Publications by authors named "Elliot Tucker-Drob"

Psychiatric disorders are highly comorbid, heritable, and genetically correlated [1-4]. The primary objective of cross-disorder psychiatric genetics research is to identify and characterize both the shared genetic factors that contribute to convergent disease etiologies and the unique genetic factors that distinguish between disorders [4, 5]. This information can illuminate the biological mechanisms underlying comorbid presentations of psychopathology, improve nosology and prediction of illness risk and trajectories, and aid the development of more effective and targeted interventions.

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A measure of lifetime brain atrophy (LBA) obtained from a single magnetic resonance imaging (MRI) scan could be an attractive candidate to boost statistical power in uncovering novel genetic signals and mechanisms of neurodegeneration. We analysed data from five young and old adult cohorts (MRi-Share, Human Connectome Project, UK Biobank, Generation Scotland Subsample, and Lothian Birth Cohort 1936 [LBC1936]) to test the validity and utility of LBA inferred from cross-sectional MRI data, i.e.

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  • Natural-experiment designs show that in-utero famine can lead to obesity, but birth rates drop during famines, raising concerns about possible selection bias in these studies.
  • The researchers studied the Dutch Hunger Winter Families Study, comparing genetically analyzed participants exposed to the 1944-1945 Dutch Famine to unexposed same-sex siblings as controls.
  • Their findings indicated that while higher genetic risk was linked to higher BMI, the difference between famine-exposed and control participants' BMI was negligible, suggesting no significant selection bias and supporting the credibility of their research method.
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  • - The study aimed to explore the genetic basis of major depressive disorder by analyzing symptoms across various clinical and community cohorts, acknowledging challenges like sample size differences and missing data patterns.
  • - Researchers performed genome-wide association studies using data from both diagnosed and undiagnosed participants, fitting models to understand the relationships between different depressive symptoms.
  • - Findings emphasized the relevance of symptom directionality (e.g., hypersomnia vs. insomnia) and the necessity of considering study design when analyzing genetic data related to depression.
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  • Non-cognitive skills, like motivation and self-regulation, are genetic traits that affect academic success, and their impact increases as children grow from ages 7 to 16.
  • A study involving over 10,000 children from England and Wales found that non-cognitive skills became more closely linked to academic achievement as the children developed.
  • Analyses showed that the genetic influence of non-cognitive skills on academic performance is not solely due to differences in family environments, suggesting a complex interaction between genes and environment.
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  • Individual sensitivity to environmental exposures is influenced by genetics, showing that different genetic makeups can lead to varying reactions to the same environment.
  • A large meta-analysis was conducted using data from nearly 22,000 monozygotic twins to explore genetic influences on seven psychiatric and neurodevelopmental traits.
  • The study found 13 significant genetic associations related to factors like stress-reactivity, growth factors, and catecholamine uptake, highlighting the potential role of genetics in understanding environmental sensitivity.
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Background: The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes.

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Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules.

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Cognitive abilities, including general intelligence and domain-specific abilities such as fluid reasoning, comprehension knowledge, working memory capacity, and processing speed, are regarded as some of the most stable psychological traits, yet there exist no large-scale systematic efforts to document the specific patterns by which their rank-order stability changes over age and time interval, or how their stability differs across abilities, tests, and populations. Determining the conditions under which cognitive abilities exhibit high or low degrees of stability is critical not just to theory development but to applied contexts in which cognitive assessments guide decisions regarding treatment and intervention decisions with lasting consequences for individuals. In order to supplement this important area of research, we present a meta-analysis of longitudinal studies investigating the stability of cognitive abilities.

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Latent factors, such as general intelligence, depression and risk tolerance, are invoked in nearly all social science research where a construct is measured via aggregation of symptoms, question responses or other measurements. Because latent factors cannot be directly observed, they are inferred by fitting a specific model to empirical patterns of correlations among measured variables. A long-standing critique of latent factor theories is that the correlations used to infer latent factors can be produced by alternative data-generating mechanisms that do not include latent factors.

