Biology subjects

Vainik, U.

Publications and source records attributed to Vainik, U..

14 recordsLinked to original sources

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↗

bioRxiv2025

Genomic structural equation modeling of impulsivity and risk-taking traits reveals three latent factors distinctly associated with brain structure and development

BackgroundImpulsivity is a multifaceted transdiagnostic trait that emerges in childhood. Research has identified genetic loci and brain systems associated with different facets of impulsivity and risk-taking. However, how these genetic underpinnings overlap across different facets, and how they are associated with brain development during childhood remain unknown. MethodsUsing genomic structural equation modeling on 17 impulsivity and risk-taking traits, we identified latent factors capturing overlapping genetic architecture. We then calculated polygenic scores for these factors using Adolescent Brain Cognitive Development Study data (N = 4,142) and examined their associations with brain structure, development, and behavior in children aged 9-14 years. Finally, we tested whether socioeconomic status modulated the associations between latent polygenic scores and brain structures. ResultsWe identified three distinct genetic latent factors, which we label lack of self-control, reward drive, and sensation seeking. In children, polygenic scores for the three factors showed associations with distinct brain patterns: lack of self-control associated with reduced prefrontal cortical thickness, reward drive with increased subcortical cellular density, and sensation seeking with increased cortical surface area and white matter integrity. Longitudinally, lack of self-control predicted slower white matter development. The association between polygenetic score for lack of self-control and white matter mean diffusivity was modulated by socioeconomic status. ConclusionsWe identified three genetically distinct dimensions of impulsivity and risk-taking with separable neurodevelopmental origins. These genetic predispositions manifested as distinct brain patterns as early as ages 9-10. Environmental experience modulated some of the genetic effects on brain development.

neuroscience↗

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↗

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↗

bioRxiv2021

Investigating the genetic and environmental basis of head micromovements during MRI

IntroductionHead motion during magnetic resonance imaging is heritable. Further, it shares phenotypical and genetic variance with body mass index (BMI) and impulsivity. Yet, to what extent this trait is related to single genetic variants and physiological or behavioral features is unknown. We investigated the genetic basis of head motion in a meta-analysis of genome-wide association studies. Further, we tested whether physiological or psychological measures, such as respiratory rate or impulsivity, mediated the relationship between BMI and head motion. MethodsWe conducted a genome-wide association meta-analysis for mean and maximal framewise head displacement (FD) in seven population neuroimaging cohorts (UK Biobank, LIFE-Adult, Rotterdam Study cohort 1-3, Austrian Stroke Prevention Family Study, Study of Health in Pomerania; total N = 35.109). We performed a pre-registered analysis to test whether respiratory rate, respiratory volume, self-reported impulsivity and heart rate mediated the relationship between BMI and mean FD in LIFE-Adult. ResultsNo variant reached genome-wide significance for neither mean nor maximal FD. Neither physiological nor psychological measures mediated the relationship between BMI and head motion. ConclusionBased on these findings from a large meta-GWAS and pre-registered follow-up study, we conclude that the previously reported genetic correlation between BMI and head motion relies on polygenic variation, and that neither psychological nor simple physiological parameters explain a substantial amount of variance in the association of BMI and head motion. Future imaging studies should thus rigorously control for head motion at acquisition and during preprocessing.

neuroscience↗

bioRxiv2021

Educational Attainment Polygenic Scores, Socioeconomic Factors, and Cortical Structure in Children and Adolescents

