Overall, detection of heterogeneity is very useful. Some polymorphisms are shown to reach genome-wide statistical significance by fixed effects calculations, but not by random effects calculations due to large between-study heterogeneity. In these cases, priority should be given to the consideration of other, correlated phenotypes and fine mapping for identifying linked, true culprit polymorphisms that yield less heterogeneous association signals. These situations are likely to be very common in the GWA setting. Tag markers are not selected based on “candidate gene variant” considerations [24]. Thus it is more likely that one may hit upon a variant that is a linked marker rather than hit directly upon the culprit causative variant. Markers will often have variable linkage disequilibrium across different populations. This will result in heterogeneous genetic effects across studies.
