On November 10, 2026, Miriam Nurm will defend octoral dissertation entitled “Population Biobanks as a Framework for Genomic Discovery, Metabolic Phenotyping, and Precision Medicine”
Supervisors:
Associate Professor Toomas Haller, Tartu Ülikool
Associate Professor Tarmo Annilo, Tartu Ülikool
dr Anu Reigo, Tartu Ülikool
Professor Neeme Tõnisson, Tartu Ülikool
Oponent:
Andrea Ganna, University of Helsinki, Finland
Summary
Only a small proportion of inherited diseases are treatable and also preventable when a timely diagnosis is reached. The data at the Estonian Biobank are of great value, enabling individuals at high genetic risk to be identified before serious health complications have a chance to occur. Two disorders of metal metabolism - Wilson's disease and hereditary hemochromatosis - can entail severe health problems as metals, copper and iron respectively, accumulate in the body over time. Importantly, both are treatable, but there is additional complexity due to being caused by gene variants whose precise effect on the onset of the disease and the nature of the symptoms is difficult to predict.
In the first part, the occurrence of disease causing Wilson's disease variants in the Estonian Biobank was examined, and on that basis biobank participants at high genetic risk were invited for recalls. The study indicated that Wilson's disease is likely to be underdiagnosed and undertreated in Estonia.
In the second part, the prevalence of gene variants associated with hereditary hemochromatosis in the Estonian Biobank was investigated, using electronic health records to determine in further detail which symptoms afflicted the carriers of these variants more than biobank participants without the variants.
In the third part, the electronic health records themselves were examined, studying diagnosis codes that could be grouped together on the basis of shared clinical laboratory measurements. The results demonstrate how data recorded in electronic health records for medical purposes can be repurposed for research.
In conclusion, this thesis shows that population-based biobanks are valuable data resources for identifying individuals at high genetic risk even before their disease manifests, help to clarify the significance of clinically relevant gene variants at the population level, and facilitate the incorporation of electronic health record data into research.
