- Email: [email protected]
Genetic parameters of finish time in Korean Thoroughbred racehorses
Livestock Science 140 (2011) 49–54
Contents lists available at ScienceDirect
Livestock Science j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / l i v s c i
Genetic parameters of finish time in Korean Thoroughbred racehorses Kyung-Do Park ⁎ Genomic Informatics Center, Hankyong National University, Anseong-si, 456-749, Republic of Korea
a r t i c l e
i n f o
Article history: Received 12 December 2010 Received in revised form 8 February 2011 Accepted 9 February 2011
Keywords: Thoroughbred Finish time Heritability Repeatability Genetic trend
a b s t r a c t This study was conducted to estimate the annual genetic and environmental trends of Thoroughbred racehorses. A total of 208,043 records collected from horses that raced at Gwacheon racecourse were analyzed. The average generation intervals of the sires of offspring and the dams of offspring were 10.82 and 10.31 years, respectively. The effects of the contemporary groups accounted for more than approximately 44% of the total variation in finish time and were the most important environmental effects in the genetic evaluation model. The range of estimated heritabilities and repeatabilities were from 0.06 to 0.30 and from 0.31 to 0.59, respectively. The estimate of heritability was highest at the racing distance of 1000 m (0.30), and decreased as the racing distance increased. Of the total variance components, the proportion of variance due to the jockey was from 0.02 to 0.06, and it was larger in longer distance races than in shorter ones. According to the results, racehorses accounted for 94.8%, and the jockey for only 5.2%, of finish time. As the racing distance increased, the percent contribution of the jockey also tended to increase. The phenotypic and environmental improvements were −2.140 and − 1.492 s over 16 years studied, respectively. On the other hand, genetic gains in racing performance of home-produced and imported racehorses in the same period were − 0.459 and − 0.369 s, respectively. The genetic improvement of racehorses in racing performance was very consistent, resulting in average gains of − 0.029 and − 0.031 s per year in home-produced and imported racehorses, respectively. © 2011 Elsevier B.V. All rights reserved.
1. Introduction The horse racing industry in Korea officially began in 1922, and on October 1st 1998, Korea, along with Turkey, became the 50th country in the world whose stud book was officially approved by the International Stud Book Committee (ISBC). Official approval of Korean Thoroughbred from the ISBC allowed equal exchange with other countries. Furthermore, the groundwork was laid not only for the global image of the Korean Racing Authority as a horse registration and horse racing enforcement organization to be improved, but also for increasing the social recognition of horse racing and horse culture as pedigree sports. As a part of national animal
⁎ Tel.: +82 31 670 5490; fax: +82 31 670 5491. E-mail address: [email protected]. 1871-1413/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.livsci.2011.02.006
improvement projects, the quality of racehorses has increased and superior racehorse lineages have been produced. In the future, Korea will strive to participate in host Part II of the International Federation of Horseracing Authorities (IFHA). In foreign countries, racehorse earnings are considered to be one of the most important traits for the evaluation of the individual (Klemetsdal, 1994; Langlois and Blouin, 2004, 2007), and in developed countries, including the USA, the Average Earning Index (AEI) determines the ranking of racehorses. When evaluating racing ability, however, traits such as earnings, placing order and winning rate are not normally distributed, and horses without earnings or winnings can be excluded from analysis. Since Thoroughbred racehorses are reproduced by natural mating, the number of progeny is relatively small compared with that of other animals. Therefore, the distribution of records is biased and environmental effects can't fully explain trends in highest or
50
K.D. Park / Livestock Science 140 (2011) 49–54
average AEI, which are important evaluation traits (Tolley et al., 1983; Buttram et al., 1988a). Finish time in each race is the only direct measure of speed and is a suitable quantitative measure that can be used to evaluate the genetic racing performance of horses. Therefore, finish time for racing distance is apparently a well-defined and clear character (Ekiz et al., 2005; Ekiz and Kocak, 2007; Buxadera and Mota, 2008). It has already been reported that the use of repeated racing records is rational when measuring racing ability changes under the current Korean horse racing system (Park and Lee, 1999). Currently, animal models using BLUP (Best Linear Unbiased Prediction) theory are utilized for the genetic evaluation of most domestic animals at the national level. Many researchers have reported that animals can be improved through the selection of economically important traits. However to achieve this, the establishment of desirable improvement traits and goals must take precedence. The objective of this study was to provide basic information for the establishment of improvement goals by analyzing the annual genetic improvement of finish time in the racing records of Korean Thoroughbred racehorses. 2. Materials and methods 2.1. Description of the data The data used in this study were 208,043 finish time records collected from 9934 Thoroughbred racehorses that raced at Gwacheon racecourse in Korea from 1990 to 2006, and was provided by the Korea Racing Authority (KRA). Finish times that were distributed outside 3.5 standard deviations from the mean at each distance were eliminated from the data, as they may represent possible injuries of the racehorses during the race or unavoidable mistakes made by the jockey. Table 1 shows the distributional properties for data structured by distance. Distribution of finish times by racing distance was slightly rightward, but most cases showed a normal shape. As the race distance increased, the standard deviation of finish time, horse weight and age increased. While, the number of records decreased due to the smaller number of longer distance races under the group winning system. Of the total records, castrated (gelding), stallion and mare comprised 28.7, 20.3 and 51.0%, respectively; and the average number of start per year was 7.5. Racehorses from 3 to 5 years of age comprised 74.5% of the total records, while 3.9% (8091)
were 2-year-old horses. Those of 6 and more than 7 years of age comprised 11.4% and 10.25%, respectively. In order to examine the relationship between the number of starts and the finish times by racing distance, finish time records were subdivided in to three groups of 1, 2–9, and 10 or more records. 2.2. Statistical model The analytical Repeatability Animal Model used for estimating the genetic parameters and expected breeding values (EBVs) was as follows: yijklmno = μ + di + sj + mk + YMDRl + jm + an + pn + eijklmno where, yijklmno =finish times (s), μ=overall mean, di =fixed effect of the ith racing distance (i =1000 m, 1200 m, 1400 m, 1700 m, 1800 m, 1900 m, 2000 m), sj =fixed effect of the jth sex (j=gelding, stallion, mare), mk =fixed effect of the kth year of age (k=2, 3, …, more than 7 years), YMDRl =fixed effect of the lth contemporary group (l =1, 2, …, 19,039), jm =random effect of the mth jockey (m = 1, 2, …, 214), a n = random additive genetic effect of the nth animal (n=1, 2, …, 19,062), pn =permanent environmental effect of individual animals (n=1, 2, …, 9934), and eijklmno =a random residual effect. Var (a)=Aσ 2a, Var(pe)=Iσ 2pe, and Var(e)=Iσ 2e, where A=numerator relationship matrix and I=identity matrix. In the model, contemporary group refers to racehorses that ran together in the same race, and effect of contemporary group for finish time was calculated using the SAS (1999) program. Variance components, genetic parameters and breeding values were estimated by the restricted maximum likelihood method for one trait animal model using the derivative-free process (DF-REML 3.1 program) (Meyer, 1998). The overall trends on finish time were calculated using the method suggested by Wilson and Willham (1986). 3. Results Thoroughbred racehorses exhibited a long generation interval from 10.21 to 10.83 years (Table 2). The average generation intervals of sires and dams of offspring were 10.82 and 10.31 years, respectively. The effect of the contemporary groups accounted for more than 43.9% (1200 m) of the total variation in finish time (Table 3). The heritability estimate was highest at the 1000 m distance (0.30), and the range of
Table 1 Distributional properties for data structure by racing distance. Racing distance (m) 1000 1200 1400 1700 1800 1900 2000 Overall a
Number of a Records
Races
Jockeys
Racehorses
Sires
Dams
50,051 49,514 42,804 20,733 21,387 12,291 11,263 208,043
4549 4514 3892 2010 1955 1114 1005 19,039
178 185 209 179 193 161 176 214
8948 8539 7199 5112 4060 2614 1846 9934
2451 2428 2213 1834 1575 1236 975 2746
6150 5975 5193 3841 3127 2117 1487 6860
Total number of jockeys, racehorses, sires and dams used across all distances (not the column sum).
