability, which could allow an independent selection process. This study has demonstrated that the genetic control of the birth weight environmental variability is possible in mice. Nevertheless, before the results are applied directly in farm animals, it would be worth confirming any other implications on other important traits, such as robustness, longevity and welfare.     

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 correlation between growth and female and male contributions to fertility in rabbit

A Bayesian bivariate Linear-Threshold Animal Model was implemented to determine the genetic correlation between fertility (F), defined as success or failure to conceive, and average daily gain (ADG) in a rabbit line selected for ADG. A total of 27 234 records of F from 7895 females and 1293 males, and 114 135 records of ADG were used for the analysis. The pedigree included 114 485 animals. The model used for ADG included the systematic effects of year-season, parity order and number of kids born alive, the animal additive effect, the maternal and paternal permanent environmental effects, the common litter permanent environmental effect and the residual. The model for the liability of F included the systematic effects of year-season and physiological status of the female, the female and male additive genetic effects, the female and male permanent environmental effects and the residual, which was divided into a permanent environmental effect related to the common litter effect for ADG, and an independent term. The estimated heritabilities were 0.15 for ADG and 0.07 and 0.04 for the female and male contributions to F, respectively. Male and female contributions to F had a positive genetic correlation (0.34). The genetic correlation between ADG and the female component of F was low to moderate and negative (-0.31), whereas it was null for the male contribution to F. Thus, it is expected that only the female contribution to reproductive performance may be impaired by selection for ADG in rabbit lines.     

Genetic and environmental influences on faecal nematode egg counts of Jamunapari goats in India

The present study was carried out from 1999 to 2003 to determine the genetic and environmental influences of faecal egg count (FEC), an indicator of host resistance, in adult Jamunapari goats with naturally acquired gastrointestinal nematode parasite infections (predominantly Haemonchus contortus). FEC data on 670 records of Jamunapari goats descended from 54 bucks and 208 does were used in this study. Analyses were carried out by restricted maximum likelihood estimation, fitting an animal model. Four different animal models ignoring or including maternal genetic or permanent environmental effects were fitted. Different environmental effects, that is, sampling year, month and the sex of the animals, significantly (P<0.01) influenced FECs in the goats. Direct heritability estimates were inflated substantially for this trait when maternal effects were ignored. The direct heritability estimates for the trait ranged from 0.11 to 0.16 depending on the model used. Low estimates of maternal heritability (m(2)=0.06) and the fraction of variance due to maternal permanent environmental effects (c(2)=0.09) for FECs were observed in the present study. The results suggest that direct and permanent environmental maternal effects were important for this trait; however, maternal additive effects had less impact on this trait. These results also indicate that modest rates of genetic progress appear possible for FECs.     

Impact of variation in length of individual testing periods on estimation of (co)variance components of a random regression model for feed intake of growing pigs

A simulation study was conducted to assess the influence of differences in the length of individual testing periods on estimates of (co)variance components of a random regression model for daily feed intake of growing pigs performance tested between 30 and 100 kg live weight. A quadratic polynomial in days on test with fixed regressions for sex, random regressions for additive genetic and permanent environmental effects and a constant residual variance was used for a bivariate simulation of feed intake and daily gain. (Co)variance components were estimated for feed intake only by means of a Bayesian analysis using Gibbs sampling and restricted maximum likelihood (REML). A single trait random regression model analogous to the one used for data simulation was used to analyse two versions of the data: full data sets with 18 weekly means of feed intake per animal and reduced data sets with the individual length of testing periods determined when tested animals reached 100 kg live weight. Only one significant difference between estimates from full and reduced data (REML estimate of genetic covariance between linear and quadratic regression parameters) and two significant differences from expected values (Gibbs estimates of permanent environmental variance of quadratic regression parameters) occurred. These differences are believed to be negligible, as the number lies within the expected range of type I error when testing at the 5% level. The course of test day variances calculated from estimates of additive genetic and permanent environmental covariance matrices also supports the conclusion that no bias in estimates of (co)variance components occurs due to the individual length of testing periods of performance‐tested growing pigs. A lower number of records per tested animal only results in more variation among estimates of (co)variance components from reduced compared with full data sets. Compared with the full data, the effective sample size of Gibbs samples from the reduced data decreased to 18% for residual variance and increased up to five times for other (co)variances. The data structure seems to influence the mixing of Gibbs chains.     

Reduced animal model with differential genetic grouping for direct and maternal effects.

