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

Allan J. Motyer^*, Chris McKendry, Sally Galbraith, Susan R. Wilson

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

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.

Original language	English
Article number	S24
Journal	BMC Proceedings
Volume	5
Issue number	SUPPL. 9
DOIs	https://doi.org/10.1186/1753-6561-5-S9-S24
Publication status	Published - 2011
Externally published	Yes

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10.1186/1753-6561-5-S9-S24

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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.",

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DO - 10.1186/1753-6561-5-S9-S24

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VL - 5

JO - BMC Proceedings

JF - BMC Proceedings

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ER -

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

Abstract

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