Estimation of genetic and phenotypic parameters of logistic growth curve and their inter-relationship in Zandi Sheep

Document Type : Genetics & breeding

Authors

• Saeed Neysi ¹
• hedayatollah roshanfekr ²
• Jamal Fayazi ³
• morteza mamouei ⁴

¹ Department of Animal Science, University of Khuzestan Agricultural Sciences and Natural Resources, Khuzestan, Iran.

² Department of Animal Science, University of Khuzestan Agricultural Sciences and Natural Resources, Khuzestan, Iran

³ Faculty of Animal Science , Agricultural Sciences and Natural Resources University of Khuzestan, Khuzestan, Iran.

⁴ Faculty of Animal Science and Food Technology, Agricultural Sciences and Natural Resources University of Khuzestan, Khuzestan, Iran.

10.22067/ijasr.v13i3.81528

Abstract

Introduction Growth, defined as changes of body weight over time, is an economically important trait in sheep that directly determines meat production.Increase in live weight or dimension against age has been described as growth. Changes in live weight or dimension for a period of time are explained by the growth curves. Animal breeders are interested in the genotypic and phenotypic relationships during all phases of growth. Knowledge of genotypic and phenotypic relationships among live weights, degree of maturity and growth rate during all phases of growth is necessary to formulate breeding programs to improve lifetime efficiency.  Growth models are mathematical functions which are applied for describing the growth pattern. Understanding, estimating, and capturing the defining characteristics of growth processes are key components of developmental research. The aim of the present study was to estimate the genetic parameters for growth traits in Zandi sheep, by determining the most appropriate animal models to be fitted. In addition, genetic, phenotypic and environmental correlations between traits were estimated.
Materials and Methods The data used in this study were obtained from the Animal Breeding Center of Iran. The data were screened several times to remove the defective and out of range records. Growth curve parameters used in study were asymptomatic mature weight (A), Growth rate (B) and maturity rate (K). The procedure of SAS software was used for studying of fix effects. Based on body weight at different ages and using different initial values, each of the growth curve parameters was estimated using SAS software version 9.1 and NLIN procedure. Estimation of (co)variance components of growth curve parameters was conducted using Bayesian approach implemented in MTGSAM and Wombat software. The number of Gibbs sampling rounds used was 200,000 rounds.  Ten percent of these numbers (20,000 rounds) was burn-in. The convergence criterion for stopping repetitions in this analysis was also considered as 10 decimals (10-10). Sampling intervals of 200 and Gouss- Seidel 10000 repetitions were considered. In order to find the best model incorporating the constant and random effects affecting each of the parameters of the growth pattern, the following models, with and without regard to maternal effects including maternal additive genetic effects and permanent maternal environmental effects in the model (Meyer’s models) were tested.
Results and Discussion Environmental factors such as year of birth and sex of lamb showed significant influence on growth curve parameters (A, B and k) in Zandi sheep. Estimates of direct heritability is based on best models using REML and Bayesian methods for A, B and K were 0.064 and 0.14, 0.17and 0.16, 0.16and 0.18 respectively. Maternal heritability was in range of 0.006 - 0.08 and Proportion of environmental variance to phenotypic variance in range of 0.03 - 0.05 parameters growth curve. Among the growth curve parameters, only A and k have biological interpretation and therefore, relationship between them may provide necessary conclusions. Estimates of direct genetic correlation between growth curve parameters were 0.323, -0.429 and 0.803 between A-B, A-K and B-K, respectively. The positive and high genetic correlation between A and B parameters is evident as expected for common genetic and physiological mechanisms controlling these traits. Positive genetic correlation between these traits suggests that selection in one parameter of the growth curve would also improve the other parameter. Residual correlations between growth curve parameters varied form −0.296 (between A-K) to 0.732 (between B-K). Phenotypic correlations between growth curve parameters varied form −0.184 (between A-K) to 0.743 (between B-K). The phenotypic and genetic antagonism between A and k indicates that rapid reduction in growth rate after inflexion point results in lower mature weights. This finding would be helpful for improving selection by identifying the animal who reaches inflexion point earlier and attend higher mature weights later.
 Conclusion Current genetic estimates for growth curve parameters in Zandi sheep could be applied in designing selection program in this breed. The low estimates of heritability for A, B and K parameters could be assigned to the high phenotypic variance arising from large environmental variation. This therefore implies that much of the improvement in these growth curve parameters could be obtained by improvement of environment rather than genetic selection. It is important to provide good environmental conditions along with optimal management strategies in the flock to achieve a desired shape of growth curve through changing the parameters of model.
 

