Threshold-linear estimation of genetic parameters for farrowing mortality, litter size, and test performance of Large White sows

Up to 109,447 records of 49,656 Large White sows were used to evaluate the genetic relationship between number of pigs born dead (BD) and number born alive (BA) in first and later parities. Performance data (n = 30,832) for ultrasound backfat (BF) at the end of the test and days to reach 113.5 kg (AD) were used to estimate their relationships with BD and BA at first parity in a four-trait threshold-linear analysis (TL). Effects were year-farm, contemporary group (CG: farm-farrowing year-farrowing month) and animal additive genetic. At first parity, estimates of heritability were 0.09, 0.09, 0.37, and 0.31 for BA, BD, AD, and BF, respectively. The estimate of genetic correlation between BD and litter size was -0.04 (BD-BA). Corresponding values with test traits were both -0.14 (BD-AD, BD-BF). Estimates of genetic correlation between BA and performance traits were 0.08 (BA-AD) and 0.05 (BA-BF). The two test traits were moderately negatively correlated (-0.22). For later parities, a six-trait (BD, BA in three parities) TL model was implemented. The estimates of additive genetic variances and heritability increased with parity for BD and BA. Estimates of heritabilities were: 0.09, 0.10, and 0.11 for BD, and 0.09, 0.12, and 0.12 for BA in parities one to three, respectively. Estimates of genetic correlations between different parities were high (0.91 to 0.96) for BD, and slightly lower (0.74 to 0.95) for BA. Genetic correlations between BD and BA were low and positive (0.02 to 0.17) for BA in Parities 1 and 2, but negative (-0.04 to -0.10) for BA in Parity 3. Selection for increased litter size should have little effect on farrowing piglet mortality. Intense selection for faster growth and increased leanness should increase farrowing piglet mortality of first-parity sows. A repeatability model with a simple correction for the heterogeneity of variances over parities could be implemented to select against farrowing mortality. The genetic components of perinatal piglet mortality are independent of the ones for litter size in the first parity, and they show an undesirable, but not strong, genetic association in second parity.     

Genetic analysis of sow longevity and sow lifetime reproductive traits using censored data

Sow longevity is a key component for efficient and profitable pig farming; however, approximately 50% of sows are removed annually from a breeding herd. There is no consensus in the scientific literature regarding a definition for sow longevity; however, it has been suggested that it can be measured using several methods such as stayability and economic indicators such as lifetime piglets produced. Sow longevity can be improved by genetic selection; however, it is rarely included in genetic evaluations. One reason is elongated time intervals required to collect complete lifetime data. The effect of genetic parameter estimation software in handling incomplete data (censoring) and possible early indicator traits were evaluated analysing a 30% censored data set (12 725 pedigreed Landrace × Large White sows that included approximately 30% censored data) with DMU6, THRGIBBS1F90 and GIBBS2CEN. Heritability estimates were low for all the traits evaluated. The results show that the binary stayability traits benefited from being analysed with a threshold model compared to analysing with a linear model. Sires were ranked very similarly regardless if the program handled censoring when all available data were included. Accumulated born alive and stayability were good indicators for lifetime born alive traits. Number of piglets born alive within each parity could be used as an early indicator trait for sow longevity.     

