Genome-wide association analysis by lasso penalized logistic regression.

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 interact

SUBMITTER: Wu TT 

PROVIDER: S-EPMC2732298 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature