Low-frequency and rare variants may contribute to elucidate the genetics of major depressive disorder

Identification of a strong genetic risk factor for major depressive disorder in the human virome

The heritability of major depressive disorder (MDD) is reportedly 30-50%. However, the genetic basis of its heritability remains unknown. Within SITH-1, a risk factor for MDD in human herpesvirus 6B (HHV-6B), we discovered a gene polymorphism with a large odds ratio for an association with MDD. It was a sequence whose number of repeats was inversely correlated with SITH-1 expression. This number was significantly lower in MDD patients. Rates for 17 or fewer repeats of the sequence were 67.9% for MDD and 28.6% for normal controls, with an odds ratio of 5.28. For patients with 17 or less repeats, the rate for presence of another MDD patient in their families was 47.4%, whereas there were no MDD patients in the families of patients with more than 17 repeats. Since HHV-6B is transmitted primarily mother to child and within families and persists for life, this gene polymorphism could potentially influence heritability of MDD.

Cauchy combination methods for the detection of gene-environment interactions for rare variants related to quantitative phenotypes

The characterization of gene-environment interactions (GEIs) can provide detailed insights into the biological mechanisms underlying complex diseases. Despite recent interest in GEIs for rare variants, published GEI tests are underpowered for an extremely small proportion of causal rare variants in a gene or a region. By extending the aggregated Cauchy association test (ACAT), we propose three GEI tests to address this issue: a Cauchy combination GEI test with fixed main effects (CCGEI-F), a Cauchy combination GEI test with random main effects (CCGEI-R), and an omnibus Cauchy combination GEI test (CCGEI-O). ACAT was applied to combine p values of single-variant GEI analyses to obtain CCGEI-F and CCGEI-R and p values of multiple GEI tests were combined in CCGEI-O. Through numerical simulations, for small numbers of causal variants, CCGEI-F, CCGEI-R and CCGEI-O provided approximately 5% higher power than the existing GEI tests INT-FIX and INT-RAN; however, they had slightly higher power than the existing GEI test TOW-GE. For large numbers of causal variants, although CCGEI-F and CCGEI-R exhibited comparable or slightly lower power values than the competing tests, the results were still satisfactory. Among all simulation conditions evaluated, CCGEI-O provided significantly higher power than that of competing GEI tests. We further applied our GEI tests in genome-wide analyses of systolic blood pressure or diastolic blood pressure to detect gene-body mass index (BMI) interactions, using whole-exome sequencing data from UK Biobank. At a suggestive significance level of 1.0 × 10-4, KCNC4, GAR1, FAM120AOS and NT5C3B showed interactions with BMI by our GEI tests.

Disruptive mutations in the serotonin transporter associate serotonin dysfunction with treatment-resistant affective disorder

Affective or mood disorders are a leading cause of disability worldwide. The serotonergic system has been heavily implicated in the complex etiology and serves as a therapeutic target. The serotonin transporter (SERT) is a major regulator of serotonin neurotransmission, yet the disease-relevance of impaired SERT function remains unknown. Here, we present the first identification and functional characterization of disruptive coding SERT variants found in patients with psychiatric diseases. In a unique cohort of 144 patients characterized by treatment-resistant chronic affective disorders with a lifetime history of electroconvulsive therapy, we identified two previously uncharacterized coding SERT variants: SERT-N217S and SERT-A500T. Both variants were significantly enriched in the patient cohort compared to GnomAD (SERT-N217S: OR = 151, P = 0.0001 and SERT-A500T: OR = 1348, P = 0.0022) and ethnicity-matched healthy controls (SERT-N217S: OR ≥ 17.7, P ≤ 0.013 and SERT-A500T: OR = ∞, P = 0.029). Functional investigations revealed that the mutations exert distinct perturbations to SERT function, but their overall effects converge on a partial loss-of-function molecular phenotype. Thus, the SERT-A500T variant compromises the catalytic activity, while SERT-N217S disrupts proper glycosylation of SERT with a resulting dominant-negative trafficking deficiency. Moreover, we demonstrate that the trafficking deficiency of SERT-N217S is amenable to pharmacochaperoning by noribogaine. Collectively, our findings describe the first disease-associated loss-of-function SERT variants and implicate serotonergic disturbances arising from SERT dysfunction as a risk factor for chronic affective disorders.

