برآورد پارامترهای ژنتیکی زنده‌مانی در بره‌های گوسفند عربی با استفاده از دو مدل خطی و ویبال

نوع مقاله : علمی پژوهشی- ژنتیک و اصلاح دام و طیور

نویسندگان

1 گروه علوم دامی، دانشگاه کشاورزی و منابع طبیعی خوزستان، خوزستان، ایران

2 گروه علوم دامی، دانشگاه کشاورزی و منابع طبیعی خوزستان، خوزستان، ایران.

3 گروه علوم دامی، دانشگاه جیرفت، جیرفت، ایران.

چکیده

بهره­وری اقتصادی پرورش گوسفند تا حد زیادی تحت تاثیر میزان زنده­مانی آن است. هدف این مطالعه بررسی پارامترهای ژنتیکی صفات زنده­مانی گوسفند عربی از تولد تا یک‌سالگی بود. بدین­منظور از تعداد 5452 رکورد­ زنده­مانی بره­های نژاد عربی که طی سال­های1371 تا 1383 توسط سازمان جهاد کشاورزی شهرستان اهواز جمع­آوری شده بود استفاده گردید. داده­ها با مدل­های خطی و نسبت خطر با تابع ویبال تجزیه شدند. این مدل­ها شامل اثر عوامل ثابت سال و ماه تولد بره، نوع تولد، سن مادر و متغیر کمکی وزن تولد بره­ها به صورت درجه دوم و اثرات تصادفی ژنتیکی افزایشی مستقیم، ژنتیکی افزایشی مادری، محیط دائمی مادری و باقی­مانده بودند. وراثت­پذیری مستقیم میزان زنده­مانی بره­ها با مدل­های مختلف خطی، در بازه 025/0 تا 061/0 برآورد گردید. وراثت­پذیری­های مستقیم در مقیاس لگاریتمی، مقیاس اولیه و وراثت­پذیری موثر نسبت خطر به­دست آمده از مدل پدری دارای تابع ویبال دامنه 13/0 تا 75/0 را نشان داد. نتایج بیانگر وراثت­پذیری کم برای مدل­های خطی و متوسط تا بالا برای تابع ویبال بود. تخمین­های متوسط تا بالای وراثت­پذیری صفات بقا، با استفاده از مدل­های نسبت خطر با تابع ویبال، می­تواند ایده بهبود بقای بره از طریق انتخاب درون­نژادی و گنجاندن آن در شاخص انتخاب نژاد گوسفند عربی را مورد توجه قرار دهد.

کلیدواژه‌ها


عنوان مقاله [English]

Estimation of Genetic Parameters for Lamb Survival Traits of Arabi sheep using Linear and Weibull Models

نویسندگان [English]

  • Nasir Karimi 1
  • Mohammad Taghi Beigi Nasiri 2
  • Arsalan Barazande 3
1 Department, Ramin Agriculture and Natural Resources University, Khuzestan.Khuzestan.
2 Department of Animal Sciences, Ramin Agriculture and Natural Resources University, Khuzestan, Khuzestan, Iran.
3 Animal Science Department, University of Jiroft, Jiroft, Iran.
چکیده [English]

Introduction[1] Arabi sheep population includes almost 55% of the local sheep population in Khuzestan province. Lamb mortality is a universal problem in sheep breeding that may be reached to 20-40% of total lambs born and could impact the genetic improvement, animal welfare and economic viability of sheep breeding, adversely. Research on improving survival of lambs is likely to have a higher pay-off than research on improving the number of lambs conceived. Lamb survival is a compound trait affected by many various factors related to climate, management, lamb and ewe behavior and genetic effects. Lamb survivability is controlled by genetics of the animal, contributed by direct genetic and maternal effects and also by environmental effects. There are disagreements among researchers about using a suitable model to analyze the survival traits, and each researcher has suggested the use of specific models. In general, the use of linear and threshold models has been suggested for survival trait analysis. Although survival has great economic importance, in the studies conducted on Iranian livestock, less attention has been paid to it. The aim of this study was to analyze the genetic parameters for the survival of Arabi lambs from birth to one year of age using linear and Weibull models.
Materials and Methods in this study, 5452 lamb survival records collected by the Jahad Agricultural Organization of Ahvaz from 1993 to 2005 were used. Traits included were cumulative survival from birth to the end of one year and on a monthly basis. In order to estimate genetic parameters using linear models, the Restricted Maximum Likelihood (REML) method was used in Wombat software based on a univariate analysis. The Weibull model and Matvec software were also used for estimating variance component and genetic parameters of Survival rate.
Results and Discussion Different models compared using the likelihood ratio test. For survival traits until 1, 4, 5, and 10 months, the model 4 was suitable, which include the direct additive genetic effect, maternal additive genetic effect and their covariance. In the case of until 2 months, the best model was the model 3, which include the direct and maternal additive genetic effects. For until 3, 8, 9, and 12 months, model 1 including direct additive genetic effect was selected. And for other traits Model 5 (direct additive genetic, maternal additive genetic, and maternal permanent environmental effects) was chosen as the best model. The direct heritability of survival rate estimated from different linear models was in the range of 0.025 to 0.061. In general, despite the high economic importance of survival in the breeds until one year of age, due to low estimates of the inheritance of these traits using linear models, it cannot be expected that genetic selection alone can make significant genetic progress. The genetic variance component among sires, heritability on the logarithmic scale, heritability on the original scale, and effective heritability obtained from Weibull sire model were increased to a peak point at 4 months. After that, a decline occurred until 5 months, and then a fluctuation was observed until 9 months. A limited increase was found in the 11 and 12 months. The heritability of sire model, in the logarithmic scale, had a low to medium range (0.13-0.25), and in the original scale had a medium to high range (0.39-0.75). The effective heritability was estimated in the medium range. Estimated values of the survival heritability using the Weibull model was greater than the value obtained from the linear animal model. Although heritability estimations for survival and mortality is low, it is possible that genetic progress may be enhanced by selecting lambs with higher breeding value for survival.
Conclusion Estimation of the genetic parameters for survival lambs from birth to one year of age using linear and Weibull models were low vs medium to high, respectively. Therefore, based on the results of linear models, response to  direct selection to improve the survival of lambs in this breed will be very slow, and more attention should be paid to improving non-genetic factors and indirect selection and outbreeding, but based on The results of Weibull models, it seems that the rate of response to genetic selection to improve survival trait is faster using these models compared to the linear models, suggest that lamb survival could be improved  through direct selection and could be included in the Arabi sheep selection  index.
 

