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Random regression models to estimate genetic parameters for weights in Murrah buffaloes
Author(s) -
Ferreira Flavia Rita,
Araujo Neto Francisco Ribeiro,
Borges Henrique Barbosa,
AspilcuetaBorquis Rusbel Raul,
HurtadoLugo Naudim Alejandro,
Oliveira Henrique Nunes,
Albuquerque Lucia Galvão,
Tonhati Humberto
Publication year - 2017
Publication title -
animal science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.606
H-Index - 38
eISSN - 1740-0929
pISSN - 1344-3941
DOI - 10.1111/asj.12758
Subject(s) - heritability , statistics , mathematics , selection (genetic algorithm) , random effects model , genetic correlation , herd , population , residual , regression analysis , regression , linear regression , zoology , animal model , polynomial , biology , demography , genetic variation , medicine , genetics , mathematical analysis , meta analysis , algorithm , artificial intelligence , sociology , computer science , endocrinology
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.