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Method of identifying high immune response animals The invention relates to a method and use of a method of identifying high immune response animals under stress. The animals are identified by a ranking procedure that classifies the animal's immune response to an antigen over a period of time that spans the stress.
Primary Examiner: Nolan; Patrick J. Attorney, Agent or Firm: This application claims priority to U.S. Provisional Application No. 60/068,750, filed Dec. 24, 1997. We claim: 1. A method of ranking the immune response of a test animal within a population of animals under a stress of periparturition comprising: (a) immunizing the animals with at least one antigen at least once before onset of the stress and at least once during the stress; and (b) for each of the animals within the population, measuring an antibody response to the at least one antigen at least once before the onset of the stress and at least three times during the stress, and at least once after the stress (c) calculating the mathematical index of the antibody response wherein the mathematical index is: y=primary response+secondary response+tertiary response+quaternary response wherein, (i) y is the total antibody response; (ii) the primary response is the difference in antibody quantity at a first period of time preperipartum and at a second period of time prepartum, wherein the animal is immunized at the first period of time preperipartum; (iii) the secondary response is the difference in antibody quantity at the second period of time prepartum and at about parturition, wherein the animal is immunized at the second period of time prepartum; (iv) the tertiary response is the difference in antibody quantity at about parturition and at a first period of time postpartum, wherein the animal is immunized at about parturition; and (v) the quaternary response is the difference in antibody quantity at the first period of time postpartum and a second period of time post peripartum, wherein animals exhibiting negative secondary or tertiary responses are weighted with a positive coefficient and the test animal having a y value greater than about one standard deviation above the average of the population is a high immune responder. 2. A method according to claim 1 wherein the antigen is selected from the group consisting of hen egg white lysozyme, human serum albumin, tyrosine-glutamine-alanine-lysine (SEQ.ID.NO.1) copolymer, and ovalbumin. 3. A method according to claim 2 wherein the antigen is ovalbumin. 4. A method according to claim 1 wherein the antigen is formulated into a vaccine. 5. A method according to claim 1 wherein a source for measuring antibody response is selected from the group consisting of milk and blood. FIELD OF THE INVENTION The invention relates to a method of identifying and breeding high immune response animals within a population of animals under stress, such as during peripartum. BACKGROUND OF THE INVENTION It has been found that there is an association between stress and disease occurrence in animals (T. Molitor and L. Schwandtdt, "Role Of Stress On Mediating Disease In Animals", Proc. Stress Symposia: Mechanisms, Responses, Management. Ed., N. H. Granholm, South Dakota State University Press, Apr. 6-7, 1993). Further it has been suggested that stress can lead to a compromised immune system. (T. Molitor and L. Schwandtdt, "Role Of Stress On Mediating Disease In Animals", Proc. Stress Symposia: Mechanisms, Responses, Management. Ed., N. H. Granholm, South Dakaota State University Press, Apr. 6-7, 1993/ Morrow-Tesch J. L. et al. 1996 J. Therm. Biol. 21(2):101-108) This can have significant effect on populations of animals such as commercial livestock including cattle, pigs, poultry, horses, and fish, wherein stress can be related to growth inhibition, infertility, and decreased milk or egg production (where applicable). It has been shown that the peripartum period or periparturition, in animals is a period of stress. (L. G. Johnson, "Temperature Tolerance, Temperature Stress, and Animal Development", Proc. Stress Symposia: Mechanisms, Responses, Management. Ed., N. H. Granholm, South Dakaota State University Press, Apr. 6-7, 1993; J. J. McGloner, "Indicators Of Stress In Livestock And Implications For Advancements In Livestock Housing", Proc. Stress Symposia, : Mechanisms, Responses, Management. Ed., N. H. Granholm, South Dakaota State University Press, Apr. 6-7, 1993; T. Molitor and L. Schwandtdt, "Role Of Stress On Mediating Disease In Animals", Proc. Stress Symposia: Mechanisms, Responses, Management. Ed., N. H. Granholm, South Dakaota State University Press, Apr. 6-7, 1993; M. J. C. Hessing et al, "Social Rank And Disease Susceptibility In Pigs", Vet Immunol. Immunopath 43:373-387, 1994; F. Blecha, "Immunoligcal Reactions Of Pigs Regrouped At Or Near Weaning", Am. J. Vet. Res. 46(9): 1934-1937, 1985; D. L. Thompson et al., "Cell Mediated Immunity In Marek's Disease Virus-Infected Chickens Genetically Selected For High and Low Concentrations Of Plasma Corticosterone", Am. J. Vet. Res. 41(1):91-96, 1980; Kehrli, H. E. et al., 1989a & b, Am. J. Vet. Res. 50(2):207 and 215). Impairment of bovine host defense during the peripartum period may be associated with high concurrent disease occurrence. Impaired resistance may be due to endocrine factors associated with metabolic and physical changes occurring during gestation, parturition and lactation (Smith et al., 1973; Guidry et al., 1976; Burton et al., 1993). Infectious diseases of the peripartum period include mastitis, metritis and pneumonia. Metabolic and some reproductive diseases also predominate during this period and include retained placenta, milk fever, ketosis, and displaced abomasum. Mastitis is the most economically relevant disease. Estimated annual losses from mastitis are $35 billion (U.S) worldwide (Giraudo et al. 1997), $2 billion (U.S.) in the United States (Harmon, 1994) and $ 17 million (Can.) in Canada ($140-300 Can./cow) (Zhang et al., 1993). Mastitis is an inflammation of the mammary gland characterized by local and systemic responses (Burvenich et al., 1994). Mastitis can be clinical or subclinical, when signs are not directly observable, but somatic cell counts in milk (SCC) increase and overall production performance decreases. Mastitis is caused by a number of Gram positive and Gram negative bacteria which are either major or minor pathogens. Major pathogens induce the greatest compositional changes in milk and have the greatest economic impact (Harmon, 1994). They include Staphylococcus aureus, Escherichia coli, Streptococcus agalactiae, Klebsiella spp., and others, while minor pathogens include coagulase negative staphylococci, and Corynebacterium bovis. The incidence of udder infection and clinical mastitis is usually highest at parturition and during early lactation (Smith et al., 1985). Coliforms such as E. coli and Klebsiella are the most common major pathogen during this period. Since coliform mastitis is difficult to treat, natural defence mechanisms of the mammary gland have been investigated in pursuit of control procedures (Burvenich et al., 1994). Coliform mastitis may be peracute and fatal, or subclinical. Most commonly it is acute clinical mastitis, with local and systemic signs of disease. Coliforms are Gram-negative microorganisms from the family Enterobacteriaceae which include important species from the genera Escherichia, Klebsiella, Enterobacter, Citrobacter and Proteus (Harmon, 1994; Kremer et al., 1994). The structure of the cell wall of coliform bacteria plays an important role in the virulence of the bacteria and subsequently in the pathogenesis of mastitis. The cell wall of E. coli has an inner cytoplasmic membrane, a peptidoglycan layer, an outer membrane that consists of two layers: a phospholipid protein layer and an outer lipopolysaccharide layer (LPS), and finally some strains possess an additional capsular polysaccharide layer. The LPS layer has three components: the O-specific polysaccharide chain, a polysaccharide core, and lipid A. Lipid A mediates the biological properties of LPS (endotoxin). Endotoxemia causes clinical signs of disease including high fever, drowsiness, appetite loss, dehydration, loss in milk production, cardiovascular failure, shock and often death (Kremer et al., 1994; Burvenich et al., 1994). Factors which contribute to susceptibility to mastitis include the complex environment (pasture, bedding, cleanliness of holding areas), management (milking practices, antibiotic therapy during lactation and dry-off) and physical trauma to the teat and/or udder (Cullor, 1995). Various attempts have been made to develop vaccines against S. aureus as a treatment for mastitis, but without success. Vaccines have included toxoid, protein A, capsule and fibronectin in varying combinations and concentrations (reviewed by Sordillo, 1995). While these preparations may reduce the severity and duration of mastitis, new infections are not prevented. Inclusion of capsular polysaccharide in vaccine preparation slightly reduced the rate of new infection (Watson and Schwartskoff, 1990). More recently, the combination of a crude extract of S. aureus exopolysaccharides and inactivated unencapsulated S. aureus and Streptococcus spp. in a vaccine decreased incidence of intramammary infections caused by S. aureus (Giraudo et al., 1997). Newer vaccines against environmental coliforms contain rough or R-mutants of E. coli or Salmonella typhimurium. The surface core antigens of these mutants induces formation of cross-protective antibody that provides protection against various gram-negative diseases of animals including mastitis and calf scours. (Parker et al., 1994). These vaccines decrease incidence and severity of clinical disease but do not affect prevalence of coliform infections (Sordillo, 1995). Direct selection for disease resistance may be done either by selecting the most disease-resistant breeding stock under normal environmental conditions, or by challenging the breeding stock with specific pathogens (Hutt, 1959). Indirect selection is based on identification of reliable indirect markers of disease resistance (Detilleux et al., 1993). Phenotypic indicators include morphological markers (eg. eye margin pigmentation in bovine infectious keraconjunctivitis), physiological markers (eg. hemoglobin type in malaria), and innate or immune response traits (eg. PMN function, antibody response and CMI). Genotypic indicators include candidate genes (eg. MHC genes, Ig genes, TcR genes), and anonymous molecular genetic markers (eg. RFLPs, tandem repeats loci, microsatellite loci) (Detilleux et al., 1993). Experiments using immune response variation as selection criteria have been successful at directing response to be high or low (Biozzi et al., 1968; Ibanez et al., 1980; Siegel et al., 1980; Van der Zijpp et al., 1983; and Mallard et al., 1992). The continuous distribution antibody response suggests that response is under multigenic control (Puel and Mouton, 1996) and that characteristic quantitative antibody responsiveness is controlled by several independently segregating loci (Stiffel et al., 1987). The first selection experiment using antibody response following immunization was reported in guinea pigs assortatively mated for five generations. The immunogen used was diphtheria anatoxin and the immune responses of progeny were progressively modified in upward and downward directions (Shiebel, 1943). A similar experiment was conducted using rabbits selected for two generations based on antibody produced to Streptococcus sp. (Eichmann et al., 1971). A more extensive examination of antibody response variability in mice was demonstrated by Biozzi et al. (1979). Several independent selective matings were carried out with mice for antibody responsiveness to sheep red blood cells (SRBCs). SRBCs are multideterminant antigens which are strongly immunogenic in all strains of mice (Puel and Mouton, 1996). Assortative mating of mice with extreme phenotypes in upward or downward directions were repeated for successive generations until maximal divergence of the two lines was achieved (Biozzi et al., 1972). The relevance of this dichotomy pertains to the ability of mice to mount strong responses, either antibody or cell mediated immune response, to extra or intra cellular organisms. The low line (L line) was determined to be more resistant than the high line (H line) to intra-cellular organisms such as Salmonellae, Yersinia, Mycobacteria, and Brucellae, and when the macrophage provides the dominant defensive barrier. The H line was more resistant to extracellular microorganism including Pneumococcus, Klebsiella, Plasmodia, and Trypanosoma. The major genetic modification which explained differences between these selected lines was at the level of the macrophage. Antigen was observed to be slowly catabolized and persisted on the macrophage membrane of the H line mice, whereas it was rapidly destroyed in L line macrophages. Selection of chickens based on antibody response to SRBC has also demonstrated variation and the consequent divergence of high and low lines of chickens (Siegel and Gross, 1980; Van der Zijpp et al., 1983; Pinard et al., 1992). Antibody response to SRBC and chicken erythrocytes was similarly evaluated in guinea pigs, which diverged to high and low immune response lines after successive selection for 8 generations (Ibanez et al., 1980). Yorkshire pigs selected using estimated breeding values (EBVs) for both antibody and cell mediated immune response, were reported to diverge into high and low immune response lines (Mallard et al., 1992). The maximum divergence of high and low responses were observed between generation 1(G.sub.1) and 3 (G.sub.3) with little or no response to selection after generation 4 (G.sub.4) (Mallard et al., 1997). Although a few studies have examined the effect that selecting for milk production has on various innate and immune response parameters, no breeding studies have been conducted using immune response variation as selection criteria. Selective breeding of cattle for resistance to mastitis using somatic cell count (SCC) is currently under evaluation. Current industry trends favour a