Genetic and economic relationships between ... - Semantic Scholar

Animal Science 2001, 73: 19-28 © 2001 British Society of Animal Science

1357-7298/01/07720019$20·00

Genetic and economic relationships between somatic cell count and clinical mastitis and their use in selection for mastitis resistance in dairy cattle H. N. Kadarmideen† and J. E. Pryce Animal Breeding and Genetics Department, Animal Biology Division, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK † E-mail [email protected]

Abstract Clinical mastitis (CM) and monthly test-day somatic cell count (SCC) records on Holstein cows were used to investigate the genetic and economic relationship of lactation average (of natural logarithms of) monthly test-day SCC (LSCC) with CM. After editing, there were 23663 lactation records on 17937 cows from 257 herds. Three groups of herds were first identified as having low (L), medium (M) and high (H) incidences of CM from the original or pooled (P) data set. Genetic parameters were estimated for the original and three data sub-sets (derived from the three herd groups). Expected genetic responses to selection against CM were calculated using genetic parameters of each data set separately, with an adapted version of the UK national index (£PLI-profitable lifetime index). Indirect economic values of SCC (EVSCC) were calculated as the direct cost of CM per cow per lactation weighted by the genetic regression coefficient of CM lactation records on their sires’ predicted transmitting ability for SCC (PTASCC). All genetic regression analyses were based on linear and threshold-liability models. Heritabilities and repeatabilities, respectively, were 0·034 and 0·111 for CM and 0·120 and 0·347 for LSCC in the original data set. Genetic, permanent environmental, residual and phenotypic correlations between CM and LSCC for the original (pooled) data set were 0·70, 0·44, 0·13 and 0·20, respectively. Parameter estimates for the three herd groups differed, with magnitude of the estimates increasing with increase in incidence from L to H herd groups. The EVSCC per unit of PTASCC for L, M, H and P herd groups, respectively, were £0·04, £0·15, £0·33 and £0·18 on the observed and £0·86, £0·96, £1·22 and £1·10 on the underlying-liability scales. Selection for mastitis resistance, using SCC as an indicator trait in an extended version of £PLI, resulted in a selection response of 0·9, 2·1, 1·7 and 1·9 more cases per 100 cows after 10 years of selection in L, M, H and P herd groups, respectively. These results suggest that genetic responses to selection for CM resistance as well as the EVSCC are specific to herd incidence and hence would be appropriate for customized selection indexes. The increase in CM cases was greater when CM was excluded from the £PLI (2·8 v. 1·9), hence it is recommended that CM should be included in the breeding goal in order to arrest further decline or to make improvement in genetic resistance to clinical mastitis. Keywords: dairy cattle, economic values, genetic parameters, mastitis, somatic cell count.

Introduction

includes PTAs for milk, fat and protein, weighted by the same economic values as PIN, but also includes a PTA for lifespan (Brotherstone et al., 1998). Although lifespan, defined as the number of lactations a cow is expected to survive, is correlated to some health and fertility traits (Pryce and Brotherstone, 1999), extra economic benefit is expected by including some health and fertility measures directly in the breeding

In the UK, two national genetic indexes are currently available for dairy farmers and breeding organizations to use. These are PIN (profit index) and £PLI (profitable lifetime index). PIN is based on predicted transmitting abilities (PTAs) for milk, fat and protein weighted by their assumed future economic values. £PLI is a ‘descendant’ of PIN and 19

