The importance of MSTN for harness racing performance in the Norwegian- Swedish Coldblooded Trotter and the Finnhorse BD Velie 1, L Bas Conn 1, K Petäjistö 1, KH Røed 2, CF Ihler 3, E Strand 3, KJ Fegraeus 1, G Lindgren 1 1 Department of Animal Breeding & Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden 2 Department of Basic Sciences and Aquatic Medicine, Norwegian University of Life Sciences, Oslo, Norway 3 Department of Companion Animal Clinical Sciences, Norwegian School of Veterinary Science, Oslo, Norway Summary Understanding the application of genomic studies of complex performance traits across breeds is needed in order to accurately evaluate the relevance of performance genes in specific populations. In the Nordic countries, apart from the Standardbred, there are two additional breeds of harness racing horses - the Norwegian-Swedish Coldblooded trotter (CBT) and the Finnhorse (FH). While both breeds compete under similar conditions and rules of racing as Standardbreds, the unique heavy horse origins of CBTs and FHs suggests that the effects and relative importance of the myostatin gene (MSTN) in these breeds may differ from other racing breeds. Thus, three SNPs within MSTN were genotyped in 481 CBTs and 223 FHs. Subsequent association analyses between each variant and racing performance traits were then carried out using the statistical software R. Significant associations were present between one MSTN variant and two performance traits in FHs. However, no other significant associations were apparent in either breed. MSTN variants evaluated do not appear to impact the racing performance of the modern CBT, but perhaps play a significant role in the performance of FHs. Keywords: racehorse, genetic, Coldblooded trotter, Finnhorse Introduction Variants in the myostatin (MSTN) gene have been reported in certain horse racing breeds as predictive of genetic potential and athletic phenotype (Hill et al., 2010; Tozaki et al., 2011; Dall Olio et al., 2014). In Thoroughbreds (TB), not only has a MSTN gene variant been described as the best predictor of distance aptitude, but additional variants in the gene have been associated with performance rank and lifetime earnings (Hill et al., 2010; Tozaki et al., 2011). To date, the majority of information connected with the racing performance effects of MSTN in horses has predominantly focused on TBs (Hill et al., 2010; Tozaki et al., 2011; Petersen et al., 2014). While the knowledge gained from the study of this gene in TBs is assuredly valuable, the advantages gained from certain gene variants may not be consistent or even significant across racing breeds, particularly when comparing gallop racing breeds to harness racing breeds. Therefore, it is vital to contextualize within each racing breed the importance of these genes as well as other genes associated with racing performance.
In the Nordic countries, there are three distinct breeds of harness racing horses - the globally popular Standardbred trotter (SB) and the nationally popular Norwegian-Swedish Coldblooded trotter (CBT) and Finnhorse (FH). Competing under similar conditions and rules, but in breed specific races, it is possible that the effects and relative importance of MSTN are the same across the three breeds. However, contrary to SBs, the CBT and the FH originate from heavy horse breeds (Hendricks, 2007). These unique heavy horse origins suggest that similarities between these three harness racing breeds may in fact be limited (Hendricks, 2007). With this in mind, the current study investigates the effects of MSTN on racing performance in the CBT and the FH. In doing so, we aim to assist not only the Nordic harness racing industry, but the global racing industry as a whole to better understand the relevance of MSTN for racing performance across breeds. Materials and methods Hair and/or blood samples for 486 CBT born between 2000 and 2010 were provided by the CBT pedigree registration authorities in Norway (Department of Basic Sciences and Aquatic Medicine, Norwegian University of Life Sciences) and Sweden (Animal Genetics Laboratory, Swedish University of Agricultural Sciences). Hair samples for 223 FH born between 1981 and 2011 were collected via personal contacts and online postings. Genomic DNA was extracted from hair and blood samples using standard hair and blood preparation procedures. Three SNPs (ECA18: g.65868604g>t [PR8604], g.66493737c>t [PR3737] and g.66495826a>g [PR5826]) within MSTN were genotyped using Custom TaqMan Genotyping assays (Applied Biosystems). Racing performance Racing performance results and breeding values, as of 31 December 2015 for CBT and 31 December 2016 for FH, were provided by the Swedish Trotting Association, the Norwegian Trotting Association, and the Finnish Trotting and Breeding Association. Cumulative earnings were calculated as the total amount of prize money a horse had earned as of 31 December 2015 for CBT and 31 December 2016 for FH. Earnings per start was calculated as the average amount of prize money earned per race start. Earnings details for the majority of CBT were provided in Swedish currency (SEK). However, for CBT with earnings in Norwegian currency (NOK) earnings were converted to SEK based on the estimated average exchange rate of 0.95 SEK/NOK from 2003 to 2016 (EUROINVESTOR, 2016). Concerning the FH, cumulative earnings and earnings per race start were calculated in euros (EUR). The number of wins was calculated as the total number of times in which a horse finished a race in first place. The number of placings was calculated as the number of times in which a horse finished a race in first, second, or third place. The number of times a horse was not ranked was calculated as the total number of races in which a horse failed to finish in the top 3 positions. Frequencies were calculated as the proportion of total races in which a horse won, placed, or was not ranked. Race times for two different starting methods, volt-start and auto-start, were included in the study. Volt-start is a starting method in which the horses start in pens that dispose 20 metres of volt space and trot in a circular pattern to then hit the starting line as a group. Auto-start is a starting method where a car is used to set the starting line. The car is placed 260 (CBT) or 350
(FH) metres before the start line and gradually increases the speed so it hits the start line at 52 km/hr (Swedish Trotting Association, 2016; Finnish Trotting and Breeding Association, 2017). Race times for each horse are recorded for each race and measured as average time per kilometer. Best race times for each horse were defined as the lowest one km time from each of the respective starting methods. Data analysis Data were structured for analyses using custom scripts written in the statistical software R (R Development Core Team, 2015). Horses that were not successfully genotyped (n = 5 CBT; n = 5 FH) for all three SNPs were excluded from the study. Hardy-Weinberg equilibrium (HWE) of the genotyped SNPs was evaluated using the R package SNPassoc (González et al., 2007). Normality of each racing performance trait was assessed by the Shapiro-Wilk test. Traits that were not normally distributed were log-transformed (log 10 ) for all analyses. Horses with no races using auto-start (n=166 CBT, n=36 FH) or volt-start (n=19 CBT, n=3 FH) were excluded from all analyses concerning the corresponding starting method. Fixed effects and covariates for the racing performance traits were assessed with ANOVA Type 3. With the exception of EBV analyses, these factors included sex and birth year. Only significant fixed effects and covariates (P<0.05) were retained in the final models. The SNPassoc package in R was used for all SNP association analyses. The package can be used to perform both whole genome association studies as well as associations with only one SNP. Using a general linear model, traits can be adjusted for fixed effects and covariates and, when applicable, the association with the SNP is tested under five different modes of inheritance: codominant, dominant, recessive, overdominant, and log-additive (González et