Mixed-model equations for the reduced animal model with maternal effects and different genetic grouping of unknown parents for additive direct and maternal effects are derived. The matrices that relate the expected value and the variance of the breeding values of non-parents to the parents, as well as the different contributions of parental and non-parental breeding values, to the resulting mixed-model equations are presented. Mis-specification of additive maternal variance and the additive covariance between direct and maternal effects, arising from missing information on the dams of known individuals with records, is discussed. To avoid an incorrect specification of the variance-covariance matrix of the records without having to invert a nondiagonal variance of the residual terms, the breeding values of the unknown dams of individuals with records are included in the equations. Breeding values of non-parents are back-solved after the solutions for genetic groups and breeding values of parents are computed as simply as in cases in which maternal effects are absent. A numerical example is included to illustrate the derivations.     

Estimation of correlation between maternal permanent environmental effects of related dams in beef cattle

Weaning weights from Gelbvieh (GV; n = 82,138) and Limousin (LM; n = 88,639) calves were used to estimate genetic and environmental variance components with models that included different values for the correlation (lambda) between permanent environmental effects of dams and their daughters. Each analysis included fixed discrete effects of contemporary group, sex of calf, age of dam at calving, and month of calving, a fixed continuous effect of age of calf, random direct and maternal additive genetic effects, permanent environmental effects due to dams, and residual effects. The REML procedure was employed with a "grid search," in which the likelihood was computed for a series of values for lambda. For both breeds, models that included a nonzero value for lambda fitted the data significantly better than the model that did not include lambda. The maximum restricted likelihood was obtained for lambda of approximately -0.2 for both breeds. Estimates of residual and direct genetic variances were similar for all values of lambda, including zero; however, estimates of maternal genetic variance and maternal heritability increased slightly, and maternal permanent environmental variance and the proportion of the maternal variance to the total (phenotypic) variance decreased slightly, when the correlated structure for permanent environmental effects was assumed. As the value of lambda became more negative, absolute values of the direct-maternal genetic covariance and direct-maternal correlation estimates were decreased. Pearson and rank correlations for direct genetic, maternal genetic, and maternal environmental effects estimated with and without lambda were very high (>0.99). These results indicated that the linear relationship between maternal permanent environmental effects of dams and their daughters for weaning weight is negative but low in both breeds. Considering this relationship in the operational model did not significantly affect estimated breeding values, and thus, it may not be important in genetic evaluations.     

Comparison of variance components for test-day electrical conductivity of milk and test-day somatic cell score for first lactation cows in an experimental herd

In recent decades, electrical conductivity (EC) has been introduced as an indicator of mastitis, and genetic selection based on this trait may be possible. In this study, genetic parameters for test-day EC and test-day somatic cell score (SCS) were compared. Data were collected from a Danish experimental herd, including daily records of EC and SCS from 265 first lactation cows. Different genetic models were tested, and a random regression animal model with a 4th order Legendre polynomial for the permanent environmental effect for both traits, a 1st order Legendre polynomial for the additive genetic effect of EC and a 2nd order Legendre polynomial for the additive genetic effect of SCS, gave the best fit. The patterns of the curves were similar for both permanent environmental and additive genetic variance for the two traits. Heritability estimates ranged from 0.05 to 0.12, and from 0.01 to 0.09, for EC and SCS, respectively. The estimate of genetic correlation between the traits was high, and ranged from 0.86 to 0.98. Based on these results, EC could be a potential indicator trait in a breeding programme where selection for increased mastitis resistance is included.     

Genetic parameters and evaluation of the Tunisian dairy cattle population for milk yield by Bayesian and BLUP analyses

A Markov Chain Monte Carlo Bayesian method and BLUP analyses were used on Tunisian dairy cattle data. Data included 92,106 lactation records collected on 37,536 animals over 19 freshening years, from 1983 to 2001. Each record was partitioned into the fixed effects of herd-year, month of calving, and age-parity, a permanent environmental effect, an additive genetic effect, and a residual effect.

Posterior conditional distributions were determined for variance components and model effects. Solutions (BLUE) and posterior means for levels of herd-year, month of calving, and age-parity showed similar patterns. Posterior means of heritability and repeatability were 0.17 ± 18 × 10^− 5 and 0.39 ± 8 × 10^− 5, respectively. Posterior means of bull's breeding values were compared to bull's BLUP solutions. BLUP solutions were obtained using 0.17 and 0.39, estimated from the data, and 0.25 and 0.40 estimates for heritability and repeatability, respectively. Rank correlations between bull's posterior means and BLUP breeding values were 0.998 and 0.994 using genetic parameters estimated from the data and from the literature, respectively. This correlation coefficient was 0.995 between bull's BLUP solutions using either of the two sets of genetic parameters.     

