The association of imputation procedures with low density chips can offer a convenient way to enhance the cost efficiency ratio and statistical power of a GWAS, since more individuals/markers can be typed by the same cost [12]. Several reports have compared the overall accuracy and statistical power of different imputation methods and highlighted the high genotype prediction accuracy of existing methods especially in genomic regions showing high LD (Linkage Disequilibrium) between markers[9]. Since imputation methods accuracy is closely related to the quality of the empiric frequencies used as an input, we initially determined the complete set of markers that were both directly genotyped and imputed by the multipoint imputation algorithm in WTCCC ([8]) diabetes II GWAS. This resulted in a set of 387,668 markers that were further evaluated. Using this set of markers, we tested a series of different quality criteria thresholds for MAF (minor allele frequencies), calling probabilities and Hardy Weinberg equilibrium deviation and analyzed the overall correlation between minus log transformed P-values of empiric and imputed allelic frequencies under a log-additive model of inheritance. We used a combined
