Genome-wide association study of recurrent early-onset major depressive disorder

The genetics of severe depression

Genome-wide association studies (GWASs) of major depressive disorder (MDD) have recently achieved extremely large sample sizes and yielded substantial numbers of genome-wide significant loci. Because of the approach to ascertainment and assessment in many of these studies, some of these loci appear to be associated with dysphoria rather than with MDD, potentially decreasing the clinical relevance of the findings. An alternative approach to MDD GWAS is to focus on the most severe forms of MDD, with the hope that this will enrich for loci of larger effect, rendering their identification plausible, and providing potentially more clinically actionable findings. Here we review the genetics of severe depression by using clinical markers of severity including: age of onset, recurrence, degree of impairment, and treatment with ECT. There is evidence for increased family-based and Single Nucleotide Polymorphism (SNP)-based estimates of heritability in recurrent and early-onset illness as well as severe functional impariment. GWAS have been performed looking at severe forms of MDD and a few genome-wide loci have been identified. Several whole exome sequencing studies have also been performed, identifying associated rare variants. Although these findings have not yet been rigorously replicated, the elevated heritability seen in severe MDD phenotypes suggests the value of pursuing additional genome-wide interrogation of samples from this population. The challenge now is generating a cohort of adequate size with consistent phenotyping that will allow for careful and robust classifications and distinctions to be made. We are currently pursuing such a strategy in our 50-site worldwide Gen-ECT-ics consortium.

White matter microstructure and functional connectivity in the brains of infants with Turner syndrome

Turner syndrome, caused by complete or partial loss of an X-chromosome, is often accompanied by specific cognitive challenges. Magnetic resonance imaging studies of adults and children with Turner syndrome suggest these deficits reflect differences in anatomical and functional connectivity. However, no imaging studies have explored connectivity in infants with Turner syndrome. Consequently, it is unclear when in development connectivity differences emerge. To address this gap, we compared functional connectivity and white matter microstructure of 1-year-old infants with Turner syndrome to typically developing 1-year-old boys and girls. We examined functional connectivity between the right precentral gyrus and five regions that show reduced volume in 1-year old infants with Turner syndrome compared to controls and found no differences. However, exploratory analyses suggested infants with Turner syndrome have altered connectivity between right supramarginal gyrus and left insula and right putamen. To assess anatomical connectivity, we examined diffusivity indices along the superior longitudinal fasciculus and found no differences. However, an exploratory analysis of 46 additional white matter tracts revealed significant group differences in nine tracts. Results suggest that the first year of life is a window in which interventions might prevent connectivity differences observed at later ages, and by extension, some of the cognitive challenges associated with Turner syndrome.

An epidemiological study of season of birth, mental health, and neuroimaging in the UK Biobank

Environmental exposures during the perinatal period are known to have a long-term effect on adult physical and mental health. One such influential environmental exposure is the time of year of birth which affects the amount of daylight, nutrients, and viral load that an individual is exposed to within this key developmental period. Here, we investigate associations between season of birth (seasonality), four mental health traits (n = 137,588) and multi-modal neuroimaging measures (n = 33,212) within the UK Biobank. Summer births were associated with probable recurrent Major Depressive Disorder (β = 0.026, pcorr = 0.028) and greater mean cortical thickness in temporal and occipital lobes (β = 0.013 to 0.014, pcorr<0.05). Winter births were associated with greater white matter integrity globally, in the association fibers, thalamic radiations, and six individual tracts (β = -0.013 to -0.022, pcorr<0.05). Results of sensitivity analyses adjusting for birth weight were similar, with an additional association between winter birth and white matter microstructure in the forceps minor and between summer births, greater cingulate thickness and amygdala volume. Further analyses revealed associations between probable depressive phenotypes and a range of neuroimaging measures but a paucity of interactions with seasonality. Our results suggest that seasonality of birth may affect later-life brain structure and play a role in lifetime recurrent Major Depressive Disorder. Due to the small effect sizes observed, and the lack of associations with other mental health traits, further research is required to validate birth season effects in the context of different latitudes, and by co-examining genetic and epigenetic measures to reveal informative biological pathways.

Genetics, epigenetics, and neurobiology of childhood-onset depression: an umbrella review

Depression is a serious and persistent psychiatric disorder that commonly first manifests during childhood. Depression that starts in childhood is increasing in frequency, likely due both to evolutionary trends and to increased recognition of the disorder. In this umbrella review, we systematically searched the extant literature for genetic, epigenetic, and neurobiological factors that contribute to a childhood onset of depression. We searched PubMed, EMBASE, OVID/PsychInfo, and Google Scholar with the following inclusion criteria: (1) systematic review or meta-analysis from a peer-reviewed journal; (2) inclusion of a measure assessing early age of onset of depression; and (3) assessment of neurobiological, genetic, environmental, and epigenetic predictors of early onset depression. Findings from 89 systematic reviews of moderate to high quality suggest that childhood-onset depressive disorders have neurobiological, genetic, environmental, and epigenetic roots consistent with a diathesis-stress theory of depression. This review identified key putative markers that may be targeted for personalized clinical decision-making and provide important insights concerning candidate mechanisms that might underpin the early onset of depression.

Peripheral Neuroinflammation and Pain: How Acute Pain Becomes Chronic

The number of individuals suffering from severe chronic pain and its social and financial impact is staggering. Without significant advances in our understanding of how acute pain becomes chronic, effective treatments will remain out of reach. This mini review will briefly summarize how critical signaling pathways initiated during the early phases of peripheral nervous system inflammation/ neuroinflammation establish long-term modifications of sensory neuronal function. Together with the recruitment of non-neuronal cellular elements, nociceptive transduction is transformed into a pathophysiologic state sustaining chronic peripheral sensitization and pain. Inflammatory mediators, such as nerve growth factor (NGF), can lower activation thresholds of sensory neurons through posttranslational modification of the pain-transducing ion channels transient-receptor potential TRPV1 and TRPA1. Performing a dual role, NGF also drives increased expression of TRPV1 in sensory neurons through the recruitment of transcription factor Sp4. More broadly, Sp4 appears to modulate a nociceptive transcriptome including TRPA1 and other genes encoding components of pain transduction. Together, these findings suggest a model where acute pain evoked by peripheral injury-induced inflammation becomes persistent through repeated cycles of TRP channel modification, Sp4-dependent overexpression of TRP channels and ongoing production of inflammatory mediators.

Genetic Landscape of Major Depressive Disorder: Assessment of Potential Diagnostic and Antidepressant Response Markers

BACKGROUND

The clinical heterogeneity in major depressive disorder (MDD), variable treatment response, and conflicting findings limit the ability of genomics toward the discovery of evidence-based diagnosis and treatment regimen. This study attempts to curate all genetic association findings to evaluate potential variants for clinical translation.

METHODS

We systematically reviewed all candidates and genome-wide association studies for both MDD susceptibility and antidepressant response, independently, using MEDLINE, particularly to identify replicated findings. These variants were evaluated for functional consequences using different in silico tools and further estimated their diagnostic predictability by calculating positive predictive values.

RESULTS

A total of 217 significantly associated studies comprising 1200 variants across 545 genes and 128 studies including 921 variants across 412 genes were included with MDD susceptibility and antidepressant response, respectively. Although the majority of associations were confirmed by a single study, we identified 31 and 18 replicated variants (in at least 2 studies) for MDD and antidepressant response. Functional annotation of these 31 variants predicted 20% coding variants as deleterious/damaging and 80.6% variants with regulatory effect. Similarly, the response-related 18 variants revealed 25% coding variant as damaging and 88.2% with substantial regulatory potential. Finally, we could calculate the diagnostic predictability of 19 and 5 variants whose positive predictive values ranges from 0.49 to 0.66 for MDD and 0.36 to 0.66 for response.

