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 › Meeting abstract › 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
Number of pages	4
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
Event	Genetic Analysis Workshop 17: Unraveling Human Exome Data - Boston, United States Duration: 13 Oct 2010 → 16 Oct 2010

Keywords

Akaike Information Criterion
Minor Allele Frequency
Bayesian Information Criterion
Model Selection Procedure
Lasso Method

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10.1186/1753-6561-5-S9-S24Licence: CC BY

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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.",

keywords = "Akaike Information Criterion, Minor Allele Frequency, Bayesian Information Criterion, Model Selection Procedure, Lasso Method",

author = "Motyer, \{Allan J.\} and Chris McKendry and Sally Galbraith and Wilson, \{Susan R.\}",

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AU - Motyer, Allan J.

AU - McKendry, Chris

AU - Galbraith, Sally

AU - Wilson, Susan R.

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - Akaike Information Criterion

KW - Minor Allele Frequency

KW - Bayesian Information Criterion

KW - Model Selection Procedure

KW - Lasso Method

UR - https://www.scopus.com/pages/publications/82455211260

U2 - 10.1186/1753-6561-5-S9-S24

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

M3 - Meeting abstract

C2 - 22373266

AN - SCOPUS:82455211260

SN - 1753-6561

VL - 5

JO - BMC Proceedings

JF - BMC Proceedings

IS - Suppl. 9

M1 - S24

T2 - Genetic Analysis Workshop 17: Unraveling Human Exome Data

Y2 - 13 October 2010 through 16 October 2010

ER -

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

Abstract

Keywords

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