Abstract
Analyses were carried out for the estimation of (co)variance components and genetic parameters for birth weight (BWT), 6-month weight (6WT), 12-month weight (12WT), 18-month weight (18WT), 24-month weight (24WT), 30-month weight (30WT), 36-month weight (36WT), weight at first service (WFS), and weight at first calving(WFC) in Sahiwal cattle. Data for 802 lifetime records (raw data) were collected over a period of 30 years (1990–2019) for various growth traits in the herd for Sahiwal cows maintained at the livestock farm unit of ICAR-NDRI Karnal, Haryana, India. Bayesian estimates using the multi-trait Gibbs sampling animal model approach were calculated in the present study. Total heritability for BWT, 6WT, 12WT, 18WT, 24WT, 30WT, 36WT, WFS, and WFC by Bayesian modeling was estimated as 0.22 ± 0.0052, 0.47 ± 0.0037, 0.30 ± 0.0025, 0.65 ± 0.0021, 0.32 ± 0.0039, 0.33 ± 0.0027, 0.39 ± 0.0031, 0.49 ± 0.0020, and 0.57 ± 0.0023, respectively, along with its Monte Carlo error in Sahiwal cattle. Direct genetic covariances between body weight traits were ranging from − 2762.5 for 18WT and WFC to 4739.6 between WFS and WFC. Environmental covariances were ranging from − 169.98 for 30WT and 36WT to 4539.4 between WFS and WFC. Family relationships as well as the existing interaction effects between two or more traits in opposite direction effect lead to negative estimates for genetic covariances between some of the combinations with various growth traits. Although most of the estimates for posteriori were somewhat skewed, the marginalization effect enabled them to fit into the Gaussian distribution, by comparing the mean, mode, and median with each other. Results suggest that genetic progress through growth traits can be achieved if the selection is carried out for highly heritable 18-month weight as well as for the selection of pubertal and fertility traits, viz., 24WT, 30WT, 36WT, WFS, and WFC with a balanced feeding and optimum management.
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The data generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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References
Andersen, H. and Plum, M., 1965. Gestation length and birth weight in cattle and buffaloes: a review. Journal of Dairy Science, 48(9), 1224-1235.
Aristodemou, K., 2014. New regression methods for measures of central tendency (Doctoral dissertation).
Beaumont, M. A. and Rannala, B., 2004. The Bayesian revolution in genetics. Nature Reviews Genetics, 5(4), 251-261.
Bhatnagar, D. S., Nagarcenkar, R., Gumani, M. and Sharma, R. C., 1974. Crossbreeding of Zebu (Sahiwal and Red Sindhi) with Brown Swiss. Annual Report, NDRI, Karnal, Haryana.
Blasco, A., Sorensen, D. and Bidanel, J. P., 1998. A Bayesian analysis of genetic parameters and selection response for litter size components in pigs. Genetics.149:301–306.
Bourdon, R. M. and Brinks, J. S., 1982. Genetic, environmental and phenotypic relationships among gestation length, birth weight, growth traits and age at first calving in beef cattle. Journal of Animal Science, 55(3), 543-553.
Brownlee, J., 2019. A gentle introduction to cross-entropy for machine learning. Machine Learning Mastery. https://machinelearningmastery.com/cross-entropy-for-machine-learning.
Cowan, G., 2006. Computing and Statistical Data Analysis. University of London Lectures.
Faria, C. U. D., Magnabosco, C. D. U., Reyes, A. D. L., Lôbo, R. B., Bezerra, L. A. F. and Sainz, R. D., 2007. Bayesian inference in a quantitative genetic study of growth traits in Nelore cattle (Bos indicus). Genet. Mol. Bio.30(3), 545-551.
Faria, C. U., Guimarães, P. H. R., Gomes, M. M. A., de Medeiros Miguel, J., de Oliveira Lemos, L., Pereira, L. S., and Lôbo, R. B., 2019. Bayesian analysis on the growth traits in nelore mocho cattle raised in cerrado biome. Vet. Not., 95–111.
Gandhi, R. S., and Kumar, A., 2014. Genetic analysis of growth performance of Sahiwal cows. Indian J. Anim. Res, 48(3), 214-216.
Garcia-Cortés, L. A., Rico, M. and Groeneveld, E., 1998. Using coupling with the Gibbs sampler to assess convergence in animal models, Journal of Animal Science, 76:441-447.
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(6):721–741.
Geweke, J., 1992. Evaluating the accuracy of sampling-based approaches to the calculations of posterior moments, Bayesian statistics, 4:641-649.
Geyer, C. J., 1992. Practical Markov chain Monte Carlo, Statistical Science, 7:473–483.
Gianola, D. and Tyler, W. J., 1974. Influences on birth weight and gestation period of Holstein-Friesian cattle. Journal of Dairy Science, 57(2), 235-240.
