Altered HCAR3 expression may underlying the blunted niacin responses of the psychiatric disorders and the risk of schizophrenia

AIM

Blunted niacin response (BNR) was an endophenotype of schizophrenia, but the underlying mechanism remains unclarified. The objective of this study was to verify whether genes associated with BNR pathway constitute the genetic basis and the pathological mechanism of BNR phenotypic psychiatric patients.

METHODS

Two independent sample sets consisting of 971 subjects were enrolled in this study. A total of 62 variants were genotyped in the discovery set, then the related variants were verified in the verification set. The published PGC GWAS data were used to validate the associations between the variants and psychiatry disorders. RT-PCR analysis, eQTL data, and Dual-Luciferase Reporter experiment were used to investigate the potential molecular mechanisms of the variants underlying BNR.

RESULTS

The results showed that two SNPs, rs56959712 in HCAR2 and rs2454721 in HCAR3 were significantly associated with niacin response. The risk allele T of rs2454721 could affect the niacin responses of psychiatric patients through elevated HCAR3 gene expression. These two genes, especially HCAR3, were significantly associated with the risk of schizophrenia, as identified in this study and verified using the published GWAS data.

CONCLUSION

HCAR3 is a novel schizophrenia susceptibility gene which is significantly associated with blunted niacin response in schizophrenia. In-depth investigation of HCAR3 is of great significance for uncovering the pathogenesis and propose new therapeutic targets for psychiatric disorders, especially for the BNR subgroup patients.

Pathophysiology and Genetic Associations of Varicose Veins: A Narrative Review

Varicose veins (VVs) have a high prevalence worldwide and have become a major medical burden. Their pathophysiology involves a complex interplay of inflammation and tissue remodeling, and current treatment is limited by its impact on the pathophysiological mechanisms. In addition, despite clear environmental factors, family history is an important risk factor, suggesting a genetic component to the risk of developing VVs. Our understanding of the pathogenesis of these diseases has benefited greatly from the expansion of population genetic studies, from pioneering family studies to large genome-wide association studies; we now find multiple risk loci for each venous disease. This review considers the pathophysiology of VVs, highlighting the current state of genetic knowledge. We also propose future directions for research.

Investigating the role of lipid genes in liver disease using fatty liver models of alcohol and high fat in zebrafish (Danio rerio)

BACKGROUND

Accumulation of lipid in the liver is the first hallmark of both alcohol-related liver disease (ALD) and non-alcohol-related fatty liver disease (NAFLD). Recent studies indicate that specific mutations in lipid genes confer risk and might influence disease progression to irreversible liver cirrhosis. This study aimed to understand the function/s of lipid risk genes driving disease development in zebrafish genetic models of alcohol-related and non-alcohol-related fatty liver.

METHODS

We used zebrafish larvae to investigate the effect of alcohol and high fat to model fatty liver and tested the utility of this model to study lipid risk gene functions. CRISPR/Cas9 gene editing was used to create knockdowns in 5 days post-fertilisation zebrafish larvae for the available orthologs of human cirrhosis risk genes (pnpla3, faf2, tm6sf2). To establish fatty liver models, larvae were exposed to ethanol and a high-fat diet (HFD) consisting of chicken egg yolk. Changes in morphology (imaging), survival, liver injury (biochemical tests, histopathology), gene expression (qPCR) and lipid accumulation (dye-specific live imaging) were analysed across treatment groups to test the functions of these genes.

RESULTS

Exposure of 5-day post-fertilisation (dpf) WT larvae to 2% ethanol or HFD for 48 h developed measurable hepatic steatosis. CRISPR-Cas9 genome editing depleted pnpla3, faf2 and tm6sf2 gene expression in these CRISPR knockdown larvae (crispants). Depletion significantly increased the effects of ethanol and HFD toxicity by increasing hepatic steatosis and hepatic neutrophil recruitment ≥2-fold in all three crispants. Furthermore, ethanol or HFD exposure significantly altered the expression of genes associated with ethanol metabolism (cyp2y3) and lipid metabolism-related gene expression, including atgl (triglyceride hydrolysis), axox1, echs1 (fatty acid β-oxidation), fabp10a (transport), hmgcra (metabolism), notch1 (signalling) and srebp1 (lipid synthesis), in all three pnpla3, faf2 and tm6sf2 crispants. Nile Red staining in all three crispants revealed significantly increased lipid droplet size and triglyceride accumulation in the livers following exposure to ethanol or HFD.

CONCLUSIONS

We identified roles for pnpla3, faf2 and tm6sf2 genes in triglyceride accumulation and fatty acid oxidation pathways in a zebrafish larvae model of fatty liver.

Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data

Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics. scPagwas effectively prioritizes trait-relevant genes, which facilitates identification of trait-relevant cell types/populations with high accuracy in extensive simulated and real datasets. Cellular-level association results identified a novel subpopulation of naive CD8+ T cells related to COVID-19 severity and oligodendrocyte progenitor cell and microglia subsets with critical pathways by which genetic variants influence Alzheimer's disease. Overall, our approach provides new insights for the discovery of trait-relevant cell types and improves the mechanistic understanding of disease variants from a pathway perspective.

New "drugs and targets" in the GWAS era of bipolar disorder

OBJECTIVE

Due to the phenotypic heterogeneity and etiological complexity of bipolar disorder (BD), many patients do not respond well to the current medications, and developing novel effective treatment is necessary. Whether any BD genome-wide association study (GWAS) risk genes were targets of existing drugs or novel drugs that can be repurposed in the clinical treatment of BD is a hot topic in the GWAS era of BD.

