Biology subjects

Bogdan, R.

Publications and source records attributed to Bogdan, R..

6 recordsLinked to original sources

bioRxiv2026

Intergenerational epigenetic signatures of prenatal adversity and their role in emerging child psychopathology

BackgroundAdversity during pregnancy is associated with alterations in offspring brain development. DNA methylation (DNAm) is a type of epigenetic modification is a putative mechanism for the intergenerational transfer of prenatal adversity on developmental outcomes. We examined the effects of prenatal social disadvantage (PSD) and prenatal psychosocial stress (PSS) on offspring epigenome-wide DNAm at four time points from infancy to age 4, and tested whether persistent DNAm signals mediated associations between PSD, PSS, and child psychopathology at ages 4-6. MethodsLongitudinal DNAm data (birth, Y1, Y2, Y3) was derived from salivary tissue of 281 infants (43.7% female) in the eLABE study. We examined epigenome-wide associations between DNAm and PSD and PSS. Linear models were adjusted for age, child sex, child race, maternal tobacco smoking, cell type composition, and batch effects. Mediation analyses tested whether birth DNAm mediated associations between prenatal exposures and internalizing and externalizing symptoms at ages 4-6. ResultsPSD was associated with 47 FDR-significant CpGs at birth and 3 at year 1. PSS was associated with 3 FDR-significant CpGs at birth. Thirteen CpGs showed PSD-associated significance across all four timepoints, including two CpGs associated with genes involved with neuronal maturation (BCL11B) and one CpG associated with a gene implicated in brain vascular health (ZNF474). Exploratory mediation analyses revealed an indirect effect of methylation at ZNF474 (cg19980369; p=0.028; pFDR=0.360) on the association between PSD and year 4-6 externalizing symptoms. ConclusionsPSD was associated with epigenetic signatures at birth, with a subset of associations persisting across early childhood and converging on cellular stress response biology. PSS showed minimal epigenetic associations, suggesting differential biological embedding of structural versus psychological dimensions of adversity.

genomics↗

bioRxiv2025

Longitudinal Trajectories of Cognition and Neural Metrics as Predictors of Persistent Distressing Psychotic-Like Experiences Across Middle Childhood and Early Adolescence

ObjectivesPsychotic-like experiences (PLEs) may arise from genetic and environmental risk leading to worsening cognitive and neural metrics over time, which in turn lead to worsening PLEs. Persistence and distress are factors that distinguish more clinically significant PLEs. Analyses used three waves of unique longitudinal Adolescent Brain Cognitive Development Study data (ages 9-13) to test whether changes in cognition and structural neural metrics attenuate associations between genetic and environmental risk with persistent distressing PLEs. MethodsMultigroup univariate latent growth models examined three waves of cognitive metrics and global structural neural metrics separately for three PLE groups: persistent distressing PLEs (n=356), transient distressing PLEs (n=408), and low-level PLEs (n=7901). Models then examined whether changes in cognitive and structural neural metrics over time attenuated associations between genetic liability (i.e., schizophrenia polygenic risk scores/family history) or environmental risk scores (e.g., poverty) and PLE groups. ResultsPersistent distressing PLEs showed greater decreases (i.e., more negative slopes) of cognition and neural metrics over time compared to those in low-level PLE groups. Associations between environmental risk and persistent distressing PLEs were attenuated when accounting for lowered scores over time on cognitive (e.g., picture vocabulary) and to a lesser extent neural (e.g., cortical thickness, volume) metrics. ConclusionsAnalyses provide novel evidence for extant theories that worsening cognition and global structural metrics may partially account for associations between environmental risk with persistent distressing PLEs.

neuroscience↗

bioRxiv2023

No evidence for the association between COVID-19 and neuroinflammation: A diffusion basis spectrum imaging study.

COVID-19 remains a significant international public health concern. Yet, the mechanisms through which symptomatology emerges remain poorly understood. While SARS-CoV-2 infection may induce prolonged inflammation within the central nervous system, the evidence primarily stems from limited small-scale case investigations. To address this gap, our study capitalized on longitudinal UK Biobank neuroimaging data acquired prior to and following COVID-19 testing (N=416 including n=224 COVID-19 cases; Mage=58.6). Putative neuroinflammation was assessed in gray matter structures and white matter tracts using non-invasive Diffusion Basis Spectrum Imaging (DBSI), which estimates inflammation-related cellularity (DBSI-restricted fraction; DBSI-RF) and vasogenic edema (DBSI-hindered fraction; DBSI-HF).We hypothesized that COVID-19 case status would be associated with increases in DBSI markers after accounting for potential confound (age, sex, race, body mass index, smoking frequency, and data acquisition interval) and multiple testing. COVID-19 case status was not significantly associated with DBSI-RF (|{beta}|s<0.28, pFDR >0.05), but with greater DBSI-HF in left pre- and post-central gyri and right middle frontal gyrus ({beta}s>0.3, all pFDR=0.03). Intriguingly, the brain areas exhibiting increased putative vasogenic edema had previously been linked to COVID-19-related functional and structural alterations, whereas brain regions displaying subtle differences in cellularity between COVID-19 cases and controls included regions within or functionally connected to the olfactory network, which has been implicated in COVID-19 psychopathology. Nevertheless, our study might not have captured acute and transitory neuroinflammatory effects linked to SARS-CoV-2 infection, possibly due to symptom resolution before the imaging scan. Future research is warranted to explore the potential time- and symptom-dependent neuroinflammatory relationship with COVID-19.

