Biomedical subjects

P Bijma

Publications and source records attributed to P Bijma.

At least 19 recordsLinked to original sources

PubMed2007

Identification of environmental factors affecting the prevalence of insect bite hypersensitivity in Shetland ponies and Friesian horses in The Netherlands.

REASONS FOR PERFORMING STUDY: It is expected that climate and habitat factors influence the prevalence of culicoides and, therefore, the prevalence of insect bite hypersensitivity (IBH), but very little is described in the literature to prove the association of these factors. Prevalence varies widely from 3% in certain areas of Great Britain to 60% in certain parts of Australia. OBJECTIVES: To describe the influence of environmental factors on the prevalence of IBH in Shetland ponies and Friesian horses in The Netherlands. METHODS: Data on 3284 Shetland and 2824 Friesian mares (n = 6108) were collected in The Netherlands, based on 90 regions, according to postal codes. The climate components, amount of rainfall, number of warm days, number of cold days, and habitat components of soil type and type of vegetation were collected for each region. RESULTS: Prevalence of IBH varied widely from 0-71.4% per region. The results showed that the environment with low IBH-prevalence had high rainfall, many cold days and few warm days per year. Habitats with a low IBH-prevalence were based along the coast line. Habitats with increasing prevalence of IBH had soils of clay with heather and woody vegetation. Friesian mares had a higher IBH prevalence than Shetland mares, which could indicate an effect of genetic background or an effect of year. CONCLUSIONS: There is an environmental effect on IBH prevalence within The Netherlands, which is caused by climate and habitat factors. POTENTIAL RELEVANCE: The results provide a more accurate description of environmental factors and their impact on development of IBH; and should help better to understand habitat and climate effects, and to distinguish these from other effects, such as animal factors (genetics, age or sex).

Animals↗

PubMed2006

Estimating maternal genetic effects in livestock.

This study investigates the estimation of direct and maternal genetic (co)variances, accounting for environmental covariances between direct and maternal effects. Estimated genetic correlations between direct and maternal effects presented in the literature have often been strongly negative, and their validity has been questioned. Explanations of extreme estimates have focused on the existence of environmental covariances between dam and offspring. As a solution, models including a regression on dam-phenotype have been proposed, but have yielded biased estimates. The performance of models that implement the variance structure arising from the classical model of Willham, however, has not been evaluated. This study investigated the covariance structure of the parts of the residual term that arise from Willham's model. Results show that a correlation between the residual of the record of an individual and that of its dam is a direct consequence of combining Willham's model with the usual assumption that phenotypic covariances between different traits are the sum of additive genetic and environmental covariances. Stochastic simulations show that fitting this structure yields unbiased estimates of the genetic (co)variances. When correlated residuals were ignored in the cases investigated, the bias in the estimated genetic correlations was approximately equal to the value of the environmental correlation. In contrast to models including a regression on dam-phenotype, there were no difficulties with interpretation of results, and the approach was consistent with standard quantitative genetic theory. The use of Willham's model while accounting for correlated residuals is conceptually appealing and yields unbiased results, with no need for regression on dam phenotype. Inclusion of the ability to fit the residual variance structure required for maternal effects into existing software packages would be helpful to animal breeders.

Animals↗

PubMed2006

Benefits of cooperation between breeding programs in the presence of genotype by environment interaction.

Dairy cattle breeding programs and dairy farmers are selecting sires and dams across environments. Genotype x environment interaction (G x E) limits the possibilities for cooperation between breeding programs operating in different environments. The objectives of this study were 2-fold: 1) to investigate the effects of heritability, selection intensity, number of progeny per bull, and size of breeding programs on possibilities for cooperation between dairy cattle breeding programs in the short and long term in the presence of G x E, and 2) to quantify the effect of such cooperation on genetic gain. A dairy cattle situation with 2 breeding programs operating in 2 environments was simulated using a deterministic pseudo-BLUP selection index model. Long-term cooperation between the 2 breeding programs was possible in the presence of G x E, when the genetic correlation was higher than 0.80 to 0.90, resulting in up to 15% extra genetic gain. In addition, in the initial generations of selection, the breeding programs could benefit from mutually selecting sires and dams from each other when the genetic correlation was as low as 0.40 to 0.60. With more intense selection, breeding programs were less likely to benefit from cooperation with breeding programs in other environments. Heritability and number of progeny per bull had little effect on possibilities for cooperation, unless the heritabilities and the number of progeny per bull were extremely different in the 2 environments. Small breeding programs benefited more from cooperation than did large breeding programs, and benefits were possible even at lower values (i.e., <0.80) of the genetic correlation. Possibilities for cooperation across environments would affect the optimal design of dairy cattle breeding programs considering genetic gain, inbreeding, and costs.