METHODS

A list of 425 protein-coding BD risk genes was distilled through the BD GWAS, and 4479 protein-coding druggable targets were retrieved from the druggable genome. The overlapped genes/targets were subjected to further analyses in DrugBank, Pharos, and DGIdb datasets in terms of their FDA status, mechanism of action and primary indication, to identify their potential for repurposing.

RESULTS

We identified 58 BD GWAS risk genes grouped as the druggable targets, and several genes were given higher priority. These BD risk genes were targets of antipsychotics, antidepressants, antiepileptics, calcium channel antagonists, as well as anxiolytics and analgesics, either existing clinically-approved drugs for BD or the drugs than can be repurposed for treatment of BD in the future. Those genes were also likely relevant to BD pathophysiology, as many of them encode ion channel, ion transporter or neurotransmitter receptor, or the mice manipulating those genes are likely to mimic the phenotypes manifest in BD patients.

CONCLUSIONS

This study identifies several targets that may facilitate the discovery of novel treatments in BD, and implies the value of conducting GWAS into clinical translation.

Eat4Genes: a bioinformatic rational gene targeting app and prototype model for improving human health

Introduction and aims

Dietary Rational Gene Targeting (DRGT) is a therapeutic dietary strategy that uses healthy dietary agents to modulate the expression of disease-causing genes back toward the normal. Here we use the DRGT approach to (1) identify human studies assessing gene expression after ingestion of healthy dietary agents with an emphasis on whole foods, and (2) use this data to construct an online dietary guide app prototype toward eventually aiding patients, healthcare providers, community and researchers in treating and preventing numerous health conditions.

Methods

We used the keywords "human", "gene expression" and separately, 51 different dietary agents with reported health benefits to search GEO, PubMed, Google Scholar, Clinical trials, Cochrane library, and EMBL-EBI databases for related studies. Studies meeting qualifying criteria were assessed for gene modulations. The R-Shiny platform was utilized to construct an interactive app called "Eat4Genes".

Results

Fifty-one human ingestion studies (37 whole food related) and 96 key risk genes were identified. Human gene expression studies were found for 18 of 41 searched whole foods or extracts. App construction included the option to select either specific conditions/diseases or genes followed by food guide suggestions, key target genes, data sources and links, dietary suggestion rankings, bar chart or bubble chart visualization, optional full report, and nutrient categories. We also present user scenarios from physician and researcher perspectives.

Conclusion

In conclusion, an interactive dietary guide app prototype has been constructed as a first step towards eventually translating our DRGT strategy into an innovative, low-cost, healthy, and readily translatable public resource to improve health.

Genetic Insights of Schizophrenia via Single Cell RNA-Sequencing Analyses

BACKGROUND

Schizophrenia is a complex and heterogeneous disorder involving multiple regions and types of cells in the brain. Despite rapid progress made by genome-wide association studies (GWAS) of schizophrenia, the mechanisms of the illness underlying the GWAS significant loci remain less clear.

STUDY DESIGN

We investigated schizophrenia risk genes using summary-data-based Mendelian randomization based on single-cell sequencing data, and explored the types of brain cells involved in schizophrenia through the expression weighted cell-type enrichment analysis.

RESULTS

We identified 54 schizophrenia risk genes (two-thirds of these genes were not identified using sequencing data of bulk tissues) using single-cell RNA-sequencing data. Further cell type enrichment analysis showed that schizophrenia risk genes were highly expressed in excitatory neurons and caudal ganglionic eminence interneurons, suggesting putative roles of these cells in the pathogenesis of schizophrenia. We also found that these risk genes identified using single-cell sequencing results could form a large protein-protein interaction network with genes affected by disease-causing rare variants.

CONCLUSIONS

Through integrative analyses using expression data at single-cell levels, we identified 54 risk genes associated with schizophrenia. Notably, many of these genes were only identified using single-cell RNA-sequencing data, and their altered expression levels in particular types of cells, rather than in the bulk tissues, were related to the increased risk of schizophrenia. Our results provide novel insight into the biological mechanisms of schizophrenia, and future single-cell studies are necessary to further facilitate the understanding of the disorder.

Characterization of gene expression patterns in mild cognitive impairment using a transcriptomics approach and neuroimaging endophenotypes

INTRODUCTION

Identification of novel therapeutics and risk assessment in early stages of Alzheimer's disease (AD) is a crucial aspect of addressing this complex disease. We characterized gene-expression patterns at the mild cognitive impairment (MCI) stage to identify critical mRNA measures and gene clusters associated with AD pathogenesis.

METHODS

We used a transcriptomics approach, integrating magnetic resonance imaging (MRI) and peripheral blood-based gene expression data using persistent homology (PH) followed by kernel-based clustering.

RESULTS

We identified three clusters of genes significantly associated with diagnosis of amnestic MCI. The biological processes associated with each cluster were mitochondrial function, NF-kB signaling, and apoptosis. Cluster-level associations with cortical thickness displayed canonical AD-like patterns. Driver genes from clusters were also validated in an external dataset for prediction of amyloidosis and clinical diagnosis.

DISCUSSION

We found a disease-relevant transcriptomic signature sensitive to prodromal AD and identified a subset of potential therapeutic targets associated with AD pathogenesis.

