A Novel Relationship for Schizophrenia, Bipolar, and Major Depressive Disorder. Part 8: a Hint from Chromosome 8 High Density Association Screen

Whole Genome Sequencing Revealed Inherited Rare Oligogenic Variants Contributing to Schizophrenia and Major Depressive Disorder in Two Families

Schizophrenia and affective disorder are two major complex mental disorders with high heritability. Evidence shows that rare variants with significant clinical impacts contribute to the genetic liability of these two disorders. Also, rare variants associated with schizophrenia and affective disorders are highly personalized; each patient may carry different variants. We used whole genome sequencing analysis to study the genetic basis of two families with schizophrenia and major depressive disorder. We did not detect de novo, autosomal dominant, or recessive pathogenic or likely pathogenic variants associated with psychiatric disorders in these two families. Nevertheless, we identified multiple rare inherited variants with unknown significance in the probands. In family 1, with singleton schizophrenia, we detected four rare variants in genes implicated in schizophrenia, including p.Arg1627Trp of LAMA2, p.Pro1338Ser of CSMD1, p.Arg691Gly of TLR4, and Arg182X of AGTR2. The p.Arg691Gly of TLR4 was inherited from the father, while the other three were inherited from the mother. In family 2, with two affected sisters diagnosed with major depressive disorder, we detected three rare variants shared by the two sisters in three genes implicated in affective disorders, including p.Ala4551Gly of FAT1, p.Val231Leu of HOMER3, and p.Ile185Met of GPM6B. These three rare variants were assumed to be inherited from their parents. Prompted by these findings, we suggest that these rare inherited variants may interact with each other and lead to psychiatric conditions in these two families. Our observations support the conclusion that inherited rare variants may contribute to the heritability of psychiatric disorders.

Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder

Depression (major depressive disorder, MDD) is a common and serious medical illness. Globally, it is estimated that 5% of adults suffer from depression. Recently, imaging genetics receives growing attention and become a powerful strategy for discoverying the associations between genetic variants (e.g., single-nucleotide polymorphisms, SNPs) and multi-modality brain imaging data. However, most of the existing MDD imaging genetic research studies conducted by clinicians usually utilize simple statistical analysis methods and only consider single-modality brain imaging, which are limited in the deeper discovery of the mechanistic understanding of MDD. It is therefore imperative to utilize a powerful and efficient technology to fully explore associations between genetic variants and multi-modality brain imaging. In this study, we developed a novel imaging genetic association framework to mine the multi-modality phenotype network between genetic risk variants and multi-stage diagnosis status. Specifically, the multi-modality phenotype network consists of voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI). Thereafter, an association model based on multi-task learning strategy was adopted to fully explore the relationship between the MDD risk SNP and the multi-modality phenotype network. The multi-stage diagnosis status was introduced to further mine the relation among the multiple modalities of different subjects. A multi-modality brain imaging data and genotype data were collected by us from two hospitals. The experimental results not only demonstrate the effectiveness of our proposed method but also identify some consistent and stable brain regions of interest (ROIs) biomarkers from the node and edge features of multi-modality phenotype network. Moreover, four new and potential risk SNPs associated with MDD were discovered.

Identifying pleiotropic genes for major psychiatric disorders with GWAS summary statistics using multivariate adaptive association tests

BACKGROUND

Genome wide association studies (GWAS) have discovered a few of single nucleotide polymorphisms (SNPs) related to major psychiatric disorders. However, it is not completely clear which genes play a pleiotropic role in multiple disorders. The study aimed to identify the pleiotropic genes across five psychiatric disorders using multivariate adaptive association tests.

METHODS

Summary statistics of five psychiatric disorders were downloaded from Psychiatric Genomics Consortium. We applied linkage disequilibrium score regression (LDSC) to estimate genetic correlation and conducted tissue and cell type specificity analyses based on Multi-marker Analysis of GenoMic Annotation (MAGMA). Then, we identified the pleiotropic genes using MTaSPUsSet and aSPUs tests. We ultimately performed the functional analysis for pleiotropic genes.

RESULTS

We confirmed the significant genetic correlation and brain tissue and neuron specificity among five disorders. 100 pleiotropic genes were detected to be significantly associated with five psychiatric disorders, of which 55 were novel genes. These genes were functionally enriched in neuron differentiation and synaptic transmission.

LIMITATIONS

The effect direction of pleiotropic genes couldn't be distinguished due to without individual-level data.

CONCLUSION

We identified pleiotropic genes using multivariate adaptive association tests and explored their biological function. The findings may provide novel insight into the development and implementation of prevention and treatment as well as targeted drug discovery in practice.