CONCLUSIONS

The replicated variants presented in our data are promising for disease diagnosis and improved response outcomes. Although these quantitative assessment measures are solely directive of available observational evidence, robust homogenous validation studies are required to strengthen these variants for molecular diagnostic application.

Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysis

Background

Alzheimer's disease (AD) is a heterogeneously progressive neurodegeneration disorder with varied rates of deterioration, either between subjects or within different stages of a certain subject. Estimating the course of AD at early stages has treatment implications. We aimed to analyze disease progression to identify distinct patterns in AD trajectory.

Methods

We proposed a deep learning model to identify underlying patterns in the trajectory from cognitively normal (CN) to a state of mild cognitive impairment (MCI) to AD dementia, by jointly predicting time-to-conversion and clustering out distinct subgroups characterized by comprehensive features as well as varied progression rates. We designed and validated our model on the ADNI dataset (1370 participants). Prediction of time-to-conversion in AD trajectory was used to validate the expression of the identified patterns. Causality between patterns and time-to-conversion was further inferred using Mendelian randomization (MR) analysis. External validation was performed on the AIBL dataset (233 participants).

Findings

The proposed model clustered out patterns characterized by significantly different biomarkers and varied progression rates. The discovered patterns also showed a strong prediction ability, as indicated by hazard ratio (CN→MCI, HR = 3.51, p < 0.001; MCI→AD, HR = 8.11, p < 0.001), C-Index (CN→MCI, 0.618; MCI→AD, 0.718), and AUC (CN→MCI, 3 years 0.802, 5 years 0.876; MCI→AD, 3 years 0.914, 5 years 0.957). In the external validation cohort, our model demonstrated competitive performance on conversion time prediction (CN→MCI, C-Index = 0.693; MCI→AD, C-Index = 0.752). Moreover, suggestive associations between CN→MCI/MCI→AD patterns with four/three SNPs were mediated and MR analysis indicated a causal link between MCI→AD patterns and time-to-conversion in the first three years.

Interpretation

Our proposed model identifies biologically and clinically meaningful patterns from real-world data and provides promising performance on time-to-conversion prediction in AD trajectory, which could promote the understanding of disease progression, facilitate clinical trial design, and provide potential for decision-making.

Funding

The National Key Research and Development Program of China, the Key R&D Program of Zhejiang, and the National Nature Science Foundation of China.

The genetic basis of major depressive disorder

The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.

Associating broad and clinically defined polygenic scores for depression with depression-related phenotypes

Twin studies indicate that 30-40% of the disease liability for depression can be attributed to genetic differences. Here, we assess the explanatory ability of polygenic scores (PGS) based on broad- (PGSBD) and clinical- (PGSMDD) depression summary statistics from the UK Biobank in an independent sample of adults (N = 210; 100% European Ancestry) who were extensively phenotyped for depression and related neurocognitive traits (e.g., rumination, emotion regulation, anhedonia, and resting frontal alpha asymmetry). The UK Biobank-derived PGSBD had small associations with MDD, depression severity, anhedonia, cognitive reappraisal, brooding, and suicidal ideation but only the association with suicidal ideation remained statistically significant after correcting for multiple comparisons. Similarly small associations were observed for the PGSMDD but none remained significant after correcting for multiple comparisons. These findings provide important initial guidance about the expected effect sizes between current UKB PGSs for depression and depression-related neurocognitive phenotypes.

Genetic and environmental factors influencing neonatal resting-state functional connectivity

Functional magnetic resonance imaging has been used to identify complex brain networks by examining the correlation of blood-oxygen-level-dependent signals between brain regions during the resting state. Many of the brain networks identified in adults are detectable at birth, but genetic and environmental influences governing connectivity within and between these networks in early infancy have yet to be explored. We investigated genetic influences on neonatal resting-state connectivity phenotypes by generating intraclass correlations and performing mixed effects modeling to estimate narrow-sense heritability on measures of within network and between-network connectivity in a large cohort of neonate twins. We also used backwards elimination regression and mixed linear modeling to identify specific demographic and medical history variables influencing within and between network connectivity in a large cohort of typically developing twins and singletons. Of the 36 connectivity phenotypes examined, only 6 showed narrow-sense heritability estimates greater than 0.10, with none being statistically significant. Demographic and obstetric history variables contributed to between- and within-network connectivity. Our results suggest that in early infancy, genetic factors minimally influence brain connectivity. However, specific demographic and medical history variables, such as gestational age at birth and maternal psychiatric history, may influence resting-state connectivity measures.

Children with Early-Onset Psychosis Have Increased Burden of Rare GRIN2A Variants

BACKGROUND

Children and adolescents with early-onset psychosis (EOP) have more rare genetic variants than individuals with adult-onset forms of the illness, implying that fewer EOP participants are needed for genetic discovery. The Schizophrenia Exome Sequencing Meta-analysis (SCHEMA) study predicted that 10 genes with ultra-rare variation were linked to adult-onset schizophrenia. We hypothesized that rare variants predicted "High" and "Moderate" by the Variant Effect Predictor Algorithm (abbreviated as VEPHMI) in these 10 genes would be enriched in our EOP cohort.

METHODS

We compared rare VEPHMI variants in individuals with EOP (N = 34) with race- and sex-matched controls (N = 34) using the sequence kernel association test (SKAT).

RESULTS

GRIN2A variants were significantly increased in the EOP cohort (p = 0.004), with seven individuals (20% of the EOP cohort) carrying a rare VEPHMI variant. The EOP cohort was then compared to three additional control cohorts. GRIN2A variants were significantly increased in the EOP cohort for two of the additional control sets (p = 0.02 and p = 0.02), and trending towards significance for the third (p = 0.06).

CONCLUSION

Despite a small sample size, GRIN2A VEPHMI variant burden was increased in a cohort of individuals with EOP in comparison to controls. GRIN2A variants have been associated with a range of neuropsychiatric disorders including adult-onset psychotic spectrum disorder and childhood-onset schizophrenia. This study supports the role of GRIN2A in EOP and emphasizes its role in neuropsychiatric disorders.

Development of cross-species translational paradigms for psychiatric research in the Research Domain Criteria era

The past 30 years of IBNS has included research attempting to treat the cognitive and behavioral deficits observed in people with psychiatric conditions. Early work utilized drugs identified from tests thought to be cognition-relevant, however the high failure rate crossing the translational-species barrier led to focus on developing valid cross-species translational tests. The face, predictive, and neurobiological validities used to assess animal models of psychiatry can be used to validate these tests. Clinical sensitivity is another important aspect however, for if the clinical population targeted for treatment does not exhibit task deficits, then why develop treatments? This review covers some work validating cross-species translational tests and suggests future directions. Also covered is the contribution IBNS made to fostering such research and my role in IBNS, making it more available to all including fostering mentor/mentee programs plus spearheading diversity and inclusivity initiatives. All science needs support and IBNS has supported research recreating the behavioral abnormalities that define psychiatric conditions with the aim to improve the lives of people with such conditions.

