Biomedical subjects

I Misztal

Publications and source records attributed to I Misztal.

At least 55 records · Page 3Linked to original sources

PubMed2001

Genetic parameter estimates from joint evaluation of purebreds and crossbreds in swine using the crossbred model.

Records on lifetime daily gain and backfat from two purebred lines A (n = 6,022), B (n = 24,170), and their reciprocal crosses C (n = 6,135) were used to estimate genetic parameters using within-line and terminal-cross models. The models that were fitted included fixed (contemporary group and sex), random additive A and(or) random additive B, random dominance, and random litter effects. Model for purebreds included only one additive effect, whereas the model for crossbreds included two additive effects. End weight was included as a covariable for backfat. Heritability estimates for lifetime daily gain were 0.26, 0.28, and 0.23 with within-line models for lines A, B, and C, respectively, and 0.26, 0.30, and 0.27 with the crossbred model, respectively. Heritability estimates for backfat were 0.52, 0.35, and 0.29 with within-line models for lines A, B, and C, respectively, and 0.51, 0.38, and 0.29 with the crossbred model, respectively. The genetic correlations between purebreds and crossbreds (r(pc)) for lifetime daily gain were 0.99 (A-C) and 0.62 (B-C); for backfat the correlations were 0.32 (A-C) and 0.70 (B-C). The amount of dominance variance from the crossbred model expressed as a proportion of phenotypic variance for lifetime daily gain was 0.39, 0.16, and 0.29 for lines A, B, and C respectively. Dominance variance for backfat was estimated as 0. A joint evaluation of purebreds and crossbreds would be most efficient with the crossbred model. The dominance variation should be accounted for lifetime daily gain.

Adipose Tissue↗

PubMed2001

Comparison of threshold vs linear and animal vs sire models for predicting direct and maternal genetic effects on calving difficulty in beef cattle.

This study compared the accuracy of several models for obtaining genetic evaluations of calving difficulty. The models were univariate threshold animal (TAM), threshold sire-maternal grandsire (TSM), linear animal (LAM), and linear sire-maternal grandsire (LSM) models and bivariate threshold-linear animal (TLAM), threshold-linear sire-maternal grandsire (TLSM), linear-linear animal (LLAM), and linear-linear sire-maternal grandsire (LLSM) models for calving difficulty and birth weight. Data were obtained from the American Gelbvieh Association and included 84,420 first-parity records of both calving difficulty and birth weight. Calving difficulty scores were distributed as 73.4% in the first category (no assistance), 18.7% in the second, 6.3% in the third, and 1.6% in the fourth. Included in the animal models were fixed sex of calf by age of dam subclasses, random herd-year-season effects, and random animal direct and maternal breeding values. Sire-maternal grandsire models were similar to the animal models, with animal and maternal effects replaced by sire and maternal grandsire effects. Models were compared using a data splitting technique based on the correlation of estimated breeding values from two samples, with one-half of the calving difficulty records discarded randomly in the first sample and the remaining calving difficulty records discarded in the second sample. Reported correlations are averages of 10 replicates. The results obtained using animal models confirmed the slight advantage of TAM over LAM (0.69 vs 0.63) and TLAM over LLAM (0.90 vs 0.86). Bivariate analyses greatly improved the accuracy of genetic prediction of direct effects on calving difficulty relative to univariate analyses. Similar ranking of the models was found for maternal effects, but smaller correlations were obtained for bivariate models. For sire-maternal grandsire models, no differences between sire or maternal grandsire correlations were observed for TLSM compared to LLSM, and small differences were observed between TSM and LSM. The threshold model offered advantages over the linear model in animal models but not in sire-maternal grandsire models. For genetic evaluation of calving difficulty in beef cattle, the threshold-linear animal model seems to be the best choice for predicting both direct and maternal effects.

Animal Husbandry↗

PubMed2001

Estimation of genetic covariances with method R.

