Overview of LASSO-related penalized regression methods for quantitative trait mapping and genomic selection

@article{Li2012OverviewOL,
  title={Overview of LASSO-related penalized regression methods for quantitative trait mapping and genomic selection},
  author={Zitong Li and Mikko J. Sillanp{\"a}{\"a}},
  journal={Theoretical and Applied Genetics},
  year={2012},
  volume={125},
  pages={419-435}
}
Quantitative trait loci (QTL)/association mapping aims at finding genomic loci associated with the phenotypes, whereas genomic selection focuses on breeding value prediction based on genomic data. Variable selection is a key to both of these tasks as it allows to (1) detect clear mapping signals of QTL activity, and (2) predict the genome-enhanced breeding values accurately. In this paper, we provide an overview of a statistical method called least absolute shrinkage and selection operator… CONTINUE READING