Genome-wide association analysis by lasso penalized logistic regression.
BACKGROUND
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.


METHODS
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.


BACKGROUND
The software discussed is available in Mendel 9.0 at the UCLA Human Genetics web site.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Wu
Tong Tong
TT
Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA.
Chen
Yi Fang
YF
Hastie
Trevor
T
Sobel
Eric
E
Lange
Kenneth
K
D019295:Computational Biology / Q000379:methods; D055106:Genome-Wide Association Study; D020407:Internet; D016015:Logistic Models; D020641:Polymorphism, Single Nucleotide; D012984:Software