Estimating coverage and power for genetic association studies using near-complete variation data

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Abstract
Tushar Bhangale, Mark Reider, and Deborah Nickerson report estimates of coverage and power by commercial genotyping arrays using a variation dataset for 76 genes resequenced as part of the SeattleSNPs program. Although studies suggest that SNPs derived from HapMap provide promising coverage and power for association studies, the lack of alternative variation datasets limits independent analysis. Using near-complete variation data for 76 genes resequenced in HapMap samples, we find that coverage of common variation by commercial genotyping arrays is substantially lower compared to the HapMap-based estimates. We quantify the power offered by these arrays for a range of disease models.