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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Multi-Omics Data Fusion for Cancer Molecular Subtyping Using Sparse Canonical Correlation Analysis.
TL;DR: Wang et al. as mentioned in this paper proposed sparse canonical correlation analysis for cancer classification (SCCA-CC), which projects each type of single-omics data onto a unified space for data fusion, followed by clustering and classification analysis.
Dissertation
Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases
TL;DR: It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model.
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[Genetic risk assessment of the joint effect of several genes: Critical appraisal].
TL;DR: Various algorithms for the model selection (searching the significant predictor combinations) are considered, beginning from the common marginal screening of the “top” predictors to LASSO and other modern algorithms of compressed sensing.
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On multi-marker tests for association in case-control studies
TL;DR: A theoretical benchmark is established by quantifying the maximum power achievable for multi-marker tests of association in case-control studies, achievable only when the causal marker is known, and a set of weights for the markers that maximize the non-centrality parameter are developed.
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