Comparison of regression tree-based methods in genomic selection.

The aim of this study was to compare the predictive performance of tree-based methods including regression tree (RT), random forest (RF) and Boosting (BT) in genomic selection. To do this, a genome comprised of five chromosomes was simulated for 1000 individuals on which 5000 single-nucleotide polymorphisms were evenly distributed. Comparison of methods was made in different scenarios of genetic architecture (number of QTL and distribution of QTL effects) and heritability level (0.