Finish time (s)
Age (year)
Horse mean weight (kg)
65.39 ± 1.54 78.99 ± 1.69 92.16 ± 1.96 116.24 ± 2.18 122.91 ± 2.31 129.67 ± 2.32 136.11 ± 2.43 –
3.8 4.1 4.5 4.9 5.2 5.6 6.0 4.5
445 447 449 452 454 457 461 450
K.D. Park / Livestock Science 140 (2011) 49–54
51
Table 2 Generation intervals for Thoroughbred racehorses. Pathway
Number of pairs
Sire → offspring(LSO) Sire → son(LSS) Sire → daughter(LSD) Dam → offspring(LDO) Dam → son(LDS) Dam → daughter(LDD) Parent → offspring(LPO) a
9716 4191 5525 9687 4189 5498 19,404
Generation interval (year) Mean ± SD a
Skewness
Median
Mode
10.82 ± 3.59 10.81 ± 3.62 10.83 ± 3.58 10.31 ± 3.80 10.21 ± 3.79 10.39 ± 3.82 10.57 ± 3.71
0.91 0.95 0.88 0.76 0.80 0.74 0.81
10 10 10 10 10 10 10
9 8 9 8 8 8 8
Standard deviation.
estimated heritabilities and repeatabilities were from 0.06 to 0.30 and from 0.31 to 0.59, respectively (Table 4). The proportion of variance due to the jockey was from 0.02 to 0.06, and racehorses accounted for 94.8% and the jockey for only 5.2% of the finish time (Table 5). Phenotypic improvement and temporary and permanent environmental improvements were −0.132, −0.103 and −0.003 per race year, respectively. Genetic improvement of racehorses in racing ability was very consistent, resulting in average improvements of −0.029 and −0.031 s per year, in homeproduced and imported racehorses, respectively (Table 6). 4. Discussion 4.1. Selection criteria Selecting the best traits with which to evaluate the genetic potential of a racehorse is a very important subject. They also argue that in the records, racehorses that have not won prize money are eliminated from the data. Also, neither the best finish time nor the average finish time can be used to evaluate the genetic potential of racehorses because they do not properly account for the environmental differences that affect racing time traits. Even though Ojala and Van Vleck (1981) and Ojala et al. (1987) have reported that the individual best finish time is an important characteristic of a racehorse for genetic evaluation, it seems impractical to use in evaluating racehorse potential in Korea because there are no specific racing distances here. Rather, racing is done according to classes and it is difficult to adjust the prize money according to inflation with great fluctuations by race year. Racehorses that had only raced once showed the best finish time at each distance, which is contrary to the report by Buttram et al. (1988a), which found that the average finish time decreased as the number of starts increased in Quarter Horse.
However, the finish time among the first-class racehorses in the 1900 m and 2000 m races improved as the number of races in which they had taken part increased. The reason for this unusual result in Korean racehorses is due to the national racing system. Racehorses are divided into 6 classes and races vary by distance and class. The number of wins and earnings establish a racehorse's classification, but this criterion may be adjusted according to the situation. The griffin class is operated separately. Generally, new racehorses run in the griffin class (1000 m). When a racehorse wins the short distance races, they enter into longer distance races, which is why superior racehorses tend to have only one race at shorter distances. Therefore, when the evaluation of racehorses is done according to distance, elimination of the racehorses with only one record will not yield accurate results. It is desirable to evaluate the performance of all racehorses using repeated finish times in order to improve both the record taking process and the quality of participating horses. 4.2. Generation interval Table 2 shows the estimated generation intervals of Thoroughbred racehorses. Generation interval is the average interval of time between the birth of parents and the birth of their offspring. The average generation intervals of sires and dams of offspring were 10.82 and 10.31 years, respectively. The average generation interval of parents of offspring was 10.57 years, which was similar to the 10.5 years reported by Langlois et al. (1983) and shorter than the generation interval of 10.93 years of Brazilian Thoroughbred (Taveira et al., 2004). 4.3. Contemporary group The average size of the contemporary group was 10.9 heads. The effect of different racing methods according to year was accounted for by the year of race, the season effect
Table 3 Percentages of the total variance in finish time (s) accounted for by race years, months, days and individual races by racing distance. Source of variation
Years (%) Months (%) Days (%) Races (%) Errors (%) Total variance (s2)
Racing distance (m) 1000
1200
1400
1700
1800
1900
2000
17.7 7.4 4.7 14.4 55.8 2.399
11.9 9.2 3.6 19.2 56.1 2.877
18.4 11.3 3.7 18.7 47.9 3.879
18.5 11.1 2.3 25.4 42.7 4.822
20.0 11.0 2.1 26.5 40.4 5.398
20.5 11.7 12.0 16.4 39.4 5.469
23.3 12.7 4.2 22.7 37.1 5.986
52
K.D. Park / Livestock Science 140 (2011) 49–54
Table 4 Additive genetic (σ 2a), permanent environmental (σ 2pe), jockey (σ 2j), error variance (σ 2e) components, heritabilities (h2), standard errors (SE), repeatabilities (r), and ratios between jockey and total variance components (J/T) for finish time by racing distance. Racing distance (m)
σ 2a
σ 2pe
σ 2j
σ 2e
h2 ± SE
r
J/ T
1000 1200 1400 1700 1800 1900 2000 ≤ 1400 ≥ 1700 Overall
0.5404 0.5557 0.1537 0.3286 0.1428 0.2732 0.1285 0.5717 0.3879 0.5533
0.5276 0.5425 0.5447 0.3605 0.4424 0.4712 0.8206 0.5813 0.4729 0.5588
0.0280 0.0397 0.0522 0.0848 0.1107 0.1131 0.0935 0.0485 0.0977 0.0614
0.7178 0.8468 0.9782 1.1193 1.1793 1.2614 1.1320 0.9364 1.2048 1.0866
0.30 ± 0.02 0.28 ± 0.01 0.09 ± 0.03 0.17 ± 0.06 0.08 ± 0.03 0.13 ± 0.07 0.06 ± 0.06 0.27 ± 0.07 0.18 ± 0.04 0.24 ± 0.03
0.59 0.55 0.40 0.36 0.31 0.35 0.44 0.54 0.40 0.49
0.02 0.02 0.03 0.04 0.06 0.05 0.04 0.02 0.05 0.03
on race was accounted for by the month of race, the condition of racetrack was accounted for by the date of race, and the individual race accounted for the sampling variation of the racehorse classes and the presence of the winning racehorses in a contemporary group (Buttram et. al., 1988b; Oki et al., 1994). Competitive horse racing is not always recorded in a consistent manner. The individual race level influences on finish time (Bugislaus et al., 2005) and the racing speed can vary depending on the records of the contemporary group of horses taking part in the race. Therefore, it is desirable to include the records of contemporary group horses in the evaluation model (Buttram et al., 1988b; Oki et al., 1994). The percentages of total variance attributable to individual races and race years were larger than those due to other effects and ranged from 14.4% to 26.5% and from 11.9% to 23.3%, respectively. Race months within race years accounted for 7.4% to 12.7% of total variance, but variance of race days within years and months was relatively small (2.1% to 12.0%). As shown in Table 3, the effect of the contemporary groups accounted for more than approximately 44% of the total variation in finish time, and was the most important environmental effect in the genetic evaluation model. 4.4. Genetic parameters The estimate of variance components and genetic parameters for finish time by racing distance are presented in Table 4. Heritability estimates of the racing time of Japanese Thoroughbred racehorses were in the range of 0.086–0.217 (Oki et al., 1995a), and those of Brazilian Thoroughbred were
