Theoretical and Applied Genetics

, Volume 125, Issue 3, pp 419-435

First online:

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

  • Zitong LiAffiliated withDepartment of Mathematics and Statistics, University of Helsinki
  • , Mikko J. SillanpääAffiliated withDepartment of Mathematics and Statistics, University of HelsinkiDepartment of Mathematical Sciences, University of OuluDepartment of Biology, University of OuluBiocenter OuluDepartment of Agricultural Sciences, University of Helsinki Email author 

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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 (LASSO) and two of its generalizations named elastic net and adaptive LASSO in the contexts of QTL mapping and genomic breeding value prediction in plants (or animals). We also briefly summarize the Bayesian interpretation of LASSO, and the inspired hierarchical Bayesian models. We illustrate the implementation and examine the performance of methods using three public data sets: (1) North American barley data with 127 individuals and 145 markers, (2) a simulated QTLMAS XII data with 5,865 individuals and 6,000 markers for both QTL mapping and genomic selection, and (3) a wheat data with 599 individuals and 1,279 markers only for genomic selection.