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Selection of livestock and poultry for disease resistance
C H A P T E R
18 Selection of livestock and poultry for disease resistance O U T L I N E Challenges of selecting for disease resistance
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Breeding for disease resistance
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Direct selection for disease resistance may be applicable as per three methods Selection based on natural infection Selection based on challenge study for individuals to be selected Selection based on challenge study for relatives or clones
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Selection criterion or basis of selection Selection index Response to selection Generation interval
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Predicting response to selection for a single trait
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Response to selection on multiple traits Mathematical modeling A case study
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Indirect selection for disease resistance
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Marker-assisted or genomic selection for disease resistance with molecular biological tools
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Disease resistance Selection for disease traits (threshold traits) Genetic assimilation
Selection objective
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Further reading
Selection for farm animals is very important. Selection of more immune animals at the early stages of their lives will reduce the huge cost of disease management, including treatment, and will minimize the losses incurred due to high mortality and morbidity. Disease resistance is a complex phenomenon. There are two modes of selectionddirect and indirect. Indirect selection is more applicable to disease-resistant traits. Say for the selection of animals resistant to parasitic infestation, fecal egg count (FEC) is most applicable. Somatic cell score/count is a good indicator for mastitis. Other hematological and immunological parameters are also equally applicable in the case of indirect selection. Hemoglobin percentage, erythrocyte sedimentation rate, total erythrocyte count, total or differential leukocyte counts as hematological traits, lymphocyte proliferation index, and neutrophil assays as immunological traits may be utilized for indirect selection, and are equally important.
Challenges of selecting for disease resistance 1. Identifying the phenotype for disease resistance is difficult. If we proceed for selection based only on phenotypic traitsdthat is, selecting only healthy animalsdit is assumed that all healthy animals are disease resistant. But practically, this assumption is not true. Apparently healthy animals may have subclinical infection, and although they do not clinically manifest disease symptoms, they may become carriers or reservoirs for the pathogen or infectious agent. There is an underlying cause of infectious agents that is continuously distributed. As explained
Genetics and Breeding for Disease Resistance of Livestock https://doi.org/10.1016/B978-0-12-816406-8.00018-8
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© 2020 Elsevier Inc. All rights reserved.
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18. Selection of livestock and poultry for disease resistance
in earlier chapters, whenever the continuous distribution of infection exceeds the threshold, perhaps in terms of host immunity, there is manifestation of disease symptoms, and we call the animal diseased. Inheritance of disease traits is governed by threshold traits, as discussed in Chapter 17. Often the clinical expression of a disease can be confounded by a similar disease; for example, pneumonia can be confused with bronchitis, emphysema, pleuritis, pulmonary adenomatosis, upper respiratory infection, and pleural fibrosis. Accurate disease diagnosis is costly and time-consuming. Thus the selection for a diseaseresistant trait is limited, as it becomes sometimes difficult to identify and measure the trait, or more accurately, to define the phenotype for disease resistance correctly. Thus selection for disease traits is far complicated than selection for production traits. Challenge study for animals with a pathogen is one of the most efficient methods for selection of animals for disease resistance. But challenge study has many constraints, as it is costly and possesses certain ethical concerns. Disease resistance is a complicated phenomenon. Selection for disease resistance is not only a subject for genetics and breeding. It leads to the consideration of many other disciplines such as microbiology, epidemiology, immunology, pathology, hostepathogen interaction, host biology, and livestock production systems. Selection for animals resistant to a particular pathogen may result in indirect selection for a more virulent pathogen, or the development of animals highly resistant to one specific pathogen may lead to the animals being more susceptible to another pathogen. Keeping the host’s immune defense system in homeostasis may be difficult. The justification for including disease resistance in breeding programs can be challenging to establish. Most importantly, the economic cost of the disease must be sufficiently high to rationalize selecting for resistance. In spite of the many constraints discussed earlier, genetic selection has certain definite advantages: If consumers wish to have a product free from antibiotic residue or nontreatable communicable diseases (e.g., BSE, avian influenza) because of its potential health threat, selection may be a favorable alternative. Microbial resistance against certain drugs or antibiotics constitute a major constraint for the treatment of certain diseases. In these cases, genetic selection is the only alternative. For certain diseases, therapeutics or vaccination is not effective. Genetic selection will be effective in such cases. Genetic selection is also effective in case of diseases with multietiological agents. Say that mastitis is an economically important disease of dairy animals, caused by many pathogens ranging from gram-positive to gramnegative bacteria, such as Pseudomonas aeruginosa, Staphylococcus aureus, Staphylococcus epidermidis, Streptococcus agalactiae, Streptococcus uberis, Brucella melitensis, Corynebacterium bovis, Mycoplasma spp. (including Mycoplasma bovis), Escherichia coli (E. coli), Klebsiella pneumoniae, Klebsiella oxytoca, Enterobacter aerogenes, Pasteurella spp., Trueperella pyogenes, Arcanobacterium pyogenes, Proteus spp., Prototheca zopfii (achlorophyllic algae), and Prototheca wickerhamii (achlorophyllic algae). It is difficult to treat such diseases and also difficult to develop vaccine. Selection is also effective for avian influenza. The virus is highly mutagenic due to frequent antigenic shift as well as drift. Treatment is not available. Due to frequent mutations in nature, effective vaccination is not available. Stamping out of the birds in a 5 km radius causes due economic loss and ethical concerns. Hence, the development of a disease-resistant stock of birds is the only alternative. Organic meat production systems are gaining popularity. Hence, genetically diseased animals may be reared in an organic way, without the use of any drug or therapeutics or vaccination. Usually, it has been observed that a disease trait is inversely correlated to a production trait. Thus selection favoring disease resistance may lead to depressed production. A common example is mastitis, which is negatively correlated with milk production. As we proceed to select animals resistant to mastitis, automatically the production would decrease. Another common example is that our indigenous nondescript cattle population is mostly resistant to mastitis but automatically has reduced milk production efficiency that is selected through evolution. Milk yield in dairy cattle has a positive correlation with many disease traits. Selection for growth rate in turkeys increases their susceptibility to Newcastle disease. The solution for these situations, when selection for a negatively correlated trait is necessary, when both are important, can be obtained through index selection. It can cope with relative economic values of different traits defined. It is possible to maintain production levels while selecting for disease resistance through index selection.
Direct selection for disease resistance may be applicable as per three methods
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Breeding for disease resistance Selection for disease resistance is costly. Potential costs associated with measuring disease resistance include reduced production, mortality, decreased longevity, diagnostic costs, and therapeutic expenses. It is extremely essential to understand host immunity (both innate or acquired) while considering selection for disease resistance. For example, if the breeding goal is to reduce bacterial diarrhea in young calves, selection traits might include the dam’s genetic potential for producing specific colostrum antibodies (passive immunity) and the calf’s genetic potential for developing an innate and acquired immune system early in life that responds to the diarrhea-causing pathogen. There may be further problems because negative genetic correlations between dam and calf resistance to some diseases have been estimated (e.g., bovine respiratory disease). Selection for disease resistance is costly. Approaches to disease control are prioritized. Genetic improvements could reduce the need for treatment and culling but would not reduce the need for proper management and sanitation. It has been reported that there is a rise in the incidences of disease and cost as we select animals for improved milk production. Disease-resistant traits are reported to be polygenic in inheritance, suggesting the involvement of a large number of loci. A few loci such as major histocompatibility complex (MHC) have been identified to account for genetic variance in disease. Other loci and the involvement of genes have been discussed in earlier chapters. The rate of genetic gain from selection for a major locus alone or in combination with performance is discussed. Genetic variation in disease incidence is important economically and needs to be included in the breeding program. However, the major problem includes low heritability for disease traits and lack of industry-wide standards for recording and accumulating field data for diseases. The negative genetic correlation of milk yield with disease traits have been illustrated.
Direct selection for disease resistance may be applicable as per three methods Selection based on natural infection In this approach, it is assumed that there is the presence of a naturally occurring pathogen and all animals are randomly exposed to the pathogen. Only the healthy animals (without representing any sign or symptom for the disease) will be selected. The animals need to be closely observed to classify them as healthy. Accuracy of selection decreases if the animals are not randomly subjected or exposed to the pathogen. Some healthy animals in the natural course may have escaped exposure to the pathogen, or it may so happen that the degree of exposure to the pathogen is different. Suppose on a large farm, the microenvironment for an extreme corner of the farm may be different from that of the other corner. The prevalence of pathogens largely depends on the microenvironment. In that case, the assumption does not hold true. Also, disease exposure in natural environments depends on temporal and spatial clustering of disease incidence. Diseases often occur in clusters of time (years, seasons, production cycles, etc.) and space (herd, pasture, farm, region, etc.). The accuracy of selection increases in cases of high incidence for disease. This is because the probability of identification of a disease-resistant animal increases with better accuracy. But on the contrary, in years of low incidence, accuracy will be diminished. Although the accuracy of selection may be less, the greatest advantage of this method is that it is easier to employ, involves less cost, and does not possess any ethical concern. Examples: Santa Ines sheep of Brazil are well adapted to climate and have significantly lower worm burdens and fewer nodular lesions than Suffolk and Ile de France lambs on the same pasture reared under natural infection. In Kenya, Red Maasai sheep were observed to be more resistant than the South African Dorper breed to Haemonchus infection during natural exposure to parasites. Sheep that are genetically resistant to Haemonchus block the initial colonization of Haemonchus larvae and have an efficacious Th2-type response (e.g., increases in blood and tissue eosinophils, specific IgE class antibodies, mast cells, IL-5, IL-13, and TNFa) that protects them against the infection, and susceptible sheep do not have this kind of immune response (Hurtado et al., 2013).