Development and Validation of a Risk Prediction Model for Breast Cancer Prognosis Based on Depression-Related Genes

Background

Depression plays a significant role in mediating breast cancer recurrence and metastasis. However, a precise risk model is lacking to evaluate the potential impact of depression on breast cancer prognosis. In this study, we established a depression-related gene (DRG) signature that can predict overall survival (OS) and elucidate its correlation with pathological parameters and sensitivity to therapy in breast cancer.

Methods

The model training and validation assays were based on the analyses of 1,096 patients from The Cancer Genome Atlas (TCGA) database and 2,969 patients from GSE96058. A risk signature was established through univariate and multivariate Cox regression analyses.

Results

Ten DRGs were determined to construct the risk signature. Multivariate analysis revealed that the signature was an independent prognostic factor for OS. Receiver operating characteristic (ROC) curves indicated good performance of the model in predicting 1-, 3-, and 5-year OS, particularly for patients with triple-negative breast cancer (TNBC). In the high-risk group, the proportion of immunosuppressive cells, including M0 macrophages, M2 macrophages, and neutrophils, was higher than that in the low-risk group. Furthermore, low-risk patients responded better to chemotherapy and endocrine therapy. Finally, a nomogram integrating risk score, age, tumor-node-metastasis (TNM) stage, and molecular subtypes were established, and it showed good agreement between the predicted and observed OS.

Conclusion

The 10-gene risk model not only highlights the significance of depression in breast cancer prognosis but also provides a novel gene-testing tool to better prevent the potential adverse impact of depression on breast cancer prognosis.

A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

BACKGROUND

Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear.

METHODS

We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders.

RESULTS

We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders.

CONCLUSIONS

Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

Co-sparse reduced-rank regression for association analysis between imaging phenotypes and genetic variants

MOTIVATION

The association analysis between genetic variants and imaging phenotypes must be carried out to understand the inherited neuropsychiatric disorders via imaging genetic studies. Given the high dimensionality in imaging and genetic data, traditional methods based on massive univariate regression entail large computational cost and disregard many-to-many correlations between phenotypes and genetic variants. Several multivariate imaging genetic methods have been proposed to alleviate the above problems. However, most of these methods are based on the l1 penalty, which might cause the over-selection of variables and thus mislead scientists in analyzing data from the field of neuroimaging genetics.

RESULTS

To address these challenges in both statistics and computation, we propose a novel co-sparse reduced-rank regression model that identifies complex correlations in a dimensional reduction manner. We developed an iterative algorithm based on a group primal dual-active set formulation to detect simultaneously important genetic variants and imaging phenotypes efficiently and precisely via non-convex penalty. The simulation studies showed that our method achieved accurate and stable performance in parameter estimation and variable selection. In real application, the proposed approach successfully detected several novel Alzheimer's disease-related genetic variants and regions of interest, which indicate that our method may be a valuable statistical toolbox for imaging genetic studies.

AVAILABILITY AND IMPLEMENTATION

The R package csrrr, and the code for experiments in this article is available in Github: https://github.com/hailongba/csrrr.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Severe Pain in Chronic Pancreatitis Patients: Considering Mental Health and Associated Genetic Factors

Pain is the most distressing and disruptive feature of recurrent acute pancreatitis (RAP) and chronic pancreatitis (CP) resulting in low quality of life (QOL) and disabilities. There is no single, characteristic pain pattern in patients with RAP and CP. Abdominal imaging features of CP accurately reflect morphologic features but they do not correlate with pain. Pain is the major driver of poor quality of life (QOL) and it is the constant pain, rather than intermittent pain that drives poor QOL. Furthermore, the most severe constant pain experience in CP is also a complex condition. The ability to target the etiopathogenesis of severe pain requires new methods to detect the exact pain mechanisms in an individual at cellular, tissue, system and psychiatric levels. In patients with complex and severe disease, it is likely that multiple overlapping mechanisms are simultaneously driving pain, anxiety and depression. Quantitative sensory testing (QST) shows promise in detecting alterations in central processing of pain signals and to classify patients for mechanistic and therapeutic studies. New genetic research suggests that genetic loci for severe pain in CP overlap with genetic loci for depression and other psychiatric disorders, providing additional insights and therapeutic targets for individual patients with severe CP pain. Well-designed clinical trials that integrate clinical features, QST, genetics and psychological assessments with targeted treatment and assessment of responses are required for a quantum leap forward. A better understanding of the context and mechanisms contributing to severe pain experiences in individual patients is predicted to lead to better therapies and quality of life.

