THL Biobank contains a large amount of data on the health and lifestyles of the Finnish population, the collection of which began already in the 1960s. When this is combined with genetic data stored in the biobank and with national health registers, illness risk factors can be effectively identified and predicted.
Thanks to the FinnGen research project, THL Biobank may offer genetic data for other biobank studies. The goal of the FinnGen project, started in autumn 2017, is to collect the genome data of half a million Finns. The project utilises samples collected by all Finnish biobanks. Genome data is combined with data available in national healthcare registers. This gives a better understanding on how diseases develop, and identify new treatments.
Postdoctoral researcher and genome expert at THL Biobank Heidi Marjonen processes genomic data of all THL Biobank cohorts.
– Now it will be possible to create personalized treatment methods. When lifestyle data is combined with genetic data, better drug treatments can be developed, says Marjonen.
– The polygenic risk score is a major research trend, says Heidi Marjonen. The risk score is a single value that reveals the genetic burden of a disease.
– Researchers receive information about the genome in a convenient way and allows to study the effect of genome on a disease or other traits in an individual.
CSC´s sensitive data services and ePouta enable a secure transfer between the portal’s user interface and the database.
FINRISK is one of the world’s longest-running population survey time series
The large-scale FINRISK population survey of risk factors for chronic, noncommunicable diseases has been used to collect health data on the population every five years since 1972. The data can be analyzed to identify risk factors for chronic diseases. More and more genetic data is also being collected, and when combined with registry data it makes it possible to develop measures to prevent diseases and to create more effective treatments.
There are many Finnish survey datasets, but according to Kati Kristiansson of the Finnish Institute for Health and Welfare (THL), FINRISK contains exceptionally rich and diverse data on the health of the Finnish population. The participants are randomly selected from populations in different regions of the country. They are asked about their lifestyle, family history of illness, mood and other factors related to health and wellbeing. Registry and survey data can be combined with genetic samples.
– When all these laboratory measurements and questionnaire data are combined with health registry data, we can learn about people’s medical histories, what medications they have taken and all the causes of death in the population.
In 2015, the FINRISK data collections were transferred to the THL Biobank. Two years later, the FINRISK and Health 2000 surveys were combined into a new FinHealth population survey. Naturally, there are also other biobank datasets containing genomic data and data from health examinations. What makes the FINRISK data of exceptional quality is its time span.
According to Kristiansson, what is particularly valuable in the FINRISK population data is the monitoring over time after the initial measurements have been taken.
– This kind of analysis helps in determining the factors that increase the risk of future illness. Hereditary and lifestyle factors are a very useful aid in this regard.
The Finnish ELIXIR node CSC is developing the Finnish FEGA service and it will be available for users in 2022. Finnish FEGA is a national service based on EGA. Data, as well as the public metadata, can be uploaded to the FEGA. FEGA is a service for storing and sharing all types of biomedical data consented for research but not for fully public dissemination. In the future, FINRISK data and genomic information should be stored on the CSC’s FEGA service. FEGA allows to store sensitive data in Finland in a way that fulfils all the requirements of the General Data Protection Regulation (GDPR).
Read more:
FINRISK: one of the world’s longest-running population survey time series
This article was first published on 16 June by CSC.
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