Objectives: Discrete blocks of low haplotype diversity exist within the human genome. The non-redundant subset of 'haplotype tagging' single nucleotide polymorphisms (htSNPs) in such blocks can distinguish a majority of the haplotypes. Several approaches have been proposed to determine htSNPs, ranging from visual inspection to formal analytic procedures. Optimal htSNPs can be estimated using a small subgroup of an association study population that have been genotyped for a dense SNP map, and it is just these htSNPs that are genotyped in the remainder of the samples. We investigated by simulation how the size of the subsample affects the power of association studies, and what type of subjects it should include.
Methods: We used the program tagSNPs [Stram et al., Hum Hered 2003;55:27-36], which selects htSNPs to minimize the uncertainty in predicting common haplotypes for individuals with unphased genotype data.
Results: On average, 27% of the SNPs were designated as htSNPs. Genotyping as few as 25 unphased individuals to select the htSNPs did not appear to reduce the power of an association study, as compared with using all SNPs. For the disease models considered, selecting htSNPs based on cases, controls, or a mixture of both gave similar results.
Conclusions: These results suggest that the genotyping effort in an association study can be substantially reduced with little loss of power by identifying htSNPs in a small subsample of individuals.
Copyright 2003 S. Karger AG, Basel