Literature DB >> 7628942

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

F A Rodríguez-Almeida1, L D Van Vleck, R L Willham, S L Northcutt.   

Abstract

Dominance and additive x additive genetic variances were estimated for birth and weaning traits of calves from three synthetic lines of beef cattle differing in mature size. Data consisted of 3,992 and 2,877 records from lines of small-, medium-, and large-framed calves in each of two research herds located at Rhodes and McNay, IA, respectively. Variance components were estimated separately by herd and line for birth weight (BWT), birth hip height (BH), 205-d weight (WW), and 205-d hip height (WH) by derivative-free REML with an animal model. Model 1 included fixed effects of year, sex, and age of dam. Random effects were additive direct (a) and additive maternal (m) genetic with covariance (a,m), maternal permanent environmental, and residual. Model 2 also included dominance (d) and model 3 included dominance plus additive x additive (a:a) effects. In general, only slight changes occurred in other variance components estimates when day was included in Model 2. However, large estimates of additive x additive genetic variances obtained with Model 3 for 4 out of 24 analyses were associated with reductions in estimates of direct additive variances. Direct (maternal) heritability estimates averaged across herd-line combinations with Model 2 were .53(.11), .42(.04), .27(.12), and .35(.04) for BWT, BH, WW, and WH, respectively. Corresponding covariance (a,m) estimates as fractions of phenotypic variance (sigma p2) were .00, .01, .01, and .06, respectively. For maternal permanent environmental effects in Model 2, average estimates of variances as fractions of sigma p2 across herd-line combinations were .03, .00, .05, and .02, for BW, BH, WW, and WH, respectively. Dominance effects explained, on average, 18, 26, 28, and 11% of total variance for BWT, BH, WW, and WH, respectively. Most of the estimates for additive x additive variances were negligible, except for one data set for BWT, two for BH, and one for WH, where the relative estimates of this component were high (.21 to .45). These results suggest that most of the non-additive genetic variance in the traits studied is accounted for by dominance genetic effects.

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Year:  1995        PMID: 7628942     DOI: 10.2527/1995.7341002x

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  8 in total

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Journal:  Genetics       Date:  2014-10-15       Impact factor: 4.562

2.  Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods.

Authors:  Anderson Antonio Carvalho Alves; Rebeka Magalhães da Costa; Tiago Bresolin; Gerardo Alves Fernandes Júnior; Rafael Espigolan; André Mauric Frossard Ribeiro; Roberto Carvalheiro; Lucia Galvão de Albuquerque
Journal:  J Anim Sci       Date:  2020-06-01       Impact factor: 3.159

3.  Genomic prediction with non-additive effects in beef cattle: stability of variance component and genetic effect estimates against population size.

Authors:  Akio Onogi; Toshio Watanabe; Atsushi Ogino; Kazuhito Kurogi; Kenji Togashi
Journal:  BMC Genomics       Date:  2021-07-07       Impact factor: 3.969

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

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Journal:  Trop Anim Health Prod       Date:  2019-10-17       Impact factor: 1.559

5.  Non-additive genetic variation in growth, carcass and fertility traits of beef cattle.

Authors:  Sunduimijid Bolormaa; Jennie E Pryce; Yuandan Zhang; Antonio Reverter; William Barendse; Ben J Hayes; Michael E Goddard
Journal:  Genet Sel Evol       Date:  2015-04-02       Impact factor: 4.297

6.  Marker-Based Estimates Reveal Significant Nonadditive Effects in Clonally Propagated Cassava (Manihot esculenta): Implications for the Prediction of Total Genetic Value and the Selection of Varieties.

Authors:  Marnin D Wolfe; Peter Kulakow; Ismail Y Rabbi; Jean-Luc Jannink
Journal:  G3 (Bethesda)       Date:  2016-11-08       Impact factor: 3.154

7.  Genomic heritability estimates in sweet cherry reveal non-additive genetic variance is relevant for industry-prioritized traits.

Authors:  Julia Piaskowski; Craig Hardner; Lichun Cai; Yunyang Zhao; Amy Iezzoni; Cameron Peace
Journal:  BMC Genet       Date:  2018-04-10       Impact factor: 2.797

8.  Estimating dominance genetic variances for growth traits in American Angus males using genomic models.

Authors:  Carolina A Garcia-Baccino; Daniela A L Lourenco; Stephen Miller; Rodolfo J C Cantet; Zulma G Vitezica
Journal:  J Anim Sci       Date:  2020-01-01       Impact factor: 3.159

  8 in total

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