Priit Palta

Intrahepatic cholestasis of pregnancy, which affects 0.2-2% of pregnancies, is characterized by pruritus, increased aminotransferase activity and elevated serum bile acids. Previous studies have implicated liver-enriched genes in intrahepatic cholestasis of pregnancy. We conducted a meta-analysis of intrahepatic cholestasis of pregnancy genome-wide association studies in the FinnGen study, deCODE, Estonian Biobank, the Danish Blood Donor Study and Copenhagen Hospital Biobank with 4,738 women with prior ICP and 436,834 female controls. The analysis found 26 genome-wide significant associations of which 10 were novel. Genes in the associated loci were prioritized using lead SNP expression quantitative trait loci associations and colocalization analysis to assess potential causality. The associated loci implicate bile acid synthesis, LDL cholesterol, and lipid metabolism. Additionally, comorbidity, genetic correlation and polygenic risk score analyses further indicated a link between intrahepatic cholestasis of pregnancy and pancreatitis, suggesting shared genetic underpinnings.

Circadian rhythms not only coordinate the timing of wake and sleep but also regulate homeostasis within the body, including glucose metabolism. The genetic variants that contribute to the temporal control of glucose levels have not been previously examined. Using genome-wide data from ~420,000 individuals from the UK Biobank and replication in ~100,000 individuals from the Estonian Biobank, ~500,000 from FinnGen, ~160,000 from the VA Million Veteran Program, and ~52,000 from the MGB Biobank, we show that glucose levels are under diurnal genetic control. We discover a robust temporal association of glucose levels at the Melatonin receptor 1B ( MTNR1B , rs10830963, P = 1×10 ⁻²² ) and a canonical circadian pacemaker gene Cryptochrome 2 ( CRY2) loci (rs12419690, P = 1×10 ⁻¹⁶ ). Furthermore, we show that sleep modulates glucose levels, and the genetic variants have an independent role in diurnal glucose control. Finally, we show that these variants independently modulate risk of type 2 diabetes and that sleep medications including melatonin associate with type 2 diabetes. Our findings, together with earlier genetic and epidemiological evidence, show a clear connection between sleep and metabolism and highlight genetic variation at MTNR1B and CRY2 in the control of diurnal glucose levels.

Interpreting the association of genetic variants with complex traits can be improved by gaining a greater understanding of the molecular consequences of these variants. Although genome-wide association studies (GWAS) for complex diseases routinely profile over one million individuals ^1–5 , studies of molecular traits have lagged behind. Here 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. We identified 88,127 common and low-frequency locus–trait associations from 8,398 loci that converged on shared genes and pathways. Using statistical fine mapping, systematic phenome-wide colocalization and cis -Mendelian randomization, we explored putative causal links between metabolic traits and disease outcomes. We predict that although plasma branched-chain amino acids (BCAAs) have been associated with type 2 diabetes in observational studies ^6,7 , lowering BCAA levels by targeting the BCAA catabolism pathway is unlikely to reduce type 2 diabetes risk. Leveraging our large sample size and high-quality genotype imputation, we found that 19.4% of the confidently fine-mapped variants had minor allele frequencies between 0.1 and 1%, and these variants were twofold enriched for predicted missense and splice-altering variants. Our results highlight the value of integrating low-frequency variants into genetic association studies.

Myopia is a rapidly escalating global public health challenge, yet the biological mechanisms linking modern lifestyles to abnormal eye growth remain unclear. Circadian rhythms have been implicated in refractive development, but causal evidence is limited. Here, we integrate population-scale human data with an experimental animal model to determine whether circadian misalignment contributes to myopia. In >265,000 individuals from the Estonian and UK Biobanks, late chronotype was consistently associated with myopia. To assess causality, we experimentally disrupted the alignment between behavioural and environmental rhythms in mice by housing them in non-24-hour light-dark schedules. Exposure to a lengthened cycle (T26) induced a myopic shift that was, notably, reversible in early adulthood. Retinal transcriptomics revealed enrichment of mitochondrial and hypoxia-related plasticity pathways, with transcriptional changes distributed across multiple retinal cell classes. Together, these findings identify circadian misalignment as a conserved and modifiable driver of myopia, highlighting opportunities for novel preventive and therapeutic approaches.

Lumbar spinal stenosis (LSS) affects over 100 million people globally, with an increasing incidence due to an ageing population. While LSS is known to be heritable, its genetic basis remains poorly understood. We conduct a genome-wide meta-analysis of LSS in 40,303 cases and 741,469 controls. We identify 73 previously unreported loci in addition to 15 known loci, and highlight spinal degeneration as a key pathogenic mechanism. In 12,784 surgically treated cases, we discover five loci specifically associated with severe disease. Age-of-onset analyses show that most variants influence risk after midlife, but some confer susceptibility as early as age 34. Mendelian randomization further demonstrates causal effects of higher body mass and fat-free mass on LSS risk. Overall, our findings expand knowledge of the genetic background of LSS and inform future translational research.