in the range of 0.10–0.32 (Mota, 2006). In 2007, Ekiz and Kocak reported that heritability and repeatability estimates ranged from 0.177 to 0.353 and from 0.289 to 0.404, respectively and it was also reported that heritability estimates on the racing time of Iranian Thoroughbred were in the range of 0.09–0.13 (Bakhtiari and Kashan, 2009). They also reported that as the racing distance increased, the heritability estimates decreased. The range of estimated heritabilities and repeatabilities were from 0.06 to 0.30 and from 0.31 to 0.59, respectively. The heritability estimate was highest at the 1000 m distance (0.30), and it decreased as the racing distance increased. Similar results were reported by Oki et al. (1995a), Mota (2006), Ekiz and Kocak (2007) and Bakhtiari and Kashan (2009). The reason may be that in the short distance racehorses can run at full speed with minimal environmental effects, but as the racing distance increases, the jockey and temporary environmental factors have a stronger effect on the finish time. Oki et al. (1995b) reported that the jockey was found to have highly significant (pb 0.01) effects on racing time. The skill of the jockey was an important source of variation in racing time across distances, and therefore, it should be considered in deriving adjustment factors, estimating genetic parameters and predicting genetic values for racehorses. In contrast, Pinchbeck et al. (2002) reported that the jockey contributed very little to the variation in the risk of falling. In our experiment, the proportion of variance due to the jockey was from 0.02 to 0.06, and it was larger in the longer distance races than in the shorter ones. Hintz and Van Vleck (1978) for Standardbred Trotter horses reported that the relative contribution to the racing
Table 5 Ratios between horse and jockey variance components by racing distance.
a
Racing distance (m)
σ 2ah
σ 2j
σ 2h + σ 2j
Horse (%)b
Jockey (%)c
1000 1200 1400 1700 1800 1900 2000 ≤ 1400 ≥ 1700 Overall
1.0679 1.0982 0.6984 0.6891 0.5852 0.7444 0.9491 1.1530 0.8607 1.1121
0.0280 0.0397 0.0522 0.0848 0.1107 0.1131 0.0935 0.0485 0.0977 0.0614
1.0960 1.1379 0.7507 0.7740 0.6959 0.8575 1.0425 1.2016 0.9584 1.1735
97.4 96.5 93.0 89.0 84.1 86.8 91.0 96.0 89.8 94.8
2.6 3.5 7.0 11.0 15.9 13.2 9.0 4.0 10.2 5.2
σ 2h = (σ 2a + σ 2j) bσ 2h/(σ2h + σ2j), cσ 2j/(σ 2h + σ 2j).
K.D. Park / Livestock Science 140 (2011) 49–54 Table 6 The overall linear trends on finish time by race year for all distance data. Variables
Linear estimates
Year of race
b ± SE
R2
Phenotypic trend Temporary environmental trend Permanent environmental trend Jockey trend Genetic trend a Genetic trend b
− 0.132 ± 0.022 ⁎⁎⁎ − 0.103 ± 0.022 ⁎⁎⁎ − 0.003 ± 0.001 ⁎⁎ 0.000 ± 0.001 NS − 0.029 ± 0.002 ⁎⁎⁎ − 0.031 ± 0.003 ⁎⁎⁎
0.71 0.60 0.51 0.02 0.91 0.85
a
Home-produced racehorse. Imported racehorse. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001. NS None significant. b
performance made by the racehorse and the jockey are 68–83% and 17–32%, respectively. But according to the results from our data, racehorses accounted for 94.8% and the jockey for only 5.2% of the finish time (Table 5). As the racing distance increases the percent contribution of the jockey also tends to increase.
4.5. Genetic trends The phenotypic, environmental and genetic trends in finish time by the race year are depicted in Fig. 1. The genetic trend in finish time was calculated on the basis of weighted mean using the predicted breeding value for each animal against each year of race. The phenotypic and environmental improvements were about − 2.140 and − 1.492 s over the last 16 years, respectively. On the other hand, genetic gains of the home-produced and imported racehorses in racing performance over 16 years were − 0.459 and − 0.69 s, respectively. Genetic improvement of racehorses in racing ability was very consistent, resulting in average improvements of − 0.029 and − 0.031 s per year, in home-produced and imported racehorses, respectively. Also, Oki and Sasaki (1995c) reported that genetic trends in Japanese Thoroughbred were − 0.0170 and −0.0084 s per birth year for racing time on turf and sand tracks, respectively and for the American Quarter Horses, it was reported genetic trends on racing time were −0.0088 and −0.0090 s per race year for distances of 320 m and 360 m, respectively (Wilson et al., 1998). 1.0
53
Mota et al. (2005) reported genetic trends in Brazilian Thoroughbred ranged from −0.0154 to −0.0310 s per year of birth at less than 1300 m race, which is in agreement with those obtained from the present study. However, in the race of 1400 m or more, the estimates were relatively small, compared with other reports. It was also reported that the genetic trends for racing time were approximately five times higher at 1000 and 1100 m than at distances of 1500 and 1600 m. Taveira et al. (2004) in Brazilian Thoroughbred reported that genetic trends on winning time for animals with records were −0.0027 and −0.0016 s/birth year. The reason for that seems to be due to the fact they were estimated using winning time. In this experiment, the estimates on genetic trends were higher than those of previous results. No trend for the jockey was recorded as the coefficient of determination (R2) value was approximately equal to zero. The genetic trends in finish time for sires and dams by nationality are depicted in Fig. 2. The annual genetic gain of domestic sires was highest (−0.053), probably as a result of the continuous importation of very superior sires by Korea Racing Authority. As shown in Table 6, the improvement in finish time of home-produced horses was not due to genetic factors, but mainly due to environmental factors. These results suggest that the environmental conditions (training, feed and care and track management system, etc.) for horse racing improved over the study period, while genetic improvements were relatively low. Therefore, it is urgent that goals for the qualitative genetic improvement of racehorses are established. 5. Conclusion It is very important to develop an effective evaluation method for the purpose of selection and improvement of Thoroughbred racehorses using BLUP. The contemporary group effect should be considered in the evaluation model in order to avoid possible bias in estimation of genetic parameters and breeding values. It can be concluded that the genetic evaluation of racehorses in Korea should utilize the finish time records of all the different distances together because the number of sires with 2 or less progeny represents 53.5% of all race data, and the number of sires with only one progeny accounts for 71.9% of all the racehorses participating in the 2000 m race. Though the best finish times, earnings,
2000
2002
2004
2006
Genetic
Fig. 1. Genetic, phenotypic and environmental trends in finish time (s) by race year using all distance data.