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The German Socio-Economic Panel (SOEP) serves a global research community by providing representative annual longitudinal data of respondents living in private households in Germany. The dataset offers a valuable life course panorama, encompassing living conditions, socioeconomic status, familial connections, personality traits, values, preferences, health, and well-being. To amplify research opportunities further, we have extended the SOEP Innovation Sample (SOEP-IS) by collecting genetic data from 2,598 participants, yielding the first genotyped dataset for Germany based on a representative population sample (SOEP-G).

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Natural-experiment designs that compare survivors of in-utero famine exposure to unaffected controls suggest that in-utero undernutrition predisposes to development of obesity. However, birth rates drop dramatically during famines. Selection bias could arise if factors that contribute to obesity also protect fertility and/or fetal survival under famine conditions.

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Background And Objectives: Methylation profile scores (MPSs) index biological aging and aging-related disease in adults and are cross-sectionally associated with social determinants of health in childhood. MPSs thus provide an opportunity to trace how aging-related biology responds to environmental changes in early life. Information regarding the stability of MPSs in early life is currently lacking.

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Identifying circulating proteins associated with cognitive function may point to biomarkers and molecular process of cognitive impairment. Few studies have investigated the association between circulating proteins and cognitive function. We identify 246 protein measures quantified by the SomaScan assay as associated with cognitive function (p < 4.

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Importance: Children who are socioeconomically disadvantaged are at increased risk for high body mass index (BMI) and multiple diseases in adulthood. The developmental origins of health and disease hypothesis proposes that early life conditions affect later-life health in a manner that is only partially modifiable by later-life experiences.

Objective: To examine whether epigenetic measures of BMI developed in adults are valid biomarkers of childhood BMI and if they are sensitive to early life social determinants of health.

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The human skeletal form underlies bipedalism, but the genetic basis of skeletal proportions (SPs) is not well characterized. We applied deep-learning models to 31,221 x-rays from the UK Biobank to extract a comprehensive set of SPs, which were associated with 145 independent loci genome-wide. Structural equation modeling suggested that limb proportions exhibited strong genetic sharing but were independent of width and torso proportions.

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Article Synopsis
  • Major depressive disorder shows varied symptoms, and genetic analysis can help identify specific subtypes and clinical profiles.
  • Challenges in integrating symptom data arise from differences in sample sizes and patterns of missing data in clinical vs. community groups.
  • The study used genome-wide association studies to find that a model including unique symptom factors and accounting for missing data best represented the symptoms of depression, highlighting the need to consider symptom directionality and study design when analyzing genetic data.
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Genetic liability to substance use disorders can be parsed into loci that confer general or substance-specific addiction risk. We report a multivariate genome-wide association meta-analysis that disaggregates general and substance-specific loci for published summary statistics of problematic alcohol use, problematic tobacco use, cannabis use disorder, and opioid use disorder in a sample of individuals of European descent and African descent. Nineteen independent SNPs were genome-wide significant ( < 5e-8) for the general addiction risk factor (), which showed high polygenicity.

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Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean |r| = 0.

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  • Noncognitive skills, like motivation and self-regulation, are partially inherited and play a significant role in predicting academic success, even more so than cognitive skills.
  • A study of over 10,000 children in England and Wales showed that the influence of noncognitive skills on academic achievement grows as children age, from 7 to 16 years old.
  • Genetic analyses suggest that the impact of noncognitive traits on academic performance strengthens over time, and findings indicate that this relationship isn't solely due to environmental factors, highlighting the interplay between genetics and development in education.
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  • Noncognitive skills like motivation and self-regulation are shown to predict academic success even more than cognitive skills, but the influence of genetics and the environment on this relationship is still not fully understood.
  • A study involving over 10,000 children in England and Wales demonstrated that noncognitive skills become more significant in predicting academic achievement as children grow from ages 7 to 16.
  • Using genetic methods, the research revealed that the genetic influence of noncognitive skills on academic achievement increases with age, highlighting the complex interactions between genetics and the environment in academic development.
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Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules.

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Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis (Genomic PCA) to model general dimensions of brain-wide morphometry at the level of their underlying genetic architecture.

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