Usefulness of subjective ovine milk scores: II. Genetic parameter estimates

Genetic parameters for a subjective milk score given to ewes within 24 h of parturition were estimated to determine the usefulness of milk score as a selection trait to improve milk production, which influences total litter weight weaned. Heritability of milk score and the genetic correlation of milk score with litter weight weaned were estimated by REML separately for four sheep breeds, Rambouillet (n = 1,731), Targhee (n = 1,638), Columbia (n = 1,731), and Polypay (n = 1,129). Litter weight weaned was the total weight of lambs weaned at approximately 120 d of age under a western range production system. Observed heritability estimates for milk score at first parity were moderate and similar among breeds, ranging from 0.18 to 0.32. Heritability estimates adjusted for a binomial distribution of milk scores at first parity were high (Columbia, 0.43; Polypay, 0.35; Rambouillet, 0.50; Targhee, 0.84). Estimates of observed heritability for second-parity milk score were moderate to high, ranging from 0.23 to 0.46. Milk score at first or second parity was genetically correlated with milk score records at maturity (third parity and greater), with estimates ranging from 0.69 to 1.00. Milk score and litter weight weaned were genetically correlated at first or second parity in Rambouillet (r(g) = 1.00) and Targhee breeds (r(g) = 1.00 and 0.61, respectively), but not in the Columbia and Polypay breeds. Estimates of heritability for lifetime records for milk score ranged from 0.16 to 0.26 across breeds. Estimates of genetic correlations of annual lifetime milk score records with litter weight weaned were high (Columbia, 1.00; Polypay, 0.81; Rambouillet, 1.00; and Targhee, 0.77). Repeatability estimates for milk score were similar across breeds, 0.23 for Columbia, Rambouillet, and Targhee ewes and 0.28 for Polypay ewes. Milk score measured at first or second parity may be a good predictor of future potential milking ability. Further, milk score can be used as a selection trait to improve maternal ability for increasing litter weight weaned. The need for increasing ewe milking performance and lamb growth rate at first parity in commercial range sheep production systems may be addressed by selection for milk score at first parity.     

Genetic relationship between purebred and crossbred sow longevity

Background: The overall breeding objective for a nucleus swine selection program is to improve crossbred commercial performance. Most genetic improvement programs are based on an assumed high degree of positive relationship between purebred performance in a nucleus herd and their relatives' crossbred performance in a commercial herd. The objective of this study was to examine the relationship between purebred and crossbred sow longevity performance. Sow longevity was defined as a binary trait with a success occurring if a sow remained in the herd for a certain number of parities and including the cumulative number born alive as a measure of reproductive success. Heritabilities, genetic correlations, and phenotypic correlations were estimated using THRGIBBS1F90.Results: Results indicated little to no genetic correlations between crossbred and purebred reproductive traits.This indicates that selection for longevity or lifetime performance at the nucleus level may not result in improved longevity and lifetime performance at the crossbred level. Early parity performance was highly correlated with lifetime performance indicating that an indicator trait at an early parity could be used to predict lifetime performance. This would allow a sow to have her own record for the selection trait before she has been removed from the herd.Conclusions: Results from this study aid in quantifying the relationship between purebred and crossbred performance and provide information for genetic companies to consider when developing a selection program where the objective is to improve crossbred sow performance. Utilizing crossbred records in a selection program would be the best way to improve crossbred sow productivity.     

Evaluation of genetic trends and determination of the optimal number of cumulative records of parity required in reproductive traits in a Large White pig population

Genetic improvement of the reproductive performance of pigs is important for pig breeding despite their low heritabilities. The objectives of this study were to investigate the effectiveness of selection concerning reproductive traits and to determine the optimal number of parity records required for accurate estimation of breeding values (BVs) in the open population of a commercial pig breeding company. The study used records of 2220 purebred Large White pigs (9845 litters) farrowed between 1998 and 2009 in the two herds of the Pacific Ocean Breeding Co. Ltd. The traits studied included farrowing interval (FI), total number of piglets at birth (TNB), average weaning weight per litter (AWW), and raising rate (RR). A statistical model was applied to the 4‐trait repeatability animal model. The heritabilities of FI, TNB, AWW and RR were low. The genetic trends in TNB (h² = 0.09) showed approximately 1.0 increase in 6 years from 2003 to 2008. The predicted error variances indicated that up to fourth parity records are necessary for accurate genetic evaluation. The present study results indicated that even reproductive traits with low heritability can be improved.     

