Development of an animal model for across-herd genetic evaluation of number born alive in swine

An animal model and computer software were developed to conduct across-herd genetic evaluations using data from producers participating in the Sow Productivity Index program of the American Yorkshire Club. The final data set consisted of 61,596 litter records from 1986 to early 1990. The animal model included fixed contemporary group effects and random additive direct, service sire, permanent environmental, and residual effects. Additive genetic relationships among animals were included. A separate relationship matrix for service sires and their sires was also included. A data set similar to the Yorkshire field data was simulated to use in testing the animal model. The simulated data set consisted of 40 herds, each with 120 reproducing dams and either four or five sires. Six generations of simulated data were produced, resulting in 20,605 litter records. These records were then evaluated using the animal model for number of pigs born alive. Finally, correlations between the true breeding values from the simulation and the predicted breeding values were computed. The correlation between the 918 true and predicted sire breeding values was considerably lower for the animal model without a service sire effect than when it was included (.53 vs .74, respectively). However, the difference was cut in half (.66 vs .77) when only sires with greater than five daughter records were included. The high accuracy of the animal model with a random service sire effect indicates that the proposed model adequately accounts for the variation found in records for number of pigs born alive.     

Breed group effects on milk production of Brazilian crossbred dairy cows

Lactation records (n = 2362) of 1402 crossbred cows in 22 cooperating dairy herds in southeastern Brazil were evaluated. Cows were mixtures of Zebu (Gir, Guzera, and unknown) and European breeding (mostly Holstein). Lactation milk yields were expressed as total, 3050-d, or deviated 305-d yields, either adjusted or unadjusted for days in milk (DIM). Mean DIM was 280. Arithmetic means unadjusted for DIM were 1942, 1666, and 5 kg per record. Milk yields of daughters from sires of 6/8 and 7/8 European breeds were higher than yields of daughters from sires of 5/8 European breeds when data were either adjusted or unadjusted for DIM. The differences associated with breed group of sire were only slightly reduced when records were adjusted for DIM. There was no evidence of a decline in milk yield as the fraction of European breeding of the sire increased from 6/8 to 7/8. For a given breed group of sire, whether the grandsire was purebred or crossbred had no detectable effect. These results should be useful in determining strategies for crossbreeding of dairy cows in tropical areas, particularly when crossbred sires are used.     

Genetic analysis of clinical lameness in dairy cattle

Scores for clinical lameness from two separate studies were combined, and genetic parameters were estimated based on linear and threshold models. Cows were from 24 herds in Minnesota, Wisconsin, and Virginia. To evaluate clinical lameness, cows were observed walking and were assigned a score between 0 and 4 (where 0 = no observable problems to 4 = inability to walk). Data included 1624 records on 1342 cows. The models included fixed effects of herd visit, parity, and stage of lactation. Random effects were additive genetics, permanent environment to account for repeated records, and residual. Estimates of heritability were 0.10 and 0.22 from the linear and threshold models, respectively. The correlation between ETA from linear and threshold models based on all animals was 0.974. Deregressed ETA of sires and REML were used to estimate genetic correlations between clinical lameness and conformation traits. Among the type traits, foot angle, rear legs (rear view), and rump width had strongest associations with clinical lameness; absolute values for genetic correlations between these traits and clinical lameness were approximately 0.65. Low foot angle, hocking in, and wide rumps were associated with increased clinical lameness. Correlations with strength and body depth ranged from 0.20 to 0.43, indicating that heavier cows were more prone to clinical lameness.     

Application of an autoregressive process to estimate genetic parameters and breeding values for daily milk yield in a tropical herd of Lucerna cattle and in United States Holstein herds

The objectives of this study were to estimate from test day records the genetic and environmental (co)variance components, correlations, and breeding values to increase genetic gain in milk yield of Lucerna and US Holstein cattle. The effects of repeated observations (within cow) were explained by first-order autoregressive processes within and across lactations using an animal model. Estimates of variance components and correlation coefficients between test days were obtained using derivative-free REML methodology. The autoregressive structure significantly reduced the model error component by disentangling the short-term environmental effects. The additional information and the more heterogeneous environmental variances between lactations in the multiple-lactation test day model than in the first lactation model provided substantially larger estimates of additive genetic variance (0.62 kg2 for Lucerna; 14.73 kg2 for Holstein), heritability (0.13 for Lucerna; 0.42 for Holstein), and individual genetic merit. Rank correlations of breeding values from multiple lactations and from first lactations ranged from 0.18 to 0.37 for females and from 0.73 to 0.89 for males, respectively. Consequently, more selection errors and less genetic gain would be expected from selection decisions based on an analysis of first lactation only, and greater accuracy would be achieved from multiple lactations. Results indicated that substantial genetic gain was possible for milk yield in the Lucerna herd (34 kg/yr). Estimates of genetic variance for Holsteins were larger than previously reported, which portends more rapid genetic progress in US herds also; under our assumptions, increases would be from 173 to 197 kg/yr.     

