{"id":499,"date":"2025-07-15T12:50:25","date_gmt":"2025-07-15T09:50:25","guid":{"rendered":"https:\/\/sisu.ut.ee\/permedcenter\/?page_id=499"},"modified":"2026-01-26T15:44:52","modified_gmt":"2026-01-26T13:44:52","slug":"scientific-publications","status":"publish","type":"page","link":"https:\/\/sisu.ut.ee\/permedcenter\/scientific-publications\/","title":{"rendered":"Scientific publications"},"content":{"rendered":"<figure class=\"wp-block-image aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"174\" src=\"https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/866\/TPM-logos_new-1024x174.png\" alt=\"\" class=\"wp-image-536\" srcset=\"https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/866\/TPM-logos_new-1024x174.png 1024w, https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/866\/TPM-logos_new-300x51.png 300w, https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/866\/TPM-logos_new-768x131.png 768w, https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/866\/TPM-logos_new-1536x261.png 1536w, https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/866\/TPM-logos_new-2048x348.png 2048w, https:\/\/sisu.ut.ee\/wp-content\/uploads\/sites\/866\/TPM-logos_new-1920x326.png 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\"><\/figure>\n\n\n<div class=\"accordion-block mb-3\">\n\t\t<div class=\"accordion \" id=\"accordion-accordion-69d4ad0fa32fe\">\n\t\t\t\t\t\t\t<div class=\"accordion-item accordion-item--white\">\n\t\t\t\t\t<h2 class=\"accordion-header\" id=\"accordion-69d4ad0fa32fe-heading-1\">\n\t\t\t\t\t\t<button class=\"accordion-button collapsed\" type=\"button\" data-bs-toggle=\"collapse\" data-bs-target=\"#accordion-69d4ad0fa32fe-collapse-1\" aria-expanded=\"true\" aria-controls=\"accordion-69d4ad0fa32fe-collapse-1\">\n\t\t\t\t\t\t\tMilani, L., Alver, M., Laur, S. et al. The Estonian Biobank\u2019s journey from biobanking to personalized medicine. Nature Communications 16, 3270 (2025)\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/h2>\n\t\t\t\t\t<div id=\"accordion-69d4ad0fa32fe-collapse-1\" class=\"accordion-collapse collapse \" aria-labelledby=\"accordion-69d4ad0fa32fe-heading-1\">\n\t\t\t\t\t\t<div class=\"accordion-body\">\n\t\t\t\t\t\t\t<p><a href=\"https:\/\/doi.org\/10.1038\/s41467-025-58465-3\">The Estonian Biobank\u2019s Journey from Biobanking to Personalized Medicine.<\/a><br>\n<em>Nature Communications<\/em>, 16, 3270 (2025)<br>\nDOI: 10.1038\/s41467-025-58465-3<\/p>\n<p><strong>Authors<\/strong><br>\nLili Milani, Maris Alver, Sven Laur, Sulev Reisberg, Toomas Haller, Oliver Aasmets, Erik Abner, Helene Alavere, Annely Allik, Tarmo Annilo, Krista Fischer, Robin Hofmeister, Georgi Hudjashov, Maarja J\u00f5eloo, Mart Kals, Liis Karo-Astover, Silva Kasela, Anastassia Kolde, Kristi Krebs, Kertu Liis Krigul, Jaanika Kronberg, Karoliina Kruusmaa, Viktorija Kuku\u0161kina, Kadri K\u00f5iv, Kelli Lehto, Liis Leitsalu, Sirje Lind, Laura Birgit Luitva, Kristi L\u00e4ll, Kreete L\u00fcll, Kristjan Metsalu, Mait Metspalu, Ren\u00e9 M\u00f5ttus, Mari Nelis, Tiit Nikopensius, Miriam Nurm, Margit N\u00f5ukas, Marek Oja, Elin Org, Marili Palover, Priit Palta, Vasili Pankratov, Kateryna Pantiukh, Natalia Pervjakova, Nat\u00e0lia Pujol-Gualdo, Anu Reigo, Ene Reimann, Steven Smit, Diana Rogozina, Dage S\u00e4rg, Nele Taba, Harry-Anton Talvik, Maris Teder-Laving, Neeme T\u00f5nisson, Mariliis Vaht, Uku Vainik, Urmo V\u00f5sa, Burak Yelmen, T\u00f5nu Esko, Raivo Kolde, Reedik M\u00e4gi, Jaak Vilo, Triin Laisk &amp; Andres Metspalu<\/p>\n<p><strong>Abstract<\/strong><br>\nLarge 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.<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"accordion-item accordion-item--white\">\n\t\t\t\t\t<h2 class=\"accordion-header\" id=\"accordion-69d4ad0fa32fe-heading-2\">\n\t\t\t\t\t\t<button class=\"accordion-button collapsed\" type=\"button\" data-bs-toggle=\"collapse\" data-bs-target=\"#accordion-69d4ad0fa32fe-collapse-2\" aria-expanded=\"true\" aria-controls=\"accordion-69d4ad0fa32fe-collapse-2\">\n\t\t\t\t\t\t\tHiie, L., Kolde, A., Pervjakova, N. et al. Pathway level metabolomics analysis identifies carbon metabolism as a key factor of incident hypertension in the Estonian Biobank. Scientific Reports 15, 8470 (2025)\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/h2>\n\t\t\t\t\t<div id=\"accordion-69d4ad0fa32fe-collapse-2\" class=\"accordion-collapse collapse \" aria-labelledby=\"accordion-69d4ad0fa32fe-heading-2\">\n\t\t\t\t\t\t<div class=\"accordion-body\">\n\t\t\t\t\t\t\t<p><a