Gianola, D., Rodriguez-Zaz, S. and Shook, G.E., 1994. The Gibbs sampler in the animal model: A primer. SéminaireModele Animal, INRA, La Collesur Loup, 47-56.
Greenwood, P. L., Cafe, L. M., Hearnshaw, H., Hennessy, D. W., Thompson, J. M. and Morris, S. G., 2006. Long-term consequences of birth weight and growth to weaning on carcass, yield and beef quality characteristics of Piedmontese-and Wagyu-sired cattle. Australian Journal of Experimental Agriculture, 46(2), 257-269.
Harvey, W. R., 1990. Mixed model least squares and Maximum likelihood computer program(LSMLMW and MIXMDL PC-2 version). Purdue University, West Lafayette, Indiana, USA.
Henderson, C. R., 1975. Comparison of alternative sire evaluation methods, Journal of Animal Science, 41:760-773.
Henderson, C. R., 1984. Applications of linear models in animal breeding (University of Guelph Press, Guelph, Ontario).
Holtmann, J., Koch, T., Lochner, K. and Eid, M., 2016. A comparison of ML, WLSMV, and Bayesian methods for multilevel structural equation models in small samples: A simulation study. Multivariate behavioral research, 51(5), pp.661-680.
Ilatsia, E. D., Migose, S. A., Muhuyi, W. B.andKahi, A. K., 2011. Sahiwal cattle in semi-arid Kenya: Genetic aspects of growth and survival traits and their relationship to milk production and fertility. Tropical Animal Health and Production, 43(8):1575-1582.
Jensen, J. Wang, C. S., Sorensen, D. A. and Gianola, D., 1994. Bayesian-inference on variance and covariance components for traits influenced by maternal and direct genetic-effects, using the Gibbs sampler. ActaAgriculturaeScandinavica - Section A: Animal Science. 44:193– 201.
Khan, U.N., Dahlin, A., Zafar, A.H., Saleem, M., Chaundhry, M.A. and Philipsson, J., 1999. Sahiwal cattle in Pakistan: genetic and environmental causes of variation in body weight and reproduction and relationship to milk production. Animal Science, 68:97– 108.
Kour, A., Deb, S.M., Nayee, N., Niranjan, S.K., Raina, V.S., Mukherjee, A., Gupta, I.D. and Patil, C.S., 2021. Novel insights into genome-wide associations in Bos indicus reveal genetic linkages between fertility and growth. Animal Biotechnology, 1–17.
Kriese, L. A., Bertrand, J. K. and Benyshek, L. L., 1991. Age adjustment factors, heritabilities and genetic correlations for scrotal circumference and related growth traits in Hereford and Brangus bulls, Journal of Animal Science, 69:478−489.
Lee, S. Y. and Song, X. Y., 2004. Evaluation of the Bayesian and maximum likelihood approaches in analyzing structural equation models with small sample sizes. Multivariate behavioral research, 39(4), pp.653-686.
Lopes, F. B., Ferreira, J. L., Lôbo, R. B., and Rosa, G. J. M., 2017. Bayesian analyses of genetic parameters for growth traits in Nellore cattle raised on pasture. Genetics and Molecular Research, 16(3), 1-10.
Manoj, M. 2009. Evolving multi-trait selection criteria using body weight and first lactation traits in Sahiwal cattle (M.V.Sc. Thesis, NDRI, Karnal, Haryana, India).
Manoj, M., Gandhi, R.S., Raja, T.V., Verma, A., Singh, A., Sachdeva, G.K. and Kumar, A., 2014. Genetic parameters of body weights at different ages in Sahiwal heifers. Indian Journal of Animal Research, 48(3):217-220.
Manzi, M., Rydhmer, L., Ntawubizi, M., Karege, C. and Strandberg, E., 2018. Growth traits of crossbreds of Ankole with Brown Swiss, Holstein Friesian, Jersey, and Sahiwal cattle in Rwanda. Tropical animal health and production, 50(4), 825-830.
Misztal, I., Tsuruta, S., Lourenco, D., Aguilar, I., Legarra, A. and Vitezica, Z., 2014. Manual for BLUPF90 family of programs. Athens:University of Georgia, 199.
Misztal, I. and Perez-Enciso, M., 1998. FSPAK90A Fortran 90 interface to sparse-matrix package FSPAK with dynamic memory allocation and sparse matrix structure, Proceedings of 6th World Congress on Genetics Applied to Livestock Production,27:467–468.
Mulindwa, H. E., Kifaro, G. C. and Ssewannyana, E., 2012. Comparative pre-weaning growth of zebu cattle and their crosses with Sahiwal and Boran. Uganda Journal of Agricultural Sciences, 13(1), 35-44.
