LASSO model selection with post-processing for a genome-wide association study data set

Allan J Motyer; Chris McKendry; Sally Galbraith; Susan R Wilson

doi:10.1186/1753-6561-5-S9-S24

LASSO model selection with post-processing for a genome-wide association study data set

BMC Proc. 2011 Nov 29;5 Suppl 9(Suppl 9):S24. doi: 10.1186/1753-6561-5-S9-S24.

Authors

Allan J Motyer¹, Chris McKendry, Sally Galbraith, Susan R Wilson

Affiliation

¹ Prince of Wales Clinical School, University of New South Wales, New South Wales 2052, Australia. [email protected].

Abstract

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.