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

Genetics Selection Evolution

Vincent Ducrocq²  Jesús Piedrafita¹  Joaquim Tarrés¹ 

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

关键词: longevity;    reliability;    genetic correlation;    simulation;   

Others  :  1094165
DOI  :  10.1186/1297-9686-38-1-65

 received in 2005-06-17, accepted in 2005-09-20,  发布年份 2006

【 摘 要 】

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.

【 授权许可】

   
2006 INRA, EDP Sciences