Biology subjects

Dunn, E. C.

Publications and source records attributed to Dunn, E. C..

9 recordsLinked to original sources

bioRxiv2026

Inflections in adolescent cortical maturation signal psychopathology risk

Cerebral cortical maturation supports emotional, social, and cognitive development during adolescence1. Neurodevelopmental theories of psychiatric illnesses, which peak in onset around age 182, implicate subtle alterations in adolescent cortical development3. However, mechanistic links between cortical maturation and emergent psychopathology have remained elusive. This gap likely reflects a need for more nuanced understanding of cortical development4, larger, longitudinal samples powered to detect small effects,5 and more uniform image quality control.6 Using 29,482 structural MRI scans from 11,720 adolescents in the Adolescent Brain Cognitive Development (ABCD) study, we demonstrate reproducible differences in the contour and tempo of maturation across the cortical surface. Regional trajectories are robust to image quality variance and align with increasing functional specialization over time. Thicker cortex in early-maturing sensory regions predicts psychiatric symptoms as early as four years prior to their emergence, a pattern that subsequently spreads to association cortex. Overlap with spatially and temporally organized gene co-expression networks implicates prenatal programming of this sequence of maturation and its links to psychopathology. These findings present a novel, versatile neurodevelopmental framework for cortical maturation during adolescence that sequentially implicates sensory and association cortex in risk for emergent psychiatric symptoms.

neuroscience↗

bioRxiv2026

Identification of Dynamic Genetic Influences on DNA Methylation from Birth to Adulthood

Whether and how genetic regulation of DNA methylation (DNAm) change across the lifespan remains unclear. Here, we map age-dependent methylation quantitative trait loci (longitudinal mQTLs) using linear mixed models applied to repeated blood DNAm measures from birth, childhood, adolescence and adulthood in the Avon Longitudinal Study of Parents and Children. We identify 2,210 longitudinal mQTLs (2,393 SNP-CpG pairs; 7.3% trans) and observe consistent genotype-by-age effects in two independent cohorts of diverse ancestries (Pearsons r = 0.85 in the Generation R Study; r = 0.56 in the Drakenstein Child Health Study). Longitudinal mQTLs show increasing effects with age at half of loci and associations with multiple phenotypes. CpGs with longitudinal mQTLs are more heritable and enriched in regulatory elements and pathways related to multicellular organism development and cell adhesion. These results chart dynamic genetic influences on the human methylome and provide a novel perspective on epigenetic regulation.

genomics↗

bioRxiv2022

Associations between methylation age and brain age in late adolescence

Recent research suggests that biological age, based on DNA methylation or neuroimaging measures, may predict health traits in adulthood more accurately than chronological age. However, whether these findings apply to earlier stages in life is unknown. We therefore aimed to characterise the performance of and interdependence between measures of biological age during adolescence, leveraging longitudinal data from a subsample of young adolescents from the population-based ALSPAC cohort (n=386). We derived four methylation age measures in late adolescence (17-19 years) and a measure of brain age derived from structural neuroimaging data (18-24 years). We then examined associations between these measures of biological age, and their relationship with five measures of physical, cognitive and mental health (8-18 years). Brain age was largely independent of different measures of methylation age, after accounting for age, cell type composition, array and study (beta range: -0.60 to 0.60, all p>0.05). Smoking and BMI were associated with three measures of advanced methylation age (beta range: -0.35 to 0.52, all p<0.05), but not brain age. Depressive symptoms and cognitive ability were unrelated to all measures of biological age. Our findings emphasize the variability of and independence between these methylation- and brain-based measures of age in adolescents. They also highlight the importance of tracking the mosaic of ageing in younger populations.

neuroscience↗

bioRxiv2021

Characterizing the human methylome across the life course: findings from eight UK-based studies

Variation in DNA methylation (DNAm) is associated with multiple biological processes that track growth and development, ageing and age-related diseases. However, there is little understanding of what constitutes typical patterns of DNAm variation and how these patterns change across the life course. In this study, we synthesised a map of the human methylome across the life course, focussing on changes in variability and mean DNAm. Harmonizing DNAm datasets across eight longitudinal and cross-sectional UK-based studies, we meta-analysed n=13,215 blood samples from n=7,037 unique individuals from birth to 98 years of age. Changes in CpG-specific variability and means were described across the life course using a meta-regression framework. CpG-specific associations of variability or mean DNAm in relation to the likelihood of association with 100 traits linked to environmental exposures, health and disease were tested within and across ten developmental age bins across the life course. Age was linked to DNAm variability at 29,212 CpG sites. On average, we observed a 1.26 fold increase in DNAm variability per year across the life course. 33,730 CpGs displayed changes in mean DNAm, with 64% of these loci showing decreases in DNAm over time. CpG sites linked to traits were in general more variable across the life course. Our study provides, for the first time, a map of the human methylome across the life course, which is publicly accessible through a searchable online database. This resource allows researchers to query CpG-specific trajectories from birth to old age and link these to health and disease.