NBAGR, 2020. Registered breeds. National Bureau of Animal Genetic Resources. http://www.nbagr.res.in/registeredbreed.html (Accessed on 17th march 2020).
Nobre, P. R. C., Lopes, P. S., Torres, R. A., Silva, L. O. C., Regazzi, A. J., Torres Júnior, R. A. A., and Misztal, I., 2003. Analyses of growth curves of Nellore cattle by Bayesian method via Gibbs sampling. Arquivo Brasileiro de Medicina Veterinária e Zootecnia, 55, 480-490.
Pathak, P., Mukherjee, A. and Mumtaz, S., 2020. Seasonal and periodical rhythmicity of economic traits and various genetic parameter analysis in sahiwal cows under sub-tropical environment. Journal of Animal Research, 10(5), pp.791-798.
Reynolds, W. L., DeRouen, T. M., Moin, S. and Koonce, K. L., 1980. Factors influencing gestation length, birth weight and calf survival of Angus, Zebu and Zebu cross beef cattle. Journal of animal science, 51(4), 860-867.
Selvan, A. S., Tantia, M. S., Kumaresan, A., Kumar, A., Kumar, D. R., Karuthadurai, T. and Upadhyay, A., 2018. Phenotypic and genetic parameters estimation for birth weight in Zebu and crossbred calves born under organized farm conditions in India. International Journal of Livestock Research, 8(06), 48-58.
Sewalem, A. and Johansson, K., 2000. Egg weight and reproduction traits in laying hens: Estimation of direct and maternal genetic effects using Bayesian approach via Gibbs sampling, Animal Sciences.70(1), 9-16.
Singh, M., Lathwal, S. S., Prasad, K. C., Dey, D., Gupta, A. and Saini, M., 2019. Availability of feed sources and nutritional status of Hariana cattle in different seasons in the breeding tract. Biological Rhythm Research, 52(6): 862-868. https://doi.org/10.1080/09291016.2019.1607222
Sorensen, D. A., Andersen, S., Gianola, D. and Korsgaard, I., 1995. Bayesian inference in threshold models using Gibbs sampling. Genetics Selection Evolution, 27(3), 229-249.
Spiegelhalter, D., Best, N. G., Carlin, B. P. and van der Linde, A., 2003. Bayesian measures of model complexity and fit. Quality control and applied statistics, 48(4):431-432.
Tsuruta, S. and Misztal, I., 2006. THRGIBBSF90 for estimation of variance components with threshold and linear models. Proceedings of 8th World Congress on Genetics Applied to Livestock Production Belo Horizonte, Brazil. CD-ROM communication. 27–31.
Van De Schoot, R., Broere, J. J., Perryck, K. H., Zondervan-Zwijnenburg, M. and Van Loey, N. E., 2015. Analyzing small data sets using Bayesian estimation: The case of posttraumatic stress symptoms following mechanical ventilation in burn survivors. European journal of psychotraumatology, 6(1), p.25216.
Van Tassell, C. P. and Van Vleck, L. D., 1996. Multiple-trait Gibbs sampler for animal models: Flexible programs for Bayesian and likelihood-based (co)variance component inference, Journal of Animal Science, 74:2586-2597.
Wakchaure, R.S. and Meena, R., 2010. Factors Affecting, birth weight, age and weight at first calving in Sahiwal cattle. Indian Journal of Animal Research, 44(3):173-177.
Williams, T. and Kelley, C., 2004. GNUPLOT version 4.0: An Interactive Plotting Program.
Yazdi, M. H., Johansson, K., Gates, P., Näsholm, A., Jorjani, H. and Liljedahl, L. E., 1999. Bayesian analysis of birth weight and litter size in Baluchi sheep using Gibbs sampling, Journal of animal science, 77(3):533-540.
Yu, J. and Qin, S. J., 2008. Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models. AIChE Journal, 54(7), 1811-1829.
Acknowledgements
The authors acknowledge the director, ICAR-NDRI, for constant moral support and providing all the facilities to carry out this work; the Livestock Research Station for providing first-hand access to recorded information; and ICAR-NDRI, Karnal, Haryana, for financial support to the first author for this research work.
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The authors thankfully acknowledge the research grant received from ICAR-NDRI, Karnal, Haryana, India, for carrying out this study.
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NY carried out the research work and finalized the manuscript; SM and SI helped with data analysis; GG helped in manuscript drafting, visualization, and manuscript preparation; and AM conceptualized the study. SM and AM edited the manuscript. All authors approved the manuscript.
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Yadav, N., Illa, S.K., Mukherjee, S. et al. Bayesian estimates for genetic and phenotypic parameters of growth traits in Sahiwal cattle. Trop Anim Health Prod 55, 30 (2023). https://doi.org/10.1007/s11250-022-03446-7
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DOI: https://doi.org/10.1007/s11250-022-03446-7