Genome wide analysis implicates upregulation of proteasome pathway in major depressive disorder.
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ABSTRACT:
SUBMITTER: Belaish S
PROVIDER: S-EPMC8319154 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: Not available
| S-EPMC3883627 | biostudies-literature
Project description:Prior genome-wide association studies (GWAS) of major depressive disorder (MDD) have met with limited success. We sought to increase statistical power to detect disease loci by conducting a GWAS mega-analysis for MDD. In the MDD discovery phase, we analyzed more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18 759 independent and unrelated subjects of recent European ancestry (9240 MDD cases and 9519 controls). In the MDD replication phase, we evaluated 554 SNPs in independent samples (6783 MDD cases and 50 695 controls). We also conducted a cross-disorder meta-analysis using 819 autosomal SNPs with P<0.0001 for either MDD or the Psychiatric GWAS Consortium bipolar disorder (BIP) mega-analysis (9238 MDD cases/8039 controls and 6998 BIP cases/7775 controls). No SNPs achieved genome-wide significance in the MDD discovery phase, the MDD replication phase or in pre-planned secondary analyses (by sex, recurrent MDD, recurrent early-onset MDD, age of onset, pre-pubertal onset MDD or typical-like MDD from a latent class analyses of the MDD criteria). In the MDD-bipolar cross-disorder analysis, 15 SNPs exceeded genome-wide significance (P<5 × 10(-8)), and all were in a 248 kb interval of high LD on 3p21.1 (chr3:52 425 083-53 822 102, minimum P=5.9 × 10(-9) at rs2535629). Although this is the largest genome-wide analysis of MDD yet conducted, its high prevalence means that the sample is still underpowered to detect genetic effects typical for complex traits. Therefore, we were unable to identify robust and replicable findings. We discuss what this means for genetic research for MDD. The 3p21.1 MDD-BIP finding should be interpreted with caution as the most significant SNP did not replicate in MDD samples, and genotyping in independent samples will be needed to resolve its status.
| S-EPMC3837431 | biostudies-literature
Project description:While genome-wide association studies are ongoing to identify sequence variation influencing susceptibility to major depressive disorder (MDD), epigenetic marks, such as DNA methylation, which can be influenced by environment, might also play a role. Here we present the first genome-wide DNA methylation (DNAm) scan in MDD. We compared 39 postmortem frontal cortex MDD samples to 26 controls. DNA was hybridized to our Comprehensive High-throughput Arrays for Relative Methylation (CHARM) platform, covering 3.5 million CpGs. CHARM identified 224 candidate regions with DNAm differences >10%. These regions are highly enriched for neuronal growth and development genes. Ten of 17 regions for which validation was attempted showed true DNAm differences; the greatest were in PRIMA1, with 12-15% increased DNAm in MDD (p = 0.0002-0.0003), and a concomitant decrease in gene expression. These results must be considered pilot data, however, as we could only test replication in a small number of additional brain samples (n = 16), which showed no significant difference in PRIMA1. Because PRIMA1 anchors acetylcholinesterase in neuronal membranes, decreased expression could result in decreased enzyme function and increased cholinergic transmission, consistent with a role in MDD. We observed decreased immunoreactivity for acetylcholinesterase in MDD brain with increased PRIMA1 DNAm, non-significant at p = 0.08.While we cannot draw firm conclusions about PRIMA1 DNAm in MDD, the involvement of neuronal development genes across the set showing differential methylation suggests a role for epigenetics in the illness. Further studies using limbic system brain regions might shed additional light on this role.
| S-EPMC3325245 | biostudies-literature
Project description:EPIC array data were generated from 2 MDD case control cohorts. EWAS was performed in each cohort, followed by meta-analysis between the 2 cohort. Cohort 1: A total of 191 blood samples from 112 patients with MDD was collected up till the interim analysis (wave 1 samples) from an observational clinical study OBSERVEMDD0001 (ClinicalTrials.gov Identifier: NCT02489305) compared to 32 healthy controls; Cohort 2: The MDD cases (N = 359) were drawn from the Molecular Biomarkers of Antidepressant Response study compared to 68 healthy controls.
2022-11-03 | GSE198904 | GEO
Project description:A genome-wide association study was carried out in 1020 case subjects with recurrent early-onset major depressive disorder (MDD) (onset before age 31) and 1636 control subjects screened to exclude lifetime MDD. Subjects were genotyped with the Affymetrix 6.0 platform. After extensive quality control procedures, 671 424 autosomal single nucleotide polymorphisms (SNPs) and 25 068 X chromosome SNPs with minor allele frequency greater than 1% were available for analysis. An additional 1 892 186 HapMap II SNPs were analyzed based on imputed genotypic data. Single-SNP logistic regression trend tests were computed, with correction for ancestry-informative principal component scores. No genome-wide significant evidence for association was observed, assuming that nominal P<5 × 10(-8) approximates a 5% genome-wide significance threshold. The strongest evidence for association was observed on chromosome 18q22.1 (rs17077540, P=1.83 × 10(-7)) in a region that has produced some evidence for linkage to bipolar-I or -II disorder in several studies, within an mRNA detected in human brain tissue (BC053410) and approximately 75 kb upstream of DSEL. Comparing these results with those of a meta-analysis of three MDD GWAS data sets reported in a companion article, we note that among the strongest signals observed in the GenRED sample, the meta-analysis provided the greatest support (although not at a genome-wide significant level) for association of MDD to SNPs within SP4, a brain-specific transcription factor. Larger samples will be required to confirm the hypothesis of association between MDD (and particularly the recurrent early-onset subtype) and common SNPs.
