آنالیز ژنتیکی صفات تولیدی گاوهای هلشتاین منطقه ی مدیترانه ای ایران به روش رگرسیون تصادفی و مدل دام

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

نویسندگان

1 زنجان

2 دانشگاه زنجان

3 ارومیه

4 گیلان

چکیده

در این مطالعه به منظور برآورد پارامترهای ژنتیکی و فنوتیپی و نیز روند فنوتیپی و ژنتیکی صفات تولیدی گاوهای هلشتاین منطقه ی مدیترانه ای ایران(استانهای اردبیل، همدان، زنجان، آدربایجان شرقی و غربی) از اطلاعات مربوط به8808 گله ی گاو هلشتاین، شامل 105118 رکورد روزآزمون استفاده شد. ویرایش لازم روی تمامی رکورد ها با استفاده از نرم افزارهای Access2010 و Fox Pro 8.0 انجام گرفت. برازش اثرات ثابت موثر برروی رکوردهای روزآزمون با استفاده از رویه GLM نرم افزار SAS انجام گرفت که تاثیر سال- فصل، گله- تاریخِ روزآزمون و استان روی داده های روزآزمون و برای صفات مقدار شیر، درصد چربی و پروتین شیر معنی دار بود. همچنین اثر خطی و درجه دوم سن زایش به عنوان عامل کمکی بر روی هر سه صفت معنی دار بود. برای برآورد روند ژنتیکی و فنوتیپی، از نرم افزار SAS و رویه ی GLM5 استفاده شد. میزان وراثت پذیری برای صفات مقدار شیر، در صد چربی و درصد پروتئین با استفاده از رکودهای روزآزمون به ترتیب، 126/0، 02/0 و059/0 برآورد شد. نتایج نشان می دهند که دقت حاصل از رکورد های روزآزمون با استفاده از روش رگرسیون تصادفی بالاتر از رکودهای تصحیح شده برای 305 روز است و لذا در این مطالعه بر نتایج حاصل از رکوردهای روزآزمون و روش رگرسیون تصادفی تاکید می شود. نتایج حاصل از مدل رگرسیون تصادفی و رکوردهای روزآزمون نشان می دهند که وراثت پذیری تولید شیر، بیشترین و وراثت پذیری درصد چربی کمترین مقدار را به خود اختصاص داده و وراثت پذیری درصد پروتئین در حد وسط قرار گرفته است.

کلیدواژه‌ها


عنوان مقاله [English]

Genetic analysis of production traits of Holstein cows in the Mediterranean climate of Iran using random regression and animal model

نویسندگان [English]

  • Mohammad Jabarzadeh Ivrigh 1
  • Morad Pasha Eskandarinasab 2
  • fatemeh purbayramian 3
  • hassan khanzade 4
1 Zanjan University
2 zanjan
3 Uromia University
4 Guilan University
چکیده [English]

Introduction Productive traits such as milk production and fat and protein percentage have economic importance in the livestock industry. Accurate prediction of breeding value of animals is one of the best tools available for maximizing response to selection program. It is a fact that the main objective of the breeding program, is to achieve the maximum economic benefit. For breeders of dairy cattle, milk, fat, and protein are the main sources of income that are the most important traits in the firm goals. For evaluating the dairy cattle based on these traits (milk production, fat, and protein percentage), prediction of breeding values is essential. The present study was performed in order to estimate the genetic and phenotypic parameters and genetic and phenotypic trends of production traits in the Mediterranean climate of Iran (including; Ardebil, Hamadan, East and West Azerbaijan and Zanjan provinces) using 105118 records for Test Day and 30985 records for 305-day lactation records Related 8808 Herd of first lactation Holstein Cattle calving between 2003 to 2013. All records collected by Animal Breeding Center of Iran.
Materials and Methods Records were edited using Fox pro 8.0 and ACCESS 2010 software and the wrong and unusual records were removed from the dataset. All analyses were performed using the RR (random regression) routine of the WOMBAT software package using AIREML algorithm on Linux operation system. Test day records were analyzed with the following random regression model (RRM):

