Gene Variant Databases and Sharing: Creating a Global Genomic Variant Database for Personalized Medicine

Abstract

Revolutionary changes in sequencing technology and the desire to develop therapeutics for rare diseases have led to the generation of an enormous amount of genomic data in the last 5 years. Large-scale sequencing done in both research and diagnostic laboratories has linked many new genes to rare diseases, but has also generated a number of variants that we cannot interpret today. It is clear that we remain a long way from a complete understanding of the genomic variation in the human genome and its association with human health and disease. Recent studies identified susceptibility markers to infectious diseases and also the contribution of rare variants to complex diseases in different populations. The sequencing revolution has also led to the creation of a large number of databases that act as "keepers" of data, and in many cases give an interpretation of the effect of the variant. This interpretation is based on reports in the literature, prediction models, and in some cases is accompanied by functional evidence. As we move toward the practice of genomic medicine, and consider its place in "personalized medicine," it is time to ask ourselves how we can aggregate this wealth of data into a single database for multiple users with different goals.

Keywords: data sharing; database; inherited disease; mutation; variant.

Figure 1

Amalgamation of databases to create a global database (blue circle). Data can assimilated from the four main domains where current variant data resides (open squares). These four domains include the LSDBs, disease specific cohorts and patient registries, databases in the public domain, such as ClinVar, and clinical laboratories databases, such as ClinVitae, EmVClass and ARUP, as well as databases in private domain, such as HGMD. Each of these arms brings a different strength to the global database and creates uniformity in data accessibility and presentation for usage for clinical trials and understanding the role genomic variants in personalized medicine.