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

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© INRA, EDP Sciences 2005