Selection based on challenge study for individuals to be selected In this approach, attempts were done to improve the accuracy of selection.
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The animals under study are uniformly challenged with infections of similar doses of the infective agent. This methodology is more accurate by having random distributions of the infectious agent among the animals under study. However, the main constraint is that the process is costly depending upon the pathogen’s virulence and clinical expression of the disease and possesses ethical concerns, but it is a reliable measure of disease resistance. This may require isolation of the population to prevent transmission to nonbreeding stock. For example, challenge study has been used for selection or study of disease resistance in sheep against strongyle infection. Increased resistance was observed in many breeds such as Barbados Blackbelly lambs of 3e7 months old compared with lambs of a French wool composite breed, INRA401, during primary and secondary artificial challenges. In studies, it is revealed that sex also has an effect on parasite resistance. Females have lower FEC during the second challenge compared with the first, whereas males have no reduction in FEC (Kmm dissertation).
Selection based on challenge study for relatives or clones This approach is aimed to challenge relatives or clones of the breeding stock. This approach is very useful in cases for which the disease has a high mortality rate. Expected predicted difference needs to be calculated for the accurate prediction of breeding value of the animals under breeding stock. Ideally, such direct approaches of phenotyping animals for disease resistance would take place in a highly controlled and isolated environment. This is probably not practical for cattle associations, but publicly funded institutions may develop such testing facilities in the future. One limitation for direct selection gives rise to biasness. These approaches do not consider the immunological background of animals under study. Once an animal is exposed to an infection, the particular animal is naturally vaccinated. On an infection for the second time, it will not suffer. Researchers need to ascertain the importance of immunological background for biasing the observed animal response to a disease challenge. In cattle, direct selection for reducing brucellosis had a favorable response. It was reported that the natural resistance of calves to brucellosis was observed to increase from 20% to 59% after breeding cows to a naturally resistant bull.
Indirect selection for disease resistance Indirect selection for disease resistance can also be achieved by selection for indicators of disease resistance. Indicators of disease resistance include pathogen products (i.e., pathogen reproductive rates, pathogen byproducts) and biological or immunological responses of the host. In Chapter 3, we described indicator traits in detail. Indicator traits for endoparasitic infection have been reported, such as FEC. Similarly, selection for somatic cell count is an indicator trait for mastitis. In the case where we want to select for a mastitis-resistant animal, we need to select the animal with a lower somatic cell count. Immune responsiveness, challenging an animal with an antigen or vaccine and measuring antibody response or production, has been useful in poultry and swine. In any breeding program, selection is applied for the lower somatic cell score, which will ultimately lead to better genetic improvement for total economic merit. However it has been observed that when selection is simultaneously applied for lower somatic cell score, it may lead to a marginal decrease in genetic gain for milk yield, but ultimately there is an increase in total economic merit. Upon selection with selection indexes, the rate of increase in mastitis would be slowed down. It is not the case that the incidence of mastitis would decrease. Immune responsiveness was suggested to be a useful indicator of disease resistance in cattle. In a single gene targeted selection process, immune response is more beneficial. However, studies in swine have indicated that selection for immune responsiveness can improve disease resistance to certain diseases while at the same time increasing susceptibility to others. For effective selection, indicator traits must be heritable, highly genetically correlated with resistance to the disease or diseases of interest, accurate to measure, and affordable. Genotypeeenvironment interaction is important in the process of selection. Suppose we select livestock for resistance to a particular disease in one particular environment; the selected livestock may suffer from the same
Selection objective
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disease in a different environment. Thus, selection programs may have to be environment-specific, with the selection environment matching the commercial production environment.
Marker-assisted or genomic selection for disease resistance with molecular biological tools Genetic selection is also a form of indirect selection. Resistance to most diseases is likely to be controlled by polygenes. Concerted interventions of physiological, genetic, and molecular genetic approaches are needed to investigate polygenic control of immune response and disease resistance. Genetic selection aided by molecular markers can be used to improve resistance to diseases. But ultimately, a detail understanding of the genetic basis of immune response is essential. Host disease resistance is governed by a number of genes conferring immunity. Three sets of genes (immuneresponse genes) the govern the response of vertebrate host to infection are 1. Genes controlling innate immunity 2. Genes determining the specificity of acquired immune response 3. Genes affecting the quality of acquired immunity Marker-assisted selection (MAS) for disease resistance involves identification of markers aided through polymorphism for the immune-response genes or identification of single-nucleotide polymorphisms (SNPs) by RFLP, SSCP, AFLP, etc. The candidate gene approach is very important for MAS. SNPs for the immune-response gene have been reported for the CD14 gene in goats (Pal and Chatterjee, 2009; Pal and Chatterjee, 2013b), cattle (Pal et al., 2011), and buffalo (Pal et al., 2013). Mastitis has been controlled by polygenes, and the candidate genes responsible for mastitis resistance have been reviewed by Pal and Chatterjee (2012). The candidate gene approach is also a form of indirect selection. Since disease-resistant traits are polygenic in inheritance, there are some constraints in the MAS process. A marker should be heritabledi.e., transmitted from generation to generation. An advantage of MAS is that these are codominant in nature and information may be available at an early age of life, even at the day-old stage. It saves the huge cost of rearing the animals to their age of production. It should be heritable. Considering the polygenic nature of inheritance, the better approach to MAS is genomic selection. This involves due consideration of a number of genes involved in disease occurrence. It consists of genome-wide association studies (GWASs). The SNPs for the genes involved in disease resistance will be identified, preferably with SNP chips, and GWASs will be conducted with suitable software. Statistical analysis involved with the disease-resistant traits is very important. Since the phenotypic traits pertaining to diseases do not follow normal distributions, they are instead threshold or all-or-none traits. Apart from usual analysis, logit or probit models for data analysis are very much applicable and widely used. We cannot at this time predict whether selection for disease resistance can be effective in livestock. Basic research into the complexities underlying diseases will likely reveal effective approaches for many disease problems. For example, the discovery that contagious keratoconjunctivitis (pinkeye) is heritable led to the discovery of a chromosomal region associated with its disease incidence. In the near future, it is likely that selection for disease resistance in most livestock species, especially cattle, will not be widely accepted by the industry because of the lack of knowledge about how best to select for disease resistance and poorly understood genetic correlations between disease resistance and economically important production traits. Selection for disease resistance will be disease dependent. It may be possible to select directly against the disease, select for indicator traits (indirect selection), to select directly for the gene(s) that confer resistance, or some combination of these approaches. The potential seems great for identifying breeding stock that is healthier because of its immune responsiveness. Although it may be difficult to select for animals resistant to a wide range of diseases, it may be possible to breed or identify animals that are genetically more responsive to antiviral vaccines or other therapies.
Selection objective The first decision to be made by a breeder setting up a selection scheme is the selection objective. The selection objective is the character (or characters) that we wish to improve by selection. It is also known as the breeding
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objective, breeding aim, breeding goal, and selection goal. It is chosen irrespective of whether it is easy, difficult, or even impossible to measure. Examples include reduced incidence of clinical mastitis in dairy cattle, reduced fecal nematode egg production in sheep, reduced numbers of ticks on beef cattle, and dogs without clinical hip dysplasia. There should be a clear-cut objective for selection for genetic improvement. Disease resistance could mean resistance to infection or the absence of clinical signs despite infection. As breeders usually wish to improve their return on investment, most breeding objectives aim to improve the profitability of animals. Every heritable trait influencing economic return related to disease resistance should be included in the selection objective for profitability. In practice, we do not have sufficient information to do this, and only the most important characters are included. The different components of an objective will vary in their economic value and are given greater or lesser weight according to their importance. In statistical terms, selection objective ¼ a1 Y1 þ a2 Y2 þ / þ an Yn ; where Y1, Y2, ., Yn are the traits in the selection objective and a1, a2, ., an are the relative economic values of the traits. Economic values are often defined as the marginal profit resulting from a change of one unit in that trait while all other traits remain unchanged. It is economic values relative to each other that are important. Estimating economic values for disease traits is not straightforward. For example, with parasitic infections such as sea louse in salmon and nematodes in sheep, the parasites are evolving resistance to drugs used to treat them. Consequently, economic impact will vary among sites; the impact of parasites is much greater on those sites that lack effective treatment. Ideally, we would like to eliminate all disease, but this is not feasible. Some diseases are very rare and individually unimportant enough to warrant inclusion in a selection index. Including more traits in the selection objective can reduce the selection pressure on each trait and slow the response in other traits such as production. For rare diseases, this cost outweighs the potential benefits. We lack adequate information to define resistant hosts for other diseases. Mastitis is economically the most important disease of dairy cattle, while nematode infection is the most important disease of small ruminants. In pigs and poultry, there are many important diseases. Consequently, selection for resistance against a single disease is not appropriate. In these industries, selection is focused against the major causes of reduced productivity and those diseases for which useful genetic markers exist. Selection for resistance against a specific disease usually involves testing directly for resistance to that disease. Two alternative selection strategies to reduce the impact of disease have been suggested. The first is to select for enhanced immune responses. This approach has been applied to both pigs and cattle. Animals with enhanced immune responses are more resistant to some diseases but more susceptible to others; however, enhanced resistance is more important than increased susceptibility. Yorkshire pigs selected for increased immune responses matured earlier, and this early maturity was valuable in itself. These individuals had both higher and more optimally balanced AMIR and CMIR, two key components of the adaptive immune system that are referred to as high immune responders. A patented test system has been developed to quickly identify these animals within dairy herds, and this method is referred to as High Immune Response (HIR) technology. Interestingly, HIR dairy cows showed lower occurrences of economically important infectious diseases as well as ketosis, emphasizing the relationships between infectious disease, immune function, and metabolic disorders. Dairy cows with the highest AMIR responses tended to have lower milk production, while those with the highest CMIR had higher milk production (Mallard, personal communication). These results emphasize the need to select animals simultaneously for AMIR and CMIR if both health and production traits are to be maintained or improved. This is a promising approach that deserves and is receiving more attention. The second alternative approach is to select for increased resilience. Resilience is defined as the ability to be productive despite infection. Some animals develop subclinical infections, and these animals could be used for breeding. In order to select for disease resistance, selection objective should include the trait for disease resistance as well as the trait for enhanced production. Thus the breeder can select for both resilience as well as resistance. Disadvantages will be there in situations where susceptibility to subclinical infection may result in economic losses for other reasons, such as increased somatic cell count in a dairy herd. Another potential disadvantage is that resilient animals may act as sources of infection for other individuals.