Genetic correlation between thyroid hormones and Parkinson's disease

Parkinson’s disease (PD) was reported to be connected with thyroid diseases clinically, which might be a critical clew to immune pathogenesis of PD. However, there was no further research to study the pathogenesis correlation between PD and thyroid diseases. In this study, except for investigating the difference in thyroid hormone between PD and the control group, we explored genetic correlation between thyroid and PD. We tried to find their shared molecular pathway by analyzing the effect of PD risk genes on thyroid function. Interestingly, most of those 12 meaningful SNPs we found could affect PD and thyroid function through immune mechanism, which is consistent with our original conjecture and provides significant evidence for the immune pathogenesis of PD.

Association of the top 20 Alzheimer's disease risk genes with [18F]flortaucipir PET

Introduction

We previously reported genetic associations of the top Alzheimer's disease (AD) risk alleles with amyloid deposition and neurodegeneration. Here, we report the association of these variants with [18F]flortaucipir standardized uptake value ratio (SUVR).

Methods

We analyzed the [18F]flortaucipir scans of 352 cognitively normal (CN), 160 mild cognitive impairment (MCI), and 54 dementia (DEM) participants from Alzheimer's Disease Neuroimaging Initiative (ADNI)2 and 3. We ran step-wise regression with log-transformed [18F]flortaucipir meta-region of interest SUVR as the outcome measure and genetic variants, age, sex, and apolipoprotein E (APOE) ε4 as predictors. The results were visualized using parametric mapping at familywise error cluster-level-corrected P < .05.

Results

APOE ε4 showed significant (P < .05) associations with tau deposition across all disease stages. Other significantly associated genes include variants in ABCA7 in CN, CR1 in MCI, BIN1 and CASS4 in MCI and dementia participants.

Discussion

We found significant associations to tau deposition for ABCA7, BIN1, CASS4, and CR1, in addition to APOE ε4. These four variants have been previously associated with tau metabolism through model systems.

Network-based analysis revealed significant interactions between risk genes of severe COVID-19 and host genes interacted with SARS-CoV-2 proteins

Whether risk genes of severe coronavirus disease 2019 (COVID-19) from genome-wide association study could play their regulatory roles by interacting with host genes that were interacted with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins was worthy of exploration. In this study, we implemented a network-based approach by developing a user-friendly software Network Calculator (https://github.com/Haoxiang-Qi/Network-Calculator.git). By using Network Calculator, we identified a network composed of 13 risk genes and 28 SARS-CoV-2 interacted host genes that had the highest network proximity with each other, with a hub gene HNRNPK identified. Among these genes, 14 of them were identified to be differentially expressed in RNA-seq data from severe COVID-19 cases. Besides, by expression enrichment analysis in single-cell RNA-seq data, compared with mild COVID-19, these genes were significantly enriched in macrophage, T cell and epithelial cell for severe COVID-19. Meanwhile, 74 pathways were significantly enriched. Our analysis provided insights for the underlying genetic etiology of severe COVID-19 from the perspective of network biology.

Identification of Parkinson's Disease-Causing Genes via Omics Data

Parkinson's disease (PD) is the second most frequent neurogenic disease after Alzheimer's disease. The clinical manifestations include mostly motor disorders, such as bradykinesia, myotonia, and static tremors. Since the cause of this pathological features remain unclear, there is currently no radical treatment for PD. Environmental and genetic factors are thought to contribute to the pathology of PD. To identify the genetic factors, some studies employed the Genome-Wide Association Studies (GWAS) method and detected certain genes closely related to PD. However, the functions of these gene mutants in the development of PD are unknown. Combining GWAS and expression Quantitative Trait Loci (eQTL) analysis, the biological meaning of mutation could be explained to some extent. Therefore, the present investigation used Summary data-based Mendelian Randomization (SMR) analysis to integrate of two PD GWAS datasets and four eQTL datasets with the objective of identifying casual genes. Using this strategy, we found six Single Nucleotide Polymorphism (SNP) loci which could cause the development of PD through altering the susceptibility gene expression, and three risk genes: Synuclein Alpha (SNCA), Mitochondrial Poly(A) Polymerase (MTPAP), and RP11-305E6.4. We proved the accuracy of results through case studies and inferred the functions of these genes in PD. Overall, this study provides insights into the genetic mechanism behind PD, which is crucial for the study of the development of this disease and its diagnosis and treatment.

Methylation factor MRPL15 identified as a potential biological target in Alzheimer's disease

Alzheimer’s disease (AD) is the most common form of dementia. However, the molecular basis of the development and progression of AD is still unclear. To elucidate the molecular processes related to AD, we obtained the expression profiles and analyzed the differentially expressed genes (DEGs). The genes potentially involved in the AD process were identified by PPI network and STEM analysis. The molecular mechanisms related to the recognition of AD were determined by GSEA and enrichment analysis. The differences from immune cells in AD were calculated. The methylation factors were identified by methylation difference analysis. Among them, MRPL15 was identified as suitable for diagnosing AD. Its expression trend had been verified in GSE5281. Importantly, MRPL15 was also a methylation factor. In addition, macrophages and neutrophils were up-regulated in AD patients. This was consistent with previous immune inflammation responses identified as being involved in the development of AD. The results of the present study revealed the genetic changes and molecular mechanisms involved in the process of the development and deterioration of AD patients. The potential AD risk genes and potential biological targets were identified. It provided evidence that immune inflammation and immune cells play an important role in AD.

