Literature DB >> 27939540

Including nonadditive genetic effects in mating programs to maximize dairy farm profitability.

H Aliloo1, J E Pryce2, O González-Recio3, B G Cocks2, M E Goddard4, B J Hayes2.   

Abstract

We compared the outcome of mating programs based on different evaluation models that included nonadditive genetic effects (dominance and heterozygosity) in addition to additive effects. The additive and dominance marker effects and the values of regression on average heterozygosity were estimated using 632,003 single nucleotide polymorphisms from 7,902 and 7,510 Holstein cows with calving interval and production (milk, fat, and protein yields) records, respectively. Expected progeny values were computed based on the estimated genetic effects and genotype probabilities of hypothetical progeny from matings between the available genotyped cows and the top 50 young genomic bulls. An index combining the traits based on their economic values was developed and used to evaluate the performance of different mating scenarios in terms of dollar profit. We observed that mating programs with nonadditive genetic effects performed better than a model with only additive effects. Mating programs with dominance and heterozygosity effects increased milk, fat, and protein yields by up to 38, 1.57, and 1.21 kg, respectively. The inclusion of dominance and heterozygosity effects decreased calving interval by up to 0.70 d compared with random mating. The average reduction in progeny inbreeding by the inclusion of nonadditive genetic effects in matings compared with random mating was between 0.25 to 1.57 and 0.64 to 1.57 percentage points for calving interval and production traits, respectively. The reduction in inbreeding was accompanied by an average of A$8.42 (Australian dollars) more profit per mating for a model with additive, dominance, and heterozygosity effects compared with random mating. Mate allocations that benefit from nonadditive genetic effects can improve progeny performance only in the generation where it is being implemented, and the gain from specific combining abilities cannot be accumulated over generations. Continuous updating of genomic predictions and mate allocation programs are required to benefit from nonadditive genetic effects in the long term. The Authors. Published by the Federation of Animal Science Societies and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Entities:  

Keywords:  inbreeding; nonadditive genetic effect; planned mating; specific combining ability

Mesh:

Year:  2016        PMID: 27939540     DOI: 10.3168/jds.2016-11261

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  17 in total

1.  Genomic Model with Correlation Between Additive and Dominance Effects.

Authors:  Tao Xiang; Ole Fredslund Christensen; Zulma Gladis Vitezica; Andres Legarra
Journal:  Genetics       Date:  2018-05-09       Impact factor: 4.562

2.  Genomic Prediction Methods Accounting for Nonadditive Genetic Effects.

Authors:  Luis Varona; Andres Legarra; Miguel A Toro; Zulma G Vitezica
Journal:  Methods Mol Biol       Date:  2022

3.  Impacts of additive, dominance, and inbreeding depression effects on genomic evaluation by combining two SNP chips in Canadian Yorkshire pigs bred in China.

Authors:  Quanshun Mei; Zulma G Vitezica; Jielin Li; Shuhong Zhao; Andres Legarra; Tao Xiang
Journal:  Genet Sel Evol       Date:  2022-10-22       Impact factor: 5.100

4.  Evaluation of nonadditive effects in yearling weight of tropical beef cattle.

Authors:  Fernanda S S Raidan; Laercio R Porto-Neto; Yutao Li; Sigrid A Lehnert; Zulma G Vitezica; Antonio Reverter
Journal:  J Anim Sci       Date:  2018-09-29       Impact factor: 3.159

5.  Genomic prediction of hybrid crops allows disentangling dominance and epistasis.

Authors:  David González-Diéguez; Andrés Legarra; Alain Charcosset; Laurence Moreau; Christina Lehermeier; Simon Teyssèdre; Zulma G Vitezica
Journal:  Genetics       Date:  2021-05-17       Impact factor: 4.562

6.  Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects.

Authors:  Seema Yadav; Xianming Wei; Priya Joyce; Felicity Atkin; Emily Deomano; Yue Sun; Loan T Nguyen; Elizabeth M Ross; Tony Cavallaro; Karen S Aitken; Ben J Hayes; Kai P Voss-Fels
Journal:  Theor Appl Genet       Date:  2021-04-26       Impact factor: 5.574

7.  Dissection of additive, dominance, and imprinting effects for production and reproduction traits in Holstein cattle.

Authors:  Jicai Jiang; Botong Shen; Jeffrey R O'Connell; Paul M VanRaden; John B Cole; Li Ma
Journal:  BMC Genomics       Date:  2017-05-30       Impact factor: 3.969

8.  Genomic Prediction of Additive and Dominant Effects on Wool and Blood Traits in Alpine Merino Sheep.

Authors:  Shaohua Zhu; Hongchang Zhao; Mei Han; Chao Yuan; Tingting Guo; Jianbin Liu; Yaojing Yue; Guoyan Qiao; Tianxiang Wang; Fanwen Li; Shuangbao Gun; Bohui Yang
Journal:  Front Vet Sci       Date:  2020-11-11

Review 9.  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

10.  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

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