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Kadarmideen and Pryce

goal (e.g. Philipsson et al., 1994; Lindhe and Philipsson, 2001). Among health or disease traits in dairy cattle, clinical mastitis (henceforth abbreviated as CM) is an economically important disease, and therefore a potential trait to be considered in the breeding goal. Pryce et al. (1999b) showed that there are substantial economic benefits in expanding the UK’s current breeding goal (production and lifespan) to include CM. In many countries, genetic improvement of CM resistance is hindered by low quality and quantity of CM records for genetic evaluation. However, somatic cell count (SCC) in milk has been shown to be highly associated with the occurrence of CM (positive genetic correlations of about 0·7), therefore indirect selection for CM resistance is possible through selection on SCC (Shook and Schutz, 1994; Mrode and Swanson, 1996; Mrode et al., 1998; Rupp and Boichard, 1999). At present PTAs for SCC are available in the UK but not for CM. Furthermore, as SCC is recorded in most countries, international genetic evaluations for SCC should be available soon, whereas international genetic evaluations for CM are unlikely in the near future (Banos, 1999). Mrode et al. (1998) provided genetic parameters and evaluations for SCC using records from one of the major milk recording organizations (MRO) in the UK, National Milk Records (NMR). Although this study involved estimating genetic correlations of SCC with many production and type traits, CM was not considered. Livestock Services UK (LSUK) Ltd is another major MRO in the UK that introduced an optional recording service for individual cow SCC in 1991, similar to NMR but also introduced a voluntary recording service for many diseases including CM, in 1994. Genetic parameters for SCC and CM using the LSUK database have not been estimated. One of the objectives of this study was therefore to estimate genetic parameters and correlations for SCC and CM using records from the LSUK database and incorporate those parameters in an adopted version of £PLI that includes CM and SCC. Inclusion of SCC in a national economic index is expected to bring extra revenue by reducing the incidence of CM and related costs, improving milk price (as there is a penalty banding structure in the UK), quality, hygiene and welfare issues (Veerkamp et al., 1998). The problem in doing this using £PLI, stems from the fact that the method of payment for milk in the UK is based on the bulk-tank mean SCC (BTSCC) that varies on a herd basis. Differences between herds in milk price make it difficult to derive a single economic value for SCC suitable for all herds and, therefore, prevents inclusion of SCC in

an overall economic index (Veerkamp et al., 1998). The approach used by Veerkamp et al. (1998) was based on the penalty banding structure for SCC itself and aimed at reducing SCC with index selection. Stott et al. (2000) also derived direct economic values of individual cow somatic cell count (ICSCC) within the SCC penalty banding structure, based on cow’s relative contribution to BTSCC and, hence to herd average milk price. However, direct economic values of ICSCC reported in their study still varied depending on the BTSCC and were £0·52, £0·74 and £0·78 per cow per year for BTSCC of 151, 228 and 308 kcounts per ml, respectively. If the objective is to use SCC as a tool to reduce CM cases then it may be worthwhile to include SCC as a selection criteria in a selection index where one of the goal traits is CM resistance. Accordingly, an economic value for SCC, applicable to the whole population, could be assigned based solely on the economic value of CM and its genetic relationship with SCC. Such an approach is different from Veerkamp et al. (1998) and Stott et al. (2000) because they consider direct economic value of SCC itself. Philipsson et al. (1995) used sire breeding values for SCC and CM to investigate the nature of the relationship between SCC and CM in Sweden. They reported that it was linear, implying that an economic value for SCC based on that for CM could be derived using a measure of the relationship between these two traits. Genetic regression coefficients and correlations predict such a genetic relationship. However a single economic value for SCC derived through this procedure may still be inappropriate if the differences in herd incidences for CM result in different regression coefficients or correlations. The dependency of regression coefficients on CM incidence would make economic values for SCC based on the economic value of CM variable. Similarly, the dependency of genetic correlations on CM incidence would make responses to genetic selection vary depending on the herd incidence. In terms of benefit to farmers, it may also be useful to know which specific genetic responses to selection are to be expected with the use of current national genetic index (£PLI) at a herd level and if different economic values of SCC are to be used depending on the herd characteristics. The main objectives of this study were, therefore, to estimate genetic parameters for CM and whole lactation SCC, to investigate genetic responses by incorporating these traits in a national economic index and, to derive economic values for SCC, separately, for different herd groups classified according to CM frequency.

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Mastitis and somatic cell counts as potential dairy cattle selection traits

