Genome based genetic evaluation and genome wide selection using supervised dimension reduction based on partial least squares

Abstract

The method of partial least squares was applied to the prediction of genetic merit using whole genome scan data consisting of 10715 SNP. The method is particularly suited to data sets that have many more markers than observations and in which markers are collinear due to high linkage disequilibrium. A SNP ranking method was applied to select a subset of markers which have equal predictive power compared to using all SNP simultaneously