href=\"https:\/\/www.nature.com\/articles\/s41598-025-92840-w#citeas\">Pathway level metabolomics analysis identifies carbon metabolism as a key factor of incident hypertension in the Estonian Biobank<\/a><br>\n<em>Scientific Reports<\/em> 15, 8470 (2025)<br>\nDOI: 10.1038\/s41598-025-92840-w<\/p>\n<p><strong>Authors<br>\n<\/strong>Liis Hiie, Anastassia Kolde, Natalia Pervjakova, Anu Reigo, Estonian Biobank Research Team, Erik Abner, Urmo V\u00f5sa, T\u00f5nu Esko, Krista Fischer, Priit Palta &amp; Jaanika Kronberg<\/p>\n<p><strong>Abstract<\/strong><br>\nThe purpose of this study was to find metabolic changes associated with incident hypertension in the volunteer-based Estonian Biobank. We used a subcohort of the Estonian Biobank where metabolite levels had been measured by mass-spectrometry (LC-MS, Metabolon platform). We divided annotated metabolites of 989 individuals into KEGG pathways, followed by principal component analysis of metabolites in each pathway, resulting in a dataset of 91 pathway components. Next, we defined incident hypertension cases and controls based on electronic health records, resulting in a dataset of 101 incident hypertension cases and 450 controls. We used Cox proportional hazards models and replicated the results in a separate cohort of the Estonian Biobank, assayed with LC-MS dataset of the Broad platform and including 582 individuals. Our results show that body mass index and a component of the carbon metabolism KEGG pathway are associated with incident hypertension in both discovery and replication cohorts. We demonstrate that a high-dimensional dataset can be meaningfully reduced into informative pathway components that can subsequently be analysed in an interpretable way, and replicated in a metabolomics dataset from a different platform.<strong><br>\n<\/strong><\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"accordion-item accordion-item--white\">\n\t\t\t\t\t<h2 class=\"accordion-header\" id=\"accordion-69d4ad0fa32fe-heading-3\">\n\t\t\t\t\t\t<button class=\"accordion-button collapsed\" type=\"button\" data-bs-toggle=\"collapse\" data-bs-target=\"#accordion-69d4ad0fa32fe-collapse-3\" aria-expanded=\"true\" aria-controls=\"accordion-69d4ad0fa32fe-collapse-3\">\n\t\t\t\t\t\t\tRizzo, N., C\u00e1ceres, M. &amp; V. M\u00e4kinen. Exploiting uniqueness: seed-chain-extend alignment on elastic founder graphs, Bioinformatics, 41 (2025)\u00a0\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/h2>\n\t\t\t\t\t<div id=\"accordion-69d4ad0fa32fe-collapse-3\" class=\"accordion-collapse collapse \" aria-labelledby=\"accordion-69d4ad0fa32fe-heading-3\">\n\t\t\t\t\t\t<div class=\"accordion-body\">\n\t\t\t\t\t\t\t<p><a href=\"https:\/\/academic.oup.com\/bioinformatics\/article\/41\/Supplement_1\/i265\/8199399\">Exploiting uniqueness: seed-chain-extend alignment on elastic founder graphs<\/a><br>\n<em>Bioinformatics<\/em>, 41 (2025)<br>\nDOI: 10.1093\/bioinformatics\/btaf225<\/p>\n<p><strong>Authors<br>\n<\/strong>Nicola Rizzo, Manuel C\u00e1ceres &amp; Veli M\u00e4kinen<\/p>\n<p><strong>Abstract<\/strong><br>\nSequence-to-graph alignment is a central challenge of computational pangenomics. To overcome the theoretical hardness of the problem, state-of-the-art tools use\u00a0<em>seed-and-extend<\/em>\u00a0or\u00a0<em>seed-chain-extend<\/em>\u00a0heuristics to alignment. We implement a complete seed-chain-extend alignment workflow based on\u00a0<em>indexable elastic founder graphs<\/em>\u00a0(iEFGs) that support linear-time exact searches unlike general graphs. We show how to construct iEFGs, find high-quality seeds, chain, and extend them at the scale of a telomere-to-telomere assembled human chromosome.<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"accordion-item accordion-item--white\">\n\t\t\t\t\t<h2 class=\"accordion-header\" id=\"accordion-69d4ad0fa32fe-heading-4\">\n\t\t\t\t\t\t<button class=\"accordion-button collapsed\" type=\"button\" data-bs-toggle=\"collapse\" data-bs-target=\"#accordion-69d4ad0fa32fe-collapse-4\" aria-expanded=\"true\" aria-controls=\"accordion-69d4ad0fa32fe-collapse-4\">\n\t\t\t\t\t\t\tPuusepp T., P\u00f5ld A., Milani L., et al. Development and validation of a risk prediction algorithm for high-risk populations combining genetic and conventional risk factors of cardiovascular disease. PLoS One 20 (2025)\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/h2>\n\t\t\t\t\t<div id=\"accordion-69d4ad0fa32fe-collapse-4\" class=\"accordion-collapse collapse \" aria-labelledby=\"accordion-69d4ad0fa32fe-heading-4\">\n\t\t\t\t\t\t<div