Animals↗

PubMed2006

Optimization of dairy cattle breeding programs for different environments with genotype by environment interaction.

Dairy cattle breeding organizations tend to sell semen to breeders operating in different environments and genotype x environment interaction may play a role. The objective of this study was to investigate optimization of dairy cattle breeding programs for 2 environments with genotype x environment interaction. Breeding strategies differed in 1) including 1 or 2 environments in the breeding goal, 2) running either 1 or 2 breeding programs, and 3) progeny testing bulls in 1 or 2 environments. Breeding strategies were evaluated on average genetic gain of both environments, which was predicted by using a pseudo-BLUP selection index model. When both environments were equally important and the genetic correlation was higher than 0.61, the highest average genetic gain was achieved with a single breeding program with progeny-testing all bulls in both environments. When the genetic correlation was lower than 0.61, it was optimal to have 2 environment-specific breeding programs progeny-testing an equal number of bulls in their own environment only. Breeding strategies differed by 2 to 12% in average genetic gain, when the genetic correlation ranged between 0.50 and 1.00. Ranking of breeding strategies, based on the highest average genetic gain, was relatively insensitive to heritability, number of progeny per bull, and the relative importance of both environments, but was very sensitive to selection intensity. With more intense selection, running 2 environment-specific breeding programs was optimal for genetic correlations up to 0.70-0.80, but this strategy was less appropriate for situations where 1 of the 2 environments had a relative importance less than 10 to 20%. Results of this study can be used as guidelines to optimize breeding programs when breeding dairy cattle for different parts of the world.

Animals↗

PubMed2005

Selection strategies for body weight and reduced ascites susceptibility in broilers.

Ascites syndrome is a metabolic disorder in broilers. Mortality due to ascites results in significant economic losses and has a negative impact on animal welfare. It has been shown that genetic factors play a considerable role in susceptibility of birds to ascites, which offers perspectives for selection against this syndrome. The aim of the present study was to evaluate the consequences of alternative selection strategies for BW and resistance to ascites syndrome using deterministic simulation. In addition to the consequences of current selection (i.e., selection for increased BW only) alternative selection strategies including information on different ascites-related traits measured under normal or cold conditions and the consequences of having information on the underlying genes (i.e., MAS) were quantified. Five different breeding schemes were compared based on the selection response for BW, ascites susceptibility, and the rate of inbreeding. Traits investigated in the index as indicators for ascites were hematocrit value (HCT) and ratio of right ventricle to the total ventricular weight of the heart (RV:TV). The results indicated that by ignoring ascites susceptibility in the breeding goal, the gain for BW is 130 g and the birds will become more susceptible to ascites. Testing 50% of the birds under cold temperature conditions and including information of ascites related traits (HCT and RV:TV) measured under normal and cold conditions makes it possible to achieve a relatively high gain for BW (111.4 g) while controlling the genetic level for ascites susceptibility (selection response was 0). The results of scenarios including QTL information of ascites susceptibility showed that QTL information could be used very effectively in controlling ascites susceptibility.

Animals↗

PubMed2005

Effects of genotype x environment interaction on genetic gain in breeding programs.