Foreign dam
Domestic sire
Domestic dam
06
04
02
00
98
96
Foreign sire
20
1998
Environmental
20
1996
20
1994
Phenotypic
20
1992
19
1990
19
90
-2.5
19
-2.0
94
-1.5
19
-1.0
19
0.0 -0.5
92
0.2 0.1 0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8
0.5
Fig. 2. Genetic trends in finish time (s) of sires and dams by nationality.
54
K.D. Park / Livestock Science 140 (2011) 49–54
and placing orders are very important traits for racehorse selection, finish time will be the most reasonable selection trait for the estimation of genetic improvement racehorse speed. According to the various results which have been reported so far, genetic performance cannot be improved much through the selection on winning time, and so to reduce the generation interval and to increase the genetic gain and accuracy of breeding value, it is necessary to establish improvement goals and a new evaluation system such as genomic selection. Acknowledgements The research was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (iPET). References Bakhtiari, J., Kashan, N.E.G., 2009. Estimation of genetic parameters of racing performance in Iranian Thoroughbred horses. Livest. Prod. Sci. 120, 151–157. Bugislaus, A.E., Roehe, R., Kalm, E., 2005. Comparison of two different statistical models considering individual races or racetracks for evaluation of German trotters. Livest. prod. sci. 92, 69–76. Buttram, S.T., Willham, R.L., Wilson, D.E., Heird, J.C., 1988a. Genetics of racing performance in the American Quarter Horse: I. Description of the data. J. Anim. Sci. 66, 2791–2799. Buttram, S.T., Willham, R.L., Wilson, D.E., 1988b. Genetics of racing performance in the American Quarter Horse: II. Adjustment factors and contemporary groups. J. Anim. Sci. 66, 2800–2807. Buxadera, A.M., Mota, M.D.S., 2008. Variance component estimations for race performance of Thoroughbred horses in Brazil by random regression model. Livest. Sci. 117, 298–307. Ekiz, B., Kocak, O., Yilmaz, A., 2005. Phenotypic and genetic parameters estimatives for racing traits of Thoroughbred horses in Turkey. Arch. Tierz. Dummerstorf 18, 121–129. Ekiz, B., Kocak, Ö., 2007. Estimates of genetic parameters for racing times of Thoroughbred horses. Turk. J. Vet. Anim. Sci. 31 (1), 1–5. Hintz, R.L., Van Vleck, L.D., 1978. Factors influencing racing performance of the Standardbred pacer. J. Anim. Sci. 46, 60–68.
Klemetsdal, G., 1994. Application of standardized, accumulated transformed earnings in breeding of Norwegian trotters. Livest. Sci. 38, 245–253. Langlois, B., Minkema, D., Bruns, E., 1983. Genetic problems in horse breeding. Livest. Prod. Sci. 10, 69–81. Langlois, B., Blouin, C., 2004. Practical efficiency of breeding value estimations based on annual earnings of horses for jumping, trotting, and galloping races in France. Livest. Prod. Sci. 87, 99–107. Langlois, B., Blouin, C., 2007. Annual, career or single race records for breeding value estimation in race horse. Livest. Sci. 107, 132–141. Meyer, K., 1998. DFREML Programs to Estimate Variance Components by Restricted Maximum Likelihood Using a Derivative-Free Algorithm. User Notes. Mota, M.D.S., Abrahao, A.R., Oliveira, H.N., 2005. Genetic and environmental parameters for racing time at different racing distances in Brazilian Thoroughbreds. J. Anim. Breed. Genet. 122, 393–399. Mota, M.D.S., 2006. Genetic correlations between performance at different racing distances in Thoroughbreds. Livest. Sci. 104, 227–232. Ojala, M., Van Vleck, L.D., 1981. Measures of racetrack performance with regard to breeding evaluation of trotters. J. Anim. Sci. 53, 611–619. Ojala, M., Van Vleck, L.D., Quaas, R.L., 1987. Factors influencing best annual racing time in finnish horses. J. Anim. Sci. 64, 109–116. Oki, H., Sasaki, Y., Willham, R.L., 1994. Genetics of racing performance in the Japanese Thoroughbred horse: II. Environmental variation of racing time on turf and dirt tracks and the influence of sex, age, and weight carried on racing time. J. Anim. Breed. Genet. 111, 128–137. Oki, H., Sasaki, Y., Willham, R.L., 1995a. Genetic parameter estimates for racing time by restricted maximum likelihood in the Thoroughbred horse of Japan. J. Anim. Breed. Genet. 112, 146–150. Oki, H., Sasaki, Y., Lin, C.Y., Willham, R.L., 1995b. Influence of jockeys on racing time in Thoroughbred horses. J. Anim. Breed. Genet. 112, 171–175. Oki, H., Sasaki, Y., 1995c. Estimation of genetic trend in racing time of Thoroughbred horses in Japan. Equine Research Institute, Setagaya-Ku, Tokyo 154. Park, K.D., Lee, K.J., 1999. Genetic evaluation of Thoroughbred racehorses in Korea. Kor. J. Anim Sci. 41 (2), 135–140. Pinchbeck, G.L., Clegg, P.D., Proudman, C.J., Morgan, K.L., Wood, J.L.N., French, N.P., 2002. Risk factors and sources of variation in horse falls in steeplechase racing in the UK. Prev. Vet. Med. 55, 179–192. SAS User's Guide: Statistics, Ver 9.1 Edition. 1999. SAS Inst., Inc., Cary, NC. Taveira, R.Z., Mota, M.D.S., Oliveira, H.N., 2004. Population parameters in Brazilian Thoroughbred. J. Anim. Breed. Genet. 121, 384–391. Tolley, E.A., Notter, D.R., Marlowe, T.J., 1983. Heritability and repeatability of speed 2- and 3-yr-old Standardbred race horses. J. Anim. Sci. 56, 1294–1305. Wilson, D.E., Willham, R.L., 1986. Within-herd phenotypic, genetic and environmental trend lines for beef cattle breeders. J. Anim. Sci. 63, 1087–1094.