Keywords

• Bayesian
• correlation
• Growth rate
• Heritability
• Restricted maximum likelihood

20.1001.1.20083106.1400.13.3.9.7

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References

1. Abegaz, S., J. B. Van Wyk, and J. J. Olivier. 2010. Estimation of genetic and phenotypic parameters of growth curve and their relationship with early growth and productivity in Horro sheep. Archives Animal Breeding, 53(1): 85-94.
2. Akaike, H. 1974. A new look at the statistical model identification. In Selected Papers of Hirotugu Akaike (pp. 215-222). Springer, New York, NY.‏
3. Akaike, H. 1983. Information measures and model selection. Proceedings of the 44th session of the international statistical institute, 1: 277-291.
4. Arzani, H., J. Motamedi, A. Nikhkhah, H. Azrinvand and M. GHorbani. 2013. Animal Unit Equivalent (AUE) and daily requirement energy for Torki Ghashghaei breed sheep grazing on rangelands of Fars Province. Iranian Journal of Range and Desert Research, 20(3): 433-444. (In Persian).
5. Bahreini Behzadi, M. 2015. Comparison of different growth models and artificial neural network to fit the growth curve of Lori-Bakhtiari sheep. Journal of Ruminant Research, 3(2): 125-148. (In Persian).
6. Bahreini Behzadi, M. R., A. A. Aslaminejad, A. R. Sharifi, and H. Simianer. 2014. Comparison of Mathematical Models for Describing the Growth of Baluchi Sheep. Journal of Agricultural Science and Technology, 16(1): 57-68.
7. Bassam, B. J., and G. Caetano-Anolles. 1993. Silver staining of DNA in polyacrylamide gels. Applied biochemistry and biotechnology, 42(2-3):181-188.‏
8. Bathaei, S., and P. Leroy. 1998. Genetic and phenotypic aspects of the growth curve characteristics in Mehraban Iranian fat-tailed sheep. Small Ruminant Research, 29(3): 261–269.
9. Da Silva, L. S. A., A. B. Fraga, F. D. L. Da Silva, P. M. G. Beelen, R. M. D. O. Silva, H. Tonhati and C. D. C. Barros. 2012. Growth curve in Santa Ines sheep. Small Ruminant Research, 105(1-3): 182-185.
10. Deimi Ghias Abadi, P., S. Alijani, J. Shodja Ghias, and N. Pirani. 2013. Comparison of Tow Restricted Maximum Likelihood (REML) and Bayesian Statistical Methods for Estimating Genetic Parameter of Some Economically Important Traits in Fars Native Chickens. Research on Animal Production (Scientific and Research), 3:1-13. (In Persian)
11. Garnero, A. D. V., C. R. Marcondes, R. J. Gunski, H. N. Oliveira, and R. B. Lobo. 2006. Genetic trends in the expected progeny difference of the asymptotic weight of Nelore females. Genetics and Molecular Biology, 29(4): 648-652.‏
12. Ghavi Hossein-Zadeh, N. 2015. Bayesian estimates of genetic relationships between growth curve parameters in shall sheep via Gibbs sampling. Iranian Journal of Applied Animal Science, 5(4): 897-904.
13. Hojati, S. F., and N. Ghavi Hoseinzadeh. 2016. Genetic and phenotypic relationships between growth curve parameters in Mehraban sheep. Journal of animal production, 18(4): 687-696. (In Persian).
14. Hosseinpour mashhadi, M., M. Elahi Torshizi, and Sh. Ehtesham Gharaee. 2017. Description of Growth Curve in Male and Female Lambs of Baluchi Breed by Application of Nonlinear Growth Model. Journal of research on animal production, 8(15): 155-160. (In Persian).
15. Jasori, M., S. Alijani, N. Pirany, M. Baghernejad, and R. Jafarzadeh. 2011. EstimationOf genetic parameters of Holstein dairy cattle using Bayesian procedure. 4th Iranian Animal Science Congress. Tehran, Iran. pp: 3022-3025
16. Kopuzlu, S., E. Sezgin, N. Esenbuga, and O. C. Bilgin. 2014. Estimation of growth curve characteristics of Hemsin male and female sheep. Journal of applied animal research, 42(2):228-232.‏
