Using LASSO to estimate marker effects for Genomic Selection

Abstract

Here we suggest a least absolute shrinkage and selection operator (LASSO) approach to estimate the marker effects for genomic selection using the least angle regression (LARS) algorithm, modified to include a cross-validation step to define the best subset of markers to involve in the model. The LASSO-LARS was tested on simulated data which consisted of 5,865 individuals and 6,000 SNPs. The last generations of this dataset were the selection candidates. Using only animals from generations prior to the candidates, three approaches to splitting the population into training and validation sets for cross-validation were evaluated. Furthermore, different sizes of the validation sample were tested..