Integrative genomics analysis identifies five promising genes implicated in insomnia risk based on multiple omics datasets

In recent decades, many genome-wide association studies on insomnia have reported numerous genes harboring multiple risk variants. Nevertheless, the molecular functions of these risk variants conveying risk to insomnia are still ill-studied. In the present study, we integrated GWAS summary statistics (N=386,533) with two independent brain expression quantitative trait loci (eQTL) datasets (N=329) to determine whether expression-associated SNPs convey risk to insomnia. Furthermore, we applied numerous bioinformatics analyses to highlight promising genes associated with insomnia risk. By using Sherlock integrative analysis, we detected 449 significant insomnia-associated genes in the discovery stage. These identified genes were significantly overrepresented in six biological pathways including Huntington’s disease (P=5.58 × 10⁻⁵), Alzheimer’s disease (P=5.58 × 10⁻⁵), Parkinson’s disease (P=6.34 × 10⁻⁵), spliceosome (P=1.17 × 10⁻⁴), oxidative phosphorylation (P=1.09 × 10⁻⁴), and wnt signaling pathways (P=2.07 × 10⁻⁴). Further, five of these identified genes were replicated in an independent brain eQTL dataset. Through a PPI network analysis, we found that there existed highly functional interactions among these five identified genes. Three genes of LDHA (P=0.044), DALRD3 (P=5.0 × 10⁻⁵), and HEBP2 (P=0.032) showed significantly lower expression level in brain tissues of insomnic patients than that in controls. In addition, the expression levels of these five genes showed prominently dynamic changes across different time points between behavioral states of sleep and sleep deprivation in mice brain cortex. Together, the evidence of the present study strongly suggested that these five identified genes may represent candidate genes and contributed risk to the etiology of insomnia.

Dissection of the Genetic Association between Anorexia Nervosa and Obsessive-Compulsive Disorder at the Network and Cellular Levels

Anorexia nervosa (AN) and obsessive-compulsive disorder (OCD) exhibit a high co-morbidity rate, similar symptoms, and a shared genetic basis. However, an understanding of the specific underlying mechanisms of these commonalities is currently limited. Here, we collected Genome-Wide Association Analysis results for AN and OCD, and obtained genes hit by the top SNPs as the risk genes. We then carried out an integrative coexpression network analysis to explore the convergence and divergence of AN and OCD risk genes. At first, we observed that the AN risk genes were enriched in coexpression modules that involved extracellular matrix functions and highly are expressed in the postnatal brain, limbic system, and non-neuronal cell types, while the OCD risk genes were enriched in modules of synapse function, the prenatal brain, cortex layers, and neurons. Next, by comparing the expressions from the eating disorder and OCD postmortem patient brain tissues, we observed both disorders have similar prefrontal cortex expression alterations influencing the synapse transmission, suggesting that the two diseases could have similar functional pathways. We found that the AN and OCD risk genes had distinct functional and spatiotemporal enrichment patterns but carried similar expression alterations as a disease mechanism, which may be one of the key reasons they had similar but not identical clinical phenotypes.

Identification of Gene Loci That Overlap Between Mental Disorders and Poor Prognosis of Cancers

Background: Co-morbid psychiatric disorders are common in patients with cancers, which make the treatment more difficult. Studying the connection between mental disease-related genes and the prognosis of cancers may potentially lead to novel therapeutic methods. Method: All mental disorders genes were selected from published articles. The correlations between the expression of these genes and the prognosis of different cancers were analyzed by starBase v2.0 and TIMER. The molecular functions, reactome pathways, and interactions among diverse genes were explored via the STRING tool. Results: 239 genes were identified for further survival analysis, 5 of which were overlapping genes across at least five cancer types, including RHEBL1, PDE4B, ANKRD55, EPHB2, and GIMAP7. 146 high-expression and 157 low-expression genes were found to be correlated with the unfavorable prognosis of diverse cancer types. Tight links existed among various mental disease genes. Besides, risk genes were mostly related to the dismal outcome of low-grade glioma (LGG) and kidney renal clear cell carcinoma (KIRC) patients. Gene Ontology (GO) and reactome pathway analysis revealed that most genes involved in various critical molecular functions and primarily related to metabolism, signal transduction, and hemostasis. Conclusions: To explore co-expression genes between mental illnesses and cancers may aid in finding preventive strategies and therapeutic methods for high-risk populations and patients with one or more diseases.

Gene Expression in Spontaneous Experimental Autoimmune Encephalomyelitis Is Linked to Human Multiple Sclerosis Risk Genes

Recent genome-wide association studies have identified over 230 genetic risk loci for multiple sclerosis. Current experimental autoimmune encephalomyelitis (EAE) models requiring active induction of disease may not be optimally suited for the characterization of the function of these genes. We have thus used gene expression profiling to study whether spontaneous opticospinal EAE (OSE) or MOG-induced EAE mirrors the genetic contribution to the pathogenesis of multiple sclerosis more faithfully. To this end, we compared gene expression in OSE and MOG EAE models and analyzed the relationship of both models to human multiple sclerosis risk genes and T helper cell biology. We observed stronger gene expression changes and an involvement of more pathways of the adaptive immune system in OSE than MOG EAE. Furthermore, we demonstrated a more extensive enrichment of human MS risk genes among transcripts differentially expressed in OSE than was the case for MOG EAE. Transcripts differentially expressed only in diseased OSE mice but not in MOG EAE were significantly enriched for T helper cell-specific transcripts. These transcripts are part of immune-regulatory pathways. The activation of the adaptive immune system and the enrichment of both human multiple sclerosis risk genes and T helper cell-specific transcripts were also observed in OSE mice showing only mild disease signs. These expression changes may, therefore, be indicative of processes at disease onset. In summary, more human multiple sclerosis risk genes were differentially expressed in OSE than was observed for MOG EAE, especially in T_H1 cells. When studying the functional role of multiple sclerosis risk genes and pathways during disease onset and their interactions with the environment, spontaneous OSE may thus show advantages over MOG-induced EAE.

