A novel algorithm for simultaneous SNP selection in high-dimensional genome-wide association studies
1 Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Härtelstr. 16–18, D-04107 Leipzig, Germany
2 Faculdade de Economia e Gestão & CEGE, Catholic University of Portugal, Rua Diogo Botelho 1327, 4169-005 Porto, Portugal
Citation and License
BMC Bioinformatics 2012, 13:284 doi:10.1186/1471-2105-13-284Published: 31 October 2012
Identification of causal SNPs in most genome wide association studies relies on approaches that consider each SNP individually. However, there is a strong correlation structure among SNPs that needs to be taken into account. Hence, increasingly modern computationally expensive regression methods are employed for SNP selection that consider all markers simultaneously and thus incorporate dependencies among SNPs.
We develop a novel multivariate algorithm for large scale SNP selection using CAR score regression, a promising new approach for prioritizing biomarkers. Specifically, we propose a computationally efficient procedure for shrinkage estimation of CAR scores from high-dimensional data. Subsequently, we conduct a comprehensive comparison study including five advanced regression approaches (boosting, lasso, NEG, MCP, and CAR score) and a univariate approach (marginal correlation) to determine the effectiveness in finding true causal SNPs.
Simultaneous SNP selection is a challenging task. We demonstrate that our CAR score-based algorithm consistently outperforms all competing approaches, both uni- and multivariate, in terms of correctly recovered causal SNPs and SNP ranking. An R package implementing the approach as well as R code to reproduce the complete study presented here is available from http://strimmerlab.org/software/care/ webcite.