| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
ANIMAL GENETICS |
Department of Farm Animal Health, Veterinary Faculty, Utrecht University, Utrecht, The Netherlands
Abstract
Long-term selection on production results in increased environmental sensitivity. This often is expressed through decreased fertility and increased health problems. The phenomenon has been described in all common farm animal species. One theory is that potential resource intake is insufficient to express production potential. Additional resources are drawn away from fitness-related traits, such as fertility and health, to further increase observed production. In addition, resources for maintaining fitness depend on the demands by the environment. In a harsh environment, more resources are required for fitness-related traits than in an optimal environment. Literature results show that selection in an optimal environment will increase sensitivity to less optimal environments. The objectives of this paper were to increase understanding of the underlying mechanism behind the development of environmental sensitivity and to gain insight into correlated response(s) when selection is on observed production. A resource allocation model was defined where observed production depended on production potential, resource intake potential, and the allocation of resources to production or fitness, including maintenance, health, and reproduction. Penalties for reproductive performance and probability of survival were included when the proportion of resources assigned to fitness dropped below a certain, environment-related, threshold. Mass selection was practiced on observed production during 40 generations using stochastic simulation. Depending on the heritabilities of the underlying components and on the environment, selection on observed production resulted in a decrease in reproductive rate and in the development of environmental sensitivity when resource intake becomes limiting. Correlations of observed production with underlying components changed across generations, following a nonlinear pattern. The proposed model is simple, but increases the understanding of underlying mechanisms and consequences of selection for production when resources are limiting.
Key Words: Environmental Sensitivity • Metabolic Stress • Resource Allocation • Selection • Stochastic Simulation
Introduction
In animal production, negative correlations are observed between production and fitness-related traits, such as fertility and health (Rauw et al., 1998
). In lactating animals, poor BCS during the period of negative energy balance results in decreased fertility (e.g., Pryce et al., 2000). In poultry, long-term selection for increased growth resulted in decreased fertility (Marks, 1996; Nestor et al., 1996). It seems that energy allocated to production cannot be applied to other body functions, resulting in increased health and fertility problems (Collard et al., 2000).
Apart from the balance between production and health and fertility, the selection environment influences animal performance. For example, the effect of the negative energy balance has been partly compensated for by improving the environment. However, despite these actions, negatively correlated responses to increased production are becoming stronger: environmental sensitivity increases and is especially expressed in decreased fertility. Animals tend to adapt to the environment they are selected in, which may result in the development of a genotype x environment (G x E) interaction. Results presented in the literature suggest that the size of difference between environments especially determines whether a G x E interaction will develop (e.g., Emanuelson et al., 1999; Cameron et al., 2000, 2003).
It is clear that selection for production may lead to problems in health and fertility, and that under some circumstances, G x E may develop, but there is still little understanding of the mechanism behind these matters. This paper presents a model to describe the long-term consequences of artificial selection on observed production, based on the allocation of recourses. The aim of this article is to increase understanding of the mechanism behind the development of environmental sensitivity and to gain insight into the correlated responses to selection for observed production.
Materials and Methods
Model
A model was developed after De Jong and Van Noordwijk. (1992) describing the allocation of resources (to a production trait and fitness of an animal (Van der Waaij et al., 2002). Note that all traits are expressed in energy units. In this study, "resources" was defined as "potential feed intake." It was assumed that sufficient resources were available at all times. "Production" is defined as the potential production level (Pp) of an animal that can be realized under optimal metabolic circumstances (i.e., when resource intake enables full expression of production and creates no limitations to fitness). "Fitness" was defined as a combination of maintenance, health, and reproduction. A proportion of resources was assigned to fitness, rather than an absolute amount, for it was assumed that increased production would lead to proportionally increased resource demand for fitness. Resource (R) allocation is controlled by a factor "c," so that resources for fitness (Rf) and for production (Rp) are as follows:
 |
so that R = Rf + Rp. It was assumed that c, Pp, and R were uncorrelated. The observed production (Po) was defined as the production level that is expressed. When Pp 
Rp, or when the demand for resources for expression of Pp is smaller than the amount of resources available for production, Po will be equal to Pp, and resource intake is assumed to be expressed accordingly, so not to its full potential. In reality, the extra resource intake potential most likely would be used for fitness, if required, or for building up body reserves, although this is not taken into account here. When Pp > Rp, or the demand is larger than the genetic potential for resource intake, Po will be equal to Rp, and the animal is in metabolic stress. It was assumed that, in contrast to natural selection, production gets first, but not unlimited, priority. When selection is on Po and resource intake becomes limited, selection pressure, consequently, is shifted toward resource intake and allocation of proportionally more resources for production and away from fitness.