low somatic cell count in milk secretions. A SCC that is too low may be detrimental to innate mechanisms of resistance to mastitis and therefore must be used with caution. Genetic correlation between SCC and mastitis vary, but values are mainly positive (r=0.81; Madsen, 1989; r=0.3, Weller et al., 1996). SCC is now considered the primary trait used to evaluate susceptibility to mastitis which enables indirect selection for resistance to mastitis (Shook, 1994; Dekkers et al., 1998). Selection based on occurrence of clinical mastitis is unreliable since it is not routinely recorded, it has complex aetiology, and observations on the occurrence and severity of mastitis are subjectively evaluated by producers. Several records on SCC are available through dairy herd improvement corporations which provide a substantial database from which to determine estimated breeding values for SCC. SCC and its logarithmic transformation, SCS, have higher heritability (h.sup.2 =ranging between 0.10-0.12) (Emmanuelson et al., 1988; Banos and Shook, 1990; Boettcher et al., 1992) than clinical mastitis (h.sup.2 =0.03) (Emmanuelson, 1988; Madsen, 1989). However, low heritability estimates of SCS, in contrast to some production traits, indicate that SCS is not influenced to a greater degree by environmental factors. Low heritabilities suggest that SCS and mastitis will respond more slowly to genetic improvement than milk yield (Shook, 1993; Boettcher et al., 1992). Research conducted in Ontario by Dekkers and Burnside (1994) evaluating estimated transmitting abilities (ETAs) for linear somatic cell score (LSCS) indicated that daughters of the poorest sires had double the average SCC (transformed from LSCS) of daughters of the best sires, and, sires whose daughters had a higher LSCS tend to have more mastitis problems. This research indicated that, although adding LSCS to genetic selection will reduce genetic progress for production by <2 percent, it will also slow down the current genetic deterioration of resistance to mastitis. Its inclusion would be relevant since there would be lower treatment and other related mastitis costs and there would be an increase in the revenue per cow per year by 0.3 to 1.0 percent, despite a slight decrease in milk sales. While there is some benefit to using SCS as a selection tool, it is not as heritable as some aspects of immune response phenotype. Antibody response to ovalbumin (OVA) in dairy calves was reported by Burton et al. (1989) to be moderately heritable (h.sup.2 =0.48), and in contrast to SCS may be more promising as a selection tool for improved inherent disease resistance (Burton et al., 1989). Dekkers et al. (1996a) recently developed a sire index called the total economic value index (TEV) which includes economically weighted traits of importance. It includes production, herd life and udder health. Production accounts for 64% of the TEV, herd life for 26% and udder health, which includes SCS, accounts for 10% of the TEV. While production still is the most economically important, more emphasis can now be placed on the costs associated with mastitis by evaluating SCS. Once more heritable candidate markers of immune response are determined, more information about udder health could be added to the TEV. SUMMARY OF THE INVENTION The present invention relates to a method of identifying high immune response animals under stress and a method of determining an animal's susceptibility to stress related disease. The method involves evaluating the animal's antibody response to an antigen over a time interval spanning the stress, for example in periparturition, the pre- and postpartum period. Based on the response to the antigen, the animals can be classified as a high, average or low immune responder. Accordingly the present invention provides a method of ranking the immune response of a test animal within a population of animals under stress comprising: (a) immunizing the animals with at least one antigen at least once before onset of the stress; and (b) for each of the animals within the population, measuring an antibody response to the at least one antigen at least once before the onset of the stress and at least once during the stress, wherein an antibody response from the test animal that is greater than the average antibody response of the population during the stress indicates that the test animal is a high immune responder. According to another embodiment of the present invention there is provided a method of ranking the immune response of a test animal within a population of animals under stress comprising: (a) immunizing the animals with at least one antigen at least once before onset of the stress and at least once during the stress; and (b) for each of the animals within the population, measuring an antibody response to the at least one antigen at least once before the onset of the stress and at least once during the stress, wherein an antibody response from the test animal that is greater than the average antibody response of the population during the stress indicates that the test animal is a high immune responder. Where the stress is periparturition, the high immune responders comprise animals that have a sustained antibody response in both the pre and postpartum period, (herein referred to as Group 1 animals). These animals are least likely to develop peripartum disease. The average (herein referred to as Group 2 animals) and the low (herein referred to as Group 3 animals) immune responders comprise animals that initially have an average antibody response which declines either prior to, or at, partuition. In particular, the average immune responders comprise animals that have an average antibody response up until parturition, and thereafter show a lack of measurable antibody response. The low immune responders comprise animals that have an average antibody response until several weeks prepartum, (e.g., 3 weeks) and show a progressive decline in antibody response thereafter. Measuring the antibody responses to the antigen over time intervals, rather than at a discreet point in time, allowed the present inventors to develop a mathematical index which can be used to rank the animals. The mathematical index as part of the immunization and measurement schedules of the present invention provide a method of ranking the immune response of a test animal within a population of animals under the stress of periparturition. The method with the index comprise the following: (a) immunizing the animals with at least one antigen at least once before onset of the stress and at least once during the stress; and (b) for each of the animals within the population, measuring an antibody response to the at least one antigen at least once before the onset of the stress and at least three times during the stress, and at least once after the stress, (c) calculating the mathematical index of the antibody response wherein the mathematical index is: y=primary response+secondary response+tertiary response+quaternary response wherein, (i) y is the total antibody response; (ii) the primary response is the difference in antibody quantity at a first period of time preperipartum and at a second period of time prepartum, wherein the animal is immunized at the first period of time preperipartum; (iii) the secondary response is the difference in antibody quantity at the second period of time prepartum and at about parturition, wherein the animal is immunized at the second period of time prepartum; (iv) the tertiary response is the difference in antibody quantity at about parturition and at a first period of time postpartum, wherein the animal is immunized at about parturition; and (v) the quaternary response is the difference in antibody quantity at the first period of time postpartum and a second period of time post peripartum, wherein animals exhibiting negative secondary or tertiary responses are weighted with a positive coefficient and the test animal having a y value greater than about one standard deviation above the average of the population is a high immune responder. The inventors have also shown that exposing a population of animals to an antigen which can evoke a cell mediated immune response (CMIR) and measuring at least one indicator of the CMIR of each animal during stress, when combined with the immunization and measurement of antibody schedule of the present invention, there is provided yet another embodiment of the present invention for ranking the immune response of a test animal within a population of animals under stress. According to this embodiment of the invention the method comprises: (a) immunizing the animals with at least one antigen at least once before onset of the stress; (b) for each of the animals within the population, measuring antibody response to the at least one antigen at least once before the onset of the stress and at least once during the stress; (c) exposing the animals to an antigen which can evoke a cell mediated immune response (CMIR); and (d) measuring at least one indicator of the CMIRof each animal during the stress, wherein the measurement of the indicator is combined with the measurement of the antibody response to provide an immune response and a test animal having an immune response greater than the average immune response of the population indicates that the test animal is a high immune responder. The mathematical index as part of the immunization and measurement schedules of the present invention according to the embodiment just described provides a further embodiment of a method of ranking the immune response of a test animal within a population of animals under the stress of periparturition. The method with the index comprise the following: (a) immunizing the animals with at least one antigen at least once before onset of the stress; (b) for each of the animals within the population, measuring antibody response to the at least one antigen at least once before the onset of the stress and at least once during the stress; (c) exposing the animals to an antigen which can evoke a cell mediated immune response (CMIR); (d) measuring at least one indicator of the CMIRof each animal of the population during the stress; and (e) calculating the mathematical index of the antibody response and CMIR wherein the mathematical index is: y=primary antibody response+secondary antibody response+tertiary antibody response+quaternary antibody response+CMIR wherein, (i) y is the total antibody response; (ii) the primary response is the difference in antibody quantity at a first period of time preperipartum and at a second period of time prepartum, wherein the animal is immunized at the first period of time preparipartum; (iii) the secondary response is the difference in antibody quantity at the second period of time prepartum and at about parturition, wherein the animal is immunized at the second period of time prepartum; (iv) the tertiary response is the difference in antibody quantity at about parturition and at a first period of time postpartum, wherein the animal is immunized at about parturition; (v) the quaternary response is the difference in antibody quantity at the first period of time postpartum and a second period of time post peripartum; and (vi) CMIR is the measurement obtained from at least one method of determining CMIR, wherein animals exhibiting negative secondary or tertiary antibody responses are weighted with a positive coefficient and a test animal having a y value greater than about one standard deviation above the average of the population is a high immune responder. The methods of ranking the animals according to the present invention can be used to identify animals that are least susceptible to developing a postpartum disease. In particular, the present inventors have demonstrated that high immune responder dairy cows have a lower incidence of mastitis as compared to animals that are ranked as average or low immune responders. Accordingly, the present invention provides a use of a method of the invention to identify animals that are selected from the group consisting of: animals that are less susceptible to developing a peripartum disease wherein antibody quantity and quality are relevant host resistance factors; animals that are less susceptible to developing a peripartum disease wherein antibody quantity and quality and CMIR mediate broad-based disease resistance; animals with increased growth hormone; and animals with increased IGF-1 outside the peripartum period and with decreased IGF-1 inside the peripartum period. Once animals have been ranked by the method of the present invention, the high immune responder animals may be selectively breeded in order to produce animals that have lower incidence of peripartum disease. The methods of the present invention may be used in a wide range of animals including cows, pigs, chickens and other commercially useful animals. Other features and advantages of the present invention will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples while indicating preferred embodiments of the invention are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description. BRIEF DESCRIPTION OF THE DRAWINGS The invention will now be described in relation to the drawings in which: FIGS. 1A and B are graphs showing the anti-OVA antibody levels versus time for animals of Group 1, Group 2 and Group 3. FIG. 2 is a bar graph showing the percentage of disease occurrence in the animals of Group 1, Group 2 and Group 3. FIG. 3 is a graph showing the anti-OVA antibody levels versus time for the animals of Group 1, Group 2 and Group 3. FIGS. 4A-C are graphs showing the anti-OVA antibody levels in whey versus time for the animals in Group 1, Group 2 and Group 3. FIGS. 5A-C is a graph showing the anti-E. coli antibody levels versus time for the animals of Group 1, Group 2 and Group 3. FIGS. 6A and B are bar graphs showing antibody levels versus time in the animals of Group 1, Group 2 and Group 3. FIG. 7 is a bar graph showing the rate of mastitis occurrence based on antibody response within a herd. FIGS. 8A-C is a graph showing the somatic cell score versus time for the animals in Herd 1, Herd 2 and Herd 3. FIG. 9 is a graph showing Con A stimulated lymphocyte proliferatives versus time for the