Imputation algorithms are a convenient and low cost solution to increase the coverage and power of a performed GWAS, allowing comparison of already generated results and bridging the gap of distinct sets of markers in different GWAS platforms. Despite their, already evaluated, overall high accuracy for genotypic prediction, we describe that even after traditional filtering criteria, a considerable amount of markers may still present important problems when one is to evaluate the association statistics derived from these markers. We serially tested a group of features known as predictors for a low accurate genotype imputation. Mostly, these features were not able to robustly identify those markers from whom association statistics are significantly biased. One solution that seems to be robust is the use of information provided by flanking markers with the use of our sliding window procedure. It is expected that concordant imputed markers, showing agreement with association statistics derived from directly genotyped allelic frequencies, are located in haplotypic blocks composed by other markers showing, at least, a moderate association with the phenotype under study. Our results highlight the immense potential
