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

Joaquim Tarrés, Jesús Piedrafita, Vincent Ducrocq

Research output: Contribution to journalArticleResearchpeer-review

18 Citations (Scopus)


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. © INRA, EDP Sciences, 2005.
Original languageEnglish
Pages (from-to)65-83
JournalGenetics Selection And Evolution
Publication statusPublished - 1 Jan 2006


  • Genetic correlation
  • Longevity
  • Reliability
  • Simulation


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