Advertisement

Estimation of additive and non-additive genetic variance component for growth traits in Adani goats

  • Seyed Abu Taleb Sadeghi
  • Mohammad RokoueiEmail author
  • Mehdi Vafaye Valleh
  • Mokhtar Ali Abbasi
  • Hadi Faraji-Arough
Regular Articles
  • 40 Downloads

Abstract

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.

Keywords

Heritability Dominance Epistasis Accuracy Correlation 

Notes

Acknowledgements

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.

References

  1. Al-Saef, A.M., 2013. Genetic and phenotypic parameters of body weights in Saudi Aradi goat and their crosses with Syrian Damascus goat. Small Ruminant Research, 112, 35–38.CrossRefGoogle Scholar
  2. Barbosa, L.T., Santos, G.D.B., Muniz, E.N., Azevedo, H.C. and Fagundes, J.L., 2015. Genetic parameters for growth traits of santa ines sheep using Gibbs sampling. Revista Caatinga, 28(4), pp.211–216.CrossRefGoogle Scholar
  3. Bolormaa, S., Pryce, J.E., Zhang, Y., Reverter, A., Barendse, W., Hayes, B.J., Goddard, M.E., 2015. Non-additive genetic variation in growth, carcass and fertility traits of beef cattle. Genetics, Selection, Evolution : GSE 47, 26.CrossRefGoogle Scholar
  4. Boujenane, I., Diallo, I.T., 2017. Estimates of genetic parameters and genetic trends for pre-weaning growth traits in Sardi sheep. Small Ruminant Research, 146, 61–68.CrossRefGoogle Scholar
  5. Boujenane, I., Hazzab, A.E., 2008. Genetic parameters for direct and maternal effects on body weights of Draa goats. Small Ruminant Research, 80, 16–21.CrossRefGoogle Scholar
  6. Boujenane, I., Chikhi, A., Ibnelbachyr, M., Mouh, F., 2015. Estimation of genetic parameters and maternal effects for body weight at different ages in D’man sheep. Small Ruminant Research 130, 27–35.CrossRefGoogle Scholar
  7. Cemal, I., Karaman, E, Firat, M.Z., Yilmaz, O., Ata, N. and Karaca, O., 2017. Bayesian inference of genetic parameters for ultrasound scanning traits of Kivircik lambs. Animal, 11, 375.381.CrossRefGoogle Scholar
  8. 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
  9. Ebrahimi, K., Dashab, G.R., Faraji-Arough, H. and Rokouei, M., 2018. Estimation of additive and non-additive genetic variances of body weight in crossbreed populations of the Japanese quail. Poultry Science, 98(1), pp.46–55.CrossRefGoogle Scholar
  10. 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
  11. Ertl, J., Legarra, A., Vitezica, Z.G., Varona, L., Edel, C., Emmerling, R., Gotz, K.U., 2014. Genomic analysis of dominance effects on milk production and conformation traits in Fleckvieh cattle. Genetics, Selection, Evolution : GSE, 46, 40.CrossRefGoogle Scholar
  12. Gengler, N., Misztal, I., Bertrand, J.K., 1997. Relationships between estimates of heterosis and dominance variance for post-weaning gain in US Limousin cattle. Journal of Animal Science, 75, 149.CrossRefGoogle Scholar
  13. Gholizadeh, M., Ghafouri-Kesbi, F., 2015. Estimation of genetic parameters for growth-related traits and evaluating the results of a 27-year selection program in Baluchi sheep. Small Ruminant Research, 130, 8–14.CrossRefGoogle Scholar
  14. Gowane, G.R., Chopra, A., Prakash, V., Arora, A.L., 2011. Estimates of (co)variance components and genetic parameters for growth traits in Sirohi goat. Tropical Animal Health and Production, 43, 189–198.CrossRefGoogle Scholar
  15. 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
  16. Jembere, T., Dessie, T., Rischkowsky, B., Kebede, K., Okeyo, A.M., Haile, A., 2017. Meta-analysis of average estimates of genetic parameters for growth, reproduction and milk production traits in goats. Small Ruminant Research, 153, 71–80.CrossRefGoogle Scholar
