Podcast recap: Samuli Ripatti on turning biobank data into clinical impact

On the most recent episode of The Genetics Podcast, Patrick speaks with Samuli Ripatti, director of the Institute for Molecular Medicine Finland (FIMM) and professor of biometry at the University of Helsinki. His research has focused on using large biobanks, particularly FinnGen, to understand cardiovascular disease and other common conditions.

From early genetics to cardiovascular risk

Samuli’s career began in statistical genetics, but he quickly gravitated toward population-scale data. Finland’s unique bottleneck population, deep medical records, and strong culture of research participation created the foundation for studies that could identify novel associations and shed light on how genetic risk translates into disease. His efforts have been central in showing how biobanks can move beyond discovery toward clinically relevant insights.

He first engaged with genetics through lipid studies. Lipids provided a useful test case because LDL receptors and other core genes were already well known, allowing researchers to benchmark their findings. Early genome-wide association studies were small by today’s standards, often just a few thousand people, but they highlighted the need for collaboration across countries and institutions. By working across borders, Samuli and colleagues showed that combining datasets could accelerate discovery and reveal consistent signals.

Polygenic risk scores in the clinic

One of Samuli’s main interests today is how polygenic risk scores (PRS) can be implemented in real clinical settings. For cardiovascular disease, PRS can complement existing check-ups by identifying individuals at especially high lifetime risk. For breast cancer, scores layered on top of BRCA1 and BRCA2 screening help explain risk for the many women who do not carry those mutations but still develop the disease.

Samuli argues that scores are most useful when added to existing clinical pathways rather than introduced in isolation. For heart disease, this means incorporating PRS into cholesterol and blood pressure assessments. For breast cancer, it means integrating scores into current genetic screening programs to refine decisions about monitoring and prevention. These examples highlight that PRS is not just an academic tool, but something that could reshape how patients are identified and managed.

Using medications and prognosis as genetic readouts

Samuli and his team have also investigated the link between genetics and medication use. By analyzing prescription records for statins, antihypertensives, and diabetes drugs, they asked whether genetic risk predicts how long people stay on treatment or whether they switch to stronger therapies. They found that higher polygenic risk often correlates with persistence, even when patients are unaware of their scores.

This work illustrates how biobanks with long-term health records can offer new phenotypes beyond diagnosis alone. Prescription histories, combined with genetics, can uncover who benefits most from specific drugs and who is likely to need escalation of therapy. These insights may eventually inform clinical trials and help design interventions tailored to patient subgroups.

The power of Finnish biobanks

Much of this progress has been possible because of Finland’s investment in nationwide biobank infrastructure. FinnGen links genetic data from more than 500,000 individuals with decades of health records, cancer registries, hospitalization data, and most recently laboratory tests. This provides a longitudinal and comprehensive view of health that is rare internationally.

The model is also a public–private partnership, with academic researchers and pharmaceutical companies working together to share results. This structure allows Finland not only to identify genetic associations, but also to connect them to function through proteomics, metabolomics, and cellular profiling. It positions the country to play a leading role in drug discovery and in testing how biobank-based approaches can support clinical trials.

Looking ahead

Samuli sees three priorities for the coming years. First, biobank data should be used to refine phenotypes and understand heterogeneity within diseases, rather than only maximizing case numbers. Second, PRS should be tested not just for risk prediction but also for prognosis and treatment response. Third, researchers and clinicians need to pilot these tools in real-world healthcare systems, building the evidence base for implementation.

The ultimate goal is to bridge the gap between large-scale genetics and everyday medicine. With resources like FinnGen and international collaboration, Samuli believes the field is well positioned to move from discovery to impact, particularly in cardiovascular disease and other common conditions. His work shows how population-scale genetics can directly inform prevention, treatment, and healthier aging.

Listen to the full episode below.