Estimates of genetic and phenotypic covariance functions for postweaning growth and mature weight of beef cows

Annual weights of cows from 19 to 119 months of age in two herds were analysed fitting a random regression model, regressing on orthogonal polynomials of age in months. Estimates of covariances between random regression coefficients were obtained by restricted maximum likelihood, and the resulting estimates of covariance functions were used to construct covariance matrices for all ages in the data. Analyses were carried out fitting regression coefficients corresponding to overall animal effects only and fitting regressions for animals' additive genetic and permanent, environmental effects. Different definitions of fixed effects subclasses were examined. Models were compared using likelihood ratio tests and estimated standard deviations for the ages in the data. Cubic regressions were sufficient to model both population trajectories and individual growth curves. Random regression coefficients were highly correlated, so that estimation forcing their covariance matrices to have reduced rank (2 or 3) did not reduce likelihoods significantly, allowing parsimonious modelling. Results showed that records were clearly not repeated measurements of a single trait with constant variances. As cows grew up to about 5 years of age, variances. As cows grew up to about 5 years of age, variances increased. Estimates of genetic correlations between 3-year-old and older cows were close to unity in one herd but more erratic in the other. For both herds, genetic correlations between weights on 2-year-old cows and older animals were clearly less than unity.     

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.     

Relationship between litters per sow per year sire breeding values and sire progeny means for farrowing rate,removal parity and lifetime born alive

The objective of this study was to determine the relationship between individual sire estimated breeding values (EBV) for litters/sow/year (LSY) and sire progeny means for farrowing rate (FR), removal parity and lifetime born alive (LTBA). Genetic parameters and breeding values were estimated using ASREML. The heritability estimate for LSY was 0.11. When all sires with 10 or more daughters with records were included in the analysis, Spearman rank correlations between the sire's LSY EBV and the sires' daughter means for FR, removal parity and LTBA were 0.49, 0.23 and 0.25 (p < 0.01). The sire EBV for LSY was favourably correlated with sires' daughter means for all three traits. This provides evidence that selecting sires with high EBV for LSY could improve herd FR, removal parity and LTBA. By including LSY as part of the selection criterion, the LTBA may be indirectly improved. The positive genetic correlation between LTBA and LSY may be a result of the improved longevity of sows with greater LSY compared with sows with lower LSY. The relationships between LSY and FR, removal parity and LTBA are strongly supported by the correlations between the sire progeny means for each trait and the sire LSY EBV.     

Genetic parameters for total lactation yields of milk,fat, protein,and somatic cell score in New Zealand dairy goats

The aim of this study was to estimate genetic parameters for lactation yields of milk (MY), fat (FY), protein (PY), and somatic cell score (SCS) of New Zealand dairy goats. The analysis used 64,604 lactation records from 23,583 does, kidding between 2004 and 2017, distributed in 21 flocks and representing 915 bucks. Estimates of genetic and residual (co) variances, heritabilities, and repeatabilities were obtained using a multiple‐trait repeatability animal model. The model included the fixed effects of contemporary group (does kidding in the same flock and year), age of the doe (in years), and as covariates, kidding day, proportion of Alpine, Nubian, Toggenburg, and “unknown” breeds (Saanen was used as the base breed), and heterosis. Random effects included additive animal genetic and doe permanent environmental effects. Estimates of heritabilities were 0.25 for MY, 0.24 for FY, 0.24 for PY, and 0.21 for SCS. The phenotypic correlations between MY, FY, and PY ranged from 0.90 to 0.96, and the genetic correlations ranged from 0.81 to 0.93. These results indicate lactation yield traits exhibit useful heritable variation and that multiple trait selection for these traits could improve milk revenue produced from successive generations of New Zealand dairy goats.     

Selecting for changes in average “parity curve” pattern of litter size in Large White pigs

This study aimed to analyse genetic background of variation in reproductive performance between parities of a sow and to investigate selection strategies to change the “parity curve”. Total number born (TNB) recorded in Large White sows was provided by Topigs Norsvin. Analysis with basic (BM) and random regression (RRM) models was done in ASReml 4.1. The BM included only a fixed “parity curve”, while RRM included 3rd order polynomials for additive genetic and permanent sow effects. Parameters from RRM were used in simulations in SelAction 2.1. Based on Akaike information criterion, RRM was a better model for TNB data. Genetic variance and heritability estimates of TNB from BM and RRM were increasing with parity from parity 2. Genetically, parity 1 is the most different from parities 7 to 10, whereas most similar to parities 2 and 3. This indicates presence of genetic variation to change the “parity curve”. Based on simulations, the selection to increase litter size in parity 1 only increases TNB in all parities, but does not change the observed shape of “parity curve”, whereas selection for increased TNB in parity 1 and reduced TNB in parity 5 decreases differences between parities, but also reduces overall TNB in all parities. Changing the “parity curve” will be difficult as the genetic and phenotypic relationships between the parities are hard to overcome even when selecting for one parity.     

