Biology subjects

Estonian Biobank Research Team,

Publications and source records attributed to Estonian Biobank Research Team,.

14 recordsLinked to original sources

bioRxiv2026

Detecting CYP2C19 deletions from genotyping array signals using neural networks

Since copy number variations (CNVs) in pharmacogenes can cause significant alterations in drug metabolism, their reliable detection is of high importance both for large-scale studies and personalized medicine. Whole-genome sequencing, and specifically long-read sequencing, is the gold standard for CNV detection. Despite increasing availability of these technologies, genotyping arrays are still widely used as cost-effective alternatives in biobank and clinical settings, yet calling CNVs based on array intensity signals is challenging due to low base pair resolution. In this work, we developed a neural network model, nnCNV, to predict deletions in the CYP2C19 pharmacogene region from array intensity signals. We compared our method to the most widely used algorithm, PennCNV, and demonstrated better performance reaching 100% accuracy in the test dataset. Furthermore, we predicted probe-by-probe CYP2C19 deletion coordinates for all Estonian Biobank samples using nnCNV and PennCNV, and validated these predictions using an identity-by-descent (IBD) sharing method, which also demonstrated superior nnCNV performance. For the deletion samples with conflicting PennCNV and nnCNV predictions, we performed PCR analysis for validation, which showed 97% precision for nnCNV compared to 23% for PennCNV. Finally, we assessed the gradient-based feature importance maps and showed that nnCNV utilizes signal intensity information not only from deletion probes, but also from probes in flanking regions. Our results demonstrate that long-range information, which cannot be utilized by hidden Markov models, can improve CNV calling.

bioinformatics↗

bioRxiv2026

Sex-specific associations between education-related genetic factors and fertility extend beyond educational attainment

Educational attainment (EA) is strongly associated with fertility, yet the mechanisms underlying this relationship remain poorly understood. Using data from ~40,000 women and ~10,000 men in the Estonian Biobank, we examine sex-specific associations between EA polygenic scores (EA PGS) and completed fertility. The EA PGS captures an individual's genetic predisposition to completing higher levels of education, and can also be constructed separately for cognitive and non-cognitive EA components. We find that EA PGS is negatively associated with fertility among women, with a significantly stronger association for the non-cognitive than the cognitive EA polygenic score. The association of EA PGS with fertility is only partly explained by EA and is moderated by EA and reproductive timing, changing sign across age-at-first-pregnancy strata. Within-family estimates are not attenuated relative to population estimates, consistent with a predominant role of direct genetic effects. Among men, associations between EA PGS and fertility are non-significant or slightly positive. Overall, education-related genetic variation is associated with fertility through pathways largely independent of EA, operating through sex-specific and timing-dependent mechanisms. These results contribute to our understanding of the micro-level mechanisms shaping macro-level demographic change.

genetics↗

bioRxiv2026

Circadian rhythms regulate refractive development across species

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.

neuroscience↗

bioRxiv2026

Genetic influences on educational outcomes during and after the Soviet era: Revisiting evidence from Estonia

The origins of individual differences in socioeconomic outcomes, including educational attainment and occupational status, reflect a combination of genetic and environmental factors whose relative contributions may differ across historical contexts. Previous research suggested a doubling of the relative role of genetics in explaining variation in these traits in independent Estonia compared with the Soviet era. Using the Estonian Biobank, now tenfold larger than at the time of the original study, we aimed to replicate and extend these findings. We found converging evidence that the relative contribution of genetic factors to educational attainment, but not to occupational status, was higher in the post-Soviet era, although the differences were smaller than previously reported and varied across analytical approaches. However, analyses using narrower birth cohorts revealed a more complex pattern, with the relative genetic contribution to educational attainment decreasing gradually across successive cohorts. Thus, while the Soviet-to-post-Soviet transition was associated with an increase in the relative role of genetic factors in explaining individual differences in educational attainment, it represents only one component of the broader historical changes that have shaped genetic associations with educational attainment in Estonia over the last century.