animals of Group 1, Group 2 and Group 3. FIG. 10 is a bar graph showing the percent increase in skin thickness after challenge with PPD in cows and heifers. FIG. 11 is a bar graph showing the lymphocyte counts versus time for the animals in Group 1, Group 2 and Group 3. FIGS. 12A-C are bar graphs showing the production versus antibody response for the animals of Group 1, Group 2 and Group 3. FIGS. 13A and B are graphs showing the anti-OVA antibody levels versus time for the animals of Group 1, Group 2 and Group 3. FIGS. 14A-C are graphs showing the hormone concentration versus time for the animals of Group 1, Group 2 and Group 3. FIG. 15 is a bar graph showing the percentage disease occurrence and the antibody response in the animals of Group 1, Group 2 and Group 3. DETAILED DESCRIPTION OF THE INVENTION As hereinbefore mentioned, the present invention is directed to a method of ranking the immune response of an animal within a population of animals. Further the present invention is directed to a method of calculating a mathematical index of the immune response in an animal. The present invention is also directed to the use of the methods of the invention to decrease the incident of disease, to enhance growth hormone (GH) and IGF-1 levels in animals during periods of stress and to breed high immune response animals. More particularly, the invention is directed to a method of ranking the immune response of a test animal within a population of animals under stress comprising: (a) immunizing the animals with at least one antigen at least once before the onset of the stress; and (b) for each of the animals within the population, measuring antibody response to the at least one antigen at least once during the stress, wherein an antibody response from the test animal that is greater than the average antibody response of the population during the stress indicates that the test animal is a high immune responder. In a preferred embodiment, each animal is further immunized at least once during the stress. In yet a further embodiment, the antibody response of the animals of the population is also measured at least once before the onset of the stress. According to another embodiment of the invention, the method of ranking the immune response further comprises exposing the animals of a population to an antigen, preferably under stress, which can evoke a cell mediated immune response (CMIR), measuring an indicator of the CMIR at least once during the stress and combining it with the measurement for antibody response, to obtain an immune response, wherein an immune response of a test animal that is greater than the average immune response of the population during stress indicates that the test animal is a high immune responder. Preferably, the CMIR is specific to the antigen. The antigen used to evoke the CMIR is preferably different than the antigen used to invoke the antibody response. Suitable indicators of CMIR include, but are not limited to: the measurement of one or more predetermined cytokines [for example, as described in L. T. Jordan et al. "Interferon Induction in SLA-Defined Pigs", Res. Vet. Sci. 58:282-283, 1995; J. Reddy et al., "Construction Of An Internal Control To Quantitate Multiple Porcine Cytokine mRNAs by rtPCR", BioTechniques 21:868-875, 1996; W. C. Brown et al., "Bovine Type 1 And Type 2 Responses", Vet. Immunl. Immunopath 63:45-55, 1998]; measuring delayed-type hypersensitivity (for example as described in Mallard, 1992, PCT/CA93/00533); and measuring in vitro lymphocyte proliferation to at least one antigen (for example, as described in Mallard B. A. et al., Animal Biotech 1992 ref. PCT/CA93/00533). "Stress" as defined herein, is any acute or chronic increase in physical, metabolic, or production related pressure to the animal. It is the sum of the biological reactions to any adverse stimulus, physical, metabolic, mental or emotional, internal or external, that tends to disturb an organisms homeostasis. Should an animal's compensating reactions be inadequate or inappropriate, stress may lead to various disorders. Many events can place an animal under stress. These include, but are not limited to: weaning, castration, dehorning, branding, social disruption, change in ration, temperature exercise and parturition. Examples of social disruption include, but are not limited to: change of location, shipping, and addition or removal of animals from immediate environment. The onset of parturition (also known as "prepartum"), parturition and after parturition (also known as "postpartum"), herein collectively referred to as "periparturition" or "peripartum", are also known causes of stress in animals. The time of periparturition, the time around parturition, is hereinafter referred to as the "peripartum period". In cows the peripartum period is from about three weeks before to about three weeks after parturition. Therefore, in cows, about 8 weeks prior to parturition would be prior to onset of the periparturition stress; about 3 weeks prior to parturition to about 3 weeks postparturition would be during the periparturition stress; and after about 3 weeks postparturition would be after the peripartum stress. Although the examples below use cows as the animal model, a person skilled in the art, upon reading this description, would understand that the present invention could be applied to other animals, preferably animals used for commercial use, such as pigs, poultry, fish, horses, and companion animals such as dogs and cats. Accordingly, "animal" as used herein includes, all members of the animal kingdom. In a preferred embodiment of the invention, the animals used are from the bovine genus and more preferably are selected from the group consisting of multiparous and primiparous cows. Further, it is understood that when conducting a method of the invention relatives may be used as the animal to define the rank of other relatives. One skilled in the art would appreciate that the gestation period differs between animal species. As such, when peripartum is the stress, such a person upon reading this description would know that the optimum times for immunizing and measuring an animal's immune response, as provided in this description for cows, may have to be adjusted, if another animal species is used. In one embodiment of the invention, pre-peripartum or before the on-set of stress, preferably refers to 2 or more weeks before the onset of stress. For instance, a person skilled in the art would appreciate that the actual time an animal is immunized before the onset of stress will depend on the antigen and animal species used. According to one embodiment of the invention, when periparturition is the stress and cows are the animals, the animals are immunized at least once before the stress at about 8 weeks before parturition and at least once during the stress at about 3 weeks before parturition and at about parturition. According to a preferred embodiment of the invention, when periparturition is the stress and cows are the animals, the antibody response is preferably measured at about 8 weeks before parturition, at about 3 weeks before partuition and at about parturition. In a more preferred embodiment the antibody response is further measured at about 3 weeks after parturition. "At about 8 weeks before parturition", as used herein, means at 8 weeks before parturition +/-4 days. "At about 3 weeks before parturition", as used herein, means at 3 weeks before parturition +/-4 days. "At about parturition", as used herein, means at or up to one week after parturition, but not before parturition. "At about 3 weeks after parturition", as used herein means at one week, and preferably at or up to 3 days, after parturition +/-4 days. "Antigen" as used herein, refers to any agent to which an animal is exposed and elicits an immune response. Suitable antigens for use in the present invention can be of bacterial, viral , synthetic, or other origin. For instance in cows, suitable antigens include but are not limited to ovalbumin, hen egg white lysozyme, human seralbumin, red blood cells from any animal other than the cow; tyrosine-glutamine-alaninelysine (SEQ. ID. NO. 1) co-polymer (a synthetic antigen). In choosing suitable antigens for the present invention, the antigens are preferably ones to which the animal is not normally exposed, and preferably one to which they have not been exposed. The antigens can be formulated into a vaccine, such as Ecoli J5, as used in the examples discussed herein. Examples of other possible vaccine antigens for use in cows include but are not limited to: Presponse (Merial) and IBR/PI3/BVD/BRSV combination vaccine (Bovilan 4K) etc. The term "greater than average antibody response" as used herein means the production of antibody in response to an antigen in an amount that is greater than approximately one standard deviation (sd) above that of the population mean. The preferred source for measuring antibody response in the present invention is milk or blood. "Milk", as used herein, is meant to include both the milk and the colostrum. The term "greater than average immune response" as used herein means a measure of an indicator of cell mediated immune response combined with the indicator of antibody response, which together provide a value that is one standard deviation above the population mean. The term "population" as used herein refers to a group of animals of the same species in which the measurements are obtained. For instance, in the examples of the present invention, three different groups or herds are used to obtain the population data. Population as used herein can also refer to a sample of the population, in so far as obtaining the ranking of immune response in a significant sample of a population can enable one to estimate or predict the immune response ranking of other related animals within the population. According to one embodiment of the present invention, there is a method of ranking the immune response of a test animal within a population of animals under the stress of periparturition comprising: (a) immunizing the animals with at least one antigen at least once before onset of the stress and at least twice during the stress; and (b) for each animal of the population, measuring antibody response to the at least one antigen at least once before the onset of the stress, at least three times during stress and at least once after the stress, calculating the mathematical index of the antibody response wherein the mathematical index is: y=primary response+secondary response+tertiary response+quaternary response wherein, (i) y is the total antibody response; (ii) the primary response is the difference in antibody quantity at a first period of time before preperipartum and at a second period of time during prepartum, wherein the animal is immunized at the first period of time prepartum; (iii) the secondary response is the difference in antibody quantity at a second period of time prepartum and at about parturition, wherein the animal is immunized at the second period of time before prepartum; (iv) the tertiary response is the difference in antibody quantity at about parturition and at a first period of time postpartum, wherein the animal is immunized at about parturition; and (v) the quaternary response is the difference in antibody quantity at the first period of time postpartum and at a second period of time after post partum, wherein animals exhibiting negative secondary or tertiary responses are weighted with a positive co-efficient, preferably about 1.5. This is done to discriminate against animals with low antibody response during stress. Test animals having a y value greater than about one standard deviation above the average of the population are high immune responders. The mathematical index of the total immune response can also be obtained with the method of the present invention, wherein the CMIR is added to the above-noted equation and results in y=primary response+secondary response+tertiary response+quartenary response+CMIR, wherein "y" is the total immune response of each animal of a population, and test animals having a "y" value greater than about one standard deviation above the average of the population are high immune responders. In one embodiment the present invention relates to a modification of the mathematical index in which all phenotypic indicators of immune response are converted to estimated breeding values. The use of this method is as previously described and includes: to identify animals with high immune response; and allow breeding of animals with increased accuracy for inherent increases in immune responsiveness. The methods of this invention can be used to identify preferred animals selected from the group consisting of: animals that are less susceptible to developing a peripartum disease wherein antibody quantity and quality are relevant host resistance factors; animals that are less susceptible to developing a peripartum disease wherein antibody quantity and quality and CMIR mediate broad-based disease resistance; animals with increased growth hormone; and animals with increased IGF-1 outside the peripartum period and with decreased IGF-1 inside the peripartum period. The methods of the invention can also be used to obtain a population of animals through traditional hereditary breeding techniques by calculating estimated breeding values (EBVs) of the indicators of immune responsiveness (Veterinary Genetics, F. W. Nicholas, Oxford Science Publications, 1987; D. S. Falconer. An introduction to quantitative genetics. Longman, London, 1981), preferably cows, which are high, average or low immune responders. The methods of the invention can also be used to predict or estimate the immune response ranking of an animal by having knowledge of the immune response ranking of at least one of the animal's relatives. Factors which would increase the accuracy of the estimate or prediction of such an immune response ranking of an animal, include but are not limited to: (i) Degree of separation from the animal (the knowledge of the ranking of the animal's full siblings and parents would result in a better estimate than with knowledge of the ranking of only cousins or partial siblings); (ii) The amount of data (the