Selection criterion or basis of selection
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Selection criterion or basis of selection The second decision is the selection criteria. The selection criteria are the traits that will actually be measured. Apart from individual selection, selection criteria might include information on relatives such as full-sibs, half-sibs or offspring. Pedigree selection based on pedigree information in a statistically sophisticated mixed-model analysis has proved efficient. For disease-resistance traits, the selection criterion could be clinical incidence for diseases. In countries like Norway, where clinical disease is routinely recorded as a part of routine investigation, the incidence of clinical mastitis in daughters is used to select bulls with superior breeding values for resistance to mastitis. Indicator traits such as somatic cell count can be equally applicable. For other diseases, such as infection with endoparasites or ectoparasites, clinical disease is often not much informative, and indicator traits are used. FECs are used to indicate susceptibility to nematode infection, while tick or louse counts can be used to identify genetically resistant cattle. Family selection is most effective for sex limited traits; for example, mastitis for dairy cattle is expressed only in cows and not in bulls. In cattle breeding, bulls do not produce milk nor develop mastitis. There is sufficient variation due to the method of assessment in many measurements of disease. One example is FEC following nematode infection. A consequence of measurement variation is that the importance of genetic variation is often underestimated. Taking multiple measurements can reduce measurement variation and provide a more accurate and heritable assessment of individual breeding value. In order to study resistance to tick infestation, researchers have developed scoring approaches or manually count ticks, and widely divergent heritability estimates for the trait have been observed. Seasonality is an important environmental effect for the estimation of genetic parameters for parasitic infestation. Immune response against nematode infection was observed to be better with increased heritability in older sheep than in young lambs. The time of year at which measurements of tick infestation were undertaken was observed to have a strong influence on heritability estimates. Genetic correlations among parasitism and production traits also change over time. A number of genetic markers for resistance to specific diseases have been identified. These genetic markers are very important to include in selection criteria. MHC is an important marker, but it has the ability to explain only 10% of phenotypic variance. Most loci have much smaller effects, and for most diseases the distribution of gene effects is L-shaped with most loci having very small effects. MHC is associated with resistance to mastitis and nematode infection, but because of its complexity, it has seldom been included in commercial breeding schemes. SNPs have been observed to be one of the most common sources of genetic variation. A single nucleotide site can have two, three, or even four alleles. SNP occurs approximately once every 1000 base pairs in humans and probably at a similar frequency in most livestock species. Nowadays, SNP chips are available, and GWAS analysis gives a conclusive result for the association leading to the most conclusive and effective selection process, “genomic selection.” Genomic selection is applicable for testing a large population that is well characterized. All values are then used to create a genomic breeding value for each individual. High genetic correlation has been observed between genomic breeding values with the estimated breeding values (EBVs) derived by conventional methods. A breeding value for each individual can be estimated from each polymorphism. An advantage of genomic selection is the ability to identify disease-resistant individuals with natural selection even in the absence of disease challenge.
Selection index Methods of selection are applicable when selection is applied for multiple traits or more than a single trait at a time. Three methods are applicable: tandem selection, independent culling method, and index selection. Selection index is the most efficient technique when individual weight is given to each trait based on economic value. In the tandem method of selection, selection is applied for one trait at a time for several generations. Then selection is applied for a second trait. The efficacy of the tandem method of selection is much less than that of the index selection method. Another method for multitrait selection is the independent culling level method. Independent culling levels select only those animals that exhibit more than a particular level of performance for each criterion. An animal will not be selected unless it is above the cutoff value for any one trait or criterion irrespective of its performance on other traits or criteria. The use of independent culling levels is only possible if all traits in the selection objective can be measured in each individual.
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Selection index is the most efficient method of combining different selection criteria. The construction of an index results in a single aggregate value that weights the different selection criteria according to their relative economic values. Selection index is an effective method to rank animals for their genetic merit (i.e., their breeding value). The traits that are to be measured can be denoted as X1, X2, ., Xm. The coefficients that give the most efficient response to selection are b1, b2, ., bm. Then, the selection index ¼ b1 X1 þ b2 X2 þ / þ bm Xm . Estimating the values of b1, b2, ., bm requires information about several factors: 1. Variation exists among animals (their phenotypic variances). 2. Differences in one trait are associated with differences in another trait in the selection index (their phenotypic covariances). 3. Genetic covariances exist between the traits. 4. There are genetic variances and covariances of the traits in the selection objective. The equation to estimate the selection index coefficients is Pb ¼ Ga, where P is a matrix of the phenotypic variances and covariances in the selection index, and G is a matrix of the genetic covariances between the traits in the selection index and traits in the selection criterion. The vector of economic values is represented by a, while the vector of coefficients to be estimated is represented by b. For a single trait that is measured only once on each animal, the selection index value is simply the trait value multiplied by the heritability. For any process of selection to be effective, it is not practically feasible to select only the disease-resistant trait. Unless the animal is in production, it is useless and not economically viable to select for only production traits. Calculation of the selection index value in monetary terms is essential, which represents the expected return from using each animal. Breeding for disease resistance leads to both genetic and epidemiological benefits because resistant animals may produce less infectious agents, thus lowering the rate of disease transmission. Mathematical models may be helpful for capturing the full response to selection. Index selection was reported to be quite effective for any disease-resistant trait. The metabolic disease resistance (MDR) index is one such important index. Six EBVs are considered in the MDR index. These are subclinical ketosis (BHB data), clinical ketosis (producer recorded), and displaced abomasum (producer recorded) for first and later parities. The traits under consideration for the genetic evaluation of these traits include body condition score from the first lactation classification and fat:protein (F:P) from the first test-day of lactation as indicator traits to improve accuracy. The weightings for the MDR index as proposed are 50% subclinical ketosis, 25% clinical ketosis, and 25% displaced abomasum, where the included EBVs are averages of the first and later lactation EBVs of each trait.
Response to selection The response to selection for a single character mainly depends upon five factors: 1. 2. 3. 4. 5.
Generation interval Variation in breeding values Intensity of selection Effective population size Accuracy of selection It has been observed practically that selection based on relatives and those with multiple traits has better accuracy.
Generation interval The generation interval (L) is the average age of parents when the offspring are born. Response to selection is inversely proportional to the generation interval. Hence if the parents are bred at an early age, the generation interval will be less and accordingly the response to selection will be more. The generation interval is calculated separately for males and females, then averaged. The generation interval will be influenced by the selection method.