Histories of Dermatan Sulfate Epimerase and Dermatan 4-O-Sulfotransferase from Discovery of Their Enzymes and Genes to Musculocontractural Ehlers-Danlos Syndrome

Dermatan sulfate (DS) and its proteoglycans are essential for the assembly of the extracellular matrix and cell signaling. Various transporters and biosynthetic enzymes for nucleotide sugars, glycosyltransferases, epimerase, and sulfotransferases, are involved in the biosynthesis of DS. Among these enzymes, dermatan sulfate epimerase (DSE) and dermatan 4-O-sulfotranserase (D4ST) are rate-limiting factors of DS biosynthesis. Pathogenic variants in human genes encoding DSE and D4ST cause the musculocontractural type of Ehlers-Danlos syndrome, characterized by tissue fragility, joint hypermobility, and skin hyperextensibility. DS-deficient mice exhibit perinatal lethality, myopathy-related phenotypes, thoracic kyphosis, vascular abnormalities, and skin fragility. These findings indicate that DS is essential for tissue development as well as homeostasis. This review focuses on the histories of DSE as well as D4ST, and their knockout mice as well as human congenital disorders.

Differential expression of MDGA1 in major depressive disorder

The identification of gene expression-based biomarkers for major depressive disorder (MDD) continues to be an important challenge. In order to identify candidate biomarkers and mechanisms, we apply statistical and machine learning feature selection to an RNA-Seq gene expression dataset of 78 unmedicated individuals with MDD and 79 healthy controls. We identify 49 genes by LASSO penalized logistic regression and 45 genes at the false discovery rate threshold 0.188. The MDGA1 gene has the lowest P-value (4.9e-5) and is expressed in the developing brain, involved in axon guidance, and associated with related mood disorders in previous studies of bipolar disorder (BD) and schizophrenia (SCZ). The expression of MDGA1 is associated with age and sex, but its association with MDD remains significant when adjusted for covariates. MDGA1 is in a co-expression cluster with another top gene, ATXN7L2 (ataxin 7 like 2), which was associated with MDD in a recent GWAS. The LASSO classification model of MDD includes MDGA1, and the model has a cross-validation accuracy of 79%. Another noteworthy top gene, IRF2BPL, is in a close co-expression cluster with MDGA1 and may be related to microglial inflammatory states in MDD. Future exploration of MDGA1 and its gene interactions may provide insights into mechanisms and heterogeneity of MDD.

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We use penalized regression to select differentially expressed genes and characterize their relationships through clustering.

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We identify MDGA1 as the most differentially expressed gene between MDD and healthy controls using RNA-Seq.

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Previous studies have implicated MDGA1 in psychiatric disorders, such as schizophrenia and bipolar disorder, but not in MDD.

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Different psychiatric disorders have some genetic associations in common due to shared neural pathways between disorders.

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A top gene, IRF2BPL, in a close co-expression cluster with MDGA1 may be related to microglial inflammatory states in MDD.

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Future investigation of interactions of MDGA1 and IRF2BPL may provide insights into mechanisms and heterogeneity of MDD.

The Specific Role of Dermatan Sulfate as an Instructive Glycosaminoglycan in Tissue Development

The crucial roles of dermatan sulfate (DS) have been demonstrated in tissue development of the cutis, blood vessels, and bone through construction of the extracellular matrix and cell signaling. Although DS classically exerts physiological functions via interaction with collagens, growth factors, and heparin cofactor-II, new functions have been revealed through analyses of human genetic disorders as well as of knockout mice with loss of DS-synthesizing enzymes. Mutations in human genes encoding the epimerase and sulfotransferase responsible for the biosynthesis of DS chains cause connective tissue disorders including spondylodysplastic type Ehlers–Danlos syndrome, characterized by skin hyperextensibility, joint hypermobility, and tissue fragility. DS-deficient mice show perinatal lethality, skin fragility, vascular abnormalities, thoracic kyphosis, myopathy-related phenotypes, acceleration of nerve regeneration, and impairments in self-renewal and proliferation of neural stem cells. These findings suggest that DS is essential for tissue development in addition to the assembly of collagen fibrils in the skin, and that DS-deficient knockout mice can be utilized as models of human genetic disorders that involve impairment of DS biosynthesis. This review highlights a novel role of DS in tissue development studies from the past decade.

Multi-Omics Characterization of Early- and Adult-Onset Major Depressive Disorder

Age at depressive onset (AAO) corresponds to unique symptomatology and clinical outcomes. Integration of genome-wide association study (GWAS) results with additional “omic” measures to evaluate AAO has not been reported and may reveal novel markers of susceptibility and/or resistance to major depressive disorder (MDD). To address this gap, we integrated genomics with metabolomics using data-driven network analysis to characterize and differentiate MDD based on AAO. This study first performed two GWAS for AAO as a continuous trait in (a) 486 adults from the Pharmacogenomic Research Network-Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS), and (b) 295 adults from the Combining Medications to Enhance Depression Outcomes (CO-MED) study. Variants from top signals were integrated with 153 p180-assayed metabolites to establish multi-omics network characterizations of early (<age 18) and adult-onset depression. The most significant variant (p = 8.77 × 10−8) localized to an intron of SAMD3. In silico functional annotation of top signals (p < 1 × 10−5) demonstrated gene expression enrichment in the brain and during embryonic development. Network analysis identified differential associations between four variants (in/near INTU, FAT1, CNTN6, and TM9SF2) and plasma metabolites (phosphatidylcholines, carnitines, biogenic amines, and amino acids) in early- compared with adult-onset MDD. Multi-omics integration identified differential biosignatures of early- and adult-onset MDD. These biosignatures call for future studies to follow participants from childhood through adulthood and collect repeated -omics and neuroimaging measures to validate and deeply characterize the biomarkers of susceptibility and/or resistance to MDD development.

Small Molecules as Modulators of Voltage-Gated Calcium Channels in Neurological Disorders: State of the Art and Perspectives

Voltage-gated calcium channels (VGCCs) are widely expressed in the brain, heart and vessels, smooth and skeletal muscle, as well as in endocrine cells. VGCCs mediate gene transcription, synaptic and neuronal structural plasticity, muscle contraction, the release of hormones and neurotransmitters, and membrane excitability. Therefore, it is not surprising that VGCC dysfunction results in severe pathologies, such as cardiovascular conditions, neurological and psychiatric disorders, altered glycemic levels, and abnormal smooth muscle tone. The latest research findings and clinical evidence increasingly show the critical role played by VGCCs in autism spectrum disorders, Parkinson's disease, drug addiction, pain, and epilepsy. These findings outline the importance of developing selective calcium channel inhibitors and modulators to treat such prevailing conditions of the central nervous system. Several small molecules inhibiting calcium channels are currently used in clinical practice to successfully treat pain and cardiovascular conditions. However, the limited palette of molecules available and the emerging extent of VGCC pathophysiology require the development of additional drugs targeting these channels. Here, we provide an overview of the role of calcium channels in neurological disorders and discuss possible strategies to generate novel therapeutics.

Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals

Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesity-associated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals.

Corrigendum: Potential genetic overlap between insomnia and sleep symptoms in major depressive disorder: A polygenic risk score analysis

[This corrects the article DOI: 10.3389/fpsyt.2021.734077.].

The underestimated sex: a review on female animal models of depression

Major depression (MD) is the most common psychiatric disorder, predicted to affect around 264 million people worldwide. Although the etiology of depression remains elusive, the interplay between genetics and environmental factors, such as early life events, stress, exposure to drugs and health problems appears to underlie its development. Whereas depression is twice more prevalent in women than in men, most preclinical studies are performed in male rodents. In fact, females' physiology and reproductive experience are associated with changes to brain, behavior and endocrine profiles that may influence both stress, an important precipitating factor for depression, and response to treatment. These specificities emphasize the need to choose the most suitable models and readouts in order to better understand the pathophysiological mechanisms of depression in females. With this review, we aim to provide an overview of female animal models of depression highlighting the major differences between models, regarding behavioral, physiological, and molecular readouts, but also the major gaps in research, attending to the role of etiological factors, protocol variability and sex.