neuroscience↗

bioRxiv2022

Multi-omics analyses cannot identify true-positive novel associations from underpowered genome-wide association studies of four brain-related traits

BackgroundThe integration of multi-omics information (e.g., epigenetics and transcriptomics) can be useful for interpreting findings from genome-wide association studies (GWAS). It has additionally been suggested that multi-omics may aid in novel variant discovery, thus circumventing the need to increase GWAS sample sizes. We tested whether incorporating multi-omics information in earlier and smaller sized GWAS boosts true-positive discovery of genes that were later revealed by larger GWAS of the same/similar traits. MethodsWe applied ten different analytic approaches to integrating multi-omics data from twelve sources (e.g., Genotype-Tissue Expression project) to test whether earlier and smaller GWAS of 4 brain-related traits (i.e., alcohol use disorder/problematic alcohol use [AUD/PAU], major depression [MDD], schizophrenia [SCZ], and intracranial volume [ICV]) could detect genes that were revealed by a later and larger GWAS. ResultsMulti-omics data did not reliably identify novel genes in earlier less powered GWAS (PPV<0.2; 80% false-positive associations). Machine learning predictions marginally increased the number of identified novel genes, correctly identifying 1-8 additional genes, but only for well-powered early GWAS of highly heritable traits (i.e., ICV and SCZ). Multi-omics, particularly positional mapping (i.e., fastBAT, MAGMA, and H-MAGMA), was useful for prioritizing genes within genome-wide significant loci (PPVs = 0.5 - 1.0). ConclusionsAlthough the integration of multi-omics information, particularly when multiple methods agree, helps prioritize GWAS findings and translate them into information about disease biology, it does not substantively increase novel gene discovery in brain-related GWAS. To increase power for discovery of novel genes and loci, increasing sample size is a requirement.

genetics↗

bioRxiv2018

Convergent evidence for predispositonal effects of brain volume on alcohol consumption

BackgroundAlcohol use has been reliably associated with smaller subcortical and cortical regional gray matter volumes (GMVs). Whether these associations reflect shared predisposing risk factors and/or causal consequences of alcohol use remains poorly understood.\n\nMethodsData came from 3 neuroimaging samples (total n=2,423), spanning childhood/adolescence to middle age, with prospective or family-based data. First, we identified replicable GMV correlates of alcohol use. Next, we used family-based and longitudinal data to test whether these associations may plausibly reflect a predispositional liability for alcohol use, and/or a causal consequence of alcohol use. Finally, we evaluated whether GWAS-defined genomic risk for alcohol consumption is enriched for genes preferentially expressed in regions identified in our neuroimaging analyses, using heritability and gene-set enrichment, and transcriptome-wide association study (TWAS) approaches.\n\nResultsSmaller right dorsolateral prefrontal cortex (DLPFC; i.e., middle and superior frontal gyri) and insula GMVs were associated with increased alcohol use across samples. Family-based and prospective longitudinal data suggest these associations are genetically conferred and that DLPFC GMV prospectively predicts future use and initiation. Genomic risk for alcohol use was enriched in gene-sets preferentially expressed in the DLPFC and associated with differential expression of C16orf93, CWF19L1, and C18orf8 in the DLPFC.\n\nConclusionsThese data suggest that smaller DLPFC and insula GMV plausibly represent predispositional risk factors for, as opposed to consequences of, alcohol use. Alcohol use, particularly when heavy, may potentiate these predispositional risk factors. DLPFC and insula GMV represent promising biomarkers for alcohol consumption liability and related psychiatric and behavioral phenotypes.

neuroscience↗

bioRxiv2017

Association Between Substance Use Disorder And Polygenic Liability To Schizophrenia

BackgroundThere are high levels of comorbidity between schizophrenia and substance use disorder, but little is known about the genetic etiology of this comorbidity.\n\nMethodsHere, we test the hypothesis that shared genetic liability contributes to the high rates of comorbidity between schizophrenia and substance use disorder. To do this, polygenic risk scores for schizophrenia derived from a large meta-analysis by the Psychiatric Genomics Consortium were computed in three substance use disorder datasets: COGEND (ascertained for nicotine dependence n=918 cases, 988 controls), COGA (ascertained for alcohol dependence n=643 cases, 384 controls), and FSCD (ascertained for cocaine dependence n=210 cases, 317 controls). Phenotypes were harmonized across the three datasets and standardized analyses were performed. Genome-wide genotypes were imputed to 1000 Genomes reference panel.\n\nResultsIn each individual dataset and in the mega-analysis, strong associations were observed between any substance use disorder diagnosis and the polygenic risk score for schizophrenia (mega-analysis pseudo R2 range 0.8%-3.7%, minimum p=4x10-23).\n\nConclusionsThese results suggest that comorbidity between schizophrenia and substance use disorder is partially attributable to shared polygenic liability. This shared liability is most consistent with a general risk for substance use disorder rather than specific risks for individual substance use disorders and adds to increasing evidence of a blurred boundary between schizophrenia and substance use disorder.

genetics↗