Genotype x environment interaction (G x E) is increasingly important, because breeding programs tend to be more internationally oriented. The aim of this theoretical study was to investigate the effects of G x E on genetic gain in sib-testing and progeny-testing schemes. Loss of genetic gain due to G x E was predicted for different values of heritability, number of progeny per dam, number of progeny per sire, proportion of selected sires, and population size in the selection environment. Two environments were considered: a selection environment (SLE) and a production environment (PDE). The breeding goal was only for performance in PDE. A pseudo-BLUP selection index was used to predict genetic gain. Recording of half-sibs or progeny in PDE limited the loss in genetic gain in PDE due to G x E between SLE and PDE. Progeny-testing schemes had less loss in genetic gain than sib-testing schemes. Higher heritability increased the loss in genetic gain, whereas increasing the number of progeny per sire in PDE decreased the loss in genetic gain. The number of progeny per sire required to minimize loss in genetic gain due to G x E was greater for sib-testing schemes than for progeny-testing schemes. More progeny per dam slightly increased the loss in genetic gain. Genetic gains for sex-limited and carcass traits were less affected by G x E than traits measured on both sexes. Loss in genetic gain was due to decreased accuracy of selection in most situations, but it was due to decreased selection intensity in situations with small population size and a low proportion of selected sires. It was concluded that recording performance of relatives in PDE minimizes loss in genetic gain due to G x E, and that progeny-testing schemes rather than sib-testing schemes are preferable in situations with low to moderate heritability (h(2) <or= 0.3), relatively short generation interval of progeny-tested sires (L(prog)/L(sib) <or= 1.7), and moderate to severe G x E interaction (r(g) <or= 0.8).

Animals↗

PubMed2005

Relationship between profitability and type traits and derivation of economic values for reproduction and survival traits in Chianina beef cows.

The objectives of this study were 1) to propose a profit function for Italian Chianina beef cattle; 2) to derive economic values for some biological variables in beef cows, specifically, production expressed as the number of calves born alive per year (NACY), age at the insemination that resulted in the birth of the first calf (FI), and length of productive life (LPL); and 3) to investigate the relationship between the phenotypic profit function and type traits as early predictors of profitability in the Chianina beef cattle population. The average profit was 196 Euros/(cow.yr) for the length of productive life (LPL) and was obtained as the difference between the average income of 1,375 Euros/(cow.yr) for LPL and costs of 1,178 Euros/(cow.yr) of LPL. The mean LPL was equal to 5.97 yr, so the average total phenotypic profit per cow on a lifetime basis was 1,175 Euros. A normative approach was used to derive the economic weights for the biological variables. The most important trait was the number of calves born alive (+4.03.cow(-1).yr(-1) and +24.06 Euros/cow). An increase of 1 d in LPL was associated with an increase of +0.19 Euros/(cow.yr) and +1.65 Euros/cow on a lifetime basis. Increasing FI by 1 d decreased profit by 0.42 Euros/(cow.yr) and 2.51 Euros/cow. Phenotypic profit per cow had a heritability of 0.29. Heritabilities for eight muscularity traits ranged from 0.16 to 0.23, and for the seven body size traits between 0.21 and 0.30. The conformation trait final score can be used as an early predictor of profitability. The sale price of the animal and differences in the revenue and costs of offspring due to muscularity should be included in a future profit function.

Age Factors↗

PubMed2005

Genetic progress in multistage dairy cattle breeding schemes using genetic markers.

The aim of this paper was to explore general characteristics of multistage breeding schemes and to evaluate multistage dairy cattle breeding schemes that use information on quantitative trait loci (QTL). Evaluation was either for additional genetic response or for reduction in number of progeny-tested bulls while maintaining the same response. The reduction in response in multistage breeding schemes relative to comparable single-stage breeding schemes (i.e., with the same overall selection intensity and the same amount of information in the final stage of selection) depended on the overall selection intensity, the selection intensity in the various stages of the breeding scheme, and the ratio of the accuracies of selection in the various stages of the breeding scheme. When overall selection intensity was constant, reduction in response increased with increasing selection intensity in the first stage. The decrease in response was highest in schemes with lower overall selection intensity. Reduction in response was limited in schemes with low to average emphasis on first-stage selection, especially if the accuracy of selection in the first stage was relatively high compared with the accuracy in the final stage. Closed nucleus breeding schemes in dairy cattle that use information on QTL were evaluated by deterministic simulation. In the base scheme, the selection index consisted of pedigree information and own performance (dams), or pedigree information and performance of 100 daughters (sires). In alternative breeding schemes, information on a QTL was accounted for by simulating an additional index trait. The fraction of the variance explained by the QTL determined the correlation between the additional index trait and the breeding goal trait. Response in progeny test schemes relative to a base breeding scheme without QTL information ranged from +4.5% (QTL explaining 5% of the additive genetic variance) to +21.2% (QTL explaining 50% of the additive genetic variance). A QTL explaining 5% of the additive genetic variance allowed a 35% reduction in the number of progeny tested bulls, while maintaining genetic response at the level of the base scheme. Genetic progress was up to 31.3% higher for schemes with increased embryo production and selection of embryos based on QTL information. The challenge for breeding organizations is to find the optimum breeding program with regard to additional genetic progress and additional (or reduced) cost.

