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.

Identifying individuals at high risk of chronic diseases via easily measured biomarkers could enhance efforts to prevent avoidable illness and death. Using ’omic data can stratify risk for many diseases simultaneously from a single measurement that captures multiple molecular predictors of risk. Here we present nuclear magnetic resonance metabolomics in blood samples from 700,217 participants in three national biobanks. We built metabolomic scores that identify high-risk groups for diseases that cause the most morbidity in high-income countries and show consistent cross-biobank replication of the relative risk of disease for these groups. We show that these metabolomic scores are more strongly associated with disease onset than polygenic scores for most of these diseases. In a subset of 18,709 individuals with metabolomic biomarkers measured at two time points we show that people whose scores change have different risk of disease, suggesting that repeat measurements capture changes both to health status and disease risk possibly due to treatment, lifestyle changes or other factors. Lastly, we assessed the incremental predictive value of metabolomic scores over existing clinical risk scores for multiple diseases and found modest improvements in discrimination for several diseases whose clinical utility, while promising, remains to be determined.

Parent-of-origin effects (POEs) occur when the effect of a genetic variant depends on its parental origin¹. Traditionally linked to genomic imprinting, POEs are believed to occur due to parental conflict over resource allocation to offspring, resulting in opposing parental influences². Despite their importance, POEs remain underexplored in complex traits, owing to the lack of parental genomes. Here we present an approach to infer the parent of origin of alleles without parental genomes, leveraging interchromosomal phasing, mitochondrial and X chromosome data, and sex-specific crossover in siblings. Applied to the UK Biobank, this enabled parent-of-origin inference for up to 109,385 individuals. Genome-wide association study scans for 59 complex traits and over 14,000 protein quantitative trait loci contrasting maternal and paternal effects identified over 30 POEs and confirmed more than 50% of known associations. More than one third of these showed opposite parental influences, especially for traits related to growth (for example, IGF1 and height) and metabolism (for example, type 2 diabetes and triglyceride levels). Replication in up to 85,050 individuals from the Estonian Biobank and 42,346 offspring from the Norwegian Mother, Father and Child Cohort Study (MoBa) validated 87% of testable associations. Overall, our findings highlight the contribution of POEs to complex traits and support the parental conflict hypothesis, providing compelling evidence for this understudied evolutionary phenomenon.

A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.

The joint impact of exposure to multiple urban environmental factors on asthma remains unclear. We analysed data from 14 European cohorts to assess the impact of the urban exposome on asthma incidence across the life course. We linked three external exposome domains (air pollution, built environment, ambient temperature) to the participants’ home addresses at baseline. We performed k-means clustering within each domain and assessed associations of clusters with asthma adjusting for potentially relevant covariates in cohort-specific analyses, with subsequent separate meta-analyses for birth and adult cohorts. An environmental risk score using a coefficient-weighted sum approach was used to assess the impact of combining the three domains. A total of 7428 incident asthma cases were identified among 349,037 participants (from birth up to age 70+). Overall, we observed higher risks of asthma for clusters characterized by high particulate matter and nitrogen dioxide exposure in adults (OR_meta = 1.13, 95%CI:1.01–1.25), and clusters characterized by high built-up area and low levels of greenness in both children and adults (OR_meta = 1.36, 95%CI: 1.14–1.64 for birth cohorts and OR_meta = 1.15, 95%CI: 1.03–1.28 for adult cohorts, respectively). The joint exposure using the environment risk score combining the three domains was consistently associated with higher risks of incident asthma (OR_meta = 1.13, 95%CI: 1.07–1.20 for birth cohorts, OR_meta = 1.15, 95%CI: 1.10–1.20 for adult cohorts per 20% increase). On average 11.6% of the incident asthma cases could be attributed to environmental risk score above cohort-specific median levels. Multiple environmental exposures jointly contribute to incident asthma risk across the life course. Urban planning accounting for these factors may help mitigate asthma development. This study was funded by the 10.13039/501100007601 European Union’s Horizon 2020 research and innovation program under agreement No 874627 (EXPANSE).

Interpreting genetic associations with complex traits can be greatly improved by detailed understanding of the molecular consequences of these variants. However, although genome-wide association studies (GWAS) for common complex diseases routinely profile 1M+ individuals, studies of molecular phenotypes have lagged behind. We performed a GWAS meta-analysis for 249 circulating metabolic traits in the Estonian Biobank and the UK Biobank in up to 619,372 individuals, identifying 88,604 significant locus-metabolite associations and 8,774 independent lead variants, including 987 lead variants with a minor allele frequency less than 1%. We demonstrate how common and low-frequency associations converge on shared genes and pathways, bridging the gap between rare-variant burden testing and common-variant GWAS. We used Mendelian randomisation (MR) to explore putative causal links between metabolic traits, coronary artery disease and type 2 diabetes (T2D). Surprisingly, up to 85% of the tested metabolite-disease pairs had statistically significant genome-wide MR estimates, likely reflecting complex indirect effects driven by horisontal pleiotropy. To avoid these pleiotropic effects, we used cis -MR to test the phenotypic impact of inhibiting specific drug targets. We found that although plasma levels of branched-chain amino acids (BCAAs) have been associated with T2D in both observational and genome-wide MR studies, inhibiting the BCAA catabolism pathway to lower BCAA levels is unlikely to reduce T2D risk. Our publicly available results provide a valuable novel resource for GWAS interpretation and drug target prioritisation.