Another possibility is bias. Incorporating between-study heterogeneity in the summary calculations has the advantage to penalize associations where results are inconsistent across studies due to population-specific biases and gives higher ranks to the consistent associations [25]. The 3 GWA investigations on type 2 diabetes paid meticulous attention to methodological detail and their design was exemplary. Careful genotyping controls were set and population stratification was controlled with principal component analysis [26]. Nevertheless, minute biases affecting particular polymorphisms with minute odds ratios around 1.12 cannot be excluded. Even if some major systematic errors (e.g. population stratification, genotyping error, phenotype misclassification) are controlled, not all biases are foreseeable. Moreover, minimized average biases do not exclude much larger differential biases for a few polymorphisms. P-values for testing the observed genetic effects against the null effect hypothesis account for random, not systematic, error.