Integrative genomics analysis identifies promising SNPs and genes implicated in tuberculosis risk based on multiple omics datasets

More than 10 GWASs have reported numerous genetic loci associated with tuberculosis (TB). However, the functional effects of genetic variants on TB remains largely unknown. In the present study, by combining a reported GWAS summary dataset (N = 452,264) with 3 independent eQTL datasets (N = 2,242) and other omics datasets downloaded from public databases, we conducted an integrative genomics analysis to highlight SNPs and genes implicated in TB risk. Based on independent biological and technical validations, we prioritized 26 candidate genes with eSNPs significantly associated with gene expression and TB susceptibility simultaneously; such as, CDC16 (rs7987202, rs9590408, and rs948182) and RCN3 (rs2946863, rs2878342, and rs3810194). Based on the network-based enrichment analysis, we found these 26 highlighted genes were jointly connected to exert effects on TB susceptibility. The co-expression patterns among these 26 genes were remarkably changed according to Mycobacterium tuberculosis (MTB) infection status. Based on 4 independent gene expression datasets, 21 of 26 genes (80.77%) showed significantly differential expressions between TB group and control group in mesenchymal stem cells, mice blood and lung tissues, as well as human alveolar macrophages. Together, we provide robust evidence to support 26 highlighted genes as important candidates for TB.

Identifying Risk Genes and Interpreting Pathogenesis for Parkinson's Disease by a Multiomics Analysis

Genome-wide association studies (GWAS) have identified tens of genetic variants associated with Parkinson’s disease (PD). Nevertheless, the genes or DNA elements that affect traits through these genetic variations are usually undiscovered. This study was the first to combine meta-analysis GWAS data and expression data to identify PD risk genes. Four known genes, CRHR1, KANSL1, NSF and LRRC37A, and two new risk genes, STX4 and BST1, were identified. Among them, CRHR1 is a known drug target, indicating that hydrocortisone may become a potential drug for the treatment of PD. Furthermore, the potential pathogenesis of CRHR1 and LRRC37A was explored by applying DNA methylation (DNAm) data, indicating a pathogenesis whereby the effect of a genetic variant on PD is mediated by genetic regulation of transcription through DNAm. Overall, this research identified the risk genes and pathogenesis that affect PD through genetic variants, which has significance for the diagnosis and treatment of PD.

Genomic mechanisms in Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disease and, owing to its increasing prevalence, represents one of the leading public health problems in aging populations. The molecular causes underlying the onset and progression of AD are manifold and hitherto still incompletely understood. Research over nearly four decades has clearly delineated genetics to play a crucial role in AD susceptibility, likely in concert with non-genetic factors. The field has gained considerable momentum and novel insights over the past 10 years owing to the advent and application of high-throughput genomics technologies in datasets of increasing size. In this contribution to the Mini-Symposium on the Molecular Etiology of Alzheimer's Disease, we review the current status of genomics research in AD. To this end, we scrutinize and discuss the main findings from the two largest and most current genome-wide association studies (GWAS) in the field, that is, the papers published by Jansen et al (Nat Genet 51:404-413) and Kunkle et al (Nat Genet 51:414-430). Particular focus is laid on genomics findings overlapping across both studies and on the novel insights they provide in terms of improving our understanding of the "genomic mechanisms" underlying this devastating disease.

Mucosal Profiling of Pediatric-Onset Colitis and IBD Reveals Common Pathogenics and Therapeutic Pathways

Pediatric-onset colitis and inflammatory bowel disease (IBD) have significant effects on the growth of infants and children, but the etiopathogenesis underlying disease subtypes remains incompletely understood. Here, we report single-cell clustering, immune phenotyping, and risk gene analysis for children with undifferentiated colitis, Crohn's disease, and ulcerative colitis. We demonstrate disease-specific characteristics, as well as common pathogenesis marked by impaired cyclic AMP (cAMP)-response signaling. Specifically, infiltration of PDE4B- and TNF-expressing macrophages, decreased abundance of CD39-expressing intraepithelial T cells, and platelet aggregation and release of 5-hydroxytryptamine at the colonic mucosae were common in colitis and IBD patients. Targeting these pathways by using the phosphodiesterase inhibitor dipyridamole restored immune homeostasis and improved colitis symptoms in a pilot study. In summary, comprehensive analysis of the colonic mucosae has uncovered common pathogenesis and therapeutic targets for children with colitis and IBD.