An insufficient proportion of resources allocated to fitness may result in decreased health, fertility, and energy available for maintenance, with consequences for reproduction rate and probability of survival. It was assumed that in a good environment, a smaller proportion of resources is required for expression of full fitness potential (fp) than in a poor environment (e.g., temperature difference, disease pressure). Fitness potential was defined as the probability of survival and reproduction. It was assumed that there are two thresholds for (1 - c), with defined values depending on the environment. The lower (L) threshold represents the value for (1 - c) below which the animal will not survive (fp = 0, and consequently Po = 0). The upper (U) threshold represents the value for (1 - c) above which animals survive and show full reproduction potential (fp = 1). In between both thresholds, the probability of survival and reproduction was reduced linearly going from U to L:
 |
For each animal in a defined environment (male and female), fp was determined based on its value for (1 - c). It was assumed that reduced reproduction rate only occurred in females, but reduced survival probability occurred in both sexes. Consequences for survival and reproduction were determined for each animal individually. For survival, if the animal had a value for (1 - c) that was between both thresholds, a random number was drawn from a uniform distribution (0, 1) for each animal that had a value for (1 - c) that was between both thresholds. The animal survived when the random number was smaller than the animal’s value for fp. Animals with values for (1 - c) above U survived, and those below L died. For reproduction, each dam had a potential maximal number of offspring. For dams with value for (1 - c) above U, all offspring were born. For dams with a value for (1 - c) that was between thresholds, a random number was drawn from a uniform distribution (0, 1) for each potential offspring separately. Each individual offspring was born when the accompanying random number was smaller than fp. As discrete generations were assumed, decreased reproduction rate can be regarded as either decreased litter size (e.g., due to poor quality oocytes) but with only one reproduction cycle per dam, or multiple reproduction cycles of a single potential offspring each, but with decreased success rate. Females with a value for (1 - c) below L had already died and thus were never selected. In this study, the same thresholds were applied to define survival probability and reproduction probability. In reality, these thresholds may be different, depending on the priority given to reproduction.
To summarize, Pp, R, and c are causal, partly heritable parameters; U and L are set by the environment; and fitness (represented by survival and female reproductive performance) and Po rates are resulting phenotypic parameters. This model allows for the development over time of negative relationships between fitness and traits under artificial selection that are counteracted by natural selection. The size of the conflict between fitness and production will depend on the contribution of R and c to the genetic variability in Po; large variance due to c will increase conflicts, whereas large variance due to R will avoid conflicts.
Population and Parameters
An initial population was simulated with 240 males and 240 females. In total, 40 discrete generations were simulated (200 replicates), and in each generation, the aim was to select 24 males and 120 females on their own performance for observed production (mass selection). No adjustment was made to the maximal number of offspring per female or to the maximal number of females selected, even if the desired number of selected animals could not be met, resulting in a fluctuating population size. Each dam had a maximum of four offspring. The average number of offspring per dam was calculated across all dams selected. Each offspring born had a probability of 0.5 to be male, otherwise it was female.
Mean and phenotypic SD were 0.2 and 0.05 for Pp, 1.0 and 0.1 for R, and 0.5 and 0.05 for c, respectively. The heritabilities for Pp and R were set to 0.3, and for c, heritability was 0.1, 0.3, or 0.5. Changing the genetic variance varied heritabilities; the phenotypic variance was assumed to remain constant. The initial means and variances were chosen such that a proportion of animals was not under metabolic stress during the first generations of selection. Two types of environments were assumed, good or poor, and were indicated by the values of the thresholds. The L threshold was either 0.4, representing a good environment, or 0.5, representing a poor environment. The distance between the L and U thresholds varied by 1 or 3 phenotypic SD (depending on the type of environmental stress), resulting in U = 0.45 (1 SD) or 0.55 (3 SD), representing a good environment, and U = 0.55 (1 SD) or 0.65 (3 SD), representing a poor environment. Animals were fed ad libitum with feed of constant quality in all cases, and it was assumed that energy intake was not negatively affected due to loss of appetite. In other words, each animal was able to express full resource intake potential.
