Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps
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Matt Silver
Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways.We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our “pathways group lasso with adaptive weights” (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets.In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- The Inheritance Procedure: Multiple Testing of Tree-structured Hypotheses
- Optimality Criteria for the Design of 2-Color Microarray Studies
- Stopping-Time Resampling and Population Genetic Inference under Coalescent Models
- A Mixture-Model Approach for Parallel Testing for Unequal Variances
- Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps
- MicroRNA Transcription Start Site Prediction with Multi-objective Feature Selection
- A Context Dependent Pair Hidden Markov Model for Statistical Alignment
- Fast Wavelet Based Functional Models for Transcriptome Analysis with Tiling Arrays
- Alignment-free Sequence Comparison for Biologically Realistic Sequences of Moderate Length
- Transcriptional Network Inference from Functional Similarity and Expression Data: A Global Supervised Approach
- Improving Hidden Markov Models for Classification of Human Immunodeficiency Virus-1 Subtypes through Linear Classifier Learning
