Estimation of genetic parameters and breeding values for growth traits using random regression model in Landrace × desi crossbred pigs
393 / 250
Keywords:
Breeding value, Crossbred pigs, Genetic parameters, Growth traits, Random regression modelAbstract
The experiment was conducted to estimate genetic parameters and breeding value of pre-weaning and postweaning growth traits in Landrace × Desi (indigenous) crossbred pigs. Random regression model (RRM) with fixed effects, random effects of direct additive, maternal genetic, maternal permanent environmental and individual permanent environmental effects and a heterogeneous residual variance structure over growth measurement at different time point was applied to estimate the genetic parameters. Among different order of Legendre polynomial fitted, RRM with heterogeneous error variance (RRM–HET) with fourth order fit for direct genetic, maternal genetic and maternal permanent environmental effect and third order fit for individual permanent environmental effect was found as the best model. The direct genetic heritability estimates were 0.299±0.021 at birth, which was low (0.011±0.006) at W12 and high at W36 (0.582±0.03). The first eigenvalue accounted for more than 98% variation in body weight and the corresponding trajectories had positive values displaying almost linear pattern till weaning and then increased rapidly in post weaning age. This showed that the selection of pigs on the basis of mean body weight at early age can also result in higher body weight in post-weaning stages. The average breeding value for the birth weight, W8 and W32 was 0.951 kg, 10.162 kg and 49.598 kg, respectively. The 42.86% of sire population at W12 and 56.76% at W24 had breeding value higher than the population's mean breeding value indicating existence of genetic variation and thus the scope for increasing the selection pressure.
Downloads
References
Arango J A, Cundiff L V and Van Vleck L D. 2004. Covariance functions and random regression models for cow weight in beef cattle. Journal of Animal Science 82: 54–67. DOI: https://doi.org/10.2527/2004.82154x
Chimonyo M, Dzama K and Bhebhe E. 2008. Genetic determination of mothering ability and piglet growth in indigenous Mukota sows of Zimbabwe. Livestock Science 113: 74–80. DOI: https://doi.org/10.1016/j.livsci.2007.02.014
Fischer T M, Van der Werf J, Banks R G and Ball A J. 2004. Description of lamb growth using random regression on field DOI: https://doi.org/10.1016/j.livprodsci.2004.02.004
data. Livestock Production Sciences 89: 175–85.
Ghafouri–Kesbi G F, Eskandarinasab M and Shahir M H. 2008. Estimation of direct and maternal effects on body weight in Mehraban sheep using random regression models. Archiv fur Tierzucht Dummerstorf 51: 235–46. DOI: https://doi.org/10.5194/aab-51-235-2008
Huynh–Tran V H, Gilbert H and David, I. 2017. Genetic structured antedependence and random regression models applied to the longitudinal feed conversion ratio in growing large white pigs. Journal of Animal Science 95(11): 4752–63. DOI: https://doi.org/10.2527/jas2017.1864
Jamrozik J, Schaeffer L R and Dekkers J C M. 1997. Genetic evaluation of dairy cattle using test day yields and random regression model. Journal of Dairy Science 80: 1217–26. DOI: https://doi.org/10.3168/jds.S0022-0302(97)76050-8
Kirkpatrick M, Lofsvold D and Bulmer M. 1990. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124: 979–93. DOI: https://doi.org/10.1093/genetics/124.4.979
Koivula M, Sevo n–Aimonen M L, Stranden I, Matilainen K, Serenius T and Stalder K J. 2008. Genetic (co)variances and breeding value estimation of Gompertz growth curve parameters in Finnish Yorkshire boars, gilts and barrows. Journal of Animal Breeding and Genetics 125: 168–75. DOI: https://doi.org/10.1111/j.1439-0388.2008.00726.x
Meyer K. 1999. Estimates of genetic and phenotypic covariance functions for post-weaning growth and mature weight of beef cows. Journal of Animal Breeding and Genetics 116: 181– 205. DOI: https://doi.org/10.1046/j.1439-0388.1999.00193.x
Meyer K. 2005. Random regression analyses using B–splines to model growth of australian angus cattle. Genetic Selection Evolution 37: 473–500. DOI: https://doi.org/10.1186/1297-9686-37-6-473
Meyer K. 2007. WOMBAT–A tool for mixed model analyses in quantitative genetics by REML. Journal of Zhejiang University Science B 8: 815–21. DOI: https://doi.org/10.1631/jzus.2007.B0815
Molina A, Menendez–Buxadera A, Valera M and Serradilla J M. 2007. Random regression model of growth during the first three months of age in Spanish Merino sheep. Journal of Animal Science 85: 2830–39. DOI: https://doi.org/10.2527/jas.2006-647
Mondal S K, Kumar A, Dubey P P, Sivamani B and Dutt T. 2014. Estimation of variance and genetic parameters for pre–weaning weights of individual Landrace × Desi synthetic piglets. Journal of Applied Animal Research 42(3): 338–44. DOI: https://doi.org/10.1080/09712119.2013.875901
Prakash V, Gupta A K, Singh M, Ambhore G S, Singh A and Gandhi R S. 2017. Random regression test–day milk yield models as a suitable alternative to the traditional 305–day lactation model for genetic evaluation of Sahiwal cattle. Indian Journal of Animal Sciences 87(3): 340–44.
Schaeffer L R and Dekkers J C M. 1994. Random regressions in animal models for test–day production in dairy cattle. Proceedings of 5th World Congress of Genetics Applied Livestock Production 5: 443–46.
Solane F X, Grandinson K, Rydhmer L, Stern S, Anderson K and Lundeheim N. 2004. Direct and maternal influences on the early growth, fattening performance, and carcass traits of pigs. Livestock Science 88: 199–212. DOI: https://doi.org/10.1016/j.livprodsci.2003.12.002
Venkataramanan R. 2016. Random regressions to model growth in Nilagiri sheep of South India. Small Ruminant Research 144: 242–47. DOI: https://doi.org/10.1016/j.smallrumres.2016.10.002
Wetten M, Odegard J, Vangen O and Meuwissen T H E. 2012. Simultaneous estimation of daily weight and feed intake curves for growing pigs by random regression. Animal 6(3): 433–39. DOI: https://doi.org/10.1017/S1751731111001832
Downloads
Submitted
Published
Issue
Section
License
Copyright (c) 2019 The Indian Journal of Animal Sciences

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright of the articles published in The Indian Journal of Animal Sciences is vested with the Indian Council of Agricultural Research, which reserves the right to enter into any agreement with any organization in India or abroad, for reprography, photocopying, storage and dissemination of information. The Council has no objection to using the material, provided the information is not being utilized for commercial purposes and wherever the information is being used, proper credit is given to ICAR.