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The comparison of standard and fully recursive multivariate models for genetic evaluation of growth traits in Markhoz goat: predictive ability of models and ranking of animals

Published online by Cambridge University Press:  23 October 2020

Mohammad Razmkabir*
Affiliation:
Department of Animal Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran
Morteza Mokhtari
Affiliation:
Department of Animal Science, Faculty of Agriculture, University of Jiroft, P.O. Box 364, Jiroft, Iran
Peyman Mahmoudi
Affiliation:
Department of Animal Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran
Amir Rashidi
Affiliation:
Department of Animal Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran
*
Author for correspondence: Mohammad Razmkabir, E-mail: [email protected]

Abstract

Data of 2780 Markhoz kids originated from 1216 dams and 211 sires during 1993–2016 in Markhoz Goat Breeding Station, located in Sanandaj, Iran, were used. Traits investigated were body weights at birth, weaning, six-month age [six months weight (6MW)], nine-month age and yearling age [yearling weight (YW)]. Two considered multivariate models including standard multivariate model (SMM) and fully recursive multivariate model (FRM) were compared using deviance information criterion (DIC) and predictive ability measures including mean square of error (MSE) and Pearson's correlation coefficient between the observed and predicted values $(r(y,\hat{y}))$ of records. Spearman's rank correlation coefficients between posterior means of direct genetic effects of the studied traits of kids under SMM and FRM were also calculated across all, 50, 10 and 1% top-ranked animals. In general, FRM performed better than SMM in terms of lower DIC and MSE and also higher $r\lpar y\comma \;\hat{y}\rpar$. For all traits, the lowest MSE and the highest $r\lpar y\comma \;\hat{y}\rpar$ were obtained under FRM. All structural coefficients estimated under FRM were statistically significant except for that of 6MW on YW. Comparisons of Spearman's rank correlations between posterior means of direct genetic effects of kids for growth traits under SMM and FRM revealed that taking the causal relationships among the studied growth traits of Markhoz goat into account may cause considerable re-ranking for the animals in terms of estimated breeding values, especially for the top-ranked animals. It may be concluded that FRM had more plausibility over SMM for genetic evaluation of the studied growth traits in Markhoz goat.

