Although recent reports from genome-wide association studies have identified a large number of associations between chromosomal loci and complex human diseases (5), to date, most of these studies have had few measures in common (6–8). It is important to compare findings across studies to validate results and to detect relatively weak statistical associations that are commonly found when multiple genetic polymorphisms make small contributions to common disorders. Moreover, there are environmental exposures that can have important ramifications. These include the effects of environmental factors, including ambient environment, personal behaviors, and treatments that can influence susceptibility, presentation, and progression of disease. Several groups of investigators have successfully expanded study populations by incorporating extracted metadata from complementary studies (9, 10). For some diseases, such as diabetes and Crohn’s disease, pooling of multiple genome-wide association studies by meta-analysis has led to the discovery of new gene associations (11–13). However, standard measures could greatly simplify the task of combining studies and validating findings. Over time, the use of standard measures should make it possible to build larger populations for cross-study analysis, thus providing increased statistical power and the ability to detect moderate associations and gene-gene and gene-environment interactions.