Contents lists available at ScienceDirect
Livestock Science j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / l i v s c i
Genetic parameters of finish time in Korean Thoroughbred racehorses Kyung-Do Park ⁎ Genomic Informatics Center, Hankyong National University, Anseong-si, 456-749, Republic of Korea
a r t i c l e
i n f o
Article history: Received 12 December 2010 Received in revised form 8 February 2011 Accepted 9 February 2011
Keywords: Thoroughbred Finish time Heritability Repeatability Genetic trend
a b s t r a c t This study was conducted to estimate the annual genetic and environmental trends of Thoroughbred racehorses. A total of 208,043 records collected from horses that raced at Gwacheon racecourse were analyzed. The average generation intervals of the sires of offspring and the dams of offspring were 10.82 and 10.31 years, respectively. The effects of the contemporary groups accounted for more than approximately 44% of the total variation in finish time and were the most important environmental effects in the genetic evaluation model. The range of estimated heritabilities and repeatabilities were from 0.06 to 0.30 and from 0.31 to 0.59, respectively. The estimate of heritability was highest at the racing distance of 1000 m (0.30), and decreased as the racing distance increased. Of the total variance components, the proportion of variance due to the jockey was from 0.02 to 0.06, and it was larger in longer distance races than in shorter ones. According to the results, racehorses accounted for 94.8%, and the jockey for only 5.2%, of finish time. As the racing distance increased, the percent contribution of the jockey also tended to increase. The phenotypic and environmental improvements were −2.140 and − 1.492 s over 16 years studied, respectively. On the other hand, genetic gains in racing performance of home-produced and imported racehorses in the same period were − 0.459 and − 0.369 s, respectively. The genetic improvement of racehorses in racing performance was very consistent, resulting in average gains of − 0.029 and − 0.031 s per year in home-produced and imported racehorses, respectively. © 2011 Elsevier B.V. All rights reserved.
1. Introduction The horse racing industry in Korea officially began in 1922, and on October 1st 1998, Korea, along with Turkey, became the 50th country in the world whose stud book was officially approved by the International Stud Book Committee (ISBC). Official approval of Korean Thoroughbred from the ISBC allowed equal exchange with other countries. Furthermore, the groundwork was laid not only for the global image of the Korean Racing Authority as a horse registration and horse racing enforcement organization to be improved, but also for increasing the social recognition of horse racing and horse culture as pedigree sports. As a part of national animal
⁎ Tel.: +82 31 670 5490; fax: +82 31 670 5491. E-mail address: [email protected]. 1871-1413/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.livsci.2011.02.006
improvement projects, the quality of racehorses has increased and superior racehorse lineages have been produced. In the future, Korea will strive to participate in host Part II of the International Federation of Horseracing Authorities (IFHA). In foreign countries, racehorse earnings are considered to be one of the most important traits for the evaluation of the individual (Klemetsdal, 1994; Langlois and Blouin, 2004, 2007), and in developed countries, including the USA, the Average Earning Index (AEI) determines the ranking of racehorses. When evaluating racing ability, however, traits such as earnings, placing order and winning rate are not normally distributed, and horses without earnings or winnings can be excluded from analysis. Since Thoroughbred racehorses are reproduced by natural mating, the number of progeny is relatively small compared with that of other animals. Therefore, the distribution of records is biased and environmental effects can't fully explain trends in highest or
50
K.D. Park / Livestock Science 140 (2011) 49–54
average AEI, which are important evaluation traits (Tolley et al., 1983; Buttram et al., 1988a). Finish time in each race is the only direct measure of speed and is a suitable quantitative measure that can be used to evaluate the genetic racing performance of horses. Therefore, finish time for racing distance is apparently a well-defined and clear character (Ekiz et al., 2005; Ekiz and Kocak, 2007; Buxadera and Mota, 2008). It has already been reported that the use of repeated racing records is rational when measuring racing ability changes under the current Korean horse racing system (Park and Lee, 1999). Currently, animal models using BLUP (Best Linear Unbiased Prediction) theory are utilized for the genetic evaluation of most domestic animals at the national level. Many researchers have reported that animals can be improved through the selection of economically important traits. However to achieve this, the establishment of desirable improvement traits and goals must take precedence. The objective of this study was to provide basic information for the establishment of improvement goals by analyzing the annual genetic improvement of finish time in the racing records of Korean Thoroughbred racehorses. 2. Materials and methods 2.1. Description of the data The data used in this study were 208,043 finish time records collected from 9934 Thoroughbred racehorses that raced at Gwacheon racecourse in Korea from 1990 to 2006, and was provided by the Korea Racing Authority (KRA). Finish times that were distributed outside 3.5 standard deviations from the mean at each distance were eliminated from the data, as they may represent possible injuries of the racehorses during the race or unavoidable mistakes made by the jockey. Table 1 shows the distributional properties for data structured by distance. Distribution of finish times by racing distance was slightly rightward, but most cases showed a normal shape. As the race distance increased, the standard deviation of finish time, horse weight and age increased. While, the number of records decreased due to the smaller number of longer distance races under the group winning system. Of the total records, castrated (gelding), stallion and mare comprised 28.7, 20.3 and 51.0%, respectively; and the average number of start per year was 7.5. Racehorses from 3 to 5 years of age comprised 74.5% of the total records, while 3.9% (8091)
were 2-year-old horses. Those of 6 and more than 7 years of age comprised 11.4% and 10.25%, respectively. In order to examine the relationship between the number of starts and the finish times by racing distance, finish time records were subdivided in to three groups of 1, 2–9, and 10 or more records. 2.2. Statistical model The analytical Repeatability Animal Model used for estimating the genetic parameters and expected breeding values (EBVs) was as follows: yijklmno = μ + di + sj + mk + YMDRl + jm + an + pn + eijklmno where, yijklmno =finish times (s), μ=overall mean, di =fixed effect of the ith racing distance (i =1000 m, 1200 m, 1400 m, 1700 m, 1800 m, 1900 m, 2000 m), sj =fixed effect of the jth sex (j=gelding, stallion, mare), mk =fixed effect of the kth year of age (k=2, 3, …, more than 7 years), YMDRl =fixed effect of the lth contemporary group (l =1, 2, …, 19,039), jm =random effect of the mth jockey (m = 1, 2, …, 214), a n = random additive genetic effect of the nth animal (n=1, 2, …, 19,062), pn =permanent environmental effect of individual animals (n=1, 2, …, 9934), and eijklmno =a random residual effect. Var (a)=Aσ 2a, Var(pe)=Iσ 2pe, and Var(e)=Iσ 2e, where A=numerator relationship matrix and I=identity matrix. In the model, contemporary group refers to racehorses that ran together in the same race, and effect of contemporary group for finish time was calculated using the SAS (1999) program. Variance components, genetic parameters and breeding values were estimated by the restricted maximum likelihood method for one trait animal model using the derivative-free process (DF-REML 3.1 program) (Meyer, 1998). The overall trends on finish time were calculated using the method suggested by Wilson and Willham (1986). 3. Results Thoroughbred racehorses exhibited a long generation interval from 10.21 to 10.83 years (Table 2). The average generation intervals of sires and dams of offspring were 10.82 and 10.31 years, respectively. The effect of the contemporary groups accounted for more than 43.9% (1200 m) of the total variation in finish time (Table 3). The heritability estimate was highest at the 1000 m distance (0.30), and the range of
Table 1 Distributional properties for data structure by racing distance. Racing distance (m) 1000 1200 1400 1700 1800 1900 2000 Overall a
Number of a Records
Races
Jockeys
Racehorses
Sires
Dams
50,051 49,514 42,804 20,733 21,387 12,291 11,263 208,043
4549 4514 3892 2010 1955 1114 1005 19,039
178 185 209 179 193 161 176 214
8948 8539 7199 5112 4060 2614 1846 9934
2451 2428 2213 1834 1575 1236 975 2746
6150 5975 5193 3841 3127 2117 1487 6860
Total number of jockeys, racehorses, sires and dams used across all distances (not the column sum).
