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Notice that in all previous experiments, genotype imputation was performed using a simple regression-based algorithm that we have previously described, which allowed us to efficiently run the exceptionally large number of comparisons already presented here. We analyzed the Coriell Institute��s publicly available http://www.selleck.cn/products/sch772984.html dataset for the study of Parkinson��s disease. The Parkinson��s dataset contains 270 cases and 271 controls assayed for 396,591 SNPs (Fung et al., 2006). All the participants are of European-American ancestry and thus the HapMap CEPH Europeans were used as a reference population. This dataset contains 369,627 SNPs in common with the HapMap CEU dataset. This subset of markers from the reference HapMap European population was used to identify tSNPs and determine prediction coefficients for the tagged ones (for genotype prediction using our simple regression-based method) as well as reference haplotypes (for genotype imputation using Beagle). We targeted 98% accuracy while varying the eigenSNP parameter between 10 and 20. In the original and reconstructed datasets, we compared the Armitage trend test statistic for those SNPs that have also been genotyped for the HapMap CEU population. A?P-value less than 10?4 was set as a threshold for reporting significant correlation with affection status. It should be noted that the?P-values of (Fung et al., 2006) cannot be reproduced exactly as a result of our choice to fill in missing entries for illustration http://www.selleckchem.com/products/obeticholic-acid.html purposes throughout this paper. Figure 7 demonstrates the performance of the selected tSNPs using both a simple regression-based method and the much more sophisticated algorithm implemented in Beagle. http://www.selleckchem.com/products/Adriamycin.html It is worth noting that, despite the relatively low density of SNPs in the reference sample (markers in the Illumina chips have been chosen to cover the entire genome), our results still uncover considerable redundancy. When the 98% accuracy and 10 eigenSNPs parameter combination is used, 62% of the SNPs are selected as tagging. Beagle takes more than 30 h in order to impute genomewide genotypes but is very successful in genotype prediction with an error of 3.2%. The regression-based method on the other hand takes less than 1 h yielding an error of 5.2%. Reflecting the more accurate prediction, Beagle produces no false positive associations while the regression-based method results in eleven false positive associations, seven of which were originally weakly associated with the disease (P?