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For example, the generation interval will be shorter for individual selection or sib-testing than it is for progeny testing. There is a trade-off between the generation interval and the proportion of animals selected, which affects selection intensity. It is possible to decrease the generation interval by using younger animals for breeding. However, if a greater proportion of offspring are used to replace parents, selection intensity be decreased. If all available ewe lambs are used to replace the older ewes in a flock, the generation interval will be small but selection intensity will be zero. Alternatively, if half the ewe lambs are selected as replacements, their mean value for the selected traits will be greater than that of their parents (i.e., the selection differential will be positive), but the generation interval will be larger. The use of assisted reproductive techniques such as artificial insemination (AI), multiple ovulation and embryo transfer (MOET), or in vitro fertilization (IVF) will improve the rate of genetic improvement. In the case of artificial insemination, a smaller number of male animals are needed to produce a given number of offspring, although the same number of female animals will be needed. In the case of MOET and IVF, a smaller number of female animals will also be needed. These methods have the ability to substantially increase the selection differential and shorten generation intervals, as the parents can leave more offspring by the time they reach a given age. Additive genetic variance is importance for any selection process to be effective. Better additive genetic variance will lead to better heritability. Variation in breeding values is important for selection. Traits with more phenotypic variation or higher heritability will have greater variation in breeding values. The selection intensity (i) is the superiority of the selected parents standardized according to the amount of variation in the trait or character. For selection based on the phenotype of an individual, i ¼ S=sp where S is the selection differential and sp is the standard deviation of the phenotypic observations. Let us discuss with the help of suitable examples. It can be more practical to talk about selecting the top 5% of animals rather than selecting some animals such that their average performance is þ10 kg over the population mean. If the top 5% of animals are chosen, then intensity of selection, i, equals 2.04 (approximately). Obviously if we select and breed the top 10% for any character, there will be a greater improvement than that achieved by selecting the top 90%. Practically, selection intensity is limited by reproductive ability and facilities for raising offspring. Any technique that improves reproductive rate can, if properly used, increase the selection intensitydfor example, assisted reproductive techniques such as those discussed (AI, MOET, IVF, etc.). Effective population size is the size of an ideal population that meets Hardy Weinberg assumptions, which would lose heterozygosity at a rate equal to that of the observed population. Factors that influence effective population size are fluctuating population size and breeding sex ratio. Effective population size (Ne) can be measured by the following formula: Ne ¼ 4NmNf/(Nm þ Nf), where Nm ¼ number of males Nf ¼ number of females Calculation of effective population size is of practical importance, particularly for endangered breedsdsay, for example, the Vechur breed of Kerala. Selection process reduces effective population size. The effective population size (Ne) considers that not all members of a population have an equal chance of contributing gametes to the next generation. Accuracy of selection depends on the accuracy of breeding value estimation. The accuracy of selection from a single measurement is estimated as the square root of the heritability. Selecting records decreases environmental noise and accordingly increases heritability. Accuracy can be improved by taking a greater number of measurements whenever feasible or by using relatives’ records (sibs, half-sibs, or progeny). The preferred statistical method for estimating breeding values is best linear unbiased predictor, because this method is the most effective at exploring genetic versus nongenetic differences such as differences between herds or between years.
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Predicting response to selection for a single trait Response to selection (R) may be predicted as R ¼ h 2S R ¼ (Va/Vp) i sp R ¼ sa h i, where h2 is heritability, S is selection differential, Va is additive genetic variance, Vp is phenotypic variance, and i is intensity of selection. sp is the phenotypic standard deviation. sa is the standard deviation due to additive genetic effect, h is the accuracy of selection, and i is the intensity of selection. As explained, response to selection is directly proportional to selection differential, heritability, and intensity of selection. The breeders’ equation states that the response to selection (R), the expected improvement in a trait in the offspring, can be predicted by the product of selection differential (S) and heritability (h2). This is applicable to a single generation for selection of a single trait, where the response is measured in generations and a large population, with no inbreeding and no drift. As selection proceeds, there is a change in frequency; favorable genes may become fixed, and unfavorable genes may no longer be present in the population. Changes in the extent of genetic variation will influence heritability. Heritability is variance in breeding values divided by phenotypic variance (Va/Vp), and selection differential is selection intensity multiplied by the phenotypic standard deviation i sp. Rearranging terms gives response to selection as sa h i, where sa is variation in breeding values, h is accuracy of selection, and i is intensity of selection.
Response to selection on multiple traits Response to selection on multiple traits depends on the additive genetic variances and covariances of the traits. Although it is similar to that of a single trait, in multiple traits the equation is R ¼ Gb; where R is change in the multivariate phenotype, G is the additive genetic varianceecovariance matrix, and b is the selection gradient estimated from the partial regression coefficients. As with selection for a single trait, this index is best for production traits and may not capture the full benefits of selective breeding for disease resistance. Mathematical modeling is required to capture the response to selection.
Mathematical modeling Quantitative genetic theory can accurately predict the response to selective breeding of production traits but is less effective at predicting the response to selection for resistance to infectious diseases. This is because selecting resistant animals and culling susceptible animals can alter the rate of disease transmission. In other words, genetic theory assumes that the environment remains unchanged, but culling heavily infected or diseased animals can decrease the contamination level in the environment with transmission stages. This is particularly true for terrestrial livestock. For example, selective breeding for nematode resistance based on FEC reduces the number of parasite eggs shed into the environment. A large number of epidemiological models have explored variation among individuals in their contributions to infectious disease dynamics, particularly for parasite infections. However, few epidemiological models have incorporated genetic variation and genetic response to selection for disease resistance.
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A case study The production of transmission stages is more persistent and predictable for endemic metazoan parasitic infections than it is for microbial infections. As a result, modeling response to selection for parasite resistance is far easier than modeling resistance to microbial infections. Models are available that predict response to parasite resistance. For example, the model for predicting the response to selection against nematode infection of lambs is popular. The lamb model predicts relatively rapid responses to selection for lower FECs following natural nematode infection, predominantly with Teladorsagia circumcincta. The responses observed in practice were less rapid. One possible explanation is that selection in practice involves multiple traits. Another is that the model did not attempt to capture immune responses. It essentially assumed that reduced parasite numbers due to immune responses were counterbalanced by increased larval intake. However, immune response may play a stabilizing role in nematode infection as reduced levels of infection generate reduced immune responses.
Disease resistance Disease resistance mechanisms in animals lead to the view that a large number of loci are involved in disease resistance. One important locus, such as MHC, may account for a major portion of genetic variance in disease. The rate of genetic gain from selection for a major locus alone or a combination of loci with performance is important. Although disease traits have low heritability, genetic variation for disease incidence is economically important and is justified for inclusion in breeding programs. An industry-wide standard for recording and accumulating field data for disease is not currently present. Coordinated approaches for institutional relationships among segments of the animal breeding and animal health industries are important for facilitating genetic improvements in disease resistance.
Selection for disease traits (threshold traits) All disease traits are mostly threshold traits, discussed in detail in other chapters. Here we discuss selection for disease traits. The application of selection to a threshold character does not lead to the theoretical difficulties of genetic analyses. It has some practical importance regarding the reduction of the incidence of abnormalities and with changing the response of experimental animals to treatmentsdfor example, increasing or decreasing drug resistance. We shall consider a character with two visible classes and refer first to individual selection. The process of direct selection for disease traits (with challenge study) was discussed at the beginning of this chapter. In this later part, it is important to discuss facts. For the threshold trait, it is not possible to locate the underlying continuous distribution. Response to selection for disease-resistant traits based on the selection differential is similar to that of production traits. But, unlike a production trait with a continuously varying character, selection differential does not depend primarily on the proportion selected but on the incidence of disease occurrence. Breeding can be employed from those individuals in the desired phenotypic class, but discrimination between those with high and low liabilities is not possible. Thus, the selected individuals are a random sample from the desired class, and their mean is the mean of the desired class. It does not depend on whether all of the desired class or only a portion of it is selected. Thus it is not advantageous to select a smaller portion than the incidence. On the contrary, if the proportion that must be selected is greater than the incidence, it will be essential to use some individuals of the undesired class. Their mean liability will be below the population mean, so the use of undesired individuals as parents will apply some threshold charactersdnegative selectiondthe mean of the undesired class is measured as eip/(1-p), where i is the mean of the desired class whose incidence is p.
Genetic assimilation The application of the principle of changing the threshold by environmental means is the phenomenon known as “genetic assimilation”dthe term was coined by Waddington (1953). If a threshold character appears due to an environmental stimulus, and selection is applied for this character, it may appear spontaneously later, without the necessity of the environmental stimulus. Thus, an “acquired character” becomes by perfectly orthodox principles
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FIGURE 18.1 Diagram illustrating genetic assimilation.
of selection as an “inherited character.” In such a situation there are two thresholds, one spontaneous and the other induced. The spontaneous threshold is initially outside the range of variation of the population, depicting no variation of phenotype, and accordingly no selection can be applied. The induced threshold was observed to be within the range of liability covered by the population. This allows individuals toward one end of the distribution to be selected. Thus, the mean genotypic value of the population is changed. If this change continues, some individuals will eventually cross the spontaneous threshold and appear as spontaneous variants. When spontaneous incidence increases, selection may be continued without the help of the environmental stimulus, and spontaneous incidence may be further increased (Fig. 18.1).
Further reading Casas, E., Stone, R.T., 2006. Putative quantitative trait loci associated with the probability of contracting infectious bovine keratoconjunctivitis. J. Anim. Sci. 84, 3180e3184. Sacco, R.E., Nestor, K.E., Saif, Y.M., Tsai, H.J., Patterson, R.A., 1994. Effects of genetic selection for increased body weight and sex of poults on antibody response of Turkeys to Newcastle virus and Pasturella multocida vaccines. Avian Dis. 38, 33e36. Shook, G.E., May 1989. Selection for disease resistance. J. Dairy Sci. 72 (5), 1349e1362. Simianer, H., Solbu, H., Schaeffer, L.R., 1991. Estimated genetic correlations between disease and yield traits in dairy cattle. J. Dairy Sci. 74, 4358e4365. Templeton, J.W., Estes, D.M., Price, R.E., Smith III, R., Adams, L.G., 1990. Immunogenetics of natural resistance to bovine brucellosis. In: Proc. 4th World Congr. Gene. Appl. Lvstk. Prod., pp. 396e399. van Dorp, T.E., Dekkers, J.C.M., Martin, S.W., Noordhuizen, J.P.M., 1998. Genetic parameters of health disorders, and relationships with 305-day milk and conformation traits of registered Holstein cows. J. Dairy Sci. 81, 2264e2270.