Covariance components and prediction for additive and nonadditive preweaning growth genetic effects in an Angus-Brahman multibreed herd

Estimates of covariances and sire expected progeny differences of additive and nonadditive direct and maternal genetic effects for birth and weaning weights were obtained using records from 1,581 straightbred and crossbred calves from the Angus-Brahman multibreed herd at the University of Florida. Covariances were estimated by Restricted Maximum Likelihood, using a Generalized Expectation-Maximization algorithm applied to multibreed populations. Estimates of heritabilities and additive genetic correlations for straightbred and crossbred groups were within the ranges of values found in the literature for these traits. Maximum values of interactibilities (ratios of nonadditive genetic variances to phenotypic variances) and nonadditive correlations were somewhat smaller than heritabilities and additive genetic correlations. Sire additive and total direct and maternal genetic predictions for birth and weaning weight tended to increase with the fraction of Brahman alleles, whereas nonadditive direct and maternal genetic predictions were similar for sires of all Angus and Brahman fractions. These results showed that it is feasible to evaluate sires for additive and nonadditive genetic effects in a structured multibreed population. Data from purebred breeders and commercial producers will be needed to accomplish the same goal at a national level.     

Genetic variation and prediction of additive and nonadditive genetic effects for six carcass traits in an Angus-Brahman multibreed herd

Estimates of covariances and sire expected progeny differences of additive and nonadditive genetic effects for six carcass traits were obtained using records from 486 straightbred and crossbred steers from 121 sires born between 1989 and 1995 in the Angus-Brahman multibreed herd of the University of Florida. Steers were slaughtered at a similar carcass composition end point. Covariances were estimated by REML procedures, using a generalized expectation-maximization algorithm applied to multibreed populations. Straightbred and crossbred estimates of heritabilities and additive genetic correlations were within ranges found in the literature for steers slaughtered on an age- or weight-constant basis for hot carcass weight, longissimus muscle area, and shear force but equal to or less than the lower bound of these ranges for fat-related traits. Maximum values of interactibilities (i.e., ratios of nonadditive variances to phenotypic variances in the F1) and nonadditive genetic correlations were smaller than heritabilities and additive genetic correlations in straightbreds and crossbred groups. Sire additive and total direct genetic predictions for longissimus muscle area, marbling, and shear force tended to decrease with the fraction of Brahman alleles, whereas those for hot carcass weight and fat thickness over the longissimus were higher, and those for kidney fat were lower in straightbreds and F1 than in other crossbred groups. Nonadditive genetic predictions were similar across sire groups of all Angus and Brahman fractions. These results suggest that slaughtering steers on a similar carcass composition basis reduces variability of fat-related traits while retaining variability for non-fat-related traits comparable to slaughtering steers on a similar age or weight basis. Selection for carcass traits within desirable (narrow) ranges and slaughter of steers at similar compositional end point seems to be a good combination to help produce meat products of consistent quality.     

Genetic and economic responses for within-family marker-assisted selection in dairy cattle breeding schemes

Marker-assisted selection schemes that utilize information about quantitative trait loci to preselect progeny test bulls within a family are the most practical application of quantitative trait loci results in the short-term. Technical difficulties exist for across-family marker-assisted selection using BLUP procedures. Two within-family marker-assisted selection schemes were evaluated genetically and economically using stochastic simulation for a locus that explained 5% of phenotypic variance. The genetic and economic impacts of variation in the number of offspring per bull-dam were evaluated. The top down marker-assisted selection scheme identifies sires that are heterozygous for the locus based on the granddaughter design and uses the quantitative trait locus information in the preselection of grandsons entering progeny testing. The bottom up marker-assisted selection scheme identifies sires heterozygous for a quantitative trait locus based on the daughter design and uses the information in the preselection of sons entering progeny testing. The top down scheme with one progeny per bull-dam reduced the rate of genetic gain compared with that from a breeding scheme that ignored knowledge of the quantitative trait locus. The top down scheme with reproductive performance of 3 or 40 progeny per bull-dam increased genetic gain by 1 to 2%. The bottom up scheme increased the rate of genetic gain by 1.5, 3.5, and 5% for 1, 3, and 40 progeny per bull-dam, respectively. When the top down scheme was used on the maternal path and the bottom up scheme on the paternal path, increases were 9% with 40 progeny per bull-dam. The use of reproductive technologies on bull-dams is imperative to prevent gains from marker-assisted selection being eroded by the loss in polygenic selection differential that results when more bull-dams are required to enable preselection of sons using markers.     

