Genome-wide association analysis by lasso penalized logistic regression
TLDR
The performance of lasso penalized logistic regression in case-control disease gene mapping with a large number of SNPs (single nucleotide polymorphisms) predictors is evaluated and coeliac disease results replicate the previous SNP results and shed light on possible interactions among the SNPs.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 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.read more
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References
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Proceedings Article
EfficientL 1 regularized logistic regression
TL;DR: Theoretical results show that the proposed efficient algorithm for L1 regularized logistic regression is guaranteed to converge to the global optimum, and experiments show that it significantly outperforms standard algorithms for solving convex optimization problems.
Journal ArticleDOI
Identifying interacting SNPs using Monte Carlo logic regression.
TL;DR: Monte Carlo logic regression is proposed, an exploratory tool that combines Markov chain Monte Carlo and logic regression, an adaptive regression methodology that attempts to construct predictors as Boolean combinations of binary covariates such as SNPs.
Journal ArticleDOI
A Conditional Inference Framework for Extending the Transmission/Disequilibrium Test
Laura C. Lazzeroni,Kenneth Lange +1 more
TL;DR: This paper describes permutation extensions of the TDT that share the conditional inference framework and permits extensions to multiple alleles, multiple loci, unaffected siblings, and genotypic rather than allelic associations.
Posted Content
Identification of SNP interactions using logic regression
Holger Schwender,Katja Ickstadt +1 more
TL;DR: In this paper, logic regression is used to identify SNP interactions explanatory for the disease status in a case-control study and propose two measures for quantifying the importance of these interactions for classification, which are then applied to the SNP data of the GENICA study, a study dedicated to the identification of genetic and gene-environment interactions associated with sporadic breast cancer.
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