Genetic Analysis of Clinical Lameness in Dairy Cattle

Transcription

1 Genetic Analysis of Clinical Lameness in Dairy Cattle P. J. BOETTCHER,* J.C.M. DEKKERS,* L. D. WARNICK, and S. J. WELLS *Centre for Genetic Improvement of Livestock, Department of Animal and Poultry Science, University of Guelph, Guelph, Ontario, Canada N1G 2W1 Department of Clinical Science, College of Veterinary Medicine, Cornell University, Ithaca, NY Centers for Epidemiology and Animal Health, USDA, Animal and Plant Health Inspection Service, Veterinary Service, Fort Collins, CO Received March 7, Accepted December 1, ABSTRACT Scores for clinical lameness from two separate studies were combined, and genetic parameters were estimated based on linear and threshold models. Cows were from 24 herds in Minnesota, Wisconsin, and Virginia. To evaluate clinical lameness, cows were observed walking and were assigned a score between 0 and 4 (where 0 = no observable problems to 4 = inability to walk). Data included 1624 records on 1342 cows. The models included fixed effects of herd visit, parity, and stage of lactation. Random effects were additive genetics, permanent environment to account for repeated records, and residual. Estimates of heritability were 0.10 and 0.22 from the linear and threshold models, respectively. The correlation between ETA from linear and threshold models based on all animals was Deregressed ETA of sires and REML were used to estimate genetic correlations between clinical lameness and conformation traits. Among the type traits, foot angle, rear legs (rear view), and rump width had strongest associations with clinical lameness; absolute values for genetic correlations between these traits and clinical lameness were approximately Low foot angle, hocking in, and wide rumps were associated with increased clinical lameness. Correlations with strength and body depth ranged from 0.20 to 0.43, indicating that heavier cows were more prone to clinical lameness. ( Key words: clinical lameness, heritability, genetic correlation, type) Abbreviation key: CL = clinical lameness, FLS = feet and legs score, HA = Holstein Association, MACE = multiple-trait across country evaluation. INTRODUCTION Feet and leg disorders are a source of considerable concern for dairy producers because clinical lameness ( CL) causes direct and indirect economic losses. Direct losses occur because money and labor are expended in the treatment and prevention of foot and leg diseases and because some acute and chronically lame cows are involuntarily culled. Indirect losses are incurred through decreased milk production and reproductive performance of affected cows. According to Esslemont (6), average losses per cow from foot and leg disorders exceeded $45/yr per cow in the United Kingdom. Politiek et al. (17) reported that every year approximately 25% of dairy cows worldwide must be treated for locomotive disorders. Uribe et al. (24) analyzed 3 yr of data from a herd health monitoring program in Ontario and found that 3.5% of all cows were culled because of feet and leg problems. According to results compiled by the USDA National Animal Health Monitoring System (16), 15.0% of the dairy cows that were sold for slaughter were culled because they suffered from lameness or injury. Lameness was the third most common reason for involuntary culling after infertility (26.7%) and mastitis (26.5%). In addition to economic losses, feet and leg problems compromise the welfare of cows (18). Much of the variability in feet and leg health is associated with environmental effects, including differences in management and housing (5, 6, 18). However, several studies have revealed genetic sources of variation in diseases of the foot and leg (3, 12). Therefore, selection could be used to decrease the incidence of disease. In general, these studies have dealt with specific disorders of the claws, and data 1998 J Dairy Sci 81:

2 TABLE 1. The scoring system of Wells et al. (27, 28) for clinical lameness. Score Gain abnormality Description GENETICS OF CLINICAL LAMENESS None No visible gait abnormality or reluctance to walk. 1 Mild Mild variation from normal gait, including mild gait asymmetry or bilateral or quadrilateral restriction in free movement. 2 Moderate Moderate and consistent gait asymmetry or symmetric gait abnormality, but able to walk without continuous stimulation. 3 Severe Marked gait asymmetry or severe symmetric abnormality. 4 Nonambulatory Recumbent. were gathered by detailed inspection of the claws, which would likely not be feasible on a scale large enough to be useful in selection schemes. The large number of daughters of progeny-tested bulls that would have to be inspected to yield reliable ETA for susceptibility to specific disorders would make such inspection cost prohibitive. In an effort to obtain some information about susceptibility to locomotive disorders, several feet and leg traits are routinely recorded by type classification programs. These traits, such as foot angle, hoof diagonal, and rear leg set, are relatively inexpensive to record, and several studies have shown genetic associations between them and certain foot diseases (3). However, type traits provide only an indication of susceptibility to disease. Several scientists have evaluated cows for locomotive disorders by observing cows walking and