Results and Discussion
Reproduction Rate
Population size and average number of progeny per selected dam were approximately constant across generations in the good environment and very similar for both heritabilities for c. The average number of offspring of selected dams was 3.9 across generations and population size remained larger than 450 animals at all times for 
. For 
, the average number of offspring decreased from 3.9 during the first 24 generations to 3.7 in generation 40 and population size decreased from around 470 to around 450 in generation 40.
In the poor environment for , the initial average number of offspring per selected dam was lower (3.2) and population size was low (177). Consequently, the number of selection candidates was much smaller than intended (only 71 in the first generation), and the number of dams available only became equal to the target of 120 in generation 17. For , initial average number of offspring was 3.3, increasing to 3.9 in generation 11 and decreasing again to 3.7 in generation 40. The number of dams available was insufficient during the first three generations and population size increased from 199 in the initial generation to 450 in generation four, after which there was little variation. Each dam got four chances to produce offspring, often resulting in some dams not having any offspring, whereas many had four. Therefore, a strong natural selection (i.e., correlated response) for fertility rate occurred, in both the poor and good environments. Overall, there seems to be a constant, suboptimal reproduction rate of 3.7 that develops in both environments, given the current selection strategy. This can be explained by the long-term genetic contributions theory (Wray and Thompson, 1990). Animals with a high genetic potential for reproduction rate that are selected for breeding (based on observed production) have a higher potential of passing on their genes to the next generation. Also, those offspring are more likely to reproduce than are animals whose dams had a lower reproduction rate. Finally, a larger number of offspring will result in a larger chance that one of them will be selected to produce the next generation. These factors together are probably the underlying reason for the relatively high average number of offspring in later generations. In practical breeding, the best producing dams that are less reproductive often are offered more chances to reproduce, resulting in a decreased, or even negative, natural selection pressure on reproduction.
Selection Response
Figure 1 shows the results of 40 generations of selection on observed production, where the heritability for c is either 0.1 or 0.5 (results for 
were between these results and are therefore not shown) and the heritability for R is 0.3. Two environments are compared and the distance between thresholds is one SD. The starting point was an unselected population. Observed production in the figure is determined as the average production for all animals in the generation, including those with production equal to zero. Therefore, in the poor environment, Po initially is much lower than Pp due to insufficient values for (1 - c). Selection for increased observed production thus results in selection pressure on (1 - c). Selection pressure on (1 - c) remains until this is no longer a limiting factor. This will occur when (1 - c) has values around the upper threshold. Due to chance, there will always be selected parents with values for (1 - c) below the U threshold that still manage to have high Po and be reproductive. Also, parents with values for (1 - c) above the U threshold may have offspring with values below. Consequently, the population mean for (1 - c) in the final generations in Figure 1 remains below the U threshold.
|
Phenotypic Correlations
Figure 2 shows the phenotypic correlations between Po and Pp, resource intake potential, and the resource allocation factor. For the correlation with survival probability, values for Po were defined as if all animals would have been able to produce, regardless of survival. All results come from the same 200 replicates as in Figure 1
. Because Pp, resource intake potential, and resource allocation factor were assumed to be genetically uncorrelated and the environmental variance was assumed to be constant, the phenotypic correlations between these traits were zero by definition (and in the simulation results). In the good environment, the correlation between observed and potential production is initially high, but decreases across generations to approximately 0.10. When resource intake becomes limiting (i.e., under selection pressure), the correlation between resource intake potential and Po increases to approximately 0.10. Simultaneously, the correlation with c increases. In the poor environment, Po is the factor most strongly correlated to resource allocation (0.6 to 0.75 for , and 0.42 to 0.75 for ). Selection immediately increases pressure on resources for fitness, therefore metabolic stress, and thus the development of environmental sensitivity, occurs at an earlier generation in the good environment.
|
and Knap and Bishop (2000) argue that when resources become limiting, a negative correlation between production traits and fitness-related traits will result. Results in this study indicate that environmental sensitivity, indicated by the negative correlation between Po and survival probability, develops as soon as there is metabolic stress. This finding is in agreement with Kolmodin et al. (2002), who found a similar trend in environmental sensitivity in Scandinavian dairy cattle. Apparently, the animals with the highest Po (trait under selection) tend to be the animals with poorer values for c, and thus are those with increased environmental sensitivity.