Local Ancestry Adjusted Allelic Association Analysis Robustly Captures Tuberculosis Susceptibility Loci

Pulmonary tuberculosis (TB), caused by Mycobacterium tuberculosis, is a complex disease. The risk of developing active TB is in part determined by host genetic factors. Most genetic studies investigating TB susceptibility fail to replicate association signals particularly across diverse populations. South African populations arose because of multi-wave genetic admixture from the indigenous KhoeSan, Bantu-speaking Africans, Europeans, Southeast Asian-and East Asian populations. This has led to complex genetic admixture with heterogenous patterns of linkage disequilibrium and associated traits. As a result, precise estimation of both global and local ancestry is required to prevent both false positive and false-negative associations. Here, 820 individuals from South Africa were genotyped on the SNP-dense Illumina Multi-Ethnic Genotyping Array (∼1.7M SNPs) followed by local and global ancestry inference using RFMix. Local ancestry adjusted allelic association (LAAA) models were utilized owing to the extensive genetic heterogeneity present in this population. Hence, an interaction term, comprising the identification of the minor allele that corresponds to the ancestry present at the specific locus under investigation, was included as a covariate. One SNP (rs28647531) located on chromosome 4q22 was significantly associated with TB susceptibility and displayed a SNP minor allelic effect (G allele, frequency = 0.204) whilst correcting for local ancestry for Bantu-speaking African ancestry (p-value = 5.518 × 10^-7; OR = 3.065; SE = 0.224). Although no other variants passed the significant threshold, clear differences were observed between the lead variants identified for each ancestry. Furthermore, the LAAA model robustly captured the source of association signals in multi-way admixed individuals from South Africa and allowed the identification of ancestry-specific disease risk alleles associated with TB susceptibility that have previously been missed.

Toward an Extended Definition of Major Depressive Disorder Symptomatology: Digital Assessment and Cross-validation Study

Background

Diagnosing major depressive disorder (MDD) is challenging, with diagnostic manuals failing to capture the wide range of clinical symptoms that are endorsed by individuals with this condition.

Objective

This study aims to provide evidence for an extended definition of MDD symptomatology.

Methods

Symptom data were collected via a digital assessment developed for a delta study. Random forest classification with nested cross-validation was used to distinguish between individuals with MDD and those with subthreshold symptomatology of the disorder using disorder-specific symptoms and transdiagnostic symptoms. The diagnostic performance of the Patient Health Questionnaire–9 was also examined.

Results

A depression-specific model demonstrated good predictive performance when distinguishing between individuals with MDD (n=64) and those with subthreshold depression (n=140) (area under the receiver operating characteristic curve=0.89; sensitivity=82.4%; specificity=81.3%; accuracy=81.6%). The inclusion of transdiagnostic symptoms of psychopathology, including symptoms of depression, generalized anxiety disorder, insomnia, emotional instability, and panic disorder, significantly improved the model performance (area under the receiver operating characteristic curve=0.95; sensitivity=86.5%; specificity=90.8%; accuracy=89.5%). The Patient Health Questionnaire–9 was excellent at identifying MDD but overdiagnosed the condition (sensitivity=92.2%; specificity=54.3%; accuracy=66.2%).

Conclusions

Our findings are in line with the notion that current diagnostic practices may present an overly narrow conception of mental health. Furthermore, our study provides proof-of-concept support for the clinical utility of a digital assessment to inform clinical decision-making in the evaluation of MDD.

The genetic basis of major depression

Major depressive disorder (MDD) is a common, debilitating, phenotypically heterogeneous disorder with heritability ranges from 30% to 50%. Compared to other psychiatric disorders, its high prevalence, moderate heritability, and strong polygenicity have posed major challenges for gene-mapping in MDD. Studies of common genetic variation in MDD, driven by large international collaborations such as the Psychiatric Genomics Consortium, have confirmed the highly polygenic nature of the disorder and implicated over 100 genetic risk loci to date. Rare copy number variants associated with MDD risk were also recently identified. The goal of this review is to present a broad picture of our current understanding of the epidemiology, genetic epidemiology, molecular genetics, and gene-environment interplay in MDD. Insights into the impact of genetic factors on the aetiology of this complex disorder hold great promise for improving clinical care.

Congenital Disorders of Deficiency in Glycosaminoglycan Biosynthesis

Glycosaminoglycans (GAGs) including chondroitin sulfate, dermatan sulfate, and heparan sulfate are covalently attached to specific core proteins to form proteoglycans, which are distributed at the cell surface as well as in the extracellular matrix. Proteoglycans and GAGs have been demonstrated to exhibit a variety of physiological functions such as construction of the extracellular matrix, tissue development, and cell signaling through interactions with extracellular matrix components, morphogens, cytokines, and growth factors. Not only connective tissue disorders including skeletal dysplasia, chondrodysplasia, multiple exostoses, and Ehlers-Danlos syndrome, but also heart and kidney defects, immune deficiencies, and neurological abnormalities have been shown to be caused by defects in GAGs as well as core proteins of proteoglycans. These findings indicate that GAGs and proteoglycans are essential for human development in major organs. The glycobiological aspects of congenital disorders caused by defects in GAG-biosynthetic enzymes including specific glysocyltransferases, epimerases, and sulfotransferases, in addition to core proteins of proteoglycans will be comprehensively discussed based on the literature to date.

Whole genome sequencing of nearly isogenic WMI and WLI inbred rats identifies genes potentially involved in depression and stress reactivity

The WMI and WLI inbred rats were generated from the stress-prone, and not yet fully inbred, Wistar Kyoto (WKY) strain. These were selected using bi-directional selection for immobility in the forced swim test and were then sib-mated for over 38 generations. Despite the low level of genetic diversity among WKY progenitors, the WMI substrain is significantly more vulnerable to stress relative to the counter-selected WLI strain. Here we quantify numbers and classes of genomic sequence variants distinguishing these substrains with the long term goal of uncovering functional and behavioral polymorphism that modulate sensitivity to stress and depression-like phenotypes. DNA from WLI and WMI was sequenced using Illumina xTen, IonTorrent, and 10X Chromium linked-read platforms to obtain a combined coverage of ~ 100X for each strain. We identified 4,296 high quality homozygous SNPs and indels between the WMI and WLI. We detected high impact variants in genes previously implicated in depression (e.g. Gnat2), depression-like behavior (e.g. Prlr, Nlrp1a), other psychiatric disease (e.g. Pou6f2, Kdm5a, Reep3, Wdfy3), and responses to psychological stressors (e.g. Pigr). High coverage sequencing data confirm that the two substrains are nearly coisogenic. Nonetheless, the small number of sequence variants contributes to numerous well characterized differences including depression-like behavior, stress reactivity, and addiction related phenotypes. These selected substrains are an ideal resource for forward and reverse genetic studies using a reduced complexity cross.

The role of rs242941, rs1876828, rs242939 and rs110402 polymorphisms of CRHR1 gene and the depression: systematic review and meta-analysis

BACKGROUND

Several studies have evaluated the possible association between polymorphisms or variants in Corticotropin-releasing hormone 1 receptor gene (CRHR1) with depression; however, results remain contradictory and heterogeneous.

OBJECTIVE

To our knowledge, we conducted the first comprehensive systematic review and meta-analysis evaluating the association of the CRHR1 gene and the risk of depression.

METHODS

A search online was conducted in databases for any CRHR1 genetic association studies in depression. Data were extracted for evaluation of pooled estimates using meta-analytic techniques. Statistical analyses were performed using the Comprehensive Meta-analysis, v2.0 software.

RESULT

A total of 1403 cases and 2353 mentally healthy controls were included in this study. We found a significant association of rs242941, rs1876828 and rs242939 variants of the CRHR1 gene with depression. No association of CRHR1 rs110402 and depression was observed.