Material and methods Data for genetic parameter estimation Data on lactational occurrences of CM and test-day SCC from January 1994 until February 1998 were obtained from LSUK Ltd. Information on various diseases (only clinical cases) was recorded by farmers and collected on a monthly basis in conjunction with official milk recording visits by LSUK. The recording of disease events is voluntary for farms that are milk recording with LSUK. Only cows sired by Holstein bulls were considered. Before data editing, CM was re-coded as a 0/1 binary trait; presence of one or more occurrences of CM within lactation per cow was scored as 1 whereas no occurrences were scored as 0. The incidence of CM was then computed for each herd as a ratio of the total number of CM lactation records (coded as 1) to the total number of lactation records across all years. In order to avoid herds that do not record mastitis, or do record, but poorly (possible with voluntary recording schemes), data were edited by selecting lactation records from herd-years with at least five lactation records of which at least one is a CM record. Pryce et al. (1997) and Kadarmideen et al. (2000d) also chose herds on a similar basis from the same recording scheme. This editing resulted in 49% of herds being discarded. Furthermore, only records with number of days in milk greater than 200 days were considered and, only cows in the first five lactations were retained. After editing, there were 23663 lactation records (from 17937 cows) in 257 herds. For each of these records, monthly test-day observations of SCC were combined to form a lactational measure of SCC (abbreviated as LSCC) as an arithmetic mean of natural logarithms of monthly test-day SCC (e.g. Mrode et al., 1998). In many countries including USA and Canada, somatic cell score (SCS) is often used, which is the log2 transformation of SCC (SCS = log2 ((SCC/100) + 3). As reviewed in Mrode and Swanson

(1996), several studies have shown that LSCC is adequate as a lactational measure of SCC and the form of logarithmic transformation has no influence on the estimates of genetic parameters. Sub-sets of data for herd groups that differed in CM frequencies were generated as follows. First, the distribution of herds by incidence of CM (Figure 1) was split into three groups of approximately equal size, based on incidence. The three herd groups, named as low (L), medium (M) and high (H) incidence herds were created based on cut-off points for herd incidences of CM (p). The cut-off points were defined as 0·5 < p ≤ 6·0, 6·0 < p ≤ 14·0 and, 14·0 < p ≤ 57·1, for L, M and H incidence herds, respectively. Second, sub-sets of data corresponding to L, M and H herd groups (henceforth named as L, M and H data sets, respectively) were created by retrieving cow records from the appropriate herd groups in original data set. The original data set (and herds) without any classification (pooled or P data set/herds) was also retained for the analysis. Animals in the data set (Table 1) were identified by their herd-year-season of calving (4 years: 1994 to 1998 and four seasons viz.; January to March, April to June, July to September and October to December), age at calving (in months) and a lactation (parity) number (1 to 5). The pedigree for each of the four data sets was created suitable for an individual animal model analysis by tracing pedigree for each animal in the data set as far back as possible using herd book numbers of cows, sires, dams and maternal and paternal grandparents. The characteristics of the four different data sets are given in Table 1. Data for genetic regressions Data for genetic regressions were the same as the original or pooled (P) data set, but further edits were applied. PTAs for SCC (PTASCC) for sires of cows in the original data set were obtained from Animal Data

Table 1 Characteristics of data sets used to estimate genetic parameters for herd groups with low, medium and high incidence of clinical mastitis (CM) and from herd group without any classification (pooled) Description Records† Cows Sires Herds Herd-years Herd-year-seasons Incidence (p) range (%) Mean incidence (%) LSCC‡

Low

Medium

High

Pooled

7800 6739 465 88 153 534 0·5 < p ≤ 6·0 2·7 3·65

7226 5261 467 91 221 703 6·0 < p ≤ 14·0 10·4 3·51

8637 5937 462 78 268 816 14·0 < p ≤ 57·1 23·8 3·72

23663 17937 620 257 642 2053 0·5 < p ≤ 57·1 12·8 3·69

† Records up to five lactations. ‡ Arithmetic mean of the natural logarithms of lactational monthly test-day somatic cell count.

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Table 2 Characteristics of data sets used to estimate genetic regressions for herd groups with low, medium and high incidence of clinical mastitis (CM) and from herd group without any classification (pooled) Description

Low

Records† Cows Sires Herds Herd-years Herd-year-seasons Mean incidence (%) LSCC†

Medium

5921 5571 5116 4056 354 358 76 81 135 184 467 579 3·7 13·4 3·66 3·49

High

Pooled

6566 18058 4516 13688 352 486 71 228 246 565 745 1791 26·1 14·2 3·73 3·68

age at calving as covariate; β is coefficient for age; HYSj is the fixed herd-year-season of calving effect; effect of the kth lactation; and eijkl residual term.

the regression effect of the jth Lk is the fixed is the random

Parameters were estimated for all data using the ASREML software package (Gilmour et al., 1998). All analyses were based on individual animal models. Heritabilities for LSCC and CM were obtained from separate uni-variate analyses. Covariances (correlations) were estimated with bi-variate models with the starting values obtained from earlier univariate analyses.

† See Table 1.

