publication . Other literature type . Article . 2009

Genome-wide association analysis by lasso penalized logistic regression

Wu, Tong Tong; Chen, Yi Fang; Hastie, Trevor; Sobel, Eric; Lange, Kenneth;
Open Access English
  • Published: 28 Jan 2009
  • Publisher: Oxford University Press
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 cycli...
Subjects
free text keywords: Original Papers, Statistics and Probability, Computational Theory and Mathematics, Biochemistry, Molecular Biology, Computational Mathematics, Computer Science Applications, Replicate, Lasso (statistics), Genome-wide association study, Web site, Single-nucleotide polymorphism, Logistic regression, Bioinformatics, Statistics, Regression, Computer science, Genome-Wide Association Analysis
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