Criteria of the German Consortium for Hereditary Breast and Ovarian Cancer for the Classification of Germline Sequence Variants in Risk Genes for Hereditary Breast and Ovarian Cancer

More than ten years ago, the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) set up a panel of experts (VUS Task Force) which was tasked with reviewing the classifications of genetic variants reported by individual centres of the GC-HBOC to the central database in Leipzig and reclassifying them, where necessary, based on the most recent data. When it evaluates variants, the VUS Task Force must arrive at a consensus. The resulting classifications are recorded in a central database where they serve as a basis for ensuring the consistent evaluation of previously known and newly identified variants in the different centres of the GC-HBOC. The standardised VUS evaluation by the VUS Task Force is a key element of the recall system which has also been set up by the GC-HBOC. The system will be used to pass on information to families monitored and managed by GC-HBOC centres in the event that previously classified variants are reclassified based on new information. The evaluation algorithm of the VUS Task Force was compiled using internationally established assessment methods (IARC, ACMG, ENIGMA) and is presented here together with the underlying evaluation criteria used to arrive at the classification decision using a flow chart. In addition, the characteristics and special features of specific individual risk genes associated with breast and/or ovarian cancer are discussed in separate subsections. The URLs of relevant databases have also been included together with extensive literature references to provide additional information and cover the scope and dynamism of the current state of knowledge on the evaluation of genetic variants. In future, if criteria are updated based on new information, the update will be published on the website of the GC-HBOC ( https://www.konsortium-familiaerer-brustkrebs.de/ ).

The Rules of Engagement: Do Microglia Seal the Fate in the Inverse Relation of Glioma and Alzheimer's Disease?

Microglia, the immune cells of the brain, play a major role in the maintenance of brain homeostasis and constantly screen the brain environment to detect any infection or damage. Once activated by a stimulus, microglial cells initiate an immune response followed by the resolution of brain inflammation. A failure or deviation in the housekeeping function of these guardian cells can lead to multiple diseases, including brain cancer and neurodegenerative diseases such as Alzheimer's disease (AD). A small number of studies have investigated the causal relation of both diseases, thereby revealing an inverse relationship where cancer patients have a reduced risk to develop AD and vice versa. In this review, we aim to shed light on the role of microglia in the fate to develop specifically glioma as one type of cancer or AD. We will examine the common and/or opposing genetic predisposition as well as associated pathways of these diseases to unravel a possible involvement of microglia in the occurrence of either disease. Lastly, a set of guidelines will be proposed for future research and diagnostics to clarify and improve the knowledge on the role of microglia in the decision toward one pathology or another.

HLA-DQA1 and HLA-DQB1 Alleles, Conferring Susceptibility to Celiac Disease and Type 1 Diabetes, are More Expressed Than Non-Predisposing Alleles and are Coordinately Regulated

HLA DQA1*05 and DQB1*02 alleles encoding the DQ2.5 molecule and HLA DQA1*03 and DQB1*03 alleles encoding DQ8 molecules are strongly associated with celiac disease (CD) and type 1 diabetes (T1D), two common autoimmune diseases (AD). We previously demonstrated that DQ2.5 genes showed a higher expression with respect to non-CD associated alleles in heterozygous DQ2.5 positive (HLA DR1/DR3) antigen presenting cells (APC) of CD patients. This differential expression affected the level of the encoded DQ2.5 molecules on the APC surface and established the strength of gluten-specific CD4⁺ T cells response. Here, we expanded the expression analysis of risk alleles in patients affected by T1D or by T1D and CD comorbidity. In agreement with previous findings, we found that DQ2.5 and DQ8 risk alleles are more expressed than non-associated alleles also in T1D patients and favor the self-antigen presentation. To investigate the mechanism causing the high expression of risk alleles, we focused on HLA DQA1*05 and DQB1*02 alleles and, by ectopic expression of a single mRNA, we modified the quantitative equilibrium among the two transcripts. After transfection of DR7/DR14 B-LCL with HLA-DQA1*05 cDNA, we observed an overexpression of the endogenous DQB1*02 allele. The DQ2.5 heterodimer synthesized was functional and able to present gluten antigens to cognate CD4⁺ T cells. Our results indicated that the high expression of alpha and beta transcripts, encoding for the DQ2.5 heterodimeric molecules, was strictly coordinated by a mechanism acting at a transcriptional level. These findings suggested that, in addition to the predisposing HLA-DQ genotype, also the expression of risk alleles contributed to the establishment of autoimmunity.

Integrative computational approach to evaluate risk genes for postmenopausal osteoporosis

In recent years, numerous studies reported over a hundred of genes playing roles in the etiology of postmenopausal osteoporosis (PO). However, many of these candidate genes were lack of replication and results were not always consistent. Here, the authors proposed a computational workflow to curate and evaluate PO related genes. They integrate large-scale literature knowledge data and gene expression data (PO case/control: 10/10) for the marker evaluation. Pathway enrichment, sub-network enrichment, and gene-gene interaction analysis were conducted to study the pathogenic profile of the candidate genes, with four metrics proposed and validated for each gene. By using the authors' approach, a scalable PO genetic database was developed; including PO related genes, diseases, pathways, and the supporting references. The PO case/control classification supported the effectiveness of the four proposed metrics, which successfully identified eight well-studied top PO genes (e.g. TGFB1, IL6, IL1B, TNF, ESR2, IGF1, HIF1A, and COL1A1) and highlighted one recently reported PO genes (e.g. IFNG). The computational biology approach and the PO database developed in this study provide a valuable resource which may facilitate understanding the genetic profile of PO.

Recurrent copy number alterations in young women with breast cancer

Breast cancer diagnosis in young women has emerged as an independent prognostic factor with higher recurrence risk and death than their older counterparts. We aim to find recurrent somatic copy number alteration (CNA) regions identified from breast cancer microarray data and associate the CNA status of the genes harbored in the regions to the survival of young women with breast cancer.