greater the database of knowledge of the ranking of one's relatives, the better the estimate or prediction); and (iii) The similarity of environmental factors. Experimental Design Identifying variation in immune response traits during the peripartum period, and any association with disease or production traits is the first step toward breeding dairy cows with superior health attributes. To evaluate phenotypic variation in peripartum antibody and cell-mediated immune responses of dairy cows, a total of 136 Holstein dairy animals (88 cows and 49 heifers) from 2 research herds (Herd 1, n=32, 6 heifers and 26 cows; Herd 2, n=67; 34 heifers and 33 cows) and 1 commercial herd (Herd 3, n=37, 8 heifers and 29 cows) were examined weekly from dry-off (approximately eight weeks prepartum; wk-8) to six weeks postpartum (wk 6). To stimulate specific antibody response during the peripartum period, all cows and heifers received intramuscular (im) injections of a mastitis endotoxemia preventive vaccine, an Rc mutant of Escherichia coli O111:B4 (Rhone Merieux Escherichia coli J5, Rhone Merieux, Lenexa, Kans.) with the manufacturer's adjuvant. In addition, cows were simultaneously administered ovalbumin antigen (OVA, Type VII, Sigma Chemical Co., St. Louis, Mo.) approximately 8 weeks (4 mg) and 3 weeks (2 mg) prior to predicted calving dates. At parturition (wk 0), cows received an additional immunization of the OVA dissolved in phosphate buffered saline (PBS-0.1 M, pH 7.4) (2 mg, im). Peripheral blood was sampled via tail venipuncture at weeks -8, -3, 0, 3, 6, and 9 relative to parturition, and centrifuged to monitor serum IgG.sub.1&2, as well as specific antibody responses to OVA and J5 E. coli. Colostrum and milk samples were also collected to measure specific antibody to OVA and total IgG.sub.1 and IgG.sub.2 in whey. Colostrum was collected at the first milking following parturition. Milk samples were stripped from all quarters approximately 2-4 hr after morning milking. Colostrum and milk samples were stored frozen without preservative at -20.degree. C. until time of whey separation and Ig quantification. In order to evaluate delayed type hypersensitivity (DTH) as a measure of cell-mediated immune (CMI) response a subset (n=36) of cows from research Herd 2 (Ponsonby Research Station, Elora, Ontario; n=15 cows and 21 heifers) were given a 1.5 mg/mL intradermal injection of the Bacillus Calmette Guerin (BCG; Connaught, Mississauga, Ontario) vaccine in the left caudal tail fold at wk 1 postpartum. At wk 3 postpartum, animals that had received the BCG vaccine were given a 0.1 mL (250 US Tuberculin Units) intradermal injection of the purified protein derivative (PPD) of Mycobacterium tuberculosis and 0.1 mL of the control (PBS), in the right caudal tail fold. These sites were located proximally to one another, about 4 cm apart. Injection sites in the left and right caudal folds were located approximately the same distance from the base of the tail head (10 cm) and across from one another. Double skinfold thickness was measured at 48 and 72 hours using Harpenden Skin Calipers (John Bull, England). As a measure of peripartum lymphocyte proliferation, lymphocytes were harvested from whole blood at weeks -3, 0, 3, and 6 relative to parturition and cultured with OVA antigen (5 .mu.g/mL) and the T-cell mitogen concanavalin A (Con A; 5 .mu.g/mL). Production Data Production data were obtained through monthly reports from the Ontario Dairy Herd Improvement Corporation (Ontario DHIC). All monthly milk samples were tested by the Central Milk Testing Laboratory, Guelph, Ontario, for SCC, and compositional content (fat %, protein %). In addition, milk samples from cows in research Herd 1 (Shurgain Research Farm, Burford, Ontario; n=26 cows and 7 heifers) were tested weekly by Ontario DHI. Projected 305 day production parameters for milk, fat, and protein were used as a measure to compare production between cows from the three herds investigated. Three hundred and five day (305-day) projections were calculated based on at least 100 days in milk (DIM). This allows comparisons between cows which may not be at the same stage of lactation when a monthly milk test is taken and between animals with varying lactation lengths. Disease Data Occurrence of infectious and metabolic diseases were investigated throughout the study period. All disease events were recorded by the herd manager. If an animal had two or more of the same disease event, it was recorded as one event for the study period. Specific Antibody Quantification by Enzyme Linked Immunosorbent Assay (ELISA) Anti-OVA antibody Serum was separated from coagulated peripheral blood by centrifugation (700.times.g, 15 min) and stored frozen (-20.degree. C.) until time of assay. Milk samples were centrifuged twice (11000.times.g, 15 min) to separate fat from whey. Whey was stored frozen at -20.degree. C. Antibody to OVA was detected by ELISA according to the procedure described by Burton, et al., 1993. Dynatech Immulon II flat bottom 96-well polystyrene plates (Fisher Scientific, Don Mills, Ont.) were coated with a 3.11.times.10.sup.-5 M solution of OVA (OVA, Type VII, Sigma Chemical Co., St. Louis Mo.) dissolved in carbonate-bicarbonate coating buffer (pH 9.6). Plates were incubated (4.degree. C., 48 h), then washed with PBS and 0.05% Tween 20 (Fisher Scientific, Don Mills, Ontario) wash buffer, (pH 7.4) using a EL403 plate washer (Biotek, Mandel Scientific, Guelph, Ontario). Plates were then blocked with a PBS -3% Tween 20 solution and incubated (rt, 1 h). Plates were washed and diluted test sera (1/50 and 1/200) or milk whey (Neat, 1/10, 1/100 and 1/400) and controls were added using the quadrant system described by Wright (1987). After blocking, sera samples were added in duplicate, and milk whey samples were added in quadruplicate. Plates were incubated (rt, 2 h). Subsequently, alkaline phosphatase conjugate rabbit anti-bovine IgG (whole molecule) (Sigma Chemical Co., St. Louis, Mo.) was dissolved in wash buffer, added to the plates and incubated (rt, 2 h). P-Nitrophenyl Phosphate Disodium tablets (pNPP) (Sigma, St. Louis, Mo.) were dissolved in a 10% diethanolamine substrate buffer, (pH 9.8). Plates were washed with wash buffer, pNPP was added to the plates and then incubated (rt, 30 min). Plates were read on a EL311 automatic ELISA plate reader (BIO-TEK Instruments, Highland Park, Vt.) and the optical density (OD) was recorded at 405 and 630 nanometres (nm) when the positive control reached OD.gtoreq..999. The 630 filter was used as a reference filter to correct for fingerprints and irregularities in the plastic of the plates. The mean of the number of replicates added to each plate was corrected to an OD=1.0 by multiplying by the inverse of the mean of the positive controls. Corrected means of each dilution were then added together to give an additive OD value, indicative of antibody response. Negative and positive controls included a pooled sample of pre-immunization sera and a pooled sample of sera from cows 14 days post secondary immunization, respectively. Sera from 20 animals was tested by ELISA to determine antibody responses at 4 dilutions (1/50, 1/200, 1/800, and 1/3200). The dilutions 1/50 and 1/200 provided responses with minimal prozone which corresponded to anticipated antibody response curve kinetics based on the immunization schedule, and allowed a clear differentiation between positive and negative controls. Since for a small subpopulation of cows these dilutions exhibited some prozone effects, the dilutions were added together to provide an index of antibody response. Similarly, in order to determine the optimal sample dilutions that would be used to quantify antibody in milk whey, milk from two cows was serially diluted (neat, 1/2, 1/4 . . . 1/512) to determine the dilution which had a minimal prozone, and allowed optimal differentiation of responses of positive and negative control sera. Acceptable dilutions included Neat, 1/10, 1/100 and 1/400. These dilutions were added together to give and index of whey antibody response. Anti-E.coli antibody Lyophilized E.coli J5 (American Type Culture Collection, Rockville, Md., USA) was grown in 5 mL Tryptic Soy Broth (TSB) for 2 days to obtain log phase growth. This culture was then transferred to a 1 L flask of sterile TSB and sealed aseptically. The culture was incubated (37.degree. C., 12 hrs, 200 rpm) on an INNOVA platform shaker (New Brunswick Scientific, Edison, N.J.). A 1 mL sample of cells was diluted logarithmically and plated on blood agar to determine the colony forming unit count (cfu). The number of cfu was 1.13.times.10.sup.9. Live cells were then pelleted by centrifugation (5000 g, 15 min). Cells were washed in PBS and pelleted by centrifugation 3 times (first wash, 5000.times.g, 15 min; second and third washes, 7500.times.g, 15 min) Cells were suspended in PBS at a final volume of 1 L. The culture was then heat-killed by boiling for 2 hours. The final preparation was diluted until an absorbance reading=1.0 at 540 nm was obtained. The E.coli J5 was stored frozen (-20.degree. C.) until time of assay. Serum was separated from coagulated peripheral blood by centrifugation (700.times.g, 15 min) and stored frozen (-20.degree. C.) until time of assay. According to the method described by Rhone-Merieux Animal Health (Lenexa, Kans.; 1994 personal communication), heat-killed Escherichia coli strain J5 (ATCC, Rockville, Md.) was coated at a concentration of 6.25.times.10.sup.7 cfu per mL onto Dynatech Immulon II polystyrene 96 well flat bottom plates overnight at 4.degree. C. After washing with wash buffer (PBS plus 0.05% Tween 20), 1% gelatin was added to block non-specific binding and plates were incubated (rt, 1 h). Plates were washed and four replicates of test serum (dilutions of 1/1000, 1/1500, 1/2000 and 1/2500) were added using a modified quadrant system (Wright, 1987). One column with PBS -0.05% Tween 20 was used as a blank, one column of fetal calf serum (FCS, Bockneck Laboratories, Can Sera, Rexdale, Ontario, Canada) was used as a negative control and one column each of the negative and positive controls prepared from pooled pre- and post immunization sera were plated, respectively. Test sera were incubated (rt, 2 h), and then the plates were washed with PBS -0.05% Tween 20. Horseradish peroxidase conjugate goat anti-bovine IgG whole molecule in PBS (1/4000) (The Binding Site, Birmingham, England) was added and the plates were incubated (rt, 1 h). After subsequent washing with PBS -0.05% Tween 20, the substrate, 2,2'-azino-di-(4-ethyl-benzthiazoline sulphonate-6) (ABTS) was added and plates were incubated (rt, 30 min). Plates were then read on an EL311 automatic ELISA plate reader (BIO-TEK Instruments, Highland Park, Vt.) and the OD was recorded at 405 nm and 490 nm. The mean OD of the four sample replicates were corrected for each plate by multiplying by the inverse of the mean of the positive controls and used as an indicator of antibody response. Based on the immunization protocol and phenotypic observation of antibody response curve kinetics of all dilutions tested, the 1/1000 dilution consistently allowed for differentiation between positive and negative controls, exhibiting minimal prozone effect. Therefore 1/1000 was the dilution of choice for comparison between animals. The same pooled positive sera used in the OVA ELISA was tested to ensure a differentiation between pre-immune negative sera and post secondary immunization sera. This positive control was determined to be suitable for this assay since an OD of 1.0 was reached at a dilution of 1/200 while the negative sera had an OD that was <0.100. Negative control sera in this assay was prepared by absorbing boiled whole cell E.coli J5 in pooled non-vaccinated sera. FCS was also used as a negative control. Quantification of Immunoglobulin G.sub.1&2 by Radial Immunodiffusion (RID) Quantification of Total IgG.sub.1&2 in sera Radial immunodiffusion (RID) was used according to a modified method described by Mallard et al, 1992, to determine the concentrations of IgG.sub.1&2 in serum at weeks 0, 3, and 6 relative to parturition. Immunodiffusion medium was prepared by dissolving 2% Seakem agarose (FMC Bioproducts, Mandel Scientific, Guelph, Canada) and 2% Polyethylene Glycol 8000 (Carbowax 8000, Fisher Scientific, Fairlawn, N.J.) in PBS. Rabbit anti-bovine isotype specific IgG.sub.1&2 (VMRD, Pullman, Wash.) was suspended in the immunodiffusion agarose at a concentration of 33% (vol/vol) for IgG.sub.1 and 30% (vol/vol) for IgG.sub.2. Immunodiffusion medium was held in a liquid state and poured into 5 mL immunodiffusion plates. Agarose was allowed to solidify and then three rows of wells, 6 wells per row, were punched with a 3 mm glass pipette tip. Standard concentrations of IgG.sub.1 (1800 mg/100 mL) and IgG.sub.2 (1600 mg/100 mL) as controls (VMRD, Pullman, Wash.) were diluted (neat, 1/2, 1/4, 1/8, 1/16, 1/32) and five microlitres of these standard serial dilutions were added to the top row of each plate. Five .mu.L of each test sample was added to the two bottom rows of each plate. Plates were incubated (rt, 20 h) in a humidified chamber. Afterwards, ring diameters were measured using a calibrated grid held over a fluorescent light source. Ring diameters from standards were used to make a standard curve for each plate determined by linear regression. By plotting ring diameter on the x axis and the log of the concentration (mg/100 mL) on the y axis, the concentration of Ig could be determined. Quantification of Total IgG.sub.1&2 in whey In order to determine Ig concentration in colostrum, immunodiffusion medium was prepared by dissolving 2% Seakem agarose (FMC Bioproducts, Mandel Scientific, Guelph, Canada) and 2% Polyethylene Glycol 4000 (Carbowax 3350, Fisher Scientific, Fairlawn, N.J.) in PBS. Rabbit anti-bovine isotype specific IgG.sub.1&2 (VMRD, Pullman, Wash.) was suspended in the immunodiffusion agarose at a concentration of 33% (vol/vol) for IgG.sub.1 and 30% (vol/vol) for IgG.sub.2. The procedure for the preparation of RID medium and plates for colostral whey samples was essentially the same as that described for sera except that polyethylene glycol 3350 was used instead of 8000 to improve ring clarity. Colostrum samples were centrifuged twice (11000.times.g, 15 min) to separate fat from whey prior to plate application. In order to determine Ig concentration in milk, immunodiffusion medium was prepared by dissolving 2% Seakem agarose and 2% Polyethylene Glycol 4000 (Carbowax 3350, Fisher Scientific, Fairlawn, N.J.) in PBS. Rabbit anti-bovine isotype specific IgG.sub.1 (VMRD, Pullman, Wash.) was suspended in the immunodiffusion agarose at a concentration of 12.5% (vol/vol). Milk samples were centrifuged twice (11000.times.g, 15 min) to separate fat from whey. The procedure for the preparation of RID medium and plates for milk whey samples is essentially the same as that described for colostrum except that the concentration of goat-antibovine sera suspended in the immunodiffusion media was 33% for IgG1 and 30 % for IgG2. Whey from wk 3 was tested for both IgG.sub.1&2 subclasses. At wk 6 however, IgG.sub.1 only was tested in whey since very low concentrations of IgG.sub.2 exist in normal milk. Examination of the Cell-Mediated Immune Response (CMIR) Delayed Type Hypersensitivity A preliminary study was conducted to determine if the Ponsonby herd was previously exposed to Mycobacterium tuberculosis or other cross reactive antigens from Mycobacterium paratuberculosis. Five cows and six heifers were injected intradermally with 0.1 cc of the PPD of M. tuberculosis (Connaught, Mississauga) and a control dose of 0.1 cc PBS (pH 7.4) in the right caudal tail fold located proximally to one another (approx. 