assigning a CL score (25, 27, 28). Wells et al. (27) proposed a scoring procedure for CL and obtained data from several herds in Minnesota and Wisconsin. Warnick et al. (25) used the scoring system of Wells et al. (28) to evaluate cows in Virginia. This scoring approach for CL has advantages over evaluation using type traits or specific disorders. First, unlike type traits, CL is a direct measure of disease, rather than an indicator trait. Also, CL is only marginally less convenient to evaluate than most conformation traits, which do not require observation of cows while walking. Clinical lameness is, however, much more easily and inexpensively evaluated than are specific foot disorders, which require cows to be restrained for careful examination of each foot. Clinical lameness is not as precise a measurement of susceptibility to each specific disease as are observations of the disease itself, but the difference in precision may not be important. Estimates of genetic correlations between specific disorders are few, but cows with one disorder often suffer from others as well (4). For example, several specific disorders such as ulceration of the sole and toe, white line disease, and heel erosion are usually associated with a general condition termed laminitis (8), which results from insult to the vascular tissue of the foot. Unlike conformation traits of the feet and legs, genetic parameters for CL have not been well established. Wells et al. (27, 28) and Warnick et al. (25) were primarily concerned with environmental effects on CL and with phenotypic relationships of CL with other economic traits. The objective of this research was to use the pooled data of Wells et al. (27, 28) and Warnick et al. (25) to obtain estimates of genetic parameters for CL and its relationships with conformation traits. Because CL was recorded as an ordered categorical trait, analyses were performed using both linear and threshold models, and results were then compared. A secondary objective was to estimate genetic correlations between CL and type traits that are currently evaluated by the United States Holstein Association ( HA). Data MATERIALS AND METHODS Data from Wells et al. (27, 28) were from farms in Minnesota and Wisconsin that were visited twice, once during summer 1989 and again during spring Data from Warnick et al. (25) were recorded during single visits to herds in Virginia during summer In both studies, cows were observed while walking on a level surface, and two evaluators, working independently, assigned scores for CL. In the Minnesota study, the same two people evaluated all cows. In the Virginia study, three people shared the scoring duties, one of whom evaluated all cows. To ensure consistency in scoring, all three evaluators trained together prior to the start of the experiment. Both studies employed the scoring system of Wells et al. (27, 28), which is defined in Table 1. Scores ranged from 0 for cows with no observable problems to 4 for cows that were unable to walk. No cows in our data were scored a 4.

3 1150 BOETTCHER ET AL. TABLE 2. Frequencies of raw scores to establish Snell scores and the resulting Snell scores. Raw score 1Based on frequencies in original data of Wells et al. (28). 2Based on frequencies among Holstein cows in data of Warnick et al. (25). 3A pair of evaluators shared duties. Minnesota 1 Virginia 2 Evaluator 1 Evaluator 2 Evaluator 3 Evaluator 4 3 ( % ) Snell ( % ) Snell ( % ) Snell ( % ) Snell In their original studies, Wells et al. (27, 28) obtained 1654 observations on CL for Holstein cows in 17 herds. Data used for this study included records from 12 herds for which DHIA information was also available. These data were 1220 records on 887 cows. Because of the nature of the analyses done by Wells et al. (27), we were unable to distinguish specific scores for some cows that had been scored either 0 or 1 by both observers. Scores from slightly more than 50% of cows were affected. The original study of Warnick et al. (25) included CL scores for 955 cows in 12 herds from Virginia. All herds were enrolled in DHIA, and pedigree information was obtained from the Dairy Records Processing Center (Raleigh, NC). An initial edit removed 33 cows that were not purebred Holsteins. Two models were used to estimate genetic parameters for CL, a threshold model and a linear model. Scores for CL on the original scale were used directly for the threshold model, but, for the linear model, scores were transformed to normalize the data by using the procedure of Snell (22). Snell scores were computed by an evaluator according to the cumulative distributions of raw scores. The frequencies of the raw scores that were used to establish Snell scores in each study are in Table 2 along with the corresponding Snell scores. Raw scores ranged from 0 to 3 for both studies. Snell scores were adjusted to range from 0 to Distributions of raw scores were similar for both studies, although cows with no gait abnormality (score = 0) were more frequent, and moderately lame cows (score = 2) were less frequent in the Virginia data than in the Minnesota data. The proportions of severely lame cows (score = 3) were about the same for both states. Cows in the Wells et al. (27, 28) study without recorded CL scores were assigned Snell scores that were the weighted (by frequency) average