class=\"accordion-body\">\n\t\t\t\t\t\t\t<p><a href=\"https:\/\/journals.plos.org\/plosone\/article?id=10.1371\/journal.pone.0335064\">Development and validation of a risk prediction algorithm for high-risk populations combining genetic and conventional risk factors of cardiovascular disease<\/a><\/p>\n<p><em>PLOS One<\/em>, 20 (10) (2025)<br>\nDOI: 10.1371\/journal.pone.0335064<\/p>\n<p><strong>Authors<\/strong><\/p>\n<p>Tuuli Puusepp, Ave P\u00f5ld, Lili Milani, Aet Elken, Estonian Biobank Research Team, Mikk J\u00fcrisson &amp; Krista Fischer<\/p>\n<p><strong>Abstract<\/strong><\/p>\n<p>Aim: To develop a model for cardiovascular disease (CVD) risk, combining polygenic risk score (PRS) with traditional risk factors while assessing the added value of PRS in two cohorts of biobank participants.<\/p>\n<p>Methods: Data of 128 209 participants from the Estonian Biobank recruited between 2002\u20132017 and 2018\u20132022 without prevalent cardiovascular disease, was included. Hazard ratios (HR) for polygenic risk versus conventional risk factors were estimated with Cox proportional hazards models, cumulative incidence was assessed with Aalen-Johansen curves. Predictive performance was tested using a split-sample approach and competing risk modelling. Age at CVD event served as the outcome, and the impact of the PRS was evaluated by age group (25\u201359 vs. 60+), sex, and recruitment period, using HRs, Harrell\u2019s C-index, and net reclassification indices (NRI).<\/p>\n<p>Results: The estimated HR per one standard deviation (SD) of PRS ranged from 1.1, 95% CI 1.06\u20131.15 (age 60\u2009+\u2009, earlier cohort) to 1.36, 95% CI 1.24\u20131.49 (men 25\u201359, later cohort). Adding PRS to the conventional risk factors in the age group 25\u201359 increased the C-statistic by 0.028 (p\u2009&lt;\u20090.0001) for men. In the age group 60\u2009+\u2009, the increase was 0.016 (p\u2009=\u20090.0002) across all. In the independent validation set, the continuous NRI was 19.1% (95% CI 13.3%\u201324.9%) in the 25\u201359 group and 13.9% (95% CI 8.1%\u201319.6%) in the 60\u2009+\u2009group.<\/p>\n<p>Conclusions: In a high-risk population, PRS is a strong independent risk factor for CVD and should be considered in routine risk assessment, starting at a relatively young age.<\/p>\n<p>\u00a0<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"accordion-item accordion-item--white\">\n\t\t\t\t\t<h2 class=\"accordion-header\" id=\"accordion-69d4ad0fa32fe-heading-5\">\n\t\t\t\t\t\t<button class=\"accordion-button collapsed\" type=\"button\" data-bs-toggle=\"collapse\" data-bs-target=\"#accordion-69d4ad0fa32fe-collapse-5\" aria-expanded=\"true\" aria-controls=\"accordion-69d4ad0fa32fe-collapse-5\">\n\t\t\t\t\t\t\tKolde A., Koitm\u00e4e M., K\u00e4\u00e4rik M. et al.\u00a0Analysis of follow-up data in large biobank cohorts: a review of methodology. Front. Genet. 16:1534726 (2025)\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/h2>\n\t\t\t\t\t<div id=\"accordion-69d4ad0fa32fe-collapse-5\" class=\"accordion-collapse collapse \" aria-labelledby=\"accordion-69d4ad0fa32fe-heading-5\">\n\t\t\t\t\t\t<div class=\"accordion-body\">\n\t\t\t\t\t\t\t<p><a href=\"https:\/\/doi.org\/10.3389\/fgene.2025.1534726\">Analysis of follow-up data in large biobank cohorts: a review of methodology<\/a><br>\n<em>Frontiers in Genetics<\/em>, 16, 1534726 (2025)<br>\nDOI: 10.3389\/fgene.2025.1534726<\/p>\n<p><strong>Authors<br>\n<\/strong>Anastassia Kolde, Merli Koitm\u00e4e, Meelis K\u00e4\u00e4rik, M\u00e4rt M\u00f6ls &amp; Krista Fischer<\/p>\n<p><strong>Abstract<\/strong><br>\nThis study focuses on key methodological challenges in genome-wide association studies (GWAS) of biobank data with time-to-event outcomes, analyzed using the Cox proportional hazards (CPH) model. We address four primary issues: left-truncation of the data, computational inefficiency of standard model-fitting algorithms, relatedness among individuals, and model misspecification. To manage left-truncation, the common practice is to use age as the timescale, with individuals entering the risk set at their age of recruitment. We assess how this choice of timescale influences bias and statistical power, under realistic GWAS conditions of varying effect sizes and censoring rates. In addition, to alleviate the computational burden typical in large-scale data, we propose and evaluate a two-step martingale residual (MR) approach for high-dimensional CPH modeling. Our results show that the timescale choice has minimal effect on accuracy for small hazard