A decade in psychiatric GWAS research

Validation of Enhancer Regions in Primary Human Neural Progenitor Cells using Capture STARR-seq

Genome-wide association studies (GWAS) and expression analyses implicate noncoding regulatory regions as harboring risk factors for psychiatric disease, but functional characterization of these regions remains limited. We performed capture STARR-sequencing of over 78,000 candidate regions to identify active enhancers in primary human neural progenitor cells (phNPCs). We selected candidate regions by integrating data from NPCs, prefrontal cortex, developmental timepoints, and GWAS. Over 8,000 regions demonstrated enhancer activity in the phNPCs, and we linked these regions to over 2,200 predicted target genes. These genes are involved in neuronal and psychiatric disease-associated pathways, including dopaminergic synapse, axon guidance, and schizophrenia. We functionally validated a subset of these enhancers using mutation STARR-sequencing and CRISPR deletions, demonstrating the effects of genetic variation on enhancer activity and enhancer deletion on gene expression. Overall, we identified thousands of highly active enhancers and functionally validated a subset of these enhancers, improving our understanding of regulatory networks underlying brain function and disease.

Analysis of genetic diversity in patients with major psychiatric disorders versus healthy controls: A molecular-genetic study of 1698 subjects genotyped for 100 candidate genes (549 SNPs)

BACKGROUND

This study analyzed the extent to which irregularities in genetic diversity separate psychiatric patients from healthy controls.

METHODS

Genetic diversity was quantified through multidimensional "gene vectors" assembled from 4 to 8 polymorphic SNPs located within each of 100 candidate genes. The number of different genotypic patterns observed per gene was called the gene's "diversity index".

RESULTS

The diversity indices were found to be only weakly correlated with their constituent number of SNPs (20.5 % explained variance), thus suggesting that genetic diversity is an intrinsic gene property that has evolved over the course of evolution. Significant deviations from "normal" diversity values were found for (1) major depression; (2) Alzheimer's disease; and (3) schizoaffective disorders. Almost one third of the genes were correlated with each other, with correlations ranging from 0.0303 to 0.7245. The central finding of this study was the discovery of "singular genes" characterized by distinctive genotypic patterns that appeared exclusively in patients but not in healthy controls. Neural Nets yielded nonlinear classifiers that correctly identified up to 90 % of patients. Overlaps between diagnostic subgroups on the genotype level suggested that (1) diagnoses-crossing vulnerabilities are likely involved in the pathogenesis of major psychiatric disorders; (2) clinically defined diagnoses may not constitute etiological entities.

CONCLUSION

Detailed analyses of the variation of genotypic patterns in genes along with the correlation between genes lead to nonlinear classifiers that enable very robust separation between psychiatric patients and healthy controls on the genotype level.

Giant pandas in captivity undergo short-term adaptation in nerve-related pathways

BACKGROUND

Behaviors in captive animals, including changes in appetite, activity level, and social interaction, are often seen as adaptive responses. However, these behaviors may become progressively maladaptive, leading to stress, anxiety, depression, and other negative reactions in animals.

RESULTS

In this study, we investigated the whole-genome sequencing data of 39 giant panda individuals, including 11 in captivity and 28 in the wild. To eliminate the mountain range effect and focus on the factor of captivity only, we first performed a principal component analysis. We then enumerated the 21,474,180 combinations of wild giant pandas (11 chosen from 28) and calculated their distances from the 11 captive individuals. The 11 wild individuals with the closest distances were used for the subsequent analysis. The linkage disequilibrium (LD) patterns demonstrated that the population was almost eliminated. We identified 505 robust selected genomic regions harboring at least one SNP, and the absolute frequency difference was greater than 0.6 between the two populations. GO analysis revealed that genes in these regions were mainly involved in nerve-related pathways. Furthermore, we identified 22 GO terms for which the selection strength significantly differed between the two populations, and there were 10 nerve-related pathways among them. Genes in the differentially abundant regions were involved in nerve-related pathways, indicating that giant pandas in captivity underwent minor genomic selection. Additionally, we investigated the relationship between genetic variation and chromatin conformation structures. We found that nucleotide diversity (θπ) in the captive population was correlated with chromatin conformation structures, which included A/B compartments, topologically associated domains (TADs) and TAD-cliques. For each GO term, we then compared the expression level of genes regulated by the above four factors (AB index, TAD intactness, TAD clique and PEI) with the corresponding genomic background. The retained 10 GO terms were all coordinately regulated by the four factors, and three of them were associated with nerve-related pathways.

