However, we encourage researchers to take things one step further and make their data more broadly available than through repositories like dbGaP. Whereas dbGaP is good for genotypes, available phenotype data are often highly limited, and are only a tiny fraction of all the phenotypes a particular study collects. Those phenotypes, and the potential genetic associations arising from them, are never shared. In the saddest cases, they are never even analyzed by the original investigative team. Direct collaboration between like-minded investigators with similar data is a powerful way to aggregate data and increase statistical power and generalizability. In those cases where raw data cannot be shared, or if genetic data is at some point in the future considered identifiable data, it is still possible to share association summary statistics, which has been widely successful in GWAS meta-analysis.