ratios, though using time since birth as the timescale \u2013 ignoring recruitment age \u2013 yields the highest power for association detection. We find that relatedness, when ignored, does not substantially bias effect size estimates, while omitting key covariates introduces significant bias. The two-step MR approach proves to be computationally efficient, retaining power for detecting small effect sizes, making it suitable for large-scale association studies. However, when precise effect size estimates are critical, particularly for moderate or larger effect sizes, we recommend recalculating these estimates using the conventional CPH model, with careful attention to left-truncation and relatedness. These conclusions are drawn from simulations and illustrated with data from the Estonian Biobank cohort.<\/p>\n<p>\u00a0<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"accordion-item accordion-item--white\">\n\t\t\t\t\t<h2 class=\"accordion-header\" id=\"accordion-69d4ad0fa32fe-heading-6\">\n\t\t\t\t\t\t<button class=\"accordion-button collapsed\" type=\"button\" data-bs-toggle=\"collapse\" data-bs-target=\"#accordion-69d4ad0fa32fe-collapse-6\" aria-expanded=\"true\" aria-controls=\"accordion-69d4ad0fa32fe-collapse-6\">\n\t\t\t\t\t\t\tAbner, E., Batool, K., Taba, N. et al. Characterization of prevalent genetic variants in the Estonian Biobank body-mass index GWAS. Nat Commun 16, 8956 (2025)\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/h2>\n\t\t\t\t\t<div id=\"accordion-69d4ad0fa32fe-collapse-6\" class=\"accordion-collapse collapse \" aria-labelledby=\"accordion-69d4ad0fa32fe-heading-6\">\n\t\t\t\t\t\t<div class=\"accordion-body\">\n\t\t\t\t\t\t\t<p><a href=\"https:\/\/doi.org\/10.1038\/s41467-025-64006-9\">Characterization of prevalent genetic variants in the Estonian Biobank body-mass index GWAS<\/a><br>\n<em>Nature Communications<\/em>, 16, 8956 (2025)<br>\nDOI: 10.1038\/s41467-025-64006-9<\/p>\n<p><strong>Authors<br>\n<\/strong>Erik Abner, Kanwal Batool, Nele Taba, Tiit Nikopensius, Kristi L\u00e4ll, Anastasiia Alekseienko, Anders Eriksson, Joel R\u00e4m\u00f6, Hele Haapaniemi, Hanna Maria Kariis, Liis Haljasm\u00e4gi, Urmo V\u00f5sa, Taavi Tillmann, Uku Vainik, Kelli Lehto, Hanna M. Ollila, Kai Kisand, Estonian Biobank Research Team &amp; T\u00f5nu Esko<\/p>\n<p style=\"text-align: left;\"><strong>Abstract<\/strong><br>\nPopulation-specific genome-wide association studies can reveal high-impact genomic variants that influence traits like body-mass index (BMI). Using the<br>\nEstonian Biobank BMI dataset (n = 204,747 participants) we identified 214 genome-wide significant loci. Among those hits, we identified a common noncoding variant within the newly associated ADGRL3 gene (\u22120.18 kg\/m\u00b2; P = 3.21 \u00d7 10\u207b\u2079). Moreover, the missense rare variant PTPRT:p.Arg1384H is<br>\nassociated with lower BMI (\u22120.44 kg\/m\u00b2; P = 2.51 \u00d7 10\u207b\u00b9\u2070), while the proteintruncating variant POMC:p.Glu206* was associated with considerably higher<br>\nBMI (+ 0.81 kg\/m\u00b2; P = 1.48 \u00d7 10\u221212), both likely affecting the functioning of the leptin-melanocortin pathway. POMC:p.Glu206* was observed in different North-European populations, suggesting a broader, yet elusive, distribution of this damaging variant. These observations indicate the previously unrecognized roles of the ADGRL3 and PTPRT genes in body weight regulation and suggest an increased prevalence of the POMC:p.Glu206* variant in European populations, offering avenues for developing interventions in obesity management.<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"accordion-item accordion-item--white\">\n\t\t\t\t\t<h2 class=\"accordion-header\" id=\"accordion-69d4ad0fa32fe-heading-7\">\n\t\t\t\t\t\t<button class=\"accordion-button collapsed\" type=\"button\" data-bs-toggle=\"collapse\" data-bs-target=\"#accordion-69d4ad0fa32fe-collapse-7\" aria-expanded=\"true\" aria-controls=\"accordion-69d4ad0fa32fe-collapse-7\">\n\t\t\t\t\t\t\tM\u00e4gi, R. Diverse landscape of genomic research within the Estonian Biobank. Human Molecular Genetics, 1\u20136 (2025)\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/h2>\n\t\t\t\t\t<div id=\"accordion-69d4ad0fa32fe-collapse-7\" class=\"accordion-collapse collapse \" aria-labelledby=\"accordion-69d4ad0fa32fe-heading-7\">\n\t\t\t\t\t\t<div class=\"accordion-body\">\n\t\t\t\t\t\t\t<p><a href=\"https:\/\/doi.org\/10.1093\/hmg\/ddaf026\">Diverse landscape of genomic research within the Estonian Biobank<\/a><br>\n<em>Human