کلیدواژه‌ها [English]

  • Arabi Sheep
  • Heritability
  • Survival
  1. . Abdelqader, A., R. Irshaid, M. J. Tabbaa, M. Abuajamieh, H. Titi and, A. R. Al-Fataftah. 2017. Factors influencing Awassi lambs survivorship under fields conditions. Livestock Science, 199: 1-6.

    1. Aktas, A., H. S. Dursun, S. Dogan, Z. Kiyma, and U. Demirci. 2015. Effects of ewe live weight and age on reproductive performance, lamb growth, and survival in Central Anatolian Merino sheep. Archiv fuer Tierzucht, 58 (2): 451-459.
    2. Almasi, M., A. Rashidi, M. Razmkabir, and M. M. Gholambabaeian. 2015. Effect of some of genetic and non-genetic parameters on lamb survival in Baluchi, Iranblack and Zandi breed sheep. Iranian Journal of Animal Science Research, 26 (1): 157-166. (In Persian)
    3. Annor, S., K. Djang-Fordjour, and K. Gyamfi. 2007. Is growth rate more important than survival and reproduction in sheep farming in Ghana?. Journal of Science and Technology (Ghana), 27: 23-38.
    4. Barazandeh, A., S. M. Moghbeli, M. Vatankhah, and N. G. Hossein-Zadeh. 2012. Lamb survival analysis from birth to weaningin Iranian Kermani sheep. Tropical animal health and production, 44: 929-934.
    5. Brien, F., M. Hebart, D. Smith, J. H. Edwards, J. Greeff, K. Hart, G. Refshauge, T. Bird-Gardiner, G. Gaunt, and R. Behrendt. 2010. Opportunities for genetic improvement of lamb survival. Animal Production Science, 50: 1017-1025.
    6. Ceyhan, A., T. Sezenler, and I. Erdogan. 2009. The estimation of variance components for prolificacy and growth traits of Sakiz sheep. livestock Science. 122:68-72.
    7. Ducrocq, V., and G. Casella. 1996. A Bayesian analysis of mixed survival models. Genet. Sel. Evol, 28: 505-529.

    Everett-Hincks, J., H. Mathias-Davis, G. Greer, B. Auvray, and K. Dodds. 2014. Genetic parameters for lamb birth weight, survival and death risk traits. Journal of Animal Science, 92: 2885-2895.