Centre (ADC, April 1999). PTASCC were only available for pedigree registered sires, thus 4272 records of daughters of 89 out of 620 sires were discarded. Data were further edited by removing records where reliability of sire PTASCC was less than 65%, which resulted in the loss of a further 1333 records. The final data set comprised of 18058 records. The herds represented in the data set for the genetic regression analysis were not re-defined based on the new incidence of CM (due to the edits listed above). Instead, herds belonged to the original classification based on their incidence before the edits for genetic regressions were applied. The characteristics of the four different data sets are given in Table 2. Genetic parameter estimation The statistical model used to estimate variance components for all data sets was yijkl = µ + HYSj + Lk + β. AGEi + ai + pi + eijkl

(1)

where yi. is the observation; µ is the overall mean; ai is the random genetic effect, pi is the random permanent environmental effect; AGEi is the effect of

Genetic responses to CM resistance Expected genetic response to selection when CM and SCC are included in the UK selection index, £PLI, as goal and index traits, respectively, was calculated for each herd group based on the set of herd group specific genetic parameters obtained from the above analyses. Standard selection index calculations were used to calculate responses to selection. The goal was defined to include those traits already included in £PLI (milk, fat and protein yields and lifespan) all weighted by their economic values (Pryce et al., 1999b) and CM. For CM, the index trait was SCC, and for all other traits index and goal traits were the same. Genetic and phenotypic (co) variance estimates for milk, fat and protein yields and CM for the UK Holstein population were from Pryce et al. (1998) and were used in the genetic and phenotypic variance-covariance matrices. It was assumed that each sire would have an average progeny group size of 75 daughters. Annual responses were calculated as 0·22 standard deviations change in the index, approximating the selection response in ‘typical’ four-pathway dairy cattle breeding scheme (Robertson and Rendel, 1950).

Herd mastitis incidence

0·6 0·5 0·4 0·3 0·2 0·1 0 1

31

61

91 121 151 181 Herd identification

211

241

Figure 1 Distribution of herd incidences of clinical mastitis across 257 herds in the pooled data set for genetic analysis.

The total cost of clinical mastitis per cow per lactation was estimated to be £218·00 by Kossaibati and Esslemont (1997), which was based on the decrease in production, increased culling, cost of discarded milk, herdsman’s time, veterinarian’s time, drugs and incidences of mild, severe and acute nature of CM. To avoid double counting with milk yield and longevity, which are also in the breeding goal (£PLI), Pryce et al. (1999a) estimated the cost of CM as £100 after excluding the costs of decrease in production and increased culling (£118·00) from the estimate of £218·00, provided by Kossaibati and Esslemont (1997). Hence, the cost of £100 was used as an economic value of CM.

Mastitis and somatic cell counts as potential dairy cattle selection traits Linear and threshold model genetic regressions Genetic regression coefficients of cow’s CM records on their sire’s PTA for SCC were used to compute economic value for SCC (EVSCC), as described below. Regression analyses of the four different data sets were based on fixed effects linear and threshold models (see Gianola (1982) for estimation with threshold models). The statistical model used for the linear genetic regressions were the same for all data sets and was yijkl = µ + HYSj + Lk + β. AGEi + β (PTASCC) i + eijkl (2) where (PTASCC)i is the sire PTA for SCC for cow i, as a covariate, β is the genetic regression coefficient for PTASCC and all other terms are as described in model (1). For regressions based on the threshold models, the same linear models were used except that the underlying liability (zi) replaces the observed binary data (yi). The model in matrix notation is, y = Xβ + e where y is the vector of observations, β is the vector of fixed effects and X is the corresponding incidence matrix, and e is the vector of random residual effects. Genetic regression coefficients were estimated for all methods and data by the ASREML software package (Gilmour et al., 1998). For threshold model (TM) analysis, a generalized linear mixed model (GLMM), λ) with probit link function was used, with E(z) = Φ(λ β, is a fitted value on the underlying where λ = Xβ normal scale and, Φ normal cumulative density function. The variance for the probit-threshold model on the underlying scale was equal to 1 (standard normal). Economic values for SCC After estimating genetic regression coefficients, EVSCC was derived as: EVSCC = β. EVMAST

(3)

where β is a genetic regression coefficient, expressed on the same scale as SCC (i.e. in per cent scale as for PTASCC) and EVMAST is the economic value of CM. As given above, the EVSCC does not include the direct effect of SCC on milk price, which, although it varies with herd bulk tank-SCC, is different from zero. However, as mentioned earlier, the EVSCC derived here (equation 3) is essentially a weighted economic

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value of CM, with weights being the magnitude of the genetic relationship between SCC and CM (genetic regression coefficients). This is clear from the equation (3), where the economic value of SCC is zero if there is no relationship between SCC and CM (i.e. weights or β = 0). Therefore, these values are different from direct economic values of SCC itself, as addressed in Veerkamp et al. (1998) and Stott et al. (2000).