4 cm apart) PPD was injected in a designated area above the PBS site. Both injection sites were 10 cm from the base of the tail head. Prior to injection, injection sites were encircled with a coloured marker and a pre-test and pre-control thickness measurement was taken in triplicate, using Harpenden skin calipers (John Bull, England). This measurement was identified as the time=zero hours measurement. After 24 and 48 hours, skin thickness measurements were taken to assess the percent increase in skin thickness of control and test sites. It was determined that the herd had not previously been exposed to the M. tuberculosis antigen since 95% of all the animals tested had very little or no increase in skin thickness at the injection sites (i.e a 0-7% increase in skin thickness) and that the BCG/PPD test system would be suitable to measure DTH responses in this herd. Two animals from the Ponsonby herd were selected to determine the optimal time point following the injection of PPD that would yield a maximal response and ensure that actual DTH responses were induced. Animals were evaluated at 0, 6, 12, 24, 48 and 72 hours post PPD challenge. Measurements taken at 6 to 12 hours were used to ensure that the response to antigen was not characteristic of an antibody-mediated reaction. In cattle, the maximal response to PPD is normally observed around 72 hours (Radostits et al, 1990). Preliminary results indicated that the response was optimal at 48 hours, therefore both time points were evaluated for comparison between animals. Prior to immunization using PPD, and a PBS control, a pre-test and pre-control (at time=0 hours) skin thickness measurement was obtained in triplicate from each of the 36 animals evaluated. Forty eight and 72 hours after secondary challenge, these measurements were taken again. The amount of skin thickness increase at 48 and 72 hours expressed as a percent increase in skin thickness was calculated as follows: where A=mean test thickness (at time=48, 72 hours), B=mean of pre-test thickness (at time=0 hours), C=mean of control thickness (at time=48, 72 hours), D=mean of pre-control thickness (at time=0 hours). Cows could be classified according to their % increase in skin thickness as either non-responsive or low responders (less than one sd below the mean), moderate responders (between one sd below and one sd above the mean), or high responders (more than one sd above the mean). Lymphocyte Proliferative Response Lymphocyte proliferation assays were performed according to the procedure of Chang, et al. (1993). Peripheral whole blood was centrifuged (850.times.g, 15 min) and buffy coats were diluted in phosphate buffered saline (PBS 0.1 M, pH 7.4). Peripheral blood lymphocytes (PBL) were separated by density gradient centrifugation (1000.times.g, 30 min) of buffy coats using aqueous Histopaque 1.077 (Sigma Chemical Co. St. Louis, Mo.) Cell pellets were washed by centrifugation in PBS (400.times.g, 7 min) and suspended in a volume of culture medium (Rosewell Park Memorial Institute; RPMI-1640, and 100 I.U. penicillin-streptomycin, prepared by Central Media Laboratory; Ontario Veterinary College, University of Guelph, Guelph, Ontario.) and 10% FCS and brought to a final concentration of 2.0.times.10.sup.6 cells/mL in culture medium. In order to determine specific clonal proliferative responses to antigen, a stock solution (50 .mu.g/mL) of OVA (Sigma Chemical Co., St. Louis Mo.) dissolved in RPMI-1640 was prepared and stored in small aliquots at -70.degree. C. Five .mu.g/mL of OVA was added to 6 replicates of test lymphocytes in 96 well flat-bottom plates (Nunc, Fisher Scientific, Don Mills, Ontario). Medium was added to 6 well replicates of cells as non-stimulated controls and this represented background or unstimulated cell proliferation. As a general indicator of lymphocyte proliferation, Con A mitogen similarly prepared from stock solution (50 .mu.g/mL) and diluted (5 .mu.g/mL) was added to 6 replicates of cells on a separate plate containing an additional 6 wells as medium controls. Following 24 h of incubation with OVA or Con A(37.degree. C., 6% CO.sub.2) cells received an 18 h 'pulse' incubation with 0.5 .mu.Ci methyl tritiated thymidine per well (ICN Biochemical, Canada Ltd. Montreal, PQ). Plates were frozen until cells were harvested using a plate harvesting system (LKB Wallac, Turku, Finland) onto fiberglass filter mats (LKB Wallac, Turku, Finland). Radioactivity was recorded as counts per minute (cpm) of test minus non-stimulated controls of retained radioactivity measured by a beta plate liquid scintillation counter (LKB Wallac,Turku, Finland). OVA antigen preparations were tested using the above described method at a concentration of 5 .mu.g/mL, 10 .mu.g/mL, and 20 .mu.g/mL. Although lymphocyte proliferative responses did not differ significantly between the tested concentrations, 5 .mu.g/mL was selected to induce PBL proliferation in subsequent assays. To determine the concentration of the mitogen able to induce optimal PBL proliferation, Con A concentrations were tested at 2 .mu.g/mL, 5 .mu.g/mL and 10 .mu.g/mL. Five .mu.g/mL yielded maximal lymphocyte proliferative responses and was therefore selected as the concentration applied in further investigations. Flow Cytometric Assay for the detection of CD Surface Molecules on Lymphocytes either not stimulated or stimulated with Con A or OVA In order to determine which lymphocyte subsets were present after stimulation with either Con A or OVA, cells were stained with monoclonal antibodies recognizing 5 cell surface markers according to the method described by Van Kampen and Mallard (1997). The monoclonal antibodies used in this study were kindly provided by Dr. Jan Naessens of ILRAD (Institute for Animal Health, Compton, Berkshire) and included antibodies to the following cell surface markers: CD2+(IL-A43), CD4+(IL-A11), CD8+(IL-A105), WCI (IL-A29), and IgM (IL-A30). A subset of animals (n=10) from research Herd 2 (Ponsonby, Elora, Ontario; n=7) and the commercial herd (Speedvalley Holsteins, Fergus, Ontario; n=3) were evaluated for expression of these lymphocyte cell surface markers at weeks -3, 0, 3, and 6 relative to parturition. Lymphocytes were prepared and cultured as previously described for lymphocyte proliferation assays, however, each 96 well plate was divided into quadrants each with 24 wells. Twenty four replicates each of Con A stimulated, OVA stimulated (at 5 .mu.g/mL and 20 .mu.g/mL) and non-stimulated controls were cultured for 42 hours (the same total duration used in the lymphocyte proliferation assays). After 42 hours, cells were harvested by pipette, washed with PBS and transferred to 10 mL glass test tubes. Cells were centrifuged (400.times.g, 10 min), and supernatants were poured off and cells were resuspended in 250 .mu.L PBS+0.1M Azide. Immunostaining was performed in 96-well round-bottom plates (Coming, New York, N.Y.). Fifty .mu.L of cells and 50 .mu.L of diluted primary antibody were added to each well and incubated (20 min, rt). After incubation, 100 .mu.L of PBS+0.1M sodium azide (Fisher Scientific, Fairlawn, N.J.) was added to each well to wash the cells. Cells were suspended by mixing on a shaker and centrifuged (400.times.g, 6 min). Supernatants were then removed using an aspirator. This washing procedure was performed twice. Fifty .mu.L of FITC-conjugated goat anti-mouse IgG(H+L) (Cedarlane Laboratories, Hornby, Ontario) was then added to the cells and cells were incubated (rt, 20 min). After incubation, plates were washed twice as described above. Cells were fixed in 1% paraformaldehyde and transferred into 3 mL polystyrene tubes (Becton Dickinson, Lincoln Park, N.J.) containing 300 .mu.L of 1% paraformaldehyde. Tubes were covered with parafilm and refrigerated until time of assay. A FACS Scan flow cytometer (Becton Dickinson, Lincoln Park, N.J.) was used to acquire all lymphocyte subset data. LYSIS II software (Becton Dickinson, Lincoln Park, N.J.) was used for fluorescence data analyses. Lymphocytes were gated from other populations based on their forward and side scatter characteristics. Five FITC histograms were plotted for each cow, time point and culture condition observed. Histograms representing fluorescence of cells expressing CD2 (pan T cell), CD4 (helper T cells), CD8 (cytotoxic/suppressor T cells), WCI (gd T cells), and IgM (B cells) cell surface markers were examined. The region of background fluorescence was established with the negative control marker, M1. Everything to the right of this marker was considered positive. Complete Blood Cell Counts Complete Differential Blood Cell Counts were determined by the Clinical Pathology Laboratory at the Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada. Counts included the percent and number erythrocytes, banded neutrophils, segmented neutrophils, lymphocytes, monocytes, basophils, eosinophils, as well as total leukocytes. Somatic Cell Counts in Milk Weekly somatic cell counts (SCC) of the Shurgain herd were obtained using the weekly sampling service offered by Ontario DHI. Weekly samples of cows in the Ponsonby and Dunk herds sampled 1-4 hours after morning milking were tested for SCC by the Mastitis Laboratory at the Ontario Veterinary College, University of Guelph, Guelph, Ontario Canada. Monthly somatic cell counts were obtained from Ontario DHI records for all three herds. Categorization of Cows Based on Antibody Response Biological Classification Using Antibody Response Curves Serum antibody responses to OVA from the first herd investigated (Shurgain, Burford, Ontario, n=32) were graphed individually for each cow at weeks -8, -3, 0, 3, and 6 to examine response curve patterns. Evaluation of these curves during the peripartum period through to peak lactation indicated that enough variation existed to rank animals according to antibody response to OVA. Cows that showed consistently above average responses to OVA were categorized as high or Group 1. Cows that had an average antibody response up until parturition and thereafter showed a lack of measurable (LOM) antibody response were categorized as the postpartum LOM response group or Group 2. Cows that had an average antibody response until three weeks pre-partum and showed a progressive decline in measurable antibody response were categorized as the peripartum LOM group or Group 3. Subsequent investigations of immune responses between cows in the other herds studied revealed similarities. However, subtle differences in the amplitude and direction of antibody response curves, in relation to the immunization schedule, indicated that the data was continuous in nature. Thus, the groups determined for Herd 1 wouldn't necessarily apply to all herds. It was clear then, that antibody responses to OVA were on a continuum, and any classification method implemented would benefit from a quantitative approach to readily and appropriately partition phenotypic variation between cows. Quantitative Classification Using a Mathematical Index Serum antibody responses to OVA were evaluated over time intervals, rather than discrete points in time. Individual animal antibody response curves from week -8 to week 6 relative to parturition (week 0) were dissected into components reflecting the response to antigen following immunizations. Primary response was defined as the change in antibody to OVA from week -8 to week -3 relative to parturition following primary immunization at week -8 (Primary=OD value at week -3 minus OD value at week -8). Secondary response was defined as the change in antibody to OVA from week -3 to parturition following secondary immunization at week -3 (Secondary=OD value at week 0 minus OD value at week -3). Tertiary response was defined as the change in antibody to OVA from parturition to week 3 following tertiary immunization at parturition (Tertiary=OD at week 3 minus OD at week 0). Quaternary response was defined as change in antibody to OVA from week 3 to week 6 (Quaternary=OD value at week 6 minus OD value at week 3). Quaternary response was included to observe the change in antibody response between the end of the immediate postpartum period (wk 3) and peak lactation. These responses were added together to give an index of antibody response to OVA between wk -8 and wk +6 relative to parturition as follows: y.sub.index =primary+secondary+tertiary+quaternary where, y=total antibody response; primary, secondary, tertiary, and quaternary responses are as previously defined; primary, secondary, tertiary, and quaternary responses when positive, have an equal weight of 1. Animals which exhibited negative secondary or tertiary responses during the immediate pre-and post-partum period were weighted with a coefficient of 1.5 instead of 1. Only