Animals↗

PubMed2004

Phenotypic relationships between longevity, type traits, and production in Chianina beef cattle.

Longevity is an increasingly important trait in beef cattle. Increased longevity decreases costs for the farmer and increases revenue. The objective of this research was to investigate the phenotypic relationship between type traits and longevity in Chianina beef cattle, and the relationship between production and longevity, to analyze the effect of voluntary culling. Data included records on reproductive, productive, and type traits provided by the National Association of Italian Beef Cattle Breeders from 6,395 Chianina cows. The average length of productive life was 1,829 d. The herd-year had a strong effect on the risk of culling. The effects of 22 type traits were analyzed. All the muscularity traits analyzed were highly significant (P < 0.01) and as a group had the largest effect on longevity, followed by dimension, refinement, and leg traits. Cows that calved before 35 mo of age had a lower probability of being culled than cows calving after 35 mo of age. Variation in herd size had a strong effect on risk ratio, with lower risk for intermediate classes. Cows with approximately one calf per year remained in the herd longer than did cows with fewer calves. Straight-legged animals had a 59% greater probability of being replaced than cows with a moderate angle to the hock, whereas sickle-legged animals had only a 3% higher probability of being culled than average cows. Udder conformation had no effect on longevity. In summary, results of this study indicate that herd-year effects and muscularity traits were the most important factors for longevity for Chianina cows among the factors studied.

Age Factors↗

PubMed2004

Genotype x environment interaction for yield and somatic cell score with automatic and conventional milking systems.

The objective of this study was to quantify genotype by environment interaction (G x E) between automatic milking systems (AMS) and conventional milking systems (CMS) for test-day milk, fat, and protein yield and for test-day somatic cell score (SCS) in The Netherlands. The G x E was studied in 2 ways: 1) between AMS farms and CMS farms in the same period and 2) within farms comparing the period before introduction of AMS with the period after introduction of AMS. For both sub-objectives, a separate data set was generated. Test-day records were used to be more flexible with respect to the introduction date of AMS. Multivariate, fixed regression, test-day sire models were used to estimate variance components. Genetic correlations between AMS farms and CMS farms in the same period were 0.93, >0.99, 0.98, and 0.79 for test-day milk yield, fat yield, protein yield, and SCS, respectively. Genetic correlations within farms between the period before and after introduction of AMS were lower for production traits and higher for SCS: 0.89, 0.91, 0.87, and >0.99, respectively, for test-day milk yield, fat yield, protein yield, and SCS. Heterogeneity of variance was observed between AMS and CMS in both data sets. Especially the residual variance increased with automatic milking. As a consequence, the heritability tended to be lower for automatic milking. It was concluded that effects of G x E are small between AMS and CMS. Therefore, AMS farms can select sires accurately based on national rankings.

Analysis of Variance↗

PubMed2002

Using genetic markers for disease resistance to improve production under constant infection pressure.