18 Selection of livestock and poultry for disease resistance O U T L I N E Challenges of selecting for disease resistance
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Direct selection for disease resistance may be applicable as per three methods Selection based on natural infection Selection based on challenge study for individuals to be selected Selection based on challenge study for relatives or clones
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Disease resistance Selection for disease traits (threshold traits) Genetic assimilation
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Further reading
Selection for farm animals is very important. Selection of more immune animals at the early stages of their lives will reduce the huge cost of disease management, including treatment, and will minimize the losses incurred due to high mortality and morbidity. Disease resistance is a complex phenomenon. There are two modes of selectionddirect and indirect. Indirect selection is more applicable to disease-resistant traits. Say for the selection of animals resistant to parasitic infestation, fecal egg count (FEC) is most applicable. Somatic cell score/count is a good indicator for mastitis. Other hematological and immunological parameters are also equally applicable in the case of indirect selection. Hemoglobin percentage, erythrocyte sedimentation rate, total erythrocyte count, total or differential leukocyte counts as hematological traits, lymphocyte proliferation index, and neutrophil assays as immunological traits may be utilized for indirect selection, and are equally important.
Challenges of selecting for disease resistance 1. Identifying the phenotype for disease resistance is difficult. If we proceed for selection based only on phenotypic traitsdthat is, selecting only healthy animalsdit is assumed that all healthy animals are disease resistant. But practically, this assumption is not true. Apparently healthy animals may have subclinical infection, and although they do not clinically manifest disease symptoms, they may become carriers or reservoirs for the pathogen or infectious agent. There is an underlying cause of infectious agents that is continuously distributed. As explained
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in earlier chapters, whenever the continuous distribution of infection exceeds the threshold, perhaps in terms of host immunity, there is manifestation of disease symptoms, and we call the animal diseased. Inheritance of disease traits is governed by threshold traits, as discussed in Chapter 17. Often the clinical expression of a disease can be confounded by a similar disease; for example, pneumonia can be confused with bronchitis, emphysema, pleuritis, pulmonary adenomatosis, upper respiratory infection, and pleural fibrosis. Accurate disease diagnosis is costly and time-consuming. Thus the selection for a diseaseresistant trait is limited, as it becomes sometimes difficult to identify and measure the trait, or more accurately, to define the phenotype for disease resistance correctly. Thus selection for disease traits is far complicated than selection for production traits. Challenge study for animals with a pathogen is one of the most efficient methods for selection of animals for disease resistance. But challenge study has many constraints, as it is costly and possesses certain ethical concerns. Disease resistance is a complicated phenomenon. Selection for disease resistance is not only a subject for genetics and breeding. It leads to the consideration of many other disciplines such as microbiology, epidemiology, immunology, pathology, hostepathogen interaction, host biology, and livestock production systems. Selection for animals resistant to a particular pathogen may result in indirect selection for a more virulent pathogen, or the development of animals highly resistant to one specific pathogen may lead to the animals being more susceptible to another pathogen. Keeping the host’s immune defense system in homeostasis may be difficult. The justification for including disease resistance in breeding programs can be challenging to establish. Most importantly, the economic cost of the disease must be sufficiently high to rationalize selecting for resistance. In spite of the many constraints discussed earlier, genetic selection has certain definite advantages: If consumers wish to have a product free from antibiotic residue or nontreatable communicable diseases (e.g., BSE, avian influenza) because of its potential health threat, selection may be a favorable alternative. Microbial resistance against certain drugs or antibiotics constitute a major constraint for the treatment of certain diseases. In these cases, genetic selection is the only alternative. For certain diseases, therapeutics or vaccination is not effective. Genetic selection will be effective in such cases. Genetic selection is also effective in case of diseases with multietiological agents. Say that mastitis is an economically important disease of dairy animals, caused by many pathogens ranging from gram-positive to gramnegative bacteria, such as Pseudomonas aeruginosa, Staphylococcus aureus, Staphylococcus epidermidis, Streptococcus agalactiae, Streptococcus uberis, Brucella melitensis, Corynebacterium bovis, Mycoplasma spp. (including Mycoplasma bovis), Escherichia coli (E. coli), Klebsiella pneumoniae, Klebsiella oxytoca, Enterobacter aerogenes, Pasteurella spp., Trueperella pyogenes, Arcanobacterium pyogenes, Proteus spp., Prototheca zopfii (achlorophyllic algae), and Prototheca wickerhamii (achlorophyllic algae). It is difficult to treat such diseases and also difficult to develop vaccine. Selection is also effective for avian influenza. The virus is highly mutagenic due to frequent antigenic shift as well as drift. Treatment is not available. Due to frequent mutations in nature, effective vaccination is not available. Stamping out of the birds in a 5 km radius causes due economic loss and ethical concerns. Hence, the development of a disease-resistant stock of birds is the only alternative. Organic meat production systems are gaining popularity. Hence, genetically diseased animals may be reared in an organic way, without the use of any drug or therapeutics or vaccination. Usually, it has been observed that a disease trait is inversely correlated to a production trait. Thus selection favoring disease resistance may lead to depressed production. A common example is mastitis, which is negatively correlated with milk production. As we proceed to select animals resistant to mastitis, automatically the production would decrease. Another common example is that our indigenous nondescript cattle population is mostly resistant to mastitis but automatically has reduced milk production efficiency that is selected through evolution. Milk yield in dairy cattle has a positive correlation with many disease traits. Selection for growth rate in turkeys increases their susceptibility to Newcastle disease. The solution for these situations, when selection for a negatively correlated trait is necessary, when both are important, can be obtained through index selection. It can cope with relative economic values of different traits defined. It is possible to maintain production levels while selecting for disease resistance through index selection.
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Breeding for disease resistance Selection for disease resistance is costly. Potential costs associated with measuring disease resistance include reduced production, mortality, decreased longevity, diagnostic costs, and therapeutic expenses. It is extremely essential to understand host immunity (both innate or acquired) while considering selection for disease resistance. For example, if the breeding goal is to reduce bacterial diarrhea in young calves, selection traits might include the dam’s genetic potential for producing specific colostrum antibodies (passive immunity) and the calf’s genetic potential for developing an innate and acquired immune system early in life that responds to the diarrhea-causing pathogen. There may be further problems because negative genetic correlations between dam and calf resistance to some diseases have been estimated (e.g., bovine respiratory disease). Selection for disease resistance is costly. Approaches to disease control are prioritized. Genetic improvements could reduce the need for treatment and culling but would not reduce the need for proper management and sanitation. It has been reported that there is a rise in the incidences of disease and cost as we select animals for improved milk production. Disease-resistant traits are reported to be polygenic in inheritance, suggesting the involvement of a large number of loci. A few loci such as major histocompatibility complex (MHC) have been identified to account for genetic variance in disease. Other loci and the involvement of genes have been discussed in earlier chapters. The rate of genetic gain from selection for a major locus alone or in combination with performance is discussed. Genetic variation in disease incidence is important economically and needs to be included in the breeding program. However, the major problem includes low heritability for disease traits and lack of industry-wide standards for recording and accumulating field data for diseases. The negative genetic correlation of milk yield with disease traits have been illustrated.
Direct selection for disease resistance may be applicable as per three methods Selection based on natural infection In this approach, it is assumed that there is the presence of a naturally occurring pathogen and all animals are randomly exposed to the pathogen. Only the healthy animals (without representing any sign or symptom for the disease) will be selected. The animals need to be closely observed to classify them as healthy. Accuracy of selection decreases if the animals are not randomly subjected or exposed to the pathogen. Some healthy animals in the natural course may have escaped exposure to the pathogen, or it may so happen that the degree of exposure to the pathogen is different. Suppose on a large farm, the microenvironment for an extreme corner of the farm may be different from that of the other corner. The prevalence of pathogens largely depends on the microenvironment. In that case, the assumption does not hold true. Also, disease exposure in natural environments depends on temporal and spatial clustering of disease incidence. Diseases often occur in clusters of time (years, seasons, production cycles, etc.) and space (herd, pasture, farm, region, etc.). The accuracy of selection increases in cases of high incidence for disease. This is because the probability of identification of a disease-resistant animal increases with better accuracy. But on the contrary, in years of low incidence, accuracy will be diminished. Although the accuracy of selection may be less, the greatest advantage of this method is that it is easier to employ, involves less cost, and does not possess any ethical concern. Examples: Santa Ines sheep of Brazil are well adapted to climate and have significantly lower worm burdens and fewer nodular lesions than Suffolk and Ile de France lambs on the same pasture reared under natural infection. In Kenya, Red Maasai sheep were observed to be more resistant than the South African Dorper breed to Haemonchus infection during natural exposure to parasites. Sheep that are genetically resistant to Haemonchus block the initial colonization of Haemonchus larvae and have an efficacious Th2-type response (e.g., increases in blood and tissue eosinophils, specific IgE class antibodies, mast cells, IL-5, IL-13, and TNFa) that protects them against the infection, and susceptible sheep do not have this kind of immune response (Hurtado et al., 2013).