Convergent selective signaling impairment exposes the pathogenicity of latrophilin-3 missense variants linked to inheritable ADHD susceptibility

Latrophilin-3 (Lphn3; also known as ADGRL3) is a member of the adhesion G Protein Coupled Receptor subfamily, which participates in the stabilization and maintenance of neuronal networks by mediating intercellular adhesion through heterophilic interactions with transmembrane ligands. Polymorphisms modifying the Lphn3 gene are associated with attention-deficit/hyperactivity disorder (ADHD) in children and its persistence into adulthood. How these genetic alterations affect receptor function remains unknown. Here, we conducted the functional validation of distinct ADHD-related Lphn3 variants bearing mutations in the receptor's adhesion motif-containing extracellular region. We found that all variants tested disrupted the ability of Lphn3 to stabilize intercellular adhesion in a manner that was distinct between ligands classes, but which did not depend on ligand-receptor interaction parameters, thus pointing to altered intrinsic receptor signaling properties. Using G protein signaling biosensors, we determined that Lphn3 couples to Gαi1, Gαi2, Gαs, Gαq, and Gα13. However, all ADHD-related receptor variants consistently lacked intrinsic as well as ligand-dependent Gα13 coupling efficiency while maintaining unaltered coupling to Gαi, Gαs, and Gαq. Consistent with these alterations, actin remodeling functions as well as actin-relevant RhoA signaling normally displayed by the constitutively active Lphn3 receptor were impeded by select receptor variants, thus supporting additional signaling defects. Taken together, our data point to Gα13 selective signaling impairments as representing a disease-relevant pathogenicity pathway that can be inherited through Lphn3 gene polymorphisms. This study highlights the intricate interplay between Lphn3 GPCR functions and the actin cytoskeleton in modulating neurodevelopmental cues related to ADHD etiology.

Kernel-based gene-environment interaction tests for rare variants with multiple quantitative phenotypes

Previous studies have suggested that gene-environment interactions (GEIs) between a common variant and an environmental factor can influence multiple correlated phenotypes simultaneously, that is, GEI pleiotropy, and that analyzing multiple phenotypes jointly is more powerful than analyzing phenotypes separately by using single-phenotype GEI tests. Methods to test the GEI for rare variants with multiple phenotypes are, however, lacking. In our work, we model the correlation among the GEI effects of a variant on multiple quantitative phenotypes through four kernels and propose four multiphenotype GEI tests for rare variants, which are a test with a homogeneous kernel (Hom-GEI), a test with a heterogeneous kernel (Het-GEI), a test with a projection phenotype kernel (PPK-GEI) and a test with a linear phenotype kernel (LPK-GEI). Through numerical simulations, we show that correlation among phenotypes can enhance the statistical power except for LPK-GEI, which simply combines statistics from single-phenotype GEI tests and ignores the phenotypic correlations. Among almost all considered scenarios, Het-GEI and PPK-GEI are more powerful than Hom-GEI and LPK-GEI. We apply Het-GEI and PPK-GEI in the genome-wide GEI analysis of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in the UK Biobank. We analyze 18,101 genes and find that LEUTX is associated with SBP and DBP (p = 2.20×10-6) through its interaction with hemoglobin. The single-phenotype GEI test and our multiphenotype GEI tests Het-GEI and PPK-GEI are also used to evaluate the gene-hemoglobin interactions for 22 genes that were previously reported to be associated with SBP or DBP in a meta-analysis of genetic main effects. MYO1C shows nominal significance (p < 0.05) by the Het-GEI test. NOS3 shows nominal significance in DBP and MYO1C in both SBP and DBP by the single-phenotype GEI test.

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.

Variance-component-based meta-analysis of gene-environment interactions for rare variants

Complex diseases are often caused by interplay between genetic and environmental factors. Existing gene-environment interaction (G × E) tests for rare variants largely focus on detecting gene-based G × E effects in a single study; thus, their statistical power is limited by the sample size of the study. Meta-analysis methods that synthesize summary statistics of G × E effects from multiple studies for rare variants are still limited. Based on variance component models, we propose four meta-analysis methods of testing G × E effects for rare variants: HOM-INT-FIX, HET-INT-FIX, HOM-INT-RAN, and HET-INT-RAN. Our methods consider homogeneous or heterogeneous G × E effects across studies and treat the main genetic effect as either fixed or random. Through simulations, we show that the empirical distributions of the four meta-statistics under the null hypothesis align with their expected theoretical distributions. When the interaction effect is homogeneous across studies, HOM-INT-FIX and HOM-INT-RAN have as much statistical power as a pooled analysis conducted on a single interaction test with individual-level data from all studies. When the interaction effect is heterogeneous across studies, HET-INT-FIX and HET-INT-RAN provide higher power than pooled analysis. Our methods are further validated via testing 12 candidate gene-age interactions in blood pressure traits using whole-exome sequencing data from UK Biobank.

