Estimation of additive and non-additive genetic variance component for growth traits in Adani goats
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Non-additive genetic effects are important to increase the accuracy of estimating genetic parameters for growth traits. The aim of this study was to estimate genetic parameters and variance components, specially dominance and epistasis genetic effects, for growth traits (birth weight (BW), weaning weight (WW), 3 (W3), 6 (W6), 9 (W9), and 12 (W12) month weight) in Adani goats. Analyses were carried out using Bayesian method via Gibbs sampler animal model by fitting of 18 different models. All fixed effects (sex, type of birth, age of dam, and year) showed significant effects on BW, WW, W3, and W6, whereas the type of birth and age of dam were not significant on W9 and W12. With the best model, direct heritability estimates were 0.347, 0.178, 0.158, 0.359, 0.278, and 0.281 for BW, WW, W3, W6, W9, and W12 traits, respectively. Maternal permanent environmental effect was significant for BW and WW, but maternal genetic effect was significant only for W3. Dominance and epitasis effects were significant almost for all traits and as a proportion of phenotypic variance were ranged from 0.115 to 0.258 and 0.107 to 0.218, respectively. The range of accuracy of breeding values estimated for growth traits with appropriate evaluation models was from 0.521 to 0.652, 0.616 to 0.694, and 0.548 to 0.684 for the all animals, 10% of the best males and 50% of the best females, respectively. When dominance and epistasis effects added to models, the error variance was reduced and the accuracy of estimated breeding values increased. The accuracy of the best model showed a significant difference with the accuracy of other models (p < 0.01). The result of the present study suggests that non-additive genetic effects should be in genetic evaluation models for goat growth traits because of its effect on accuracy of estimated breeding values.
KeywordsHeritability Dominance Epistasis Accuracy Correlation
The authors would like to thank the Adani Goats Breeding Center and Agriculture-Jahad Organization of Bushehr province, Iran, for data collection.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Duenk, P., Calus, M.P., Wientjes, Y.C. and Bijma, P., 2017. Benefits of dominance over additive models for the estimation of average effects in the presence of dominance. G3: Genes, Genomes, Genetics, 7(10), pp.3405–3414.Google Scholar
- El-Moghazy, M.M., Metavi, H.R., Faid-Allah, E. and El-Raghi, A.A., 2015. Genetic and non genetic factors affecting body weight traits in Zaraibi goat in Egypt, Journal of Agricultural Research Kafr El-Shaikh Univesity, 41(1): 27–40.Google Scholar
- Heidaritabar, M., Wolc, A., Arango, J., Zeng, J., Settar, P., Fulton, J.E., O'Sullivan, N.P., Bastiaansen, J.W., Fernando, R.L., Garrick, D.J. and Dekkers, J.C., 2016. Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers. Journal of Animal Breeding and Genetics, 133(5), pp.334–346.CrossRefGoogle Scholar
- Misztal, I., Tsuruta, S., Strabel, T., Auvray, B., Druet, T. and Lee, D.H., 2002, August. BLUPF90 and related programs (BGF90). In Proceedings of the 7th world congress on genetics applied to livestock production, 33, 743–744).Google Scholar
- Mrode, R.A., Thompson, R., 2005. Linear Models for the Prediction of Animal Breeding Values, CABI Pub.Google Scholar
- Sorensen, D., and Gianola, D., 2007. Likelihood, Bayesian, and MCMC methods in quantitative genetics. Springer Science & Business Media.Google Scholar
- Sun, C., Van Raden, P.M., Cole, J.B., O'Connell, J.R., 2014. Improvement of prediction ability for genomic selection of dairy cattle by including dominance effects. PloS one, 9, 1–18.Google Scholar
- Varona, L., Legarra, A., Toro, M.A. and Vitezica, Z.G., 2018. Non-additive effects in genomic selection. Frontiers in Genetics, 9, p.78.Google Scholar
- Willam, A., Nitter, G., Bartenschlager, H., K., K., E., N., Graser, H.U., 2008. Z P L A N:Manual for a PC-Program to Optimize Livestock Selection Schemes. Manual Version.Google Scholar