Method R is a simple and computationally inexpensive method for estimating (co)variances. The objective of the study was to investigate properties of Method R for estimation of (co)variance components with emphasis on covariance estimation. Theoretical Method R formulas were developed for simplified single-variate and bivariate models. In single-trait models, the curve of the regression of Method R was continuous and monotonic and its slope depended on the amount of information on each animal and on the variance ratio. The curve became steeper as the number of records per animal decreased. For covariance, the curve of the regression was monotonic but not continuous. However, a regression coefficient of 1 still corresponded to the correct covariance. Similar curves were observed in analyses of simulated data sets. Because of the observed discontinuity, algorithms implementing Method R that require a continuous regression curve would not work in models with covariances. An alternative algorithm was based on a transformation matrix obtained by multiplying a matrix of numerators with the inverse of a matrix of denominators of the regression factors. Such an algorithm converged reliably for all models tested. Method R can be modified to estimate covariances in models too large for other methods.

Algorithms↗

PubMed2001

Approximation of reliabilities for multiple-trait model with maternal effects.

Reliabilities for a multiple-trait maternal model were obtained by combining reliabilities obtained from single-trait models. Single-trait reliabilities were obtained using an approximation that supported models with additive and permanent environmental effects. For the direct effect, the maternal and permanent environmental variances were assigned to the residual. For the maternal effect, variance of the direct effect was assigned to the residual. Data included 10,550 birth weight, 11,819 weaning weight, and 3,617 postweaning gain records of Senepol cattle. Reliabilities were obtained by generalized inversion and by using single-trait and multiple-trait approximation methods. Some reliabilities obtained by inversion were negative because inbreeding was ignored in calculating the inverse of the relationship matrix. The multiple-trait approximation method reduced the bias of approximation when compared with the single-trait method. The correlations between reliabilities obtained by inversion and by multiple-trait procedures for the direct effect were 0.85 for birth weight, 0.94 for weaning weight, and 0.96 for postweaning gain. Correlations for maternal effects for birth weight and weaning weight were 0.96 to 0.98 for both approximations. Further improvements can be achieved by refining the single-trait procedures.

Animals↗

PubMed2001

Use of the preconditioned conjugate gradient algorithm as a generic solver for mixed-model equations in animal breeding applications.

Utility of the preconditioned conjugate gradient algorithm with a diagonal preconditioner for solving mixed-model equations in animal breeding applications was evaluated with 16 test problems. The problems included single- and multiple-trait analyses, with data on beef, dairy, and swine ranging from small examples to national data sets. Multiple-trait models considered low and high genetic correlations. Convergence was based on relative differences between left- and right-hand sides. The ordering of equations was fixed effects followed by random effects, with no special ordering within random effects. The preconditioned conjugate gradient program implemented with double precision converged for all models. However, when implemented in single precision, the preconditioned conjugate gradient algorithm did not converge for seven large models. The preconditioned conjugate gradient and successive overrelaxation algorithms were subsequently compared for 13 of the test problems. The preconditioned conjugate gradient algorithm was easy to implement with the iteration on data for general models. However, successive overrelaxation requires specific programming for each set of models. On average, the preconditioned conjugate gradient algorithm converged in three times fewer rounds of iteration than successive overrelaxation. With straightforward implementations, programs using the preconditioned conjugate gradient algorithm may be two or more times faster than those using successive overrelaxation. However, programs using the preconditioned conjugate gradient algorithm would use more memory than would comparable implementations using successive overrelaxation. Extensive optimization of either algorithm can influence rankings. The preconditioned conjugate gradient implemented with iteration on data, a diagonal preconditioner, and in double precision may be the algorithm of choice for solving mixed-model equations when sufficient memory is available and ease of implementation is essential.

Algorithms↗

PubMed2001

Potential accuracy of genetic evaluation for calving difficulty with incomplete data on calving difficulty and/or birth weight using a bivariate threshold-linear animal model.