secondary and tertiary responses were weighted in this manner, since this is the period when lowered host resistance mechanisms are thought to contribute to increased occurrence of disease. The coefficients for weighting negative secondary and tertiary responses were optimized using the original biological assessment for grouping animals in the first herd investigated. The quantitative ranking of animals had to reflect the biological assessment of grouping animals based on the magnitude and direction of response to immunization. The mean of the antibody response index was determined and animals that exceeded one standard deviation above the mean were classified as high responders (Group 1). Animals that were one standard deviation below the mean were classified as low responders (Group 3). Animals with an index of antibody response that ranged between one standard deviation below and above the mean were classified as average responders (Group 2). Statistical Methods Least squares analysis of variance (ANOVA) and corrected means (least square means, LS Means) were generated using the General Linear Models (GLM) Procedure of the Statistical Analysis System (Helwig and Council, 1982). A model was constructed for the following dependent variables: antibody response to OVA in sera and whey, antibody response to E.coli in sera, concentration of IgG.sub.1&2 in serum and whey, background lymphocyte proliferation and lymphocyte proliferation following culture with Con A or OVA, DTH, SCS and production variables. Sources of variation included in the model for each dependent variable are summarized in Table 1. Data that did not show a normal distribution, as indicated by the univariate procedure of SAS (Helwig and Council, 1982), were transformed to natural logarithms. The Proc CORR procedure of SAS was used to generate Pearson product moment correlation coefficients between immune response parameters and production variables. Results were considered to be statistically significant if the p-value was .ltoreq.0.05 and trends were reported at a p-value .ltoreq.0.10. Models indicated are base models. Some parameters were excluded if non-significant in order to generate LS Means. Model 1 Antibody response to OVA in serum and whey, Ig in serum and whey and E. coli in serum y.sub.ijklmn =.mu.+herd.sub.i +season-yr.sub.j +cow(group*parity).sub.klm +week.sub.n +group.sub.k +parity.sub.l +(group*parity).sub.kl +(group*week).sub.kn +e.sub.ijklmno where, y.sub.ijklmno =observed response of cow m in group k and parity l for each sample week of each cow, .mu.=the population mean, herd.sub.i =fixed effect of herd (i=1,2,3), season-yr.sub.j =fixed season-year effect (j=Spring 1994, Summer 1994, Fall 1994, Winter 1994/1995, Spring 1995, Summer 1995, Fall 1995, Winter 1995/96), group.sub.k =fixed effect of group based on antibody response to OVA (k=1,2,3), parity.sub.l =fixed effect of parity (1=1,2, or >3), (group*parity).sub.kl =fixed effect of group*parity interaction, cow(group*parity).sub.klm =random effect of cow-grouped within group*parity term, week.sub.n =fixed effect of sample week (n=-8,-3, 0, 3, 6, 9), (group*week).sub.kn =fixed effect of group by week interaction term; e.sub.ijklmno =random or residual error term. When parity was not significant, the cow term was edited to reflect the appropriate nested variable. Model 2 Cell Mediated Immune Responses and Lymphocyte proliferation y.sub.ijklmnop =.mu.+herd.sub.i +season-yr.sub.j +cow(group*parity).sub.klm +week.sub.n +group.sub.k +parity.sub.l +(group*parity).sub.kl +(group*week).sub.kn +replicate.sub.o +b(cov).sub.ijklmno +e.sub.ijklmnop where all variables are as described for model 1 except, y.sub.ijklmnop =observed response of cow m in group k and parity l for each replicate o at each sample week, replicate.sub.o =fixed effect of replicate (o=1,2,3,4,5,6), and b(cov).sub.ijklmno =regression coefficient of y.sub.ijklmnop on resting cell proliferation for the klm.sup.th cow The model for DTH was: y.sub.ij =.mu.+group.sub.i +e.sub.ij ; where, .mu.=the population mean, group.sub.i =fixed effect of group based on antibody response to OVA (i=1,2,3), e.sub.ij =random or residual error term. Parity was not included in this model since it was not significant, and when included with group, did not provide enough degrees of freedom to run the analysis of variance. Tests of hypothesis of group or parity were tested against the MS random error term for cow. Type III Sums of Squares corrected for all other variable within the model were used account for the variation in immune responses. The following non-limiting examples are illustrative of the present invention: EXAMPLES Example 1 Periparturient Antibody Response Profiles of Holstein Cows: An Initial Immunobiological Assessment To evaluate phenotypic variation in peripartum immune responsiveness of dairy cattle, 33 Holstein cows were immunized with ovalbumin (OVA) and Escherichia coli J5 at weeks -8 and -3 prior to parturition. At parturition (week 0), cows received an additional immunization of OVA. Blood was collected at weeks -8, -3, 0, 3 and 6 relative to parturition to measure serum immunoglobulin (Ig) concentration, and antibody to OVA and E.coli. Colostrum and milk were also collected post-parturition to measure Ig and antibody to OVA. All cows had a measurable antibody to OVA following primary immunization, but not all cows responded to second and/or third immunizations. Antibody response to OVA was used to classify cows into three groups recognizing animals with sustained measurable antibody response before and after parturition (Group 1), animals which responded poorly or did not respond to immunization at parturition (Group 2), and animals which did not respond to immunizations at week -3 or at parturition (Group 3). The objectives of this example were threefold: 1) to investigate antibody response during the peripartum period; 2) to classify cows based on variation of antibody response; and, 3) determine if antibody response is associated with the occurrence of disease. Materials and Methods Animals and Treatments Antibody response of 33 Holstein cows were examined from approximately eight weeks prepartum (week -8), based on predicted calving dates to six weeks postpartum (week 6). Twenty-six animals were multiparous cows and seven were primiparous heifers. Cows received an intramuscular (im) injection of a mastitis endotoxemia preventive vaccine with the manufacturer's adjuvant (Rhone Merieux E. coli J5, Rhone Meerieux, Lenexa, Kans.) along with the antigen OVA (Type VII, Sigma Chemical Co., St. Louis, Mo.), at weeks -8 (4 mg OVA) and -3 (2 mg OVA). At parturition (week 0), cows received an additional immunization of OVA without adjuvant dissolved in phosphate buffered saline (PBS-0.1 M, pH 7.4) (2 mg, im). Ovalbumin was chosen as an inert soluble antigen to which these animals had likely not been previously exposed. E. coli J5 was used as a complex, insoluble, biologically relevant antigen to which most dairy cows were likely to have been previously exposed. Antibody response to OVA was used to classify cows into three groups recognizing animals with sustained measurable antibody response before and after parturition (Group 1), animals which responded poorly or did not respond to immunization at parturition (Group 2), and animals which did not respond to immunizations at week -3 or at parturition (Group 3)(FIG. 1A). Blood and Milk Sampling Schedule Blood was collected by tail venipuncture at week -8, and weekly from weeks -3 to 6 relative to parturition. Serum was used to monitor immunoglobulin G.sub.1&2 concentrations, and determine antibody to OVA and E. coli J5. Colostrum and milk were collected to determine antibody to OVA and to monitor IgG.sub.1 (weeks 0, 3, 6) and IgG.sub.2 (weeks 0 and 3) concentration. Colostrum was collected at the first milking following parturition. Milk was obtained from all quarters approximately 2-4 hr after morning milking. Colostrum and milk samples were frozen without preservative at -20.degree. C. until the time of whey separation and analysis. Anti-OVA Enzyme Linked Immunosorbent assay (ELISA) As described in the General Methods section. Anti-E. coli J5 ELISA As described in the General Methods section. Radial Immunodiffusion Assay As described in the General Methods section. Disease Occurrence As described in the General Methods section. Milk Somatic Cell Count Milk (AM/PM composite sample) was collected weekly by the herd milker during milking to determine somatic cell count (SCC). Only SCC which coincided with the day of blood sample collection for each week are reported. SCC, an indicator of subclinical mastitis, was transformed to somatic cell score (SCS) for analysis. SCS is the natural logarithm of SCC in cells/.mu.L and is calculated as follows (Shook, 1993): Statistical Methods Type III least squares analysis of variance (ANOVA) and corrected means (least square means, LS Means) were generated using the General Linear Models (GLM) Procedure of the Statistical Analysis System (SAS; Helwig and Council, 1982). The statistical models used included fixed effects of antibody response groups (1,2,3), cow nested within antibody response group, and week relative to parturition (weeks -8, -3, 0, 3, and 6). In preliminary analysis, the effect of parity was not significant and was therefore removed from all subsequent models. A model was constructed for the following dependent variables: antibody response to OVA in sera and whey, antibody response to E. coli J5 in sera, and the concentration of IgG.sub.1&2 in serum and whey. Sources of variation included in the model for each dependent variable are summarized in Table 1. Data that were not normally distributed as indicated by the univariate procedure of SAS, were transformed to natural logarithms. (whey antibody to OVA, serum antibody to E. coli, serum and whey IgG.sub.2. Pearson product moment correlation coefficients between immune response variables were generated using the correlations procedure of SAS (Proc CORR). Results were considered to be statistically significant if the P-value was .ltoreq.0.05 and trends were reported at P-values .ltoreq.0.10. Results Antibody Response to OVA Antibody in serum Serum antibody to OVA varied significantly over the peripartum period and individuals could readily be classified into three immune response groups: high responders (Group 1, n=12; 6 heifers, 6 cows) versus animals which exhibited a LOM response to immunization either postpartum (Group 2, n=12 cows) or pre- and postpartum (Group 3, n=9; 8 cows, 1 heifer). Approximately 1/3 (Group 1) of the animals had consistently above average serum antibody response to OVA following immunization at weeks -8, -3, and 0 relative to parturition. The remaining animals had OD values measuring antibody to OVA that were close to the population mean or had responses lower than the population mean and did not respond following immunization at week -3 or parturition (FIG. 1A). All cows, including those of Group 3, had serum antibody greater than background (week -8) at week -3 and therefore were considered low responders rather than non-responders. The statistical model (ANOVA) accounted for 94.19% of the total variation in serum antibody to OVA over the peripartum period. Effects of cow (P.ltoreq.0.0001), antibody response group (P.ltoreq.0.0001), week (P.ltoreq.0.0001), and the interaction between antibody response group and week (P.ltoreq.0.0001), contributed significantly to the variation in serum antibody to OVA (Table 1). Antibody in Whey Cow (P.ltoreq.0.0001), week (P.ltoreq.0.0001), and antibody response group (P.ltoreq.0.0001) contributed significantly to the variation in antibody in whey (Table 1). There was also a tendency for the interaction between antibody response group and week (P.ltoreq.0.09) to account for variation in whey antibody to OVA. Population LS Means of whey antibody to OVA declined significantly following parturition, such that at week 0 the OD value was 1.456 compared to 0.645 (P.ltoreq.0.004) at week 3 and 0.366.+-.0.20 (P.ltoreq.0.0001) at week 6 (FIG. 1B). At weeks 3 and 6, Group 1 cows were significantly higher than (P.ltoreq.0.05) Group 3 cows. Antibody Response to E. coli L5 Cow (P.ltoreq.0.0001), week (P.ltoreq.0.0001), and antibody response group (0.0001) all contributed significantly to variation in antibody response to E. coli J5. OD values of pre-immunization sera (week -8) indicated that these cows had minimal measurable E. coli J5 antibody (population mean of OD=0.296; n=33) compared to post-vaccination antibody at week -3 (0.739) and week 0 (0.789). Antibody response to E. coli J5 was positively correlated with antibody response to OVA (r.sup.2 =0.59, P.ltoreq.0.0001). IgG.sub.1 & IgG.sub.2 in serum, colostrum, and milk Antibody response group significantly contributed to the variation of serum IgG.sub.2 (P.ltoreq.0.0001) only. Group 3 cows had a significantly (P.ltoreq.0.05) higher serum IgG.sub.2 concentration than Groups 1 and 2 at parturition. Antibody to OVA was negatively and significantly correlated with serum IgG.sub.2 (r=-0.23; P.ltoreq.0.05). Disease Occurrence Fifty four and a half % of the 33 animals evaluated were considered healthy during this study. Of the diseased animals, seven cows had mastitis (21.21%), seven had ketosis (21.21%) and three cows had other diseases (9.09%). Animals in Group 1 that had above average antibody to OVA, had the lowest percent occurrence of disease (17%) (FIG. 2) and actually had no clinical mastitis. 