Biomedical subjects

J H van der Werf

Publications and source records attributed to J H van der Werf.

10 recordsLinked to original sources

PubMed1998

The use of covariance functions and random regressions for genetic evaluation of milk production based on test day records.

In the analysis of test day records for dairy cattle, covariance functions allow a continuous change of variances and covariances of test day yields on different lactation days. The equivalence between covariance functions as an infinite dimensional extension of multivariate models and random regression models is shown in this paper. A canonical transformation procedure is proposed for random regression models in large-scale genetic evaluations. Two methods were used to estimate covariance function coefficients for first parity test day yields of Holsteins: 1) a two-step procedure fitting covariance functions to matrices with estimated genetic and residual covariances between predetermined periods of lactation and 2) REML directly from data with a random regression model. The first method gave more reliable estimates, particularly for the periphery of the trajectory. The goodness of fit of a random regression model based on covariables describing the shape of the lactation curve was nearly the same as random regression on Legendre polynomials. In the latter model, two and three regression coefficients were sufficient to fit the covariance structure for additive genetic and permanent environment, respectively. The eigenfunction pattern revealed the possibility of selection for persistency. Covariance functions can be usefully implemented in large-scale test day models by means of random regressions.

Analysis of Variance↗

PubMed1995

Maximizing selection efficiency for categorical traits.

Genetic improvement of categorically recorded traits is hampered because information content of categorical records is low and ordinary linear breeding value estimation methods do not apply theoretically. The ordinary animal or linear mixed model (LMM), which ignored the categorical nature of the trait, is compared to a generalized linear mixed model (GLMMp) that assumes a linear mixed model for an underlying continuous variable. The GLMMp takes full account of the categorical nature of the trait and is a straightforward extension of LMM. In a closed nucleus breeding scheme (e.g., cattle, pigs, or poultry), rates of genetic gain increased by 1 to 2%, when GLMMp was used instead of LMM. Rates of genetic gain increased by 7 to 20%, when the best sires were used on the best herds (i.e., when there was some confounding between sire and herd effects). When considering a binary trait (e.g., disease incidence) initial incidences of 25% could be reduced to 2.8% within 10 generations of selection. Rates of gain can be increased by up to 84% by gathering more information on high-incidence categories (i.e., by dividing these categories into subcategories). Subdividing low-incidence categories (e.g., splitting diseased animals into moderately and severely diseased) hardly increased rates of gain. Direct recording of the underlying variable, which requires uncovering of the physiological background of the categorical trait, yielded 109 to 278% more genetic gain than selection for a binary trait.

Animals↗

PubMed1995

Genetic correlation and heritabilities for purebred and crossbred performance in poultry egg production traits.

Genetic correlations between purebred and crossbred performance and purebred and crossbred heritabilities were estimated for egg production traits of laying chickens using a multivariate sire model accounting for additive relationships between sires. Two sire lines, denoted lines 1 and 2, were crossed to one dam line to produce crossbred progeny. Records for egg weight, egg specific gravity, and egg number were collected on purebred and crossbred hens. In total, 99 sires in line 1 and 292 sires in line 2 were used in the analysis, each sire producing on average 45 purebred and 105 crossbred daughters. Estimates of purebred heritability in lines 1 and 2 were in range of .54 to .74 for egg number traits, .52 to .91 for egg weight traits, and .41 to .83 for egg specific gravity traits. Estimates of crossbred heritability were .04 to .51 for egg numbers, .23 to .45 for egg weight, and .13 to .31 for egg specific gravity. The sire component in crossbreds differed up to 78% from the sire component in purebreds depending on traits. The estimate of genetic correlation (rpc) between purebred and crossbred performance was .56 to .73 for egg number, .69 to .99 for egg weight, and .72 to .82 for egg specific gravity. Although crossbred parameters were strongly affected by environmental factors, the results tend to agree with the theory that traits with a larger dominance variation and a larger difference between sire components in purebreds and crossbreds show a lower rpc.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗

PubMed1994

Effects of correction for heterogeneity of variance on bias and accuracy of breeding value estimation for Dutch dairy cattle.

Data on 305-d milk yield from the Dutch dairy evaluation were used to obtain breeding value estimates from an animal model for 1984 to 1992. Changes in sire evaluations were investigated for bias and realized accuracy. Evaluations based on progeny records were generally lower than their expectation based on parent average. The average decrease was 157 kg for Black and White bulls and 73 kg for Red and White bulls. Evaluations based on test daughters changed about -50 kg when second and third lactations became available, but estimates for breeding value changed insignificantly when records on daughters from the breeding period of the bull were used. The standard deviation of changes from evaluations for first to second batch was about 14% larger than expected from population parameters. Breeding values of imported bulls, based on Dutch data, decreased 75 kg when more information became available in subsequent evaluations. Heterogeneity of variance was estimated by a quasi-likelihood approach with a model that accounted for sampling variance on estimates of variances within herd. The coefficient of variation of the variances within herd-year was 31%. A simple method for standardization of variances within herd-year decreased bias of parent averages by about 20%, and fluctuations of breeding values were within the expected range. A correction for heterogeneity of variance within herd may not remove all bias of parent averages, but a general improvement of bias and accuracy of breeding values can be expected.

Analysis of Variance↗

PubMed1993

Genetic and statistical properties of residual feed intake.

