Literature DB >> 28004872

Random regression models to estimate genetic parameters for weights in Murrah buffaloes.

Flavia Rita Ferreira1, Francisco Ribeiro de Araujo Neto1, Henrique Barbosa Borges1, Rusbel Raul Aspilcueta-Borquis2, Naudim Alejandro Hurtado-Lugo3, Henrique Nunes de Oliveira3, Lucia Galvão de Albuquerque3, Humberto Tonhati3.   

Abstract

This article reports genetic analysis of the weight at different ages of Murrah water buffaloes, using random regression models (RRM). Models ranging from third to sixth order polynomial were used to describe direct genetic and animal permanent environmental effects. Contemporary group was included as a fixed effect, and a cubic polynomial was used to model the mean curve of the population. The residual was modeled considering a log-linear function. Two models were selected for study of genetic parameters. The first model included third and sixth order polynomials for direct genetic and animal permanent environmental effects (M36). The second model included sixth order polynomials for all random effects (M66). The estimates of heritability varied from 0.16 + 0.04 (44 days) to 0.38 + 0.04 (568 days) for model M36 and from 0.16 + 0.05 (33 days) to 0.42 + 0.05 (600 days) for model M66. Regarding estimates of the correlation for all effects, the magnitude tended to decline with the increase of the time span between measurements. These results indicate that the species has potential for genetic selection based on weight at different ages, since we found favorable genetic variability within the herd, with selection likely to be more efficient at ages near 600 days.
© 2016 Japanese Society of Animal Science.

Entities:  

Keywords:  Bubalus bubalis; longitudinal traits; model selection; water buffalo

Mesh:

Year:  2016        PMID: 28004872     DOI: 10.1111/asj.12758

Source DB:  PubMed          Journal:  Anim Sci J        ISSN: 1344-3941            Impact factor:   1.749


  1 in total

1.  Genome-wide association studies for growth traits in buffaloes using the single step genomic BLUP.

Authors:  Francisco Ribeiro de Araujo Neto; Daniel Jordan de Abreu Santos; Gerardo Alves Fernandes Júnior; Rusbel Raul Aspilcueta-Borquis; André Vieira do Nascimento; Leonardo de Oliveira Seno; Humberto Tonhati; Henrique Nunes de Oliveira
Journal:  J Appl Genet       Date:  2019-11-01       Impact factor: 3.240

  1 in total

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