Molecular Genetics<\/em>, 1\u20136 (2025)<br>\nDOI: 10.1093\/hmg\/ddaf026<\/p>\n<p><strong>Authors<br>\n<\/strong>Reedik M\u00e4gi<\/p>\n<p style=\"text-align: left;\"><strong>Abstract<\/strong><br>\nThe Estonian Biobank (EstBB) is a national biobank hosted by the Institute of Genomics at the University of Tartu. Established in 2000, it is one of the largest population-based biobanks in the world, with the sample size exceeding 212 000 individuals<br>\n(including more than 74 000 parent\u2013child pairs and 38 000 sibpairs) and thus representing more than 20% of the adult population in Estonia. The biobank collects and stores health and lifestyle data, biological samples (DNA, blood plasma, buffy coat etc.), and medical information from national health databases on a large segment of the Estonian population to facilitate scientific research and the development of personalized medicine. This extensive coverage provides a population-representative sample of the Estonian population and additionally enables various analyses, including genome-wide association studies and studies involving related individuals or families.<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"accordion-item accordion-item--white\">\n\t\t\t\t\t<h2 class=\"accordion-header\" id=\"accordion-69d4ad0fa32fe-heading-8\">\n\t\t\t\t\t\t<button class=\"accordion-button collapsed\" type=\"button\" data-bs-toggle=\"collapse\" data-bs-target=\"#accordion-69d4ad0fa32fe-collapse-8\" aria-expanded=\"true\" aria-controls=\"accordion-69d4ad0fa32fe-collapse-8\">\n\t\t\t\t\t\t\tKuznetsov, I. A., Estonian Biobank Research Team, Metspalu M., Vainik U. et al. Genetic effects on migration behavior contribute to increasing spatial differentiation at trait-associated loci in Estonia. iScience  28, 12 (2025)\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/h2>\n\t\t\t\t\t<div id=\"accordion-69d4ad0fa32fe-collapse-8\" class=\"accordion-collapse collapse \" aria-labelledby=\"accordion-69d4ad0fa32fe-heading-8\">\n\t\t\t\t\t\t<div class=\"accordion-body\">\n\t\t\t\t\t\t\t<p><a href=\"https:\/\/doi.org\/10.1016\/j.isci.2025.114013\">Genetic effects on migration behavior contribute to increasing spatial differentiation at trait-associated loci in Estonia<\/a><br>\n<em>iScience<\/em>, 28, 12 (2025)<br>\nDOI: 10.1016\/j.isci.2025.114013<\/p>\n<p><strong>Authors<\/strong><br>\nIvan A. Kuznetsov, Estonian Biobank Research Team (Andres Metspalu, Lili Milani, T\u00f5nu Esko, Reedik M\u00e4gi, Mari Nelis, Georgi Hudjashov), Mait Metspalu, Uku Vainik, Luca Pagani, Francesco Montinaro &amp; Vasili Pankratov<\/p>\n<p><strong>Abstract<\/strong><br>\nEmerging evidence suggests that migration behavior can be selective with respect to individuals\u2019 genotypes, producing genotype-environment correlations that standard methods used in genetic association studies cannot correct. We investigate this phenomenon by examining the spatial dynamics of polygenic scores (PGSs) in Estonia. Our analyses show that contemporary migrations intensify inter-regional differences in PGSs for multiple traits, with educational attainment (EA) PGS showing the strongest effect and largely explaining the inter-regional variation of other PGSs. This differentiation is mainly driven by individuals with higher EA PGS migrating to Estonia\u2019s two largest cities from the rest of the country. Importantly, this pattern replicates within families: individuals migrating to the major cities have, on average, higher EA PGS than their siblings staying elsewhere. This trend has persisted since the mid-20th century, despite significant societal changes. These findings illustrate how migration shapes genetic differentiation within a population and highlight direct genetic effects influencing this process.<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"accordion-item accordion-item--white\">\n\t\t\t\t\t<h2 class=\"accordion-header\" id=\"accordion-69d4ad0fa32fe-heading-9\">\n\t\t\t\t\t\t<button class=\"accordion-button collapsed\" type=\"button\" data-bs-toggle=\"collapse\" data-bs-target=\"#accordion-69d4ad0fa32fe-collapse-9\" aria-expanded=\"true\" aria-controls=\"accordion-69d4ad0fa32fe-collapse-9\">\n\t\t\t\t\t\t\tKrebs, K., Luitva, L.B., K\u00f5re, A.C.\u00a0et al.\u00a0Pharmacokinetic recall study of Estonian Biobank participants with novel genetic variants in\u00a0CYP2C19\u00a0and\u00a0CYP2D6.\u00a0npj Genom. Med.