Results and discussion Heritabilities and repeatabilities Heritabilities and repeatabilities of CM and LSCC from uni-variate repeatability animal models are given in Table 3. Heritabilities and repeatabilities for CM were different across data sets and increased with increase in incidence (as expected for binary traits). Heritabilities and repeatabilities for LSCC were much larger than CM in all herd groups supporting earlier findings (e.g. Weller et al., 1992). Heritability and repeatability of CM reported here for pooled data set (0·034 and 0·111, respectively) agree well with published literature estimates (e.g. Uribe et al., 1995; Pryce et al., 1997; Heringstad et al., 1999; Kadarmideen et al., 2000b and d). For LSCC, heritability and repeatability estimates reported for the pooled data set (0·120 and 0·347, respectively) are close to published literature estimates for either lactation average SCC or SCS (e.g. Mrode and Swanson, 1996; Pösö and Mäntysaari, 1996; Weller and Ezra, 1997; Boettcher et al., 1998; Mrode et al., 1998; Rupp and Boichard, 1999; Castillo-Juarez et al., 2000). In estimating heritabilities of CM (or correlations; see below), repeat occurrences of diseases within a lactation on the same animal were combined into one and the disease was regarded as being present or absent, thus ignoring the repeat nature of diseases. Currently, this is the common (only) way of estimating genetic parameters or evaluating animals for CM in the single or multiple trait models involving CM (binary traits, in general), based on 305-day lactation models. Information on repeated binary traits on the same animal within and across lactations can be utilized in longitudinal test-day (threshold) models similar to those used by Kadarmideen et al. (2000c) but only under single-trait models. Estimating (genetic) correlations between repeated CM episodes and test-day SCC based on a two-trait test-day joint LM-TM models would be cumbersome and such methodology still needs development. Correlations Estimates of genetic, permanent environmental, residual and phenotypic correlations between CM

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Table 3 Phenotypic means (mean) and standard deviations (s.d.) and heritabilities (h2) and repeatabilities (r) with their standard errors (s.e.) of clinical mastitis (CM) and lactational somatic cell count (LSCC) for data sets from low, medium, high and pooled mastitis incidence herds Herd groups

Low Medium High Pooled

CM

LSCC†

Mean (s.d.)

h2

s.e. (h2)

r

s.e. (r)

Mean (s.d.)

h2

s.e. (h2)

r

s.e. (r)

0·027 (0·163) 0·104 (0·305) 0·238 (0·426) 0·128 (0·334)

0·018 0·045 0·051 0·034

0·014 0·019 0·016 0·008

0·061 0·105 0·141 0·111

0·032 0·024 0·021 0·013

3·65 (1·36) 3·51 (1·66) 3·72 (1·62) 3·69 (1·19)

0·070 0·089 0·113 0·120

0·022 0·028 0·027 0·017

0·344 0·322 0·366 0·347

0·028 0·023 0·018 0·013

† See Table 1.

and LSCC are given in Table 4. Estimates of genetic correlation (rg) were high (e.g. 0·7 for pooled data set), supporting earlier findings (as reviewed by Mrode and Swanson, 1996) that these two traits are genetically related and hence SCC could be used as an indicator trait to achieve genetic improvement for mastitis resistance (e.g. Shook and Schutz, 1994). For different herd groups, estimates of rg increased from 0·21 for L to 0·81 for H incidence herd groups. This, as stated earlier, indicates the influence of mean incidence on genetic parameter estimates for binary traits or traits that are related to them. Estimates of rg for M, H and P data sets agree well with published estimates for rg between CM and SCC or SCS (Philipsson et al., 1995; Mrode and Swanson, 1996; Rupp and Boichard, 1999) or with somatic cell production deviance (Lund et al., 1999).