genetics↗

bioRxiv2025

Bias in genome-wide association test statistics due to omitted interactions

Over the past two decades, genome-wide association studies (GWAS) enabled the discovery of thousands of variants associated with many complex human traits. However, conventional GWAS are still widely performed with linear models with the assumption that the genetic effects are predominantly additive. In this work, we investigate the test statistic behavior when linear models are used to obtain significant genotype-phenotype associations without accounting for epistasis. We first algebraically derive mean and variance shift in the null statistic due to the omitted interaction term, and define the boundary between conservative (i.e., deflated statistic tail) and anti-conservative (i.e., inflated statistic tail) regimes for the common GWAS significance threshold. We then perform phenotype simulation analyses using the Estonian Biobank genotypes and validate the mathematical model. We demonstrate that the anti-conservative regime is plausible under realistic parameter settings and models omitting interaction terms can produce spurious significance. Our findings suggest caution when interpreting statistically significant signals reported in the literature based on linear models, especially for large-scale GWAS.

bioinformatics↗

bioRxiv2025

Robust inference and widespread genetic correlates from a large-scale genetic association study of human personality

Personality traits describe stable differences in how individuals think, feel, and behave and how they interact with and experience their social and physical environments. We assemble data from 46 cohorts including 611K-1.14M participants with European-like and African-like genomes for genome-wide association studies (GWAS) of the Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience), and data from 51K participants for within-family GWAS. We identify 1,257 lead genetic variants associated with personality, including 823 novel variants. Common genetic variants explain 4.8%-9.3% of the variance in each trait, and 10.5%-16.2% accounting for measurement unreliability. Genetic effects on personality are highly consistent across geography, reporter (self vs. close other), age group, and measurement instrument, and we find minimal spousal assortment for personality in recent history. In stark contrast to many other social and behavioral traits, within-family GWAS and polygenic index analyses indicate little to no shared environmental confounding in genetic associations with personality. Polygenic prediction, genetic correlation, and Mendelian randomization analyses indicate that personality genetics have widespread, potentially causal associations with a wide range of consequential behaviors and life outcomes. The genetic architecture of personality is robust and fundamental to being a human.

genomics↗

bioRxiv2025

An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference

Polygenic indexes (PGIs) -- DNA-based predictors of individual phenotypes -- have become essential tools across biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands phenotype coverage from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGIs predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and offer a framework for interpreting results from analyses that control for parental PGIs. We illustrate the utility of parental PGIs using two applications: (1) comparing PGI associations with and without parental PGI controls for all phenotypes in two Repository datasets with family data, and (2) for BMI and diastolic blood pressure, exploring the contribution of causal versus non-causal components of PGI associations to the imperfect portability of PGIs across subgroups within a genetic ancestry. Collectively, the updates enhance predictive performance, broaden the Repositorys scope, and introduce novel resources that reduce confounding bias and improve interpretability.

genomics↗

bioRxiv2025

Separating direct, indirect and parent-of-origin genetic effects in the human population

Here, we present a novel approach to estimate the degree to which the phenotypic effect of a DNA locus is attributable to four components: alleles in the child (direct genetic effects), alleles in the mother and the father (indirect genetic effects), or is dependent upon the parent from which it is inherited (parent-of-origin, PofO effects). Applying our model, JODIE, to 30,000 child-mother-father trios with phased DNA information from the Estonian Biobank (EstBB) and the Norwegian Mother, Father, Child Cohort (MoBa), we jointly estimate the phenotypic variance attributable to these four effects unbiased of assortative mating (AM) for height, body mass index (BMI) and childhood educational test score (EA). For all three traits, direct effects make the largest contribution to the genetic effect variance. But we find that parental indirect genetic effects make an equivalent combined contribution, and that there is a non-zero PofO effect variance for all traits. We calculate the heritability that would be obtained at the populationlevel in the absence of AM for common DNA loci, and show that the proportional contribution of direct effects to these heritability values can be calculated as 64.0% for EA in MoBa, 77.1% and 63.4% for height in MoBa and EstBB, and 81.2% and 88.0% for BMI in MoBa and EstBB. Additionally, using within-family genome-wide association testing, we identify 276 independently associated DNA regions that replicate across two additional biobanks, which all show a genotype-phenotype relationship that reflects an interplay of direct, indirect and PofO effects. Determining how direct, parental and PofO genetic effects combine across loci genome-wide to influence human phenotypic variation requires joint modeling of parental and child genotypes alongside the parental origin of loci and here, we make the first attempt to do this in the human population.

