Genome-wide association analysis by lasso penalized logistic regression

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ID: 304472
2009
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Abstract
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. Contact: klange@ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W2122189635 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tong Tong Wu, Yi Fang Chen, Trevor Hastie, Eric M. Sobel, Kenneth Lange
Journal BMC Bioinformatics
Year 2009
DOI
10.1093/bioinformatics/btp041
URL
https://doi.org/10.1093/bioinformatics/btp041
Keywords Keywords not found

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