Finish time (s)
Age (year)
Horse mean weight (kg)
65.39 ± 1.54 78.99 ± 1.69 92.16 ± 1.96 116.24 ± 2.18 122.91 ± 2.31 129.67 ± 2.32 136.11 ± 2.43 –
3.8 4.1 4.5 4.9 5.2 5.6 6.0 4.5
445 447 449 452 454 457 461 450
K.D. Park / Livestock Science 140 (2011) 49–54
51
Table 2 Generation intervals for Thoroughbred racehorses. Pathway
Number of pairs
Sire → offspring(LSO) Sire → son(LSS) Sire → daughter(LSD) Dam → offspring(LDO) Dam → son(LDS) Dam → daughter(LDD) Parent → offspring(LPO) a
9716 4191 5525 9687 4189 5498 19,404
Generation interval (year) Mean ± SD a
Skewness
Median
Mode
10.82 ± 3.59 10.81 ± 3.62 10.83 ± 3.58 10.31 ± 3.80 10.21 ± 3.79 10.39 ± 3.82 10.57 ± 3.71
0.91 0.95 0.88 0.76 0.80 0.74 0.81
10 10 10 10 10 10 10
9 8 9 8 8 8 8
Standard deviation.
estimated heritabilities and repeatabilities were from 0.06 to 0.30 and from 0.31 to 0.59, respectively (Table 4). The proportion of variance due to the jockey was from 0.02 to 0.06, and racehorses accounted for 94.8% and the jockey for only 5.2% of the finish time (Table 5). Phenotypic improvement and temporary and permanent environmental improvements were −0.132, −0.103 and −0.003 per race year, respectively. Genetic improvement of racehorses in racing ability was very consistent, resulting in average improvements of −0.029 and −0.031 s per year, in homeproduced and imported racehorses, respectively (Table 6). 4. Discussion 4.1. Selection criteria Selecting the best traits with which to evaluate the genetic potential of a racehorse is a very important subject. They also argue that in the records, racehorses that have not won prize money are eliminated from the data. Also, neither the best finish time nor the average finish time can be used to evaluate the genetic potential of racehorses because they do not properly account for the environmental differences that affect racing time traits. Even though Ojala and Van Vleck (1981) and Ojala et al. (1987) have reported that the individual best finish time is an important characteristic of a racehorse for genetic evaluation, it seems impractical to use in evaluating racehorse potential in Korea because there are no specific racing distances here. Rather, racing is done according to classes and it is difficult to adjust the prize money according to inflation with great fluctuations by race year. Racehorses that had only raced once showed the best finish time at each distance, which is contrary to the report by Buttram et al. (1988a), which found that the average finish time decreased as the number of starts increased in Quarter Horse.
However, the finish time among the first-class racehorses in the 1900 m and 2000 m races improved as the number of races in which they had taken part increased. The reason for this unusual result in Korean racehorses is due to the national racing system. Racehorses are divided into 6 classes and races vary by distance and class. The number of wins and earnings establish a racehorse's classification, but this criterion may be adjusted according to the situation. The griffin class is operated separately. Generally, new racehorses run in the griffin class (1000 m). When a racehorse wins the short distance races, they enter into longer distance races, which is why superior racehorses tend to have only one race at shorter distances. Therefore, when the evaluation of racehorses is done according to distance, elimination of the racehorses with only one record will not yield accurate results. It is desirable to evaluate the performance of all racehorses using repeated finish times in order to improve both the record taking process and the quality of participating horses. 4.2. Generation interval Table 2 shows the estimated generation intervals of Thoroughbred racehorses. Generation interval is the average interval of time between the birth of parents and the birth of their offspring. The average generation intervals of sires and dams of offspring were 10.82 and 10.31 years, respectively. The average generation interval of parents of offspring was 10.57 years, which was similar to the 10.5 years reported by Langlois et al. (1983) and shorter than the generation interval of 10.93 years of Brazilian Thoroughbred (Taveira et al., 2004). 4.3. Contemporary group The average size of the contemporary group was 10.9 heads. The effect of different racing methods according to year was accounted for by the year of race, the season effect
Table 3 Percentages of the total variance in finish time (s) accounted for by race years, months, days and individual races by racing distance. Source of variation
Years (%) Months (%) Days (%) Races (%) Errors (%) Total variance (s2)
Racing distance (m) 1000
1200
1400
1700
1800
1900
2000
17.7 7.4 4.7 14.4 55.8 2.399
11.9 9.2 3.6 19.2 56.1 2.877
18.4 11.3 3.7 18.7 47.9 3.879
18.5 11.1 2.3 25.4 42.7 4.822
20.0 11.0 2.1 26.5 40.4 5.398
20.5 11.7 12.0 16.4 39.4 5.469
23.3 12.7 4.2 22.7 37.1 5.986
52
K.D. Park / Livestock Science 140 (2011) 49–54
Table 4 Additive genetic (σ 2a), permanent environmental (σ 2pe), jockey (σ 2j), error variance (σ 2e) components, heritabilities (h2), standard errors (SE), repeatabilities (r), and ratios between jockey and total variance components (J/T) for finish time by racing distance. Racing distance (m)
σ 2a
σ 2pe
σ 2j
σ 2e
h2 ± SE
r
J/ T
1000 1200 1400 1700 1800 1900 2000 ≤ 1400 ≥ 1700 Overall
0.5404 0.5557 0.1537 0.3286 0.1428 0.2732 0.1285 0.5717 0.3879 0.5533
0.5276 0.5425 0.5447 0.3605 0.4424 0.4712 0.8206 0.5813 0.4729 0.5588
0.0280 0.0397 0.0522 0.0848 0.1107 0.1131 0.0935 0.0485 0.0977 0.0614
0.7178 0.8468 0.9782 1.1193 1.1793 1.2614 1.1320 0.9364 1.2048 1.0866
0.30 ± 0.02 0.28 ± 0.01 0.09 ± 0.03 0.17 ± 0.06 0.08 ± 0.03 0.13 ± 0.07 0.06 ± 0.06 0.27 ± 0.07 0.18 ± 0.04 0.24 ± 0.03
0.59 0.55 0.40 0.36 0.31 0.35 0.44 0.54 0.40 0.49
0.02 0.02 0.03 0.04 0.06 0.05 0.04 0.02 0.05 0.03
on race was accounted for by the month of race, the condition of racetrack was accounted for by the date of race, and the individual race accounted for the sampling variation of the racehorse classes and the presence of the winning racehorses in a contemporary group (Buttram et. al., 1988b; Oki et al., 1994). Competitive horse racing is not always recorded in a consistent manner. The individual race level influences on finish time (Bugislaus et al., 2005) and the racing speed can vary depending on the records of the contemporary group of horses taking part in the race. Therefore, it is desirable to include the records of contemporary group horses in the evaluation model (Buttram et al., 1988b; Oki et al., 1994). The percentages of total variance attributable to individual races and race years were larger than those due to other effects and ranged from 14.4% to 26.5% and from 11.9% to 23.3%, respectively. Race months within race years accounted for 7.4% to 12.7% of total variance, but variance of race days within years and months was relatively small (2.1% to 12.0%). As shown in Table 3, the effect of the contemporary groups accounted for more than approximately 44% of the total variation in finish time, and was the most important environmental effect in the genetic evaluation model. 4.4. Genetic parameters The estimate of variance components and genetic parameters for finish time by racing distance are presented in Table 4. Heritability estimates of the racing time of Japanese Thoroughbred racehorses were in the range of 0.086–0.217 (Oki et al., 1995a), and those of Brazilian Thoroughbred were
