Functional annotations have been shown to improve both the discovery power and fine-mapping accuracy in genomewide association studies. However, the optimal strategy to incorporate the large number of existing annotations is still not clear. In this study, we propose a Bayesian framework to incorporate functional annotations in a systematic manner. We compute the maximum a posteriori solution and use cross validation to find the optimal penalty parameters. By extending our previous fine-mapping method CAVIARBF into this framework, we require only summary statistics as input. We also derived an exact calculation of Bayes factors using summary statistics for quantitative traits, which is necessary when a large proportion of trait variance is explained by the variants of interest, such as in fine mapping expression quantitative trait loci (eQTL). We compared the proposed method with PAINTOR using different strategies to combine annotations. Simulation results show that the proposed method achieves the best accuracy in identifying causal variants among the different strategies and methods compared. We also find that for annotations with moderate effects from a large annotation pool, screening annotations individually and then combining the top annotations can produce overly optimistic results. We applied these methods on two real data sets: a meta-analysis result of lipid traits and a cis-eQTL study of normal prostate tissues. For the eQTL data, incorporating annotations significantly increased the number of potential causal variants with high probabilities.KEYWORDS Bayesian fine mapping; annotations; summary statistics; causal variants A large amount of annotation information of genomic elements has been generated, such as the Encyclopedia of DNA Elements (The ENCODE Project Consortium 2012). Regulatory elements, such as those marked by DNase I hypersensitive sites (DHSs), have been shown to be enriched with associations and explain a large proportion of heritability (Maurano et al. 2012;Gusev et al. 2014). Incorporation of these annotations into statistical association analyses can improve both the power in genome-wide association study (GWAS) discovery (Pickrell 2014) and the accuracy in fine mapping underlying causal variants (Quintana and Conti 2013;Kichaev et al. 2014;Wen et al. 2015). However, the number of annotations is often very large and many of them may not be informative for the underlying causal genetic variants. Currently, there is no systematic and effective way to incorporate a large number of annotations in association analyses, which is crucial to the full use of the available information. In practice, usually only a small number of annotations are considered (Quintana and Conti 2013;Wen et al. 2015). A recent proposed algorithm, PAINTOR, suggests a one-by-one test of each annotation selecting the top 4-5 for inclusion. This has potential to waste the information from the remaining annotations, which may together provide significant predictive power of the causal status. Another approach...