genetics↗

bioRxiv2021

Updates to data versions and analytic methods influence the reproducibility of results from epigenome-wide association studies

IntroductionBiomedical research has grown increasingly cooperative, with several large consortia compiling and sharing epigenomic data. Since data are typically preprocessed by consortia prior to distribution, the implementation of new pipelines can lead to different versions of the same dataset. Analytic frameworks also constantly evolve to incorporate cutting-edge methods and shifting best practices. However, it remains unknown how differences in data and analytic versions alter the results of epigenome-wide analyses, which has broad implications for the replicability of epigenetic associations. Thus, we assessed the impact of these changes using a subsample of the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. MethodsWe analyzed two versions of DNA methylation data, processed using separate preprocessing and analytic pipelines, to examine associations between childhood adversity and prenatal smoking exposure on DNA methylation at age 7. We performed two sets of analyses: (1) epigenome-wide association studies (EWAS); (2) Structured Life Course Modeling Approach (SLCMA), a two-stage method that models time-dependent effects. We also compared results from the SLCMA using more recent methodological recommendations. ResultsDifferences between ALSPAC data versions impacted both EWAS and SLCMA analyses, yielding different sets of associations at conventional p-value thresholds. However, the magnitude and direction of associations was generally consistent between data versions, regardless of significance thresholds. Updating the SLCMA analytic version similarly altered top associations, but time-dependent effects remained concordant. ConclusionsChanges to data and analytic versions influenced the results of epigenome-wide studies, particularly when using p-value thresholds as reference points for successful replication and stability.

genetics↗

bioRxiv2019

A two-stage approach to identifying and validating modifiable factors for the prevention of depression

BackgroundAlthough depression is recognized as the leading cause of disability worldwide, decades of research have identified few actionable preventive factors. Using phenotypic and genomic data from the UK Biobank, we took advantage of a unique opportunity to screen a wide range of potentially modifiable factors that could offset known risk factors for depression.\n\nMethodsWe curated baseline data on more than 100 lifestyle and environmental factors in participants lives, including behavioral (e.g., exercise, sleep, media use, diet), social (e.g., support, activities), and environmental (e.g., greenspace, pollution) variables. In a follow-up survey, participants reported on their traumatic life experiences and mental health, including depression. Polygenic risk scores for depression were generated based on large-scale genome-wide association results. Excluding those meeting criteria for depression at baseline, we identified at-risk individuals at high predicted probability (> 90th percentile) for clinically significant depression at follow-up based on their (i) polygenic risk, or (ii) reported traumatic life events. Using a factors-wide design corrected for multiple testing and adjusted for potential confounders, we identified modifiable factors associated with follow-up depression in the full sample and among at-risk individuals. Using a two-sample Mendelian randomization (MR) design, we then examined which significant factors showed potential causal influences on depression risk, or vice versa.\n\nResultsA range of baseline modifiable factors were prospectively associated with follow-up depression, including factors related to social engagement, physical activity, media use, and diet. MR follow-up analyses provided further support for the effects of social support-seeking, TV use, and other factors on depression risk.\n\nConclusionAs the field increasingly quantifies the role of genetic factors in complex conditions such as depression, knowledge of modifiable factors that could offset ones genetic risk has become highly relevant. Here, we present an approach to screening for potentially modifiable factors that may offset the risk of depression in general and among at-risk individuals. In light of the burden of disease associated with depression and the urgent need for actionable preventive strategies, this approach could help prioritize candidates for follow-up studies including clinical trials for depression prevention.

genetics↗

bioRxiv2018

Genetic variation in the Major Histocompatibility Complex and association with depression