of the Snell scores for categories 0 and 1. Data from the two studies were merged, and a file with genetic relationships was created. Maternal pedigrees included at least two generations, and male ancestry was determined for three generations. Despite the time and regional differences between the studies, genetic ties existed between cows across the two studies. Twenty-five bulls had daughters in both studies, and most bulls had paternal brothers with daughters in both studies. Nearly all sires were related to at least one other bull. Sires and dams were not identified for one herd from Minnesota and for two herds from Virginia, and parentage was also not recorded for some cows in the remaining herds. Because these cows lacked pedigree information, they were of little value for estimation of genetic parameters, and their records were deleted. Removal of the cows without pedigree information had little effect on the distribution of CL scores. The final data file included 1624 records on 1342 cows. The relationship file contained 2018 animals, including 458 bulls with at least one daughter. Analyses For analysis with the linear model, Snell scores from the two evaluators were averaged and used as the observation of CL. The model equation for the linear model was y il = hv i + stage j + parity k + animal l +pe l +e il, [1] where y il = mean of Snell scores for CL of cow l during herd visit i, hv i = fixed effect of herd visit i, stage j = fixed effect of stage of lactation j on the date of the herd visit, parity k = fixed effect of parity k, animal l = breeding value for animal l, pe l = random effect of the permanent environment of animal l, and e il = random residual. Six stage of lactation classes were defined

4 GENETICS OF CLINICAL LAMENESS 1151 according to DIM at the time of scoring. Classes were 0 to 50, 51 to 100, 101 to 150, 150 to 200, 201 to 300, and greater than 300 DIM. Seven parity classes were used (1, 2,..., 6, 7). The relationship file included 2018 animals. The PEST and VCE software of Groeneveld and Kovac (11) and Groeneveld (10), respectively, were used for genetic evaluation and REML estimation of variance components. A multiple-trait linear model was also used to estimate genetic correlations between CL and production. The yield data provided by the Minnesota DHIA was mature equivalent milk yield adjusted for the value of protein and fat. We obtained the formula that Minnesota DHIA used to standardize records (Jerry Steuernagel, 1996, personal communication) and applied it to the data from Virginia and from one herd from Wisconsin. The correlation between standardized and nonstandardized milk yield was Some cows were scored twice for CL in the same lactation, but only one record could be used because it was matched with lactational milk yield. For these cows, one observation of CL was chosen randomly for use in the multiple-trait analysis. Data included 1477 records of production and CL on 1342 cows. The model for standardized yield was the same as used for CL (Equation [1]). Because CL was recorded as an ordered categorical trait and was not normally distributed, a threshold model was also used for estimation of genetic parameters. A procedure that employed Gibbs sampling was implemented. The following model equation was used to describe the data, u ilm = hv i + stage j + parity k + eval l + animal m +pe m +e ilm, [2] where u ilm = CL for cow m at herd visit i according to evaluator l but transformed to the underlying scale, hv i = fixed effect of herd visit i, stage j = fixed effect of stage of lactation j on the date of the herd visit, parity k = fixed effect of parity k, eval l = fixed effect of evaluator l, animal m = breeding value for animal m, pe m = random effect of the permanent environment of animal m, and e ilm = random residual. Stage and parity classes were the same as for the linear model. For each cow, a single raw score from one of the two evaluators was chosen randomly as the dependent variable, rather than the average of the two scores, as was done for the linear model. This procedure of randomly sampling one of the two scores was repeated five times, and five separate analyses were performed. Then, parameter estimates from each of the five analyses were averaged to yield the final estimates of genetic parameters. We had initially attempted to use scores of both evaluators simultaneously to make the analysis more similar to the linear model, by fitting a model that included an additional environmental effect that was common to both scores for the same cow during the same herd visit. However, this model was seemingly overparameterized and convergence was not reached. By repeating the sampling and analysis five times and averaging results, we expected estimates of breeding values to be similar to those resulting from a single analysis that used both scores simultaneously. Because correlations between scores from different evaluators were high, we expected to observe only small differences in estimates of heritability from using a single score rather than the mean value for two scores. The g2 software of Janss et al. (13) was used for analyses of CL on the underlying scale. Several complementary subroutines were written to transform the raw scores to the underlying scale. One set of subroutines converted raw scores from