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Bioinformatics. 2009 Mar 15;25(6):714-21. doi: 10.1093/bioinformatics/btp041. Epub 2009 Jan 28.

Genome-wide association analysis by lasso penalized logistic regression.

Author information

  • 1Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA.

Abstract

MOTIVATION:

In ordinary regression, imposition of a lasso penalty makes continuous model selection straightforward. Lasso penalized regression is particularly advantageous when the number of predictors far exceeds the number of observations.

METHOD:

The present article evaluates the performance of lasso penalized logistic regression in case-control disease gene mapping with a large number of SNPs (single nucleotide polymorphisms) predictors. The strength of the lasso penalty can be tuned to select a predetermined number of the most relevant SNPs and other predictors. For a given value of the tuning constant, the penalized likelihood is quickly maximized by cyclic coordinate ascent. Once the most potent marginal predictors are identified, their two-way and higher order interactions can also be examined by lasso penalized logistic regression.

RESULTS:

This strategy is tested on both simulated and real data. Our findings on coeliac disease replicate the previous SNP results and shed light on possible interactions among the SNPs.

AVAILABILITY:

The software discussed is available in Mendel 9.0 at the UCLA Human Genetics web site.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
19176549
[PubMed - indexed for MEDLINE]
PMCID:
PMC2732298
Free PMC Article

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