- Email: [email protected]
Heritability of hip dysplasia: Preliminary results for German Shepherd dogs in Brazil
Preventive Veterinary Medicine 171 (2019) 104745
Contents lists available at ScienceDirect
Preventive Veterinary Medicine journal homepage: www.elsevier.com/locate/prevetmed
Heritability of hip dysplasia: Preliminary results for German Shepherd dogs in Brazil
T
Adriane Yumi Babáa, Carlos Antonio Lopes de Oliveiraa, Grazyella Massako Yoshidaa, ⁎ Marcus Túlio Cavalcante Costab, Leonir Bueno Ribeiroa, Ricardo Souza Vasconcellosa, a b
Maringá State University, Department of Animal Science, 5790, Colombo avenue, Maringá, Paraná State, Zip Code 87020-900, Brazil Brazilian Society of German Shepherd Breeders, Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: Breeding value Phenotypic selection Genetic trend
The heritability of canine hip dysplasia in German Shepherd dogs was estimated using Bayesian methods. Data on hip score and status of 1632 dogs born from 1990 to 2013 were provided by the Brazilian Society of German Shepherd Breeders. Heritability estimates (mean ± standard deviation) were 0.1979 ± 0.058 for hip scores and 0.187 ± 0.055 for hip status. We observed no phenotypic trends and a small rate of genetic trend (0.52%) according to the year of birth, probably because of ineffective phenotypic selection and absence of genetic selection. The heritability estimates in this study can be used to achieve effective selective breeding and genetic gains, which in turn can result in improvements in dog health and welfare.
1. Introduction
results. In Brazil, there are no studies that have evaluated over the years the prevalence of HD in dogs. In this way, studies for selection criteria in breeding genetic selection are most important than phenotypic characteristics. Thus, this study aimed to estimate the heritability and the genetic trend of HD in German Shepherd dogs registered in Brazil.
Canine hip dysplasia (HD) is a common and complex orthopedic disorder with multiple genes and environmental factors influencing the susceptibility to this disease (Lavrijsen et al., 2014; Mäki et al., 2002; Stock et al., 2011). The German Shepherd dog is among the ten breeds with the highest prevalence of these conditions (Oberbauer et al., 2017). The HD can lead to painful secondary osteoarthritis and physical disability, and their symptoms may cause deleterious changes in dog behavior (Peterson, 2017). Medical and surgical management can help relieve the pain but cannot treat the conditions (Zhu et al., 2012). Given the high prevalence of HD in German Shepherd dogs (Oberbauer et al., 2017) and the lack of effective treatment options, genetic selection has been suggested as the most effective method for reducing dysplasia prevalence and improving dog welfare (Cachon et al., 2010; Mäki et al., 2002; Stock et al., 2011; Woolliams et al., 2011). However, the most common method used by breeders to select the animals for breeding is based on phenotypic characteristics and this practice seems to be ineffective (Lavrijsen et al., 2014; Mäki et al., 2002; Stock et al., 2011). Leppänen and Saloniemi (1999) in a retrospective study in Finland, including 69,349 dogs born from 1988 to 1995, verified that the selection of breeding animals based on phenotypic characteristics did not change over the years and the authors concluded that predicted breeding values based on progeny testing would probably give better
⁎
2. Material and methods 2.1. Dataset The information of hip dysplasia score (HDSC) and status (HDST) were obtained from the records of German Shepherd dogs born between 1990 and 2013, provided by the Brazilian Society of German Shepherd Breeders. HDSC was scored according to the Fédération Cynologique Internationale (FCI) grading system, ranging from A to E. Dogs scored A (no signs of dysplasia) or B (near normal hip joint) were considered normal, and dogs scored C (mild affected), D (moderate affected), or E (severely affected), were considered dysplastic. The letters A–E of this grading system were replaced for 1–5 for statistical analysis. HDST was Normal (1 –hip scores A or B) or dysplastic (2 –hip scores C, D, and E). 2.2. Estimation of variance components and heritability It was performed a single-trait analysis to estimate the variance
Corresponding author. E-mail address: [email protected] (R.S. Vasconcellos).
https://doi.org/10.1016/j.prevetmed.2019.104745 Received 23 January 2019; Received in revised form 6 August 2019; Accepted 10 August 2019 0167-5877/ © 2019 Elsevier B.V. All rights reserved.
