Validation of an approximate approach to compute genetic correlations between longevity and linear traits
Tarrés i Font, Joaquim (Universitat Autònoma de Barcelona. Departament de Ciència Animal i dels Aliments)
Piedrafita Arilla, Jesús (Universitat Autònoma de Barcelona. Departament de Ciència Animal i dels Aliments)
Ducrocq, Vincent (Institut national de la recherche agronomique (França))

Data: 2006
Resum: 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.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès.
Document: article ; publishedVersion
Publicat a: Genetics, selection, evolution, Vol. 38 (January 2006) , p. 65-83, ISSN 0999-193X

DOI: 10.1051/gse:2005027

19 p, 156.6 KB

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