Abstract
BackgroundAccurate prediction of genomic breeding values (GEBVs) requires numerous markers. However, predictive accuracy can be enhanced by excluding markers with no effects or with inconsistent effects among crosses that can adversely affect the prediction of GEBVs.MethodsWe present three different approaches for pre-selecting markers prior to predicting GEBVs using four different BLUP methods, including ridge regression and three spatial models. Performances of the models were evaluated using 5-fold cross-validation.Results and conclusionsRidge regression and the spatial models gave essentially similar fits. Pre-selecting markers was evidently beneficial since excluding markers with inconsistent effects among crosses increased the correlation between GEBVs and true breeding values of the non-phenotyped individuals from 0.607 (using all markers) to 0.625 (using pre-selected markers). Moreover, extension of the ridge regression model to allow for heterogeneous variances between the most significant subset and the complementary subset of pre-selected markers increased predictive accuracy (from 0.625 to 0.648) for the simulated dataset for the QTL-MAS 2010 workshop.
Highlights
Accurate prediction of genomic breeding values (GEBVs) requires numerous markers
Markers are most useful for Genomic selection (GS) if they are in high linkage disequilibrium with a QTL
If a marker is in high linkage disequilibrium with a QTL its effect should be consistent among crosses or generations
Summary
Accurate prediction of genomic breeding values (GEBVs) requires numerous markers. Predictive accuracy can be enhanced by excluding markers with no effects or with inconsistent effects among crosses that can adversely affect the prediction of GEBVs. Genomic selection (GS) is a method for predicting breeding values on the basis of a large number of molecular markers [1]. If many markers have zero effects but are estimated to be non-zero, their cumulative effects increase noise in the estimates [2]. Markers are most useful for GS if they are in high linkage disequilibrium with a QTL. If a marker is in high linkage disequilibrium with a QTL its effect should be consistent among crosses (full sib families) or generations. One option is to select against markers with inconsistent effects
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