Preventive Veterinary Medicine 171 (2019) 104745
A.Y. Babá, et al.
Table 1 Distribution of progeny number by sire and dam.
Table 3 Posterior means and standard deviation (SD) of variance components and heritability of hip score (HDSC) and hip status (HDST) in German Shepherd dogs in Brazil.
Progeny number
Sire Dam
N
Mean
Mode
Min
Max
SD
829 1,364
2.198 1.335
1 1
1 1
28 7
2.518 0.775
Model I
Table 2 Mean, standard deviation (SD), and frequency (%) of hip dysplasia in German Shepherd dogs in Brazil. Dysplasia grade (% of dogs)a
Trait
Mean
SD
σa2
a
h2
d
DIC
σa2 a
N 2d
h
Hip dysplasiab
A
B
C
D
E
61.64
24.39
10.78
2.45
0.74
1.56
0.84
3th threshold 4th threshold DIC
1632
a Hip score: A (1 = no signs of dysplasia), B (2 = near normal hip joint), C (mild affected), D (moderate affected), E (severely affected). b Hip status: 13.98% of the dogs were dysplastic.
Model III
Model IV
0.25(0.08) (0.11-0.44) 0.20(0.05) (0.10-0.30) 4651.8
0.25(0.09) (0.10-0.45) 0.20(0.06) (0.09-0.30) 4607.8
0.45(0.11) (0.26-0.68) 0.31(0.05) (0.21-0.41) 4555.6*
0.26(0.07) (0.13-0.43) 0.20(0.05) (0.12-0.31) 1.455 (0.25) 2.067 (0.41) 4685.3
0.26(0.08) (0.13-0.43) 0.20(0.05) (0.12-0.31) 1.482 (0.27) 2.124 (0.44) 4668.9*
0.42(0.09) (0.26-0.63) 0.30(0.05) (0.21-0.40) 1.560 (0.29) 2.282 (0.47) 4798.3
SD and 95% confidence intervals are shown in parentheses. * the best model, smallest DIC. a σa2 . Additive genetic variance. d h2 . Heritability.
components and the estimated breeding value (EBV) for dysplasia using the THRGIBBS1F90 program (Misztal et al., 2016). The HDSC was considered a categorical trait with 5 categories and HDST a binary trait. A total of 1,200,000 iterations of the Gibbs sampler were run with a burn-in period of 200,000 iterations, the sampling interval was 100 iterations. The general single-trait model used is as follows:
y = Xb + Za + e
HDST 0.24(0.09) (0.09-0.43) 0.19(0.06) (0.08-0.30) 4774.4 HDSC 0.25(0.07) (0.12-0.41) 0.20(0.05) (0.10-0.30) 1.467 (0.26) 2.099 (0.43) 4673.1
Model II
The posterior means of EBV and the sex and birth year effects were estimated using the POSTGIBBSF90 program, using the Gibbs samples chain provided by the THRSGIBBS1F90 program (Misztal et al., 2016). The THRGIBBS1F90 program fixes the threshold (t = 0) for binary traits (HDST) and first and second threshold (t1 = 0 and t2 = 1) for categorical traits with more than three categories (HDSC), thus for HDSC were estimated the third and fourth thresholds. Convergence diagnostics and autocorrelation statistics were assessed by the Heidelberger and Welch’s test (Cowles and Carlin, 1996). Also, the posterior means and standard deviation and the 95% confidence interval of genetic parameters were estimated using the CODA package in R software version 3.2.2 (R Development Core Team, 2015). The relationship matrix included information on 2722 dogs. The dataset contained information from 1632 animals scored by HDSC and HDST, being 1006 males and 626 females.