BackgroundGenome-wide polygenic scores for educational attainment (PGS-EA) and socioeconomic factors, which are correlated with each other, have been consistently associated with academic achievement and general cognitive ability in children and adolescents. Yet, the independent associations of PGS-EA and socioeconomic factors with specific underlying factors at the neural and neurocognitive levels are not well understood. The goal of this study was to examine the unique contributions of PGS-EA and parental education to cortical thickness (CT), cortical surface area (SA), and neurocognitive skills in children and adolescents. MethodsParticipants were typically developing children and adolescents (3-21 years of age; 53% male; N = 391). High-resolution, T1-weighted magnetic resonance imaging data were acquired. PGS-EA were computed based on the most recent genome-wide association study of educational attainment. Sustained attention, inhibitory control, working memory, vocabulary, and episodic memory were measured. ResultsPGS-EA and parental education were independently and significantly associated with SA, vocabulary, and attention outcomes but were not associated with CT. Vertex-wise analyses indicated that higher PGS-EA was significantly associated with greater SA in the left medial orbitofrontal gyrus and inferior frontal gyrus after accounting for parental education. Higher parental education was significantly associated with greater SA in the left parahippocampal gyrus after accounting for PGS-EA. ConclusionsThese findings suggest that education-linked genetics may influence SA, particularly in certain frontal regions, leading to variability in academic achievement. Results suggested genetic confounding in associations between parental education and SA in children and adolescents, with these associations remaining significant after controlling for PGS-EA.

neuroscience↗

bioRxiv2021

A critical role of brain network architecture in a continuum model of autism spectrum disorders spanning from healthy individuals with genetic liability to individuals with ASD

Studies have shown cortical alterations in individuals with autism spectrum disorders (ASD) as well as in individuals with high polygenic risk for ASD. An important addition to the study of altered cortical anatomy is the investigation of the underlying brain network architecture that may reveal brain-wide mechanisms in ASD and in polygenic risk for ASD. Such an approach has been proven useful in other psychiatric disorders by revealing that brain network architecture shapes (to an extent) the disorder-related cortical alterations. This study uses data from a clinical dataset - 560 male subjects (266 individuals with ASD and 294 healthy individuals, CTL, mean age at 17.2 years) from the Autism Brain Imaging Data Exchange database, and data of 391 healthy individuals (207 males, mean age at 12.1 years) from the Pediatric Imaging, Neurocognition and Genetics database. ASD-related cortical alterations (group difference, ASD-CTL, in cortical thickness) and cortical correlates of polygenic risk for ASD were assessed, and then statistically compared with structural connectome-based network measures (such as hubs) using spin permutation tests. Next, we investigated whether polygenic risk for ASD could be predicted by network architecture by building machine-learning based prediction models, and whether the top predictors of the model were identified as disease epicenters of ASD. We observed that ASD-related cortical alterations as well as cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. We also observed that age progression of ASD-related cortical alterations and cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. Further investigation revealed that structural connectomes predicted polygenic risk for ASD (r=0.30, p<0.0001), and two brain regions (the left inferior parietal and left suparmarginal) with top predictive connections were identified as disease epicenters of ASD. Our study highlights a critical role of network architecture in a continuum model of ASD spanning from healthy individuals with genetic risk to individuals with ASD. Our study also highlights the strength of investigating polygenic risk scores in addition to multi-modal neuroimaging measures to better understand the interplay between genetic risk and brain alterations associated with ASD.

neuroscience↗

bioRxiv2021

Schizophrenia polygenic risk during typical development reflects multiscale cortical organization

Schizophrenia is widely recognized as a neurodevelopmental disorder. Abnormal cortical development may by revealed using polygenic risk scoring for schizophrenia (PRS-SCZ). We assessed PRS-SCZ and cortical morphometry in typically developing children (3-21 years) using whole genome genotyping and T1-weighted MRI (n=390) from the Pediatric Imaging, Neurocognition and Genetics (PING) cohort. We contextualise the findings using (i) age-matched transcriptomics, (ii) histologically-defined cytoarchitectural types and functionally-defined networks, (iii) case-control differences of schizophrenia and other major psychiatric disorders. Higher PRS-SCZ was associated with greater cortical thickness, which was most prominent in areas with heightened gene expression of dendrites and synapses. PRS-SCZ related increases in vertex-wise cortical thickness were especially focused in the ventral attention network, while koniocortical type cortex (i.e. primary sensory areas) was relatively conserved from PRS-SCZ related differences. The large-scale pattern of cortical thickness increases related to PRS-SCZ mirrored the pattern of cortical thinning in schizophrenia and mood-related psychiatric disorders. Age group models illustrate a possible trajectory from PRS-SCZ associated cortical thickness increases in early childhood towards thinning in late adolescence, which resembles the adult brain phenotype of schizophrenia. Collectively, combining imaging-genetics with multi-scale mapping, our work provides novel insight into how genetic risk for schizophrenia impacts the cortex early in life.