Genetic study of individual preweaning mortality and birth weight in Large White piglets using threshold-linear models

Individual records from 49,788 Large White piglets were used to evaluate preweaning mortality and its relationship with birth weight (BW). Preweaning mortality included farrowing mortality (TM) was also divided into stillbirth (SB), early (EM), late (LM) and total (ELM) preweaning mortality. Farrowing mortality was also studied as a sow's trait as number of piglets born dead (NBD). Threshold-linear models were used via MCMC. Traits included (1) TM-BW, (2) SB-ELM-BW, (3) SB-EM-LM and (4) NBD-ELM-BW. Model for BW included parity number, litter size, sex, contemporary group (farm-farrowing year-month), litter, and direct and maternal additive genetic effects. For mortality traits, litter effect was of the nursing litter for cross-fostered piglets (4.9%). Models for SB (2, 3) and NBD (4) excluded the effect of sex. In Model 3, BW was fitted as covariable for EM and LM. Estimates of direct and maternal heritability for BW were 0.03–0.06 and 0.14–0.19; and for mortality traits 0.03–0.12 and 0.08–0.12. Direct-maternal correlations were negative for all traits. Genetic correlations between all mortality traits were positive. Results confirmed the importance of BW for the genetic evaluation of piglet mortality. Early mortality is a good candidate for improvement of TM because of larger heritability and high genetic correlations with other mortality traits. It is most efficient to treat SB at sow level and preweaning mortality at the piglet level.     

Bioeconomic evaluation of sow longevity and profitability

Sow production indicators, including litter size, litter weight, and the length of time that sows remained in the herd (sow longevity), were used to characterize sow performance and profitability. Sow longevity and production records from 148,568 sows in 32 commercial herds from Central Illinois from January 1995 to May 2001 were analyzed using survival and repeatability models, respectively. The factors studied included sow genetics (32 genetic lines), with eight major lines present in multiple herds, and the combination of herd and year of entry in the herd. The largest difference in longevity between the major genetic lines was approximately one parity. There were differences (P < 0.05) in the instantaneous sow removal rate or hazard from the major lines. These differences constitute evidence that sow longevity could be improved by using replacements from specific genetic lines. The net present value per sow (present value of future cash flows and the present value of the sow) was used to evaluate the effect of sow longevity and production traits on economic returns. Assuming a zero discount rate per parity, genetic lines with longer herd life resulted in greater profit than genetic lines with shorter herd life. This difference was reduced with increasing discount rates and was reversed with high discount rates and low net income per litter. These results suggest that the magnitude of the economic improvement attained through the use of sow genetic lines with longer longevity depends on the economic context under which the evaluation is made.     

Covariance functions and random regression models for cow weight in beef cattle

Data from the first four cycles of the Germplasm Evaluation program at the U.S. Meat Animal Research Center were used to evaluate weights of Angus, Hereford, and F1 cows produced by crosses of 22 sire and 2 dam (Angus and Hereford) breeds. Four weights per year were available for cows from 2 through 8 yr of age (AY) with age in months (AM). Weights (n = 61,798) were analyzed with REML using covariance function-random regression models (CF-RRM), with regression on orthogonal (Legendre) polynomials of AM. Models included fixed regression on AM and effects of cow line, age in years, season of measurement, and their interactions; year of birth; and pregnancy-lactation codes. Random parts of the models fitted RRM coefficients for additive (a) and permanent environmental (c) effects. Estimates of CF were used to estimate covariances among all ages. Temporary environmental effects were modeled to account for heterogeneity of variance by AY. Quadratic fixed regression was sufficient to model population trajectory and was fitted in all analyses. Other models varied order of fit and rank of coefficients for a and c. A parsimonious model included linear and quartic regression coefficients for a and c, respectively. A reduced cubic order sufficed for c. Estimates of all variances increased with age. Estimates for older ages disagreed with estimates using traditional bivariate models. Plots of covariances for c were smooth for intermediate, but erratic for extreme ages. Heritability estimates ranged from 0.38 (36 mo) to 0.78 (94 mo), with fluctuations especially for extreme ages. Estimates of genetic correlations were high for most pairs of ages, with the lowest estimate (0.70) between extreme ages (19 and 103 mo). Results suggest that although cow weights do not fit a repeatability model with constant variances as well as CF-RRM, a repeatability model might be an acceptable approximation for prediction of additive genetic effects.     

