Our Markov Chain produces three estimates of imputation quality and these can be used to focus analyses on subsets of high-quality genotypes. First, it produces a quality score that estimates the accuracy of each imputed genotype and is simply the proportion of iterations where the final imputed genotype (by taking a majority vote across all iterations) was selected. Second, it produces an overall measure of the accuracy of imputation for each marker, which is the genotype quality score averaged across all individuals. Finally, by comparing the distribution of sampled genotypes in each iteration with the estimated allele counts that result from averaging over all iterations, it produces an estimate of the r2 between imputed and true genotypes (see Methods for more details). Quality measures for individual genotypes were good predictors of imputation accuracy (Supplementary Figure 1, Right Panel) and show that most imputed genotypes are called with a high degree of confidence (Supplementary Figure 1, Left Panel). For example, as measured by their quality scores, the top 95% of genotypes had average quality scores of 98.9% and actually matched experimental
