Genome-wide association analysis by lasso penalized logistic regression

Bioinformatics. 2009 Mar 15;25(6):714-21. doi: 10.1093/bioinformatics/btp041. Epub 2009 Jan 28.


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.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Computational Biology / methods
  • Genome-Wide Association Study*
  • Internet
  • Logistic Models*
  • Polymorphism, Single Nucleotide
  • Software