Switched Environments
Long-term selection for production in one environment will lead to increased production in that particular environment. However, it does not necessarily mean that those improved animals would perform as well in a second environment with different production circumstances. The main reason is a difference in natural selection pressure on traits underlying observed production in both environments (Van der Waaij et al., 2000). Thorpe and Luiting (2000) argue that selection in a poor environment will result in more robust animals. Rauw et al. (1998) have summarized situations in practical pig, poultry, and dairy cattle breeding where this may be the case. Kolmodin et al. (2003) have shown that selection for high phenotypic values does result in increased environmental sensitivity in dairy cattle, especially when the environment is improved during the generations of selection. To validate whether the model presented here would also predict this, animals were selected in one environment for 10 or 40 generations, transferred to the opposite environment, and subsequently selected to produce generation 11 or 41.
Results in Table 1 indicate that the difference in the quality of the environments and the number of generations of selection in those environments are important factors determining the presence and size of environmental sensitivity and, consequently, the development of a G x E interaction. The heritability for c, representing the potential selection response in c, has a larger influence in the short term (10 generations) than in the long term (40 generations). Resource intake potential and resource allocation factor do not change going from one environment to the other. However, Po, survival probability, and reproduction probability do, mainly because of a change in the importance of (1 - c).
|
, going from the poor to the good environment after 10 generations of selection (0.21 to 0.79). In all cases, the correlation was lowest in the poor environment and never equal to 1 in the good environment, indicating that observed production is dependent not only on Pp, especially under harsher conditions. The correlation with resource intake generally was low, and only moderately high when transferred from a poor to a good environment after 40 generations of selection. The correlation with the resource allocation factor changes considerably going from one environment to the other, the most extreme change occurring when going from a poor to a good environment after 40 generations of selection and ; this results in a correlation change from 0.59 to -0.49. Going from a poor to a good environment, the resource intake factor, determining how much energy is assigned to fitness-related traits, can be decreased without negative consequences for reproduction probability, and thus for Po.
Transferring from a good to a poor environment causes a decrease in the correlation between Po and survival probability, especially after 40 generations of selection. Kolmodin et al. (2002) argue that environmental sensitivity develops following phenotypic selection in the presence of G x E, but the present results show that environmental sensitivity actually is the reason for the presence of G x E. Given the results in Figure 2 and Table 1, it can thus be concluded that the size of G x E depends on the number of generations of selection in separate environments. In relation to this, it is interesting to see that the (size of) change in phenotypic correlations between Po and c, resource intake, Pp, and survival probability has an important influence due to the environment, rather than to the underlying genetic parameters of c, resource intake potential, and Pp.
Long-term selection in the same environment, as, for example, in the case of indigenous breeds, causes some changes in the relationships as described above. Selection in the good environment usually results in a higher Po than when selection is in the poor environment and animals are transferred to a good environment after a number of generations. However, when and after 40 generations of selection in the poor environment, Po was higher after transfer to the good environment than after 40 generations of selection in the good environment. It should be noted that resource intake also is higher, with economic consequences. Also, in the present study, it was assumed that resources were never limited and were of equal quality in both environments. This may be valid when differences between environments are due to, for example, climate conditions in the housing system or to a difference in infection pressure. However, when the quality and/or quantity of resources in the poor environment is lower than that in the good environment, a maximum is set to resource intake. Most likely, there will be a point in time where Po no longer increases as a consequence of limiting resources, and reproduction rate may move to a lower equilibrium than in case of ad libitum feeding.
Varying Type of Environmental Stress
Differences in type of environmental stress were mimicked by varying the distance between thresholds (move the U threshold). The larger the distance, the more gradual the influence of a decrease in (1 - c) will be. For , a distance between thresholds of 2 or 3 SD, compared with the 1 SD as discussed above, had drastic consequences. Especially in the case of 3 SD, the reproductive rate in the poor environment decreased too much and the average number of offspring per selected dam did not exceed two in any generation with a survival rate of approximately 17% across generations. The survival rate decreased a bit in the good environment as well when the distance between thresholds was increased from 1 to 3 SD, but the average number of offspring was 3.3. After 40 generations, the average Po was 0.43 and the survival and reproduction probability 0.80. The average value for (1 - c) was 0.53, and for feed intake potential, it was 1.24, instead of 0.49 and 1.33, respectively.
For , the situation was slightly different. In the poor environment, the average number of offspring again was below two at first, but increased across generations, and from generation 19 onward, the population size started to rapidly increase. By then, (1 - c) had increased from 0.51 to 0.64. In generation 40, average Po was 0.32, (1 - c) was 0.62, resource intake capacity was 1.24, and survival and reproduction probability was 73%. In the good environment, the average number of offspring again was below two at first, but quickly increased to above two in generation 3. In generation 40, average Po reached 0.44, (1 - c) was 0.52, resource intake potential was 1.30, and survival and reproduction probability was 77%.
General Discussion
In this article, a model is proposed to describe the consequences of selection on Po for allocation of resources to production and fitness-related traits. By assuming simple additive relationships between the underlying components, it is shown that nonlinear relationships develop among resulting components. Depending on the heritabilities of the underlying components and on the environment, it is shown that selection on Po eventually results in a decrease in survival and reproductive rate and in the development of environmental sensitivity. Even though the proposed model is very simple, it helps increase the understanding of underlying mechanisms and consequences of selection for production when resources are limiting.
The consequences of long-term selection for a production trait have been described in the literature. In poultry (Japanese quail and turkey), long-term selection for increased premature BW resulted in increased adult BW, feed intake, and feed efficiency (Marks, 1996), egg weight, and eating bouts (Nestor et al., 1996), but also in decreased hatchability and egg production (Marks, 1996), semen production, ability to walk, and resistance to infection (Nestor et al., 1996). Selection for higher milk yields resulted in increased feed intake and decreased energy balance in cattle, sheep, and pigs (reviewed by Veerkamp, 2002). In dairy cattle, where selection has been predominantly on milk production for the past decades, fertility has become a limiting factor (Pryce et al., 2000; Royal et al., 2002). The genetic correlations between various fertility-related traits and BCS, milk, fat and protein in Holstein cattle are moderately to strongly negative (e.g., Pryce et al., 2001, 2002; Royal et al., 2002), and increase unfavorably under heat stress (López-Gatius, 2003).
Health is also influenced by a shift in the allocation of resources, as can be illustrated by ascites in broilers. The correlation between BW and ascites-related traits under cold conditions are moderate to high (Pakdel et al., 2002). These correlations are different for animals under cold vs. normal climate conditions (De Greef et al., 2001). Also, the resistance to infection with Pasteurella multocida and Newcastle virus decreased following generations of selection for increased premature BW in turkeys (Nestor et al., 1996). On the other hand, the metabolic balance may also shift toward increased fitness as a result of selection, as can be illustrated by chickens that have been under long-term selection for increased immune response. These animals are substantially smaller than control animals (Parmentier et al., 1996).
Animals tend to adapt to their environment. For example, selection in dairy cattle in more temperate regions will decrease heat tolerance in the warmer regions (Ravagnolo and Misztal, 2002), fast-growing broilers show less heat tolerance than slower growing ones (Yalçin et al., 2001), and the indigenous Red Maasai sheep is much more productive than the Dorper in the humid coastal environment of east Africa, whereas the hybrid Dorper is only a little more productive in the semi-arid environment (Baker, et al., 2002). Results like these show that G x E interactions develop, especially when the difference between environments is substantial.