Animals will show reduced production when exposed to a constant infection pressure unless they are fully resistant, the size of the reduction depending on the degree of resistance and the severity of infection. In this article, the use of QTL for disease resistance for improving productivity under constant infection pressure is investigated using stochastic simulation. A previously published model was used with two thresholds for resistance: a threshold below which production is not possible and a threshold above which production is not affected by the infection. Between thresholds, observed production under constant infection is a multiplicative function of underlying potential production and level of resistance. Some simplifications of reality were adopted in the model, such as no genetic correlation between potential production and resistance, the absence of influence of lack of resistance on reproductive capacity, and the availability of phenotypes in both sexes. Marker-assisted selection was incorporated by assuming a proportion of the genetic variance to be explained by the QTL, which thus is defined as a continuous trait. Phenotypes were available for production, not for resistance. The infection pressure may vary across time. Results were compared to mass selection on production under constant as well as intermittent infection pressure, where the infection pressure varied between but not within years. Selection started in a population with a very poor level of resistance. Incorporation of QTL information is valuable (i.e., the increase in observed production relative to mass selection) when a large proportion of the additive genetic variance is explained by the QTL (50% genetic variance explained) and when the heritability for resistance is low (h2R = 0.1). Under constant infection pressure, incorporating QTL information does not increase selection responses in observed production when the QTL effect explains less than 25% of the genetic variance. Under intermittent selection pressure, the use of QTL information gives a slightly greater increase in observed production in early generations, relative to mass selection on observed production, but still only when the QTL effect is large or the heritability for resistance is low. The additional advantage of incorporating QTL information is that use of (preventive) medical treatment is possible, or animals may be evaluated in uninfected environments.

Animals↗

PubMed2001

Predicting rates of inbreeding for livestock improvement schemes.

This article presents a deterministic method to predict rates of inbreeding (deltaF) for typical livestock improvement schemes. The method is based on a recently developed general theory to predict rates of inbreeding, which uses the concept of long-term genetic contributions. A typical livestock breeding population was modeled, with overlapping generations, BLUP selection, and progeny testing of male selection candidates. Two types of selection were practiced: animals were either selected by truncation on estimated breeding values (EBV) across age classes, or the number of parents selected from each age class was set to a fixed value and truncation selection was practiced within age classes. Bulmer's equilibrium genetic parameters were obtained by iterating on a pseudo-BLUP selection index and deltaF was predicted for the equilibrium situation. Predictions were substantially more accurate than predictions from other available methods, which ignore the effect of selection on deltaF. Predictions were accurate for schemes with up to 20 sires. Predicted deltaF was somewhat too low for schemes with more than 20 sires, which was due to the use of simple linear models to predict genetic contributions. The present method provides a computationally feasible (i.e., deterministic) tool to consider both the rate of inbreeding and the rate of genetic gain when optimizing livestock improvement schemes.

Animals↗

PubMed2000

Predicting rates of inbreeding in populations undergoing selection.

Tractable forms of predicting rates of inbreeding (DeltaF) in selected populations with general indices, nonrandom mating, and overlapping generations were developed, with the principal results assuming a period of equilibrium in the selection process. An existing theorem concerning the relationship between squared long-term genetic contributions and rates of inbreeding was extended to nonrandom mating and to overlapping generations. DeltaF was shown to be approximately (1)/(4)(1 - omega) times the expected sum of squared lifetime contributions, where omega is the deviation from Hardy-Weinberg proportions. This relationship cannot be used for prediction since it is based upon observed quantities. Therefore, the relationship was further developed to express DeltaF in terms of expected long-term contributions that are conditional on a set of selective advantages that relate the selection processes in two consecutive generations and are predictable quantities. With random mating, if selected family sizes are assumed to be independent Poisson variables then the expected long-term contribution could be substituted for the observed, providing (1)/(4) (since omega = 0) was increased to (1)/(2). Established theory was used to provide a correction term to account for deviations from the Poisson assumptions. The equations were successfully applied, using simple linear models, to the problem of predicting DeltaF with sib indices in discrete generations since previously published solutions had proved complex.

Inbreeding↗

PubMed2000

A general procedure for predicting rates of inbreeding in populations undergoing mass selection.

Predictions of rates of inbreeding (DeltaF), based on the concept of long-term genetic contributions assuming the infinitesimal model, are developed for populations with discrete or overlapping generations undergoing mass selection. Phenotypes of individuals are assumed to be recorded prior to reproductive age and to remain constant over time. The prediction method accounts for inheritance of selective advantage both within and between age classes and for changing selection intensities with age. Terms corresponding to previous methods that assume constant selection intensity with age are identified. Predictions are accurate (relative errors < or =8%), except for cases with extreme selection intensities in females in combination with high heritability. With overlapping generations DeltaF reaches a maximum when parents are equally distributed over age classes, which is mainly due to selection of the same individuals in consecutive years. DeltaF/year decreases much more slowly compared to DeltaF/generation as the number of younger individuals increases, whereas the decrease is more similar as the number of older individuals increases. The minimum DeltaF (per year or per generation) is obtained when most parents were in the later age classes, which is mainly due to an increased number of parents per generation. With overlapping generations, the relationship between heritability and DeltaF is dependent on the age structure of the population.