CONCLUSION

Our meta-analysis shows that some variants of the CRHR1 gene (rs242941, rs1876828 and rs242939) might confer susceptibility to depression. Further studies with larger sample sizes need to be conducted.

Analysis of Major Depression Risk Genes Reveals Evolutionary Conservation, Shared Phenotypes, and Extensive Genetic Interactions

Major depressive disorder (MDD) affects around 15% of the population at some stage in their lifetime. It can be gravely disabling and it is associated with increased risk of suicide. Genetics play an important role; however, there are additional environmental contributions to the pathogenesis. A number of possible risk genes that increase liability for developing symptoms of MDD have been identified in genome-wide association studies (GWAS). The goal of this study was to characterize the MDD risk genes with respect to the degree of evolutionary conservation in simpler model organisms such as Caenorhabditis elegans and zebrafish, the phenotypes associated with variation in these genes and the extent of network connectivity. The MDD risk genes showed higher conservation in C. elegans and zebrafish than genome-to-genome comparisons. In addition, there were recurring themes among the phenotypes associated with variation of these risk genes in C. elegans. The phenotype analysis revealed enrichment for essential genes with pleiotropic effects. Moreover, the MDD risk genes participated in more interactions with each other than did randomly-selected genes from similar-sized gene sets. Syntenic blocks of risk genes with common functional activities were also identified. By characterizing evolutionarily-conserved counterparts to the MDD risk genes, we have gained new insights into pathogenetic processes relevant to the emergence of depressive symptoms in man.

Discriminating bipolar depression from major depressive disorder with polygenic risk scores

BACKGROUND

Although accurate differentiation between bipolar disorder (BD) and unipolar major depressive disorder (MDD) has important prognostic and therapeutic implications, the distinction is often challenging based on clinical grounds alone. In this study, we tested whether psychiatric polygenic risk scores (PRSs) improve clinically based classification models of BD v. MDD diagnosis.

METHODS

Our sample included 843 BD and 930 MDD subjects similarly genotyped and phenotyped using the same standardized interview. We performed multivariate modeling and receiver operating characteristic analysis, testing the incremental effect of PRSs on a baseline model with clinical symptoms and features known to associate with BD compared with MDD status.

RESULTS

We found a strong association between a BD diagnosis and PRSs drawn from BD (R2 = 3.5%, p = 4.94 × 10-12) and schizophrenia (R2 = 3.2%, p = 5.71 × 10-11) genome-wide association meta-analyses. Individuals with top decile BD PRS had a significantly increased risk for BD v. MDD compared with those in the lowest decile (odds ratio 3.39, confidence interval 2.19-5.25). PRSs discriminated BD v. MDD to a degree comparable with many individual symptoms and clinical features previously shown to associate with BD. When compared with the full composite model with all symptoms and clinical features PRSs provided modestly improved discriminatory ability (ΔC = 0.011, p = 6.48 × 10-4).

CONCLUSIONS

Our study demonstrates that psychiatric PRSs provide modest independent discrimination between BD and MDD cases, suggesting that PRSs could ultimately have utility in subjects at the extremes of the distribution and/or subjects for whom clinical symptoms are poorly measured or yet to manifest.

Genetic markers for depressive disorders with earlier age at onset

Age at onset has been considered a potential indicator of underlying genetic risk in depression research. However, the variants associated with earlier age at onset of depressive disorder have not been elucidated. To evaluate the genetic architecture of depression onset, whole-exome sequencing of samples from 1000 patients with depressive disorder was performed. Cox proportional hazard models with false discovery rate-adjusted P-values were used to estimate the hazard ratios; carriers and non-carriers of individual coding variants were compared in terms of age at onset of depression with adjustment for sociodemographic and clinical characteristics. The clinical relevance of the candidate variants was also examined. Whole-exome sequencing revealed four variants in the CCL14, FYB, GPRASP1, and CTNND2 genes associated with an increased risk of depressive disorder with earlier age at onset. Although no individual variant was associated with any clinical characteristic except AAO, together they were associated with younger AAO, younger age at visit for treatment, and recurrent and atypical depression. Our data suggest novel candidate genes for depressive disorder with earlier age at onset. These genes could serve as markers allowing early identification of patients at risk of depression, and thus earlier intervention.

Identification of putative causal loci in whole-genome sequencing data via knockoff statistics

The analysis of whole-genome sequencing studies is challenging due to the large number of rare variants in noncoding regions and the lack of natural units for testing. We propose a statistical method to detect and localize rare and common risk variants in whole-genome sequencing studies based on a recently developed knockoff framework. It can (1) prioritize causal variants over associations due to linkage disequilibrium thereby improving interpretability; (2) help distinguish the signal due to rare variants from shadow effects of significant common variants nearby; (3) integrate multiple knockoffs for improved power, stability, and reproducibility; and (4) flexibly incorporate state-of-the-art and future association tests to achieve the benefits proposed here. In applications to whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) and COPDGene samples from NHLBI Trans-Omics for Precision Medicine (TOPMed) Program we show that our method compared with conventional association tests can lead to substantially more discoveries.

Therapy response prediction in major depressive disorder: current and novel genomic markers influencing pharmacokinetics and pharmacodynamics

Major depressive disorder is connected with high rates of functional disability and mortality. About a third of the patients are at risk of therapy failure. Several pharmacogenetic markers especially located in CYP450 genes such as CYP2D6 or CYP2C19 are of relevance for therapy outcome prediction in major depressive disorder but a further optimization of predictive tools is warranted. The article summarizes the current knowledge on pharmacogenetic variants, therapy effects and side effects of important antidepressive therapeutics, and sheds light on new methodological approaches for therapy response estimation based on genetic markers with relevance for pharmacokinetics, pharmacodynamics and disease pathology identified in genome-wide association study analyses, highlighting polygenic risk score analysis as a tool for further optimization of individualized therapy outcome prediction.

Transcription Factor Motifs Associated with Anterior Insula Gene Expression Underlying Mood Disorder Phenotypes

Mood disorders represent a major cause of morbidity and mortality worldwide but the brain-related molecular pathophysiology in mood disorders remains largely undefined. Because the anterior insula is reduced in volume in patients with mood disorders, RNA was extracted from the anterior insula postmortem anterior insula of mood disorder samples and compared with unaffected controls for RNA-sequencing identification of differentially expressed genes (DEGs) in (a) bipolar disorder (BD; n = 37) versus (vs.) controls (n = 33), and (b) major depressive disorder (MDD n = 30) vs. controls, and (c) low vs. high axis I comorbidity (a measure of cumulative psychiatric disease burden). Given the regulatory role of transcription factors (TFs) in gene expression via specific-DNA-binding domains (motifs), we used JASPAR TF binding database to identify TF-motifs. We found that DEGs in BD vs. controls, MDD vs. controls, and high vs. low axis I comorbidity were associated with TF-motifs that are known to regulate expression of toll-like receptor genes, cellular homeostatic-control genes, and genes involved in embryonic, cellular/organ, and brain development. Robust imaging-guided transcriptomics by using meta-analytic imaging results to guide independent postmortem dissection for RNA-sequencing was applied by targeting the gray matter volume reduction in the anterior insula in mood disorders, to guide independent postmortem identification of TF motifs regulating DEG. Our findings of TF-motifs that regulate the expression of immune, cellular homeostatic-control, and developmental genes provide novel information about the hierarchical relationship between gene regulatory networks, the TFs that control them, and proximate underlying neuroanatomical phenotypes in mood disorders.