Selection based on challenge study for individuals to be selected In this approach, attempts were done to improve the accuracy of selection.
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The animals under study are uniformly challenged with infections of similar doses of the infective agent. This methodology is more accurate by having random distributions of the infectious agent among the animals under study. However, the main constraint is that the process is costly depending upon the pathogen’s virulence and clinical expression of the disease and possesses ethical concerns, but it is a reliable measure of disease resistance. This may require isolation of the population to prevent transmission to nonbreeding stock. For example, challenge study has been used for selection or study of disease resistance in sheep against strongyle infection. Increased resistance was observed in many breeds such as Barbados Blackbelly lambs of 3e7 months old compared with lambs of a French wool composite breed, INRA401, during primary and secondary artificial challenges. In studies, it is revealed that sex also has an effect on parasite resistance. Females have lower FEC during the second challenge compared with the first, whereas males have no reduction in FEC (Kmm dissertation).
Selection based on challenge study for relatives or clones This approach is aimed to challenge relatives or clones of the breeding stock. This approach is very useful in cases for which the disease has a high mortality rate. Expected predicted difference needs to be calculated for the accurate prediction of breeding value of the animals under breeding stock. Ideally, such direct approaches of phenotyping animals for disease resistance would take place in a highly controlled and isolated environment. This is probably not practical for cattle associations, but publicly funded institutions may develop such testing facilities in the future. One limitation for direct selection gives rise to biasness. These approaches do not consider the immunological background of animals under study. Once an animal is exposed to an infection, the particular animal is naturally vaccinated. On an infection for the second time, it will not suffer. Researchers need to ascertain the importance of immunological background for biasing the observed animal response to a disease challenge. In cattle, direct selection for reducing brucellosis had a favorable response. It was reported that the natural resistance of calves to brucellosis was observed to increase from 20% to 59% after breeding cows to a naturally resistant bull.
Indirect selection for disease resistance Indirect selection for disease resistance can also be achieved by selection for indicators of disease resistance. Indicators of disease resistance include pathogen products (i.e., pathogen reproductive rates, pathogen byproducts) and biological or immunological responses of the host. In Chapter 3, we described indicator traits in detail. Indicator traits for endoparasitic infection have been reported, such as FEC. Similarly, selection for somatic cell count is an indicator trait for mastitis. In the case where we want to select for a mastitis-resistant animal, we need to select the animal with a lower somatic cell count. Immune responsiveness, challenging an animal with an antigen or vaccine and measuring antibody response or production, has been useful in poultry and swine. In any breeding program, selection is applied for the lower somatic cell score, which will ultimately lead to better genetic improvement for total economic merit. However it has been observed that when selection is simultaneously applied for lower somatic cell score, it may lead to a marginal decrease in genetic gain for milk yield, but ultimately there is an increase in total economic merit. Upon selection with selection indexes, the rate of increase in mastitis would be slowed down. It is not the case that the incidence of mastitis would decrease. Immune responsiveness was suggested to be a useful indicator of disease resistance in cattle. In a single gene targeted selection process, immune response is more beneficial. However, studies in swine have indicated that selection for immune responsiveness can improve disease resistance to certain diseases while at the same time increasing susceptibility to others. For effective selection, indicator traits must be heritable, highly genetically correlated with resistance to the disease or diseases of interest, accurate to measure, and affordable. Genotypeeenvironment interaction is important in the process of selection. Suppose we select livestock for resistance to a particular disease in one particular environment; the selected livestock may suffer from the same
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disease in a different environment. Thus, selection programs may have to be environment-specific, with the selection environment matching the commercial production environment.
Marker-assisted or genomic selection for disease resistance with molecular biological tools Genetic selection is also a form of indirect selection. Resistance to most diseases is likely to be controlled by polygenes. Concerted interventions of physiological, genetic, and molecular genetic approaches are needed to investigate polygenic control of immune response and disease resistance. Genetic selection aided by molecular markers can be used to improve resistance to diseases. But ultimately, a detail understanding of the genetic basis of immune response is essential. Host disease resistance is governed by a number of genes conferring immunity. Three sets of genes (immuneresponse genes) the govern the response of vertebrate host to infection are 1. Genes controlling innate immunity 2. Genes determining the specificity of acquired immune response 3. Genes affecting the quality of acquired immunity Marker-assisted selection (MAS) for disease resistance involves identification of markers aided through polymorphism for the immune-response genes or identification of single-nucleotide polymorphisms (SNPs) by RFLP, SSCP, AFLP, etc. The candidate gene approach is very important for MAS. SNPs for the immune-response gene have been reported for the CD14 gene in goats (Pal and Chatterjee, 2009; Pal and Chatterjee, 2013b), cattle (Pal et al., 2011), and buffalo (Pal et al., 2013). Mastitis has been controlled by polygenes, and the candidate genes responsible for mastitis resistance have been reviewed by Pal and Chatterjee (2012). The candidate gene approach is also a form of indirect selection. Since disease-resistant traits are polygenic in inheritance, there are some constraints in the MAS process. A marker should be heritabledi.e., transmitted from generation to generation. An advantage of MAS is that these are codominant in nature and information may be available at an early age of life, even at the day-old stage. It saves the huge cost of rearing the animals to their age of production. It should be heritable. Considering the polygenic nature of inheritance, the better approach to MAS is genomic selection. This involves due consideration of a number of genes involved in disease occurrence. It consists of genome-wide association studies (GWASs). The SNPs for the genes involved in disease resistance will be identified, preferably with SNP chips, and GWASs will be conducted with suitable software. Statistical analysis involved with the disease-resistant traits is very important. Since the phenotypic traits pertaining to diseases do not follow normal distributions, they are instead threshold or all-or-none traits. Apart from usual analysis, logit or probit models for data analysis are very much applicable and widely used. We cannot at this time predict whether selection for disease resistance can be effective in livestock. Basic research into the complexities underlying diseases will likely reveal effective approaches for many disease problems. For example, the discovery that contagious keratoconjunctivitis (pinkeye) is heritable led to the discovery of a chromosomal region associated with its disease incidence. In the near future, it is likely that selection for disease resistance in most livestock species, especially cattle, will not be widely accepted by the industry because of the lack of knowledge about how best to select for disease resistance and poorly understood genetic correlations between disease resistance and economically important production traits. Selection for disease resistance will be disease dependent. It may be possible to select directly against the disease, select for indicator traits (indirect selection), to select directly for the gene(s) that confer resistance, or some combination of these approaches. The potential seems great for identifying breeding stock that is healthier because of its immune responsiveness. Although it may be difficult to select for animals resistant to a wide range of diseases, it may be possible to breed or identify animals that are genetically more responsive to antiviral vaccines or other therapies.
Selection objective The first decision to be made by a breeder setting up a selection scheme is the selection objective. The selection objective is the character (or characters) that we wish to improve by selection. It is also known as the breeding
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objective, breeding aim, breeding goal, and selection goal. It is chosen irrespective of whether it is easy, difficult, or even impossible to measure. Examples include reduced incidence of clinical mastitis in dairy cattle, reduced fecal nematode egg production in sheep, reduced numbers of ticks on beef cattle, and dogs without clinical hip dysplasia. There should be a clear-cut objective for selection for genetic improvement. Disease resistance could mean resistance to infection or the absence of clinical signs despite infection. As breeders usually wish to improve their return on investment, most breeding objectives aim to improve the profitability of animals. Every heritable trait influencing economic return related to disease resistance should be included in the selection objective for profitability. In practice, we do not have sufficient information to do this, and only the most important characters are included. The different components of an objective will vary in their economic value and are given greater or lesser weight according to their importance. In statistical terms, selection objective ¼ a1 Y1 þ a2 Y2 þ / þ an Yn ; where Y1, Y2, ., Yn are the traits in the selection objective and a1, a2, ., an are the relative economic values of the traits. Economic values are often defined as the marginal profit resulting from a change of one unit in that trait while all other traits remain unchanged. It is economic values relative to each other that are important. Estimating economic values for disease traits is not straightforward. For example, with parasitic infections such as sea louse in salmon and nematodes in sheep, the parasites are evolving resistance to drugs used to treat them. Consequently, economic impact will vary among sites; the impact of parasites is much greater on those sites that lack effective treatment. Ideally, we would like to eliminate all disease, but this is not feasible. Some diseases are very rare and individually unimportant enough to warrant inclusion in a selection index. Including more traits in the selection objective can reduce the selection pressure on each trait and slow the response in other traits such as production. For rare diseases, this cost outweighs the potential benefits. We lack adequate information to define resistant hosts for other diseases. Mastitis is economically the most important disease of dairy cattle, while nematode infection is the most important disease of small ruminants. In pigs and poultry, there are many important diseases. Consequently, selection for resistance against a single disease is not appropriate. In these industries, selection is focused against the major causes of reduced productivity and those diseases for which useful genetic markers exist. Selection for resistance against a specific disease usually involves testing directly for resistance to that disease. Two alternative selection strategies to reduce the impact of disease have been suggested. The first is to select for enhanced immune responses. This approach has been applied to both pigs and cattle. Animals with enhanced immune responses are more resistant to some diseases but more susceptible to others; however, enhanced resistance is more important than increased susceptibility. Yorkshire pigs selected for increased immune responses matured earlier, and this early maturity was valuable in itself. These individuals had both higher and more optimally balanced AMIR and CMIR, two key components of the adaptive immune system that are referred to as high immune responders. A patented test system has been developed to quickly identify these animals within dairy herds, and this method is referred to as High Immune Response (HIR) technology. Interestingly, HIR dairy cows showed lower occurrences of economically important infectious diseases as well as ketosis, emphasizing the relationships between infectious disease, immune function, and metabolic disorders. Dairy cows with the highest AMIR responses tended to have lower milk production, while those with the highest CMIR had higher milk production (Mallard, personal communication). These results emphasize the need to select animals simultaneously for AMIR and CMIR if both health and production traits are to be maintained or improved. This is a promising approach that deserves and is receiving more attention. The second alternative approach is to select for increased resilience. Resilience is defined as the ability to be productive despite infection. Some animals develop subclinical infections, and these animals could be used for breeding. In order to select for disease resistance, selection objective should include the trait for disease resistance as well as the trait for enhanced production. Thus the breeder can select for both resilience as well as resistance. Disadvantages will be there in situations where susceptibility to subclinical infection may result in economic losses for other reasons, such as increased somatic cell count in a dairy herd. Another potential disadvantage is that resilient animals may act as sources of infection for other individuals.
