Abstract

Genetic connectedness assesses the extent to which estimated breeding values can be fairly compared across management units. Ranking of individuals across units based on best linear unbiased prediction (BLUP) is reliable when there is a sufficient level of connectedness due to a better disentangling of genetic signal from noise. Although a recent study showed that genomic relatedness strengthens the estimates of connectedness across management units compared to that of pedigree, the relationship between connectedness measures and prediction accuracies has been explored only to a limited extent. In this study, we examined whether increased measures of connectedness led to higher prediction accuracies evaluated by a cross-validation based on computer simulations. We found that the greater extent of connectedness enhanced accuracy of whole-genome prediction. The use of genomic information resulted in increased estimates of connectedness and improved prediction accuracies compared to those of pedigree-based models, especially when the numbers of markers and QTLs were large. Keywords: cross-validation, genomic connectedness, genomic prediction, relatedness

Haipeng Yu, Matthew Spangler, Ronald Lewis, Gota Morota

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Theory to Application 1, , 406, 2018
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