Validation of an approximate approach to compute genetic correlations between longevity and linear traits.
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.
Tarrés
Joaquim
J
Grup de Recerca en Remugants, Departament de ciència animal i dels aliments, Universitat autònoma de Barcelona, 08193 Bellaterra (Barcelona), Spain. joaquim.tarres@vit.de
Piedrafita
Jesús
J
Ducrocq
Vincent
V
D008136:Longevity / Q000235:genetics*; D008957:Models, Genetic