Next, we carried out exploratory procedures to investigate key indicators of a poor imputation performance on association statistics. Specifically, we tested how the use of different quality calling criteria and the minor allele frequency (MAF) thresholds could predict the observed bias between empiric and imputed frequencies. This feature was explored by using more stringent cutoffs for calling rates, Hardy-Weinberg disequilibrium (HWD) and the use of SNPs showing a MAF ≥1%. The number of markers excluded by these quality filters was determined. The minus log transformed association statistics of the remaining imputed or genotyped markers were compared by analyzing their degree of correlation (Additional file 4, Table S3 and Additional file 5, Table S4). Consistent with findings from recent investigations studying the accuracy of imputation algorithms over genotype determination, the use of polymorphisms with a MAF below 1% accompanied or not by lower calling rates decreases the overall agreement between results based on imputed genotypes and those obtained by truly genotyped markers. In our examined dataset, HWD had no major impact on the performance of imputation since markers with strong Hardy-Weinberg deviation were already trimmed from the dataset before publication.
