  Background: 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 need 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.
  Results: 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.
  Conclusions: 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/ .