Expected genetic responses in CM incidence and LSCC Genetic responses to selection are given in Table 5 for each of the four groups of herds, computed using genetic parameters for CM and LSCC obtained from the corresponding analyses. Genetic responses are expressed as an increase in number of CM cases per 100 cows after 10 years of selection against CM using £PLI with CM included. Genetic response varied according to herd group, with an increase of 0·9, 2·1, 1·7 and 1·9 cases per 100 cows for L, M, H and P incidence herds, respectively. These results, therefore, suggest that the benefit of SCC as an indicator trait to improve CM resistance should be specific to herd incidences. In general, the increase in CM cases were low for L and tend to be high for M and H herd groups, which shows the influence of mean incidence on responses to CM cases.

The permanent environmental correlation estimate (rpe) was medium to high (e.g. 0·44 for the pooled data set), suggesting the strong relationship between non-genetic effects of CM and LSCC, carried over parities. Estimates of rpe for L to H incidence herd groups ranged from 0·26 to 0·70. Estimates of residual correlations (re) were relatively low for all herd groups (0·09 to 0·18), suggesting that residuals (or measurement errors) for CM and LSCC are weakly related. Estimates of phenotypic correlations (rp) were lower than rg as well as rpe in all herd groups (0·11 to 0·26) but were highly significant.

The increase in CM would have been greater if mastitis was excluded from the breeding goal completely, as selection using £PLI (a multi-trait index that included production and longevity only) would result in a correlated response of 2·8 (0·9 greater) cases per 100 cows over 10 years for the pooled data set. Thus including mastitis in the breeding goal reduced the increase in the number of cases of mastitis. Even with CM as a goal trait, in our

Table 4 Genetic, permanent environmental, residual and phenotypic correlations (rg, rpe, re, rp, respectively) between clinical mastitis (CM) and lactational somatic cell count (LSCC) with their standard errors (s.e.) for data sets from low, medium, high and pooled mastitis incidence herds Herd groups Low Medium High Pooled

rg s.e. (rg) rpe s.e. (rpe) re 0·21 0·72 0·81 0·70

0·34 0·18 0·13 0·10

0·26 0·70 0·45 0·44

0·23 0·20 0·11 0·08

0·10 0·09 0·18 0·13

s.e. (re) rp s.e. (rp) 0·03 0·02 0·02 0·01

0·11 0·19 0·26 0·20

0·01 0·01 0·01 0·01

Table 5 Expected genetic response† to selection for different herd groups. Genetic response is expressed as increase in number of clinical mastitis (CM) cases per 100 cows after 10 years of selection against CM using £PLI with CM included

Herd groups

Mean incidence before selection (%)

Response in number of mastitis cases

Low Medium High Pooled

2·7 10·4 23·8 12·8

0·9 2·1 1·7 1·9

† Genetic responses in each herd group were calculated using corresponding parameter estimates given in Tables 2 and 3.

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Mastitis and somatic cell counts as potential dairy cattle selection traits study, CM incidence within each group continued to increase regardless of the incidence level and genetic parameters used for the calculations. This is because SCC is used as a predictor of CM rather than CM as an index trait in its own right and also because production traits are more valuable relative to other traits of importance in dairying, thus the greatest response is in production traits. The genetic response was calculated as part of a multi-trait index (£PLI) that included CM and SCC as goal and index traits, respectively. The aim was to investigate the efficiency of selection for CM resistance when SCC is included as its index trait, with an emphasis on herd groups with different CM frequency. But inclusion of both the SCC and CM in an index also has additional advantages because SCC has a higher heritability than CM and has a high genetic correlation with many udder health traits, as with CM, resulting in an overall genetic improvement of resistance to diseases of udder (Rupp and Boichard, 1999). It is also tempting to include CM in the genetic index because the genetic correlation between CM and LSCC is only around 0·7 and, therefore CM data would bring some additional information to predict CM breeding values, in spite of its low heritability. Philipsson et al. (1995) reported a 20% increase in selection efficiency for CM when both CM and SCC are included in a selection index. Variable genetic responses in selection index calculations across different herd groups are a result of using different parameters (heritabilities and genetic and phenotypic correlations), which in turn is a result of herd differences in incidences for a binary trait, CM. This demonstrates the difficulty in dealing with a binary trait in a selection index, either as a goal or index trait. It may be possible to obtain genetic responses that are less sensitive to observed frequencies by including estimates of correlations from a multiple-trait LM-TM method, which would simultaneously apply linear model (LM) to continuous trait and threshold model (TM) to binary traits. Although, such joint LM-TM estimation methodology has been developed recently (e.g. Gates et al., 1999; Van Tassell et al., 1999) estimates obtained from such analyses still need to be incorporated into national selection indices and tested in practice. Alternatively, predictions of selection response for binary trait in a single-trait index, which account for observed frequencies, could be based on the TM method as described in Foulley (1992). In practice, it may also be important to know selection responses in terms of number of cases given a specific incidence at a farm level or farms with similar incidence of CM. In such circumstances, these

results indicate suitability of £PLI based on a herd’s incidence of mastitis, similar to customized herd indices used in Australia (Bowman et al., 1996). It might be appropriate either to design selection indexes specifically for individual farm circumstances, or group similar farms together, as we have done here. However, a customized breeding objective per herd is optimal only if the situation of each herd is relatively constant over time in the long term. Otherwise a unique breeding objective based on the population parameters (from pooled data set) would be preferable. Genetic regressions Genetic regression coefficients (β) and their standard errors (s.e.) from LM and TM are presented in Table 6. Estimates of β are on normal-liability versus probability scales for TM and LM, respectively . On the original scale, TM estimates were higher than LM estimates of β, as parameters are estimated on different scales with TM versus LM. Estimated regression coefficients were, in general, highly significant (P < 0·001) for all data sets. The magnitude of estimated regression coefficients increased from L through M to H incidence herds for both LM and TM. With LM, every 1% increase in PTASCC resulted in 0·04%, 0·15% and 0·33% increase in CM incidence for L, M and H incidence herds, respectively. With TM, the change is interpreted as the increase in the underlying liability to CM for every 1% change in PTASCC. Table 6 also shows that the mean of regression coefficients from L, M and H herds is very close to the regression coefficient from the pooled data set (0·0017 versus 0·0018 for LM and 0·0101 versus 0·0110 for TM) indicating the existence of a linear relationship between SCC and CM incidence in a population-based analysis. As with genetic parameter estimation (Tables 3 and 4), the standard error of regression coefficients were lowest for the pooled data set. In our study, we have directly estimated the genetic regression coefficients (β) but indirect derivations are possible with the use of estimates of genetic correlations (rg) and genetic standard deviations of CM (σCM) and LSCC (σLSCC) as, β = rg(σCM/σLSCC)

(4).

However, β in this case, would correspond to predictions of CM incidence based on changes in ICSCC rather than on sire PTASCC. As our aim was to express economic values in terms of sire PTAs (for the ease of incorporation into £PLI), we have adopted direct genetic regressions but if economic values are to be interpreted based on ICSCC (as in Stott et al., 2000), then the equation (2) could still be

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Table 6 Genetic regression coefficients (β) from regression of daughter clinical mastitis (CM) records on sire PTA for SCC and corresponding economic values of somatic cell count (SCC)† for data sets from low, medium, high and pooled mastitis incidence herds (values in parentheses are standard errors of estimates) Economic values of SCC Genetic regression coefficients (β) Herd groups Low Medium High Pooled

In £ per PTASCC†

In genetic standard deviations

Linear model

Threshold model

Linear model

Threshold model

Linear model

Threshold model

0·0004 (0·0002) 0·0015 (0·0004) 0·0033 (0·0005) 0·0018 (0·0002)

0·0086 (0·0040) 0·0096 (0·0026) 0·0122 (0·0018) 0·0110 (0·0014)

0·04 (0·02) 0·15 (0·04) 0·33 (0·05) 0·18 (0·02)

0·86 (0·40) 0·96 (0·26) 1·22 (0·18) 1·10 (0·14)

0·02 (0·01) 0·07 (0·02) 0·18 (0·03) 0·07 (0·01)

0·31 (0·14) 0·48 (0·06) 0·66 (0·10) 0·45 (0·06)

† Calculated based on equation 3.

used but with β derived from the above method (equation 4). Differences in predicted means for CM incidence across herd groups indicates that the relationship between CM incidence and SCC (β or correlations) is a function of incidence. The reason behind adopting TM was to estimate a relationship (β) independent of observed incidences but the results in Table 6 show that there is still a slight dependency of TM on incidence. There is, however, a tendency for LM to place relatively less emphasis on low incidence herds than medium incidence herds (0·0004 v. 0·0015) when compared with TM (0·0086 v. 0·0096), suggesting that LM estimates are more sensitive to extreme incidences. While, theoretically, dependency on incidence is not expected with TM, incidence-dependent-parameter-estimates with TM, as obtained here, are also common (e.g. Kadarmideen et al., 2000a and d). Economic value for SCC EVSCC are also in Table 6. The EVSCC per unit of PTASCC for L, M, H and P herd groups, respectively, were £0·04, £0·15, £0·33 and £0·18 for LM and £0·86, £0·96, £1·22 and £1·10 for TM genetic regressions. The EVSCC were also expressed in genetic standard deviations for both LM and TM, in order to facilitate the comparison with foreign data. The EVSCC in genetic standard deviations were approximately half of EVSCC expressed per unit of PTASCC. The differences in EVSCC between LM and TM methods (lower for LM) are expected because weights (β in equation 3) are estimated on an unobserved liability scale for TM and an observed probability scale for LM, with liability expected to be higher than probability (e.g. Kadarmideen et al., 2000a and d). The use of LM or TM estimates of EVSCC will depend on the breeding goal. If the goal is to decrease the

SCC with an aim of reducing the underlying liability to mastitis susceptibility, then TM economic values may be considered, whereas LM values should be used if the goal is to reduce the observed mastitis incidence. EVSCC follows exactly the same pattern as β, across different herd groups and for LM versus TM because they correspond directly to the magnitude of estimated weights (β in equation 3). With LM or TM, EVSCC increased with increasing incidence from L to H herd groups, with EVSCC from pooled data closely representing the mean of all three groups. Specifically, these results show that there is a need to consider CM incidence on a herd basis in computing EVSCC. General remarks In the absence of compulsory disease recording on farms (common in many countries except Scandinavia), the data editing procedure for choosing ‘disease-recording’ herds is inevitably arbitrary. In this study, herds were selected only if they had at least one case of mastitis in a herd-year, which significantly reduces the risk of selecting herds that poorly record mastitis and eliminate herds that do not record any case of mastitis. For example, Pryce et al. (1997) and Kadarmideen et al. (2000d) adopted this editing procedure for data obtained from the same (LSUK) recording scheme. Rupp and Boichard (1999) with similar French recording schemes chose herds for analysis of CM data on the basis of an assessment of the reliability and completeness of records conducted by technicians and discarded 34% of herds. While such a complete survey was not possible in our case, herds that were used in our analysis were assessed by the LSUK to be

Mastitis and somatic cell counts as potential dairy cattle selection traits ‘disease-recording’ herds. This coupled with our editing based on herd-year incidence of mastitis has yielded a reliable data set for analyses. Therefore, estimates of genetic parameters and economic values from this study can be considered to be reliable estimates for inclusion in an overall national index, such as £PLI. Conclusions This study provided separate estimates of heritabilities and genetic, permanent environmental, residual and phenotypic correlations between clinical mastitis and LSCC for herd groups with low, medium and high incidences of mastitis as well as for all herds together (pooled). Strong genetic correlations between LSCC and CM (0·7 for the pooled data set) obtained in this study show that SCC could be used as a predictor of CM. Genetic responses to CM, with SCC as its predictor, were different for herds with different CM frequency. Low incidence herd groups tended to have less increase in CM cases than high incidence herd groups, after 10 years of selection on £PLI. Results also showed that there would have been a greater increase in cases of CM (which implied associated loss in returns) if selection on £PLI had continued completely without CM. Hence this study demonstrated that it is important to include CM for the genetic improvement of mastitis resistance in the breeding goal and hence to address emerging ethical, welfare and economical concerns of mastitis episodes in dairy cattle. As opposed to using direct economic values of SCC, this study derived and used an indirect economic value based entirely on CM determined using SCC as a predictor. As genetic responses to CM and economic values of SCC were found to be specific to herds or herds with similar characteristics (CM incidences), this study supports the use of genetic indexes at a herd or herd-group levels (customized indexes) if SCC is used as a predictor of CM.

Acknowledgements Authors thank LSUK for providing CM and SCC data and ADC for PTAs, and the Milk Development Council for funding this research. The authors also thank Mr Matthew Winter and Mr Mike Coffey for their help with pedigree information.

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