Genome-wide association analysis by lasso penalized logistic regression

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 l...

Disgrifiad llawn

Wedi'i Gadw mewn:
Manylion Llyfryddiaeth
Prif Awduron: Wu, Tong Tong, Chen, Yi Fang, Hastie, Trevor, Sobel, Eric, Lange, Kenneth
Fformat: Erthygl
Iaith:English
Cyhoeddwyd: Oxford University Press 2009
Pynciau:
Original Papers
Mynediad Ar-lein:https://ncbi.nlm.nih.gov/pmc/articles/PMC2732298/
https://ncbi.nlm.nih.gov/pubmed/19176549
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/bioinformatics/btp041
Tagiau: Ychwanegu Tag
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
id pubmed-2732298
record_format dspace
spelling pubmed-27322982010-03-15 Genome-wide association analysis by lasso penalized logistic regression Wu, Tong Tong Chen, Yi Fang Hastie, Trevor Sobel, Eric Lange, Kenneth Bioinformatics Original Papers 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. Contact: klange@ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2009-03-15 2009-01-28 /pmc/articles/PMC2732298/ /pubmed/19176549 http://dx.doi.org/10.1093/bioinformatics/btp041 Text en © The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
institution US National Library of Medicine
collection PubMed Central
language English
format Article
topic Original Papers
spellingShingle Original Papers
Wu, Tong Tong
Chen, Yi Fang
Hastie, Trevor
Sobel, Eric
Lange, Kenneth
Genome-wide association analysis by lasso penalized logistic regression
description 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. Contact: klange@ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online.
author Wu, Tong Tong
Chen, Yi Fang
Hastie, Trevor
Sobel, Eric
Lange, Kenneth
author_facet Wu, Tong Tong
Chen, Yi Fang
Hastie, Trevor
Sobel, Eric
Lange, Kenneth
author_sort Wu, Tong Tong
title Genome-wide association analysis by lasso penalized logistic regression
title_short Genome-wide association analysis by lasso penalized logistic regression
title_full Genome-wide association analysis by lasso penalized logistic regression
title_fullStr Genome-wide association analysis by lasso penalized logistic regression
title_full_unstemmed Genome-wide association analysis by lasso penalized logistic regression
title_sort genome-wide association analysis by lasso penalized logistic regression
publisher Oxford University Press
publisher_facet Oxford University Press
publishDate 2009
url https://ncbi.nlm.nih.gov/pmc/articles/PMC2732298/
https://ncbi.nlm.nih.gov/pubmed/19176549
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/bioinformatics/btp041
_version_ 1760759374589984768

Eitemau Tebyg