Genome-wide association analysis by lasso penalized logistic regression
University of California, Los Angeles · University of Maryland, College Park · +1 more institution
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…
Citation impact
- FWCI
- 28.90
- Percentile
- 100%
- References
- 37
Authors
5- TTTong Tong WuCorresponding
University of California, Los Angeles, University of Maryland, College Park, Stanford University
- YFYi Fang Chen
University of California, Los Angeles, University of Maryland, College Park, Stanford University
- THTrevor Hastie
University of California, Los Angeles, University of Maryland, College Park, Stanford University
- EMEric M. Sobel
University of California, Los Angeles, University of Maryland, College Park, Stanford University
- KLKenneth Lange
University of California, Los Angeles, University of Maryland, College Park, Stanford University
Topics & keywords
- Lasso (programming language)
- Logistic regression
- Single-nucleotide polymorphism
- Regression
- Elastic net regularization
- Computer science
- Feature selection
- Regression analysis
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