This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data
LASSO model selection with post-processing for a genome-wide association study data set
1 Prince of Wales Clinical School, University of New South Wales, New South Wales 2052, Australia
2 School of Mathematics and Statistics, University of New South Wales, New South Wales 2052, Australia
BMC Proceedings 2011, 5(Suppl 9):S24 doi:10.1186/1753-6561-5-S9-S24Published: 29 November 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.