  17. Lourenco, D.A., Fragomeni, B.O., Tsuruta, S., Aguilar, I., Zumbach, B., Hawken, R.J., Legarra, A., Misztal, I., 2015. Accuracy of estimated breeding values with genomic information on males, females, or both: an example on broiler chicken. Genetics Selection Evolution, 47, 56.CrossRefGoogle Scholar
  18. Menezes, L.M., Sousa, W.H., Cavalcanti-Filho, E.P., L.T. Gama, 2016. Genetic parameters for reproduction and growth traits in Boer goats in Brazil. Small Ruminant Research, 136, 247–256.CrossRefGoogle Scholar
  19. 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
  20. Mohammadi, H., Moradi Shahr Babak, M., Moradi Shahr Babak, H., 2012. Genetic parameter estimates for growth traits and prolificacy in Raeini Cashmere goats. Tropical Animal Health and Production, 44, 1213–1220.CrossRefGoogle Scholar
  21. Mrode, R.A., Thompson, R., 2005. Linear Models for the Prediction of Animal Breeding Values, CABI Pub.Google Scholar
  22. Nagy, I., Gorjanc, G., Curik, I., Farkas, J., Kiszlinger, H., Szendrő, Z., 2013. The contribution of dominance and inbreeding depression in estimating variance components for litter size in Pannon White rabbits. Journal of Animal Breeding and Genetics, 130, 303–311.CrossRefGoogle Scholar
  23. Rodriguez-Almeida, F.A., Van Vleck, L.D., Willham, R.L., Northcutt, S.L., 1995. Estimation of non-additive genetic variances in three synthetic lines of beef cattle using an animal model. Journal Animal Science, 73, 1002–1011.CrossRefGoogle Scholar
  24. Roy, R., Mandal, A., Notter, D.R., 2008. Estimates of (co) variance components due to direct and maternal effects for body weights in Jamunapari goats. Animal : an International Journal of Animal Bioscience, 2, 354–359.CrossRefGoogle Scholar
  25. Sorensen, D., and Gianola, D., 2007. Likelihood, Bayesian, and MCMC methods in quantitative genetics. Springer Science & Business Media.Google Scholar
  26. Spiegelhalter, D.J., Best, N.G., Carlin, B.P. and Van Der Linde, A., 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series b (Statistical Methodology), 64(4), 583–639.CrossRefGoogle Scholar
  27. Su, G., Christensen, O.F., Ostersen, T., Henryon, M., Lund, M.S., 2012. Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PloS one, 7, e45293.CrossRefGoogle Scholar
  28. 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
  29. Varona, L., Misztal, I., Bertrand, J., Lawlor, T., 1998. Effect of full sibs on additive breeding values under the dominance model for stature in United States Holsteins. Journal of Dairy Science, 81, 1126–1135.CrossRefGoogle Scholar
  30. 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
  31. 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
  32. Wolak, M., 2012. Nadiv: An R package to create relatedness matrices for estimating non-additive genetic variances in animal models. Methods in Ecology and Evolution, 3(5),792-796.CrossRefGoogle Scholar
  33. Zhang, C.-Y., Zhang, Y., Xu, D.-Q., Li, X., Su, J., Yang, L. G., 2009. Genetic and phenotypic parameter estimates for growth traits in Boer goat. Livestock Science, 124, 66–71.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Seyed Abu Taleb Sadeghi
    • 1
    • 2
  • Mohammad Rokouei
    • 1
    • 3
    Email author
  • Mehdi Vafaye Valleh
    • 1
  • Mokhtar Ali Abbasi
    • 4
  • Hadi Faraji-Arough
    • 5
  1. 1.Department of Animal Sciences, Faculty of AgricultureUniversity of ZabolZabolIran
  2. 2.Animal Science Research DepartmentBushehr Agricultural and Natural Resources Research and Education Center, AREEOBushehrIran
  3. 3.Department of BioinformaticsUniversity of ZabolZabolIran
  4. 4.Animal Science Research Institute of IranAgricultural Research, Education and Extension Organization (AREEO)KarajIran
  5. 5.Research Center of Special Domestic AnimalsUniversity of ZabolZabolIran

Personalised recommendations