Pooling data from large population-based biobanks allows researchers to study complex diseases, but common variables need to be established before information can be shared. Professor Ronald Stolk and BioSHaRE.eu project manager Lisette Giepmans tell us about their work to develop harmonised measures and standardised computing infrastructures
Pooling data to get to the roots of disease The
world’s biobanks hold vast amounts of valuable medical information that researchers can use to investigate the root causes of disease. However, while data is currently mainly used within an individual biobank, sharing data more widely could give researchers a wider perspective on causes and risk factors for disease. Researchers who want to study the causes of complex diseases and geneenvironment interactions need large numbers of participants, which is currently beyond the scope of most individual biobanks. Data sharing, more specifically data pooling, is therefore essential for these types of studies, a context in which the work of the BioSHaRE project takes on real importance. “The BioSHaRE project aims to enable datasharing between biobanks. An important tool for this is a searchable website with all study catalogues where researchers can find which biobank contains specific data. One of our aims is to establish an overview of the existing biobanks; what information do they gather and to what level of detail?” outlines Project Manager Lisette Giepmans. Before data can be pooled they must be 10
made comparable (harmonized). Researchers in the project are working to enable harmonization of study data and to develop a secure environment for central statistical analysis of locally stored individual data. Biobanks typically use their own questionnaires and hold data in different formats, an issue which researchers need to consider when pooling it. “When you pool the data you need to
example standard alcoholic drink) and the data can be analysed from the individual level data, and in real time. The analyses can be done more flexibly and more efficiently by a single investigator from a central computer. A lot of work has already been done in these areas, with BioSHaRE building on the DataSHAPER harmonization platform, initially developed by the
These biobanks include data on everybody – so if you can pool it effectively you get a huge resource of information. The big advantage of population-based biobanks compared to any other form of research is that it’s real life data – you don’t have to interpret the research to make it applicable to the population harmonize it first and provide a secure system to share it. The available data is transferred into a new common variable through an algorithm written specifically for each biobank,” says Ms Giepmans. The big advantage over the traditional metaanalysis is twofold: the variables from the different biobanks are translated or harmonized into common variables (for
Public Population Project in Genomics (P3G) in Canada. The project is also working on the Opal and Mica applications, software designed by the OBiBA initiative and the OICR in Canada to enable data sharing, Molgenis for genetic data, and the DataSHIELD application for secure federated statistical analysis of individual data.
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