The impact of data structure on genetic (co)variance components of early growth in sheep,estimated using an animal model with maternal effects

Several studies have noted high negative correlations between maternal genetic and direct additive effects and their influence on additive and maternal heritability of early growth traits in sheep. Multigeneration data from the Suffolk Sire Reference Scheme (SSRS) were used to investigate the effect of data structure on estimates of direct and maternal (co)variances for lamb 8-wk weight. In all analyses the additive, maternal genetic, maternal environmental, and residual effects were fitted along with the covariance between direct and maternal additive effects. The contributions of particular genetic relationships to the estimates were studied by analyzing subsets of the SSRS data. A further eight subsets were formed having 10% or 50% of the dams with their own records and having one or two, three or four, five or six, and more than six offspring per dam. Analysis of data having only 10% of the dams with their own record and one or two offspring records yielded a high negative correlation (-0.99) between direct and maternal genetic effects. However, the seven other data sets with more records per dam or a higher proportion of dams with their own records produced values of -0.35 to -0.51. Data structure and the number of dams and granddams with records are important determinants of estimated direct and maternal effects in early growth traits.     

Analysis of beef cattle longitudinal data applying a nonlinear model

The objective of this work was to evaluate the Nelore beef cattle, growth curve parameters using the Von Bertalanffy function in a nested Bayesian procedure that allowed estimation of the joint posterior distribution of growth curve parameters, their (co)variance components, and the environmental and additive genetic components affecting them. A hierarchical model was applied; each individual had a growth trajectory described by the nonlinear function, and each parameter of this function was considered to be affected by genetic and environmental effects that were described by an animal model. Random samples of the posterior distributions were drawn using Gibbs sampling and Metropolis-Hastings algorithms. The data set consisted of a total of 145,961 BW recorded from 15,386 animals. Even though the curve parameters were estimated for animals with few records, given that the information from related animals and the structure of systematic effects were considered in the curve fitting, all mature BW predicted were suitable. A large additive genetic variance for mature BW was observed. The parameter a of growth curves, which represents asymptotic adult BW, could be used as a selection criterion to control increases in adult BW when selecting for growth rate. The effect of maternal environment on growth was carried through to maturity and should be considered when evaluating adult BW. Other growth curve parameters showed small additive genetic and maternal effects. Mature BW and parameter k, related to the slope of the curve, presented a large, positive genetic correlation. The results indicated that selection for growth rate would increase adult BW without substantially changing the shape of the growth curve. Selection to change the slope of the growth curve without modifying adult BW would be inefficient because their genetic correlation is large. However, adult BW could be considered in a selection index with its corresponding economic weight to improve the overall efficiency of beef cattle production.     

Bayesian inference for genetic parameter estimation on growth traits for Nelore cattle in Brazil,using the Gibbs sampler

This data set consisted of over 29 245 field records from 24 herds of registered Nelore cattle born between 1980 and 1993, with calves sires by 657 sires and 12 151 dams. The records were collected in south‐eastern and midwestern Brazil and animals were raised on pasture in a tropical climate. Three growth traits were included in these analyses: 205‐ (W205), 365‐ (W365) and 550‐day (W550) weight. The linear model included fixed effects for contemporary groups (herd‐year‐season‐sex) and age of dam at calving. The model also included random effects for direct genetic, maternal genetic and maternal permanent environmental (MPE) contributions to observations. The analyses were conducted using single‐trait and multiple‐trait animal models. Variance and covariance components were estimated by restricted maximum likelihood (REML) using a derivative‐free algorithm (DFREML) for multiple traits (MTDFREML). Bayesian inference was obtained by a multiple trait Gibbs sampling algorithm (GS) for (co)variance component inference in animal models (MTGSAM). Three different sets of prior distributions for the (co)variance components were used: flat, symmetric, and sharp. The shape parameters (ν) were 0, 5 and 9, respectively. The results suggested that the shape of the prior distributions did not affect the estimates of (co)variance components. From the REML analyses, for all traits, direct heritabilities obtained from single trait analyses were smaller than those obtained from bivariate analyses and by the GS method. Estimates of genetic correlations between direct and maternal effects obtained using REML were positive but very low, indicating that genetic selection programs should consider both components jointly. GS produced similar but slightly higher estimates of genetic parameters than REML, however, the greater robustness of GS makes it the method of choice for many applications.     