    1. Fogarty, N. M. 1995. Genetic parameters for live weight, fat and muscle measurements, wool production and reproduction in sheep, a review. Anim. Breed. Abstr, 63 (3): 101-143.
    2. GhaviHossein-Zadeh, N., R. Noori, and A. A. Shadparvar. 2018. Genetic analysis of longevity and lamb survival from birth to yearling in Moghani sheep. Journal of Applied Animal Research, 46: 1363-1369.
    3. Gowane, G., C. Swarnkar, L. Prince, and A. Kumar. 2018. Geneticparameters for neonatal mortality in lambs at semi-arid region of Rajasthan India. Livestock science, 210: 85-92.
    4. Haghdoost, A., A. A. Shadparvar, M. T. B. Nasiri, and J. Fayazi. 2008. Estimates of economic values for traits of Arabisheep in village system. Small Ruminant Research, 80: 91-94.
    5. Hassan Kiyadeh, A. V., M. Rokouei, G. R. Dashab, A. R. Seyedalian, and H. Faraji-Arough. 2019. Estimation of non-genetic and genetic effects for survival trait in Zandi sheep. Animal Production, 21: 182-191. (In Persian)
    6. Hassan Kiyadeh, A. V., M. Rokouei, G. R. Dashab, A. R. Seyedalian, and H. Faraji-Arough. 2016. Genetic evaluation of survival trait in Baluchi sheep using Gibbs sampling method. Iranian Journal of Animal Science, 47 (3): 453-461. (In Persian)
    7. Hatcher, S., K. Atkins, and E. Safari. 2010. Lamb survival in Australian Merino sheep: a genetic analysis. Journal of Animal Science, 88: 3198-3205.
    8. Kalbfleisch, J. D., and R. L. Prentice. 1980. The Statistical Analysis of Failure Time Data. John Wiley and Sons. New York.
    9. Klein, J. P., and M. L. Moeschberger. 1997. Survival Analysis: Techniques for Censored and Truncated Data. Springer-Verlag. New York.
    10. Lima, M. J., M. Rokouei, G. R. Dashab, A. R. Seyedalian, and H. Faraji-Arough. 2019. Genetic and non-genetic analysis of lamb survival in Sangsari sheep by gibbs sampling method. Small Ruminant Research, 177: 56-60.
    11. Mandal, A., K. Pant, P. Rout, and R. Roy. 2004. Effects of inbreeding on lamb survival in a flock of Muzaffarnagari sheep. Asian-australasian journal of animal sciences, 17: 594-597
    12. Matos, C., D. Thomas, L. Young, and D. Gianola. 2000. Genetic analyses of lamb survival in Rambouillet and Finnsheep flocks by linear and threshold models. Animal Science, 71: 227-234.
    13. Meyer, K. 2007. WOMBAT—A tool for mixed model analyses inquantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University Science B, 8: 815-821.
    14. Mohammadinejad, F., M. R. Mohammadabadi, A. Barazandeh. 2017. Estimating genetic parameters of kid survival in Raini Cashmere goat using linear and Weibul models. Iranian Journal of Animal Science, 48 (2): 297-304. (In Persian)
    15. Mola-Abdol-Karimi, M., A. Rashidi, and G. Asgari-Jafar-Abadi. 2014. Estimation of genetic parameters for lamb survival in Zandi sheep breeds using animal, sire and threshold models. Animal Science Journal (Pajouhesh & Sazandegi), 105: 27-34. (In Persian)
    16. Moraes, A. B. D., C. H. E. C. Poli, V. Fischer, N. M. Fajardo, M. F. Aita, and G. C. D. Porciuncula. 2016. Ewe maternal behavior score to estimate lamb survival and performance during lactation. Acta Scientiarum. Animal Sciences, 38 (3): 327-332.‏
    17. Riggio, V., R. Finocchiaro, S. Bishop. 2008. Genetic parameters for early lamb survival and growth in Scottish Blackface sheep. Journal of Animal Science, 86: 1758-1764.
    18. Safari, E., N. M. Fogrty, and A. R. Gilmour. 2005. A review of genetic parameter estimates for wool, growth, meat and reproduction traits in sheep. Livestock Production Science, 92: 271-289.
    19. Saghi, D. A. 2015. The effects of genetic and non genetic factors on survival and longevity of Kourdi lambs from birth to yearling. Animal science Journal (pajouhesh & Sazandegi), 112: 68-75. (In Persian)
    20. Seasakhti, D., M. Vatankhah, H. R. Merzaei, and M. Yousef Ellahi. 2010. Estimates of some environmental factors and genetic parameters on Lori-Bakhtiari lambs survival. Animal Sciences (pajouhesh & Sazandegi), 84: 66-70. (In Persian)
    21. Southey, B., S. L. Rodriguez-Zas, and K. Leymaster. 2001. Survival analysis of lambmortality in a terminal sire composite population. Journal of Animal Science, 79: 2298-2306.
    22. Sawalha, R., J. Conington, S. Brotherstone, and B. Villanueva. 2007. Analyses of lamb survival of Scottish Blackface sheep. Animal, 1: 151-157.
    23. Vatankhah, M., M. A. Talebi, and H. Blair. 2016. Genetic analysis of Lori-Bakhtiari lamb survivalrate up to yearling age for autosomal and sex-linked. Small Ruminant Research, 136: 121-126.
    24. Vatankhah, M. 2013. Estimation of the genetic parameters for survival rate in Lori-Bakhtiari lambs using linear and Weibull proportional hazard models. Journal of Agricultural Science and Technology (JAST), 15(6):1133-1143.33. Vatankhah, M., and M. A. Talebi. 2009. Genetic and non-genetic factors affecting mortality in Lori-Bakhtiari lambs. Asian-Australasian Journal of Animal Sciences, 22: 459-464.
    25. Wang, T., R. L. Fernando, and S. D. Kachman. 2002. Matvec User’s Guide. Version 1.03. Available: http://statistics.unl.edu/faculty/steve/software/matvec/.

    35. Yazdi, M. H., P. M. Visscher, V. Ducrocq, and R. Thompson. 2002. Heritability, reliability of genetic evaluations and response to selection in proportional hazard models. Journal of Dairy Science, 85: 1563-1577.

CAPTCHA Image