Sulfur distribution in bacteriorhodopsin from multiple wavelength anomalous diffraction near the sulfur K-edge with synchrotron x-ray radiation

Estimates of variance components for test day records in an animal model that considered multiple traits over multiple lactations were calculated using REML methodology. Test day records were classified into 11 periods within first and later lactations. Missing ancestors in the relationship matrix were classified in genetic groups. Data were collected from Costa Rican dairy farms. Estimates of components for total and additive genetic variance were clearly heterogeneous during the lactation. Heritabilities for traits in later parities were slightly higher than those for traits in first parity. Heritabilities were highest for records of midlactation. Phenotypic and genetic correlations for adjacent test days were close to 1. Phenotypic correlations were lower than genetic correlations. Heterogeneity of variances during the lactation suggests the adequacy of the multiple-trait test day model to describe milk yield during the lactation. When missing ancestors were allocated to a single base population instead of genetic groups, the estimates of residual variance were lower, and the estimates of genetic variance and genetic correlations were higher. When standardized records were used instead of actual test day records, the estimates of residual and total variance were lower, and the estimates of genetic variance were higher. Consequently, estimates of heritability and genetic correlations were also higher. Use of standardized data obtained by interpolation procedures is not advised for estimation of genetic variance components in a test day model.     

Variance component estimation and multitrait genetic evaluation for type traits of dairy goats

Covariance components for final score and 13 linear type traits of dairy goats were estimated by multitrait REML using canonical transformation with an animal model. Data were 10,932 type appraisals from 1988 through 1994 from herds with > or = 40 appraisals. Heritabilities were estimated as 0.27 for final score, 0.52 for stature, 0.29 for strength, 0.24 for dairyness, 0.38 for teat diameter, 0.21 for rear legs, 0.32 for rump angle, 0.27 rump width, 0.25 for fore udder attachment, 0.25 for rear udder height, 0.19 for rear udder arch, 0.25 for udder depth, 0.33 for suspensory ligament, and 0.36 for teat placement. Genetic correlations of linear type traits and final score were positive except for dairyness (-0.15) and teat diameter (-0.10); the largest correlations with final score were 0.66 for fore udder attachment, 0.44 for rear udder arch, 0.36 for rump width, and 0.30 for strength. The largest positive correlation among linear traits was 0.63 for stature and rump width; the largest negative correlation was -0.51 for strength and dairyness. Multitrait evaluations were calculated with data from all herds. Correlations between PTA calculated with animal and sire models ranged from 0.44 to 0.70 for bucks that had a PTA with a reliability of > or = 30%.     

Maternal performance differences between porcine stress syndrome-normal and -carrier Landrace females

Differences between porcine stress syndrome (PSS) normal (NN) and carrier (Nn) Landrace dams were determined for adjusted number of pigs born alive, adjusted number of pigs at 21 d, adjusted 21-d litter weight, proportion of pigs surviving to 21 d, and farrowing interval. Data were analyzed from a total of 841 females, 623 normal (NN) and 218 carriers (Nn) having 2,231 and 869 records, respectively. Three susceptible (nn) females from two herds were dropped from the analysis because of their small contribution to the total number of records. Frequency of the recessive PSS allele ranged from .07 to .28 in the nine herds involved in this study. Data were adjusted using Landrace breed-specific adjustments and analyzed with mixed-model derivative-free REML procedures fitting the dams' PSS genotype as a fixed effect in the model. Only females having two or more successive parities were used in the analysis of farrowing interval, resulting in a reduction of total records analyzed to 2,201 (1,564 NN and 637 Nn records) from 632 females (445 NN and 187 Nn females). No differences between NN and Nn dams were observed for adjusted number of pigs born alive, adjusted number of pigs at 21 d, adjusted 21-d litter weight, proportion of pigs surviving to 21 d, and farrowing interval. The results of this investigation indicate no significant maternal performance differences between PSS NN or Nn Landrace dams.     