3.5. Somatic Cell Score (SCS) At parturition, LS Means of SCS were significantly lower (P.ltoreq.0.05) for Group 2 cows (SCS=3.2) compared to Group 1 (SCS=4.36) and Group 3 (SCS=4.98) cows. At weeks 2,3,4, and 6 after parturition, all groups differed significantly from one another, and, Group 1 cows consistently had the lowest SCS while Group 3 cows consistently had the highest SCS. Discussion Antibody response before and after parturition has not been thoroughly investigated. Antibody response to OVA, a test antigen to which these animals would normally not have and had probably not been previously exposed, was utilized to partition cows into three immune response groups recognizing animals with sustained antibody response before and after parturition (Group 1), animals which did not respond to immunization at parturition (Group 2), and animals responding poorly throughout the peripartum period (Group 3). Variation in antibody response to E. coli J5, a biologically relevant antigen, was more difficult to partition. Pre-immunization E. coli antibody was significantly lower compared to post immunization antibody in this herd. This indicates that the E. coli J5 antigen would be useful for classifying animals in the herd evaluated according to their antibody response but does not indicate that another herd will respond in the same way. Pre-immunization antibody may be higher in other herds where gram negative bacteria are frequently encountered. Nagahata et al. (1992), examined B lymphocyte populations in order to evaluate host defense in dairy cows during the periparturient period. This study found no significant changes in the number of B lymphocytes of cows from two weeks before until two weeks after parturition. However, they did report a significant decrease in antibody producing cells immediately after parturition. The authors suggested this indicated a decrease in B lymphocyte function during the immediate postpartum period. This is consistent with the low peripartum antibody response seen in some animals in the present study. Although it has been reported that serum antibodies decline at parturition and colostral antibodies increase due to the sequestration of immunoglobulins into the mammary gland (Detilleux et al., 1995), this study suggests that lower antibody in serum does not necessarily relate to Ig transport. For instance, Group 1 cows, which had the highest serum antibody responses, also tended to have higher whey antibodies to OVA postpartum, when compared to cows of Groups 2 and 3. Initially, it was questioned whether low serum antibody may be associated with higher antibody in the colostrum or milk. This data indicates that animals with high serum antibody also supply high concentrations of antibody to the mammary gland. This example has demonstrated significant individual variation during the peripartum period and confirms that not all cows have depressed antibody response. In swine, animals with inherently high and low immune response phenotypes can also be identified in a population (Mallard et al., 1992). In light of previously reported heritability (h.sup.2) estimates of bovine antibody response (Burton et al., 1989), these data from this study may suggest that Group 1 animals could be inherently better able to produce antibody, in spite of the metabolic and physical stresses of the peripartum period. Cows in Group 1 did have the lowest occurrence of peripartum disease, particularly mastitis (0% occurrence), and significantly lower SCS scores following parturition than cows in Groups 2 and 3, thus indicating that antibody response should be considered as a potential marker of peripartum disease resistance. Example 2 A Quantitative Approach to Classifying Holstein Dairy Cows Based on Antibody Response, the Relationship Between Antibody Response and Peripartum Disease Occurrence, and Heridability Estimates A quantitative approach was developed to partition phenotypic variation of peripartum antibody response profiles of Holstein cows and to determine associations with peripartum mastitis. Using a mathematical index, 136 cows and heifers from three herds were ranked as high responders (Group 1), average responders (Group 2) or low responders (Group 3) to OVA. Grouping animals by serum antibody response to OVA indicated that animals ranked similarly for antibody to OVA in whey and antibody to Escherichia coli in serum. Differences in serum and whey IgG.sub.1 concentrations between antibody response groups were not significant. Serum IgG.sub.2 concentration however, varied between group, within herd and across time. Whey IgG.sub.2 did not differ significantly between antibody response groups within herd. Occurrence of mastitis was negligible for Group 1 animals. In contrast, Group 1 animals from Herd 2, had the greatest occurrence of mastitis while Group 3 had the lowest. Milk somatic cell score (SCS), was lowest for Group 1 animals in Herd 1 and lowest for Group 3 animals in Herd 2, thus supporting the distribution frequency of clinical mastitis in those herds. Herd 3 SCS did not differ significantly between antibody response groups and did not underscore the distribution of clinical mastitis. The objective of this study was to confirm the existence of high and low antibody response profiles amongst individuals across three herds and to devise a method for quantitatively classifying cows into groups based on antibody response to standardized immunization protocols. Relationships were evaluated between antibody response, immunoglobulin concentration, milk somatic cell score, and disease occurrence with respect to antibody response group. Materials & Methods Animals and Treatments Antibody responses of 136 Holstein dairy cows and heifers from 2 research herds (Herd 1, n=32, 6 heifers and 26 cows; Herd 2, n=67; 34 heifers and 33 cows) and 1 commercial herd (Herd 3, n=37, 8 heifers and 29 cows) were examined from eight weeks prepartum (week -8) based on predicted parturition dates to six weeks postpartum (week 6). Forty nine animals were primiparous heifers, 47 animals were in their second lactation and 41 were multiparous cows (>2 lactations). Antibody responses were evaluated as previously described (Mallard et al., 1997; Ch. V ). Animals received an intramuscular (im) injection of ovalbumin (OVA; Type VII, Sigma Chemical Co., St. Louis, Mo.) and a mastitis endotoxemia preventive vaccine with the manufacturer's adjuvant (Rhone Merieux E. coli J5, Rhone Merieux, Lenexa, Kans.) at weeks -8 (4 mg) and -3 (2 mg). At parturition (week 0), animals received an additional immunization of OVA in phosphate buffered saline (PBS-0.1 M, pH 7.4) (2 mg, im). OVA was chosen as an inert test antigen to which these animals had not likely been previously exposed. E. coli J5 was used because dairy cows could be expected to have been previously exposed to E. coli, a complex antigen, having biological relevance. Blood and Milk Sampling Schedule Blood was collected by caudal tail venipuncture at approximately week -8 relative to parturition, and weekly from weeks -3 to 6 relative to parturition. Samples were used to monitor serum immunoglobulin G.sub.1&2 and serum antibody to OVA and E. coli J5. Colostrum and milk samples were collected to monitor whey IgG.sub.1&2 and antibody to OVA in whey. Colostrum was collected at the first milking following parturition. Milk samples were stripped from all quarters approximately 2-4 hr after morning milking. Colostrum and milk samples were stored frozen without preservative at -20.degree. C. until time of whey separation and immunoglobulin quantification. ELISA for OVA Antibody Detection In Serum and Whey Antibody to OVA was detected by ELISA, and quantified based on optical density measurements according to a procedure previously described (Mallard et al., 1997; Ch. V). Sera samples (weeks -8, -3, 0, 3, and 6) diluted 1/50 and 1/200 were assayed in duplicate. Whey samples (weeks 0,2,3,4, and 6) diluted 1/10, 1/100, 1/400 and undiluted were assayed in quadruplicate. ELISA for E. coli J5 Antibody Detection In Serum Antibody response to E. coli J5 was measured according to the method previously described (Mallard et al., 1997; Ch. V). Serum samples (weeks -8, -3, 0, 3, and 6) diluted 1/1000 were assayed in quadruplicate. Radial Immunodiffusion Assay Radial immunodiffusion was used according to the method described by Mallard et al. (1992) to determine the concentrations of serum IgG.sub.1&2 at weeks 0, 3, and 6 and whey IgG.sub.1 at weeks 0, 3, and 6 and whey IgG.sub.2 at weeks 0 and 3. Quantitative Classification of Animals Based on Antibody Response Serum antibody responses to OVA were evaluated over time intervals, rather than discrete points in time. Individual animal antibody response curves from week -8 to week 6 relative to parturition (week 0) were dissected into components reflecting the response to antigen following immunizations. Primary response was defined as the change in antibody to OVA from week -8 to week -3 relative to parturition following primary immunization at week -8 (Primary=OD value at week-3 minus OD value at week -8). Secondary response was defined as the change in antibody to OVA from week -3 to parturition following secondary immunization at week -3 (Secondary=OD value at week 0 minus OD value at week -3). Tertiary response was defined as the change in antibody to OVA from parturition to week 3 following tertiary immunization at parturition (Tertiary=OD at week 3 minus OD at week 0). Quaternary response was defined as change in antibody to OVA from week 3 to week 6 (Quaternary=OD value at week 6 minus OD value at week 3). Quaternary response was included to observe the change in antibody response between the end of the immediate postpartum period (wk 3) and peak lactation. These responses were added together to give an index of antibody response to OVA between wk -8 and wk +6 relative to parturition as follows: y.sub.index =primary+secondary+tertiary+quaternary where, y=total antibody response; primary, secondary, tertiary, and quaternary responses are as previously defined; primary, secondary, tertiary, and quaternary responses when positive, have an equal weight of 1. Animals which exhibited negative secondary or tertiary responses during the immediate pre-and postpartum period were weighted with a coefficient of 1.5 instead of 1. Only secondary and tertiary responses were weighted in this manner, since this is the period when lowered host resistance mechanisms are thought to contribute to increased occurrence of disease. The coefficients for weighting negative secondary and tertiary responses were optimized using the original biological assessment for grouping animals in the first herd investigated. The quantitative ranking of animals had to reflect the biological assessment of grouping animals based on the magnitude and direction of response to immunization. The mean of the antibody response index was determined and animals that exceeded one standard deviation above the mean were classified as high responders (Group 1; n=18). Animals that were one standard deviation below the mean were classified as low responders (Group 3; n=23). Animals with an index of antibody response that ranged between one standard deviation below and above the mean were classified as average responders (Group 2; n=95). Heritability Estimates Sire and error variance components of serum antibody to OVA were estimates by REML using Variance Component Estimation (VCE) software (Groeneveld, E. 1994). Sire and error variances were used to estimate paternal half-sib heritabilities for serum antibody to OVA at weeks -8, -3, 0, 3, and 6 relative to calving. Approximate standard errors were computed from the variance covariance matrix of sire and error variance component estimates. Mastitis Occurrence Occurrence of clinical mastitis was recorded throughout the study period by herd managers. Two or more events of mastitis it were recorded as one event for the study period (Martin et al., 1993). Incidence of mastitis occurrence was calculated by dividing the number of animals within an antibody response group that had at least one disease event by all the animals in that antibody response group, and multiplying this number by 100. Mastitis occurrence was evaluated for associations with antibody response group within each herd, using odds-ratio (OR) (Martin and Meek, 1987). Odds-ratios in this study was calculated on a within herd basis, as the ratio between the rate of mastitis in one antibody response group versus the rate of mastitis in the rest of the herd (i.e. the other two groups). Odds-ratio is the approximate relative risk when the rate of disease in the population is relatively infrequent (<5%) (Martin and Meek, 1987). Odds ratios values were tested for significance using the chi-square test (Martin and Meek, 1987). Milk Somatic Cell Count Milk (AM/PM composite sample) was collected weekly to determine somatic cell count (SCC), an indicator or subclinical mastitis. Only SCC which coincided with blood sample collection for each week were used in evaluation. SCC was transformed to somatic cell score (SCS) for analysis. SCS is the natural logarithm of SCC in cells/mL and is calculated as follows: Statistical Methods Type III least squares analysis of variance (ANOVA) and corrected means (least square means, LS Means) were generated using the General Linear Models (GLM) Procedure of the Statistical Analysis System (SAS; Helwig and Council, 1982) to evaluate the effects of herd, season-year, cow, antibody response group, parity, week, and their interaction terms on antibody response to OVA and E. coli, and immunoglobulin concentration (Table 2). Tests of hypothesis of main effects were tested against the MS for cow. Sources of variation that were not significant were removed from the model in order to generate LS Means. Data that did not show a normal distribution (E. coli antibody response, serum IgG.sub.2 and whey IgG.sub.2) as indicated by the univariate procedure of SAS (SAS, 1982), were transformed to natural logarithms. LS means were converted back to original units from log.sub.e transformed data. Consequently, standard errors of means are not shown. The Proc CORR procedure of SAS was used to generate Pearson product moment correlation coefficients between immune response parameters. Results were considered to be statistically significant if the p-value was <0.05 and trends were reported at the p-value <0.10. Results Serum Antibody to OVA Cow, antibody response group, week, and the interaction between antibody response group and week contributed to the variation (P<0.0001) in serum antibody to OVA (Table 2). Herd did not significantly contribute to the variation in serum antibody to OVA. As expected, the rank of antibody response to OVA was Group 1>Group 2>Group 3 except at week -8 prior to immunization and significant differences were noted between all groups at weeks -3, 0, 3, and 6. Population LS means significantly (P<0.0001) increased