(1)
Where y is the vector of HD, b is the vector of sex and birth year effects, a is the vector of random additive genetic effects, e is the vector of residual effects, and X and Z are incidence matrices relating phenotype to the effects b and a, respectively. For both traits, the presence/absence of fixed effects (sex and birth year) were tested using four models. The Model I (with sex and birth year effects); Model II (only sex effects); Model III (only the birth year effects); and Model IV (without sex and birth year effects). The deviance information criterion (DIC) was adopted to select the goodness of fit of the models for each trait, where the lowest DIC value indicated the most robust model (Spiegelhalter et al., 2002).
Fig. 1. Number of normal (black solid line) or dysplastic (grey dashed line) German Shepherd dogs registered in Brazil by year of birth. 2
Preventive Veterinary Medicine 171 (2019) 104745
A.Y. Babá, et al.
Fig. 2. Genetic (a) (estimated breeding values-EBV) and phenotypic (b) trends by Hip Dysplasia Score in German Shepherd dogs in Brazil. The error bars indicate 95% confidence intervals.
The best models were Model IV and Model III for HDST and HDSC, indicating there were no effects of sex for both traits and the year effects were significant only by HDSC. The birth year effects ranged from -0.815 to 0.254 and the estimated posterior means of threshold were 1.4824 and 2.1245 for 3th and 4th threshold (Table 3). Heritabilities were moderate 0.20 ± 0.05 and 0.31 ± 0.05 to HDSC and HDST (Table 3), respectively. The small confidence interval and low standard deviation indicated that all estimates were statistically different from zero (Table 3). Estimated breeding values (EBVs) ranged from -0.675 to 0.877 for HDSC and from −-1.4087 to 0.7067 HDST. The genetic trend of HDSC was significant (p < 0.05), the regression equation was y = 16.182 – 0.0081*(year birth), the coefficient of determination was 0.748. The relative decrease of EBV_HDSC by year was 0.52%.
2.3. Estimation of genetic and phenotypic trends Linear regression using the arithmetic mean of EBV for the HDSC (EBV_HDSC) by birth year was applied to estimate the genetic trends. The phenotypic trend was estimated adjusting a linear regression of arithmetic mean of HSDC by year birth. These analyses were performed using the LM function of R software version 3.2.2 (R Development Core Team, 2015).
3. Results The number of progenies varied between sire and dam (Table 1). Both produced relatively few number, but sires presented more progeny (2.2) than dams (1.3). The mean hip score was 1.56 (SD = 0.84). More than 86% of animals were not affected and less than 1% of animals were severely affected by HD (Table 2). As observed in Fig. 1, there was a constant prevalence of HD over the years, despite the difference between the number of normal and dysplastic dogs registered (Fig. 1). The phenotypic trends of HSDC by birth year was not significant (p < 0.05).
4. Discussion Hip dysplasia evaluations have become more common, which is expected to generate valuable information for practical breeding 3
Preventive Veterinary Medicine 171 (2019) 104745
A.Y. Babá, et al.
Shepherd Breeders for providing the dataset and the scholarship of Grazyella Massako Yoshida was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).