CONCLUSIONS

This study revealed that giant pandas in captivity undergo short-term adaptation in nerve-related pathways. Furthermore, it provides new insights into the molecular mechanism of gene expression regulation under short-term adaptation to environmental change.

The schizophrenia syndrome, circa 2024: What we know and how that informs its nature

With new data about different aspects of schizophrenia being continually generated, it becomes necessary to periodically revisit exactly what we know. Along with a need to review what we currently know about schizophrenia, there is an equal imperative to evaluate the construct itself. With these objectives, we undertook an iterative, multi-phase process involving fifty international experts in the field, with each step building on learnings from the prior one. This review assembles currently established findings about schizophrenia (construct, etiology, pathophysiology, clinical expression, treatment) and posits what they reveal about its nature. Schizophrenia is a heritable, complex, multi-dimensional syndrome with varying degrees of psychotic, negative, cognitive, mood, and motor manifestations. The illness exhibits a remitting and relapsing course, with varying degrees of recovery among affected individuals with most experiencing significant social and functional impairment. Genetic risk factors likely include thousands of common genetic variants that each have a small impact on an individual's risk and a plethora of rare gene variants that have a larger individual impact on risk. Their biological effects are concentrated in the brain and many of the same variants also increase the risk of other psychiatric disorders such as bipolar disorder, autism, and other neurodevelopmental conditions. Environmental risk factors include but are not limited to urban residence in childhood, migration, older paternal age at birth, cannabis use, childhood trauma, antenatal maternal infection, and perinatal hypoxia. Structural, functional, and neurochemical brain alterations implicate multiple regions and functional circuits. Dopamine D-2 receptor antagonists and partial agonists improve psychotic symptoms and reduce risk of relapse. Certain psychological and psychosocial interventions are beneficial. Early intervention can reduce treatment delay and improve outcomes. Schizophrenia is increasingly considered to be a heterogeneous syndrome and not a singular disease entity. There is no necessary or sufficient etiology, pathology, set of clinical features, or treatment that fully circumscribes this syndrome. A single, common pathophysiological pathway appears unlikely. The boundaries of schizophrenia remain fuzzy, suggesting the absence of a categorical fit and need to reconceptualize it as a broader, multi-dimensional and/or spectrum construct.

A genome-wide association study of social trust in 33,882 Danish blood donors

Social trust is a heritable trait that has been linked with physical health and longevity. In this study, we performed genome-wide association studies of self-reported social trust in n = 33,882 Danish blood donors. We observed genome-wide and local evidence of genetic similarity with other brain-related phenotypes and estimated the single nucleotide polymorphism-based heritability of trust to be 6% (95% confidence interval = (2.1, 9.9)). In our discovery cohort (n = 25,819), we identified one significantly associated locus (lead variant: rs12776883) in an intronic enhancer region of PLPP4, a gene highly expressed in brain, kidneys, and testes. However, we could not replicate the signal in an independent set of donors who were phenotyped a year later (n = 8063). In the subsequent meta-analysis, we found a second significantly associated variant (rs71543507) in an intergenic enhancer region. Overall, our work confirms that social trust is heritable, and provides an initial look into the genetic factors that influence it.