from pre-immunization (week -8) to week -3 (post primary immunization) in all antibody response groups and OVA antibody response varied significantly across time at weeks -3, 0, 3, and 6. (FIG. 3). Heritability Estimates of Antibody to OVA Heritability estimates (h.sup.2) of antibody to OVA at weeks -8, -3, 0, 3, and 6 relative to calving were 0.64, 0.62, 0.32, 0.50, and 0.58 respectively. Standard errors could not be calculated by VCE software due to the small sample size evaluated. These heritability estimates can be used to obtain an Estimated Breeding Value (EBV) of an animal in accordance with the procedure described in PCT/CA93/00533 to Wilkie et al., filed Dec. 9, 1992, entitled "Methodology For Developing A Superior line of Domesticated Animals" (also see, Veterinary Genetics, F. W. Nicholas, Oxford Science Publications, 1987; D. S. Falconer, An introduction to quantitative genetics, Longman, London, 1981). EBV is an indicator of an animal's inherent ability to produce an immune response and its ability to pass genes influencing these traits to offspring. For the purposes of the present invention, EBV values are useful in selecting animals to be bred in order to produce offspring which inherit the level of ability to produce a high immune response when under stress. High immune response may, in part, influence disease resistance. Whey Antibody to OVA Herd contributed significantly (P<0.01) to variation in antibody response to OVA and therefore, herds were further analyzed separately. Cow, antibody response group, and week all significantly contributed to the variation in antibody to OVA in whey (P<0.0001); however, there was no significant contribution of the interaction term antibody response group and week to the variation in response. For all herds, antibody to OVA in whey by antibody response group, ranked similarly to the antibody responses observed for serum, such that Group 1>2>3. This was consistent for colostral and milk whey from parturition until week 6 of lactation (FIGS. 4A,B, and C). Least squares means of antibody to OVA in whey for all herds declined significantly from parturition to peak lactation. Correlation analysis between antibody to OVA in sera with antibody to OVA in whey, indicated a positive and significant relationship for Herd 1 (r=0.45; P<0.0001), Herd 2 (r=0.28; P<0.001) and Herd 3 (r=0.45; P<0.001) respectively. Antibody to E. coli J5 in sera Herd contributed significantly (P<0.003) to variation in antibody response to E. coli J5 and therefore, herds were further analyzed separately (Table 2). Herd 1 Cow, antibody response group, and week each significantly (P<0.0001) contributed to the variation in antibody to E. coli J5. Although antibody OD was not significantly different between antibody response groups from week -3 to week 6, the rank of LS Means of antibody response to E. coli by antibody response group was Group 1>Group 2>Group 3 (FIG. 5A). Least squares means of antibody to E. coli J5 varied during the peripartum period (week -3 to week +3) and up to peak lactation (week +6) and were significantly higher (P<0.0001) than pre-immunization antibody at week -8 for all animals, regardless of group (OD value=0.275) (FIG. 5A). Correlation analysis, comparing antibody to E. coli J5 with antibody to OVA in sera, indicated a positive and significant relationship (r=0.56; P<0.0001). The correlation between serum anti-OVA and E. coli for Group 1, 2, and 3 was 0.66(P<0.001), 0.59 (P<0.0001) 0.38 (P<0.06), respectively. Herd 2 Cow, antibody response group by parity, parity and week significantly contributed to the variation in antibody response to E. coli J5 (P<0.0001) for Herd 2. Antibody for Group 3 animals at week -8 was significantly higher (OD value=0.386) than for animals of Group 1 (OD value=0.257; P<0.005) and Group 2 (OD value=0.292; P<0.05). Optical density values of antibody to E. coli for animals in Groups 1 and 2 from week -3 to week 6 was similar to OD values of serum antibody to OVA. Optical density values of antibody were consistently positive following the immunization but were not significantly higher than the population mean. In contrast to serum antibody to OVA, Group 3 animals had OD values that were consistent but not significantly lower than the population mean. (FIG. 5B). Least square means of antibody response to E. coli J5 at weeks -3,0,3, and 6 were significantly higher (P<0.0001) than pre-immunization antibody at week -8 regardless of group (OD value=0.307). Correlation analysis between serum antibody to E. coli J5 and serum antibody to OVA indicated a positive and significant relationship (r=0.49; P<0.0001). The correlation between antibody to E. coli J5 and antibody to OVA for Groups 1, 2, and 3 was 0.65(P<0.0001), 0.54 (P<0.0001), and 0.31 (P<0.08) respectively. Herd 3 Cow grouped within antibody response group, antibody response group, week, and the interaction between week and antibody response group significantly contributed to the variation in antibody to E. coli J5 (P<0.0001) in Herd 3. In this herd, antibody for Group 1 animals was significantly lower (P<0.05) at weeks -8 and -3 compared to Group 2 and 3 animals. At parturition, Group 1 and 2 animals had higher antibody to E. coli than Group 3 animals. At weeks 3 and 6, however, the rank of antibody response group for antibody to E. coli was similar to the other herds, in that Group 1>Group 2>Group 3 (FIG. 5C). LS Means of antibody to E. coli J5 at weeks -3,0,3, and 6 were significantly different across time and were significantly higher (P<0.0001) than pre-immunization antibody regardless of group (OD value=0.224)(FIG. 5C). Correlation analysis between serum antibody to E. coli J5 and serum antibody to OVA indicated a positive and significant relationship (0.47; P<0.0001). Correlation between serum antibody to E. coli J5 and antibody to OVA for Groups 1, 2, and 3 were 0.93(P<0.007), 0.48 (P<0.0001), and 0.36(P<0.006) respectively. IgG.sub.1 in serum and whey Analysis of variance indicated that the effect of week contributed significantly (P<0.05) and the effect of antibody response group tended (P<0.07) to contribute to variation in serum IgG.sub.1. Except at week 3, serum IgG.sub.1 did not differ significantly between groups; however, Group 1 animals tended to have lower serum IgG.sub.1 compared to animals of Group 2 and 3. Least square means of total IgG.sub.1 in sera increased significantly (P<0.0001) from parturition (430.09 mg/100 mL) to week 3 (687.46 mg/100 mL) and week 6 (799.51 mg/100 mL) (FIG. 6A, population mean). Correlations between serum IgG.sub.1 concentration and serum antibody to OVA and E. coli were not significant. The effects of week and parity contributed significantly (P<0.05) to the variation in IgG.sub.1 concentration in whey. Although antibody response group did not significantly contribute to variation in whey IgG.sub.1 (FIG. 6B), LS means of IgG.sub.1 concentration (mg/100 mL) at week 0 were significantly lower for Group 1 (768.16 mg/100 mL) and Group 3 (1081.39 mg/100 mL) compared to Group 2 (1381.60 mg/100 mL). Concentration of IgG.sub.1 did not differ significantly between groups at weeks 3 and 6. Population LS Means of IgG.sub.1 concentration in whey declined significantly from parturition (1046.28 mg/mL) to week 3 (44.93 mg/100 mL, P<0.0001). There was no significant change at week 6 (43.25 mg/100 mL). Correlation analysis between whey IgG.sub.1 concentration and whey antibody to OVA indicated a positive and significant relationship (r=0.711;P<0.0001). The correlation coefficients between whey IgG.sub.1 and whey OVA antibody response for Groups 1, 2, and 3 were 0.52(P<0.0001), 0.76(P<0.0001), and 0.69(P<0.0001), respectively. IgG.sub.2 in sera Herd contributed significantly (P<0.0001) to variation in serum IgG.sub.2 concentration and therefore, herds were analyzed separately. Herd 1 Effects of cow and the interaction between antibody response group and week contributed significantly (P.ltoreq.0.05) to variation in IgG.sub.2 concentration for Herd 1. Antibody response group did not significantly contribute to the variation in IgG.sub.2 ; however, LS means of IgG.sub.2 in sera at weeks 0 and 3 was lowest for Group 1 animals and highest for Group 3 animals. This trend reversed at week 6, such that Group 1 animals had the highest concentration of IgG.sub.2 and Group 3 animals had the lowest. LS Means of IgG.sub.2 significantly increased from 1019.43 mg/100 mL at parturition to 1534.56 mg/100 mL at week 3 but declined significantly at week 6 to 1103.23 mg/100 mL. Correlation analysis, between antibody to OVA in sera and concentration of IgG.sub.2, indicated a negative and significant relationship (r=-0.23, P<0.03). Correlations between antibody to OVA with serum IgG.sub.2 concentration indicated for Group 1, 2, and 3 were 0.07(ns), -0.35(P<0.004) and -0.33 (ns). Significant correlations were not observed between E. coli antibody response and serum IgG.sub.2 concentration, even when examined by group. Herd 2 Cow significantly contributed (P<0.05) to the variation of serum IgG.sub.2 concentration while antibody response group and the interaction between antibody response group and parity tended to contribute to the variation in serum IgG.sub.2 concentration. At parturition, groups did not significantly differ in serum IgG.sub.2. At week 6, LS means of IgG.sub.2 concentration for animals in Group 1 were significantly higher than for Group 3 animals. Least square means of IgG.sub.2 concentration did not differ significantly between weeks 0, 3, and 6. Correlation analysis between serum IgG.sub.2 concentration and serum antibody to OVA indicated a positive and significant relationship (r=0.15; P<0.03). Significant correlations were not observed between serum IgG.sub.2 concentration and serum antibody to OVA or serum antibody to E. coli J5. Herd 3 Cow (P<0.005) and parity (P<0.04) contributed significantly to the variation of serum IgG.sub.2. concentration. Week (P<0.09) tended to contribute to variation in serum IgG.sub.2 concentration. Antibody response group did not significantly contribute to variation in serum IgG.sub.2 concentration. Correlations between serum IgG.sub.2 concentration and antibody to OVA and E. coli were not significant. IgG.sub.2 in whey Herd contributed significantly (P<0.03) to the variation in serum IgG.sub.2 concentration and therefore, herds were analyzed separately. Herd 1 Week contributed significantly to variation in IgG.sub.2 concentration in whey. Whey IgG.sub.2 concentration did not differ significantly between groups (FIG. 8A). LS Means of total IgG.sub.2 concentration in whey declined significantly from week 0 (327.34 mg/100 mL) to week 3 (26.31 mg/100 mL). Correlation analysis between whey IgG.sub.2 concentration and antibody to OVA indicated a positive and significant relationship (r=0.7; P<0.0001). Correlations between whey IgG.sub.2 concentration and whey antibody to OVA were r=0.9 (P<0.002), 0.6 (P<0.0003), and 0.8 (P<0.02) for Groups 1, 2, and 3, respectively. Herd 2 None of the parameters in the linear model contributed significantly to variation in whey IgG.sub.2 concentration, and, therefore, LS means were not estimable. Correlations between whey IgG.sub.2 concentration and whey antibody to OVA indicated a positive and significant relationship (r=0.3, P<0.009). Correlations between whey IgG.sub.2 and whey antibody to OVA was 0.2(ns), 0.5(P<0.0001), and 0.6 (ns) for Groups 1, 2, and 3, respectively. Herd 3 Antibody response group and week significantly contributed to the variation in whey IgG.sub.2 concentration. Whey IgG.sub.2 concentration did not significantly differ between groups at parturition, and responses at week 3 could only be estimated for Group 3 animals since responses for Groups 1 and 2 were either low or too low to be detected. Correlation analysis indicated no significant relationships between whey IgG.sub.2 concentration and whey antibody to OVA. Mastitis Occurrence Percent mastitis occurrence varied between groups and between herds. Rates of occurrence of clinical mastitis are presented in table 3. Mastitis did not occur in Group 1 of either Herds 1 or 3. Mastitis occurrence in Herd 1 was 21.7% and 33.3% for Groups 2 and 3, respectively. Mastitis occurrence in Herd 3 was 11.5 and 10% for Groups 2 and 3 respectively. However, in Herd 2, Group 1 animals had the highest occurrence of mastitis (15.4%) which exceeded the percent occurrence of mastitis in Groups 2 (2.1%) and 3 (0%) (FIG. 7). Animals with mastitis in Herds 1 (n=6 heifers; n=26 cows) and 3 (n=8 heifers; n=29 cows) were in their second or greater parity. Animals with mastitis in Herd 2 (n=34 heifers; n=33 cows) were all heifers. Across all herds, animals in Group 3 had the highest rate of mastitis occurrence (13.6%) compared to Group 1 (11.1%) and Group 2 (9.3%) (Table 3). These differences across herds however, were not significant. Odds--Ratio for Mastitis Within herd, odds-ratio calculations comparing animals of one antibody response group with the other two groups indicated that only animals in Group 1 of Herd 2 had a statistically significant higher relative risk of having a mastitis event (by 7.57 times) compared to the animals in the rest of the herd. Although the risk of mastitis occurrence within Group 3 of Herds 1 and 3 was 2.16 and 1.8 times greater (respectively) than for other groups, these values were not significant. Somatic Cell Score For Herds 1 and 2, cow, week and antibody response group significantly contributed to the variation in SCS (Table 2). In Herd 3, only the effect of cow within antibody response group accounted for the variation in SCS. Somatic cell score was significantly different between groups in Herds 1 and 2 but not Herd 3. LS Means of SCS in Herd 1 were lowest for the high antibody responder animals, and greatest for the low antibody responder animals at weeks 3,4,5 and 6 following parturition (FIG. 8A). Conversely, LS Means of SCS in Herd 2 were significantly lower for low antibody responder animals compared to high antibody responder animals (FIG. 8B). Discussion Example 1 indicated that animals could be classified according to the amplitude and direction of their individual OVA antibody response profiles, and that this ranking had some association |