neuroscience↗

bioRxiv2020

Heritability of cortical morphology reflects a sensory-fugal plasticity gradient

Human brain plastically adapts to environmental demands. Here, we propose that naturally occuring plasticity in certain brain areas should be reflected by higher environmental influence and therefore lower heritability of the structure of those brain areas. Mesulams (1998) seminal overview proposed a hierarchy of plasticity, where higher-order multimodal areas should be more plastic than lower-order sensory areas. Using microstructural and functional gradients as proxies for Mesulams hierarchy, we seek to test whether these gradients predict heritability of brain structure. We test this model simultaneously across multiple measures of cortical structure and microstructure derived from structural magnet resonance imaging. We also account for multiple other explanations of heritability differences, such as signal-to-noise ratio and spatial autocorrelation. We estimated heritability of brain areas using 984 participants from the Human Connectome Project. Multi-level modelling of heritability differences demonstrated that heritability is explained by both signal quality, as well as by the primary microstructural gradient. Namely, sensory areas had higher heritability and limbic/heteromodal areas had lower heritability. Given the increasing availability of genetically informed imaging data, heritability could be a quick method assess brain plasticity. Highlights (up to 85 chars)Cortical areas vary in heritability. This is seen across structural measures. Heritability differences could be explained by plasticity, topography, or noise. We build a comprehensive model testing many explanations across 5 measures. Heritability is explained by noise and 1st structural gradient reflecting plasticity. Heritability could be a method to study brain plasticity.

neuroscience↗

bioRxiv2019

Non-linear effects of socioeconomic status on brain development: associations between parental occupation, cortical thickness and language skills in childhood and adolescence

Studies have pointed to the role of the brain in mediating the effects of the social environment of the developing child on life outcomes. Since brain development involves nonlinear trajectories, these effects of the childs social context will likely have age-related differential associations with the brain. However, there is still a dearth of integrative research investigating the interplay between neurodevelopmental trajectories, social milieu and life outcomes. We set out to fill this gap, focusing specifically on the role of socioeconomic status, SES (indexed by parental occupation) on brain and cognitive development by analyzing MRI scans from 757 typically-developing subjects (age = 3-21 years). We observed nonlinear interaction of age and SES on cortical thickness, specifically a significant positive association between SES and thickness around 9-13 years at several cortical regions. Using a moderated mediation model, we observed that cortical thickness mediated the link between SES and language abilities, and this mediation was moderated by age in a quadratic pattern, indicating a pronounced SES-effect during early adolescence. Our results, drawn from cross-sectional data, provide a basis for further longitudinal studies to test whether early adolescence may be a sensitive time window for the impact of SES on brain and cognitive development.

neuroscience↗

bioRxiv2018

Neural and behavioral endophenotypes of obesity

BackgroundImpulsivity increases the risk for obesity and weight gain. However, the precise role of impulsivity in the aetiology of overeating behavior and obesity is currently unknown. Here we examined the relationships between personality-related measures of impulsivity, Uncontrolled Eating, BMI, and longitudinal weight changes. Additionally, we analyzed the associations between general impulsivity domains and brain cortical thickness to elucidate brain vulnerability factors related to weight gain. MethodsStudents in their first year of university - a risky period for weight gain - completed questionnaire measures of impulsivity and eating behavior at the beginning (N = 2318) of the school year. We also collected their weight at the end of the term (N = 1197). Impulsivity was divided into factors stress reactivity, reward sensitivity and lack of self-control. Using structural equation models, we tested the plausibility of a hierarchical relationship, in which impulsivity traits were associated with Uncontrolled Eating, which in turn predicted BMI and weight change. 71 participants underwent T1-weighted MRI to investigate the correlation between impulsivity and cortical thickness. ResultsImpulsivity traits showed positive correlations with Uncontrolled Eating. Higher scores in Uncontrolled Eating were in turn associated with higher BMI. None of the impulsivity-related measurements nor Uncontrolled Eating were correlated with longitudinal weight gain. Higher stress sensitivity was associated with increased cortical thickness in the superior temporal gyrus. Lack of self-control was positively associated with increased thickness in the superior medial frontal gyrus. Finally, higher reward sensitivity was associated with lower thickness in the inferior frontal gyrus. ConclusionThe present study provides a comprehensive characterization of the relationships between different facets of impulsivity and obesity. We show that differences in impulsivity domains might be associated with BMI via Uncontrolled Eating. Our results might inform future clinical strategies aimed at fostering self-control abilities to prevent and/or treat unhealthy weight gain.