The direction of selection response obtained from the simulation seems in agreement with selection results as presented in the literature; however, we were not able to find any results in the literature on the changing correlations between Po and underlying traits across generations. Validation of the model, therefore, is difficult at present, though could be done by setting up a selection experiment, for example, in mice, which are less expensive. Several papers have shown that selection for increased preweaning growth (i.e., milk production of the mother) will have increased BW and feed intake as a correlated response (e.g., Bünger et al., 1998). However, in these studies, offspring were suckled by their own mother, resulting in entanglement of genetic effects of milk production, growth, and litter size The pups would need to be cross fostered in order to eliminate this effect. Selection should be on preweaning growth of the cross-fostered pups, as a representation of milk production. Feed intake should be measured, as well as fertility (time to pregnancy), maternal BW (as an additional indication of negative energy balance), and cumulative pup weight at a number of times (milk production). Genetic parameters could then be estimated and the simulation results could be validated.
In the study, it was assumed that c, Pp, and R were genetically uncorrelated. This assumption may or may not be realistic, depending on the situation. The reason for assuming zero correlation is that we do not know what the real correlations would be. Also, the correlations may be different across situations or traits considered (e.g., production traits such as growth, milk production, wool production). By assuming zero correlation between the underlying traits (i.e., c, Pp, and R), results give more insight into the size of the correlations that develop over time between those traits and the resulting traits (i.e., Po, reproduction probability). In a very poor environment, reproduction rate was not always sufficient to cover replacement rates and, as a consequence, population size decreased. Because of natural selection, in later generations, population size often recovered. As a consequence, the selected proportion was not always comparable across environments or the various genetic parameters, resulting in differing selection pressure overall, regardless of the underlying components. A decreasing population size often is not acceptable in practical animal breeding, and management would be adjusted to limit loss in population size. The purpose of this study was to gain understanding of underlying mechanisms and, therefore, results are presented when management is not adjusted, regardless of the results.
Selection response in feed intake in the present article occurs as a correlated response to selection for increased observed production. The fact that it is a correlated response with a time lag may explain why the response often is not sufficient to fully express the trait under selection. In cattle breeding, an increased feed intake is not considered a negative consequence, and often is stimulated. In pig and poultry breeding, however, an increased feed intake is not always considered to be advantageous. Therefore, if one fails to allow for increased feed intake, but does select for increased production, energy for fitness will be diminished. The consequences of this selection practice would be increased selection pressure on resource allocation toward the trait with highest selection weight (i.e., production), resulting in an increase in environmental sensitivity. Putting no limits on feed intake does not automatically solve the problem. Because of the time lag, not limiting feed intake is not enough, as presented in this research. By also putting sufficient selection pressure directly on feed intake, selection pressure on c may be relieved. However, it may be difficult to balance selection pressure because resource allocation of individual animals cannot be measured and too large a feed intake would result in animals that grow fat. Selection for an optimal allocation of resources, given a certain environment, would be desirable, even though it is not clear yet how that should be accomplished.
Production is a trait that is determined by many underlying components. Selection for production puts the highest selection pressure on the most limiting component. Improvement in that component may reveal a second component as most limiting, which may have, for example, a different heritability. The consequence of this shift in importance of underlying components is that correlations between Po and these components change as well. In this study, we have only discriminated between Pp, resource allocation factor, and feed intake, but in reality there are many more underlying components—all of them, together with the environment, determining the final observed production. One way of determining the size of the effect of underlying components on the trait of interest (i.e., observed production in this case) in practice would be to estimate the correlations between Po and underlying components across generations, provided these underlying components can be measured. A change in the correlations indicates a change in the importance of one of the underlying components.
Implications
Even though the present model simplifies reality, it gives insight into the consequences of selection for production in a good or poor environment. Selection always puts the greatest selection pressure on the most limiting underlying trait, resulting in a changing correlation between underlying traits across generations. Long-term phenotypic selection on observed production increases environmental sensitivity, which causes problems, especially when the animals are subsequently transferred to a poorer environment. The direction of correlated selection response agrees with literature results on reaction-norm models, but it remains important to validate the model with historical data, both to quantify the elements of the model (e.g., the heritability of the resource allocation factor) and to assess the predictive ability of the model.
1 Correspondence: P.O. Box 80151, 3508TD Utrecht (phone: +31-30-2531233; fax: +31-30-2521887; e-mail: e.h.vanderwaaij{at}vet.uu.nl).