programs. It is known that information on parental phenotype can increase the accuracy of breeding value estimation. Furthermore, a database containing hip dysplasia scores of parents and relatives can provide accurate estimations for both phenotyped and non-phenotyped individuals (Falconer and Mackay, 1996). Heritability of HDSC and HDST has been estimated for different dog breeds using various statistical methods, ranging from 0.11 to 0.64, respectively (Malm et al., 2013; Silvestre et al., 2007; Stock et al., 2011; Wilson et al., 2012; Woolliams et al., 2011), which is in agreement with the estimates obtained in the present study. The phenotypic (Fig. 2b) and genetic trends (Fig. 2a) for the HDSC in the German Shepherd population in Brazil pointed out no significant phenotypic changes during the studied period and a low rate of reduction decrease of EBV (0.52% by year), indicating an ineffective selection. Sixteen sires with ten or more progenies presented an EBV_HDSC mean -0.04 ranged from -0.31 to 0.38, for dams with three or more progenies the EBV_HDSC mean was 0.046, ranging from -0.34 to 0.65. The parents' mean of EBV_HSDC of animals registered in the last three years (2011, 2012 and 2013) were -0,021 for sires (234) and 0.015 for dams (356) and it ranged from -0,583 to 0707. These values suggested that the mating practice is random considering the genetic information (EBV). As shown by heritability estimates, environmental factors have a large influence on HDSC and HDST. Thus, the selection of animals based on phenotypic information is not effective. Leppänen et al. (2000) reported that phenotypic selection is usually ineffective because most dogs used for breeding are phenotypically classified as not dysplastic. Furthermore, it is unreliable to classify dogs phenotypically, as animals of a certain grade may be genetically close to animals of other grades. The results of this study presented for the German Shepherd dog population registered in Brazil show the importance of genetic evaluation for the breeding programs in dogs. Further studies are suggested by using a larger dataset, to support our primary results. Environmental variables, such as nutrition, and related factors, such as growth rate and body weight, strongly influence HD expression (Hemmings, 2016; Peterson, 2017). Phenotypic selection may fail to identify individuals with the best genetic combinations as HD heritabilities estimates pointed out the strong environmental influence. Therefore, we strongly recommend the incorporation of EBV into selection schemes to reduce the prevalence of HD in Brazil.
References Cachon, T., Genevois, J.P., Remy, D., Carozzo, C., Viguier, E., Maitre, P., Fau, D., 2010. Risk of simultaneous phenotypic expression of hip and elbow dysplasia in dogs. Vet. Comp. Orthop. Traumatol. 23 (01), 28–30. https://doi.org/10.3415/VCOT-08-110116. Cowles, M.K., Carlin, B.P., 1996. Markov chain monte carlo convergence diagnostics: a comparative review. J. Am. Stat. Assoc. 91, 883–904. https://doi.org/10.1080/ 01621459.1996.10476956. Falconer, D.S., Mackay, T.F.C., 1996. Introduction to Quantitative Genetics. Harlow, UK. . Hemmings, C., 2016. The importance of good nutrition in growing puppies and kittens. Vet. Nurse 7 (8), 450–456. Lavrijsen, I.C.M., Heuven, H.C.M., Meij, B.P., Theyse, L.F.H., Nap, R.C., Leegwater, P.A.J., Hazewinkel, H.A.W., 2014. Prevalence and co-occurrence of hip dysplasia and elbow dysplasia in Dutch pure-bred dogs. Prev. Vet. Med. 114, 114–122. https://doi.org/10. 1016/J.PREVETMED.2014.02.001. Leppänen, M., Mäki, K., Juga, J., Saloniemi, H., 2000. Estimation of heritability for hip dysplasia in German Shepherd Dogs in Finland. J. Anim. Breed. Genet. 117, 97–103. https://doi.org/10.1111/j.1439-0388.2000x.00229.x. Leppänen, M., Saloniemi, H., 1999. Controlling canine hip dysplasia in Finland. Prev. Vet. Med. 42, 121–131. https://doi.org/10.1016/S0167-5877(99)00059-8. Mäki, K., Groen, A.F., Liinamo, A.