In our most relevant comparisons with modest effects and large sample sizes, use of the dosage summaries was as powerful as using the mixture model methods, at a fraction of the computational cost. The exception to this result is apparent only at SNPs with very large genetic effects. In such situations of large effects, most methods will be effective at detecting an association. This difference is most pronounced at poorly imputed SNPs. In practice, many researchers routinely exclude results from poorly imputed SNPs, such as those below an R2 threshold of, say, 30%. Application of this quality-control filter to our results would tend to mitigate (tabulated) differences in power between the mixture and standard regression methods in the setting of large effect sizes. In fact, it may be fruitful, in some cases, to devote additional computational resources to some of these SNPs, such as application of mixture models. However, for the majority of settings and effect sizes detected and verified in GWA studies, use of dosage quantities appears to be effective and efficient to account for the uncertainty in the imputed genotypes.
