Literature DB >> 31625012

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

Seyed Abu Taleb Sadeghi1,2, Mohammad Rokouei3,4, Mehdi Vafaye Valleh1, Mokhtar Ali Abbasi5, Hadi Faraji-Arough6.   

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.

Entities:  

Keywords:  Accuracy; Correlation; Dominance; Epistasis; Heritability

Year:  2019        PMID: 31625012     DOI: 10.1007/s11250-019-02064-0

Source DB:  PubMed          Journal:  Trop Anim Health Prod        ISSN: 0049-4747            Impact factor:   1.559


  16 in total

1.  Effect of full sibs on additive breeding values under the dominance model for stature in United States Holsteins.

Authors:  L Varona; I Misztal; J K Bertrand; T J Lawlor
Journal:  J Dairy Sci       Date:  1998-04       Impact factor: 4.034

2.  Genetic parameter estimates for growth traits and prolificacy in Raeini Cashmere goats.

Authors:  Hossein Mohammadi; Mohammad Moradi Shahrebabak; Hossein Moradi Shahrebabak
Journal:  Trop Anim Health Prod       Date:  2012-01-03       Impact factor: 1.559

3.  Bayesian inference of genetic parameters for ultrasound scanning traits of Kivircik lambs.

Authors:  I Cemal; E Karaman; M Z Firat; O Yilmaz; N Ata; O Karaca
Journal:  Animal       Date:  2016-08-11       Impact factor: 3.240

4.  Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers.

Authors:  M Heidaritabar; A Wolc; J Arango; J Zeng; P Settar; J E Fulton; N P O'Sullivan; J W M Bastiaansen; R L Fernando; D J Garrick; J C M Dekkers
Journal:  J Anim Breed Genet       Date:  2016-06-30       Impact factor: 2.380

5.  Estimates of (co)variance components and genetic parameters for growth traits in Sirohi goat.

Authors:  Gopal R Gowane; Ashish Chopra; Ved Prakash; A L Arora
Journal:  Trop Anim Health Prod       Date:  2010-08-15       Impact factor: 1.559

6.  Estimation of non-additive genetic variances in three synthetic lines of beef cattle using an animal model.

Authors:  F A Rodríguez-Almeida; L D Van Vleck; R L Willham; S L Northcutt
Journal:  J Anim Sci       Date:  1995-04       Impact factor: 3.159

7.  Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers.

Authors:  Guosheng Su; Ole F Christensen; Tage Ostersen; Mark Henryon; Mogens S Lund
Journal:  PLoS One       Date:  2012-09-13       Impact factor: 3.240

8.  Benefits of Dominance over Additive Models for the Estimation of Average Effects in the Presence of Dominance.

Authors:  Pascal Duenk; Mario P L Calus; Yvonne C J Wientjes; Piter Bijma
Journal:  G3 (Bethesda)       Date:  2017-10-05       Impact factor: 3.154

9.  Improvement of prediction ability for genomic selection of dairy cattle by including dominance effects.

Authors:  Chuanyu Sun; Paul M VanRaden; John B Cole; Jeffrey R O'Connell
Journal:  PLoS One       Date:  2014-08-01       Impact factor: 3.240

Review 10.  Non-additive Effects in Genomic Selection.

Authors:  Luis Varona; Andres Legarra; Miguel A Toro; Zulma G Vitezica
Journal:  Front Genet       Date:  2018-03-06       Impact factor: 4.599

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