-E., Ojala, M., 2002. Genetic variances, trends and mode of inheritance for hip and elbow dysplasia in Finnish dog populations. Anim. Sci. 75, 197–207. https://doi.org/10.1017/s1357729800052966. Malm, S., Sørensen, A.C., Fikse, W.F., Strandberg, E., 2013. Efficient selection against categorically scored hip dysplasia in dogs is possible using best linear unbiased prediction and optimum contribution selection : a simulation study. J. Anim. Breed. Genet. 130, 154–164. https://doi.org/10.1111/j.1439-0388.2012.01013.x. Misztal, I., Tsuruta, S., Lourenco, D., Masuda, Y., Aguilar, I., Legarra, A., 2016. Manual for BLUPF90 Family of Programs. (Accessed 4 December 2018). http://nce.ads.uga. edu/wiki/lib/exe/fetch.php?media=blupf90_all2.pdf. Oberbauer, A.M., Keller, G.G., Famula, T.R., 2017. Long-term genetic selection reduced prevalence of hip and elbow dysplasia in 60 dog breeds. PLoS One 12, e0172918. https://doi.org/10.1371/journal.pone.0172918. Peterson, C., 2017. Canine hip dysplasia: pathogenesis, phenotypic scoring, and genetics. Duluth J. Undergrad. Biol. 4, 19–27. Silvestre, A.M., Ginja, M.M.D., Ferreira, A.J.A., Colaço, J., 2007. Comparison of estimates of hip dysplasia genetic parameters in Estrela Mountain Dog using linear and threshold models. J. Anim. Sci. 85, 1880–1884. https://doi.org/10.2527/jas.20070166. Spiegelhalter, D.J., Best, N.G., Carlin, B.P., Van Der Linde, A., 2002. Bayesian measures of model complexity and fit. J. R. Stat. Soc. Series B Stat. Methodol. 64 (4), 583–639. https://doi.org/10.1111/1467-9868.00353. Stock, K.F., Klein, S., Tellhelm, B., Distl, O., 2011. Genetic analyses of elbow and hip dysplasia in the German shepherd dog. J. Anim. Breed. Genet. 128, 219–229. https:// doi.org/10.1111/j.1439-0388.2010.00901.x. Wilson, B.J., Nicholas, F.W., James, J.W., Wade, C.M., Tammen, I., Raadsma, H.W., Castle, K., Thomson, P.C., 2012. Heritability and phenotypic variation of canine hip dysplasia radiographic traits in a cohort of australian german shepherd dogs. PLoS One 7, e39620. https://doi.org/10.1371/journal.pone.0039620. Woolliams, J.A., Lewis, T.W., Blott, S.C., 2011. Canine hip and elbow dysplasia in UK Labrador retrievers. Vet. J. 189, 169–176. https://doi.org/10.1016/J.TVJL.2011.06. 015. Zhu, L., Chen, S., Jiang, Z., Zhang, Z., Ku, H.-C., Li, X., McCann, M., Harris, S., Lust, G., Jones, P., Todhunter, R., 2012. Identification of quantitative trait loci for canine hip dysplasia by two sequential multipoint linkage analyses. J. Appl. Stat. 39, 1719–1731. https://doi.org/10.1080/02664763.2012.673121.
Declaration of Competing Interest The authors declare that they have no conflicts of interest. Acknowledgments The authors are grateful to the Brazilian Society of German
4
Contents lists available at ScienceDirect
Preventive Veterinary Medicine journal homepage: www.elsevier.com/locate/prevetmed
Heritability of hip dysplasia: Preliminary results for German Shepherd dogs in Brazil
T
Adriane Yumi Babáa, Carlos Antonio Lopes de Oliveiraa, Grazyella Massako Yoshidaa, ⁎ Marcus Túlio Cavalcante Costab, Leonir Bueno Ribeiroa, Ricardo Souza Vasconcellosa, a b
Maringá State University, Department of Animal Science, 5790, Colombo avenue, Maringá, Paraná State, Zip Code 87020-900, Brazil Brazilian Society of German Shepherd Breeders, Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: Breeding value Phenotypic selection Genetic trend
The heritability of canine hip dysplasia in German Shepherd dogs was estimated using Bayesian methods. Data on hip score and status of 1632 dogs born from 1990 to 2013 were provided by the Brazilian Society of German Shepherd Breeders. Heritability estimates (mean ± standard deviation) were 0.1979 ± 0.058 for hip scores and 0.187 ± 0.055 for hip status. We observed no phenotypic trends and a small rate of genetic trend (0.52%) according to the year of birth, probably because of ineffective phenotypic selection and absence of genetic selection. The heritability estimates in this study can be used to achieve effective selective breeding and genetic gains, which in turn can result in improvements in dog health and welfare.
