2004South African Journal of Animal ScienceOpen access

Genetic parameters for quail body weights using a random regression model

Yavuz Akbaş, Çiğdem Takma, Erdal Yaylak

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Abstract

A model including fixed and random linear regressions is described for analyzing body weights at different ages. In this study, (co)variance components, heritabilities for quail weekly weights and genetic correlations among these weights were estimated using a random regression model by DFREML under DXMRR option. Data of 1046 pedigreed quail were used. Individual live weights were obtained weekly from hatching to six weeks of age. Records for the same bird were taken as repeated measurements and single measurement error variance was assumed to be constant for all ages. Orthogonal polynomial regressions (on the Legendre scale) of sixth order were sufficient to model the additive genetic, phenotypic and permanent environmental (co)variances. Heritability estimates for ages were moderate, ranging from 0.007-0.61 and estimated measurement error variance was 9.60 g2. Correlations were found positive among weights. Genetic correlations were higher than phenotypic and permanent environmental correlations. The correlations between adjacent periods are more closely correlated than between remote periods. South African Journal of Animal Science Vol.34(2) 2004: 104-109

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A model including fixed and random linear regressions is described for analyzing body weights at different ages. In this study, (co)variance components, heritabilities for quail weekly weights and genetic correlations among these weights were estimated using a random regression model by DFREML under DXMRR option. Data of 1046 pedigreed quail were used. Individual live weights were obtained weekly from hatching to six weeks of age. Records for the same bird were taken as repeated measurements and single measurement error variance was assumed to be constant for all ages. Orthogonal polynomial regressions (on the Legendre scale) of sixth order were sufficient to model the additive genetic, phenotypic and permanent environmental (co)variances. Heritability estimates for ages were moderate, ranging from 0.007-0.61 and estimated measurement error variance was 9.60 g2. Correlations were found positive among weights. Genetic correlations were higher than phenotypic and permanent environmental correlations. The correlations between adjacent periods are more closely correlated than between remote periods. South African Journal of Animal Science Vol.34(2) 2004: 104-109

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Available abstract

A model including fixed and random linear regressions is described for analyzing body weights at different ages. In this study, (co)variance components, heritabilities for quail weekly weights and genetic correlations among these weights were estimated using a random regression model by DFREML under DXMRR option. Data of 1046 pedigreed quail were used. Individual live weights were obtained weekly from hatching to six weeks of age. Records for the same bird were taken as repeated measurements and single measurement error variance was assumed to be constant for all ages. Orthogonal polynomial regressions (on the Legendre scale) of sixth order were sufficient to model the additive genetic, phenotypic and permanent environmental (co)variances. Heritability estimates for ages were moderate, ranging from 0.007-0.61 and estimated measurement error variance was 9.60 g2. Correlations were found positive among weights. Genetic correlations were higher than phenotypic and permanent environmental correlations. The correlations between adjacent periods are more closely correlated than between remote periods. South African Journal of Animal Science Vol.34(2) 2004: 104-109

Key concepts: Heritability, Quail, Statistics, Mixed model, Mathematics, Linear regression, Random effects model, Genetic correlation

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