the observed to the underlying scale by sampling from truncated normal distributions. Thresholds were chosen such that u ilm were approximately distributed N(0,1). The process of transformation from the observed to the underlying scales simplified the handling of cows with missing scores from the study of Wells et al. (27, 28). Those cows were known to have scored either 0 or 1, so their u ilm were sampled from normal distributions that were right truncated at t 2, the threshold between scores 1 and 2. In contrast, u ilm for a known score of 1 was drawn from a normal distribution that was also left truncated at t 1. Another subroutine generated new realizations of thresholds by sampling [n] from uniform distributions. For example, t 2, the realization for threshold 2 in round n, was drawn from U(max u [n] y = 1, min u [n] y = 2) where max u [n] y = 1 is the maximum underlying value in round n among cows with a CL score of 1, and min u [n] y = 2 is the minimum underlying value in round n for cows with a CL score of 2. For these analyses, t 2 was the only threshold that was sampled. The threshold between categories 0 and 1 was fixed at 0.7, and the threshold between categories 2 and 3 was fixed at 2.3. These thresholds correspond to percentage points for the standard normal distribution and were chosen to help ensure that u ilm were approximately N(0,1). Because two thresholds were fixed, estimation of residual variance was necessary. A common approach with threshold models is to fix one threshold at 0 and residual variance at 1. The procedure for Gibbs sampling consisted of the following steps: 1) read through the data file and

5 1152 BOETTCHER ET AL. generate underlying values, 2) sample threshold t 2, 3) sample fixed effects from normal distributions, 4) sample random effects from normal distributions, 5) sample variance components from inverted chi-square distributions, and 6) return to step 1 and repeat until enough samples are obtained. Priors for variances were based on results from the linear model. For each of the five replications, two sampling chains of 100,000 cycles were generated. The first 5000 cycles of each chain were discarded to ensure that sampling was from the joint distribution. Then, realizations from every 10th cycle were saved. Lag correlations between pairs of realizations separated by a given number of rounds were calculated to determine the number of rounds between virtually independent samples. Realizations were considered to be virtually independent if the lag correlation between them was 0. The number of virtually independent samples per 200,000 cycles ranged from at least 100 for t 2 to approximately 700 for residual variance. Estimates of breeding values for each replication were the means of the resulting samples. Point estimates for the variance components were the modes of the respective posterior distributions from each replication. Estimates were averaged over the five replications to obtain final results. Relationships Between Clinical Lameness and Conformation Traits A procedure that was based on the method for multiple-trait across country evaluations ( MACE) of Schaeffer (20) was used to estimate genetic correlations between CL and type traits evaluated by the HA. For two countries, A and B, MACE estimates transmitting abilities in country A for bulls with daughters in country B by using the bull ETA in country B, the ETA of their relatives in country A, and the genetic correlation between the trait in countries A and B. Sigurdsson and Banos (21) developed a REML procedure to estimate genetic correlations between traits in different countries by using the mixed model equations for MACE. For this study, correlations were estimated between CL and type traits by effectively defining bull ETA for CL as ETA for country A and HA ETA for type traits as ETA for country B. All bulls with at least 4 daughters with CL data and official ETA for type traits were used in the analysis. Seventy-seven bulls met these criteria. Sires and maternal granddams and maternal grandsires of bulls were identified and added to the relationship matrix along with six unknown parent groups. Deregressed proofs were calculated from the ETA by using the procedure of Banos et al. ( 2 ) as recently modified by Schaeffer (1996, personal communication) to account for the country mean. Then, the procedure of Sigurdsson and Banos (21) was applied to obtain estimates of genetic correlations between CL and type traits. Thirty-four correlations were estimated between each of the 17 linear type traits and CL for both the linear and threshold models. CL RESULTS According to results from the linear model, herd visit, stage of lactation, and parity all had effects ( P < 0.05) on CL. Estimates from the linear and threshold models for the effects of stage of lactations and parity on CL are in Table 3. Lameness was more common during the earliest stage of lactation and for cows in later parities. Similar effects of age ( 9 ) and stage of lactation (19) have been reported previously. In early lactation, cows are fed high energy diets with relatively low ratios of roughage to concentrate. These diets can cause ruminal acidosis, which often leads to laminitis and CL. Older cows are at a higher risk for CL because they have been subject to greater cumulative effects of lifetime wear and stress than their younger herdmates. Effects of date of calving and date of herd visit, classified as either single months or as 3-mo seasons, were initially included in the models, but were nonsignificant and, therefore, were removed from the final analyses. Estimates of proportions of variance in CL accounted for by additive genetic, permanent environment, and residual effects for the linear and threshold models are in Table 4. Standard errors for the linear model were those estimated using the VCE software TABLE 3. Estimates of effects of stage of lactation and parity on clinical lameness according to the linear and threshold models. Model Effect n Linear Threshold Stage of lactation 50 d d d d d d Parity