in the range of 0.10–0.32 (Mota, 2006). In 2007, Ekiz and Kocak reported that heritability and repeatability estimates ranged from 0.177 to 0.353 and from 0.289 to 0.404, respectively and it was also reported that heritability estimates on the racing time of Iranian Thoroughbred were in the range of 0.09–0.13 (Bakhtiari and Kashan, 2009). They also reported that as the racing distance increased, the heritability estimates decreased. The range of estimated heritabilities and repeatabilities were from 0.06 to 0.30 and from 0.31 to 0.59, respectively. The heritability estimate was highest at the 1000 m distance (0.30), and it decreased as the racing distance increased. Similar results were reported by Oki et al. (1995a), Mota (2006), Ekiz and Kocak (2007) and Bakhtiari and Kashan (2009). The reason may be that in the short distance racehorses can run at full speed with minimal environmental effects, but as the racing distance increases, the jockey and temporary environmental factors have a stronger effect on the finish time. Oki et al. (1995b) reported that the jockey was found to have highly significant (pb 0.01) effects on racing time. The skill of the jockey was an important source of variation in racing time across distances, and therefore, it should be considered in deriving adjustment factors, estimating genetic parameters and predicting genetic values for racehorses. In contrast, Pinchbeck et al. (2002) reported that the jockey contributed very little to the variation in the risk of falling. In our experiment, the proportion of variance due to the jockey was from 0.02 to 0.06, and it was larger in the longer distance races than in the shorter ones. Hintz and Van Vleck (1978) for Standardbred Trotter horses reported that the relative contribution to the racing
Table 5 Ratios between horse and jockey variance components by racing distance.
a
Racing distance (m)
σ 2ah
σ 2j
σ 2h + σ 2j
Horse (%)b
Jockey (%)c
1000 1200 1400 1700 1800 1900 2000 ≤ 1400 ≥ 1700 Overall
1.0679 1.0982 0.6984 0.6891 0.5852 0.7444 0.9491 1.1530 0.8607 1.1121
0.0280 0.0397 0.0522 0.0848 0.1107 0.1131 0.0935 0.0485 0.0977 0.0614
1.0960 1.1379 0.7507 0.7740 0.6959 0.8575 1.0425 1.2016 0.9584 1.1735
97.4 96.5 93.0 89.0 84.1 86.8 91.0 96.0 89.8 94.8
2.6 3.5 7.0 11.0 15.9 13.2 9.0 4.0 10.2 5.2
σ 2h = (σ 2a + σ 2j) bσ 2h/(σ2h + σ2j), cσ 2j/(σ 2h + σ 2j).
K.D. Park / Livestock Science 140 (2011) 49–54 Table 6 The overall linear trends on finish time by race year for all distance data. Variables
Linear estimates
Year of race
b ± SE
R2
Phenotypic trend Temporary environmental trend Permanent environmental trend Jockey trend Genetic trend a Genetic trend b
− 0.132 ± 0.022 ⁎⁎⁎ − 0.103 ± 0.022 ⁎⁎⁎ − 0.003 ± 0.001 ⁎⁎ 0.000 ± 0.001 NS − 0.029 ± 0.002 ⁎⁎⁎ − 0.031 ± 0.003 ⁎⁎⁎
0.71 0.60 0.51 0.02 0.91 0.85
a
Home-produced racehorse. Imported racehorse. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001. NS None significant. b
performance made by the racehorse and the jockey are 68–83% and 17–32%, respectively. But according to the results from our data, racehorses accounted for 94.8% and the jockey for only 5.2% of the finish time (Table 5). As the racing distance increases the percent contribution of the jockey also tends to increase.
4.5. Genetic trends The phenotypic, environmental and genetic trends in finish time by the race year are depicted in Fig. 1. The genetic trend in finish time was calculated on the basis of weighted mean using the predicted breeding value for each animal against each year of race. The phenotypic and environmental improvements were about − 2.140 and − 1.492 s over the last 16 years, respectively. On the other hand, genetic gains of the home-produced and imported racehorses in racing performance over 16 years were − 0.459 and − 0.69 s, respectively. Genetic improvement of racehorses in racing ability was very consistent, resulting in average improvements of − 0.029 and − 0.031 s per year, in home-produced and imported racehorses, respectively. Also, Oki and Sasaki (1995c) reported that genetic trends in Japanese Thoroughbred were − 0.0170 and −0.0084 s per birth year for racing time on turf and sand tracks, respectively and for the American Quarter Horses, it was reported genetic trends on racing time were −0.0088 and −0.0090 s per race year for distances of 320 m and 360 m, respectively (Wilson et al., 1998). 1.0
53
Mota et al. (2005) reported genetic trends in Brazilian Thoroughbred ranged from −0.0154 to −0.0310 s per year of birth at less than 1300 m race, which is in agreement with those obtained from the present study. However, in the race of 1400 m or more, the estimates were relatively small, compared with other reports. It was also reported that the genetic trends for racing time were approximately five times higher at 1000 and 1100 m than at distances of 1500 and 1600 m. Taveira et al. (2004) in Brazilian Thoroughbred reported that genetic trends on winning time for animals with records were −0.0027 and −0.0016 s/birth year. The reason for that seems to be due to the fact they were estimated using winning time. In this experiment, the estimates on genetic trends were higher than those of previous results. No trend for the jockey was recorded as the coefficient of determination (R2) value was approximately equal to zero. The genetic trends in finish time for sires and dams by nationality are depicted in Fig. 2. The annual genetic gain of domestic sires was highest (−0.053), probably as a result of the continuous importation of very superior sires by Korea Racing Authority. As shown in Table 6, the improvement in finish time of home-produced horses was not due to genetic factors, but mainly due to environmental factors. These results suggest that the environmental conditions (training, feed and care and track management system, etc.) for horse racing improved over the study period, while genetic improvements were relatively low. Therefore, it is urgent that goals for the qualitative genetic improvement of racehorses are established. 5. Conclusion It is very important to develop an effective evaluation method for the purpose of selection and improvement of Thoroughbred racehorses using BLUP. The contemporary group effect should be considered in the evaluation model in order to avoid possible bias in estimation of genetic parameters and breeding values. It can be concluded that the genetic evaluation of racehorses in Korea should utilize the finish time records of all the different distances together because the number of sires with 2 or less progeny represents 53.5% of all race data, and the number of sires with only one progeny accounts for 71.9% of all the racehorses participating in the 2000 m race. Though the best finish times, earnings,
2000
2002
2004
2006
Genetic
Fig. 1. Genetic, phenotypic and environmental trends in finish time (s) by race year using all distance data.
