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A General Method to Validate Breeding Value Prediction Software

H. Leclerc^*,1,2, M. Wensch-Dorendorf^,2, J. Wensch, V. Ducrocq^* and H. H. Swalve

^* Institut National de la Recherche Agronomique, UR337, Station de Génétique Quantitative et Appliquée, F-78352 Jouyen-Josas, France
Institute of Agricultural and Nutritional Sciences, Martin-Luther University, Halle-Wittenberg, 06099 Halle, Germany
Institute of Scientific Computing, Technical University Dresden, 01062 Dresden, Germany

¹ Corresponding author: helene.leclerc{at}jouy.inra.fr

The validity of national genetic evaluations depends on the quality of input data, on the model of analysis, and on the correctness of genetic evaluation software. A general strategy was developed to validate national breeding value prediction software: performances from a real data file were replaced with simulated ones, created from simulated fixed and random effects and residuals in such a way that BLUP estimates from the evaluation software must be equal to the simulated effects. This approach was implemented for a multiple-trait model and a random regression test-day model. An example was presented on test-day observations analyzed with a random regression animal model including a lactation curve described as a sum of fixed polynomial regression and fixed spline regression on days in milk, and with genetic and permanent environmental effects modeled by using Legendre polynomials of order 2. Residuals had heterogeneous variances, and phantom parent groups were included. This method can be easily extended to other linear models. The comparison of genetic evaluation results with simulated true effects is used to demonstrate the great efficiency and usefulness of the proposed method.

Key Words: genetic evaluation • validation • best linear unbiased predictor • random regression