genetics↗

bioRxiv2024

StarPhase: Comprehensive Phase-Aware Pharmacogenomic Diplotyper for Long-Read Sequencing Data

Pharmacogenomics is central to precision medicine, informing medication safety and efficacy. Phar-macogenomic diplotyping of complex genes requires full-length DNA sequences and detection of structural rearrangements. We introduce StarPhase, a tool that leverages PacBio HiFi sequence data to diplotype 21 CPIC Level A pharmacogenes and provides detailed haplotypes and supporting visualizations for HLA-A, HLA-B, and CYP2D6. StarPhase diplotypes have high concordance with benchmarks where 99.5% are either exact matches or minor discrepancies. Manual inspection of the 0.5% mismatches indicates they were correctly called by StarPhase. With StarPhase, we update or correct 26.2% of GeT-RM pharmacogenomic diplotypes. Population distributions from StarPhase mostly reflect those of the All of Us cohort, while also highlighting gaps in existing pharmacogenomic databases that long-read sequencing can fill. With a single HiFi whole genome sequencing assay, StarPhase enables robust PGx diplotyping even as additional pharmacogenes and haplotypes are discovered.

bioinformatics↗

bioRxiv2024

Genetic variants affect diurnal glucose levels throughout the day

Circadian rhythms not only coordinate the timing of wake and sleep but also regulate homeostasis within the body, including glucose metabolism. However, the genetic variants that contribute to temporal control of glucose levels have not been previously examined. Using data from 420,000 individuals from the UK Biobank and replicating our findings in 100,000 individuals from the Estonian Biobank, we show that diurnal serum glucose is under genetic control. We discover a robust temporal association of glucose levels at the Melatonin receptor 1B (MTNR1B) (rs10830963, P = 1e-22) and a canonical circadian pacemaker gene Cryptochrome 2 (CRY2) loci (rs12419690, P = 1e-16). Furthermore, we show that sleep modulates serum glucose levels and the genetic variants have a separate mechanism of diurnal control. Finally, we show that these variants independently modulate risk of type 2 diabetes. Our findings, together with earlier genetic and epidemiological evidence, show a clear connection between sleep and metabolism and highlight variation at MTNR1B and CRY2 as temporal regulators for glucose levels.

genetics↗

bioRxiv2023

Assessing the impact of 20th century internal migrations on the genetic structure of Estonia

There is emerging evidence that migration behaviour can be selective with respect to individuals genotypes. As a result, it can generate genotype-environment correlations at migration-associated loci that cannot be corrected by standard methods used in genetic association studies. Here, we explore this phenomenon by looking into the dynamics of the spatial distribution of polygenic scores (PGSs) in the Estonian Biobank. We show that contemporary migrations intensify inter-regional differences in PGSs for many traits, with the effect of educational attainment (EA) PGS being the strongest and, to a large extent, explaining inter-regional differences in other PGSs. This differentiation is mainly driven by the migration of individuals with relatively high PGS for EA to the two largest cities from the rest of the country. Importantly, we replicate this pattern within families: individuals migrating to the major cities have, on average, higher PGS for EA relative to their siblings residing in other regions of Estonia. This trend is observed for the period starting from the mid-20th century to the present, despite substantial changes in Estonian society during this period. Thus, we show that there is increasing genetic differentiation at trait-associated loci between most urbanized regions of Estonia versus the rest of the country, leading to genotype-environment correlations that cannot be fully corrected using standard approaches. We also provide evidence for direct genetic effects on migration behaviour based on sibling analysis and discuss potential links between migration and EA.

genetics↗

bioRxiv2022

Omics-informed CNV calls reduce false positive rate and improve power for CNV-trait associations

