A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression.
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ABSTRACT:
SUBMITTER: Guintivano J
PROVIDER: S-EPMC3669121 | biostudies-literature | 2013 Mar
REPOSITORIES: biostudies-literature
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Project description:Cellular heterogeneity may bias cell type specific epigenetic patterns leading to a dramatic increase in false positive or negative findings in psychiatric epigenetic studies. To address this issue, we performed fluorescence activated cell sorting (FACS) of neuronal nuclei in post mortem frontal cortex 58 samples followed by Illumina HM450 microarray based DNA methylation profiling. We characterized the extent of neuron and glia specific DNA methylation variation independent of disease status and identified significant cell type specific epigenetic variation at 51% of loci. DNA methylation was profiled for sorted neuronal nuclei from post mortem frontal cortex of 29 major depression and 29 matched control samples using Illumina HM450 microarrays
2013-02-22 | E-GEOD-41826 | biostudies-arrayexpress
Project description:Cellular heterogeneity may bias cell type specific epigenetic patterns leading to a dramatic increase in false positive or negative findings in psychiatric epigenetic studies. To address this issue, we performed fluorescence activated cell sorting (FACS) of neuronal nuclei in post mortem frontal cortex 58 samples followed by Illumina HM450 microarray based DNA methylation profiling. We characterized the extent of neuron and glia specific DNA methylation variation independent of disease status and identified significant cell type specific epigenetic variation at 51% of loci.
2013-02-22 | GSE41826 | GEO
Project description:Background: Major Depressive Disorder (MDD) represents a major social and economic health issue and constitutes a major risk factor for MDD suicide. The molecular pathology of suicidal depression remains poorly understood, although it has been hypothesized that regulatory genomic processes are involved in the pathology of both MDD and suicidality. Methods: Genome-wide patterns of DNA methylation were assessed in depressed MDD suicide completers (n=20) and compared to non-psychiatric, sudden-death controls (n=20) using tissue from two cortical brain regions (Brodmann Area 11 (BA11) and Brodmann Area 25 (BA25)). Analyses focussed on identifying differentially methylated regions (DMRs) associated with suicidal depression, and epigenetic variation was explored in the context of polygenic risk scores for major depression and MDD suicide. Weighted gene co-methylation network analysis was used to identify modules of co-methylated loci associated with depressed MDD suicide completers and polygenic burden for MDD and MDD suicide attempt. Results: We identified a DMR upstream of the PSORS1C3 gene, subsequently validated using bisulfite-pyrosequencing and replicated in a second set of MDD suicide samples, which is characterized by significant hypomethylation in both cortical brain regions in MDD MDD suicide cases. We also identified discrete modules of co-methylated loci associated with polygenic risk burden for MDD suicide attempt, but not major depression. MDD suicide-associated co-methylation modules were enriched among gene networks implicating biological processes relevant to depression and suicidality, including nervous system development and mitochondria function. Conclusions: Our data suggest there are coordinated changes in DNA methylation associated with MDD suicide that may offer novel insights into the molecular pathology associated with depressed MDD suicide completers.
2016-11-01 | GSE88890 | GEO
Project description:The molecular mechanisms underlying major depressive disorder (MDD) are largely unknown. Limited success of previous genetics studies may be attributable to heterogeneity of MDD, aggregating biologically different subtypes. We examined the polygenic features of MDD and two common clinical subtypes (typical and atypical) defined by symptom profiles in a large sample of adults with established diagnoses. Data were from 1530 patients of the Netherlands Study of Depression and Anxiety (NESDA) and 1700 controls mainly from the Netherlands Twin Register (NTR). Diagnoses of MDD and its subtypes were based on DSM-IV symptoms. Genetic overlap of MDD and subtypes with psychiatric (MDD, bipolar disorder, schizophrenia) and metabolic (body mass index (BMI), C-reactive protein, triglycerides) traits was evaluated via genomic profile risk scores (GPRS) generated from meta-analysis results of large international consortia. Single nucleotide polymorphism (SNP)-heritability of MDD and subtypes was also estimated. MDD was associated with psychiatric GPRS, while no association was found for GPRS of metabolic traits. MDD subtypes had differential polygenic signatures: typical was strongly associated with schizophrenia GPRS (odds ratio (OR)=1.54, P=7.8e-9), while atypical was additionally associated with BMI (OR=1.29, P=2.7e-4) and triglycerides (OR=1.21, P=0.006) GPRS. Similar results were found when only the highly discriminatory symptoms of appetite/weight were used to define subtypes. SNP-heritability was 32% for MDD, 38% and 43% for subtypes with, respectively, decreased (typical) and increased (atypical) appetite/weight. In conclusion, MDD subtypes are characterized by partially distinct polygenic liabilities and may represent more homogeneous phenotypes. Disentangling MDD heterogeneity may help the psychiatric field moving forward in the search for molecular roots of depression.