Inbreeding↗

PubMed2000

Prediction of rates of inbreeding in populations selected on best linear unbiased prediction of breeding value.

Predictions for the rate of inbreeding (DeltaF) in populations with discrete generations undergoing selection on best linear unbiased prediction (BLUP) of breeding value were developed. Predictions were based on the concept of long-term genetic contributions using a recently established relationship between expected contributions and rates of inbreeding and a known procedure for predicting expected contributions. Expected contributions of individuals were predicted using a linear model, u(i)(()(x)()) = alpha + betas(i), where s(i) denotes the selective advantage as a deviation from the contemporaries, which was the sum of the breeding values of the individual and the breeding values of its mates. The accuracy of predictions was evaluated for a wide range of population and genetic parameters. Accurate predictions were obtained for populations of 5-20 sires. For 20-80 sires, systematic underprediction of on average 11% was found, which was shown to be related to the goodness of fit of the linear model. Using simulation, it was shown that a quadratic model would give accurate predictions for those schemes. Furthermore, it was shown that, contrary to random selection, DeltaF less than halved when the number of parents was doubled and that in specific cases DeltaF may increase with the number of dams.

Animal Husbandry↗

PubMed2000

Modeling selection for production traits under constant infection pressure.

This article presents a model describing the relationship between level of disease resistance and production under constant infection pressure. The model assumes that given a certain infection pressure, there is a threshold for resistance below which animals will stop producing, and that there is also a threshold for resistance above which animals produce at production potential. In between both thresholds animals will show a decrease in production, the size of decrease depending on the severity of infection and the level of resistance. The dynamic relationship between production and resistance when level of resistance changes, such as due to infection, is modeled both stochastically and deterministically. Selection started in a population with very poor level of resistance introduced in an environment with constant infection pressure. Mass selection on observed production was applied, which resulted in a nonlinear selection response for all three traits considered. When resistance is poor, selection for observed production results in increased level of resistance. With increasing level of resistance, selection response shifts to production potential and eventually selection for observed production is equivalent to selection for production potential. The rate at which resistance is improved depends on its heritability, the difference between both thresholds, and selection intensity. The model also revealed that when a zero correlation between resistance and production potential is assumed, the phenotypic correlation between resistance and observed production level increases for low levels of resistance and subsequently asymptotes to zero, whereas the phenotypic correlation between production potential and observed production asymptotes to 1.0. For most breeding schemes investigated, the deterministic model performed well in relation to the stochastic simulation results. Experimental results reported in literature support the model predictions.

Animal Husbandry↗

PubMed2000

Prediction of rates of inbreeding in populations selected on best linear unbiased prediction of breeding value

Journal Article↗

PubMed1999

Prediction of genetic contributions and generation intervals in populations with overlapping generations under selection.

A method to predict long-term genetic contributions of ancestors to future generations is studied in detail for a population with overlapping generations under mass or sib index selection. An existing method provides insight into the mechanisms determining the flow of genes through selected populations, and takes account of selection by modeling the long-term genetic contribution as a linear regression on breeding value. Total genetic contributions of age classes are modeled using a modified gene flow approach and long-term predictions are obtained assuming equilibrium genetic parameters. Generation interval was defined as the time in which genetic contributions sum to unity, which is equal to the turnover time of genes. Accurate predictions of long-term genetic contributions of individual animals, as well as total contributions of age classes were obtained. Due to selection, offspring of young parents had an above-average breeding value. Long-term genetic contributions of youngest age classes were therefore higher than expected from the age class distribution of parents, and generation interval was shorter than the average age of parents at birth of their offspring. Due to an increased selective advantage of offspring of young parents, generation interval decreased with increasing heritability and selection intensity. The method was compared to conventional gene flow and showed more accurate predictions of long-term genetic contributions.

Animals↗