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Heterogeneity of variance components for preweaning growth in Romane sheep due to the number of lambs reared

Ingrid David1*, Frédéric Bouvier2, Dominique François1, Jean-Paul Poivey13 and Laurence Tiphine4

Author Affiliations

1 INRA UR 631, Station d'Amélioration Génétique des Animaux, 31320 Castanet-Tolosan, France

2 INRA UE 0332, Domaine de la Sapinière, 18390 Osmoy, France

3 CIRAD UMR 112, SELMET, 34398 Montpellier, France

4 Institut de l'Elevage, 75012 Paris, France

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Genetics Selection Evolution 2011, 43:32  doi:10.1186/1297-9686-43-32

Published: 7 September 2011

Abstract

Background

The pre-weaning growth rate of lambs, an important component of meat market production, is affected by maternal and direct genetic effects. The French genetic evaluation model takes into account the number of lambs suckled by applying a multiplicative factor (1 for a lamb reared as a single, 0.7 for twin-reared lambs) to the maternal genetic effect, in addition to including the birth*rearing type combination as a fixed effect, which acts on the mean. However, little evidence has been provided to justify the use of this multiplicative model. The two main objectives of the present study were to determine, by comparing models of analysis, 1) whether pre-weaning growth is the same trait in single- and twin-reared lambs and 2) whether the multiplicative coefficient represents a good approach for taking this possible difference into account.

Methods

Data on the pre-weaning growth rate, defined as the average daily gain from birth to 45 days of age on 29,612 Romane lambs born between 1987 and 2009 at the experimental farm of La Sapinière (INRA-France) were used to compare eight models that account for the number of lambs per dam reared in various ways. Models were compared using the Akaike information criteria.

Results

The model that best fitted the data assumed that 1) direct (maternal) effects correspond to the same trait regardless of the number of lambs reared, 2) the permanent environmental effects and variances associated with the dam depend on the number of lambs reared and 3) the residual variance depends on the number of lambs reared. Even though this model fitted the data better than a model that included a multiplicative coefficient, little difference was found between EBV from the different models (the correlation between EBV varied from 0.979 to 0.999).

Conclusions

Based on experimental data, the current genetic evaluation model can be improved to better take into account the number of lambs reared. Thus, it would be of interest to evaluate this model on field data and update the genetic evaluation model based on the results obtained.