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Selection criterion or basis of selection The second decision is the selection criteria. The selection criteria are the traits that will actually be measured. Apart from individual selection, selection criteria might include information on relatives such as full-sibs, half-sibs or offspring. Pedigree selection based on pedigree information in a statistically sophisticated mixed-model analysis has proved efficient. For disease-resistance traits, the selection criterion could be clinical incidence for diseases. In countries like Norway, where clinical disease is routinely recorded as a part of routine investigation, the incidence of clinical mastitis in daughters is used to select bulls with superior breeding values for resistance to mastitis. Indicator traits such as somatic cell count can be equally applicable. For other diseases, such as infection with endoparasites or ectoparasites, clinical disease is often not much informative, and indicator traits are used. FECs are used to indicate susceptibility to nematode infection, while tick or louse counts can be used to identify genetically resistant cattle. Family selection is most effective for sex limited traits; for example, mastitis for dairy cattle is expressed only in cows and not in bulls. In cattle breeding, bulls do not produce milk nor develop mastitis. There is sufficient variation due to the method of assessment in many measurements of disease. One example is FEC following nematode infection. A consequence of measurement variation is that the importance of genetic variation is often underestimated. Taking multiple measurements can reduce measurement variation and provide a more accurate and heritable assessment of individual breeding value. In order to study resistance to tick infestation, researchers have developed scoring approaches or manually count ticks, and widely divergent heritability estimates for the trait have been observed. Seasonality is an important environmental effect for the estimation of genetic parameters for parasitic infestation. Immune response against nematode infection was observed to be better with increased heritability in older sheep than in young lambs. The time of year at which measurements of tick infestation were undertaken was observed to have a strong influence on heritability estimates. Genetic correlations among parasitism and production traits also change over time. A number of genetic markers for resistance to specific diseases have been identified. These genetic markers are very important to include in selection criteria. MHC is an important marker, but it has the ability to explain only 10% of phenotypic variance. Most loci have much smaller effects, and for most diseases the distribution of gene effects is L-shaped with most loci having very small effects. MHC is associated with resistance to mastitis and nematode infection, but because of its complexity, it has seldom been included in commercial breeding schemes. SNPs have been observed to be one of the most common sources of genetic variation. A single nucleotide site can have two, three, or even four alleles. SNP occurs approximately once every 1000 base pairs in humans and probably at a similar frequency in most livestock species. Nowadays, SNP chips are available, and GWAS analysis gives a conclusive result for the association leading to the most conclusive and effective selection process, “genomic selection.” Genomic selection is applicable for testing a large population that is well characterized. All values are then used to create a genomic breeding value for each individual. High genetic correlation has been observed between genomic breeding values with the estimated breeding values (EBVs) derived by conventional methods. A breeding value for each individual can be estimated from each polymorphism. An advantage of genomic selection is the ability to identify disease-resistant individuals with natural selection even in the absence of disease challenge.
Selection index Methods of selection are applicable when selection is applied for multiple traits or more than a single trait at a time. Three methods are applicable: tandem selection, independent culling method, and index selection. Selection index is the most efficient technique when individual weight is given to each trait based on economic value. In the tandem method of selection, selection is applied for one trait at a time for several generations. Then selection is applied for a second trait. The efficacy of the tandem method of selection is much less than that of the index selection method. Another method for multitrait selection is the independent culling level method. Independent culling levels select only those animals that exhibit more than a particular level of performance for each criterion. An animal will not be selected unless it is above the cutoff value for any one trait or criterion irrespective of its performance on other traits or criteria. The use of independent culling levels is only possible if all traits in the selection objective can be measured in each individual.
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Selection index is the most efficient method of combining different selection criteria. The construction of an index results in a single aggregate value that weights the different selection criteria according to their relative economic values. Selection index is an effective method to rank animals for their genetic merit (i.e., their breeding value). The traits that are to be measured can be denoted as X1, X2, ., Xm. The coefficients that give the most efficient response to selection are b1, b2, ., bm. Then, the selection index ¼ b1 X1 þ b2 X2 þ / þ bm Xm . Estimating the values of b1, b2, ., bm requires information about several factors: 1. Variation exists among animals (their phenotypic variances). 2. Differences in one trait are associated with differences in another trait in the selection index (their phenotypic covariances). 3. Genetic covariances exist between the traits. 4. There are genetic variances and covariances of the traits in the selection objective. The equation to estimate the selection index coefficients is Pb ¼ Ga, where P is a matrix of the phenotypic variances and covariances in the selection index, and G is a matrix of the genetic covariances between the traits in the selection index and traits in the selection criterion. The vector of economic values is represented by a, while the vector of coefficients to be estimated is represented by b. For a single trait that is measured only once on each animal, the selection index value is simply the trait value multiplied by the heritability. For any process of selection to be effective, it is not practically feasible to select only the disease-resistant trait. Unless the animal is in production, it is useless and not economically viable to select for only production traits. Calculation of the selection index value in monetary terms is essential, which represents the expected return from using each animal. Breeding for disease resistance leads to both genetic and epidemiological benefits because resistant animals may produce less infectious agents, thus lowering the rate of disease transmission. Mathematical models may be helpful for capturing the full response to selection. Index selection was reported to be quite effective for any disease-resistant trait. The metabolic disease resistance (MDR) index is one such important index. Six EBVs are considered in the MDR index. These are subclinical ketosis (BHB data), clinical ketosis (producer recorded), and displaced abomasum (producer recorded) for first and later parities. The traits under consideration for the genetic evaluation of these traits include body condition score from the first lactation classification and fat:protein (F:P) from the first test-day of lactation as indicator traits to improve accuracy. The weightings for the MDR index as proposed are 50% subclinical ketosis, 25% clinical ketosis, and 25% displaced abomasum, where the included EBVs are averages of the first and later lactation EBVs of each trait.
Response to selection The response to selection for a single character mainly depends upon five factors: 1. 2. 3. 4. 5.
Generation interval Variation in breeding values Intensity of selection Effective population size Accuracy of selection It has been observed practically that selection based on relatives and those with multiple traits has better accuracy.
Generation interval The generation interval (L) is the average age of parents when the offspring are born. Response to selection is inversely proportional to the generation interval. Hence if the parents are bred at an early age, the generation interval will be less and accordingly the response to selection will be more. The generation interval is calculated separately for males and females, then averaged. The generation interval will be influenced by the selection method.