Rare Functional Variants Associated with Antidepressant Remission in Mexican-Americans: Short title: Antidepressant remission and pharmacogenetics in Mexican-Americans

INTRODUCTION

Rare genetic functional variants can contribute to 30-40% of functional variability in genes relevant to drug action. Therefore, we investigated the role of rare functional variants in antidepressant response.

METHOD

Mexican-American individuals meeting the Diagnostic and Statistical Manual-IV criteria for major depressive disorder (MDD) participated in a prospective randomized, double-blind study with desipramine or fluoxetine. The rare variant analysis was performed using whole-exome genotyping data. Network and pathway analyses were carried out with the list of significant genes.

RESULTS

The Kernel-Based Adaptive Cluster method identified functional rare variants in 35 genes significantly associated with treatment remission (False discovery rate, FDR <0.01). Pathway analysis of these genes supports the involvement of the following gene ontology processes: olfactory/sensory transduction, regulation of response to cytokine stimulus, and meiotic cell cycleprocess.

LIMITATIONS

Our study did not have a placebo arm. We were not able to use antidepressant blood level as a covariate. Our study is based on a small sample size of only 65 Mexican-American individuals. Further studies using larger cohorts are warranted.

CONCLUSION

Our data identified several rare functional variants in antidepressant drug response in MDD patients. These have the potential to serve as genetic markers for predicting drug response.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00265291.

Targeted sequencing of the BDNF gene in young Chinese Han people with major depressive disorder

BACKGROUND

Adolescence and young adulthood are considered the peak age for the emergence of many psychiatric disorders, in particular major depressive disorder (MDD). Previous research has shown substantial heritability for MDD. In addition, the brain-derived neurotrophic factor (BDNF) gene is known to be associated with MDD. However, there has been no study conducting targeted sequencing of the BDNF gene in young MDD patients so far.

METHOD

To examine whether the BDNF gene is associated with the occurrence of MDD in young patients, we used targeted sequencing to detect the BDNF gene variants in 259 young Chinese Han people (105 MDD patients and 154 healthy subjects).

RESULTS

The BDNF variant rs4030470 was associated with MDD in young Chinese Han people (uncorrected p = 0.046), but this was no longer significant after applying FDR correction (p = 0.552, after FDR correction). We did not find any significant differences in genotype or haplotype frequencies between the case and control groups, and furthermore discovered no rare mutation variants any of the 259 subjects.

CONCLUSION

Our results do not support an association of the BDNF gene variants with MDD in young people in the Chinese Han population.

Heterogeneity and Polygenicity in Psychiatric Disorders: A Genome-Wide Perspective

Genome-wide association studies (GWAS) have been performed for many psychiatric disorders and revealed a complex polygenic architecture linking mental and physical health phenotypes. Psychiatric diagnoses are often heterogeneous, and several layers of trait heterogeneity may contribute to detection of genetic risks per disorder or across multiple disorders. In this review, we discuss these heterogeneities and their consequences on the discovery of risk loci using large-scale genetic data. We primarily highlight the ways in which sex and diagnostic complexity contribute to risk locus discovery in schizophrenia, bipolar disorder, attention deficit hyperactivity disorder, autism spectrum disorder, posttraumatic stress disorder, major depressive disorder, obsessive-compulsive disorder, Tourette’s syndrome and chronic tic disorder, anxiety disorders, suicidality, feeding and eating disorders, and substance use disorders. Genetic data also have facilitated discovery of clinically relevant subphenotypes also described here. Collectively, GWAS of psychiatric disorders revealed that the understanding of heterogeneity, polygenicity, and pleiotropy is critical to translate genetic findings into treatment strategies.