Accuracy of international conversions of elite sires and cows when conversion equations are based on linear regression

Conversion equations that are based on linear regression are used widely to transform estimated breeding values (EBV) of sires for production, type, health, and management traits from the genetic base, scale, and units of measurement of an exporting country to that of an importing country. One of the major deficiencies of these regression equations is that the accuracy of converted EBV of elite sires and cows, which are of primary interest in genetic selection programs, is lower than that for average animals. In this study, it is shown both mathematically and in practical examples that the standard error (SE) of prediction of elite dairy sires can be much larger than for average sires. When more than 100 sires are used to develop conversion equations, the SE of prediction for elite AI sires is up to 10% larger than for an average sire, and, when 50 sires are used to develop conversion equations, the SE of prediction is up to 25% larger than for an average sire. When fewer than 50 sires are used to develop conversion equations, the accuracy of converted EBV of elite sires is very poor, and SE can be 30 to 60% larger than for an average sire. Based on this study, it is recommended that international sire evaluations based on BLUP methodology (rather than linear regression) be made available as soon as possible for nonproduction traits in all countries and for production traits in countries that currently do not participate in routine INTERBULL (International Bull Evaluation Service) analyses.     

Genetic evaluation of body weight of lactating Holstein heifers using body measurements and conformation traits

Genetic and phenotypic parameters of body weight (BW), hip height, heart girth, and linear conformation traits were estimated from field data for 7344 lactating Holstein heifers from 560 herds. Mean BW was around 540 kg at calving, decreased to a minimum of 514 kg by wk 6, and increased to > 590 kg by wk 45 of lactation. The statistical model for BW included month of recording, lactation stage, pregnancy stage, proportion of Holstein genes, parity of the dam, calving age, herd, and animal. Variation between herds accounted for 33% of the phenotypic variation of BW. Estimated heritability was 0.33 for BW and ranged from 0.32 to 0.54 for heart girth, hip height, and conformation traits. Genetic correlations of BW with heart girth, hip height, body depth, rump width, and muscularity ranged from 0.48 to 0.77. The accuracy of a selection index was 0.90 for direct selection and 0.83 for indirect selection using information on those conformation traits of 50 daughters. Genetic variation of BW is considerable, and genetic evaluation of BW can be based on data for conformation traits from a field recording system with only a limited loss of accuracy.     

Covariance functions across herd production levels for test day records on milk, fat, and protein yields

Multiple-trait BLUP evaluations of test day records require a large number of genetic parameters. This study estimated covariances with a reduced model that included covariance functions in two dimensions (stage of lactation and herd production level) and all three yield traits. Records came from all six states in Australia, were evenly distributed across the herd production levels, but decreased with increasing lactation stage from 9693 records for the 1st mo of lactation to 4199 records for the 10th mo. Using the variance component estimation package and a bivariate animal model, 1176 genetic (co)variances and 312 environmental (co)variances were estimated for 48 traits (1, 4, 7, and 10 mo of lactation; herd production levels of < 20, 20 to 22, 22 to 24, > 24 kg of milk/d; and milk, fat, and protein yields). The genetic (co)variances could be predicted by a multiplicative model that included 1) a term dependent on which yields (milk, fat, or protein) were involved in the covariance, 2) the covariance functions for month of lactation and herd production level, and 3) a covariance function for the interaction between these. This model required only 27 parameters instead of the 1176 (co)variances. For the environmental (co)variances, a model was fitted that contained several additional covariance functions. This model reduced the number of parameters from 312 to 71. For the same trait at the same production level, genetic correlations between test days ranged from 0.59 to 1, and environmental correlations ranged from 0.17 to 0.48. Genetic correlations between milk and fat, milk and protein, and fat and protein were 0.38, 0.83, 0.59, respectively, and correlations between the herd production levels ranged from 0.79 to 0.97. Failure to consider herd production level in a test day model evaluation might result, for instance, in overweighting of early lactation information from high production herds compared with information coming from bulls tested across all production levels.     