BackgroundThe prevalence of depression is higher in individuals suffering from autoimmune diseases, but the mechanisms underlying the observed comorbidities are unknown. Epidemiological findings point to a bi-directional relationship - that depression increases the risk of developing an autoimmune disease, and vice-versa. Shared genetic etiology is a plausible explanation for the overlap between depression and autoimmune diseases. In this study we tested whether genetic variation in the Major Histocompatibility Complex (MHC), which is associated with risk for autoimmune diseases, is also associated with risk for depression.\n\nMethodWe fine-mapped the classical MHC (chr6: 29.6-33.1 Mb), imputing 216 Human Leukocyte Antigen (HLA) alleles and four Complement Component 4 (C4) haplotypes in studies from the Psychiatric Genomics Consortium (PGC) Major Depressive Disorder (MDD) working group and the UK Biobank (UKB). In the 26 PGC-MDD studies, cases met a lifetime diagnosis of MDD, determined by a structured diagnostic interview. In the UKB, cases and controls were identified from an online mental health questionnaire. The total sample size was 45,149 depression cases and 86,698 controls. We tested for association between depression status and imputed MHC variants in each study and performed an inverse-variance weighted meta-analysis across the PGC-MDD and UKB samples, applying both a conservative region-wide significance threshold (3.9-e6) and a candidate threshold (1.6e-4).\n\nResultsNo HLA alleles or C4 haplotypes were associated with depression at the conservative threshold in the PGC, UKB or meta-analysis. HLA-B*08:01 was associated with modest protection for depression at the candidate threshold in the meta-analysis. Under the conservative threshold, 70 SNPs were detected in the UKB and 143 SNPs were detected in the meta-analysis, mirroring previous findings from highly powered GWAS of depression.\n\nDiscussionWe found no evidence that HLA alleles, which play a major role in the genetic susceptibility to autoimmune diseases, or C4 haplotypes, which are strongly associated with schizophrenia, confer risk for depression. These results indicate that autoimmune diseases and depression do not share common risk loci of moderate or large effect in the MHC.

genetics↗

bioRxiv2018

Predicting cellular aging following exposure to adversity: Does accumulation, recency, or developmental timing of exposure matter?

Exposure to adversity has been linked to accelerated biological aging, which in turn has been shown to predict numerous health problems, including neuropsychiatric disease. In recent years, measures of DNA methylation-based epigenetic age - known as \"epigenetic clocks\" - have been used to estimate accelerated epigenetic aging. Yet, few studies have been conducted in children. Using data from the Avon Longitudinal Study of Parents and Children (n=973), we explored the prospective association between repeated measures of childhood exposure to seven types of adversity on epigenetic age assessed at age 7 using the Horvath and Hannum epigenetic clocks. With a Least Angle Regression variable selection procedure, we evaluated the effects of the developmental timing, accumulation, and recency of adversity exposure. We found that exposure to sexual or physical abuse, financial stress, or neighborhood disadvantage during sensitive periods in early and middle childhood best explained variability in the deviation of the Hannum epigenetic age from the chronological age. Secondary sex-stratified analyses identified particularly strong sensitive period effects, such that by age 7, girls who were exposed to abuse at age 3.5 were biologically older than their unexposed peers by almost 2 months. These effects were undetected in analyses comparing children \"exposed\" versus \"unexposed\" to adversity. Our results suggest that exposure to adversity may alter methylation processes in ways that perturb normal cellular aging and that these effects may be heightened during sensitive periods in development. Research is needed to demonstrate the effect of accelerated epigenetic aging on negative health outcomes following childhood adversity exposure.

genetics↗

bioRxiv2018

Sensitive periods for the effect of childhood adversity on DNA methylation: Results from a prospective, longitudinal study

BackgroundExposure to \"early life\" adversity is known to predict DNA methylation (DNAm) patterns that may be related to prolonged psychiatric risk. However, few studies have investigated whether adversity has time-dependent effects based on the age at exposure.\n\nMethodsUsing a two-stage structured life course modeling approach (SLCMA), we tested the hypothesis that there are sensitive periods when adversity induced greater DNAm changes. We tested this hypothesis in relation to two alternative explanations: an accumulation hypothesis, in which the effect of adversity on DNAm increases with the number of occasions exposed, regardless of timing, and a recency model, in which the effect of adversity is stronger for more proximal events. Data came from the Accessible Resource for Integrated Epigenomics Studies (ARIES), a subsample of mother-child pairs from the Avon Longitudinal Study of Parents and Children (ALSPAC; n=670-776).\n\nResultsAfter covariate adjustment and multiple testing correction, we identified 40 CpG sites that were differentially methylated at age 7 following exposure to adversity. Most loci (n=32) were predicted by the timing of adversity, namely exposures during infancy. Neither the accumulation nor recency of the adversity explained considerable variability in DNAm. A standard EWAS of lifetime exposure (vs. no exposure) failed to detect these associations.\n\nConclusionsThe developmental timing of adversity explains more variability in DNAm than the accumulation or recency of exposure. Infancy appears to be a sensitive period when exposure to adversity predicts differential DNAm patterns. Classification of individuals as exposed vs. unexposed to \"early life\" adversity may dilute observed effects.

genetics↗