In all, heterogeneity is a useful aspect of the data, rather than a nuisance, as it can often point to leads that can clarify better the nature of postulated associations in the context of meta-analysis [31]. Heterogeneity should not be ignored and should be carefully factored in the interpretation of emerging genetic associations from GWA studies. Heterogeneity has implications also for the epidemiological design of GWA studies and their replication efforts. Consistency in the definition of phenotypes and meticulous attention to quality control in genotyping and avoidance of population stratification is warranted, so as to avoid heterogeneity due to bias. However, heterogeneity due to genuine differences should not be avoided. Thus one should encourage diversity in secondary aspects of the study design across studies, such as the use of matching or not for other population characteristics, and targeting of populations of diverse racial descent with different linkage disequilibrium patterns. Finally, proper evaluation of between-study heterogeneity would ideally require complete and transparent individual-level information on genotype results from all conducted GWA investigations. Ensuring full public data availability would enhance the credibility of GWA evidence.