Impact of biological and economic variables on optimal parity for replacement in swine breed-to-wean herds

Voluntary and involuntary culling practices determine the average parity when sows are replaced in a herd. Underlying these practices is the economic effect of replacing a sow at different parities. A dynamic programming model was used to find the optimal parity and net present value in breed-to-wean swine herds. The model included income and costs per parity weighted by the discount rate and sow removal rate. Three scenarios that reflect a wide range of cases were considered: low removal rates per parity with no salvage value (LRNS), high removal rates per parity with no salvage value (HRNS), and high removal rates per parity with a percentage of the sows having a salvage value (HRYS). The optimal parity of replacement for the base biological and economic conditions was 4 and 5 parities in the high and low removal scenarios, respectively. Sensitivity analyses identified the variables influencing the optimal replacement parity. Optimal parity of replacement ranged from 3 to 7 parities in the low replacement scenario, compared with 1 to 5 parities in the high replacement scenarios. Sow replacement cost and salvage value had the greatest impact on optimal parity of replacement followed by revenues per piglet weaned. The discount rate and number of parities per year generally had little influence on optimal parity. For situations with high sow costs, low salvage values, and low revenues per piglet, the optimal parity at removal was as high as 6 to 10 parities, and for situations with low sow cost, high salvage values, and high revenues per piglet, the optimal parity at removal was as low as 1 to 2 parities depending on removal rates. The modified internal rate of return suggested that, for most LRNS and HRYS scenarios considered, investment in a swine breed-to-wean enterprise was favored over other investments involving a similar risk profile. Our results indicate that in US breeding herds, sows are culled on average near the optimal parity of 4. However, the optimization process should be a dynamic one that adapts to changes in replacement rates, salvage value, replacement cost, and revenues per piglet.     

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 relationship between milk score and litter weight for Targhee, Columbia, Rambouillet, and Polypay sheep

This study was conducted to evaluate the relationship between milk score (MS) and litter weight at 70 d (LW) for four sheep breeds in the United States. Milk score is a subjective measure of milk production used to assess milk production of range ewes when milk yield cannot be quantitatively determined. Variance components for MS and LW were estimated for each of Targhee, Columbia, Rambouillet, and Polypay breeds. Data collected from 1990 through 2000 at the U.S. Sheep Exp. Stn. in Dubois, ID, were analyzed with an animal model using REML. There were 13,900 records of MS and LW for 5,807 ewes. Records were grouped according to parity as first, second, and greater (mature), and all records (lifetime). Estimates of heritability for MS were in the range of 0.05 to 0.18 for first, 0.01 to 0.27 for second, 0.05 to 0.10 for mature, and 0.08 to 0.13 for all lifetime parity groups. Estimates of genetic correlation between MS at first and second parities ranged from 0.74 to 1.00. Similarly, mature MS was highly correlated genetically with MS at first (0.83 to 1.00) and at second (0.60 to 1.00) parities, suggesting that additive genetic value for milking ability at maturity could be evaluated as early as at first parity. Heritability estimates for LW ranged from 0.00 to 0.18 over all breeds and parity groupings. The genetic correlation between LW at first and second parity groups ranged from 0.43 to 1.00. Estimates of genetic correlation between LW at first or second parity with mature LW were mostly high and positive, except for Targhee (-0.10) and Polypay (0.14) at first parity. Litter weight for mature ewes could be improved by selection at first or second parity. Estimates of genetic correlation at first parity between MS and LW were high (1.00) for Rambouillet and Polypay, and near zero for Columbia and Targhee. At second parity, estimates of genetic correlation between MS and LW were positive and moderate for Rambouillet and Polypay but more variable for Columbia and Targhee. Estimates of genetic correlation between MS and LW were mostly positive and may be favorable with smaller estimates of standard errors using all lifetime records rather than first or second parity records. Although estimates are variable, the average of the estimates of the genetic correlation suggests that LW can be improved by selecting ewes for favorable MS.     