| S-EPMC5546325 | biostudies-literature
Project description:Major depression (MD) is a heterogeneous disorder; however, the extent to which genetic factors distinguish MD patient subgroups (genetic heterogeneity) remains uncertain. This study sought evidence for genetic heterogeneity in MD. Using UK Biobank cohort, the authors defined 16 MD subtypes within eight comparison groups (vegetative symptoms, symptom severity, comorbid anxiety disorder, age at onset, recurrence, suicidality, impairment, and postpartum depression; N ~ 3000-47000). To compare genetic component of these subtypes, subtype-specific genome-wide association studies were performed to estimate SNP-heritability, and genetic correlations within subtype comparison and with other related disorders/traits. The findings indicated that MD subtypes were divergent in their SNP-heritability, and genetic correlations both within subtype comparisons and with other related disorders/traits. Three subtype comparisons (vegetative symptoms, age at onset, and impairment) showed significant differences in SNP-heritability; while genetic correlations within subtype comparisons ranged from 0.55 to 0.86, suggesting genetic profiles are only partially shared among MD subtypes. Furthermore, subtypes that are more clinically challenging, e.g., early-onset, recurrent, suicidal, more severely impaired, had stronger genetic correlations with other psychiatric disorders. MD with atypical-like features showed a positive genetic correlation (+0.40) with BMI while a negative correlation (-0.09) was found in those without atypical-like features. Novel genomic loci with subtype-specific effects were identified. These results provide the most comprehensive evidence to date for genetic heterogeneity within MD, and suggest that the phenotypic complexity of MD can be effectively reduced by studying the subtypes which share partially distinct etiologies.
| S-EPMC9106834 | biostudies-literature
Project description:Coupling molecular biology to high throughput sequencing has revolutionized the study of biology. Molecular genomics techniques are continually refined to provide higher resolution mapping of nucleic acid interactions and nucleic acid structure. These assays are converging on single-nucleotide resolution measurements, but the sequence preferences of molecular biology enzymes can interfere with the accurate interpretation of the data. Enzymatic sequence preferences manifest more prominently as the resolution of these assays increase. We developed seqOutBias to seek out enzymatic sequence bias from experimental data and scale individual sequence reads to correct the bias. We show that this software efficiently and successfully corrects the sequence bias resulting from DNase-seq, TACh-seq, ATAC-seq, MNase-seq, and PRO-seq data.
2016-12-21 | GSE92674 | GEO
Project description:Automatic correction of intensity nonuniformity (also termed as the bias correction) is an essential step in brain MR image analysis. Existing methods are typically developed for adult brain MR images based on the assumption that the image intensities within the same brain tissue are relatively uniform. However, this assumption is not valid in infant brain MR images, due to the dynamic and regionally-heterogeneous image contrast and appearance changes, which are caused by the underlying spatiotemporally-nonuniform myelination process. Therefore, it is not appropriate to directly use existing methods to correct the infant brain MR images. In this paper, we propose an end-to-end 3D adversarial bias correction network (ABCnet), tailored for direct prediction of bias fields from the input infant brain MR images for bias correction. The "ground-truth" bias fields for training our network are carefully defined by an improved N4 method, which integrates manually-corrected tissue segmentation maps as anatomical prior knowledge. The whole network is trained alternatively by minimizing generative and adversarial losses. To handle the heterogeneous intensity changes, our generative loss includes a tissue-aware local intensity uniformity term to reduce the local intensity variation in the corrected image. Besides, it also integrates two additional terms to enhance the smoothness of the estimated bias field and to improve the robustness of the proposed method, respectively. Comprehensive experiments with different sizes of training datasets have been carried out on a total of 1492 T1w and T2w MR images from neonates, infants, and adults, respectively. Both qualitative and quantitative evaluations on simulated and real datasets consistently demonstrate the superior performance of our ABCnet in both accuracy and efficiency, compared with popularly available methods.
