Bioinformatics Advance Access published online on January 28, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp041
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Genomewide Association Analysis by Lasso Penalized Logistic Regression
1Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742
2Department of Statistics, Stanford University, Stanford, CA 94305
3Department of Biostatistics, Stanford University, Stanford, CA 94305
4Department of Human Genetics, University of California, Los Angeles, CA 90095
5Department of Biomathematics, University of California, Los Angeles, CA 90095
*To whom correspondence should be addressed. Kenneth Lange, E-mail: klange{at}ucla.edu
| Abstract |
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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 paper evaluates the performance of lasso penalized logistic regression in case-control disease gene mapping with a large number of SNP (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 single 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.
Contact: klange{at}ucla.edu
Associate Editor: Dr. Alex Bateman
Received on September 29, 2008; revised on December 11, 2008; accepted on January 18, 2009