17. Kume, K., and L. Hajno. 2011. Study of growth curve variations for kids 0-6 months old of Alpine goat breed in Albania. Archiva zootechnica, 13: 54–62.
18. Lambe, N. R., E. A. Navajas, G. Simm, and L. Bunger. 2006. A genetic investigation of various growth models to describe growth of lambs of two contrasting breeds. Journal of Animal Science, 84(10): 2642–2654.
19. Lewis, R. M., G. C. Emmans, W. S. Dingwall, and G. Simm. 2002. A description of the growth of sheep and its genetic analysis. Animal Sciences, 74(1): 51–62.
20. Malhado, C. H. M., P. L. S. Carneiro, P. R. A. M. Affonso, A. A. O. Souza, and J. L. R. Sarmento. 2009. Growth curves in Dorper sheep crossed with the local Brazilian breeds, Morada Nova, Rabo Largo, and Santa Inês. Journal of Small Ruminant Research, 84:1.16-21.
21. Mandal, A., R. Roy, and P. K. Rout. 2008. Direct and maternal effects for body measurements at birth and weaning in Muzaffarnagari sheep of India. Small Ruminant Research, 75(2-3):123-127.
22. Mavrogenis A. P., and A. Constantinou. 1990. Relationships between pre-weaning growth, post-weaning growth and mature body size in Chios sheep. Animal Production, 50(2): 271–275.
23. Maxa, J., E. Norenberg, P. Berg, and M. Milerski. 2007. Genetic parameters for body weight, longissimus muscle depth and fat depth for Suffolk sheep in the Czech Republic. Small Ruminant Research, 72(2-3): 87-91.
24. Meyer, K. 2006. WOMBAT–A program for mixed model analyses by restricted maximum likelihood. User notes. (Animal Genetic and Breeding Unit. In University of New England: Armidale) Meyer K, Graser HU.‏
25. Mohamadi, H., M. Moradi Shahrbabak, and M. Sadeghi. 2011. Estimation genetic, environmental and phenotypic trends of growth traits in Zandi sheep. Modern genetics journal, 6(2): 49-57.
26. Rashedi, A., J. Fayyazi, A. Masoodi, and R. Abdolahi. 2018. Genetic analysis of growth curve parameters obtained by nonlinear functions in Moghani sheep using Bayesian approach. Journal of Animal Science Researches, 28(3): 113-126.
27. Saghi D. A., A. Aslaminejad, M. Tahmoorespur, H. Farhangfar, M. Nassiri, and G. R. Dashab. 2012. Estimation of genetic parameters for growth traits in Baluchi sheep using Gompertz growth curve function. Indian Journal of Animal Sciences, 82(8): 889-892.
28. Sargolzaei, M., and M. A. Edriss. 2004. Estimation of phenotypic, genetic and environmental trends of some of the growth traits in Bakhtiari sheep. Journal of Science and Technology of Agriculture and Natural Resources, 8(1): 125-133.
29. SAS Institue. 2003. SAS /STAT Users Guide: Statistics. Release 8. 2. SAS Institute Inc., Cary, NC.
30. Sghaier, N., A. Gaddour, O. Mabrouk, A. Mouldi, and B. H. Mohamed. 2007. Non genetic factors affecting local kid’s growth curve under pastoral mode in Tunisian arid region. Journal of Biological Science, 7: 1005–
31. Taylor, G. 1975. Spanish versus angora in controlling browse. Pp 16-17.‏ Presentation at the Spanish Goat Conference, San Angelo, TX. Texas A&M Res. and Ext. Center.
32. Topal, M., M. Ozdemir, V. Aksakal, N. Yildiz, and U. Dogru. 2004. Determination of the best nonlinear function in order to estimate growth in Morkaraman and Awassi lambs. Small Ruminant Research, 55(1-3):229-232.
33. Van Tassell, C. P., and L. D. Van Vleck. 1995. A Manual for Use of MTGSAM. A Set of FORTRAN Programs to Apply Gibbs Sampling to Animal Models for Variance Component Estimation [DRAFT]. U.S. Department of Agriculture, Agricultural Research Service.