Random regression models to estimate genetic parameters for test‐day milk yield in Brazilian Murrah buffaloes

The objective of this work was to estimate covariance functions for additive genetic and permanent environmental effects and, subsequently, to obtain genetic parameters for buffalo’s test‐day milk production using random regression models on Legendre polynomials (LPs). A total of 17 935 test‐day milk yield (TDMY) from 1433 first lactations of Murrah buffaloes, calving from 1985 to 2005 and belonging to 12 herds located in São Paulo state, Brazil, were analysed. Contemporary groups (CGs) were defined by herd, year and month of milk test. Residual variances were modelled through variance functions, from second to fourth order and also by a step function with 1, 4, 6, 22 and 42 classes. The model of analyses included the fixed effect of CGs, number of milking, age of cow at calving as a covariable (linear and quadratic) and the mean trend of the population. As random effects were included the additive genetic and permanent environmental effects. The additive genetic and permanent environmental random effects were modelled by LP of days in milk from quadratic to seventh degree polynomial functions. The model with additive genetic and animal permanent environmental effects adjusted by quintic and sixth order LP, respectively, and residual variance modelled through a step function with six classes was the most adequate model to describe the covariance structure of the data. Heritability estimates decreased from 0.44 (first week) to 0.18 (fourth week). Unexpected negative genetic correlation estimates were obtained between TDMY records at first weeks with records from middle to the end of lactation, being the values varied from ?0.07 (second with eighth week) to ?0.34 (1st with 42nd week). TDMY heritability estimates were moderate in the course of the lactation, suggesting that this trait could be applied as selection criteria in milking buffaloes.     

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.     

Estimation of genetic parameters and prediction of breeding values for multivariate threshold and continuous data in a simulated horse population using Gibbs sampling and residual maximum likelihood

Simulated horse data were used to compare multivariate estimation of genetic parameters and prediction of breeding values (BV) for categorical, continuous and molecular genetic data using linear animal models via residual maximum likelihood (REML) and best linear unbiased prediction (BLUP) and mixed linear-threshold animal models via Gibbs sampling (GS). Simulation included additive genetic values, residuals and fixed effects for one continuous trait, liabilities of four binary traits, and quantitative trait locus (QTL) effects and genetic markers with different recombination rates and polymorphism information content for one of the liabilities. Analysed data sets differed in the number of animals with trait records and availability of genetic marker information. Consideration of genetic marker information in the model resulted in marked overestimation of the heritability of the QTL trait. If information on 10,000 or 5,000 animals was used, bias of heritabilities and additive genetic correlations was mostly smaller, correlation between true and predicted BV was always higher and identification of genetically superior and inferior animals was - with regard to the moderately heritable traits, in many cases - more reliable with GS than with REML/BLUP. If information on only 1,000 animals was used, neither GS nor REML/BLUP produced genetic parameter estimates with relative bias 50% for all traits. Selection decisions for binary traits should rather be based on GS than on REML/BLUP breeding values.     

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.     

Nuclear, cytoplasmic, and environmental effects on growth, fat, and muscle traits in suffolk lambs from a sire referencing scheme

Maternal effects are an important source of variation in early growth and body traits in sheep but are often excluded from genetic analyses. Maternal additive genetic, maternal environmental, and cytoplasmic effects were investigated in a large Suffolk breeding scheme using a range of models involving different combinations of these effects with the direct additive genetic effect. Weights at 8 wk of age and at scanning (mean age 146 d) and ultrasonically measured muscle and fat depth were analyzed using an animal model on 55,683 (8-wk weight) and 28,947 (scanning traits) lamb records. Simple additive models always overestimated the heritability of all traits when compared to more complex models. The successive inclusion of maternal environmental, maternal genetic, and the covariance between direct and maternal additive effects in the model significantly improved the fit for almost all models and all traits, as indicated by a likelihood ratio test. Under the full model, the heritability of both weight traits was low (0.14 and 0.20 for 8-wk and scanning weight, respectively). The maternal additive and maternal environmental effects, as a proportion of the phenotypic variance, were similar (0.10 and 0.08 for 8-wk weight and 0.07 and 0.06 for scanning weight). The two scanning traits had higher heritabilities (0.29 and 0.27 for muscle depth and fat depth, respectively) with low levels of maternal genetic and maternal environmental variance. No evidence was found of a cytoplasmic effect on any of the traits studied under the full model. Breeding schemes for early growth and body traits in sheep should account for maternal effects in their genetic evaluations in order to improve their accuracy. The exact model to use will depend on the trait and individual circumstances of the scheme.