Imputation of 3 million SNPs in the Arabidopsis regional mapping population

Natural variation has become a prime resource to identify genetic variants that contribute to phenotypic variation. The regional mapping (RegMap) population is one of the most important populations for studying natural variation in Arabidopsis thaliana, and has been used in a large number of association studies and in studies on climatic adaptation. However, only 413 RegMap accessions have been completely sequenced, as part of the 1001 Genomes (1001G) Project, while the remaining 894 accessions have only been genotyped with the Affymetrix 250k chip. As a consequence, most association studies involving the RegMap are either restricted to the sequenced accessions, reducing power, or rely on a limited set of SNPs. Here we impute millions of SNPs to the 894 accessions that are exclusive to the RegMap, using the 1135 accessions of the 1001G Project as the reference panel. We assess imputation accuracy using a novel cross-validation scheme, which we show provides a more reliable measure of accuracy than existing methods. After filtering out low accuracy SNPs, we obtain high-quality genotypic information for 2029 accessions and 3 million markers. To illustrate the benefits of these imputed data, we reconducted genome-wide association studies on five stress-related traits and could identify novel candidate genes.

A unified method for rare variant analysis of gene-environment interactions

Advanced technology in whole-genome sequencing has offered the opportunity to comprehensively investigate the genetic contribution, particularly rare variants, to complex traits. Several region-based tests have been developed to jointly model the marginal effect of rare variants, but methods to detect gene-environment (GE) interactions are underdeveloped. Identifying the modification effects of environmental factors on genetic risk poses a considerable challenge. To tackle this challenge, we develop a method to detect GE interactions for rare variants using generalized linear mixed effect model. The proposed method can accommodate either binary or continuous traits in related or unrelated samples. Under this model, genetic main effects, GE interactions, and sample relatedness are modeled as random effects. We adopt a kernel-based method to leverage the joint information across rare variants and implement variance component score tests to reduce the computational burden. Our simulation studies of continuous and binary traits show that the proposed method maintains correct type I error rates and appropriate power under various scenarios, such as genotype main effects and GE interaction effects in opposite directions and varying the proportion of causal variants in the model. We apply our method in the Framingham Heart Study to test GE interaction of smoking on body mass index or overweight status and replicate the Cholinergic Receptor Nicotinic Beta 4 gene association reported in previous large consortium meta-analysis of single nucleotide polymorphism-smoking interaction. Our proposed set-based GE test is computationally efficient and is applicable to both binary and continuous phenotypes, while appropriately accounting for familial or cryptic relatedness.

Seeking for potential pathogenic genes of major depressive disorder in the Gene Expression Omnibus database

INTRODUCTION

Major depressive disorder (MDD) is one of the most common mental disorders worldwide. The aim of this study was to identify potential pathological genes in MDD.

METHODS

We searched and downloaded gene expression data from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) in MDD. Then, Kyoto Encyclopedia of Genes and Genomes pathway, Gene Ontology analysis, and protein-protein interaction (PPI) network were applied to investigate the biological function of identified DEGs. The quantitative real-time polymerase chain reaction and a published dataset were used to validate the result of bioinformatics analysis.

RESULTS

A total of 514 DEGs were identified in MDD. In the PPI network, some hub genes with high degrees were identified, such as EEF2, RPL26L1, RPLP0, PRPF8, LSM3, DHX9, RSRC1, and AP2B1. The result of in vitro validation of RPL26L1, RSRC1, TOMM20L, RPLPO, PRPF8, AP2B1, STIP1, and C5orf45 was consistent with the bioinformatics analysis. Electronic validation of C5orf45, STIP1, PRPF8, AP2B1, and SLC35E1 was consistent with the bioinformatics analysis.

DISCUSSION

The deregulated genes could be used as potential pathological factors of MDD. In addition, EEF2, RPL26L1, RPLP0, PRPF8, LSM3, DHX9, RSRC1, and AP2B1 might be therapeutic targets for MDD.

Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression

PURPOSE

Despite advances in characterizing the neurobiology of emotional disorders, there is still a significant lack of scientific understanding of the pathophysiological mechanisms governing major depressive disorder (MDD). This study attempted to elucidate the molecular circuitry of MDD and to identify more potential genes associated with the pathogenesis of the disease.

PATIENTS AND METHODS

Microarray data from the GSE98793 dataset were downloaded from the NCBI Gene Expression Omnibus (GEO) database, including 128 patients with MDD and 64 healthy controls. Weighted gene coexpression network analysis (WGCNA) was performed to find modules of differentially expressed genes (DEGs) with high correlations followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to obtain further biological insight into the top three key modules. The protein-protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed, as well.

