Genome-wide association studies (GWAS) are a promising tool for the identification of genetic markers underlying phenotypes of interest and recently allowed the identification of markers associated with several human complex phenotypes[1]. These studies have accomplished their goals in improving our knowledge of genetic patterns underlying diseases such as diabetes mellitus type I [1] and II [2] and Cronh's disease [3]. Although methodologically appealing, these high-throughput experiments are not free from biases and limitations. Indeed, it is highly acknowledged that GWAS are not only prone to major drawbacks such as genotyping errors and sample failures, but also to varying levels of genome coverage across samples [4]. In practice, a further complication arises from the barrier imposed by the comparison of results among different GWAS. The commercially available GWA platforms make use of distinct sets of markers with highly heterogeneous genomic coverage ranging from hundreds of thousands to millions of typed markers [5]. This diversity in panels of markers limits even further the full potential of genome-wide association studies to uncover variants putatively implicated in the susceptibility to diseases or other complex