Ethnicity-dependent effect of rs1799971 polymorphism on OPRM1 expression in the postmortem brain and responsiveness to antipsychotics

Schizophrenia is associated with aberration of inhibitory neurons. Although the mu-opioid receptor (MOR) is an essential modulator of inhibitory neurons, the effect of rs1799971 polymorphism in the MOR gene on risk of schizophrenia is controversial. Moreover, the disturbance of opioids systems in patients with schizophrenia has not been fully examined. We firstly conducted preliminary meta-analyses integrating Asian and European populations separately over 12,000 subjects to assess the effect of rs1799971 on risk of schizophrenia. Based on the above result, we also investigated the effect on the expression levels of MOR mRNA in the prefrontal cortex (PFC) and caudate nucleus of 41 postmortem brains. In addition, we determined whether these levels were related to antemortem schizophrenia symptoms and pharmacotherapeutic effects. The rs1799971 G-allele reduced the risk of schizophrenia in Asian populations (OR: 0.56, 95%CI: 0.32-0.98, p = 0.042) but increased it in European populations (OR: 1.66, 95%CI: 1.08-2.56, p = 0.022). It decreased MOR mRNA levels in PFC in the Japanese population (p = 0.031). Increased MOR mRNA level in PFC correlated with higher total score of antemortem schizophrenia symptoms (p = 0.017). Furthermore, the pharmacotherapeutic effect of first-generation antipsychotics was higher for genotype AA than AG/GG of rs1799971 (p = 0.036). The rs1799971 affects risk of schizophrenia and MOR mRNA expression and the effect varies according to ethnicity. Overexpression of MOR might induce severe schizophrenia symptoms. Therefore, MOR modulation may be the key clue for treating antipsychotics-resistant schizophrenia, and genotyping rs1799971 may provide a better pharmacotherapeutic strategy.

Determination of genetic correlation between tobacco smoking and coronary artery disease

Backgrounds

Tobacco smoking is an important risk factor for coronary artery disease (CAD), but the genetic mechanisms linking smoking to CAD remain largely unknown.

Methods

We analyzed summary data from the genome-wide association study (GWAS) of the UK Biobank for CAD, plasma lipid concentrations (n = 184,305), and smoking (n = 337,030) using different biostatistical methods, which included LD score regression and Mendelian randomization (MR).

Results

We identified SNPs shared by CAD and at least one smoking behavior, the genes where these SNPs are located were found to be significantly enriched in the processes related to lipoprotein metabolic, chylomicron-mediated lipid transport, lipid digestion, mobilization, and transport. The MR analysis revealed a positive correlation between smoking cessation and decreased risk for CAD when smoking cessation was considered as exposure (p = 0.001), and a negative correlation between the increased risk for CAD and smoking cessation when CAD was considered as exposure (p = 2.95E-08). This analysis further indicated that genetic liability for smoking cessation increased the risk of CAD.

Conclusion

These findings inform the concomitant conditions of CAD and smoking and support the idea that genetic liabilities for smoking behaviors are strongly associated with the risk of CAD.

Mendelian Randomization Using the Druggable Genome Reveals Genetically Supported Drug Targets for Psychiatric Disorders

BACKGROUND AND HYPOTHESIS

Psychiatric disorders impose a huge health and economic burden on modern society. However, there is currently no proven completely effective treatment available, partly owing to the inefficiency of drug target identification and validation. We aim to identify therapeutic targets relevant to psychiatric disorders by conducting Mendelian randomization (MR) analysis.

STUDY DESIGN

We performed genome-wide MR analysis by integrating expression quantitative trait loci (eQTL) of 4479 actionable genes that encode druggable proteins and genetic summary statistics from genome-wide association studies of psychiatric disorders. After conducting colocalization analysis on the brain MR findings, we employed protein quantitative trait loci (pQTL) data as genetic proposed instruments for intersecting the colocalized genes to provide further genetic evidence.

STUDY RESULTS

By performing MR and colocalization analysis with eQTL genetic instruments, we obtained 31 promising drug targets for psychiatric disorders, including 21 significant genes for schizophrenia, 7 for bipolar disorder, 2 for depression, 1 for attention deficit and hyperactivity (ADHD) and none for autism spectrum disorder. Combining MR results using pQTL genetic instruments, we finally proposed 8 drug-targeting genes supported by the strongest MR evidence, including gene ACE, BTN3A3, HAPLN4, MAPK3 and NEK4 for schizophrenia, gene NEK4 and HAPLN4 for bipolar disorder, and gene TIE1 for ADHD.

CONCLUSIONS

Our findings with genetic support were more likely to be to succeed in clinical trials. In addition, our study prioritizes approved drug targets for the development of new therapies and provides critical drug reuse opportunities for psychiatric disorders.

Underlying causes for prevalent false positives and false negatives in STARR-seq data

Self-transcribing active regulatory region sequencing (STARR-seq) and its variants have been widely used to characterize enhancers. However, it has been reported that up to 87% of STARR-seq peaks are located in repressive chromatin and are not functional in the tested cells. While some of the STARR-seq peaks in repressive chromatin might be active in other cell/tissue types, some others might be false positives. Meanwhile, many active enhancers may not be identified by the current STARR-seq methods. Although methods have been proposed to mitigate systematic errors caused by the use of plasmid vectors, the artifacts due to the intrinsic limitations of current STARR-seq methods are still prevalent and the underlying causes are not fully understood. Based on predicted cis-regulatory modules (CRMs) and non-CRMs in the human genome as well as predicted active CRMs and non-active CRMs in a few human cell lines/tissues with STARR-seq data available, we reveal prevalent false positives and false negatives in STARR-seq peaks generated by major variants of STARR-seq methods and possible underlying causes. Our results will help design strategies to improve STARR-seq methods and interpret the results.

Genome-wide association studies: utility and limitations for research in physiology

Physiological systems are subject to interindividual variation encoded by genetics. Genome-wide association studies (GWAS) operate by surveying thousands of genetic variants from a substantial number of individuals and assessing their association to a trait of interest, be it a physiological variable, a molecular phenotype (e.g. gene expression), or even a disease or condition. Through a myriad of methods, GWAS downstream analyses then explore the functional consequences of each variant and attempt to ascertain a causal relationship to the phenotype of interest, as well as to delve into its links to other traits. This type of investigation allows mechanistic insights into physiological functions, pathological disturbances and shared biological processes between traits (i.e. pleiotropy). An exciting example is the discovery of a new thyroid hormone transporter (SLC17A4) and hormone metabolising enzyme (AADAT) from a GWAS on free thyroxine levels. Therefore, GWAS have substantially contributed with insights into physiology and have been shown to be useful in unveiling the genetic control underlying complex traits and pathological conditions; they will continue to do so with global collaborations and advances in genotyping technology. Finally, the increasing number of trans-ancestry GWAS and initiatives to include ancestry diversity in genomics will boost the power for discoveries, making them also applicable to non-European populations.

Structural aging of human neurons is opposite of the changes in schizophrenia

Human mentality develops with age and is altered in psychiatric disorders, though their underlying mechanism is unknown. In this study, we analyzed nanometer-scale three-dimensional structures of brain tissues of the anterior cingulate cortex from eight schizophrenia and eight control cases. The distribution profiles of neurite curvature of the control cases showed a trend depending on their age, resulting in an age-correlated decrease in the standard deviation of neurite curvature (Pearson's r = -0.80, p = 0.018). In contrast to the control cases, the schizophrenia cases deviate upward from this correlation, exhibiting a 60% higher neurite curvature compared with the controls (p = 7.8 × 10-4). The neurite curvature also showed a correlation with a hallucination score (Pearson's r = 0.80, p = 1.8 × 10-4), indicating that neurite structure is relevant to brain function. This report is based on our 3D analysis of human brain tissues over a decade and is unprecedented in terms of the number of cases. We suggest that neurite curvature plays a pivotal role in brain aging and can be used as a hallmark to exploit a novel treatment of schizophrenia.

