Estimation of Heritability of Karan Fries Cattle using Bayesian Procedure
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Authors
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Himadri Shekhar Roy
ICAR-Indian Agricultural Statistics Research Institute
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Amrit Kumar Paul
Principal Scientist
Division of Statistical Genetics
ICAR-IASRI, Pusa, New Delhi
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Ranjit Kumar Paul
Dr. Ranjit Kumar Paul, FNAAS
Scientist, ICAR-IASRI
IASRI, Library Avenue, Pusa, New Delhi-110 012
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Ramesh Kumar Singh
M.V.Sc. Ph.D. (AGB)
Asstt. Prof. Cum Jr. Scientist
Deptt. of Animal Genetics and Breeding
Bihar Veterinary College, Patna
Mob. No. 91-8986157240
Email ID: Ramesh.kumarvet@gmail.com
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MD `YEASIN
ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
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Prakash Kumar
Scientist
Division of Statistical Genetics
ICAR-IASRI, Pusa, New Delhi
Keywords:
Bayesian, Credible Interval, Heidelberg Stationarity Test, Heritability, Karan Fries, Linear Mixed ModelAbstract
  The Bayesian model was applied for analyzing the first lactation in Karan Fries cattle. First lactation data of production (305-day or less milk yield and daily milk yield) were collected from the history-cum pedigree sheet and daily milk yield registers of the division of Dairy Cattle Breeding (DCB), National Dairy Research Institute (NDRI), Karnal. In the Bayesian paradigm, MCMC methods are applied to solve complex mathematical problems to estimate a large number of unknown parameters. Assuming linear mixed model and using the different prior set up, diagnostic of MCMC (Markov Chain Monte Carlo) was carried out graphically as well as by Heidelberg stationarity test. Variance estimates of the random effects (VA) and residual variance estimation (VR) and Variance estimate location effects i.e. fixed effects were calculated along with effective sample size. Finally, heritability (h2) estimates for First lactation 305 days or less milk yield (FL305DMY) was estimated along with its credible interval.
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Ahlinder J and Sillanpää M J. 2013. Rapid Bayesian inference of heritability in animal models without convergence problems. Methods in Ecology and Evolution 4(11): 1037–46. DOI: https://doi.org/10.1111/2041-210X.12113
Basic Animal Husbandry Statistics. DAHD&F, GoI. Blasco A. 2001. The Bayesian controversy in animal breeding. Journal of Animal Science 79(8): 2023–46. DOI: https://doi.org/10.2527/2001.7982023x
Breslow N E and Clayton D G. 1993. Approximate inference in generalized linear mixed models. Journal of the American statistical Association 88(421): 9–25. DOI: https://doi.org/10.1080/01621459.1993.10594284
Brown H and Prescott R. 1999. Applied Mixed Models in Medicine. John Wiley & Sons, New York.
Chib S and Greenberg E. 1995. Understanding the metropolis-hastings algorithm. The American Statistician 49(4): 327–35. DOI: https://doi.org/10.1080/00031305.1995.10476177
Demidenko E. 2004. Mixed Models: Theory and Application. John Wiley & Sons, New Jersey. DOI: https://doi.org/10.1002/0471728438
Fong Y, Rue H, and Wakefield J. 2010. Bayesian inference for generalized linear mixed models. Biostatistics 11(3): 397–412. DOI: https://doi.org/10.1093/biostatistics/kxp053
Gianola D and Fernando R L. 1986. Bayesian methods in animal breeding theory. Journal of Animal Science 63(1): 217–44. DOI: https://doi.org/10.2527/jas1986.631217x
Gianola D and Foulley J L. 1990. Variance estimation from integrated likelihoods (VEIL). Genetics Selection Evolution 22(4): 1–15. DOI: https://doi.org/10.1186/1297-9686-22-4-403
Gianola D, Im S and Macedo F W. 1990. A framework for prediction of breeding value, pp. 210-238. Advances in Statistical Methods for Genetic Improvement of Livestock. Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-642-74487-7_11
Geman S and Geman D. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6: 721–41. DOI: https://doi.org/10.1109/TPAMI.1984.4767596
Gilks W, Richardson S and Spiegelhalter D. 1995. Markov Chain Monte Carlo in Practice. Interdisciplinary Statistics. Chapman & amp. DOI: https://doi.org/10.1201/b14835
Hadfield J D. 2010. MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. Journal of Statistical Software 33(2): 1–22. DOI: https://doi.org/10.18637/jss.v033.i02
Henderson C R. 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 423–47. DOI: https://doi.org/10.2307/2529430
Holand A M, Steinsland I, Martino S and Jensen H. 2013. Animal models and integrated nested Laplace approximations. G3: Genes, Genomes, Genetics 3(8): 1241–51. DOI: https://doi.org/10.1534/g3.113.006700
Kaam V. 1997. GIBANAL-Analyzing program for Markov Chain Monte Carlo sequences.
Mathew B, Bauer A M, Koistinen P, Reetz T C, Léon J and Sillanpää M J. 2012. Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters. Heredity 109(4): 235–45. DOI: https://doi.org/10.1038/hdy.2012.35
Meyer K. 2007. WOMBAT—A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University Science B 8(11): 815–21. DOI: https://doi.org/10.1631/jzus.2007.B0815
Nustad H E, Page C M, Reiner A H, Zucknick M and LeBlanc M. 2018. A Bayesian mixed modeling approach for estimating heritability. BMC Proceedings 12: 117–22. DOI: https://doi.org/10.1186/s12919-018-0131-z
Singh A P, Chakravarty A K, Mir M A and Arya A. 2020. Genetic parameters and animal model evaluation of first lactation 305- d milk yield and energy traits in Karan Fries cattle. Indian Journal of Animal Research 54: 405–08. DOI: https://doi.org/10.18805/ijar.B-3786
Singh A, Singh A, Singh M, Prakash V, Ambhore G S, Sahoo S K and Dash S. 2016. Estimation of genetic parameters for first lactation monthly test-day milk yields using random regression test day model in Karan Fries cattle. Asian-Australasian Journal of Animal Sciences 29(6): 775–81. DOI: https://doi.org/10.5713/ajas.15.0643
Singh R K. 2013. ‘Genetic evaluation of Karan-Fries sires using multiple trait models.’ Ph.D. Thesis, National Dairy Research Institute (Deemed University), Karnal, India.
Sorensen D and Gianola D. 2007. Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics. Springer Science and Business Media.
Wang C S, Rutledge J J and Gianola D. 1993. Marginal inferences about variance components in a mixed linear model using Gibbs sampling. Genetics Selection Evolution 25(1): 1–22. DOI: https://doi.org/10.1186/1297-9686-25-1-41
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