al., 2007). Results 481 CBTs, representing offspring from 109 sires and 431 dams, were successfully genotyped for all three SNPs. Genotype frequencies are presented in Figure 1. All SNPs deviated from HWE. Summary statistics for the racing performance traits are presented in Table 1. Concerning the FH, 218 horses representing offspring from 83 sires and 200 dams were successfully genotyped for all three SNPs. Genotype frequencies are also presented in Figure 1. All SNPs deviated from HWE. Summary statistics for the racing performance traits are presented in Table 2. Fixed effects and covariates included in the final models are listed with p-values in Table 3. Significant associations were present between one MSTN variant [PR3737] and two performance traits in FHs. No other significant associations were present (Table 4). Discussion The relatively recent expansion of genomic research to complex performance traits in horses requires rigorous investigation into the application of results across breeds (Hill et al., 2010; Tozaki et al., 2011; Dall Olio et al., 2014; Petersen et al., 2014; Rama et al., 2016). With this in mind, the current study was performed to provide insight into the effect and potential relevance of a well-known racing performance gene for the CBT and FH breeds. While multiple studies have shown a clear link between MSTN and racing performance in TBs, the results of the current study provide no evidence of a significant association between MSTN and racing performance in
the CBT (Hill et al., 2010; Tozaki et al., 2011). However, some support pertaining to the importance of MSTN for racing performance in FHs was apparent. Additionally, allele frequencies and a major deviation from HWE for two of the MSTN variants investigated suggest what may be a gradual move towards fixation of the T allele, the allele associated with stamina in TBs (Hill et al., 2010; Tozaki et al., 2011). Similar moves towards fixation have also been suggested in SBs and Spanish Trotters, perhaps indicating a greater emphasis on endurance rather than explosiveness in harness racing breeds (Dall Olio et al., 2014; Petersen et al., 2014; Rama et al., 2016). Given the conditions and distances in which CBTs and FHs race, explosiveness is unlikely to provide a competitive advantage and, considering the relationship between the C allele and Type 2B muscle fibres, may in fact impede a harness racing horse s overall ability to perform at an elite level (Hill et al., 2010; Petersen et al., 2014). Nevertheless, this is only one possible explanation for the observed allele frequencies and cannot be stated with certainty without additional exploration. References Dall Olio, S., E. Scotti, L. Fontanesi & M. Tassinari, 2014. Analysis of the 227 bp short interspersed nuclear element (SINE) insertion of the promoter of the myostatin (MSTN) gene in different horse breeds. Veter Ital Ser 50, 193-197. EUROINVESTOR, 2016. Omvandlare. Available: http://www.valuta.se [2016-06-03] Finnish Trotting and Breeding Association, 2017. Available: http://www.hippos.fi/ [2017-08-11] González, J.R., L. Armengol, X. Solé, E. Guíno, J.M. Mercader, X. Estivill & V. Moreno, 2007. SNPassoc: an R package to perform whole genome association studies. Bioinformatics 23, 644-645. Hendricks, B.L., 2007. International Encyclopedia of Horse Breeds, Norman: University of Oklahoma Press. Hill, E.W., B.A. McGivney, J. Gu, R. Whiston & D.E. MacHugh, 2010. A genome-wide SNPassociation study confirms a sequence variant (g.66493737c>t) in the equine myostatin (MSTN) gene as the most powerful predictor of optimum racing distance for Thoroughbred racehorses. BMC Genomics 11, 552. Petersen, J.L., S.J. Valberg, J.R. Mickelson & M.E. McCue, 2014. Haplotype diversity in the equine myostatin gene with focus on variants associated with race distance propensity and muscle fiber type proportions. Anim Genet 45, 827-835. R Development Core Team, 2015. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria Rama, S.N., M. Valera, A. Membrillo, M.D. Gomez, M. Sole, A. Menendez-Buxadera, G. Anaya & A. Molina, 2016. Quantitative analysis of short- and long-distance racing performance in young and adult horses and association analysis with functional candidate genes in Spanish Trotter horses. J Anim Breed Genet doi: 10.1111/jbg.12208. The Swedish Trotting Association, 2016. Avelsindex. Available: https://www.travsport.se/artikel/avelsindex [2016-05-10] Tozaki, T., E.W. Hill, K. Hirota, H. Kakoi, H. Gamahara, T. Miyake, S. Sugita, T. Hasegawa, N. Ishida, Y. Nakano & M. Kurosawa, 2011. A cohort study of racing performance in Japanese Thoroughbred racehorses using genome information on ECA18. Anim Genet 43, 42-52.
Figure 1. Genotype frequencies of MSTN variants in a sample of raced Norwegian-Swedish Coldblooded trotters (n=481) and Finnhorses (n=218). 1 MSTN_5826 = ECA18: g.66495826a>g [PR5826]; MSTN_8604 = g.65868604g>t [PR8604]; MSTN_3737 = g.66493737c>t [PR3737] 2 CBT = Norwegian-Swedish Coldblooded trotter; FH = Finnhorse
Table 1. Descriptive results of cumulative earnings, number of race starts, earnings per race start, number of wins, number of placings, race times, and estimated breeding values (EBV) for the sample of Norwegian-Swedish Coldblooded trotters. Min 25th percentile Median Mean 75th percentile Max Number of race starts Total sample 1 13 28 38 54 222 Intact males 1 2 28 67 64 84 186 Females 1 9 22 28 42 118 Geldings 1 15 30 41 57 222 Number of wins Total sample 0 0 2 4 5 53 Intact males 1 0 3 9 11 16 53 Females 0 0 1 2 4 17 Geldings 0 0 2 4 5 40 Number of placings Total sample 0 2 7 11 15 101 Intact males 1 0 12 23 27 37 101 Females 0 2 6 8 11 37 Geldings 0 3 7 11 15 72 Best auto-start time (seconds/km) Total sample 80.1 86.5 88.6 88.7 90.9 99.3 Intact males 1 80.1 83.1 84.8 84.9 87.0 92.6 Females 83.7 87.9 89.4 89.8 91.7 99.3 Geldings 80.2 86.7 88.7 88.9 91.0 97.2 Best volt-start time (seconds/km) Total sample 81.4 88.0 90.2 90.6 93.0 106.9 Intact males 1 81.4 83.9 85.5 86.6 88.9 96.6 Females 83.7 88.7 90.5 91.6 93.7 106.9 Geldings 83.1 88.1 90.3 90.7 92.8 104.4 Cumulative earnings (SEK) Total sample 0 31200 97850 232600 250900 3189000 Intact males 1 8800 132800 524200 800700 1185000 2997000 Females 0 21680 71720 141300 167700 1826000 Geldings 0 34210 93290 193500 229000 3189000 Earnings/race start Total sample 0 1979 3533 4803 5618 57070 Intact males 1 1021 5840 9317 11700 15440 53540 Females 0 1589 3202 4202 5252 57070 Geldings 0 1970 3300 3944 4944 26660 EBV Total sample 95 107 112 111.8 117 126 Intact males 1 106 112 117 116.5 121 126 Females 95 108 112 112.4 118 126 Geldings 95 106 110 110.4 115 124 1 Includes one cryptorchid
Table 2. Descriptive results of cumulative earnings, number of race starts, earnings per race start, number of wins, number of placings, race times, and estimated breeding values (EBV) for the sample of Finnhorses. Min 25th percentile Median Mean 75th percentile Max Number of race starts Total sample 1 21 43 59 79 329 Intact males 4 34 70 83 125 232 Females 1 19 36 50 62 329 Geldings 1 20 51 58 81 200 Number of wins Total sample 0 1 4 6 10 56 Intact males 0 7 12 13 17 38 Females 0 1 2 4 5 56 Geldings 0 1 4 5 8 18 Number of placings Total sample 0 4 11 18 26 123 Intact males 0 19 28 32 45 95 Females 0 3 8 13 17 123 Geldings 0 4 12 17 26 58 Best auto-start time (seconds/km) Total sample 78.6 83.7 86.8 87.6 90.2 105.3 Intact males 78.6 81.7 83.7 84.0 85.2 94.9 Females 81.5 85.3 88.9 89.0 92.1 104.0 Geldings 79.9 84.9 87.0 88.2 91.3 105.3 Best volt-start time (seconds/km) Total sample 80.3 85.6 88.6 90.2 92.5 126.6 Intact males 80.3 84.0 84.9 86.0 86.7 104.6 Females 83.3 87.3 90.4 91.8 93.3 126.6 Geldings 83.8 86.0 89.0 90.6 92.7 117.1 Cumulative earnings (EUR) Total sample 0 1759 7980 32728 39466 516230 Intact males 0 22711 50448 82288 75981 516230 Females 0 1288 4482 17748 12289 362905 Geldings 0 2034 7155 20855 25971 113890 Earnings/race start Total sample 0 74 173 438 465 7309 Intact males 0 286 542 1049 1164 7309 Females 0 57 119 248 251 4886 Geldings 0 86 156 301 383 2423 EBV Total sample 68 107 114 113.0 120 148 Intact males 82 114 122 119.2 126 148 Females 68 106 113 111.4 119 131 Geldings 72 107 113 110.9 116 138
