Cluster-Localized Sparse Logistic Regression for SNP Data
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Harald Binder
Abstract
The task of analyzing high-dimensional single nucleotide polymorphism (SNP) data in a case-control design using multivariable techniques has only recently been tackled. While many available approaches investigate only main effects in a high-dimensional setting, we propose a more flexible technique, cluster-localized regression (CLR), based on localized logistic regression models, that allows different SNPs to have an effect for different groups of individuals. Separate multivariable regression models are fitted for the different groups of individuals by incorporating weights into componentwise boosting, which provides simultaneous variable selection, hence sparse fits. For model fitting, these groups of individuals are identified using a clustering approach, where each group may be defined via different SNPs. This allows for representing complex interaction patterns, such as compositional epistasis, that might not be detected by a single main effects model. In a simulation study, the CLR approach results in improved prediction performance, compared to the main effects approach, and identification of important SNPs in several scenarios. Improved prediction performance is also obtained for an application example considering urinary bladder cancer. Some of the identified SNPs are predictive for all individuals, while others are only relevant for a specific group. Together with the sets of SNPs that define the groups, potential interaction patterns are uncovered.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- A New Explained-Variance Based Genetic Risk Score for Predictive Modeling of Disease Risk
- Hessian Calculation for Phylogenetic Likelihood based on the Pruning Algorithm and its Applications
- Cluster-Localized Sparse Logistic Regression for SNP Data
- How to analyze many contingency tables simultaneously in genetic association studies
- Incorporating the Empirical Null Hypothesis into the Benjamini-Hochberg Procedure
- Estimating the Number of One-step Beneficial Mutations
- Testing clonality of three and more tumors using their loss of heterozygosity profiles
- Correction for Founder Effects in Host-Viral Association Studies via Principal Components
- A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology
- An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping
- A Novel and Fast Normalization Method for High-Density Arrays
- Performance of MAX Test and Degree of Dominance Index in Predicting the Mode of Inheritance
- A Bayesian autoregressive three-state hidden Markov model for identifying switching monotonic regimes in Microarray time course data
- QTL Mapping Using a Memetic Algorithm with Modifications of BIC as Fitness Function
- Computing Posterior Probabilities for Score-based Alignments Using ppALIGN
