We utilize Finnish research cohorts and a global network of biobank data with longitudinal health event histories in identifying genetic associations and potential causal variants modifying the risks of diseases and develop risk models to stratify individuals based on their risks for preventive actions or stratified treatment. With health care partners, we develop approaches to apply these models in routine health care.
Revealing the molecular mechanisms underlying common complex diseases holds the promise of improved and targeted prediction, prevention, diagnosis and treatment. Building on unique resources and an extensive track record in disease genetic studies in Finland, the Academy of Finland Centre of Excellence in Complex Disease Genetics (CoECDG[LINK]), hosted by the Institute for Molecular Medicine Finland (FIMM) at HiLIFE and the Faculty of Medicine, aims to develop and apply a powerful, reliable and general strategy for comprehensive identification of risk and protective variants. The CoECDG will also develop and pilot strategies and lead national and international efforts to implement genomic findings into prevention and personalised treatment of common complex diseases.
Data science, including the development and adoption of artificial intelligence -based methods is expected to have a transformative impact on our ability to understand disease development and trajectories. It is also expected to bring about new methods for personalized medicine in which the treatment options for patients are tailored toward the specific genetic and other characteristics of the individual. Nonetheless, there remains several and significant challenges related to the advance of data science into everyday clinical practice.
The emergence of genomic sequencing and other molecular profiling methods together with the digitalization of health care data collections have created unprecedented opportunities for better treatment options. INTERVENE is an international and interdisciplinary consortium that seeks to leverage these vast, but underused data resources to generate clinically actionable knowledge for improved understanding of diseases and treatment options tailored to individuals. Specifically, INTERVENE seeks to advance AI-facilitated analyses of complex medical data to develop genetic risk scores, which summarize the estimated effect of an individual’s genetic makeup on the risk of developing a particular disease. A central aim is to meet the urgent need for clinically validated risk scores with predictive value for complex and rare diseases, applicability for disease screening, and understandable by clinicians and citizens.
The GeneRISK Study is a population-based cohort focusing on studying the behavioral changes following a return of clinical and genetic risk information. The cohort consists of 7351 middle aged participants who went through extensive questionnaire, clinical check-up and biobanking at baseline. The first follow-up visit was on average 18 months after the baseline. Both the baseline and follow-up visits have provided also rich biomarker profiles. The follow-up for GeneRISK continued for years to come.
Finnish population history with relatively small number of founders and multiple bottlenecks provides a natural experiment for genetic studies. We study Finnish genetic sequence variation and its relation to other populations.
We catalogue genetic loci modifying serum lipid levels and metabolites in national and international projects. Using population and family-based collections with rich sequence and biomarker data, we aim at identifying causal variants for dyslipidemias, modifying lipid and other metabolite levels and study their role in cardiometabolic diseases.
We are building models to measure genetic risks for complex diseases. These models are tested in large-scale biobank data with longitudinal follow-up data on disease incidence, treatment and maintenance. The validated models are piloted and applied in health care setting together with leading health care providers.
We develop statistical and computational approaches to analyze highly multivariate phenotype data. Examples of rich sets of phenotypes include longitudinal data derived from national health registries and various biomarker screens, allowing for innovative designs and novel powerful inference.