Abstract
A procedure to measure connectedness among groups in large-sized genetic evaluations is presented. It consists of two steps: (a) computing coefficients of determination (CD) of comparisons among groups of animals; and (b) building sets of connected groups. The CD of comparisons were estimated using a sampling-based method that estimates empirical variances of true and predicted breeding values from a simulated n-sample. A clustering method that may handle a large number of comparisons and build compact clusters of connected groups was developed. An aggregation criterion (Caco) that reflects the level of connectedness of each herd was computed. This procedure was validated using a small beef data set. It was applied to the French genetic evaluation of the beef breed with most records and to the genetic evaluation of goats. Caco was more related to the type of service of sires used in the herds than to herd size. It was very sensitive to the percentage of missing sires. Disconnected herds were reliably identified by low values of Caco. In France, this procedure is the reference method for evaluating connectedness among the herds involved in on-farm genetic evaluation of beef cattle (IBOVAL) since 2002 and for genetic evaluation of goats from 2007 onwards.
Highlights
The problem of disconnectedness in genetic evaluation is becoming increasingly important in animal breeding
The best linear unbiased prediction (BLUP) of breeding values allows meaningful comparisons between animals, but only when genetic links exist between the different environments (e.g. [7])
Laloë and Phocas [15] showed that both decrease in accuracy and potential bias in a genetic evaluation are due to the same phenomenon of regression towards the mean
Summary
The problem of disconnectedness in genetic evaluation is becoming increasingly important in animal breeding. Disconnectedness was originally defined for fixed effects models in terms of non-estimability [2]. Such a definition implies that disconnectedness never occurs for random effects, since their contrasts are always estimable. Laloë and Phocas [15] showed that both decrease in accuracy and potential bias in a genetic evaluation are due to the same phenomenon of regression towards the mean. These authors proved that these two effects of disconnectedness were assessed by CD of comparisons of the BLUP of breeding values of animals raised in different environments. Using a different terminology (the “standardised prediction variance” is equal to 1 – CD), Huisman et al [10] used the square root of the CD of comparisons as a criterion of connectedness
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