1. Introduction
results. In Brazil, there are no studies that have evaluated over the years the prevalence of HD in dogs. In this way, studies for selection criteria in breeding genetic selection are most important than phenotypic characteristics. Thus, this study aimed to estimate the heritability and the genetic trend of HD in German Shepherd dogs registered in Brazil.
Canine hip dysplasia (HD) is a common and complex orthopedic disorder with multiple genes and environmental factors influencing the susceptibility to this disease (Lavrijsen et al., 2014; Mäki et al., 2002; Stock et al., 2011). The German Shepherd dog is among the ten breeds with the highest prevalence of these conditions (Oberbauer et al., 2017). The HD can lead to painful secondary osteoarthritis and physical disability, and their symptoms may cause deleterious changes in dog behavior (Peterson, 2017). Medical and surgical management can help relieve the pain but cannot treat the conditions (Zhu et al., 2012). Given the high prevalence of HD in German Shepherd dogs (Oberbauer et al., 2017) and the lack of effective treatment options, genetic selection has been suggested as the most effective method for reducing dysplasia prevalence and improving dog welfare (Cachon et al., 2010; Mäki et al., 2002; Stock et al., 2011; Woolliams et al., 2011). However, the most common method used by breeders to select the animals for breeding is based on phenotypic characteristics and this practice seems to be ineffective (Lavrijsen et al., 2014; Mäki et al., 2002; Stock et al., 2011). Leppänen and Saloniemi (1999) in a retrospective study in Finland, including 69,349 dogs born from 1988 to 1995, verified that the selection of breeding animals based on phenotypic characteristics did not change over the years and the authors concluded that predicted breeding values based on progeny testing would probably give better
⁎
2. Material and methods 2.1. Dataset The information of hip dysplasia score (HDSC) and status (HDST) were obtained from the records of German Shepherd dogs born between 1990 and 2013, provided by the Brazilian Society of German Shepherd Breeders. HDSC was scored according to the Fédération Cynologique Internationale (FCI) grading system, ranging from A to E. Dogs scored A (no signs of dysplasia) or B (near normal hip joint) were considered normal, and dogs scored C (mild affected), D (moderate affected), or E (severely affected), were considered dysplastic. The letters A–E of this grading system were replaced for 1–5 for statistical analysis. HDST was Normal (1 –hip scores A or B) or dysplastic (2 –hip scores C, D, and E). 2.2. Estimation of variance components and heritability It was performed a single-trait analysis to estimate the variance
Corresponding author. E-mail address: [email protected] (R.S. Vasconcellos).
https://doi.org/10.1016/j.prevetmed.2019.104745 Received 23 January 2019; Received in revised form 6 August 2019; Accepted 10 August 2019 0167-5877/ © 2019 Elsevier B.V. All rights reserved.
Preventive Veterinary Medicine 171 (2019) 104745
A.Y. Babá, et al.
Table 1 Distribution of progeny number by sire and dam.
Table 3 Posterior means and standard deviation (SD) of variance components and heritability of hip score (HDSC) and hip status (HDST) in German Shepherd dogs in Brazil.
Progeny number
Sire Dam
N
Mean
Mode
Min
Max
SD
829 1,364
2.198 1.335
1 1
1 1
28 7
2.518 0.775
Model I
Table 2 Mean, standard deviation (SD), and frequency (%) of hip dysplasia in German Shepherd dogs in Brazil. Dysplasia grade (% of dogs)a
Trait
Mean
SD
σa2
a
h2
d
DIC
σa2 a
N 2d
h
Hip dysplasiab
A
B
C
D
E
61.64
24.39
10.78
2.45
0.74
1.56
0.84
3th threshold 4th threshold DIC
1632
a Hip score: A (1 = no signs of dysplasia), B (2 = near normal hip joint), C (mild affected), D (moderate affected), E (severely affected). b Hip status: 13.98% of the dogs were dysplastic.