Type
Animal Research Paper
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Abadi, MRM, Askari, N, Baghizadeh, A and Esmailizadeh, AK (2011) A directed search around caprine candidate loci provided evidence for microsatellites linkage to growth and cashmere yield in Rayini goats. Small Ruminant Research 81, 146151.CrossRefGoogle Scholar
Akaike, H (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716723.CrossRefGoogle Scholar
Amou Posht-e Masari, H, Hafezian, SH, Abdollahi-Arpanahi, R, Mokhtari MS, RMG and Taheri Yeganeh, A (2019) The comparison of alternative models for genetic evaluation of growth traits in Lori-Bakhtiari sheep: implications on predictive ability and ranking of animals. Small Ruminant Research 173, 5964.CrossRefGoogle Scholar
Barazandeh, A, Moghbeli, SM, Vatankhah, M and Mohammadabadi, MR (2012) Estimating non-genetic and genetic parameters of pre-weaning growth traits in Raini Cashmere goat. Tropical Animal Health and Production 44, 811817.CrossRefGoogle ScholarPubMed
Boujenane, I and Kansari, J (2002) Estimates of (co)variances due to direct and maternal effects for body weights in Timahdite sheep. Animal Science 74, 409414.CrossRefGoogle Scholar
Bouwman, AC, Valente, BD, Janss, LLG, Bovenhuis, H and Rosa, GJM (2014) Exploring causal networks of bovine milk fatty acids in a multivariate mixed model context. Genetics Selection Evolution 46, 2.CrossRefGoogle Scholar
Esmaeili, AVN, Esmaeilizadeh Koshkooieh, A, Aytolahi, AM, Mohammadabadi, MR, Babenko, OI, Bushtruk, M, Tkachenko, S, Stavetska, R and Klopenko, N (2019) Comparison of genetic diversity of leptin gene between wild goat and domestic goat breeds in Iran. Malaysian Applied Biology 48, 8593.Google Scholar
Gholizadeh, M, Rahimi Mianji, G, Hashemi, M and Hafezian, H (2010) Genetic parameter estimates for birth and weaning weights in Raeini goats. Czech Journal of Animal Science 55, 3036.CrossRefGoogle Scholar
Gianola, D and Sorensen, D (2004) Quantitative genetic models for describing simultaneous and recursive relationships between phenotypes. Genetics 167, 14071424.CrossRefGoogle ScholarPubMed
Inoue, K, Valente, BD, Shoji, N, Honda, T, Oyama, K and Rosa, GJM (2016) Inferring phenotypic causal structures among meat quality traits and the application of a structural equation model in Japanese black cattle. Journal of Animal Science 94, 41334142.CrossRefGoogle ScholarPubMed
Konig, S, Wu, XL, Gianola, D, Heringstad, B and Simianer, H (2008) Exploration of relationships between claw disorders and milk yield in Holstein cows via recursive linear and threshold models. Journal of Dairy Science 91, 395406.CrossRefGoogle ScholarPubMed
Lopez de Maturana, E, Legarra, A, Varona, L and Ugarte, E (2007) Analysis of fertility and dystocia in Holsteins using recursive models to handle censored and categorical data. Journal of Dairy Science 90, 20122024.CrossRefGoogle ScholarPubMed
Lopez de Maturana, E, de los Campos, G, Wu, XL, Gianola, D, Weigel, KA and Rosa, GJM (2010) Modeling relationships between calving traits: a comparison between standard and recursive mixed models. Genetics Selection Evolution 42, 1.CrossRefGoogle Scholar
Madsen, P, Jensen, J, Labouriau, R, Christensen, OF and Sahana, G (2014) DMU – a package for analyzing multivariate mixed models in quantitative genetics and genomics. Proceedings of the 10th World Congress on Genetics Applied to Livestock Production, Vancouver, Canada.Google Scholar
Maghsoudi, A, Vaez Torshizi, R and Safi Jahanshahi, A (2009) Estimates of (co)variance components for productive and composite reproductive traits in Iranian Cashmere goats. Livestock Science 126, 162167.CrossRefGoogle Scholar
Mioc, B, Susic, V, Antunovic, Z, Prpic, Z, Vnucec, I and Kasap, A (2011) Study on birth weight and pre-weaning growth of Croatian multicolored goat kids. Veterinarski Arhiv 81, 339347.Google Scholar
Misztal, I, Tsuruta, S, Strabel, T, Auvray, B, Druet, T and Lee, D (2002) BLUPF90 and related programs (BGF90). Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France.Google Scholar
Moghbeli, SM, Barazandeh, A, Vatankhah, M and Mohammadabadi, MR (2013) Genetics and non-genetics parameters of body weight for post-weaning traits in Raini Cashmere goats. Tropical Animal Health and Production 45, 15191524.CrossRefGoogle Scholar
Mohammadi, H, Moradi Shahrebabak, M and Moradi Shahrebabak, H (2012) Genetic parameter estimates for growth traits and prolificacy in Raeini Cashmere goats. Tropical Animal Health and Production 44, 12131220.CrossRefGoogle ScholarPubMed
Mokhtari, MS, Moghbeli Damaneh, M and Abdollahi Arpanahi, R (2018) The application of recursive multivariate model for genetic evaluation of early growth traits in Raeini Chasmere goat: a comparison with standard multivariate model. Small Ruminant Research 165, 5461.CrossRefGoogle Scholar
Rashidi, A, Sheikhahmadi, M, Rostamzadeh, J and Shrestha, JNB (2008) Genetic parameter estimates of body weight at different ages and yearling fleece weight in Markhoz goats. Asian-Australian Journal of Animal Science 21, 13951403.CrossRefGoogle Scholar
Rashidi, A, Bishop, SC and Matika, O (2011) Genetic parameter estimates for pre-weaning performance and reproduction traits in Markhoz goats. Small Ruminant Research 100, 100106.CrossRefGoogle Scholar
Rashidi, A, Mokhtari, M and Gutierrez, JP (2015) Pedigree analysis and inbreeding effects on early growth traits and greasy fleece weight in Markhoz goat. Small Ruminant Research 124, 18.CrossRefGoogle Scholar
Rosa, GJM, Valente, BD, de los Campos, G, Wu, XL, Gianola, D and Silva, MA (2011) Inferring causal phenotype networks using structural equation models. Genetics Selection Evolution 43, 6.CrossRefGoogle ScholarPubMed
Roy, R, Mandal, A and Notter, D (2008) Estimates of (co) variance components due to direct and maternal effects for body weights in Jamunapari goats. Animal 20, 354359.CrossRefGoogle Scholar
SAS (Statistical Analysis System) (2004) SAS Users’ Guide, Version 9.1. Cary, North Carolina, USA: SAS Institute Inc.Google Scholar
Shamsalddini, S, Mohammadabadi, MR and Esmailizadeh, AK (2016) Polymorphism of the prolactin gene and its effect on fiber traits in goat. Russian Journal of Genetics 52, 405408.CrossRefGoogle ScholarPubMed
Sorensen, DA and Gianola, D (2002) Likelihood, Bayesian and MCMC Methods in Quantitative Genetics. New York, 175 Fifth Avenue, New York: Springer-Verlag Inc. 10.1007/b98952CrossRefGoogle Scholar
Tosh, JJ and Kemp, RA (1994) Estimation of variance components for lamb weights in three sheep populations. Journal of Animal Science 72, 11841190.CrossRefGoogle ScholarPubMed
Valente, BD, Rosa, GJM, de los Campos, G, Gianola, D and Silva, MA (2010) Searching for recursive causal structures in multivariate quantitative genetics mixed models. Genetics 185, 633644.CrossRefGoogle ScholarPubMed
Valente, BD, Rosa, GJM, Gianola, D, Wu, XL and Weigel, K (2013) Is structural equation modeling advantageous for the genetic improvement of multiple traits? Genetics 194, 561572.CrossRefGoogle ScholarPubMed
Wright, S (1921) Correlation and causation. Journal of Agricultural Research 20, 557585.Google Scholar
Wright, S (1934) An analysis of variability in number of digits in an inbred strain of guinea pigs. Genetics 19, 506536.CrossRefGoogle Scholar