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
Citations
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TL;DR: OpenMendel as discussed by the authors is an open source software project for genomewide association studies (GWAS), which aims to enable interactive and reproducible analyses with informative intermediate results, scale to big data analytics, embrace parallel and distributed computing, adapt to rapid hardware evolution, and allow cloud computing.
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Structured Genome-Wide Association Studies with Bayesian Hierarchical Variable Selection.
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Stochastic model search with binary outcomes for genome-wide association studies.
TL;DR: A novel Bayesian model search algorithm, Binary Outcome Stochastic Search (BOSS), is introduced, which addresses the model selection problem when the number of predictors far exceeds thenumber of binary responses.
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