6 GENETICS OF CLINICAL LAMENESS 1153 TABLE 4. Estimates of the proportions of total variances in clinical lameness accounted for by additive genetic, permanent environmental, and residual effects according to the linear and threshold models. Variance component Linear model 1Mean of five replications. 2Range of modes of posterior distributions from five replicates. Threshold model Estimate SE Estimate 1 SD 1 Range 2 Additive genetic Permanent environment Residual (10). For the threshold model, the respective proportions of variance were calculated for each replication by using the modes of the posterior distributions of the three respective variances. The estimates in Table 4 are the means of these proportions across the five replicates. Two estimates of dispersion are given for the threshold model. Standard deviations are the averages of standard deviations of samples within a replicate across the five replicates. Ranges are of the modes of the respective parameters across the five replicates. The standard deviation of Monte Carlo error (23) within each replication was approximately for all variance ratios. Within each replication, the posterior distributions from the two sets of 100,000 cycles were quite similar, suggesting that convergence was obtained. The estimate of heritability (the proportion of total variance accounted for by additive genetic effects) was about twice as large when the threshold model was used as when the linear model was used. Heritability on the underlying scale was expected to be greater than on the observed scale. The formula derived by Gianola ( 7 ) was used to convert the estimate of heritability from the observed scale to the underlying scale. According to this formula, the observed heritability of suggested an underlying heritability of The inability to distinguish between 0 and 1 for some of the cows in the Minnesota data may have had a greater impact on estimates of variance components for the linear model than for the threshold model. The effect of using the mean values for scores by the two evaluators in the linear model rather than a single randomly chosen score was relatively minor because of the positive correlation ( 0.70) between the two evaluators. The linear model was also used to estimate heritabilities for five replicates of randomly sampled single scores from each cow. The average heritability across the five replicates was 0.108, which was not significantly different from the estimate that was obtained from the means of the scores from the two evaluators. Estimates of breeding values from the linear model versus the threshold model were seemingly affected less than estimates of variance components. The correlation between all EBV from the two models was Among sires with at least 4 daughters, the correlation between EBV was The top 4 sires and the bottom 4 sires were the same from both models, although the relative positions among the bottom four sires differed between models. The top 4 sires ranked the same when either model was used. Other scientists also reported high (>0.95) correlations between EBV from linear and threshold models (15, 26). According to results from the direct multiple-trait analysis of CL and production, the genetic correlation between CL and production was 0.25 but was not significantly different from 0 (SE = 0.20). The estimate was negative, however, which suggested a favorable relationship between CL and production. Estimates for heritability from this analysis were and for CL and production, respectively. TABLE 5. Estimated genetic correlations between conformation traits and clinical lameness according to estimated breeding values from the linear model and threshold model. Conformation trait Linear Model Threshold Stature Strength Body depth Dairy form Rump angle Rump width Rear legs, side view Rear legs, rear view Foot angle Feet and legs score Fore udder attachment Rear udder height Rear udder width Udder cleft Udder depth Front teat placement Teat length