Copy number variations (CNV) are believed to play an important role in a wide range of complex traits but discovering such associations remains challenging. Whilst whole genome sequencing (WGS) is the gold standard approach for CNV detection, there are several orders of magnitude more samples with available genotyping microarray data. Such array data can be exploited for CNV detection using dedicated software (e.g., PennCNV), however these calls suffer from elevated false positive and negative rates. In this study, we developed a CNV quality score that weights PennCNV calls (pCNV) based on their likelihood of being true positive. First, we established a measure of pCNV reliability by leveraging evidence from multiple omics data (WGS, transcriptomics and methylomics) obtained from the same samples. Next, we built a predictor of omics-confirmed pCNVs, termed omics-informed quality score (OQS), using only PennCNV software output parameters. Promisingly, OQS assigned to pCNVs detected in close family members was up to 35% higher than the OQS of pCNVs not carried by other relatives (P < 3.0-10-90), outperforming other scores. Finally, in an association study of four anthropometric traits in 89,516 Estonian Biobank samples, the use of OQS led to a relative increase in the trait variance explained by CNVs of up to 34% compared to raw pCNVs or previous quality scores. Overall, we put forward a flexible framework to improve any CNV detection method leveraging multi-omics evidence, applied it to improve PennCNV calls and demonstrated its utility by improving the statistical power for downstream association analyses.

bioinformatics↗

bioRxiv2021

Prioritising Autoimmunity Risk Variants for Functional Analyses by Fine-Mapping Mutations Under Natural Selection

Pathogens imposed selective pressure on humans and shaped genetic variation in immunity genes. This can also be true for a fraction of causal variants implicated in chronic inflammatory disorders. Hence, locating adaptive mutations among candidate variants for these disorders can be a promising way to prioritize and decipher their functional response to microbial stimuli and contribution to pathogenesis. This idea has been discussed for decades, but challenges in locating adaptive SNPs hindered its application in practice. Our study addresses this issue and shows that a fraction of candidate variants for inflammatory conditions evolved under moderate and weak selection regimes (sweeps), and such variants are mappable. Using a novel powerful local-tree-based methodology, we show that 204 out of 593 risk loci for 21 autoimmune disorders contain at least one candidate SNP with strong evidence of selection. More importantly, in 28% of cases, these candidates for causal variants colocalize with SNPs under natural selection that we fine-mapped in this study. Causal SNPs under selection represent promising targets for functional experiments. Such experiments will help decipher molecular events triggered by infectious agents, a likely early event in autoimmunity. Finally, we show that a large fraction (60%) of candidate variants are either hitchhikers or linked with the selected mutation. Our findings, thus, support both hitchhiking and natural selection models, with the latter having important practical implications in medicine.

genetics↗

bioRxiv2021

Long-range regulatory effects of Neandertal DNA in modern humans

The admixture between modern humans and Neandertals has resulted in [~]2% of the genomes of present-day non-Africans being composed of Neandertal DNA. Association studies have shown that introgressed DNA significantly influences phenotypic variation in people today and that several of the phenotype-associated archaic variants had links to expression regulation as well. In general, introgressed DNA has been demonstrated to significantly affect the transcriptomic landscape in people today. However, little is known about how much of that impact is mediated through long-range regulatory effects that have been shown to explain [~]20% of expression variation. Here we identified 60 transcription factors (TFs) with their top cis-eQTL SNP being of Neandertal ancestry in GTEx and predicted long-range Neandertal DNA-induced regulatory effects by screening for the predicted target genes of those TFs. We show that genes in regions devoid of Neandertal DNA are enriched among the target genes of some of these TFs. Furthermore, archaic cis-eQTLs for these TFs included multiple candidates for local adaptation and have associations with various immune traits, schizophrenia, blood cell type composition and anthropometric measures. Finally, we show that our results can be replicated in empirical trans-eQTLs with Neandertal variants. Our results suggest that the regulatory reach of Neandertal DNA goes beyond the 40% of genomic sequence that it still covers in present-day non-Africans and that via this mechanism Neandertal DNA additionally influences the phenotypic variation in people today.

genomics↗