RESULTS

We filtered 3276 genes that were considered significant DEGs for further WGCNA analysis. By performing WGCNA, we found that the turquoise, blue and brown functional modules were all strongly correlated with MDD development, including immune response, neutrophil degranulation, ribosome biogenesis, T cell activation, glycosaminoglycan biosynthetic process, and protein serine/threonine kinase activator activity. Hub genes were identified in the key functional modules that might have a role in the progression of MDD. Functional annotation showed that these modules primarily enriched such KEGG pathways as the TNF signaling pathway, T cell receptor signaling pathway, primary immunodeficiency, Th1, Th2 and Th17 cell differentiation, autophagy and RNA degradation and oxidative phosphorylation. These results suggest that these genes are closely related to autophagy and cellular immune function.

CONCLUSION

The results of this study may help to elucidate the pathophysiology of MDD development at the molecular level and explore the potential molecular mechanisms for new interventional strategies.

Genetic Advance in Depressive Disorder

Major depressive disorder (MDD) and bipolar disorder (BPD) are both chronic, severe mood disorder with high misdiagnosis rate, leading to substantial health and economic burdens to patients around the world. There is a high misdiagnosis rate of bipolar depression (BD) just based on symptomology in depressed patients whose previous manic or mixed episodes have not been well recognized. Therefore, it is important for psychiatrists to identify these two major psychiatric disorders. Recently, with the accumulation of clinical sample sizes and the advances of methodology and technology, certain progress in the genetics of major depression and bipolar disorder has been made. This article reviews the candidate genes for MDD and BD, genetic variation loci, chromosome structural variation, new technologies, and new methods.

Rare variants in SLC6A4 cause susceptibility to major depressive disorder with suicidal ideation in Han Chinese adolescents and young adults

BACKGROUND

Suicidal ideation (SI) is the most serious symptom of major depressive disorder (MDD) and considered an extreme state. The serotonin transporter gene (SLC6A4) plays a significant role in MDD and suicide pathophysiology. Previous studies have revealed an association between common variants of SLC6A4 with the risk of MDD and suicide. However, very few studies have so far focused on the degree to which rare variants of SLC6A4 are responsible for the depression observed in adolescent and young adult suicide patients. The aim of this study was to examine the impact of common and rare variants of SLC6A4 on the risk of Han Chinese adolescents and young adults suffering MDD with SI.

METHODS

Targeted sequencing of the SLC6A4 gene was conducted using FastTarget technology in Han Chinese adolescents and young adults, of which 74 were MDD patients with SI and 150 were healthy controls. Gene-based association analyses of rare variants were performed using enrichment analysis and a cumulative allele test. An allele association study was performed against common variants.

RESULTS

After sequencing and bioinformatics analysis, a total of 15 single nucleotide variants (SNVs) were detected in the targeted regions from all participants, including 9 common and 6 rare variants. Among these, 5 rare variants were identified within the study group. Enrichment analysis of rare variants demonstrated a statistical difference (p = 0.042) between the study and control groups. Using cumulative allele analysis, alternative alleles in the SLC6A4 gene exhibited an association with MDD patients with SI (cumulative allele: OR = 10.18, 95% CI = 1.18-87.32, p = 0.017). No significant association was found between the 9 common SLC6A4 variants and MDD patients with SI.

CONCLUSIONS

Our results suggest that rare variants of SLC6A4 may contribute to a genetic risk of adolescents and young adults suffering MDD with SI.

A single-nucleotide polymorphism influences brain morphology in drug-naïve patients with major depressive disorder

OBJECTIVE

Recently, a genome-wide association study successfully identified genetic variants associated with major depressive disorder (MDD). The study identified 17 independent single-nucleotide polymorphisms (SNPs) significantly associated with diagnosis of MDD. These SNPs were predicted to be enriched in genes that are expressed in the central nervous system and function in transcriptional regulation associated with neurodevelopment. The study aimed to investigate associations between 17 SNPs and brain morphometry using magnetic resonance imaging (MRI) in drug-naïve patients with MDD and healthy controls (HCs).

METHODS

Forty-seven patients with MDD and 42 HCs were included. All participants underwent T1-weighted structural MRI and genotyping. The genotype-diagnosis interactions associated with regional cortical thicknesses were evaluated using voxel-based morphometry for the 17 SNPs.

RESULTS

Regarding rs301806, an SNP in the RERE genomic regions, we found a significant difference in a genotype effect in the right-lateral orbitofrontal and postcentral lobes between diagnosis groups. After testing every possible diagnostic comparison, the genotype-diagnosis interaction in these areas revealed that the cortical thickness reductions in the MDD group relative to those in the HC group were significantly larger in T/T individuals than in C-carrier ones. For the other SNPs, no brain area was noted where a genotype effect significantly differed between the two groups.

