Prioritizing causal variants in Genome-Wide Association Studies (GWAS) is challenging due to linkage disequilibrium and high dimensionality. We propose an iterative algorithm for the selection of genetic variables that allows the integration of biological priors. The proposed algorithm requires the specification of the power function of a test statistic and incorporates a priori information on each SNP. We derive the approximate and exact distributions of several commonly used test statistics. At each step of the proposed algorithm, we select the SNP with the largest a posteriori probability of being causal. We consider several stopping criteria for the algorithm. We conduct comprehensive simulation studies to assess the performance of several versions of the proposed algorithm and to make a comparison to some existing competitors. The use of the proposed prioritization algorithm is illustrated with a cis-eQTL application to a human gene-expression data, using European discovery and Yoruba cross-population replication.

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