Exome sequencing reveals predominantly de novo variants in disorders with intellectual disability (ID) in the founder population of Finland

The genetics of autosomal recessive intellectual disability (ARID) has mainly been studied in consanguineous families, however, founder populations may also be of interest to study intellectual disability (ID) and the contribution of ARID. Here, we used a genotype-driven approach to study the genetic landscape of ID in the founder population of Finland. A total of 39 families with syndromic and non-syndromic ID were analyzed using exome sequencing, which revealed a variant in a known ID gene in 27 families. Notably, 75% of these variants in known ID genes were de novo or suspected de novo (64% autosomal dominant; 11% X-linked) and 25% were inherited (14% autosomal recessive; 7% X-linked; and 4% autosomal dominant). A dual molecular diagnosis was suggested in two families (5%). Via additional analysis and molecular testing, we identified three cases with an abnormal molecular karyotype, including chr21q22.12q22.2 uniparental disomy with a mosaic interstitial 2.7 Mb deletion covering DYRK1A and KCNJ6. Overall, a pathogenic or likely pathogenic variant was identified in 64% (25/39) of the families. Last, we report an alternate inheritance model for 3 known ID genes (UBA7, DDX47, DHX58) and discuss potential candidate genes for ID, including SYPL1 and ERGIC3 with homozygous founder variants and de novo variants in POLR2F and DNAH3. In summary, similar to other European populations, de novo variants were the most common variants underlying ID in the studied Finnish population, with limited contribution of ARID to ID etiology, though mainly driven by founder and potential founder variation in the latter case.

Animal models for the study of depressive disorder

Depressive disorder is one of the most widespread forms of psychiatric pathology, worldwide. According to a report by the World Health Organization, the number of people with depression, globally, is increasing dramatically with each year. Previous studies have demonstrated that various factors, including genetics and environmental stress, contribute to the risk of depression. As such, it is crucial to develop a detailed understanding of the pathogenesis of depressive disorder and animal studies are essential for identifying the mechanisms and genetic disorders underlying depression. Recently, many researchers have reported on the pathology of depression via various models of depressive disorder. Given that different animal models of depression show differences in terms of patterns of depressive behavior and pathology, the comparison between depressive animal models is necessary for progress in the field of the depression study. However, the various animal models of depression have not been fully compared or evaluated until now. In this paper, we reviewed the pathophysiology of the depressive disorder and its current animal models with the analysis of their transcriptomic profiles. We provide insights for selecting different animal models for the study of depression.

Potential Genetic Overlap Between Insomnia and Sleep Symptoms in Major Depressive Disorder: A Polygenic Risk Score Analysis

Background: The prevalence of insomnia and hypersomnia in depressed individuals is substantially higher than that found in the general population. Unfortunately, these concurrent sleep problems can have profound effects on the disease course. Although the full biology of sleep remains to be elucidated, a recent genome-wide association (GWAS) of insomnia, and other sleep traits in over 1 million individuals was recently published and provides many promising hits for genetics of insomnia in a population-based sample. Methods: Using data from the largest available GWAS of insomnia and other sleep traits, we sought to test if sleep variable PRS scores derived from population-based studies predicted sleep variables in samples of depressed cases [Psychiatric Genomics Consortium - Major Depressive Disorder subjects (PGC MDD)]. A leave-one-out analysis was performed to determine the effects that each individual study had on our results. Results: The only significant finding was for insomnia, where p-value threshold, p = 0.05 was associated with insomnia in our PGC MDD sample (R ² = 1.75^-3, p = 0.006). Conclusion: Our results reveal that <1% of variance is explained by the variants that cover the two significant p-value thresholds, which is in line with the fact that depression and insomnia are both polygenic disorders. To the best of our knowledge, this is the first study to investigate genetic overlap between the general population and a depression sample for insomnia, which has important treatment implications, such as leading to novel drug targets in future research efforts.

Genome-wide copy number variation-, validation- and screening study implicates a new copy number polymorphism associated with suicide attempts in major depressive disorder

BACKGROUND

The genetic basis of suicide attempts (SA) remains unclear. Especially the role of copy number variations (CNVs) remains to be elucidated. The present study aimed to identify susceptibility variants associated with SA among Chinese with major depressive disorder (MDD), covering both CNVs and single-nucleotide polymorphisms (SNPs).

METHODS

We conducted a genome-wide association study (GWAS) on MDD patients with and without SA and top results were tested in a replication study. A genome-wide CNV study was also performed. Subsequently, a validation assay using qRT-PCR technology was performed to confirm any associated CNVs and then applied to the entire cohort to examine the association.

RESULTS

Although GWAS did not identify any SNPs reaching genome-wide significance, we identified TPH2 as the top susceptibility gene (p-value = 2.75e-05) in gene-based analysis, which is a strong biological candidate for its role in the serotonergic system. As for CNV analysis, we found that the global rate of CNV was higher in SA than that in non-SA subjects (p-value = 0.023). Genome-wide CNV study revealed an SA-associated CNV region that achieved genome-wide significance (corrected p-value = 0.014). The associated CNV was successfully validated with a more rigorous qRT-PCR assay and identified to be a common variant in this cohort. Its deletion rate was higher in SA subjects [OR = 2.05 (1.02-4.12), adjusted p-value = 0.045]. Based on the GTEx database, genetic variants that probed this CNV were significantly associated with the expression level of ZNF33B in two brain regions (p-value < 4.2e-05). In stratified analysis, the CNV showed a significant effect [OR = 2.58 (1.06-6.27), p-value = 0.039] in those with high neuroticism but not in those with average or low neuroticism.

CONCLUSIONS

We identified a new common CNV likely involved in the etiology of SA. This finding sheds light on an important role of common CNVs in the pathophysiology of SA, suggesting a new promising avenue for investigating its genetic architecture.

A joint study of whole exome sequencing and structural MRI analysis in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability worldwide and influenced by both environmental and genetic factors. Genetic studies of MDD have focused on common variants and have been constrained by the heterogeneity of clinical symptoms.

METHODS

We sequenced the exome of 77 cases and 245 controls of Han Chinese ancestry and scanned their brain. Burden tests of rare variants were performed first to explore the association between genes/pathways and MDD. Secondly, parallel Independent Component Analysis was conducted to investigate genetic underpinnings of gray matter volume (GMV) changes of MDD.

RESULTS

Two genes (CSMD1, p = 5.32×10-6; CNTNAP5, p = 1.32×10-6) and one pathway (Neuroactive Ligand Receptor Interactive, p = 1.29×10-5) achieved significance in burden test. In addition, we identified one pair of imaging-genetic components of significant correlation (r = 0.38, p = 9.92×10-6). The imaging component reflected decreased GMV in cases and correlated with intelligence quotient (IQ). IQ mediated the effects of GMV on MDD. The genetic component enriched in two gene sets, namely Singling by G-protein coupled receptors [false discovery rate (FDR) q = 3.23×10-4) and Alzheimer Disease Up (FDR q = 6.12×10-4).

CONCLUSIONS

Both rare variants analysis and imaging-genetic analysis found evidence corresponding with the neuroinflammation and synaptic plasticity hypotheses of MDD. The mediation of IQ indicates that genetic component may act on MDD through GMV alteration and cognitive impairment.

Genome-wide association study of morbid obesity in Han Chinese

BACKGROUND

As obesity is becoming pandemic, morbid obesity (MO), an extreme type of obesity, is an emerging issue worldwide. It is imperative to understand the factors responsible for huge weight gain in certain populations in the modern society. Very few genome-wide association studies (GWAS) have been conducted on MO patients. This study is the first MO-GWAS study in the Han-Chinese population in Asia.

METHODS

We conducted a two-stage GWAS with 1110 MO bariatric patients (body mass index [BMI] ≥ 35 kg/m2) from Min-Sheng General Hospital, Taiwan. The first stage involved 575 patients, and 1729 sex- and age-matched controls from the Taiwan Han Chinese Cell and Genome Bank. In the second stage, another 535 patients from the same hospital were genotyped for 52 single nucleotide polymorphisms (SNPs) discovered in the first stage, and 9145 matched controls from Taiwan Biobank were matched for confirmation analysis.

RESULTS

The results of the joint analysis for the second stage revealed six top ranking SNPs, including rs8050136 (p-value = 7.80 × 10- 10), rs9939609 (p-value = 1.32 × 10- 9), rs1421085 (p-value = 1.54 × 10- 8), rs9941349 (p-value = 9.05 × 10- 8), rs1121980 (p-value = 7.27 × 10- 7), and rs9937354 (p-value = 6.65 × 10- 7), which were all located in FTO gene. Significant associations were also observed between MO and RBFOX1, RP11-638 L3.1, TMTC1, CBLN4, CSMD3, and ERBB4, respectively, using the Bonferroni correction criteria for 52 SNPs (p < 9.6 × 10- 4).

CONCLUSION

The most significantly associated locus of MO in the Han-Chinese population was the well-known FTO gene. These SNPs located in intron 1, may include the leptin receptor modulator. Other significant loci, showing weak associations with MO, also suggested the potential mechanism underlying the disorders with eating behaviors or brain/neural development.

MiR-137 Deficiency Causes Anxiety-Like Behaviors in Mice

Anxiety and depression are major public health concerns worldwide. Although genome-wide association studies have identified several genes robustly associated with susceptibility for these disorders, the molecular and cellular mechanisms associated with anxiety and depression is largely unknown. Reduction of microRNA-137 (miR-137) level has been implicated in the etiology of major depressive disorder. However, little is known about the in vivo impact of the loss of miR-137 on the biology of anxiety and depression. Here, we generated a forebrain-specific miR-137 knockout mouse line, and showed that miR-137 is critical for dendritic and synaptic growth in the forebrain. Mice with miR-137 loss-of-function exhibit anxiety-like behavior, and impaired spatial learning and memory. We then observe an elevated expression of EZH2 in the forebrain of miR-137 knockout mice, and provide direct evidence that knockdown of EZH2 can rescue anxious phenotypes associated with the loss of miR-137. Together our results suggest that loss of miR-137 contributes to the etiology of anxiety, and EZH2 might be a potential therapeutic target for anxiety and depressive phenotypes associated with the dysfunction of miR-137.

Integrative analyses of major histocompatibility complex loci in the genome-wide association studies of major depressive disorder

Recent European genome-wide association studies (GWAS) have revealed strong statistical correlations between MDD and numerous zero-to-high linked variants in the genomic region containing major histocompatibility complex (MHC) genes (MHC region), but the underlying biological mechanisms are still unclear. To better understand the roles of this genomic region in the neurobiology of MDD, we applied a convergent functional genomics approach to integrate GWAS data of MDD relevant biological phenotypes, gene-expression analyses results obtained from brain samples, and genetic analyses of independent Chinese MDD samples. We observed that independent MDD risk variants in the MHC region were also significantly associated with the relevant biological phenotypes in the predicted directions, including the emotional and cognitive-related phenotypes. Gene-expression analyses further revealed that mRNA expression levels of several MHC region genes in the human brain were associated with MDD risk SNPs and diagnostic status. For instance, a brain-enriched gene ZNF603P consistently showed lower mRNA levels in the individuals carrying MDD risk alleles and in MDD patients. Remarkably, we further found that independent MDD risk SNPs in the MHC region likely converged to affect the mRNA level(s) of the same gene(s), and Europeans and Han Chinese populations have a substantial shared genetic and molecular basis underlying MDD risk associations in the MHC region. These results highlighted several potential pivotal genes at the MHC region in the pathogenesis of MDD. Their common impacts on multiple psychiatric relevant phenotypes also implicated the neurological processes shared by different psychological processes, such as mood and/or cognition, shedding lights on their potential biological mechanisms.

The GWAS Risk Genes for Depression May Be Actively Involved in Alzheimer's Disease

Depression is one of the most frequent psychiatric symptoms observed in people during the development of Alzheimer's disease (AD). We hypothesized that genetic factors conferring risk of depression might affect AD development. In this study, we screened 31 genes, which were located in 19 risk loci for major depressive disorder (MDD) identified by two recent large genome-wide association studies (GWAS), in AD patients at the genomic and transcriptomic levels. Association analysis of common variants was performed by using summary statistics of the International Genomics of Alzheimer's Project (IGAP), and association analysis of rare variants was conducted by sequencing the entire coding region of the 31 MDD risk genes in 107 Han Chinese patients with early-onset and/or familial AD. We also quantified the mRNA expression alterations of these MDD risk genes in brain tissues of AD patients and AD mouse models, followed by protein-protein interaction network prediction to show their potential effects in AD pathways. We found that common and rare variants of L3MBTL2 were significantly associated with AD. mRNA expression levels of 18 MDD risk genes, in particular SORCS3 and OAT, were differentially expressed in AD brain tissues. 13 MDD risk genes were predicted to physically interact with core AD genes. The involvement of HACE1, NEGR1, and SLC6A15 in AD was supported by convergent lines of evidence. Taken together, our results showed that MDD risk genes might play an active role in AD pathology and supported the notion that depression might be the "common cold" of psychiatry.

Genome-wide Burden of Rare Short Deletions Is Enriched in Major Depressive Disorder in Four Cohorts

BACKGROUND

Major depressive disorder (MDD) is moderately heritable, with a high prevalence and a presumed high heterogeneity. Copy number variants (CNVs) could contribute to the heritable component of risk, but the two previous genome-wide association studies of rare CNVs did not report significant findings.

METHODS

In this meta-analysis of four cohorts (5780 patients and 6626 control subjects), we analyzed the association of MDD to 1) genome-wide burden of rare deletions and duplications, partitioned by length (<100 kb or >100 kb) and other characteristics, and 2) individual rare exonic CNVs and CNV regions.

RESULTS

Patients with MDD carried significantly more short deletions than control subjects (p = .0059) but not long deletions or short or long duplications. The confidence interval for long deletions overlapped with that for short deletions, but long deletions were 70% less frequent genome-wide, reducing the power to detect increased burden. The increased burden of short deletions was primarily in intergenic regions. Short deletions in cases were also modestly enriched for high-confidence enhancer regions. No individual CNV achieved thresholds for suggestive or significant association after genome-wide correction. p values < .01 were observed for 15q11.2 duplications (TUBGCP5, CYFIP1, NIPA1, and NIPA2), deletions in or near PRKN or MSR1, and exonic duplications of ATG5.

CONCLUSIONS

The increased burden of short deletions in patients with MDD suggests that rare CNVs increase the risk of MDD by disrupting regulatory regions. Results for longer deletions were less clear, but no large effects were observed for long multigenic CNVs (as seen in schizophrenia and autism). Further studies with larger sample sizes are warranted.

Differential effects on neurodevelopment of FTO variants in obesity and bipolar disorder suggested by in silico prediction of functional impact: An analysis in Mexican population

INTRODUCTION

Several studies indicate that polygenic obesity is linked to fat-mass and obesity-associated (FTO) genetic variants. Nevertheless, the link between variants in FTO and mental disorders has been barely explored. The present work aims to determine whether FTO genetic variants are associated with bipolar disorder and obesity, and to perform an in silico prediction of variant-dependent functional impact on the developing brain transcriptome.

