title: LASSO model selection with post-processing for a genome-wide association study data set.
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.
authors.affiliation: Prince of Wales Clinical School, University of New South Wales, New South Wales 2052, Australia. a.motyer@unsw.edu.au.
authors.forename: Allan J
authors.initials: AJ
authors.lastname: Motyer
authors.forename: Chris
authors.initials: C
authors.lastname: McKendry
authors.forename: Sally
authors.initials: S
authors.lastname: Galbraith
authors.forename: Susan R
authors.initials: SR
authors.lastname: Wilson