\u00a0(2026)\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/h2>\n\t\t\t\t\t<div id=\"accordion-69d4ad0fa32fe-collapse-9\" class=\"accordion-collapse collapse \" aria-labelledby=\"accordion-69d4ad0fa32fe-heading-9\">\n\t\t\t\t\t\t<div class=\"accordion-body\">\n\t\t\t\t\t\t\t<p><a href=\"https:\/\/doi.org\/10.1038\/s41525-025-00549-6\">Pharmacokinetic recall study of Estonian Biobank participants with novel genetic variants in <i>CYP2C19<\/i>\u00a0and\u00a0<i>CYP2D6<\/i><\/a><br>\n<i>npj Genomic Medicine<\/i> (2026)<br>\nDOI: 10.1038\/s41525-025-00549-6<\/p>\n<p><strong>Authors<\/strong><br>\nKristi Krebs, Laura Birgit Luitva, Anette Caroline K\u00f5re, Raul Kokasaar, Maarja J\u00f5eloo, Georgi Hudjashov, Kadri Maal, Elisabet St\u00f8rset, Birgit Malene Wollmann, Liis Karo-Astover, Krista Fischer, Estonian Biobank Research Team, Volker M. Lauschke, Magnus Ingelman-Sundberg, Espen Molden, Alar Irs, Kersti Oselin, Jana Lass &amp; Lili Milani<\/p>\n<p><strong>Abstract<\/strong><br>\nCYP2C19 and CYP2D6 are involved in the hepatic metabolism of approximately 35\u201340% of clinically used drugs. We conducted an in vivo phenotyping study encompassing 114 Estonian Biobank participants to evaluate the functional impact of rare or novel single-nucleotide and structural variants in the\u00a0<i>CYP2C19<\/i>\u00a0and\u00a0<i>CYP2D6<\/i>\u00a0genes using omeprazole and metoprolol as respective probe drugs. Plasma concentrations of these drugs and their metabolites were measured at 10 time points, and parent drug-to-metabolite ratios were calculated to determine enzymatic activity. Long-read sequencing enabled high-resolution star allele calling. Our results provide the first in vivo confirmation that partial gene and intragenic deletions in\u00a0<i>CYP2C19<\/i>\u00a0(<i>CYP2C19*37<\/i>\u00a0and\u00a0<i>CYP2C19*42<\/i>), enriched in Estonians and Finns, are associated with poor metaboliser phenotypes (<i>P<\/i>\u2009&lt;\u20091.2 \u00d7 10<sup>\u22127<\/sup>). Additionally, we offer in vivo evidence of reduced metabolic activity of the\u00a0<i>CYP2D6*124<\/i>\u00a0allele and a novel missense variant (c.940C&gt;A) in exon 6 of\u00a0<i>CYP2D6<\/i>. Furthermore, we observed that inhibitor exposure was significantly associated with higher metabolic ratios for both CYP2C19 (<i>P<\/i>\u2009=\u20093.0 \u00d7 10<sup>\u22126<\/sup>) and CYP2D6 (<i>P<\/i>\u2009=\u20090.02). Our findings emphasise the importance of identifying genetic variants in\u00a0<i>CYP2C19<\/i>\u00a0and\u00a0<i>CYP2D6<\/i>\u00a0beyond commonly assessed star alleles and that profiling for drug interactions can provide more precise assignments of metabolic phenotypes and improve personalised treatment.<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t<\/div>\n\n\n\n<h1 class=\"wp-block-heading\">Resources in Estonian<br><\/h1>\n\n\n<div class=\"accordion-block mb-3\">\n\t\t<div class=\"accordion \" id=\"accordion-accordion-69d4ad0fa3b64\">\n\t\t\t\t\t\t\t<div class=\"accordion-item accordion-item--white\">\n\t\t\t\t\t<h2 class=\"accordion-header\" id=\"accordion-69d4ad0fa3b64-heading-1\">\n\t\t\t\t\t\t<button class=\"accordion-button collapsed\" type=\"button\" data-bs-toggle=\"collapse\" data-bs-target=\"#accordion-69d4ad0fa3b64-collapse-1\" aria-expanded=\"true\" aria-controls=\"accordion-69d4ad0fa3b64-collapse-1\">\n\t\t\t\t\t\t\tWATCH: Seminar \"How health data help to prevent and treat diseases?\"\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/h2>\n\t\t\t\t\t<div id=\"accordion-69d4ad0fa3b64-collapse-1\" class=\"accordion-collapse collapse \" aria-labelledby=\"accordion-69d4ad0fa3b64-heading-1\">\n\t\t\t\t\t\t<div class=\"accordion-body\">\n\t\t\t\t\t\t\t<p><!-- wp:paragraph --><\/p>\n<p>Seminar \u201cHow health data help to prevent and treat diseases?\u201d (<em>Kuidas terviseandmed aitavad haigusi ennetada ja ravida?<\/em>)\u00a0in co-operation with the Estonian Academy of Sciences (EAS). 24.03.2025<\/p>\n<p>Large volumes of diverse health data are generated in healthcare systems on a daily basis. The best modern medical care relies on extensive individual patient health data, which are highly valuable for analysing how disease prevention can be improved, treatments made more effective, and informed healthcare policy decisions taken.<\/p>\n<p><!-- \/wp:paragraph --> <!-- wp:paragraph --><\/p>\n<p>At present, only a small proportion of the available data is being used. Although data-processing capacity has increased dramatically over the past decade, continued attention to data quality remains essential. Presenters discussed how Estonian researchers have recently advanced the management of health data and what would be a sensible way forward.