CONCLUSIONS

We found that a RERE gene SNP was associated with cortical thickness reductions in the right-lateral orbitofrontal and postcentral lobes in drug-naïve patients with MDD. The effects of RERE gene polymorphism and gene-environment interactions may exist in brain structures of patients with MDD.

Genetic effects on white matter integrity in drug-naive patients with major depressive disorder: a diffusion tensor imaging study of 17 genetic loci associated with depressive symptoms

BACKGROUND

A genome-wide association study using megadata identified 17 single-nucleotide polymorphisms (SNPs) in candidate genes for major depressive disorder (MDD). These MDD susceptibility polymorphisms may affect white matter (WM) integrity. This study aimed to investigate the relationship between WM alterations and 17 SNPs in candidate genes for MDD in the first depressive episode of drug-naive MDD patients using a tract-based spatial statistics (TBSS) method.

METHODS

Thirty-five drug-naive MDD patients with a first depressive episode and 47 age-and sex-matched healthy subjects underwent diffusion tensor imaging scans and genotyping. The genotype-diagnosis interactions related to WM integrity were evaluated using TBSS for the 17 SNPs.

RESULTS

For the anterior thalamic radiation, cingulum, corticospinal tract, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus, uncinate fasciculus, forceps major, and forceps minor, the genotype effect significantly differed between diagnosis groups (P<0.05, family-wise error corrected) in only one SNP, rs301806, in the arginine-glutamic acid dipeptide (RE) repeats (RERE) gene.

CONCLUSION

The RERE polymorphism was associated with WM alterations in first-episode and drug-naive MDD patients, which may be at least partially related to the manifestation of MDD. Future studies are needed to explore the gene-environment interactions with regard to individual WM integrity.

iMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes

BACKGROUND

A range of rare and common genetic variants have been discovered to be potentially associated with mental diseases, but many more have not been uncovered. Powerful integrative methods are needed to systematically prioritize both variants and genes that confer susceptibility to mental diseases in personal genomes of individual patients and to facilitate the development of personalized treatment or therapeutic approaches.

METHODS

Leveraging deep neural network on the TensorFlow framework, we developed a computational tool, integrated Mental-disorder GEnome Score (iMEGES), for analyzing whole genome/exome sequencing data on personal genomes. iMEGES takes as input genetic mutations and phenotypic information from a patient with mental disorders, and outputs the rank of whole genome susceptibility variants and the prioritized disease-specific genes for mental disorders by integrating contributions from coding and non-coding variants, structural variants (SVs), known brain expression quantitative trait loci (eQTLs), and epigenetic information from PsychENCODE.

RESULTS

iMEGES was evaluated on multiple datasets of mental disorders, and it achieved improved performance than competing approaches when large training dataset is available.

CONCLUSION

iMEGES can be used in population studies to help the prioritization of novel genes or variants that might be associated with the susceptibility to mental disorders, and also on individual patients to help the identification of genes or variants related to mental diseases.

Genetic and Epigenetic Alterations Underlie Oligodendroglia Susceptibility and White Matter Etiology in Psychiatric Disorders

Numerous genetic risk loci are found to associate with major neuropsychiatric disorders represented by schizophrenia. The pathogenic roles of genetic risk loci in psychiatric diseases are further complicated by the association with cell lineage- and/or developmental stage-specific epigenetic alterations. Besides aberrant assembly and malfunction of neuronal circuitry, an increasing volume of discoveries clearly demonstrate impairment of oligodendroglia and disruption of white matter integrity in psychiatric diseases. Nonetheless, whether and how genetic risk factors and epigenetic dysregulations for neuronal susceptibility may affect oligodendroglia is largely unknown. In this mini-review, we will discuss emerging evidence regarding the functional interplay between genetic risk loci and epigenetic factors, which may underlie compromised oligodendroglia and myelin development in neuropsychiatric disorders. Transcriptional and epigenetic factors are the major aspects affected in oligodendroglia. Moreover, multiple disease susceptibility genes are connected by epigenetically modulated transcriptional and post-transcriptional mechanisms. Oligodendroglia specific complex molecular orchestra may explain how distinct risk factors lead to the common clinical expression of white matter pathology of neuropsychiatric disorders.