Genome-wide association studies have successfully found many links between genetic variation and phenotypes. However, over the past decade, the statistical and software tools used for doing these studies have substantially changed and improved.
Major improvements have taken GWAS from sampling looking at for significant differences in frequency of a genetic variants between cases and control to models that include covariates, principle components to account for population structure and finally linear mixed models.
With all the methods out there, a formally bench marking study needs to be carried out. This study will use simulated data where the underlying causal genetics is known. The study will run each of the commonly used GWAS methods on the simulated data. It will compare the true discovery rates, false discovery rates, family-wise error rates and other commonly used statistics across the methods.