Where; Pk; kth fixed effect of province, YSl; lth fixed effect of year-season of calving, Yklimnptv; test day record i obtained at dimt of cow p calved at the nth age group in herd-test day m, HTDm; fixed effect of mth herd-test date, Cf; The fth fixed regression coefficient for calving age, agen; The nth calving age, k; The order of fit for fixed regression coefficients (k=4), βr; The rth fixed regression coefficient, ka; The order of fit for additive genetic random regression coefficients, kp; The order of fit for permanent environmental random regression coefficients, αpr; The rth random regression coefficient of additive genetic value for pth cow, γpr; The rth random regression coefficient of permanent environmental effect for pth cow, ∅r)dimt (; The rth coefficient of Legendre polynomials evaluated at days in milk t, emnptv; is The residual effect.
Results and Discussion The heritability of milk yield, fat percentage, and protein percentage during days 5 to 305 of lactation were 0.07 to 0.2, 0.019 to 0.041, and 0.019 to 0.217, respectively. The repeatability of milk yield, fat percentage, and protein percentage during this period of lactation were 0.65, 0.09, and 0.16, respectively. The study of production traits suggested that during the last 10 years in the Mediterranean climate of Iran, Genetic trend of Milk production was positive, but the genetic trend of fat and protein percentage, negative or zero. Figures 2 and 3 clearly indicate that using both types of records; test day records and 305-corrected records, genetic trend for milk production compared with fat and protein percentage was positive and increasing. Heritability of production traits in early lactation was low. The great influence of environment on animals and the negative energy balance are the reasons for the low heritability in this period (2). This amount was being increased and reaches its maximum in the second half of lactation. Increased heritability in the second half of lactation is a function of increasing additive genetic variance and sharp decline in the variance of permanent environment. Similar trends for the results of other studies were reported in the country (1, 14).
Conclusion Recent studies showed that the accuracy of test day records using random regression method was higher than 305 days lactation records. The results of random regression method and Test Day records showed that the heritability of milk production is the highest and heritability of fat percentage is. Result of this study showed that, during the last 10 years (from 2003 to 2013) in the Mediterranean climate of Iran, Genetic trend was positive in the amount of milk production, but genetic trend of fat and protein percentage, negative or near zero.

کلیدواژه‌ها [English]

  • Holstein cows
  • Mediterranean climate of Iran
  • Production traits
  • Random regression model
  • Univariate animal model
1- Abdullahpour, R., M. Moradi Shahrbabak., A. Nejati Javaremi., and R. Vaez Torshizi. 2010. Genetic analysis of daily milk, fat percentage and protein percentage of Iranian first lactation Holstein cattle. World Applied Science, 10: 1042-1046.
2- Beerda, B., W. Ouweltjes., L. B. Sebek., J. J. Windig., and R. F. Veerkamp. 2007. Effects of genotype by environment interactions on milk yield, energy balance andprotein balance. Journla of Dairy Science, 90: 219-228.
3- Dekkers, J. 2002. Models for genetic analysis of longitudinal data. Course Notes, Univesity of Guelph, Canada. (Sited in: The http://www.anslab. iastate.edu/ class/AnS657/RR_models_1.doc).
4- Kettunen, A., E. A. Montysaari., and J. Poso. 2000. Estimation of genetic parameters for dairy milk yield of primiparous Ayrshire cow by random regression test-day models. Livestock Production Science, 66:251-261.
5- Kham chin Moghadam, F. V., and H. Rezaei Pajand. 2009. Criticism climate classification for maximum daily precipitation using linear torques. J. of Islamic Azad University of Mashhad. No 2.( In Persian)
6- Lewis, R. M., and S. Brotherstone. 2002. A genetic evaluation of growth in sheep using random regression techniques. Animal Science, 74: 60-70
7- Liu, Y. X., J. Zhang., L. R. Schaeffer., R. G. Yang., and W. L. Zhang. 2006. Optimal random regression models for milk production in dairy cattle. Journal of Dairy Science, 89: 2233-2235.
8- Meyer, K. 1998. Estimating covariance functions for longitudinal data using a random regression model. Genetic Selection Evolution, 30: 221-240.
9- Meyer. K. 2000. Random regressions to model phenotypic variation in monthly weights of Australian beef cows. Livestock Production Science, 62: 19-38.
10- Meyer, K. 2011. Wombat A program for mixed model analyses by restrictec maximum likelihood. University of New England. pp: 102.
11- Razavi.S,M., M. Vatankhah., H. R. Mirzaei., and M. Rokouei. 2007. Estimation of genetic trends for production traits of Holstein cattle in Markazi province. Pajouhesh & Sazandegi No 77 pp: 55-62. ( In Persian)
12- Sesana, R. C., A. B. Bignardi., R. R. Borquis., L. El Faro., L. F. Baldi., L. G. Albuquerque., and H. Tonhati. 2010. Random regression models to estimate genetic parameters for test-day milk yield in Brazilian Murrah buffaloes. Journal of Animal Breedind Genetics, 127:369-379.
13- Seyyed Dokht, A., A. A. Aslami Nejad., and M. Tahmouraspour. 2012. Genetic analysis of milk yield of Holstein cows in Tehran using the Test Day Model. Journal of Animal Science, Research.No 2 PP:168-174.( In Persian)
14- Shadparvar, A. A., and M. S. Yazdanshenas. 2005. Genetic parameters of milk yield and milk fat percentage test-day records of Iranian Holstein cows. Asian-Aust. Journal of Animal Science, 18: 1231-1236.