Table 3. P-values resulting from the ANOVA assessment of potential fixed effects and covariates 1. Trait Sex Birthyear Country of registration Number of starts Cumulative earnings Norwegian-Swedish Coldblooded Trotters Number of race starts 0.244 < 0.001 0.028 - < 0.001 Number of wins < 0.001 0.918 0.541 < 0.001 - Frequency of wins < 0.001 0.113 0.082 - - Number of placings < 0.001 0.849 0.053 < 0.001 - Frequency of placings < 0.001 0.144 0.003 - - Number of races not ranked 0.015 < 0.001 0.002 < 0.001 - Frequency of race not ranked 0.001 0.004 0.587 - - Best auto-start time (seconds/km) < 0.001 < 0.001 < 0.001 < 0.001 - Best volt-start time (seconds/km) < 0.001 0.002 0.224 < 0.001 - Cumulative earnings (SEK) 0.018 0.001 0.136 < 0.001 - Earnings/race start < 0.001 0.044 < 0.001 - - Estimated breeding value - - - - - Finnhorses Number of race starts 0.174 < 0.001 - - < 0.001 Number of wins < 0.001 0.499 - < 0.001 - Frequency of wins < 0.001 0.017 - - - Number of placings < 0.001 0.02 - < 0.001 - Frequency of placings < 0.001 0.001 - - - Number of races not ranked < 0.001 0.001 - < 0.001 - Frequency of race not ranked < 0.001 0.008 - - - Best auto-start time (seconds/km) < 0.001 < 0.001 - < 0.001 - Best volt-start time (seconds/km) < 0.001 < 0.001 - < 0.001 - Cumulative earnings (EUR) < 0.001 < 0.001 - < 0.001 - Earnings/race start < 0.001 0.001 - - - Estimated breeding value - - - - - 1 - indicates a fixed effect or covariate that was not included in the analysis
MSTN_3737 1 Table 4. Final model p-values for the association analyses between MSTN gene variants and harness racing performance traits. Number of race starts Number of wins Frequency of wins Number of placings Frequency of placings Number of races not ranked Frequency of not ranked Best autostart time 2 Norwegian-Swedish Coldblooded Trotters Best voltstart time Cumulative earnings Earnings/race start Codominant 0.791 0.553 0.980 0.791 0.574 0.728 0.718 0.914 0.324 0.729 0.640 0.438 MSTN_8604 1 Codominant 0.544 0.255 0.305 0.836 0.559 0.514 0.800 0.293 0.575 0.321 0.323 0.128 MSTN_5826 1 Codominant 0.472 0.302 0.588 0.614 0.673 0.985 0.690 0.983 0.680 0.414 0.420 0.899 Dominant 0.821 0.809 0.312 0.331 0.473 0.863 0.873 0.861 0.489 0.900 0.423 0.937 Recessive 0.252 0.122 0.692 0.999 0.698 0.953 0.424 0.928 0.504 0.186 0.240 0.644 Overdominant 0.617 0.907 0.350 0.323 0.412 0.871 0.725 0.876 0.586 0.855 0.592 0.978 Log-Additive 0.970 0.576 0.302 0.363 0.557 0.861 0.982 0.855 0.428 0.687 0.314 0.862 MSTN_3737 1 Finnhorses Codominant 0.783 0.090 0.104 0.285 0.542 0.777 0.599 0.067 0.185 0.426 0.224 0.002 Dominant 0.603 0.429 0.035 0.335 0.352 0.477 0.367-0.112 0.294 0.143 0.005 Recessive 0.597 0.278 0.610 0.172 0.478 0.962 0.568-0.267 0.368 0.274 0.021 Overdominant 0.663 0.063 0.041 0.456 0.413 0.479 0.416-0.158 0.364 0.196 0.015 Log-Additive 0.557 0.032 0.033 0.249 0.311 0.487 0.335-0.086 0.246 0.111 0.002 MSTN_8604 1 Codominant 0.721 0.969 0.681 0.184 0.522 0.963 0.657 0.422 0.996 0.756 0.778 0.257 Dominant 0.962 0.801 0.382 0.068 0.258 0.789 0.378 0.207 0.934 0.700 0.492 0.099 Recessive 0.426 0.992 0.853 0.615 0.766 0.923 0.711 0.843 0.989 0.557 0.934 0.733 Overdominant 0.858 0.801 0.395 0.079 0.275 0.800 0.408 0.191 0.932 0.628 0.479 0.109 Log-Additive 0.937 0.806 0.381 0.065 0.254 0.785 0.363 0.237 0.938 0.779 0.514 0.099 MSTN_5826 1 Codominant 0.947 0.274 0.653 0.618 0.606 0.726 0.689 0.249 0.384 0.981 0.692 0.548 Dominant 0.746 0.886 0.651 0.852 0.369 0.427 0.452 0.131 0.173 0.881 0.398 0.284 EBV
Recessive 0.959 0.119 0.362 0.332 0.469 0.706 0.512 0.911 0.817 0.865 0.666 0.552 Overdominant 0.745 0.652 0.898 0.859 0.499 0.485 0.585 0.095 0.174 0.926 0.464 0.361 Log-Additive 0.771 0.546 0.504 0.646 0.323 0.428 0.399 0.221 0.219 0.857 0.394 0.274 1 MSTN_5826 = ECA18: g.66495826a>g [PR5826]; MSTN_8604 = g.65868604g>t [PR8604]; MSTN_3737 = g.66493737c>t [PR3737] 2 - indicates possible modes of inheritance that were unable to be tested due to insufficient variation in the genotypes available for this trait