METHODS

Four hundred and forty-six Mexican mestizos were included in a genetic association analysis. SNP-sequence kernel association test and linear mixed models were implemented for genetic association assessment. For functional impact prediction, we analyzed the mapping of regulatory elements, the modification of binding sites of transcription factors and the expression of transcription factors in the brain developing transcriptome, searching on different databases.

RESULTS

In the set-based analysis, we found different associated regions to BD (bipolar disorder) and obesity. The promoter flanking region of FTO intron 1 was associated with differential effects on BMI, while intron 2 of RPGRIP1L and FTO upstream regions were associated with BD. The prediction analysis showed that FTO BD-associated variants disturb binding sites of SP1 and SP2; obesity-associated variants, on the other hand, disturb binding sites of FOXP1, which are transcription factors highly expressed during prenatal development stages of the brain.

CONCLUSION

Our results suggest a possible effect of FTO variants on neurodevelopment in obesity and bipolar disorder, which gives new insights into the molecular mechanism underlying this association.

Systems Approach to Identify Common Genes and Pathways Associated with Response to Selective Serotonin Reuptake Inhibitors and Major Depression Risk

Despite numerous studies on major depressive disorder (MDD) susceptibility, the precise underlying molecular mechanism has not been elucidated which restricts the development of etiology-based disease-modifying drug. Major depressive disorder treatment is still symptomatic and is the leading cause of (~30%) failure of the current antidepressant therapy. Here we comprehended the probable genes and pathways commonly associated with antidepressant response and MDD. A systematic review was conducted, and candidate genes/pathways associated with antidepressant response and MDD were identified using an integrative genetics approach. Initially, single nucleotide polymorphisms (SNPs)/genes found to be significantly associated with antidepressant response were systematically reviewed and retrieved from the candidate studies and genome-wide association studies (GWAS). Also, significant variations concerning MDD susceptibility were extracted from GWAS only. We found 245 (Set A) and 800 (Set B) significantly associated genes with antidepressant response and MDD, respectively. Further, gene set enrichment analysis revealed the top five co-occurring molecular pathways (p ≤ 0.05) among the two sets of genes: Cushing syndrome, Axon guidance, cAMP signaling pathway, Insulin secretion, and Glutamatergic synapse, wherein all show a very close relation to synaptic plasticity. Integrative analyses of candidate gene and genome-wide association studies would enable us to investigate the putative targets for the development of disease etiology-based antidepressant that might be more promising than current ones.

Role of inflammation in depression relapse

Major depressive disorder (MDD) is a leading cause of disability worldwide. After the first episode, patients with remitted MDD have a 60% chance of experiencing a second episode. Consideration of therapy continuation should be viewed in terms of the balance between the adverse effects of medication and the need to prevent a possible relapse. Relapse during the early stages of MDD could be prevented more efficiently by conducting individual risk assessments and providing justification for continuing therapy. Our previous work established the neuroimaging markers of relapse by comparing patients with recurrent major depressive disorder (rMDD) in depressive and remitted states. However, it is not known which of these markers are trait markers that present before initial relapse and, consequently, predict disease course. Here, we first describe how inflammation can be translated to subtype-specific clinical features and suggest how this could be used to facilitate clinical diagnosis and treatment. Next, we address the central and peripheral functional state of the immune system in patients with MDD. In addition, we emphasize the important link between the number of depressive episodes and rMDD and use neuroimaging to propose a model for the latter. Last, we address how inflammation can affect brain circuits, providing a possible mechanism for rMDD. Our review suggests a link between inflammatory processes and brain region/circuits in rMDD.

Identification and characterization of the lncRNA signature associated with overall survival in patients with neuroblastoma

Neuroblastoma (NB) is a commonly occurring cancer among infants and young children. Recently, long non-coding RNAs (lncRNAs) have been using as prognostic biomarkers for therapeutics and interventions in various cancers. Considering the poor survival of NB, the lncRNA-based therapeutic strategies must be improved. This work proposes an overall survival time estimator called SVR-NB to identify the lncRNA signature that is associated with the overall survival of patients with NB. SVR-NB is an optimized support vector regression (SVR)-based method that uses an inheritable bi-objective combinatorial genetic algorithm for feature selection. The dataset of 231 NB patients that contains overall survival information and expression profiles of 783 lncRNAs was used to design and evaluate SVR-NB from the database of gene expression omnibus accession GSE62564. SVR-NB identified a signature of 35 lncRNAs and achieved a mean squared correlation coefficient of 0.85 and a mean absolute error of 0.56 year between the actual and estimated overall survival time using 10-fold cross-validation. Further, we ranked and characterized the 35 lncRNAs according to their contribution towards the estimation accuracy. Functional annotations and co-expression gene analysis of LOC440896, LINC00632, and IGF2-AS revealed the association of co-expressed genes in Kyoto Encyclopedia of Genes and Genomes pathways.

Network Analysis of Depression-Related Transcriptomic Profiles

Major depressive disorder is a common debilitating disorder that is associated with increased morbidity and mortality. However, the molecular mechanism underlying depression remains largely unknown. The current study investigated the association of depression with blood gene expression using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Depression was measured by the geriatric depression scale, and the blood gene expression was measured by the Affymetrix Human Genome U219 Array. Linear regression was used to test the association between gene expression and depression, and the model was adjusted for age and sex. A total of 671 participants were included in our study (mean age 75 ± 8 years, 43.2% women). We found three genes were associated with depression, including COL1A2 (P = 8.9 × 10^-8), RNF150 (P = 1.4 × 10^-7) and CTGF (P = 8.3 × 10^-7). An interaction network was built, and the pathway analysis indicated that many depression-related genes were involved in the neurotrophin signaling pathway (P = 2.1 × 10^-7). Future studies are necessary to validate our findings and further investigate potential mechanism of depression.

Transcription factor Sp4 is required for hyperalgesic state persistence

Understanding how painful hypersensitive states develop and persist beyond the initial hours to days is critically important in the effort to devise strategies to prevent and/or reverse chronic painful states. Changes in nociceptor transcription can alter the abundance of nociceptive signaling elements, resulting in longer-term change in nociceptor phenotype. As a result, sensitized nociceptive signaling can be further amplified and nocifensive behaviors sustained for weeks to months. Building on our previous finding that transcription factor Sp4 positively regulates the expression of the pain transducing channel TRPV1 in Dorsal Root Ganglion (DRG) neurons, we sought to determine if Sp4 serves a broader role in the development and persistence of hypersensitive states in mice. We observed that more than 90% of Sp4 staining DRG neurons were small to medium sized, primarily unmyelinated (NF200 neg) and the majority co-expressed nociceptor markers TRPV1 and/or isolectin B4 (IB4). Genetically modified mice (Sp4+/-) with a 50% reduction of Sp4 showed a reduction in DRG TRPV1 mRNA and neuronal responses to the TRPV1 agonist-capsaicin. Importantly, Sp4+/- mice failed to develop persistent inflammatory thermal hyperalgesia, showing a reversal to control values after 6 hours. Despite a reversal of inflammatory thermal hyperalgesia, there was no difference in CFA-induced hindpaw swelling between CFA Sp4+/- and CFA wild type mice. Similarly, Sp4+/- mice failed to develop persistent mechanical hypersensitivity to hind-paw injection of NGF. Although Sp4+/- mice developed hypersensitivity to traumatic nerve injury, Sp4+/- mice failed to develop persistent cold or mechanical hypersensitivity to the platinum-based chemotherapeutic agent oxaliplatin, a non-traumatic model of neuropathic pain. Overall, Sp4+/- mice displayed a remarkable ability to reverse the development of multiple models of persistent inflammatory and neuropathic hypersensitivity. This suggests that Sp4 functions as a critical control point for a network of genes that conspire in the persistence of painful hypersensitive states.