Genetics of limb development and congenital hand malformations

Differences in breeding values between dominance and additive models were examined theoretically and with field data. Data included 5.2 million records on stature from 3.0 million US Holsteins. The largest full-sib family had 29 animals, and 7% of all animals had at least one full sib. The dominance model, which accounted for dominance covariances, included the following effects: management, age, stage of lactation, permanent environment, animal additive, and parental dominance (one-quarter of dominance variance) as well as a regression coefficient for inbreeding percentage. Two reduced models were also assumed; in the first, the parental dominance effect was removed, and, in the second, the inbreeding regression coefficient was also removed. The correlations between breeding values in the three models were > 0.999, but breeding values of some animals from full-sib families changed > 5 standard deviations of parental dominance. The largest changes were observed for parents with large numbers of full-sib progeny, with limited information from parents, and without individual performance records. On average, the differences were up to four times larger for cows than for bulls and up to five times larger for dams than for sires. The greatest differences in breeding values between the dominance and the additive models were observed for dams with full-sib progeny, female full sibs, and low reliability bulls with full sibs in the extended family. Animals with large amounts of additive information as progeny-tested bulls were influenced little by the inclusion of dominance. Animals with a large proportion of information coming from animals with dominance relationships, such as cows originating via embryo transfer changed the most.     

Adjusting for missing data due to culling before testing in genetic evaluations of swine

The aim of this study was to evaluate data augmentation in genetic evaluations as a method of adjusting for missing data due to culling of pigs before testing. A stochastic simulation was used to generate 10 yr of data for age at test (AGE) and fat thickness (FAT) in a breeding unit with 100 sows and 15 boars. Culling was performed at random (C-RAND), within litters (C-W/IN) or over litters (C-OVER), by deleting two-thirds of the records from the simulated data sets. The culling variate (CVAR) used had genetic and phenotypic correlations of 1.00, .75, .50, and .25 with AGE [r(CVAR, AGE)], whereas culling was uncorrelated with AGE in C-RAND. Missing records for AGE were replaced with their expectations (dummy records), based on the phenotypic average of the tested animals and selection intensities applied. With missing records, predictions were seriously biased for AGE in C-W/IN and especially in C-OVER, when r(CVAR, AGE) exceeded .50 and .25, respectively. The ranking of the animals was more affected in C-OVER than in C-W/IN. With dummy records, bias was removed effectively in cases with a high r(CVAR, AGE) in C-W/IN and C-OVER, whereas a larger bias was created in the opposite direction when r(CVAR, AGE) was less than .50 and for C-RAND. In conclusion, this method was beneficial for adjusting missing data owing to culling, when the correlation between CVAR and AGE was .50 or greater.     

Production and reproductive responses to use of DairyMAN: a management information system for New Zealand dairy herds

To assess the benefits of on-farm use of a computerized management information system (DairyMAN, Department of Veterinary Clinical Science, Massey University, Palmerston North, New Zealand) on seasonally calving herds of New Zealand, data for 144 herds using this system were compared with a stratified random sample of 294 herds using only the centralized National Dairy Database system during the season 1993 to 1994. Demographic, reproductive performance, and milk yield indices were compared. Herds using DairyMAN had superior reproductive outcomes, measured as a higher percentage (+8.4%) of cows calving during the desired seasonal period (critical in this seasonal system) and a higher percentage of cows (+9.7%) mated at the optimal time to achieve a concentrated calving in the following year. Multiple analysis of covariance was used to differentiate effects. The superior performance was not associated with differences in some of the more commonly used reproductive indices (efficiency of estrus detection, first service nonreturn rate, and pregnancy rate), but the data used in the study do not accurately reflect true performance. Herds using DairyMAN had more information on breeding performance because pregnancy data were available and natural breedings were recorded for a longer period. With cows of equivalent genetic merit, herds using DairyMAN produced more milk (+1.2 L/d per cow) indicating that users achieved better management of the herd through improved attention to managerial details. This improved production was associated with the adoption of DairyMAN but is not shown by this study to be a direct consequence of it. DairyMAN user herds were larger (245 cows) than were herds not using the system (181 cows), but this difference did not significantly affect the various outcomes measured.     