| S-EPMC8316417 | biostudies-literature
Project description:Aberrant affective neural processing and negative emotional bias are trait-marks of major depression disorders (MDDs). However, most research on biased emotional perception in depression has only focused on unimodal experimental stimuli, the neural basis of potentially biased emotional processing of multimodal inputs remains unclear. Here, we addressed this issue by implementing an audiovisual emotional task during functional MRI scanning sessions with 37 patients with MDD and 37 gender-, age- and education-matched healthy controls. Participants were asked to distinguish laughing and crying sounds while being exposed to faces with different emotional valences as background. We combined general linear model and psychophysiological interaction analyses to identify abnormal local functional activity and integrative processes during audiovisual emotional processing in MDD patients. At the local neural level, MDD patients showed increased bias activity in the ventromedial prefrontal cortex (vmPFC) while listening to negative auditory stimuli and concurrently processing visual facial expressions, along with decreased dorsolateral prefrontal cortex (dlPFC) activity in both the positive and negative visual facial conditions. At the network level, MDD exhibited significantly decreased connectivity in areas involved in automatic emotional processes and voluntary control systems during perception of negative stimuli, including the vmPFC, dlPFC, insula, as well as the subcortical regions of posterior cingulate cortex and striatum. These findings support a multimodal emotion dysregulation hypothesis for MDD by demonstrating that negative bias effects may be facilitated by the excessive ventral bottom-up negative emotional influences along with incapability in dorsal prefrontal top-down control system.
| S-EPMC8837587 | biostudies-literature
Project description:ObjectivesBrain molecular aging, the pervasive and consistent transcriptome changes associated with normal brain aging, appears to overlap with disease pathways and may be anticipated in neurodegenerative and neuropsychiatric diseases, including major depressive disorder (MDD). Here, we characterize the global interaction of MDD-related gene changes with age, starting from our previous report of downregulated brain-derived neurotrophic factor (BDNF) and BDNF-dependent genes in the amygdala of women with MDD.MethodsA large-scale gene expression data set in the amygdala from a postmortem cohort of 21 women with MDD and 21 age-matched controls (age range: 16-74 years) was analyzed for correlations of gene transcript changes with age, in the presence or absence of a diagnosis of MDD.Results1) The age-related decrease in BDNF transcripts observed in control subjects corresponds with further age-related decreases in BDNF and BDNF-dependent gene expression in MDD subjects; 2) most MDD-related genes are frequently age-regulated in both MDD and control subjects; 3) the effects of MDD and age are positively correlated; 4) most genes that are age-dependent in control subjects display greater age effects in MDD subjects; and 5) the increased prevalence of age effects in MDD corresponds to similar trends in controls, rather than representing de novo age effects.ConclusionsMDD strongly associates with robust and anticipated gene expression changes that occur during normal aging of the brain, suggesting that an older molecular age of the brain represents an early biological event and/or a marker of risk for subsequent onset of MDD symptoms.
| S-EPMC3615087 | biostudies-literature
Project description:A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models learned from data can reinforce these structural inequalities or biases. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches regularly present biased behaviors. We also show that mitigation techniques, both standard and our own post-hoc method, can be effective in reducing the level of unfair bias. There is no one best ML model for depression prediction that provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions. Finally, we also identify positive habits and open challenges that practitioners could follow to enhance fairness in their models.
| S-EPMC10991528 | biostudies-literature