The federated database – a basis for biobank-based post-genome studies, integrating phenome and genome data from 600 000 twin pairs in Europe

Abstract

Integration of complex data and data management represent major challenges in large-scale biobankbased post-genome era research projects like GenomEUtwin (an international collaboration between eight Twin Registries) with extensive amounts of genotype and phenotype data combined from different data sources located in different countries. The challenge lies not only in data harmonization and constant update of clinical details in various locations, but also in the heterogeneity of data storage and confidentiality of sensitive health-related and genetic data. Solid infrastructure must be built to provide secure, but easily accessible and standardized, data exchange also facilitating statistical analyses of the stored data. Data collection sites desire to have full control of the accumulation of data, and at the same time the integration should facilitate effortless slicing and dicing of the data for different types of data pooling and study designs. Here we describe how we constructed a federated database infrastructure for genotype and phenotype information collected in seven European countries and Australia and connected this database setting via a network called TwinNET to guarantee effortless data exchange and pooled analyses. This federated database system offers a powerful facility for combining different types of information from multiple data sources. The system is transparent to end users and application developers, since it makes the set of federated data sources look like a single system. The user need not be aware of the format or site where the data are stored, the language or programming interface of the data source, how the data are physically stored, whether they are partitioned and/or replicated or what networking protocols are used. The user sees a single standardized interface with the desired data elements for pooled analyses.

From Data Islands to Sharing Data in the Cloud: the Evolution of Data Integration in Biological Data Repositories

“…The next phase is the BBMRI-ERIC (European Research Infrastructure Consortium), which integrates all these resources into a hub-and-spoke [Muilu et al 2007] network properly embedded into the European scientific, ethical, legal, and societal frameworks. The hub-and-spoke model consists in creating or choosing a central resource and connecting all others to it instead of creating one connection between each resource pair.…”

“…The GenomEUtwin [Litton et al 2003, Muilu et al 2007] is an international collaboration between eight repositories providing information about more than 600 000 human twins pairs. They propose the TwinNET, a federation of local data warehouses, combined with a global mediator that provides transparent access to them through database instances and the use of DiscoveryLink.…”

“…The availability of large sample collections accelerates medical breakthroughs, and researchers aim to analyze genomes from whole populations instead of from a few individuals [Muilu et al 2007]. Fetching all genomes of interest to a private infrastructure (i.e., data shipping) before processing them is each time more impractical.…”

Inter- and Intra-Biobank Networks: Classification of Biobanks

“…With the exception of a few examples, e.g. genomeEUtwin [25] , most networks incorporate retrospective case-control biobanks, and issues relating to 'blanket consent' or 'consent waivers' are not as relevant for retrospective biobanks. Retrospective biobanks rarely collect any data after the initial recruitment period other than perhaps mortality data.…”

Necessary and sufficient conditions for transaction-consistent global checkpoints in a distributed database system

“…FDBSs are suitable for integrating complex data. For example, as Muilu et al [20] point out, large-scale biobank-based post-genome era research projects like GenomEUtwin (an international collaboration between eight Twin Registries) require extensive amounts of genotype and phenotype data combined from different data sources located in different countries. Building a solid infrastructure for accessing such data requires using the model of federated databases.…”