By using the interval graph-based algorithm we developed, and the CNA data consisting of a Discovery set with 130 young women and a Validation set with 125 young women, we identified 38 validated recurrent CNAs containing 39 protein encoding genes. CNA gain regions encompassing genes CAPN2, CDC73 and ASB13 are the top 3 with the highest occurring frequencies in both the Discovery and Validation dataset, while gene SGCZ ranked top for the recurrent CNA loss regions. The mutation status of 9 of the 39 genes shows significant associations with breast cancer specific survival. Interestingly, the expression level of 2 of the 9 genes, ASB13 and SGCZ, shows significant association with survival outcome. Patients with CNA mutations in both of these genes had a worse survival outcome when compared to patients without the gene mutations. The mutated CNA status in gene ASB13 was associated with a higher gene expression, which predicted patient survival outcome. Together, identification of the CNA events with prognostic significance in young women with breast cancer may be used in genomic-guided treatment.

Loss-of-function of neuroplasticity-related genes confers risk for human neurodevelopmental disorders

High and increasing prevalence of neurodevelopmental disorders place enormous personal and economic burdens on society. Given the growing realization that the roots of neurodevelopmental disorders often lie in early childhood, there is an urgent need to identify childhood risk factors. Neurodevelopment is marked by periods of heightened experience-dependent neuroplasticity wherein neural circuitry is optimized by the environment. If these critical periods are disrupted, development of normal brain function can be permanently altered, leading to neurodevelopmental disorders. Here, we aim to systematically identify human variants in neuroplasticity-related genes that confer risk for neurodevelopmental disorders. Historically, this knowledge has been limited by a lack of techniques to identify genes related to neurodevelopmental plasticity in a high-throughput manner and a lack of methods to systematically identify mutations in these genes that confer risk for neurodevelopmental disorders. Using an integrative genomics approach, we determined loss-of-function (LOF) variants in putative plasticity genes, identified from transcriptional profiles of brain from mice with elevated plasticity, that were associated with neurodevelopmental disorders. From five shared differentially expressed genes found in two mouse models of juvenile-like elevated plasticity (juvenile wild-type or adult Lynx1-/- relative to adult wild-type) that were also genotyped in the Mount Sinai BioMe Biobank we identified multiple associations between LOF genes and increased risk for neurodevelopmental disorders across 10,510 patients linked to the Mount Sinai Electronic Medical Records (EMR), including epilepsy and schizophrenia. This work demonstrates a novel approach to identify neurodevelopmental risk genes and points toward a promising avenue to discover new drug targets to address the unmet therapeutic needs of neurodevelopmental disease.

Perinatal Asphyxia in Rat Alters Expression of Novel Schizophrenia Risk Genes

Epidemiological studies suggest that obstetric complications, particularly those related to hypoxia during labor and delivery, are a risk factor for development of schizophrenia. The impact of perinatal asphyxia on postnatal life has been studied in a rodent model of global hypoxia, which is accompanied by cesarean section birth. This asphyxia model shows several behavioral, pharmacological, neurochemical, and neuroanatomical abnormalities in adulthood that have relevance to schizophrenia. Further, it is suggested that schizophrenia has a strong genetic component, and indeed novel candidate genes were recently identified by a genome-wide association study. Here, we examined alteration in the novel schizophrenia risk genes, CNNM2, CSMD1, and MMP16 in the brains of rats undergoing cesarean section with or without global hypoxia. The brain regions studied were the prefrontal cortex, striatum, and hippocampus, which are all relevant to schizophrenia. Risk gene expression was measured at three time periods: neonatal, adolescence, and adulthood. We also performed an in vitro analysis to determine involvement of these genes in CNS maturation during differentiation of human neuronal and glial cell lines. Cnnm2 expression was altered in the brains of asphyxia model rats. However, Csmd1 and Mmp16 showed altered expression by exposure to cesarean section only. These findings suggest that altered expression of these risk genes via asphyxia and cesarean section may be associated, albeit through distinct pathways, with the pathobiology of schizophrenia.

Translational utility of experimental autoimmune encephalomyelitis: recent developments

Multiple sclerosis (MS) is a complex autoimmune condition with firmly established genetic and environmental components. Genome-wide association studies (GWAS) have revealed a large number of genetic polymorphisms in the vicinity of, and within, genes that associate to disease. However, the significance of these single-nucleotide polymorphisms in disease and possible mechanisms of action remain, with a few exceptions, to be established. While the animal model for MS, experimental autoimmune encephalomyelitis (EAE), has been instrumental in understanding immunity in general and mechanisms of MS disease in particular, much of the translational information gathered from the model in terms of treatment development (glatiramer acetate and natalizumab) has been extensively summarized. In this review, we would thus like to cover the work done in EAE from a GWAS perspective, highlighting the research that has addressed the role of different GWAS genes and their pathways in EAE pathogenesis. Understanding the contribution of these pathways to disease might allow for the stratification of disease subphenotypes in patients and in turn open the possibility for new and individualized treatment approaches in the future.