On the rationality of decision-making studies: Part 2: Divergent rationalities

We examine likelihood based methods aimed at analysing the causal effect of actual exposure to drug treatment on a (repeated) binary outcome in two randomized trials with partial compliance. Starting with the univariate compliance summary 'total treatment dose history', we apply a method for ordinal compliance and monotone dose response, proposed by Goetghebeur and Molenberghs. In a short duration trial of blood pressure reduction, this summary leads to meaningful effect estimators. However, in the analysis of a vitamin A trial, this method reaches a boundary solution; the estimated possible benefit from vitamin A for children who did not receive any pills on the treatment arm is zero. In our formulation the number of pills that were taken captures part of the outcome, and the corresponding effect parameters suffer from this confounding. To gain additional insight, we account explicitly for the temporal structure of compliance. We extend the likelihood based methodology for univariate ordered compliance to more dimensional compliance with only a partial order structure on exposure. The randomization assumptions in the causal formulation of Rubin are translated to this setting. We motivate a set of parametric assumptions on the joint distribution of potential outcomes and observed compliance levels and reanalyse the vitamin A trial. Our findings suggest that one capsule of vitamin A had a large impact on mortality during the first 4 months. The greatest reduction in risk was estimated amongst children who received two doses. This supports findings from a vitamin A trial in Ghana and in Nepal. Finally, we discuss extensions of this method, covering uncensored and censored grouped survival data.     

Genetic evaluation by BLUP in two-breed terminal crossbreeding systems under dominance

To obtain estimates of breeding values by BLUP using Henderson's mixed-model equations, it is necessary to invert the covariance matrix for each random effect in the model. In a model in which the genotypic value is included as a random effect (genotypic model), it is necessary to invert the genotypic covariance matrix. Under additive inheritance, the inverse of the genotypic covariance matrix can be computed efficiently. Under dominance inheritance, however, an efficient method to invert the genotypic covariance matrix has not yet been developed, especially for crossbred populations. Thus, the use of a genotypic model for BLUP is not suitable for genetic evaluation in large, crossbred populations. We present an equivalent model in which the genotypic effect is partitioned into additive and dominance effects. With this equivalent model, methods used for within-breed genetic evaluation by BLUP can be used for a two-breed terminal cross under dominance.     

11Beta-hydroxysteroid dehydrogenase type 2 in human pregnancy and reduced expression in intrauterine growth restriction

The evolutionary theory of aging predicts that the equilibrium gene frequency for deleterious mutations should increase with age at onset of mutation action because of weaker (postponed) selection against later-acting mutations. According to this mutation accumulation hypothesis, one would expect the genetic variability for survival (additive genetic variance) to increase with age. The ratio of additive genetic variance to the observed phenotypic variance (the heritability of longevity) can be estimated most reliably as the doubled slope of the regression line for offspring life span on paternal age at death. Thus, if longevity is indeed determined by late-acting deleterious mutations, one would expect this slope to become steeper at higher paternal ages. To test this prediction of evolutionary theory of aging, we computerized and analyzed the most reliable and accurate genealogical data on longevity in European royal and noble families. Offspring longevity for each sex (8409 records for males and 3741 records for females) was considered as a dependent variable in the multiple regression model and as a function of three independent predictors: paternal age at death (for estimation of heritability of life span), paternal age at reproduction (control for parental age effects), and cohort life expectancy (control for cohort and secular trends and fluctuations). We found that the regression slope for offspring longevity as a function of paternal longevity increases with paternal longevity, as predicted by the evolutionary theory of aging and by the mutation accumulation hypothesis in particular.     

Genetic and environmental parameters for mature weight and other growth measures in Polled Hereford cattle

Variances and covariances were estimated for birth weight (BWT), weaning weight (WW), yearling gain (YG), yearling weight (YW), yearling height (YH), relative growth from birth to weaning (RGW) and weaning to yearling (RGY), and mature weight (MW). Field records on 572,446 Polled Hereford cattle were analyzed. Because multiple dam weights were collected on a cow the MW used in the analysis was calculated using a nonlinear regression correction factor computed by fitting a quadratic with a plateau to the data. If the cow had an observation(s) beyond the inflection point (IP), the closest weight to that point was used. If the cow only had observation(s) before the IP the closest weight to that point was nonlinearly adjusted to the plateau. The IP for this data set was 1,506 d and the plateau was 554.7 kg. Heritabilities and genetic and environmental correlations were calculated from the appropriate (co)variances and phenotypic correlations were computed. Heritability estimates for BWT, WW, YG, YW, YH, RGW, RGY, and MW were .49, .24, .23, .30, .59, .24, .15, and .52, respectively. Genetic correlations between MW with BWT, WW, YG, YW, YH, RGW, and RGY were .64, .80, .76, .89, .73, -.29, and .35, respectively, environmental correlations were .15, .43, .05, .40, 1.03, .32, and -.10, respectively, and phenotypic correlations were .33, .32, .28, .46, .70, .00, and .07, respectively.