Model III
Model IV
0.25(0.08) (0.11-0.44) 0.20(0.05) (0.10-0.30) 4651.8
0.25(0.09) (0.10-0.45) 0.20(0.06) (0.09-0.30) 4607.8
0.45(0.11) (0.26-0.68) 0.31(0.05) (0.21-0.41) 4555.6*
0.26(0.07) (0.13-0.43) 0.20(0.05) (0.12-0.31) 1.455 (0.25) 2.067 (0.41) 4685.3
0.26(0.08) (0.13-0.43) 0.20(0.05) (0.12-0.31) 1.482 (0.27) 2.124 (0.44) 4668.9*
0.42(0.09) (0.26-0.63) 0.30(0.05) (0.21-0.40) 1.560 (0.29) 2.282 (0.47) 4798.3
SD and 95% confidence intervals are shown in parentheses. * the best model, smallest DIC. a σa2 . Additive genetic variance. d h2 . Heritability.
components and the estimated breeding value (EBV) for dysplasia using the THRGIBBS1F90 program (Misztal et al., 2016). The HDSC was considered a categorical trait with 5 categories and HDST a binary trait. A total of 1,200,000 iterations of the Gibbs sampler were run with a burn-in period of 200,000 iterations, the sampling interval was 100 iterations. The general single-trait model used is as follows:
y = Xb + Za + e
HDST 0.24(0.09) (0.09-0.43) 0.19(0.06) (0.08-0.30) 4774.4 HDSC 0.25(0.07) (0.12-0.41) 0.20(0.05) (0.10-0.30) 1.467 (0.26) 2.099 (0.43) 4673.1
Model II
The posterior means of EBV and the sex and birth year effects were estimated using the POSTGIBBSF90 program, using the Gibbs samples chain provided by the THRSGIBBS1F90 program (Misztal et al., 2016). The THRGIBBS1F90 program fixes the threshold (t = 0) for binary traits (HDST) and first and second threshold (t1 = 0 and t2 = 1) for categorical traits with more than three categories (HDSC), thus for HDSC were estimated the third and fourth thresholds. Convergence diagnostics and autocorrelation statistics were assessed by the Heidelberger and Welch’s test (Cowles and Carlin, 1996). Also, the posterior means and standard deviation and the 95% confidence interval of genetic parameters were estimated using the CODA package in R software version 3.2.2 (R Development Core Team, 2015). The relationship matrix included information on 2722 dogs. The dataset contained information from 1632 animals scored by HDSC and HDST, being 1006 males and 626 females.
(1)
Where y is the vector of HD, b is the vector of sex and birth year effects, a is the vector of random additive genetic effects, e is the vector of residual effects, and X and Z are incidence matrices relating phenotype to the effects b and a, respectively. For both traits, the presence/absence of fixed effects (sex and birth year) were tested using four models. The Model I (with sex and birth year effects); Model II (only sex effects); Model III (only the birth year effects); and Model IV (without sex and birth year effects). The deviance information criterion (DIC) was adopted to select the goodness of fit of the models for each trait, where the lowest DIC value indicated the most robust model (Spiegelhalter et al., 2002).
Fig. 1. Number of normal (black solid line) or dysplastic (grey dashed line) German Shepherd dogs registered in Brazil by year of birth. 2
Preventive Veterinary Medicine 171 (2019) 104745
A.Y. Babá, et al.
Fig. 2. Genetic (a) (estimated breeding values-EBV) and phenotypic (b) trends by Hip Dysplasia Score in German Shepherd dogs in Brazil. The error bars indicate 95% confidence intervals.
The best models were Model IV and Model III for HDST and HDSC, indicating there were no effects of sex for both traits and the year effects were significant only by HDSC. The birth year effects ranged from -0.815 to 0.254 and the estimated posterior means of threshold were 1.4824 and 2.1245 for 3th and 4th threshold (Table 3). Heritabilities were moderate 0.20 ± 0.05 and 0.31 ± 0.05 to HDSC and HDST (Table 3), respectively. The small confidence interval and low standard deviation indicated that all estimates were statistically different from zero (Table 3). Estimated breeding values (EBVs) ranged from -0.675 to 0.877 for HDSC and from −-1.4087 to 0.7067 HDST. The genetic trend of HDSC was significant (p < 0.05), the regression equation was y = 16.182 – 0.0081*(year birth), the coefficient of determination was 0.748. The relative decrease of EBV_HDSC by year was 0.52%.