Residual feed intake is defined as the difference between actual feed intake and that predicted on the basis of requirements for production and maintenance of body weight. Formulas were developed to obtain genetic parameters of residual feed intake from knowledge of the genetic and phenotypic parameters of the component traits. Genetic parameters of residual feed intake were determined for a range of heritabilities (h2 = .1, .3, or .5) for component traits of feed intake and production, and genetic (rg = .1, .5, or .9) and environmental (re = .1, .5, or .9) correlations between them. Resulting heritability of residual feed intake ranged from .03 to .84 and the genetic correlation between residual feed intake and production ranged from -.90 to .87. Heritability of residual feed intake depends considerably on the environmental correlation between feed intake and production. Residual feed intake based on phenotypic regression of feed intake on production usually contains a genetic component due to production. Residual feed intake based on genotypic regression of feed intake on production is genetically independent of production and its use is equivalent to use of a selection index restricted to hold production constant. Multiple-trait selection on residual feed intake, based on either phenotypic or genetic regressions, and production is equivalent to multiple-trait selection on feed intake and production. Residual energy intake in dairy cattle was examined as an example. Heritability of residual energy intake based on genotypic regression was close to zero and indicated that measurement of feed intake provides little additional genetic information over and above that provided by milk production and body weight. The principles outlined in this study have broader application than just to residual feed intake and apply to any trait that is defined as a linear function of other traits.

Animals↗

PubMed1993

Animal model estimation of additive and dominance variances in egg production traits of poultry.

An animal model analysis was used to estimate simultaneously additive (sigma 2a) and dominance (sigma 2d) variances for egg production traits within three White Leghorn lines. The data consisted of information for three generations on egg number (EN) produced at 18 to 25 (EN1), 26 to 65 (EN2), and 18 to 65 wk of age (EN3); egg weight (EW) measured at 30 to 35 (EW1) and 40 to 45 wk (EW2); and egg specific gravity (ESG) measured at 30 to 35 (ESG1) and 40 to 45 wk (ESG2). A transformation was used for EN2 and EN3 because of a skewed distribution. In total, 813 sires, 2,575 dams, and 28,649 daughters were involved in the analyses. Three genetic models (sire-dam, additive, and dominance) were compared in estimating heritability (h2). The sire-dam model underestimated h2 because it ignored animal relationships. The h2 estimates from the additive model were approximately 9 to 52% higher for EN and 2 to 18% higher for EW and ESG than those from the dominance model. The differences between the h2 estimates from the additive and dominance models were increased for larger dominance variance sigma 2d. Ratios of sigma 2d to total variance were high for EN (10 to 20%) and low for EW and ESG (1 to 13%). Ratios of sigma 2d to total genetic variance for EN1, EN2, EN3, EW1, EW2, ESG1, and ESG2 were 18 to 36, 29 to 43, 29 to 56, 1 to 26, 3 to 8, 20 to 27, and 2 to 14%, respectively.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗

PubMed1993

Economic values of optimum traits: the example of meat quality in pigs.

In this paper a method is outlined to derive marginal-income functions and to calculate economic values for traits with an intermediate optimum such as meat-quality traits. A normal distribution of the quality trait was assumed, but the method can be used for other distributions as well. The parameters necessary to use this method are distribution of the quality trait, population mean and the standard deviation of the quality trait, optimum range, and price differences between products within and outside the optimum range. Especially, the optimum range for the quality trait and the price differences to be used have to be derived from consumer and processing research. Some alternative methods that can be used for selection on quality traits, such as restricted selection index, desired-gains index, and indices based on a quadratic aggregate genotype, are discussed.

Animals↗

PubMed1992

Variance decomposition in the estimation of genetic variance with selected data.

Estimation of genetic variance in populations under selection involves assumptions on base animals. Base animals are often considered unselected and it also has been proposed to treat selected base animals as fixed. The consequences of assumptions on base animals in the estimation of genetic variance in selected populations are not fully understood. Variance decompositions are introduced for simple designs to quantify the differences between models that treat base animals in different ways. Independent contrasts were constructed and REML estimates of variance components were compared for different designs and selection rules. The method shows how selection is accounted for in a complete model and why estimation of variance components can become biased when base animals are treated as fixed.

Animals↗

PubMed1992

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

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

Animals↗

PubMed1990

Estimation of additive genetic variance when base populations are selected.

A population of size 40 was simulated 1,000 times for 10 generations. Five out of twenty males were selected each generation, and each male was mated to four females to have two progeny. The additive genetic variance (sigma 2a) before selection was 10, and the initial heritability was .5. Due to covariances among animals, inbreeding and gametic disequilibrium, the genetic variance was reduced to 6.72 after 10 generations of selection. Reduction of variance was lower in another population simulated with size 400 and 10% of the males selected. Restricted Maximum Likelihood was used to estimate sigma 2a using an animal model. The estimate of sigma 2a was empirically unbiased when all data and all relationships were used. Omitting data from selected ancestors caused biased estimates of sigma 2a due to not accounting for all gametic disequilibrium. Including additional relationships between assumed base animals adjusted for inbreeding and for covariances. Bias from gametic disequilibrium decreased slightly with the use of more relationship information, and it was smaller in the small population and(or) when selection had been practiced for just a few generations.

Animals↗