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Loci involved in local adaptation can be identified by an unusual correlation between allele frequencies and important ecological variables, or simply by extreme allele frequency differences between geographic regions. However, such comparisons are complicated by differences in sample sizes and the neutral correlation of allele frequencies across populations due to shared history and gene flow. To overcome these difficulties we have developed a Bayesian method that estimates the empirical pattern of covariance in allele frequencies between populations from a set of markers, and then uses this as a null model for a test at individual SNPs. This test is shown through power simulations to outperform other correlation tests used in the literature. We also demonstrate that the method can be used to perform a test for allele frequency differentiation, that offers a powerful alternative to tests based on pairwise $F_{st}$ or global $F_{st}$.