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For example, the generation interval will be shorter for individual selection or sib-testing than it is for progeny testing. There is a trade-off between the generation interval and the proportion of animals selected, which affects selection intensity. It is possible to decrease the generation interval by using younger animals for breeding. However, if a greater proportion of offspring are used to replace parents, selection intensity be decreased. If all available ewe lambs are used to replace the older ewes in a flock, the generation interval will be small but selection intensity will be zero. Alternatively, if half the ewe lambs are selected as replacements, their mean value for the selected traits will be greater than that of their parents (i.e., the selection differential will be positive), but the generation interval will be larger. The use of assisted reproductive techniques such as artificial insemination (AI), multiple ovulation and embryo transfer (MOET), or in vitro fertilization (IVF) will improve the rate of genetic improvement. In the case of artificial insemination, a smaller number of male animals are needed to produce a given number of offspring, although the same number of female animals will be needed. In the case of MOET and IVF, a smaller number of female animals will also be needed. These methods have the ability to substantially increase the selection differential and shorten generation intervals, as the parents can leave more offspring by the time they reach a given age. Additive genetic variance is importance for any selection process to be effective. Better additive genetic variance will lead to better heritability. Variation in breeding values is important for selection. Traits with more phenotypic variation or higher heritability will have greater variation in breeding values. The selection intensity (i) is the superiority of the selected parents standardized according to the amount of variation in the trait or character. For selection based on the phenotype of an individual, i ¼ S=sp where S is the selection differential and sp is the standard deviation of the phenotypic observations. Let us discuss with the help of suitable examples. It can be more practical to talk about selecting the top 5% of animals rather than selecting some animals such that their average performance is þ10 kg over the population mean. If the top 5% of animals are chosen, then intensity of selection, i, equals 2.04 (approximately). Obviously if we select and breed the top 10% for any character, there will be a greater improvement than that achieved by selecting the top 90%. Practically, selection intensity is limited by reproductive ability and facilities for raising offspring. Any technique that improves reproductive rate can, if properly used, increase the selection intensitydfor example, assisted reproductive techniques such as those discussed (AI, MOET, IVF, etc.). Effective population size is the size of an ideal population that meets Hardy Weinberg assumptions, which would lose heterozygosity at a rate equal to that of the observed population. Factors that influence effective population size are fluctuating population size and breeding sex ratio. Effective population size (Ne) can be measured by the following formula: Ne ¼ 4NmNf/(Nm þ Nf), where Nm ¼ number of males Nf ¼ number of females Calculation of effective population size is of practical importance, particularly for endangered breedsdsay, for example, the Vechur breed of Kerala. Selection process reduces effective population size. The effective population size (Ne) considers that not all members of a population have an equal chance of contributing gametes to the next generation. Accuracy of selection depends on the accuracy of breeding value estimation. The accuracy of selection from a single measurement is estimated as the square root of the heritability. Selecting records decreases environmental noise and accordingly increases heritability. Accuracy can be improved by taking a greater number of measurements whenever feasible or by using relatives’ records (sibs, half-sibs, or progeny). The preferred statistical method for estimating breeding values is best linear unbiased predictor, because this method is the most effective at exploring genetic versus nongenetic differences such as differences between herds or between years.
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Predicting response to selection for a single trait Response to selection (R) may be predicted as R ¼ h 2S R ¼ (Va/Vp) i sp R ¼ sa h i, where h2 is heritability, S is selection differential, Va is additive genetic variance, Vp is phenotypic variance, and i is intensity of selection. sp is the phenotypic standard deviation. sa is the standard deviation due to additive genetic effect, h is the accuracy of selection, and i is the intensity of selection. As explained, response to selection is directly proportional to selection differential, heritability, and intensity of selection. The breeders’ equation states that the response to selection (R), the expected improvement in a trait in the offspring, can be predicted by the product of selection differential (S) and heritability (h2). This is applicable to a single generation for selection of a single trait, where the response is measured in generations and a large population, with no inbreeding and no drift. As selection proceeds, there is a change in frequency; favorable genes may become fixed, and unfavorable genes may no longer be present in the population. Changes in the extent of genetic variation will influence heritability. Heritability is variance in breeding values divided by phenotypic variance (Va/Vp), and selection differential is selection intensity multiplied by the phenotypic standard deviation i sp. Rearranging terms gives response to selection as sa h i, where sa is variation in breeding values, h is accuracy of selection, and i is intensity of selection.
Response to selection on multiple traits Response to selection on multiple traits depends on the additive genetic variances and covariances of the traits. Although it is similar to that of a single trait, in multiple traits the equation is R ¼ Gb; where R is change in the multivariate phenotype, G is the additive genetic varianceecovariance matrix, and b is the selection gradient estimated from the partial regression coefficients. As with selection for a single trait, this index is best for production traits and may not capture the full benefits of selective breeding for disease resistance. Mathematical modeling is required to capture the response to selection.
Mathematical modeling Quantitative genetic theory can accurately predict the response to selective breeding of production traits but is less effective at predicting the response to selection for resistance to infectious diseases. This is because selecting resistant animals and culling susceptible animals can alter the rate of disease transmission. In other words, genetic theory assumes that the environment remains unchanged, but culling heavily infected or diseased animals can decrease the contamination level in the environment with transmission stages. This is particularly true for terrestrial livestock. For example, selective breeding for nematode resistance based on FEC reduces the number of parasite eggs shed into the environment. A large number of epidemiological models have explored variation among individuals in their contributions to infectious disease dynamics, particularly for parasite infections. However, few epidemiological models have incorporated genetic variation and genetic response to selection for disease resistance.
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A case study The production of transmission stages is more persistent and predictable for endemic metazoan parasitic infections than it is for microbial infections. As a result, modeling response to selection for parasite resistance is far easier than modeling resistance to microbial infections. Models are available that predict response to parasite resistance. For example, the model for predicting the response to selection against nematode infection of lambs is popular. The lamb model predicts relatively rapid responses to selection for lower FECs following natural nematode infection, predominantly with Teladorsagia circumcincta. The responses observed in practice were less rapid. One possible explanation is that selection in practice involves multiple traits. Another is that the model did not attempt to capture immune responses. It essentially assumed that reduced parasite numbers due to immune responses were counterbalanced by increased larval intake. However, immune response may play a stabilizing role in nematode infection as reduced levels of infection generate reduced immune responses.
Disease resistance Disease resistance mechanisms in animals lead to the view that a large number of loci are involved in disease resistance. One important locus, such as MHC, may account for a major portion of genetic variance in disease. The rate of genetic gain from selection for a major locus alone or a combination of loci with performance is important. Although disease traits have low heritability, genetic variation for disease incidence is economically important and is justified for inclusion in breeding programs. An industry-wide standard for recording and accumulating field data for disease is not currently present. Coordinated approaches for institutional relationships among segments of the animal breeding and animal health industries are important for facilitating genetic improvements in disease resistance.
Selection for disease traits (threshold traits) All disease traits are mostly threshold traits, discussed in detail in other chapters. Here we discuss selection for disease traits. The application of selection to a threshold character does not lead to the theoretical difficulties of genetic analyses. It has some practical importance regarding the reduction of the incidence of abnormalities and with changing the response of experimental animals to treatmentsdfor example, increasing or decreasing drug resistance. We shall consider a character with two visible classes and refer first to individual selection. The process of direct selection for disease traits (with challenge study) was discussed at the beginning of this chapter. In this later part, it is important to discuss facts. For the threshold trait, it is not possible to locate the underlying continuous distribution. Response to selection for disease-resistant traits based on the selection differential is similar to that of production traits. But, unlike a production trait with a continuously varying character, selection differential does not depend primarily on the proportion selected but on the incidence of disease occurrence. Breeding can be employed from those individuals in the desired phenotypic class, but discrimination between those with high and low liabilities is not possible. Thus, the selected individuals are a random sample from the desired class, and their mean is the mean of the desired class. It does not depend on whether all of the desired class or only a portion of it is selected. Thus it is not advantageous to select a smaller portion than the incidence. On the contrary, if the proportion that must be selected is greater than the incidence, it will be essential to use some individuals of the undesired class. Their mean liability will be below the population mean, so the use of undesired individuals as parents will apply some threshold charactersdnegative selectiondthe mean of the undesired class is measured as eip/(1-p), where i is the mean of the desired class whose incidence is p.
Genetic assimilation The application of the principle of changing the threshold by environmental means is the phenomenon known as “genetic assimilation”dthe term was coined by Waddington (1953). If a threshold character appears due to an environmental stimulus, and selection is applied for this character, it may appear spontaneously later, without the necessity of the environmental stimulus. Thus, an “acquired character” becomes by perfectly orthodox principles
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A
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FIGURE 18.1 Diagram illustrating genetic assimilation.
of selection as an “inherited character.” In such a situation there are two thresholds, one spontaneous and the other induced. The spontaneous threshold is initially outside the range of variation of the population, depicting no variation of phenotype, and accordingly no selection can be applied. The induced threshold was observed to be within the range of liability covered by the population. This allows individuals toward one end of the distribution to be selected. Thus, the mean genotypic value of the population is changed. If this change continues, some individuals will eventually cross the spontaneous threshold and appear as spontaneous variants. When spontaneous incidence increases, selection may be continued without the help of the environmental stimulus, and spontaneous incidence may be further increased (Fig. 18.1).
Further reading Casas, E., Stone, R.T., 2006. Putative quantitative trait loci associated with the probability of contracting infectious bovine keratoconjunctivitis. J. Anim. Sci. 84, 3180e3184. Sacco, R.E., Nestor, K.E., Saif, Y.M., Tsai, H.J., Patterson, R.A., 1994. Effects of genetic selection for increased body weight and sex of poults on antibody response of Turkeys to Newcastle virus and Pasturella multocida vaccines. Avian Dis. 38, 33e36. Shook, G.E., May 1989. Selection for disease resistance. J. Dairy Sci. 72 (5), 1349e1362. Simianer, H., Solbu, H., Schaeffer, L.R., 1991. Estimated genetic correlations between disease and yield traits in dairy cattle. J. Dairy Sci. 74, 4358e4365. Templeton, J.W., Estes, D.M., Price, R.E., Smith III, R., Adams, L.G., 1990. Immunogenetics of natural resistance to bovine brucellosis. In: Proc. 4th World Congr. Gene. Appl. Lvstk. Prod., pp. 396e399. van Dorp, T.E., Dekkers, J.C.M., Martin, S.W., Noordhuizen, J.P.M., 1998. Genetic parameters of health disorders, and relationships with 305-day milk and conformation traits of registered Holstein cows. J. Dairy Sci. 81, 2264e2270.