Received for publication September 16, 2003. Accepted for publication December 10, 2003.
Literature Cited
Baker, R. L., J. M. Mugambi, J. O. Audho, A. B. Carles, and W. Thorpe. 2002. Comparison of Red Maasai and Dorper sheep for resistance to gastro-intestinal nematode parasites, productivity and efficiency in a humid and semi-arid environment in Kenya. Proc. 7th World Congr. Genet. Appl. Livest. Prod., Montpellier, France, Communication No. 13–10.
Beilharz, R. G., B. G. Luxford, and J. L. Wilkinson. 1993. Quantitative genetics and evolution: Is our understanding of genetics sufficient to explain evolution? J. Anim. Br. Gen. 110:161–170.
Bünger L., U. Renne, G. Dietl, and S. Kuhla. 1998. Long-term selection for protein amount over 70 generations in mice. Genet. Res. 72:93–109.[Medline]
Butler, W. R. 2003. Energy balance relationships with follicular development, ovulation and fertility in postpartum dairy cows. Livest. Prod. Sci. 83:211–218.
Cameron, N. D., M. Enser, G. R. Nute, F. M. Whittington, J. C. Penman, A. C. Fisken, A. M. Perry, and J. D. Wood. 2000. Genotype with nutrition interaction on fatty acid composition of intramuscular fat and the relationship with flavor of pig meat. Meat Sci. 55:187–195.
Cameron, N. D., G. B. Garth, J. C. Penman, and A. Fiskin. 2003. Sensitivity to dietary lysine: energy content in pigs divergently selected for components of efficient lean growth rate. Anim. Sci. 76:175–190.
Collard, B. L., P. J. Boettcher, J. C. M. Dekkers, D. Petitclerc, and L. R. Schaeffer. 2000. Relationships between energy balance and health traits of dairy cattle in early lactation. J. Dairy Sci. 83:2683–2690.[Abstract]
De Greef, K. H., L. L. G. Janss, A. L. J. Vereijken, R. Pit, and C. L. M. Gerritsen. 2001. Disease induced variability of genetic correlations: ascites in broilers as a case study. J. Anim. Sci. 79:1723–1733.
De Jong, G., and A. J. Van Noordwijk. 1992. Acquisition and allocation of resources: Genetic (co)variances, selection, and life histories. Am. Nat. 139:749–770.
Emanuelson, U., G. Banos, and J. Philipsson. 1999. Interbull Centre Report. Pages 1–6 in Interbull Bulletin 22. Proc. of the 1999 Interbull Mtg., Zürich, Switzerland.
Knap, P. W., and S. C. Bishop. 2000. Relations between genetic change and infectious disease in domestic livestock. Pages 65–80 in Occas. Pub. Br. Soc. Anim. Sci. No. 27. BSAS, Edinburgh, Scotland.
Kolmodin, R., E. Strandberg, H. Jorjani, and B. Danell. 2003. Selection in presence of genotype by environment interaction: Response in environmental sensitivity. Anim. Sci. 76:375–386.
Kolmodin, R., E. Strandberg, E. Madsen, J. Jensen, and H. Jorjani. 2002. Genotype by environment interaction in Nordic dairy cattle studied using reaction norms. Acta Agric. Scand. 52:11–24.
López-Gatius, F. 2003. Is fertility declining in dairy cattle? A retrospective study in north-eastern Spain. Theriogenology 60:89–99.[Medline]
Luiting, P., and E. M. Urff. 1991. Optimization of a model to estimate residual feed consumption in the laying hen. Livest. Prod. Sci., 27:321–338.
Marks, H. L. 1996. Long-term selection for body weight in Japanese quail under different environments. Poult. Sci. 75:1198–1203.[Medline]
Nestor, K. E., D. O. Noble, N. J. Zhu, and Y. Moritsu. 1996. Direct and correlated responses to long-term selection for increased body weight and egg production in turkeys. Poult. Sci. 75:1180–1191.[Medline]
Pakdel, A., J. A. M. Van Arendonk, A. L. J. Vereijken, and H. Bovenhuis 2002. Genetic and phenotypic correlations for ascites related traits in broilers. Communication No. 13–16. Proc. 7th World Congr. Genet. Appl. Livest. Prod., Montpellier, France.