The purpose of this study was to evaluate the potential loss of accuracy in direct and maternal predicted breeding values (PBV) for calving difficulty (CD) with different levels of missing records of CD and/or birth weight (BW), using a bivariate threshold-linear animal model. Data obtained from the American Gelbvieh Association included 84,420 first-parity records with both CD and BW available. The final pedigree file included 178,858 animals. The model included fixed calf-sex-dam-age, random herd-year-season, and animal direct and maternal effects. Different levels of missing observations for CD and BW were obtained by randomly deleting 0, 25, 50, 75, and 100% of records for both traits in various combinations. Correlation estimates between PBV for CD obtained with complete and incomplete data were used to measure the changes in PBV for different levels of missing records. Reported correlations are means of three replicates. The results suggest that the information on direct and maternal PBV provided by CD records is more reliable than the information provided by BW records. The difference was especially large when a high proportion of CD records were missing. Correlations above 0.96 and 0.95 for direct and maternal PBV, respectively, when missing 25% or 0% of the CD or BW records suggest that small changes would be predicted with a low proportion incomplete data. For genetic prediction of popular sires (with > 100 pogeny), a higher proportion of missing records could be tolerated. The results suggest that the bivariate threshold-linear animal model is useful for routine genetic evaluation of CD with incomplete field data.

Journal Article↗

PubMed2001

Generating data in models including direct and maternal dominance effects.

Algorithms are presented to simulate multiple generations of animal data by a model including direct additive genetic, maternal additive genetic, direct dominance, maternal dominance and permanent environmental effects. Dominance effects were computed as parental subclasses. Testing involved five single trait models that included direct contemporary group and direct additive effects, and different combinations of maternal, permanent environmental, and dominance effects. Simulated populations included 5 generations of animals and 20 contemporary groups per generation. The base population contained 200 sires and 600 dams. Variance components were estimated by Average-Information Restricted Maximum Likelihood (AIREML). No significant bias was observed. The simulation algorithms can be used in research involving dominance models, such as evaluation of mating systems exploiting special combining abilities of prospective parents.

Journal Article↗

PubMed2000

Variance and covariance components for weaning weight for Herefords in three countries.

Records from the Hereford Associations of the United States (USA), Canada, and Uruguay were used to estimate genetic and phenotypic variances and covariances for weaning weight. Estimation was done using a complete animal model, relatively large data sets, and the same methodology for the three countries in order to determine whether genetic parameters for weaning weight were homogeneous across environments. Data were composed of 2,322,722, 487,661, and 102,986 edited weaning weight records for USA, Canada, and Uruguay, respectively. Ten samples were obtained from each country by eliminating data from small herds with fewer than 500 records, selecting herds at random from the entire data set after removing the small herds, and then retaining the direct-sire-connected contemporary groups within each sample. The final sample sizes ranged from 9,832 to 46,377 records. An accelerated EM-REML algorithm was used in estimating the (co)variance components in each sample. The estimates were pooled by calculating the arithmetic mean of the 10 samples from within each country. Direct and maternal (in parentheses) heritability estimates were .24 (.16), .20 (.16), and .23 (.18) for USA, Canada, and Uruguay, respectively. Maternal heritabilities reported here are nearly 50% smaller than the values currently used in national genetic evaluation for the breed, which were estimated using sire-maternal grandsire models. Covariance between direct and maternal was negative in all countries, accounting for 6, 8, and 10% of the total phenotypic variation, and the total dam effect was 32.5, 37.0, and 34.0% in USA, Canada, and Uruguay, respectively. Total heritabilities were similar among the countries, with values of .19, .19, and .17 for the three respective countries. The similarity of genetic and environmental parameters across the three countries suggests that joint genetic evaluation is feasible across environments provided that the genotype x environment interaction is negligible and can be ignored.

Animals↗

PubMed2000

Investigation of genotype x environment interactions for weaning weight for Herefords in three countries.

