Genome wide association studies are a promising tool for the determination of genetic signatures that could, when associated with environmental factors, predispose an individual to a phenotype of interest. Quality control of data in a GWAS study has been implicated as an important source of bias and loss of power in both linkage analyses and population-based association studies [6]. Imputation algorithms use allelic frequencies of typed markers and the haplotypic structure information to infer the expected allelic frequencies of a low quality or missing marker. These algorithms are considered a near zero cost alternative to allow the combination of results generated by different platforms with distinct genome coverage. The combination of directly genotyped and imputed allelic frequencies allowed the identification of SNPs that were strongly associated to diseases of interest such as hypertension and diabetes [1,8]. Genome wide association studies, like any other large scale experiments, are prone to false negative associations due to the impressive amount of hypothesis tests being performed and a small percentage of low quality SNPs can cause important statistical problems ([11]). These statistical limitations demand