Circadian clocks govern the 24-hour rhythmic activity of organs across the human body and these nycthemeral rhythms are critical for human physical and mental health. Here, we analysed 14,886 samples across 45 human tissues and inferred tissue-specific local phases using CHIRAL to define the rhythmic transcriptome. Local phase inference identified a median of 5,747 rhythmic transcripts across tissues, including 5,531 across brain tissues, indicating that prior donor-level approaches have substantially underestimated the extent of rhythmic transcription in the human brain. In the brain, rhythmic genes were enriched for neurotransmitter pathways, the synaptic vesicle cycle, circadian entrainment and major neurodegenerative disorders. Strikingly, more than 200 genes in Alzheimer’s, Parkinson’s, Huntington’s and prion disease pathways displayed significant 24-hour rhythmicity across cortical, striatal and hippocampal regions, segregating into distinct diurnal and nocturnal phase clusters. These included Parkinson’s disease genes SNCA and PRKN , Alzheimer’s disease genes PSEN1 and APP , prion disease genes PRNP and EIF2AK3 ( PERK ), and Huntington’s disease genes HTT and GPR52 . Among these disease-associated rhythmic transcripts were several targets already under therapeutic investigation for which administration at specific times of day may improve therapeutic efficacy or tolerability. Here, we observe that human tissues, including the brain, exhibit distinct local rhythmic organisation. These findings provide novel insights into disease mechanisms and highlight opportunities for target discovery, drug development and chronotherapeutic intervention.

An increased emphasis on disease prevention is essential to improve population health and reduce healthcare resource needs. Key requirements for effective population scale prevention programmes are an optimal balance between simplicity of screening and accuracy of risk prediction at individual level. We developed models of varying complexity to predict the incident risk of 24 diseases in the UK Biobank population, testing combinations of modalities including established clinical variable-based risk scoring, ¹ H-NMR metabolomics, polygenic risk scores (PRS), and self-answered questionnaires with individual electronic health record (EHR)-based past medical history (PMH). Our results show that prediction models that utilise just questionnaire and PMH data (“No Needle” model) or with added metabolomics and PRS (“Single Blood Draw” model) exhibit robust discriminative performance, at least as good or better than a comprehensive clinical variable model or established cardiovascular disease (CVD) risk scores. The “No Needle” model was also validated in an independent prospective cohort, the Estonian Biobank. We also tested scenarios for deployment of these models to improve the effectiveness of the NHS Health Check, a population-scale UK screening programme. Our results suggest high potential for less resource-intensive approaches than current clinically-based paradigms for prediction of incident disease.

Polygenic risk scores (PRSs) can improve type 2 diabetes (T2D) risk prediction beyond clinical risk factors, but most exclude low-frequency, rare, and population-specific variants. We hypothesized that incorporation of rare variants via large-scale, well-imputed or whole-genome sequence-based genome-wide association (GWAS) meta-analyses and expanded linkage disequilibrium (LD) reference panels would improve risk prediction for T2D. We constructed a GWAS meta-analysis (230,675 T2D cases and 991,401 T2D controls), enabling the inclusion of rare variants (minor allele frequency [MAF] range= 1×10 ⁻⁵ - 0.01) to construct three T2D PRSs: (i) CTSLEB, which utilizes a custom ancestry-matched LD panel of 79.5 million variants and 83K participants to specifically model LD of rare variants; (ii) PRS-CS (TAGIT), using a reference panel expanded to 2.3 million variants to better capture low-frequency and population specific variants (population-specific MAF ≥ 0.01); (iii) PRS-CS (HM3), using a standard LD panel with HapMap3 variants (1.2 million variants). Performance was evaluated in the All of Us Research Program (20,301 T2D cases; 30,617 T2D controls) and compared to a benchmark multi-ancestry PRS (MAF≥0.01), developed by the D-PRISM consortium and derived from a significantly larger set of ancestry-specific meta-analyses (totaling 359,891 T2D cases and 1,825,792 controls). Expanding variant coverage with PRS-CS (TAGIT) and CTSLEB improved risk prediction relative to PRS-CS (HM3). While PRS-CS (TAGIT) showed greater prediction accuracy in the overall population, CTSLEB uniquely captured risk driven by rare variants, showing greater prediction accuracy for carriers of rare and low-frequency variants compared to PRS-CS (TAGIT) (AUC = 0.832 vs. 0.823 p(DeLong test) = 7.9×10 ⁻⁵ ) and PRS-CS (HM3) (AUC = 0.832 vs 0.818, p(DeLong test) = 2.39×10 ⁻⁷ ). The benchmark D-PRISM PRS showed the highest predictive performance for all ancestries except in African ancestry populations, where CTSLEB performed similarly for the overall population (CTSLEB AUC = 0.786 vs. D-PRISM AUC = 0.784, p(DeLong test) = 0.57) and significantly better for rare variant carriers (CSTLEB AUC = 0.775 vs. D-PRISM AUC = 0.768, p(DeLong test) = 8.71×10 ⁻³ ). These results demonstrate the value in incorporating rare and population-specific variants into PRS construction, improving genetic risk prediction in diverse populations.

Infections of the dental pulp are common sequelae of microbial activity and host susceptibility, affecting >80% of adult population. We performed a genome-wide association study on endodontic infections utilizing Finnish health registry and genotype data from FinnGen. Cases [132,124 (27.2%)] had at least one ICD10-diagnosis code of pulpal or apical diseases, whereas 353,106 individuals without diagnoses served as controls. We investigated two clinical sub-phenotypes, Pulpitis and Necrosis of pulp or apical periodontitis. Our analysis resulted in significant associations in 12 chromosomes and 15 independent loci, such as those near HORMAD2 gene and those in the HLA region. The imputed HLA alleles, especially DRB1 * 04:01 and DQB1 * 03:01, were associated with endodontic infections. Bioinformatic analysis of the top variants indicated several potential regulatory variants which are involved in MHC class II protein complex, humoral immune responses, and antigen processing. Our study widens understanding on how immune dysregulation resulting from immunogenetic variation is involved in the pathogenesis of endodontic infections.

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.