Foreign dam
Domestic sire
Domestic dam
06
04
02
00
98
96
Foreign sire
20
1998
Environmental
20
1996
20
1994
Phenotypic
20
1992
19
1990
19
90
-2.5
19
-2.0
94
-1.5
19
-1.0
19
0.0 -0.5
92
0.2 0.1 0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8
0.5
Fig. 2. Genetic trends in finish time (s) of sires and dams by nationality.
54
K.D. Park / Livestock Science 140 (2011) 49–54
and placing orders are very important traits for racehorse selection, finish time will be the most reasonable selection trait for the estimation of genetic improvement racehorse speed. According to the various results which have been reported so far, genetic performance cannot be improved much through the selection on winning time, and so to reduce the generation interval and to increase the genetic gain and accuracy of breeding value, it is necessary to establish improvement goals and a new evaluation system such as genomic selection. Acknowledgements The research was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (iPET). References Bakhtiari, J., Kashan, N.E.G., 2009. Estimation of genetic parameters of racing performance in Iranian Thoroughbred horses. Livest. Prod. Sci. 120, 151–157. Bugislaus, A.E., Roehe, R., Kalm, E., 2005. Comparison of two different statistical models considering individual races or racetracks for evaluation of German trotters. Livest. prod. sci. 92, 69–76. Buttram, S.T., Willham, R.L., Wilson, D.E., Heird, J.C., 1988a. Genetics of racing performance in the American Quarter Horse: I. Description of the data. J. Anim. Sci. 66, 2791–2799. Buttram, S.T., Willham, R.L., Wilson, D.E., 1988b. Genetics of racing performance in the American Quarter Horse: II. Adjustment factors and contemporary groups. J. Anim. Sci. 66, 2800–2807. Buxadera, A.M., Mota, M.D.S., 2008. Variance component estimations for race performance of Thoroughbred horses in Brazil by random regression model. Livest. Sci. 117, 298–307. Ekiz, B., Kocak, O., Yilmaz, A., 2005. Phenotypic and genetic parameters estimatives for racing traits of Thoroughbred horses in Turkey. Arch. Tierz. Dummerstorf 18, 121–129. Ekiz, B., Kocak, Ö., 2007. Estimates of genetic parameters for racing times of Thoroughbred horses. Turk. J. Vet. Anim. Sci. 31 (1), 1–5. Hintz, R.L., Van Vleck, L.D., 1978. Factors influencing racing performance of the Standardbred pacer. J. Anim. Sci. 46, 60–68.
Klemetsdal, G., 1994. Application of standardized, accumulated transformed earnings in breeding of Norwegian trotters. Livest. Sci. 38, 245–253. Langlois, B., Minkema, D., Bruns, E., 1983. Genetic problems in horse breeding. Livest. Prod. Sci. 10, 69–81. Langlois, B., Blouin, C., 2004. Practical efficiency of breeding value estimations based on annual earnings of horses for jumping, trotting, and galloping races in France. Livest. Prod. Sci. 87, 99–107. Langlois, B., Blouin, C., 2007. Annual, career or single race records for breeding value estimation in race horse. Livest. Sci. 107, 132–141. Meyer, K., 1998. DFREML Programs to Estimate Variance Components by Restricted Maximum Likelihood Using a Derivative-Free Algorithm. User Notes. Mota, M.D.S., Abrahao, A.R., Oliveira, H.N., 2005. Genetic and environmental parameters for racing time at different racing distances in Brazilian Thoroughbreds. J. Anim. Breed. Genet. 122, 393–399. Mota, M.D.S., 2006. Genetic correlations between performance at different racing distances in Thoroughbreds. Livest. Sci. 104, 227–232. Ojala, M., Van Vleck, L.D., 1981. Measures of racetrack performance with regard to breeding evaluation of trotters. J. Anim. Sci. 53, 611–619. Ojala, M., Van Vleck, L.D., Quaas, R.L., 1987. Factors influencing best annual racing time in finnish horses. J. Anim. Sci. 64, 109–116. Oki, H., Sasaki, Y., Willham, R.L., 1994. Genetics of racing performance in the Japanese Thoroughbred horse: II. Environmental variation of racing time on turf and dirt tracks and the influence of sex, age, and weight carried on racing time. J. Anim. Breed. Genet. 111, 128–137. Oki, H., Sasaki, Y., Willham, R.L., 1995a. Genetic parameter estimates for racing time by restricted maximum likelihood in the Thoroughbred horse of Japan. J. Anim. Breed. Genet. 112, 146–150. Oki, H., Sasaki, Y., Lin, C.Y., Willham, R.L., 1995b. Influence of jockeys on racing time in Thoroughbred horses. J. Anim. Breed. Genet. 112, 171–175. Oki, H., Sasaki, Y., 1995c. Estimation of genetic trend in racing time of Thoroughbred horses in Japan. Equine Research Institute, Setagaya-Ku, Tokyo 154. Park, K.D., Lee, K.J., 1999. Genetic evaluation of Thoroughbred racehorses in Korea. Kor. J. Anim Sci. 41 (2), 135–140. Pinchbeck, G.L., Clegg, P.D., Proudman, C.J., Morgan, K.L., Wood, J.L.N., French, N.P., 2002. Risk factors and sources of variation in horse falls in steeplechase racing in the UK. Prev. Vet. Med. 55, 179–192. SAS User's Guide: Statistics, Ver 9.1 Edition. 1999. SAS Inst., Inc., Cary, NC. Taveira, R.Z., Mota, M.D.S., Oliveira, H.N., 2004. Population parameters in Brazilian Thoroughbred. J. Anim. Breed. Genet. 121, 384–391. Tolley, E.A., Notter, D.R., Marlowe, T.J., 1983. Heritability and repeatability of speed 2- and 3-yr-old Standardbred race horses. J. Anim. Sci. 56, 1294–1305. Wilson, D.E., Willham, R.L., 1986. Within-herd phenotypic, genetic and environmental trend lines for beef cattle breeders. J. Anim. Sci. 63, 1087–1094.