NRG1, PIP4K2A, and HTR2C as Potential Candidate Biomarker Genes for Several Clinical Subphenotypes of Depression and Bipolar Disorder

GSK3B, BDNF, NGF, NRG1, HTR2C, and PIP4K2A play important roles in molecular mechanisms of psychiatric disorders. GSK3B occupies a central position in these molecular mechanisms and is also modulated by psychotropic drugs. BDNF regulates a number of key aspects in neurodevelopment and synaptic plasticity. NGF exerts a trophic action and is implicated in cerebral alterations associated with psychiatric disorders. NRG1 is active in neural development, synaptic plasticity, and neurotransmission. HTR2C is another important psychopharmacological target. PIP4K2A catalyzes the phosphorylation of PI5P to form PIP2, the latter being implicated in various aspects of neuronal signal transduction. In the present study, the six genes were sequenced in a cohort of 19 patients with bipolar affective disorder, 41 patients with recurrent depressive disorder, and 55 patients with depressive episode. The study revealed a number of genetic variants associated with antidepressant treatment response, time to recurrence of episodes, and depression severity. Namely, alleles of rs35641374 and rs10508649 (NRG1 and PIP4K2A) may be prognostic biomarkers of time to recurrence of depressive and manic/mixed episodes among patients with bipolar affective disorder. Alleles of NC_000008.11:g.32614509_32614510del, rs61731109, and rs10508649 (also NRG1 and PIP4K2A) seem to be predictive biomarkers of response to pharmacological antidepressant treatment on the 28th day assessed by the HDRS-17 or CGI-I scale. In particular, the allele G of rs10508649 (PIP4K2A) may increase resistance to antidepressant treatment and be at the same time protective against recurrent manic/mixed episodes. These results support previous data indicating a biological link between resistance to antidepressant treatment and mania. Bioinformatic functional annotation of associated variants revealed possible impact for transcriptional regulation of PIP4K2A. In addition, the allele A of rs2248440 (HTR2C) may be a prognostic biomarker of depression severity. This allele decreases expression of the neighboring immune system gene IL13RA2 in the putamen according to the GTEx portal. The variant rs2248440 is near rs6318 (previously associated with depression and effects of psychotropic drugs) that is an eQTL for the same gene and tissue. Finally, the study points to several protein interactions relevant in the pathogenesis of mood disorders. Functional studies using cellular or animal models are warranted to support these results.

Molecular linkage between post-traumatic stress disorder and cognitive impairment: a targeted proteomics study of World Trade Center responders

Existing work on proteomics has found common biomarkers that are altered in individuals with post-traumatic stress disorder (PTSD) and mild cognitive impairment (MCI). The current study expands our understanding of these biomarkers by profiling 276 plasma proteins with known involvement in neurobiological processes using the Olink Proseek Multiplex Platform in individuals with both PTSD and MCI compared to either disorder alone and with unaffected controls. Participants were World Trade Center (WTC) responders recruited through the Stony Brook WTC Health Program. PTSD and MCI were measured with the PTSD Checklist (PCL) and the Montreal Cognitive Assessment, respectively. Compared with unaffected controls, we identified 16 proteins associated with comorbid PTSD-MCI at P < 0.05 (six at FDR < 0.1), 20 proteins associated with PTSD only (two at FDR < 0.1), and 24 proteins associated with MCI only (one at FDR < 0.1), for a total of 50 proteins. The multiprotein composite score achieved AUCs of 0.84, 0.77, and 0.83 for PTSD-MCI, PTSD only, and MCI only versus unaffected controls, respectively. To our knowledge, the current study is the largest to profile a large set of proteins involved in neurobiological processes. The significant associations across the three case-group analyses suggest that shared biological mechanisms may be involved in the two disorders. If findings from the multiprotein composite score are replicated in independent samples, it has the potential to add a new tool to help classify both PTSD and MCI.

A logical relationship for schizophrenia, bipolar, and major depressive disorder. Part 1: Evidence from chromosome 1 high density association screen

Familial clustering of schizophrenia (SCZ), bipolar disorder (BPD), and major depressive disorder (MDD) was investigated systematically (Aukes et al., Genetics in Medicine, 2012, 14, 338-341) and any two or even three of these disorders could coexist in some families. Furthermore, evidence from symptomatology and psychopharmacology also imply the existence of intrinsic connections between these three major psychiatric disorders. A total of 71,445 SNPs on chromosome 1 were genotyped on 119 SCZ, 253 BPD (type-I), 177 MDD cases and 1000 controls and further validated in 986 SCZ patients in the population of Shandong province of China. Outstanding psychosis genes are systematically revealed( ATP1A4, ELTD1, FAM5C, HHAT, KIF26B, LMX1A, NEGR1, NFIA, NR5A2, NTNG1, PAPPA2, PDE4B, PEX14, RYR2, SYT6, TGFBR3, TTLL7, and USH2A). Unexpectedly, flanking genes for up to 97.09% of the associated SNPs were also replicated in an enlarged cohort of 986 SCZ patients. From the perspective of etiological rather than clinical psychiatry, bipolar, and major depressive disorder could be subtypes of schizophrenia. Meanwhile, the varied clinical feature and prognosis might be the result of interaction of genetics and epigenetics, for example, irreversible or reversible shut down, and over or insufficient expression of certain genes, which may gives other aspects of these severe mental disorders.