7 1154 BOETTCHER ET AL. Correlations with Type Traits Estimates of genetic correlations between CL and the linear type traits are in Table 5. Results are given for both the linear and threshold models. Not surprisingly, the greatest correlations between conformation and CL were for several traits that described structure of the feet, legs, and rump. For both the linear and threshold models, the greatest (absolute value) correlation was between CL and foot angle. Estimates were 0.76 and 0.64 from the linear and threshold models, respectively, indicating that decreased foot angle was genetically associated with increased CL. Wells et al. (27) found a similar relationship between foot angle and CL on the phenotypic scale. They reported an odds ratio of 2.4 for a decrease of 10 in the angle of the rear lateral claw. The genetic correlation between rear legs, rear view, and CL was also quite substantial: 0.68 and 0.64 for the linear and threshold models, respectively. These results indicate that cows that tend to walk or stand with their hocks pointing inward and toes pointing outward are genetically predisposed to CL. Genetic correlations between rump width and CL were also greater than 0.60 for both models (Table 5), indicating that CL is more common among the daughters of bulls that transmit genes for wider rumps. The genetic correlations between feet and legs score ( FLS) and CL were not as exceptionally high as were the correlations between CL and the three previously mentioned traits. This result was somewhat surprising, because CL and FLS are both general assessments of feet and leg form and function and because Holstein classifiers consider mobility, if possible, when evaluating cows for FLS (2). Genetically, CL and FLS were seemingly not the same trait. Among the plausible explanations for this result is that classifiers are often not able to observe the cows walking while assigning an FLS. Also, CL is a measure of gait alteration in response to the pain of disease, but FLS differentiates among the observed feet and leg structures of seemingly healthy cows. Another contributing factor is that many breeders trim the feet of their cattle prior to classification, which may mask some of the genetic variation in FLS. Conformation traits are also usually evaluated earlier in life than CL was for some cows. Locomotive form and function may be a different trait genetically as cows age. Genetic correlations between rear legs, side view, and CL were not much different from 0 (Table 5). Among the other type traits, genetic correlations between dairy form and CL were moderately high, especially for the linear model (Table 5) (0.60), indicating that increased sharpness and decreased body condition were associated with increased CL. This result agrees with the phenotypic relationship reported by Wells et al. (27). In their study, the average condition score was 2.32 for clinically lame cows and 2.50 for healthy cows. Manson and Leaver (14) also reported a phenotypic relationship between decreased body condition and increased CL. The causes and effects of this relationship are nebulous. Both body condition scores and CL may be indicators of susceptibility to metabolic diseases such as rumen acidosis. Cows in severe negative energy balance are likely to have poorer condition than healthy cows. Such cows are also more prone to laminitis. Genetic correlations between body depth and strength and CL were moderately high and positive (Table 5). These correlations, and the high correlation of CL with rump width, indicate that sires with larger, wider, and possibly heavier daughters tended to be predisposed to CL. The genetic correlations between stature and CL were only about 0.10, however, suggesting that, genetically, increased body weight relative to frame size may be a more important risk factor for CL than absolute body weight. Wells et al. (27) reported that the lame cows in their study were significantly heavier than the cows that were not lame. Weight was estimated by measuring the heart girth of each cow, and lame cows probably had more body depth and strength (width of chest) than did cows that were not lame. Rowlands et al. (19) previously reported a positive phenotypic relationship between heart girth and CL. Genetic correlations between CL and udder depth and udder cleft were moderately high, but negative (Table 5). Well-attached udders were associated with decreased CL. This genetic relationship was not easily explained. Phenotypically, cows may have to alter their gaits if udders are deep and pendulous. Comparison of the ETA of type traits for bulls with extreme EBV for CL provided another perspective on the relationship between traits for CL and feet and legs. Table 6 has the differences in average ETA for feet and legs of the 5 best bulls and the 5 poorest bulls for CL according to their EBV from the linear and threshold models, respectively. Although the differences between the groups of sires are not significant in all instances, the results clearly suggest that CL is less common among daughters of bulls that tend to transmit improved conformation of feet and legs. Bulls with the most favorable EBV for CL tended to transmit straighter legs, especially from the rear view, and steeper foot angles. Overall, FLS and composites for feet and legs were also greater for the 5 best bulls than for the 5 poorest bulls. Several

8 GENETICS OF CLINICAL LAMENESS 1155 specific sires had EBV for CL that were consistent with their reputations among breeders. For example, the poorest ranking sire was Walkway Chief Mark, a generally outstanding bull that was infamous for transmitting poor feet and legs. The two highest ranking sires were Wardin Bell Gene and Carlin-M Ivanhoe Bell, 2 bulls that transmitted exceptional feet and legs. Deregressed EBV for CL were regressed on linear, quadratic, and cubic effects of ETA for feet and legs traits to test for nonlinear relationships. The FLS was the only trait for which quadratic effects ( P < 0.05) were found. Figure 1 shows the regression curve for the relationship between CL from the threshold model and FLS. The lowest ETA for FLS were not associated with the poorest CL. Scatter plots of CL versus FLS revealed that the 5 bulls that had the poorest FLS were slightly better than average for CL. This nonlinearity may have contributed to the lower genetic correlation between CL and FLS than between CL and foot angle and rear legs, rear view. DISCUSSION The data that were used for these analyses were not originally collected with the intent of combining them and performing a genetic analysis of CL. The two studies were conducted by different researchers in different regions of the US more than 3 yr apart. For this analysis, the precision of observations for data from one of the experiments was decreased because specific individual CL scores were not maintained for all observations. Thus, the data were less than ideal for a precise genetic analysis. However, both studies used the same procedure to evaluate CL, and genetic ties existed between their groups of cows; the decreased precision of scores was addressed in the analyses. As a result, the joint analysis was still very informative. TABLE 6. Differences 1 in mean estimated transmitting abilities for feet and leg traits among the 5 best and 5 poorest bulls for clinical lameness according to EBV from the linear and threshold models. Model Trait Linear Threshold Rear legs, side view Rear legs, rear view 1.18* 1.73 Foot angle 1.86* 2.30* Feet and legs score Feet and legs composite Mean of the 5 best bulls for clinical lameness minus mean of the 5 poorest bulls. P < *P < Figure 1. Regression curve of deregressed evaluation of sire EBV for clinical lameness on sire ETA for feet and legs score. The heritability estimate of approximately 0.20 from the threshold model indicated that direct selection for reduced CL could be moderately successful. Also, results from the multiple-trait analysis of CL and milk production indicated a slightly favorable but nonsignificant genetic correlation between CL and milk production. Thus, selection for decreased CL should not compromise genetic progress for production. Estimates of genetic correlations between several type traits were moderate to high. Correlations were highest for CL with foot angle and rear legs, rear view. Bulls that transmitted steeper foot angles and straighter legs from the rear view had fewer daughters with CL. Correlations between CL and FLS were moderate. The FLS and CL were both general measures of feet and leg form and function but were different traits genetically. Their relationship was nonlinear. The correlation between CL and rear legs, side view was essentially 0, indicating that neither posty nor straight hocks were strongly associated with CL. Genetic correlations between CL and body depth, strength, and rump width were moderate and positive, but correlations were close to 0 for stature. Daughters from bulls that sired cows that were relatively heavy in proportion to their frame size were more susceptible to CL. The magnitude of these correlations indicate that a selection index with foot angle; rear legs, rear view; FLS; and some body size traits could be used to indirectly select for CL. In this study, the poorest bulls for CL had average ETA for feet and legs traits that were one to two standard deviations lower than the best bulls for CL. The potential for either direct or indirect selection for decreased CL seems promising, but one question

9 1156 BOETTCHER ET AL. that remains unanswered by this study is whether CL is actually the trait pertaining to feet and legs that should be incorporated in an overall breeding goal. Perhaps another trait or combination of traits including CL would better explain the economic losses that result from locomotive disorders. Clinical lameness may be evaluated most accurately if evaluation is independent of other type traits to avoid effects of trimming, but such data collection may not be feasible. Additional research that examines all of the genetic factors that contribute to economic losses from problems with feet and legs is warranted. ACKNOWLEDGMENTS A number of people contributed to this research. We first thank the producers for allowing the collection of data for this project. We also thank Jerry Steuernagel from Minnesota DHIA and Ken Butcher from the Dairy Records Processing Center in Raleigh, North Carolina for providing production and pedigree information for the cows in the study. We also thank Les Hansen and John Galgowski for providing DHIA information for research herds in Minnesota and Wisconsin, respectively. We thank Paul VanRaden and Ryan Starkenberg from the USDA for providing us with pedigree information for the bulls with daughters in the study. We also recognize Tom Lawlor from the US Holstein Association for providing ETA for type traits of the bulls. We thank Eildert Groeneveld, Agust Sigurdsson, Georgios Banos, Larry Schaeffer, and Luc Janss for use of their software and Geert Jongert for data processing. Finally, we thank the Cattle Breeders Research Council of Canada for their financial support of this research. REFERENCES 1 Banos, G., L. R. Schaeffer, and E. B. 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