The objective of this study was to investigate the possibility of genotype x environment interactions for weaning weight (WWT) between different regions of the United States (US) and between Canada (CA), Uruguay (UY), and US for populations of Hereford cattle. Original data were composed of 487,661, 102,986, and 2,322,722 edited weaning weight records from CA, UY, and US, respectively. A total of 359 sires were identified as having progeny across all three countries; 240 of them had at least one progeny with a record in each environment. The data sets within each country were reduced by retaining records from herds with more than 500 WWT records, with an average contemporary group size of greater than nine animals, and that contained WWT records from progeny or maternal grand-progeny of the across-country sires. Data sets within each country were further reduced by randomly selecting among remaining herds. Four regions within US were defined: Upper Plains (UP), Cornbelt (CB), South (S), and Gulf Coast (GC). Similar sampling criteria and common international sires were used to form the within-US regional data sets. A pairwise analysis was done between countries and regions within US (UP-CB vs S-GC, UP vs CB, and S vs GC) for the estimation of (co)variance components and genetic correlation between environments. An accelerated EM-REML algorithm and a multiple-trait animal model that considered WWT as a different trait in each environment were used to estimate parameters in each pairwise analysis. Direct and maternal (in parentheses) estimated genetic correlations for CA vs UY, CA vs US, US vs UY, UP-CB vs S-GC, UP vs CB, and S vs GC were .88 (.84), .86 (.82), .90 (.85), .88 (.87), .88 (.84), and .87 (.85), respectively. The general absence of genotype x country interactions observed in this study, together with a prior study that showed the similarity of genetic and environmental parameters across the three countries, strongly indicates that a joint WWT genetic evaluation for Hereford cattle could be conducted using a model that treated the information from CA, UY, and US as a single population using single population-wide genetic parameters.

Animals↗

PubMed2000

Strategies for estimating the parameters needed for different test-day models.

Currently, most analyses of parameters in test-day models involve two types of models: random regression, where various functions describe variability of (co)variances with regard to days in milk, and multiple traits, where observations in adjacent days in milk are treated as one trait. The methodologies used for estimation of parameters included Bayesian via Gibbs sampling, and REML in the form of derivative-free, expectation-maximization, or average-information algorithms. The first method is simpler and uses less memory but may need many rounds to produce posterior samples. In REML, however, the stopping point is well established. Because of computing limitations, the largest estimations of parameters were on fewer than 20,000 animals. The magnitude and pattern of heritabilities varied widely, which could be caused by simplifications in the model, overparameterization, small sample size, and unrepresentative samples. Patterns of heritability differ among random regression and multiple-trait models. Accurate parameters for large multi-trait random regression models may be difficult to obtain at the present time. Parameters that are sufficiently accurate in practice may be obtained outside the complete prediction model by a constructive approach, where parameters averaged over the lactation would be combined with several typical curves for (co)variances for days in milk. Obtained parameters could be used for any model, and could also aid in comparison of models.

Analysis of Variance↗

PubMed2000

Method R estimates of additive genetic, dominance genetic, and permanent environmental fraction of variance for yield and health traits of Holsteins.

Fractions of variance accounted for by additive genetic, dominance genetic, and permanent environmental effects for milk, fat, and protein yields; somatic cell score; and productive life were estimated from Holstein data used for national genetic evaluations. Contemporary group assignments were determined using the national procedure. Data included 1,973,317 milk and fat records for 812,659 cows, 1,019,421 protein records for 462,067 cows, 468,374 lactation average somatic cell score (SCS) records for 232,909 cows, and 735,256 cows with productive-life records. Variance components were estimated with the JAADOM program, which uses iteration on data and second-order Jacobi iteration for obtaining solutions to the mixed-model equations and Method R for estimation of variance components. Ten different random data subsets were used to estimate parameters for each trait. Estimated additive genetic, dominance genetic, and permanent environmental fractions of variance were 0.34, 0.05, and 0.10 for milk yield; 0.34, 0.05, and 0.11 for fat yield; 0.31, 0.05, and 0.10 for protein yield; and 0.17, 0.01, and 0.16 for lactation average SCS. Estimated additive genetic and dominance genetic fractions of variance were 0.12 and 0.06 for productive life. Mean empirical standard errors of additive genetic, dominance genetic, and permanent environmental variance fractions were 0.003, 0.006, and 0.006.

Animals↗

PubMed2000

Genetic Component of Heat Stress in Dairy Cattle, Development of Heat Index Function.

