Computational protocol: TagSNP transferability and relative loss of variability prediction from HapMap to an admixed population

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Protocol publication

[…] The tagSNP transferability study was conducted using the Stampa algorithm [] implemented on the Gevalt package []. This algorithm aims to maximize the expected accuracy of predicting untyped SNPs based on genotype data of the tagSNPs []. To conduct this study, first the variability prediction accuracy for each gene was assessed to calculate the coverage of the HapMap phase II data in relation to the total number of available SNPs in each region: number of common SNPs - with minor allele frequency (MAF) > 0.05; number of SNPs required to capture 100% of SNP prediction; maximum prediction using the same number of SNPs as in the study; and the prediction for the selected set of SNPs. Then, the set of SNPs selected with average distances of 5 Kb had their variability prediction calculated based on two until the maximum number of tagSNPs for all five samples. Finally, the relative loss of variability prediction (in percentage points; pp) was calculated by subtracting the variability prediction of tagSNPs selected for BRA from the relative prediction obtained when using the tagSNPs selected for each of the HapMap populations and the pooled sample in the Brazilian group.Measures of linkage disequilibrium (LD) between pairs of SNP loci (D' and r2) were calculated by the Gerbil algorithm [], implemented in Gevalt, using the standard maximum-likelihood and expectation-maximization algorithm methods. Only the SNPs accounted for in all five populations were evaluated. A pairwise population LD analysis was carried out using a Spearman's correlation coefficient. […]

Pipeline specifications

Software tools SNPinfo, GERBIL
Applications Population genetic analysis, GWAS