<\/p>\n<p>Click on the presentation title to watch the recording (in Estonian).<\/p>\n<p><a href=\"https:\/\/youtu.be\/Qo7n3tyRk3Q\" data-type=\"link\" data-id=\"https:\/\/youtu.be\/Qo7n3tyRk3Q\">Introduction<\/a> (<em>Sissejuhatus teemasse<\/em>). Sander Pajusalu, MD, Associate Professor of Clinical Genetics, University of Tartu; Head of the Genetics and Personalised Medicine Clinic, Tartu University Hospital<br>\n<a href=\"https:\/\/youtu.be\/1liUSDrKPls\">Health informatics \u2013 how data are generated and how they are used <\/a>(<em>Terviseinformaatika \u2013 kuidas andmed tekivad ja kuidas neid kasutatakse<\/em>). Jaak Vilo, Professor of Bioinformatics, University of Tartu<br>\n<a href=\"https:\/\/youtu.be\/5rAdQRhYkkI\">From data to medicines <\/a>(<em>Andmetest ravimiteni<\/em>). Kadi Liis Saar, Researcher, University of Cambridge<br>\n<a href=\"https:\/\/youtu.be\/b0Iw5rfNNaE\">The black box of colorectal cancer<\/a> (<em>Soolev\u00e4hi must kast<\/em>). Indrek Seire, MD, Junior Research Fellow, Institute of Clinical Medicine, University of Tartu<br>\n<a href=\"https:\/\/youtu.be\/bqMXe9cCI8g\">Beautiful health data <\/a>(<em>Ilusad terviseandmed<\/em>). Maarja Pajusalu, Junior Research Fellow, Institute of Computer Science, University of Tartu<br>\n<a href=\"https:\/\/youtu.be\/n-BmJOZPBg0\">Health data in joint replacement<\/a> (<em>Endoproteesimise terviseandmed<\/em>). Kaspar Tootsi, MD, Researcher in Orthopaedics, University of Tartu<\/p>\n<p><!-- \/wp:list-item --><\/p>\n<p><!-- \/wp:list --><\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"accordion-item accordion-item--white\">\n\t\t\t\t\t<h2 class=\"accordion-header\" id=\"accordion-69d4ad0fa3b64-heading-2\">\n\t\t\t\t\t\t<button class=\"accordion-button collapsed\" type=\"button\" data-bs-toggle=\"collapse\" data-bs-target=\"#accordion-69d4ad0fa3b64-collapse-2\" aria-expanded=\"true\" aria-controls=\"accordion-69d4ad0fa3b64-collapse-2\">\n\t\t\t\t\t\t\tLISTEN: KUKU Radio programme \u201cKuue samba taga\u201d episode \u201cGenetic study of cardiovascular health\" with Mikk J\u00fcrisson\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/h2>\n\t\t\t\t\t<div id=\"accordion-69d4ad0fa3b64-collapse-2\" class=\"accordion-collapse collapse \" aria-labelledby=\"accordion-69d4ad0fa3b64-heading-2\">\n\t\t\t\t\t\t<div class=\"accordion-body\">\n\t\t\t\t\t\t\t<p><strong>Listen to episode \u201c<a href=\"https:\/\/kuku.pleier.ee\/podcast\/kuue-samba-taga\/214069\">Genetic study of cardiovascular health<\/a><\/strong>\u201c<strong> (<em>S\u00fcdametervise geeniuuring<\/em>). 17.11.2025<\/strong><\/p>\n<p>What questions should a five-year genetic study on cardiovascular health answer? The study includes 2,700 participants and forms part of the TeamPerMed project, which focuses on advancing personalised medicine. The topic is discussed by Mikk J\u00fcrisson, Associate Professor of Public Health at University of Tartu.<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"accordion-item accordion-item--white\">\n\t\t\t\t\t<h2 class=\"accordion-header\" id=\"accordion-69d4ad0fa3b64-heading-3\">\n\t\t\t\t\t\t<button class=\"accordion-button collapsed\" type=\"button\" data-bs-toggle=\"collapse\" data-bs-target=\"#accordion-69d4ad0fa3b64-collapse-3\" aria-expanded=\"true\" aria-controls=\"accordion-69d4ad0fa3b64-collapse-3\">\n\t\t\t\t\t\t\tLISTEN: KUKU Radio programme \u201cKuue samba taga\u201d episode \u201cPersonalised medicine\u201d with Mikk J\u00fcrisson\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/h2>\n\t\t\t\t\t<div id=\"accordion-69d4ad0fa3b64-collapse-3\" class=\"accordion-collapse collapse \" aria-labelledby=\"accordion-69d4ad0fa3b64-heading-3\">\n\t\t\t\t\t\t<div class=\"accordion-body\">\n\t\t\t\t\t\t\t<p><!-- wp:paragraph --><\/p>\n<p><strong>Listen to episode \u201c<a href=\"https:\/\/kuku.pleier.ee\/podcast\/kuue-samba-taga\/106753\">Personalised medicine\u201d<\/a> (<em>Personaalmeditsiin<\/em>). 13.09.2025<\/strong><\/p>\n<p><!-- \/wp:paragraph --> <!-- wp:paragraph --><\/p>\n<p>Which genetic data could general practitioners use? What do studies show about people\u2019s health behaviour, and what do researchers recommend?<br>\nThese questions are addressed by Mikk J\u00fcrisson, Associate Professor of Public Health at University of Tartu.<\/p>\n<p><!