The Estonian Biobank's journey from biobanking to personalized medicine

Abstract

Large biobanks have set a new standard for research and innovation in human genomics and implementation of personalized medicine. The Estonian Biobank was founded a quarter of a century ago, and its biological specimens, clinical, health, omics, and lifestyle data have been included in over 800 publications to date. What makes the biobank unique internationally is its translational focus, with active efforts to conduct clinical studies based on genetic findings, and to explore the effects of return of results on participants. In this review, we provide an overview of the Estonian Biobank, highlight its strengths for studying the effects of genetic variation and quantitative phenotypes on health-related traits, development of methods and frameworks for bringing genomics into the clinic, and its role as a driving force for implementing personalized medicine on a national level and beyond.

Fig. 1. Overview of the Estonian Biobank.

A Timeline depicting major milestones in EstBB; B Overview of the age and sex distribution of EstBB participants and comparison to the whole Estonian population in 2023. Different coloured bars correspond to male, female, and deceased participants (blue, purple, and navy, respectively) in each age category, while the grey outline corresponds to the age and sex distribution in the whole Estonian population; C Distribution of inferred relatedness in the EstBB cohort. Relatedness was inferred using the KING software v2.2.7; D Overview of different phenotype and omics datasets available in EstBB.

Fig. 2. Overview of derived datasets in the Estonian Biobank.

A CNVs in the EstBB cohort. Deletion (blue) and duplication (yellow) frequencies (y-axis) at GSA probe positions (x-axis) are presented. The dashed line indicates 1% frequency. Loci with CNV frequencies >5% are labelled with cytogenic bands. B Numbers of detected CNVs (x-axis) with copy number 0 to 4 (y-axis). C Number of individuals (y-axis) and frequencies of assessed pharmacogenetic phenotypes of nine major pharmacogenes (x-axis). The proportions of PGx phenotypes among all analysed individuals (N = 211,257) are shown with percentages on each bar. Different colours correspond to the established PGx phenotypes for each gene. D Observed number of microbial species (y-axis) among the microbiome cohort participants who had taken 0, 1–5 or >5 courses of antibiotics within 5 years prior to microbiome sample collection. Individuals who used antibiotics within 6 months before sampling were excluded.

Fig. 3. Medication purchase and disease trajectories in the Estonian Biobank.

A Number of EstBB participants with top 10 purchased medications from 2004 to 2023. Different coloured lines indicate different medication classes and reflect the number of EstBB participants who were prescribed the respective drugs each year. B Illustration of disease and treatment trajectories detected in EstBB, exemplified by dorsalgia as a starting point. Prior dorsalgia significantly increases the relative risk of observing subsequent diagnoses (purple) and medications (blue) in the dataset. Node size indicates the number of patients, and numbers on the arrows denote the relative risk (only shown if relative risk is greater than 3).

Fig. 4. Timeline depicting recall by genotype studies carried out at EstBB.

Above the timeline, return of results directly from the biobank, below the timeline, return of results provided in a clinical setting. The recall studies are based on CNVs—copy number variants, ACMG—Americal College of Medical Genetics list of genes where incidental findings should be reported, PRS—polygenic risk scores for CVD (cardiovascular disease) or breast cancer, PGx—pharmacogenetics—novel predicted LoF (loss-of-function) or nonsyn (non-synonymous) variants in CYP2D6 or CYP2C19. N indicate number of individuals with high risk PRS or variant carriers that participated in the study.