2.3. Estimation of genetic and phenotypic trends Linear regression using the arithmetic mean of EBV for the HDSC (EBV_HDSC) by birth year was applied to estimate the genetic trends. The phenotypic trend was estimated adjusting a linear regression of arithmetic mean of HSDC by year birth. These analyses were performed using the LM function of R software version 3.2.2 (R Development Core Team, 2015).
3. Results The number of progenies varied between sire and dam (Table 1). Both produced relatively few number, but sires presented more progeny (2.2) than dams (1.3). The mean hip score was 1.56 (SD = 0.84). More than 86% of animals were not affected and less than 1% of animals were severely affected by HD (Table 2). As observed in Fig. 1, there was a constant prevalence of HD over the years, despite the difference between the number of normal and dysplastic dogs registered (Fig. 1). The phenotypic trends of HSDC by birth year was not significant (p < 0.05).
4. Discussion Hip dysplasia evaluations have become more common, which is expected to generate valuable information for practical breeding 3
Preventive Veterinary Medicine 171 (2019) 104745
A.Y. Babá, et al.
Shepherd Breeders for providing the dataset and the scholarship of Grazyella Massako Yoshida was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).
programs. It is known that information on parental phenotype can increase the accuracy of breeding value estimation. Furthermore, a database containing hip dysplasia scores of parents and relatives can provide accurate estimations for both phenotyped and non-phenotyped individuals (Falconer and Mackay, 1996). Heritability of HDSC and HDST has been estimated for different dog breeds using various statistical methods, ranging from 0.11 to 0.64, respectively (Malm et al., 2013; Silvestre et al., 2007; Stock et al., 2011; Wilson et al., 2012; Woolliams et al., 2011), which is in agreement with the estimates obtained in the present study. The phenotypic (Fig. 2b) and genetic trends (Fig. 2a) for the HDSC in the German Shepherd population in Brazil pointed out no significant phenotypic changes during the studied period and a low rate of reduction decrease of EBV (0.52% by year), indicating an ineffective selection. Sixteen sires with ten or more progenies presented an EBV_HDSC mean -0.04 ranged from -0.31 to 0.38, for dams with three or more progenies the EBV_HDSC mean was 0.046, ranging from -0.34 to 0.65. The parents' mean of EBV_HSDC of animals registered in the last three years (2011, 2012 and 2013) were -0,021 for sires (234) and 0.015 for dams (356) and it ranged from -0,583 to 0707. These values suggested that the mating practice is random considering the genetic information (EBV). As shown by heritability estimates, environmental factors have a large influence on HDSC and HDST. Thus, the selection of animals based on phenotypic information is not effective. Leppänen et al. (2000) reported that phenotypic selection is usually ineffective because most dogs used for breeding are phenotypically classified as not dysplastic. Furthermore, it is unreliable to classify dogs phenotypically, as animals of a certain grade may be genetically close to animals of other grades. The results of this study presented for the German Shepherd dog population registered in Brazil show the importance of genetic evaluation for the breeding programs in dogs. Further studies are suggested by using a larger dataset, to support our primary results. Environmental variables, such as nutrition, and related factors, such as growth rate and body weight, strongly influence HD expression (Hemmings, 2016; Peterson, 2017). Phenotypic selection may fail to identify individuals with the best genetic combinations as HD heritabilities estimates pointed out the strong environmental influence. Therefore, we strongly recommend the incorporation of EBV into selection schemes to reduce the prevalence of HD in Brazil.
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Declaration of Competing Interest The authors declare that they have no conflicts of interest. Acknowledgments The authors are grateful to the Brazilian Society of German
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