Production data obtained from AIPL USDA included 119,337 first-parity, test-day records of 15,012 Holsteins from 134 Georgia farms collected in 1990 to 1997. Weather information was obtained from the Georgia Automated Environmental Monitoring Network and included daily minimum, average, and maximum temperatures and humidity for 21 stations throughout the state. Each test-day record was augmented with weather information from the closest weather station. Analyses were based on models that included effects of herd-year-season, age, test day, milking frequency, and several types of heat and humidity. The best model used a temperature-humidity index. With this model, the average test-day yield for milk was about 26.3 kg for a temperature-humidity index <72 and decreased at about 0.2 kg per unit increase in the temperature-humidity index for a temperature-humidity index > or =72. For fat and protein, the test yield was 0.92 and 0.85 kg at a temperature-humidity index <72, respectively, and declined at a rate of 0.012 and 0.009 kg per degree of the temperature-humidity index, respectively. The temperature-humidity index calculated with the available weather information can be used to account for the effect of heat stress on production.

Animals↗

PubMed2000

Genetic component of heat stress in dairy cattle, parameter estimation.

Our data included 119,205 first-parity, test-day records from 15,002 Holsteins in 134 Georgia farms with temperature and humidity data from 21 weather stations throughout Georgia. The test-day model included the effects of herd test date, days-in-milk (DIM) classes, age, milking frequency, general additive effect, random regression on the heat-humidity index for heat-tolerance additive effect, general permanent environment, and the random regression on the heat-humidity index for a permanent environment. The general effects, which corresponded to effects in the current repeatability models, were assumed to be correlated with the heat-tolerance effects. Variance components were estimated by REML. For heat-humidity indices below 72, heritability for milk was 0.17, and additive variance of heat tolerance was 0. For a heat-humidity index of 86 (which would correspond to temperatures of 36 degrees C at 50% humidity), the additive variance of heat tolerance was as high as for general effect, and the genetic correlation between the two effects was -0.36. Results for fat and protein were similar. Current selection for production reduces heat tolerance. Joint selection for heat tolerance and production is possible.

Animals↗

PubMed2000

Bias in genetic evaluations by records of cows treated with bovine somatotropin.

Records from Dairy Records Management Systems in Raleigh were used to estimate effects of bovine somatotropin (bST) treatment and to predict breeding values for milk production traits. The data comprised 5245 test-day records of bST-treated cows and 126,223 test-day records of untreated cows in first lactation for milk, fat, and protein yields. Fixed effects of bST treatment were estimated from test-day animal models with herd-test-date as another fixed factor. Percentage increases due to bST treatment ranged from 7 to 8% for test-day milk, fat, and protein yields. Random regression coefficients for additive genetic and permanent environmental effects were included in the model. To assess the potential for bias in genetic evaluations when some and not all cows are treated with bST, breeding values predicted by the test-day model with and without effects of bST treatment were compared for cows and sires. Correlations between breeding values predicted from models with and without effects of bST treatment were 0.99. However, relatively large bias was found for individual animals. This result suggests that bias in genetic evaluation caused by ignoring bST treatment may be significant.

Animals↗

PubMed1999

Threshold-linear versus linear-linear analysis of birth weight and calving ease using an animal model: I. Variance component estimation.

Birth weight and calving difficulty were analyzed with Bayesian methodology using univariate linear models, a bivariate linear model, a threshold model for calving difficulty, and a joint threshold-linear model using a probit approach. Field data included 26,006 records of Gelbvieh cattle. Simulated populations were generated using parameters estimated from the field data. The Gibbs sampler was used to obtain estimates of the marginal posterior mean and standard deviation of the (co)variance components, heritabilities, and correlations. In the univariate analyses, the posterior mean of direct heritability for calving difficulty was .23 with the threshold model and .18 with the linear model. Maternal heritabilities were .10 and .08, respectively. In the bivariate analysis, posterior means of direct heritability for calving difficulty were .21 and .18 for the bivariate linear-threshold and linear-linear model, respectively. Maternal heritabilities were .09 and .06, respectively. Direct heritability for birth weight was .25 for the univariate model and .26 for bivariate models. Maternal heritability was .05 for the linear-threshold model and the univariate model and .06 for the bivariate linear model. Genetic correlation between direct genetic effects in both traits was .81 for the linear-threshold model and .79 for the bivariate linear. Residual correlation was .35 for the bivariate linear model and .50 for the bivariate linear-threshold. A simulation study confirmed that the posterior mean of the marginal distribution was suitable as a point estimate for univariate threshold and bivariate linear-threshold models.