neuroscience↗

bioRxiv2017

Neurobehavioural Correlates of Obesity are Largely Heritable

Recent molecular genetic studies have shown that the majority of genes associated with obesity are expressed in the central nervous system. Obesity has also been associated with neurobehavioural factors such as brain morphology, cognitive performance, and personality. Here, we tested whether these neurobehavioural factors were associated with the heritable variance in obesity measured by body mass index (BMI) in the Human Connectome Project (N=895 siblings). Phenotypically, cortical thickness findings supported the \"right brain hypothesis\" for obesity. Namely, increased BMI associated with decreased cortical thickness in right frontal lobe and increased thickness in the left frontal lobe, notably in lateral prefrontal cortex. In addition, lower thickness and volume in entorhinal-parahippocampal structures, and increased thickness in parietal-occipital structures in obese participants supported the role of visuospatial function in obesity. Brain morphometry results were supported by cognitive tests, which outlined obesitys negative association with visuospatial function, verbal episodic memory, impulsivity, and cognitive flexibility. Personality-obesity correlations were inconsistent. We then aggregated the effects for each neurobehavioural factor for a behavioural genetics analysis and demonstrated the factors genetic overlap with obesity. Namely, cognitive test scores and brain morphometry had 0.25 - 0.45 genetic correlations with obesity, and the phenotypic correlations with obesity were 77-89% explained by genetic factors. Neurobehavioural factors also had some genetic overlap with each other. In summary, obesity has considerable genetic overlap with brain and cognitive measures. This supports the theory that obesity is inherited via brain function, and may inform intervention strategies.\n\nSignificance StatementObesity is a widespread heritable health condition. Evidence from psychology, cognitive neuroscience, and genetics has proposed links between obesity and the brain. The current study tested whether the heritable variance in obesity is explained by brain and behavioural factors in a large brain imaging cohort that included multiple related individuals. We found that the heritable variance in obesity had genetic correlations 0.25 - 0.45 with cognitive tests, cortical thickness, and regional brain volume. In particular, obesity was associated with frontal lobe asymmetry and differences in temporal-parietal perceptual systems. Further, we found genetic overlap between certain brain and behavioural factors. In summary, the genetic vulnerability to obesity is expressed in the brain. This may inform intervention strategies.

neuroscience↗

bioRxiv2017

Network Connectivity Predicts Cortical Thinning and Cognitive Decline in Early Parkinson’s Disease

Parkinsons Disease (PD) is a progressive neurodegenerative disorder characterized by motor and cognitive deficits. The neurodegenerative process is thought to move stereotypically from the brainstem up to the cerebral cortex, possibly reflecting the spread of toxic alpha-synuclein molecules. Using a large, longitudinal, multi-center database of de novo PD patients, we tested whether focal reductions in cortical thickness could be explained by disease spread from a subcortical \"disease reservoir\" along the brains connectome. PD patients (n=105) and matched controls (n=57) underwent T1-MRI at entry and one year later. Over this period, PD patients demonstrated significantly greater loss of cortical thickness than healthy controls in parts of the left occipital and bilateral frontal lobes and right somatomotor-sensory cortex. Cortical regions with greater connectivity (measured functionally or structurally) to a \"disease reservoir\" evaluated via MRI at baseline demonstrated greater atrophy one year later. The atrophy pattern in the ventral frontal lobes resembled one described in certain cases of Alzheimers disease. Moreover, a multiple linear regression model suggested that cortical thinning was associated with impaired cognitive function at follow-up. Our findings suggest that disease propagation to the cortex in PD follows neural connectivity, and that disease spread to the cortex may herald the onset of cognitive impairment.

neuroscience↗