Synaptic plasticity, neural circuits, and the emerging role of altered short-term information processing in schizophrenia

Synaptic plasticity alters the strength of information flow between presynaptic and postsynaptic neurons and thus modifies the likelihood that action potentials in a presynaptic neuron will lead to an action potential in a postsynaptic neuron. As such, synaptic plasticity and pathological changes in synaptic plasticity impact the synaptic computation which controls the information flow through the neural microcircuits responsible for the complex information processing necessary to drive adaptive behaviors. As current theories of neuropsychiatric disease suggest that distinct dysfunctions in neural circuit performance may critically underlie the unique symptoms of these diseases, pathological alterations in synaptic plasticity mechanisms may be fundamental to the disease process. Here we consider mechanisms of both short-term and long-term plasticity of synaptic transmission and their possible roles in information processing by neural microcircuits in both health and disease. As paradigms of neuropsychiatric diseases with strongly implicated risk genes, we discuss the findings in schizophrenia and autism and consider the alterations in synaptic plasticity and network function observed in both human studies and genetic mouse models of these diseases. Together these studies have begun to point toward a likely dominant role of short-term synaptic plasticity alterations in schizophrenia while dysfunction in autism spectrum disorders (ASDs) may be due to a combination of both short-term and long-term synaptic plasticity alterations.

Convergent synaptic and circuit substrates underlying autism genetic risks

There has been a surge of diagnosis of autism spectrum disorders (ASD) over the past decade. While large, high powered genome screening studies of children with ASD have identified numerous genetic risk factors, research efforts to understanding how each of these risk factors contributes to the development autism has met with limited success. Revealing the mechanisms by which these genetic risk factors affect brain development and predispose a child to autism requires mechanistic understanding of the neurobiological changes underlying this devastating group of developmental disorders at multifaceted molecular, cellular and system levels. It has been increasingly clear that the normal trajectory of neurodevelopment is compromised in autism, in multiple domains as much as aberrant neuronal production, growth, functional maturation, patterned connectivity, and balanced excitation and inhibition of brain networks. Many autism risk factors identified in humans have been now reconstituted in experimental mouse models to allow mechanistic interrogation of the biological role of the risk gene. Studies utilizing these mouse models have revealed that underlying the enormous heterogeneity of perturbed cellular events, mechanisms directing synaptic and circuit assembly may provide a unifying explanation for the pathophysiological changes and behavioral endophenotypes seen in autism, although synaptic perturbations are far from being the only alterations relevant for ASD. In this review, we discuss synaptic and circuit abnormalities obtained from several prevalent mouse models, particularly those reflecting syndromic forms of ASD that are caused by single gene perturbations. These compiled results reveal that ASD risk genes contribute to proper signaling of the developing gene networks that maintain synaptic and circuit homeostasis, which is fundamental to normal brain development.

Beta-amyloid toxicity modifier genes and the risk of Alzheimer's disease

Late-onset Alzheimer's disease (LOAD) is a complex and multifactorial disease. So far ten loci have been identified for LOAD, including APOE, PICALM, CLU, BIN1, CD2AP, CR1, CD33, EPHA1, ABCA7, and MS4A4A/MS4A6E, but they explain about 50% of the genetic risk and thus additional risk genes need to be identified. Amyloid beta (Aβ) plaques develop in the brains of LOAD patients and are considered to be a pathological hallmark of this disease. Recently 12 new Aβ toxicity modifier genes (ADSSL1, PICALM, SH3KBP1, XRN1, SNX8, PPP2R5C, FBXL2, MAP2K4, SYNJ1, RABGEF1, POMT2, and XPO1) have been identified that potentially play a role in LOAD risk. In this study, we have examined the association of 222 SNPs in these 12 candidate genes with LOAD risk in 1291 LOAD cases and 958 cognitively normal controls. Single site and haplotype analyses were performed using PLINK. Following adjustment for APOE genotype, age, sex, and principal components, we found single nucleotide polymorphisms (SNPs) in PPP2R5C, PICALM, SH3KBP1, XRN1, and SNX8 that showed significant association with risk of LOAD. The top SNP was located in intron 3 of PPP2R5C (P=0.009017), followed by an intron 19 SNP in PICALM (P=0.0102). Haplotype analysis revealed significant associations in ADSSL1, PICALM, PPP2R5C, SNX8, and SH3KBP1 genes. Our data indicate that genetic variation in these new candidate genes affects the risk of LOAD. Further investigation of these genes, including additional replication in other case-control samples and functional studies to elucidate the pathways by which they affect Aβ, are necessary to determine the degree of involvement these genes have for LOAD risk.

Schizophrenia, psychiatric genetics, and Darwinian psychiatry: an evolutionary framework

The evolutionary origins of one of the most dramatic and seemingly deleterious behavioral phenotypes, the syndrome known as schizophrenia, are mysterious. Schizophrenia occurs in all cultures and is inherited. Although most phenotypes are said to be "selected for" based on adaptive qualities, it is difficult to understand how the genetic basis of schizophrenia could have operated under a similar framework. This has lead several theorists analyzing the proposed evolutionary origins of other disease states to that of schizophrenia. To date, several models have been applied. We have tried to conceptualize schizophrenia in a compensatory advantage framework whereby incomplete penetrance of the full disorder, or alternatively, the inheritance of risk alleles insufficient in number to manifest as the classic clinical syndrome, may manifest as a behavioral phenotype with adaptive advantages (eg, creative behavior or novel illuminating ideas). The idea that even full penetrance can also be advantageous has been offered as applied to religious experience and ancient social standing, but is unlikely. Can complex behavioral phenotypes such as schizophrenia, and particularly those that seem purely deleterious, be explained by mechanisms of Darwinian psychiatry? Can models from other disease classes be applied successfully to schizophrenia? Such ideas have generated intense speculation, but often in the absence of testable models. In this article, we will examine some of these proposed ideas and offer suggestions for future research.