High peroxidasin-like expression is a potential and independent prognostic biomarker in breast cancer

Breast cancer is a frequent female malignant tumor with high mortality and poor prognosis. Peroxidasin like (PXDNL) has many biological functions, including characteristic activity of hormone biosynthesis, host defense, and cell motility. In addition, PXDNL is closely connected with the progression of breast cancer. In this study, we found that PXDNL may be an independent prognostic biomarker of breast cancer.We tested the mRNA expression of PXDNL in breast cancer by detecting The Cancer Genome Atlas (TCGA) database. The chi-squared test was used to evaluate clinical correlation. The receiver operating characteristic (ROC) curves were drawn to evaluate diagnosis potential in breast cancer. Subsequently, survival analyses were performed to identify the relevance between the expression of PXDNL and the overall survival/relapse-free survival of patients with breast cancer. Univariate/multivariate Cox regression model was executed to detect risk factors affecting the prognosis of patients with breast cancer.PXDNL is highly expressed in breast cancer tissues and is related to survival status of patients. The ROC curve showed that PXDNL had beneficial diagnostic ability in breast cancer. Survival analysis indicated that patients with breast cancer with high PXDNL expression generally had decreased overall survival/relapse-free survival. Univariate/multivariate Cox model analyses further suggested an association between PXDNL expression and prognosis of patients with breast cancer.High PXDNL expression is a potential and independent prognostic biomarker in breast cancer.

Linking Cell Polarity to Cortical Development and Malformations

Cell polarity refers to the asymmetric distribution of signaling molecules, cellular organelles, and cytoskeleton in a cell. Neural progenitors and neurons are highly polarized cells in which the cell membrane and cytoplasmic components are compartmentalized into distinct functional domains in response to internal and external cues that coordinate polarity and behavior during development and disease. In neural progenitor cells, polarity has a prominent impact on cell shape and coordinate several processes such as adhesion, division, and fate determination. Polarity also accompanies a neuron from the beginning until the end of its life. It is essential for development and later functionality of neuronal circuitries. During development, polarity governs transitions between multipolar and bipolar during migration of postmitotic neurons, and directs the specification and directional growth of axons. Once reaching final positions in cortical layers, neurons form dendrites which become compartmentalized to ensure proper establishment of neuronal connections and signaling. Changes in neuronal polarity induce signaling cascades that regulate cytoskeletal changes, as well as mRNA, protein, and vesicle trafficking, required for synapses to form and function. Hence, defects in establishing and maintaining cell polarity are associated with several neural disorders such as microcephaly, lissencephaly, schizophrenia, autism, and epilepsy. In this review we summarize the role of polarity genes in cortical development and emphasize the relationship between polarity dysfunctions and cortical malformations.

Aspects of Additional Psychiatric Disorders in Severe Depression/Melancholia: A Comparison between Suicides and Controls and General Pattern

OBJECTIVE

Additional and comorbid diagnoses are common among suicide victims with major depressive disorder (MDD) and have been shown to increase the suicide risk. The aim of the present study was first, to investigate whether patients with severe depression/melancholia who had died by suicide showed more additional psychiatric disorders than a matched control group. Second, general rates of comorbid and additional diagnoses in the total group of patients were estimated and compared with literature on MDD.

METHOD

A blind record evaluation was performed on 100 suicide victims with severe depression/melancholia (MDD with melancholic and/or psychotic features: MDD-M/P) and matched controls admitted to the Department of Psychiatry, Lund, Sweden between 1956 and 1969 and monitored to 2010. Diagnoses in addition to severe depression were noted.

RESULTS

Less than half of both the suicides and controls had just one psychiatric disorder (47% in the suicide and 46% in the control group). The average number of diagnoses was 1.80 and 1.82, respectively. Additional diagnoses were not related to an increased suicide risk. Anxiety was the most common diagnosis. Occurrence of suspected schizophrenia/schizotypal or additional obsessive-compulsive symptoms were more common than expected, but alcohol use disorders did not appear very frequent.

CONCLUSIONS

The known increased risk of suicide in MDD with comorbid/additional diagnoses does not seem to apply to persons with MDD-M/P (major depressive disorder-depression/Melancholia). Some diagnoses, such as schizophrenia/schizotypal disorders, were more frequent than expected, which is discussed, and a genetic overlap with MDD-M/P is proposed.

Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables

Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline.