LASSO model selection with post-processing for a genome-wide association study data set.
Allan J Motyer, Chris McKendry, Sally Galbraith, Susan R Wilson
BMC Proceedings | Springer Science and Business Media LLC | Published : 2011
Model selection procedures for simultaneous analysis of all single-nucleotide polymorphisms in genome-wide association studies are most suitable for making full use of the data for a complex disease study. In this paper we consider a penalized regression using the LASSO procedure and show that post-processing of the penalized-regression results with subsequent stepwise selection may lead to improved identification of causal single-nucleotide polymorphisms.