Coverage and power in genomewide association studies

Abstract

The ability of genomewide association studies to decipher genetic traits is driven in part by how well the measured single-nucleotide polymorphisms "cover" the unmeasured causal variants. Estimates of coverage based on standard linkage-disequilibrium measures, such as the average maximum squared correlation coefficient (r2), can lead to inaccurate and inflated estimates of the power of genomewide association studies. In contrast, use of the "cumulative r2 adjusted power" measure presented here gives more-accurate estimates of power for genomewide association studies.

Figure 1

Cumulative distribution of maximum r² values from the study by Hinds et al.

Figure 2

Effects of varying parameter values on average and cumulative r² adjusted power. Plots are based on a case-control study with 1,000 cases and 1,000 unmatched controls, disease-allele frequency 0.3, OR 1.5, α level 10⁻⁶, and a log additive (multiplicative) model. For each plot, power was calculated by varying one parameter while holding the other parameters constant. AA = African American; EA = European American. A, Sample size is varied from 100 cases and 100 controls to 3,000 cases and 3,000 controls. B, OR is varied from 1.2 to 2.0. C, Disease-allele frequency is varied from 0.01 to 0.99. D, The α level is varied from 10⁻⁸ to 0.05.