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.     

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.     

Genetic analysis of age at first service, return rate, litter size, and weaning-to-first service interval of gilts and sows

The aim of this study was to estimate genetic parameters of seven traits related to sow reproductive performance. Data on all Norwegian Landrace pigs (NL) born in nucleus herds and raised in nucleus or multiplying herds from 1990 to 2000 were extracted from the Norwegian national recording scheme. Reproductive traits investigated were age at first service (AFS), return rate in gilts (RRg), age at first farrowing (AFF), live-born piglets in the first litter (NBA1), interval from weaning to first service after first litter (WTS1), return rate after first litter (RR1), live-born piglets in the second litter (NBA2), and interval from weaning to first service after second litter (WTS2). After editing, the data set comprised 12,583 to 56,042 records, depending on the trait. A mixed linear and a joint linear threshold animal model were used to estimate (co)variance components. A full Bayesian approach via Gibbs sampling was adopted. The statistical model used for analysis included contemporary groups of herd-year (-season), purebred or crossbred litter, single or double insemination, mating type, parity in which the animal was born, a regression on lactation length, and an additive genetic effect. Neither the estimated heritabilities nor the genetic correlations differed much between the two approaches, but there was a tendency for higher genetic correlations using the joint linear threshold model approach. Average heritabilities were as follows: AFS = 0.31; RRg = 0.03; RR1 = 0.02; NBA1 = 0.12; NBA2 = 0.14; WTS1 = 0.08; and WTS2 = 0.03. The highest genetic correlations were estimated between NBA1 and NBA2 (r(g) = 0.95), RR1 and WTS1 (r(g) = 0.93), and between WTS1 and WTS2 (r(g) = 0.78). The estimated genetic correlation between NBA and WTS were close to zero. Selection for increased NBA will slightly increase AFS and reduce the probability of a return. Selection for decreased AFS will have a favorable effect on WTS intervals; however, selection for decreased AFS seems to have an unfavorable effect on return rate both on gilts and sows. Conversely, selection for decreased WTS intervals will reduce the probability of a return. Potential selection candidates to include in a multivariate fertility index are AFS, NBA, and WTS1. Due to the low heritability and low, but favorable, genetic correlations to NBA and WTS, RR is not recommended as a selection candidate.     

Direct and maternal variances and covariances and maternal phenotypic effects on preweaning growth of beef cattle

Birth weights (BW) and weaning weights (WW) of 4,423 non-creep-fed Hereford calves were used to estimate direct and maternal sources of variation and maternal phenotypic effects (fm). Seventeen different (co)variances among relatives were estimated through Henderson's Method III and restricted estimated maximum likelihood procedures. Direct and maternal (co)variances and fm were evaluated by multiple regression procedures. Estimates of h2 for BW and WW were .28 and .28 respectively, by the paternal half-sib procedure and .45 and .88, respectively, based on full-sibs. Repeatability estimates were .21 for BW and .30 for WW. Heritabilities based on regression of offspring on dam and offspring on sire were .45 and .21 for BW and .28 and .06 for WW, respectively. Negative correlations were found between solutions for additive genetic direct and additive maternal effects (rG). Estimates of rG ranged from -.86 to -1.05 for BW and from -.57 to -.79 for WW. Estimates of heritability for direct effects (h2o), for maternal effects (h2m) and for total additive genetic effects (h2T) were .16 to .27, .18 to .63 and -.02 to .05 for BW and .26 to .32, .27 to .67 and .10 to .20 for WW. Dominance affected both direct and maternal effects for BW and WW. Values of -.15 (BW) and -.25 (WW) were found for fm (path coefficient between the maternal phenotypes of dam and daughter). These results indicated that selection response would be decreased due to the negative genetic correlation between direct and maternal effects.