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

Joaquim Tarrés1*, Jesús Piedrafita1 and Vincent Ducrocq2

Author Affiliations

1 Grup de Recerca en Remugants, Departament de ciència animal i dels aliments, Universitat autònoma de Barcelona, 08193 Bellaterra (Barcelona), Spain

2 Station de génétique quantitative et appliquée, Institut national de la recherche agronomique, 78352 Jouy-en-Josas Cedex, France

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Genetics Selection Evolution 2006, 38:65-83  doi:10.1186/1297-9686-38-1-65


The electronic version of this article is the complete one and can be found online at: http://www.gsejournal.org/content/38/1/65


Received:17 June 2005
Accepted:20 September 2005
Published:15 January 2006

© 2006 INRA, EDP Sciences

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.

Keywords:
simulation; genetic correlation; reliability; longevity

Research

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