Journal
BIOINFORMATICS
Volume 25, Issue 6, Pages 714-721Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btp041
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Funding
- USPHS [GM53275, MH59490]
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [0926194] Funding Source: National Science Foundation
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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 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.
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