Modeling gene-covariate interactions in sparse regression with group structure for genome-wide association studies
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Yun Li
,
George T. O’Connor
Abstract
In genome-wide association studies (GWAS), it is of interest to identify genetic variants associated with phenotypes. For a given phenotype, the associated genetic variants are usually a sparse subset of all possible variants. Traditional Lasso-type estimation methods can therefore be used to detect important genes. But the relationship between genotypes at one variant and a phenotype may be influenced by other variables, such as sex and life style. Hence it is important to be able to incorporate gene-covariate interactions into the sparse regression model. In addition, because there is biological knowledge on the manner in which genes work together in structured groups, it is desirable to incorporate this information as well. In this paper, we present a novel sparse regression methodology for gene-covariate models in association studies that not only allows such interactions but also considers biological group structure. Simulation results show that our method substantially outperforms another method, in which interaction is considered, but group structure is ignored. Application to data on total plasma immunoglobulin E (IgE) concentrations in the Framingham Heart Study (FHS), using sex and smoking status as covariates, yields several potentially interesting gene-covariate interactions.
Acknowledgments
This research is supported by National Institute Health grants ES020827, DK078616, N01 HC25195 and P01 AI050516 (in part). A portion of this research was conducted using the Linux Clusters for Genetic Analysis (LinGA) computing resources at Boston University Medical Campus.
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©2015 by De Gruyter
Artikel in diesem Heft
- Frontmatter
- Research Articles
- A novel method to prioritize RNAseq data for post-hoc analysis based on absolute changes in transcript abundance
- A mutual information estimator with exponentially decaying bias
- Bayes factors based on robust TDT-type tests for family trio design
- Modeling gene-covariate interactions in sparse regression with group structure for genome-wide association studies
- Weighted Kolmogorov Smirnov testing: an alternative for Gene Set Enrichment Analysis
- Application of the fractional-stable distributions for approximation of the gene expression profiles
- Software and Application Notes
- CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data
- TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists
Artikel in diesem Heft
- Frontmatter
- Research Articles
- A novel method to prioritize RNAseq data for post-hoc analysis based on absolute changes in transcript abundance
- A mutual information estimator with exponentially decaying bias
- Bayes factors based on robust TDT-type tests for family trio design
- Modeling gene-covariate interactions in sparse regression with group structure for genome-wide association studies
- Weighted Kolmogorov Smirnov testing: an alternative for Gene Set Enrichment Analysis
- Application of the fractional-stable distributions for approximation of the gene expression profiles
- Software and Application Notes
- CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data
- TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists
