Issues of publication bias, lack of replicability and false discovery have long plagued the genetics community. Proper utilization of public and shared data resources presents an opportunity to ameliorate these problems. We present an approach to public database management that we term Quality Preserving Database (QPD). It enables perpetual use of the database for testing statistical hypotheses while controlling false discovery and avoiding publication bias on the one hand, and maintaining testing power on the other hand. We demonstrate it on a use case of a replication server for GWAS findings, underlining its practical utility. We argue that a shift to using QPD in managing current and future biological databases will significantly enhance the community's ability to make efficient and statistically sound use of the available data resources. The prevalence and importance of public databases and shared data resources in the genetics and molecular biology community have been constantly increasing, and this trend is expected to continue and accelerate with the growth in genetic data generation. For example, publicly available GWAS data sources like WTCCC (Wellcome Trust Case Control Consortium, 2010) and dbGaP (Mailman et al., 2007) have had a major contribution in genetic research well beyond the findings in the original studies which collected the data. An important class of data sharing efforts is the formation of scientific consortia for collection and analysis of genetic data, ranging in size from several collaborating scientists to entire research communities (Rich et al., 2007) . The coming years are expected to see the formation of multi-purpose public genetic databases of unprecedented size and utility (Couzin-Frankel, 2012) .
Concerned with the above issues and the importance of data sharing in the genetics community, REF proposed an approach to public database management, called Quality Preserving Database (QPD).
Novel Statistical Tools for Management of Public Databases Facilitate Community-Wide Replicability and Control of False Discovery
Genetic epidemiology
Genetic epidemiology
Biology Medicine