| S-EPMC6486400 | biostudies-literature
Project description:Major depressive disorder (MDD) is a common complex disorder with a partly genetic etiology. We conducted a genome-wide association study of the MDD2000+ sample (2431 cases, 3673 screened controls and >1?M imputed single-nucleotide polymorphisms (SNPs)). No SNPs achieved genome-wide significance either in the MDD2000+ study, or in meta-analysis with two other studies totaling 5763 cases and 6901 controls. These results imply that common variants of intermediate or large effect do not have main effects in the genetic architecture of MDD. Suggestive but notable results were (a) gene-based tests suggesting roles for adenylate cyclase 3 (ADCY3, 2p23.3) and galanin (GAL, 11q13.3); published functional evidence relates both of these to MDD and serotonergic signaling; (b) support for the bipolar disorder risk variant SNP rs1006737 in CACNA1C (P=0.020, odds ratio=1.10); and (c) lack of support for rs2251219, a SNP identified in a meta-analysis of affective disorder studies (P=0.51). We estimate that sample sizes 1.8- to 2.4-fold greater are needed for association studies of MDD compared with those for schizophrenia to detect variants that explain the same proportion of total variance in liability. Larger study cohorts characterized for genetic and environmental risk factors accumulated prospectively are likely to be needed to dissect more fully the etiology of MDD.
| S-EPMC3252611 | biostudies-literature
Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.
2011-10-04 | E-GEOD-19738 | biostudies-arrayexpress
Project description:Major depressive disorder (MDD) is a complex, heritable psychiatric disorder. Advanced statistical genetics for genome-wide association studies (GWASs) have suggested that the heritability of MDD is largely explained by common single nucleotide polymorphisms (SNPs). However, until recently, there has been little success in identifying MDD-associated SNPs. Here, based on an empirical Bayes estimation of a semi-parametric hierarchical mixture model using summary statistics from GWASs, we show that MDD has a distinctive polygenic architecture consisting of a relatively small number of risk variants (~17%), e.g., compared to schizophrenia (~42%). In addition, these risk variants were estimated to have very small effects (genotypic odds ratio ≤ 1.04 under the additive model). Based on the estimated architecture, the required sample size for detecting significant SNPs in a future GWAS was predicted to be exceptionally large. It is noteworthy that the number of genome-wide significant MDD-associated SNPs would rapidly increase when collecting 50,000 or more MDD-cases (and the same number of controls); it can reach as much as 100 SNPs out of nearly independent (linkage disequilibrium pruned) 100,000 SNPs for ~120,000 MDD-cases.
| S-EPMC6032046 | biostudies-literature
Project description:Genetic variants conferring risk for autism spectrum disorder (ASD) have been identified, but the role of post-transcriptional mechanisms in ASD is not well understood. We performed genome-wide microRNA (miRNA) expression profiling in post-mortem brains from individuals with ASD and controls and identified miRNAs and co-regulated modules that were perturbed in ASD. Putative targets of these ASD-affected miRNAs were enriched for genes that have been implicated in ASD risk. We confirmed regulatory relationships between several miRNAs and their putative target mRNAs in primary human neural progenitors. These include hsa-miR-21-3p, a miRNA of unknown CNS function that is upregulated in ASD and that targets neuronal genes downregulated in ASD, and hsa_can_1002-m, a previously unknown, primate-specific miRNA that is downregulated in ASD and that regulates the epidermal growth factor receptor and fibroblast growth factor receptor signaling pathways involved in neural development and immune function. Our findings support a role for miRNA dysregulation in ASD pathophysiology and provide a rich data set and framework for future analyses of miRNAs in neuropsychiatric diseases.
| S-EPMC5841760 | biostudies-literature
Project description:Obsessive-compulsive disorder (OCD) has a complex etiology involving both genetic and environmental factors. However, the genetic causes of OCD are largely unknown, despite the identification of several promising candidate genes and linkage regions.Our objective was to conduct genetic linkage studies of the type of OCD thought to have the strongest genetic etiology (i.e., childhood-onset OCD), in 33 Caucasian families with ?2 childhood-onset OCD-affected individuals from the United States (n = 245 individuals with genotype data). Parametric and nonparametric genome-wide linkage analyses were conducted with Morgan and Merlin in these families using a selected panel of single nucleotide repeat polymorphisms from the Illumina 610-Quad Bead Chip. The initial analyses were followed by fine-mapping analyses in genomic regions with initial heterogeneity logarithm of odds (HLOD) scores of ?2.0.We identified five areas of interest (HLOD score ?2) on chromosomes 1p36, 2p14, 5q13, 6p25, and 10p13. The strongest result was on chromosome 1p36.33-p36.32 (HLOD = 3.77, suggestive evidence for linkage after fine mapping). At this location, several of the families showed haplotypes co-segregating with OCD.The results of this study represent the strongest linkage finding for OCD in a primary analysis to date and suggest that chromosome 1p36, and possibly several other genomic regions, may harbor susceptibility loci for OCD. Multiple brain-expressed genes lie under the primary linkage peak (approximately 4 megabases in size). Follow-up studies, including replication in additional samples and targeted sequencing of the areas of interest, are needed to confirm these findings and to identify specific OCD risk variants.
| S-EPMC3437244 | biostudies-literature