-- \/wp:paragraph --><\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"accordion-item accordion-item--white\">\n\t\t\t\t\t<h2 class=\"accordion-header\" id=\"accordion-69d4ad0fa3b64-heading-4\">\n\t\t\t\t\t\t<button class=\"accordion-button collapsed\" type=\"button\" data-bs-toggle=\"collapse\" data-bs-target=\"#accordion-69d4ad0fa3b64-collapse-4\" aria-expanded=\"true\" aria-controls=\"accordion-69d4ad0fa3b64-collapse-4\">\n\t\t\t\t\t\t\tLISTEN: Podcast S\u00dcNAPS episode \"Genetic research and personalised medicine\" with Sander Pajusalu\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/h2>\n\t\t\t\t\t<div id=\"accordion-69d4ad0fa3b64-collapse-4\" class=\"accordion-collapse collapse \" aria-labelledby=\"accordion-69d4ad0fa3b64-heading-4\">\n\t\t\t\t\t\t<div class=\"accordion-body\">\n\t\t\t\t\t\t\t<p><strong>Listen to episode <a href=\"https:\/\/tervisemuuseum.ee\/sunaps-93-sander-pajusalu-geeniuuringud-ja-personaalmeditsiin\/\">\u201cGenetic research and personalised medicine\u201d<\/a> (<em>Geeniuuringud ja personaalmeditsiin<\/em>). 02.04.2025<\/strong><\/p>\n<p>\u00a0<\/p>\n<p>Estonian Biobank is sending invitations to selected gene donors to participate in a large-scale cardiovascular health research project. Based on the data collected, researchers aim to determine whether cardiovascular disease can be prevented more effectively by prescribing cholesterol-lowering treatment according to an individual\u2019s genetic risk.<\/p>\n<p>The opportunities offered by genetic research are discussed in more detail by medical geneticist and Head of the Genetics and Personalised Medicine Clinic at Tartu University Hospital, Dr Sander Pajusalu.<\/p>\n<p><strong>\u00a0<\/strong><\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"accordion-item accordion-item--white\">\n\t\t\t\t\t<h2 class=\"accordion-header\" id=\"accordion-69d4ad0fa3b64-heading-5\">\n\t\t\t\t\t\t<button class=\"accordion-button collapsed\" type=\"button\" data-bs-toggle=\"collapse\" data-bs-target=\"#accordion-69d4ad0fa3b64-collapse-5\" aria-expanded=\"true\" aria-controls=\"accordion-69d4ad0fa3b64-collapse-5\">\n\t\t\t\t\t\t\tLISTEN: Vikeraadio programme \u201cKajalood\u201d with Margus Viigimaa\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t<\/h2>\n\t\t\t\t\t<div id=\"accordion-69d4ad0fa3b64-collapse-5\" class=\"accordion-collapse collapse \" aria-labelledby=\"accordion-69d4ad0fa3b64-heading-5\">\n\t\t\t\t\t\t<div class=\"accordion-body\">\n\t\t\t\t\t\t\t<p><strong>Listen to episode \u201c<a href=\"https:\/\/vikerraadio.err.ee\/1609655537\/kajalood-margus-viigimaa-mu-suda-roomustab-kardioloogide-uue-polvkonna-ule?fbclid=IwY2xjawOw3PxleHRuA2FlbQIxMABicmlkETFLUlJESjZzMDVLcEp5UXM5c3J0YwZhcHBfaWQQMjIyMDM5MTc4ODIwMDg5MgABHqBEh5Sxo_JsY6KwakByT5_mW_PJIBmZUnfHmg4AWtlxLvnvjiBel1pwoEys_aem_saeNPHybVorDFo2tBriaaw\">Margus Viigimaa, MD: My heart rejoices over the new generation of cardiologists<\/a>\u201d (Margus Viigimaa: mu s\u00fcda r\u00f5\u00f5mustab kardioloogide uue p\u00f5lvkonna \u00fcle). 19.04.2025<\/strong><\/p>\n<p data-start=\"108\" data-end=\"314\">As a practising cardiologist, Dr Margus Viigimaa consults up to twenty patients on some days at the Heart Centre of the Regional Hospital, while also conducting scientific research at Estonian universities.<\/p>\n<p data-start=\"550\" data-end=\"880\">Dr Viigimaa talks about cutting-edge cardiac genetic studies recently launched with his involvement and leadership, as well as new biological therapies, wireless pacemakers and other innovations that have only just been introduced into cardiology.<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t<\/div>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><br><br><\/p>\n\n\n\n<p><br><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Resources in Estonian<\/p>\n","protected":false},"author":771,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"class_list":["post-499","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/sisu.ut.ee\/permedcenter\/wp-json\/wp\/v2\/pages\/499","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sisu.ut.ee\/permedcenter\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sisu.ut.ee\/permedcenter\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sisu.ut.ee\/permedcenter\/wp-json\/wp\/v2\/users\/771"}],"replies":[{"embeddable":true,"href":"https:\/\/sisu.ut.ee\/permedcenter\/wp-json\/wp\/v2\/comments?post=499"}],"version-history":[{"count":31,"href":"https:\/\/sisu.ut.ee\/permedcenter\/wp-json\/wp\/v2\/pages\/499\/revisions"}],"predecessor-version":[{"id":784,"href":"https:\/\/sisu.ut.ee\/permedcenter\/wp-json\/wp\/v2\/pages\/499\/revisions\/784"}],"wp:attachment":[{"href":"https:\/\/sisu.ut.ee\/permedcenter\/wp-json\/wp\/v2\/media?parent=499"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}