Free Access
Genet. Sel. Evol.
Volume 38, Number 1, January-February 2006
Page(s) 65 - 83
Published online 21 December 2005
Genet. Sel. Evol. 38 (2006) 65-83
DOI: 10.1051/gse:2005027

Validation of an approximate approach to compute genetic correlations between longevity and linear traits

Joaquim Tarrésa, Jesús Piedrafitaa and Vincent Ducrocqb

a  Grup de Recerca en Remugants, Departament de ciència animal i dels aliments, Universitat autònoma de Barcelona, 08193 Bellaterra (Barcelona), Spain
b  Station de génétique quantitative et appliquée, Institut national de la recherche agronomique, 78352 Jouy-en-Josas Cedex, France

(Received 17 June 2005; accepted 20 September 2005; published online 21 December 2005)

Abstract - The estimation of genetic correlations between a nonlinear trait such as longevity and linear traits is computationally difficult on large datasets. A two-step approach was proposed and was checked via simulation. First, univariate analyses were performed to get genetic variance estimates and to compute pseudo-records and their associated weights. These pseudo-records were virtual performances free of all environmental effects that can be used in a BLUP animal model, leading to the same breeding values as in the (possibly nonlinear) initial analyses. By combining these pseudo-records in a multiple trait model and fixing the genetic and residual variances to their values computed during the first step, we obtained correlation estimates by AI-REML and approximate MT-BLUP predicted breeding values that blend direct and indirect information on longevity. Mean genetic correlations and reliabilities obtained on simulated data confirmed the suitability of this approach in a wide range of situations. When nonzero residual correlations exist between traits, a sire model gave nearly unbiased estimates of genetic correlations, while the animal model estimates were biased upwards. Finally, when an incorrect genetic trend was simulated to lead to biased pseudo-records, a joint analysis including a time effect could adequately correct for this bias.

Key words: simulation / genetic correlation / reliability / longevity

Correspondence and reprints:

© INRA, EDP Sciences 2005

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.