Article

Journal of Agricultural, Biological, and Environmental Statistics

, 12:300

First online:

Incorporating LASSO effects into a mixed model for quantitative trait loci detection

  • Scott D. FosterAffiliated withSchool of Agriculture, Food and Wine, The University of Adelaide Email author 
  • , Arūnas P. VerbylaAffiliated withSchool of Agriculture, Food and Wine, The University of Adelaide
  • , Wayne S. PitchfordAffiliated withSchool of Agriculture, Food and Wine, The University of Adelaide

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Abstract

The identification of quantitative trait loci (QTL) can be viewed as a subset selection problem. In a simulation study the least absolute selection and shrinkage operator (LASSO) is shown to be a useful and powerful tool for QTL identification. LASSO effects are embedded into a mixed model allowing simultaneous modeling of genetic and experimental effects. This provides the flexibility to model the experiment in conjunction with the power of LASSO QTL identification. Estimation is performed using an approximation to the restricted likelihood and modified Gaussian elimination. The extended mixed model is used to analyze a cattle gene mapping dataset.

Key Words

Adjusted scores Partial Laplace approximation Quantitative trial loci Restricted likelihood Subset selection