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

BMC Proceedings

Proceedings

Allan J Motyer¹  Chris McKendry¹  Sally Galbraith²  Susan R Wilson² 

[1] Prince of Wales Clinical School, University of New South Wales, 2052, New South Wales, Australia;Prince of Wales Clinical School, University of New South Wales, 2052, New South Wales, Australia;School of Mathematics and Statistics, University of New South Wales, 2052, New South Wales, Australia;

关键词: Akaike Information Criterion;    Minor Allele Frequency;    Bayesian Information Criterion;    Model Selection Procedure;    Lasso Method;   

DOI  :  10.1186/1753-6561-5-S9-S24

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Motyer et al; licensee BioMed Central Ltd. 2011