Parmentier, H. K., M. G. B. Nieuwland, E. Rijke, G. De Vries Reilingh, and J. W. Schrama. 1996. Divergent antibody responses to vaccines and divergent body weight of chicken lines selected for high and low humoral responsiveness to sheep red blood cells. Avian Dis. 40:634–644.[Medline]
Pryce, J. E., M. P. Coffey, and S. Brotherstone. 2000. The genetic relationship between calving interval, body condition score and linear type and management traits in registered Holsteins. J. Dairy Sci. 83:2664–2671.[Abstract]
Pryce, J. E., M. P. Coffey, S. H. Brotherstone, and J. A. Wooliams. 2002. Genetic relationships between calving interval and body condition score conditional on milk yield. J. Dairy Sci. 85:1590–1595.[Abstract]
Pryce, J. E., M. P. Coffey, and G. Simm. 2001. The relationship between body condition score and reproductive performance. J. Dairy Sci. 84:1508–1515.[Abstract]
Rauw, W. M., E. Kanis, E. N. Noordhuizen-Stassen, and F. J. Grommers. 1998. Undesirable side effects of selection for high production efficiency in farm animals: A review. Livest. Prod. Sci. 56:15–33.
Ravagnolo, O., and I. Misztal. 2002. Genetic component of heat stress in dairy cattle, parameter estimation. J. Dairy Sci., 83:2126–2130.
Royal, M. D., J. E. Pryce, J. A. Woolliams, and A. P. F. Flint. 2002. The genetic relationship between commencement of luteal activity and calving interval, body condition score, production, and linear type traits in Holstein-Friesian dairy cattle. J. Dairy Sci. 85:3071–3080.
Thorpe, B. H., and E. Luiting. 2000. Breeding for resistance to production diseases in poultry. Pages 357–377 in Breeding for Disease Resistance in Farm Animals. 2nd ed. R. F. E. Axford, S. C. Bishop, F. W. Nicholas, and J. B. Owen, ed. CAB Int., Oxon, U.K.
Van der Waaij, E. H., P. Bijma, S. C. Bishop, and J. A. M. Van Arendonk. 2000. Modeling selection for production traits under selection under constant infection pressure. J. Anim. Sci. 78:2809–2820.
Van der Waaij, E. H., L. L. G. Janss, and P. Bijma. 2002. A resource allocation model describing consequences of artificial selection under metabolic stress. Communication No. 23–11. Proc. 7th World Congr. Genet. Appl. Livest. Prod., Montpellier, France.
Veerkamp, R. F. 2002. Feed intake and energy balance in lactating animals. Proc. 7th World Congr. Genet. Appl. Livest. Prod., Montpellier, France, Communication No. 10–01.
Wray, N. R., and R. Thompson. 1990. Prediction of rates of inbreeding in selected populations. Genet. Res. Camb. 55:41–54.[Medline]
Yalçin, S., S. Özkan, T. Türknut, and P. B. Siegel. 2001. Responses to heat stress in commercial and local broiler stocks. Br. Poult. Sci. 42:149–152.[Medline]
This article has been cited by other articles:
|
|
 |
|
  S. Konig, X. L. Wu, D. Gianola, B. Heringstad, and H. Simianer Exploration of Relationships Between Claw Disorders and Milk Yield in Holstein Cows via Recursive Linear and Threshold Models J Dairy Sci, January 1, 2008; 91(1): 395 - 406. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Konig, F. Bosselmann, U. U. von Borstel, and H. Simianer Genetic Analysis of Traits Affecting the Success of Embryo Transfer in Dairy Cattle J Dairy Sci, August 1, 2007; 90(8): 3945 - 3954. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 X. Ye, S. Avendano, J. C. M. Dekkers, and S. J. Lamont Association of twelve immune-related genes with performance of three broiler lines in two different hygiene environments. Poult. Sci., September 1, 2006; 85(9): 1555 - 1569. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Arango, I. Misztal, S. Tsuruta, M. Culbertson, and W. Herring Study of codes of disposal at different parities of Large White sows using a linear censored model J Anim Sci, September 1, 2005; 83(9): 2052 - 2057. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