Animals↗

PubMed1999

Threshold-linear versus linear-linear analysis of birth weight and calving ease using an animal model: II. Comparison of models.

Several models were evaluated in terms of predictive ability for calving difficulty. Data included birth weight and calving difficulty scores provided by the American Gelbvieh Association from 26,006 calves born to first-parity cows and five simulated populations of 6,200 animals each. Included in the model were fixed age of dam x sex interaction effects, random herd-year-season effects, and random animal direct and maternal effects. Bivariate linear-threshold and linear-linear models for birth weight/calving ease and univariate threshold and linear models for calving ease were applied to the data sets. For each data set and model, one-half of calving ease records were randomly discarded. Predictive ability of the different models was defined with the mean square error (MSE) for the difference between a deleted calving ease score and its prediction obtained from the remaining data. In terms of correlation between simulated and predicted breeding values, the threshold models had a 1% advantage for direct genetic effects and 3% for maternal genetic effects. In simulation, the average MSE was .29 for linear-threshold, .32 for linear-linear, .37 for threshold, and .39 for linear model. For the field data set, the MSE was .31, .33, .39, and .40, respectively. Although the bivariate models for calving ease/birth weight were more accurate than univariate models, the threshold models showed a greater advantage under the bivariate model. For the purpose of genetic evaluation for calving difficulty in beef cattle, the use of the linear-threshold model seems justified. In dairy cattle, the evaluation for calving ease can benefit from recording birth weight.

Animals↗

PubMed1999

Supply of genetic information--amount, format, and frequency.

The volume and complexity of genetic information is increasing because of new traits and better models. New traits may include reproduction, health, and carcass. More comprehensive models include the test day model in dairy cattle or a growth model in beef cattle. More complex models, which may include nonadditive effects such as inbreeding and dominance, also provide additional information. The amount of information per animal may increase drastically if DNA marker typing becomes routine and quantitative trait loci information is utilized. In many industries, evaluations are run more frequently. They result in faster genetic progress and improved management and marketing opportunities but also in extra costs and information overload. Adopting new technology and making some organizational changes can help realize all the added benefits of the improvements to the genetic evaluation systems at an acceptable cost. Continuous genetic evaluation, in which new records are accepted and breeding values are updated continuously, will relieve time pressures. An online mating system with access to both genetic and marketing information can result in mating recommendations customized for each user. Such a system could utilize inbreeding and dominance information that cannot efficiently be accommodated in the current sire summaries or off-line mating programs. The new systems will require a new organizational approach in which the task of scientists and technicians will not be simply running the evaluations but also providing the research, design, supervision, and maintenance required in the entire system of evaluation, decision making, and distribution.

Animals↗

PubMed1999

Prediction of parental dominance combinations for planned matings, methodology, and simulation results.

Optimal use of dominance information requires a mating system and predictions of specific combining abilities for each set of prospective parents. Current evaluation procedures provide such predictions only for a limited number of parents. A procedure is described that predicts the specific combining ability for any parents. In this procedure, for each set of parents and their ancestors, the additive relationship matrix is created as a dense matrix. This matrix is then used to create a parental dominance matrix in a sparse matrix form, in which the rows of the matrix correspond to all parental combinations for which predictions are already available. Each new prediction requires a solution of the system of equations with the parental dominance matrix as the left-hand side. The efficiency of the mating system that accounts for dominance was evaluated in a simulation study. The simulated data files varied with respect to proportion of males and females selected, proportion of cattle born through embryo transfer, and additive and dominance variance. Sires and dams were preselected based on the additive merits only, but specific matings were arranged based on the combined additive plus dominance merit. The response to selection with consideration of dominance increased from 3.8 to 16.6% of the response from one generation of additive selection. The response was greater when the additive variance was smaller, the dominance variance was larger, the intensity of additive selection was lower, and the proportion of full sibs was greater. Use of dominance in the mating system is feasible and results in an additional genetic response to selection.

Animals↗