Larval zebrafish as a model for studying individual variability in translational neuroscience research

The larval zebrafish is a popular model for translational research into neurological and psychiatric disorders due to its conserved vertebrate brain structures, ease of genetic and experimental manipulation and small size and scalability to large numbers. The possibility of obtaining in vivo whole-brain cellular resolution neural data is contributing important advances into our understanding of neural circuit function and their relation to behavior. Here we argue that the larval zebrafish is ideally poised to push our understanding of how neural circuit function relates to behavior to the next level by including considerations of individual differences. Understanding variability across individuals is particularly relevant for tackling the variable presentations that neuropsychiatric conditions frequently show, and it is equally elemental if we are to achieve personalized medicine in the future. We provide a blueprint for investigating variability by covering examples from humans and other model organisms as well as existing examples from larval zebrafish. We highlight recent studies where variability may be hiding in plain sight and suggest how future studies can take advantage of existing paradigms for further exploring individual variability. We conclude with an outlook on how the field can harness the unique strengths of the zebrafish model to advance this important impending translational question.

Identification of schizophrenia symptom-related gene modules by postmortem brain transcriptome analysis

Schizophrenia is a multifactorial disorder, the genetic architecture of which remains unclear. Although many studies have examined the etiology of schizophrenia, the gene sets that contribute to its symptoms have not been fully investigated. In this study, we aimed to identify each gene set associated with corresponding symptoms of schizophrenia using the postmortem brains of 26 patients with schizophrenia and 51 controls. We classified genes expressed in the prefrontal cortex (analyzed by RNA-seq) into several modules by weighted gene co-expression network analysis (WGCNA) and examined the correlation between module expression and clinical characteristics. In addition, we calculated the polygenic risk score (PRS) for schizophrenia from Japanese genome-wide association studies, and investigated the association between the identified gene modules and PRS to evaluate whether genetic background affected gene expression. Finally, we conducted pathway analysis and upstream analysis using Ingenuity Pathway Analysis to clarify the functions and upstream regulators of symptom-related gene modules. As a result, three gene modules generated by WGCNA were significantly correlated with clinical characteristics, and one of these showed a significant association with PRS. Genes belonging to the transcriptional module associated with PRS significantly overlapped with signaling pathways of multiple sclerosis, neuroinflammation, and opioid use, suggesting that these pathways may also be profoundly implicated in schizophrenia. Upstream analysis indicated that genes in the detected module were profoundly regulated by lipopolysaccharides and CREB. This study identified schizophrenia symptom-related gene sets and their upstream regulators, revealing aspects of the pathophysiology of schizophrenia and identifying potential therapeutic targets.

Examining the shared etiology of psychopathology with genome-wide association studies

Genome-wide association studies (GWASs) have ushered in a new era of reproducible discovery in psychiatric genetics. The field has now identified hundreds of common genetic variants that are associated with mental disorders, and many of them influence more than one disorder. By advancing the understanding of causal biology underlying psychopathology, GWAS results are poised to inform the development of novel therapeutics, stratification of at-risk patients, and perhaps even the revision of top-down classification systems in psychiatry. Here, we provide a concise review of GWAS findings with an emphasis on findings that have elucidated the shared genetic etiology of psychopathology, summarizing insights at three levels of analysis: 1) genome-wide architecture; 2) networks, pathways, and gene sets; and 3) individual variants/genes. Three themes emerge from these efforts. First, all psychiatric phenotypes are heritable, highly polygenic, and influenced by many pleiotropic variants with incomplete penetrance. Second, GWAS results highlight the broad etiological roles of neuronal biology, system-wide effects over localized effects, and early neurodevelopment as a critical period. Third, many loci that are robustly associated with multiple forms of psychopathology harbor genes that are involved in synaptic structure and function. Finally, we conclude our review by discussing the implications that GWAS results hold for the field of psychiatry, as well as expected challenges and future directions in the next stage of psychiatric genetics.

Genomic analysis of firework fear and noise reactivity in standard poodles

BACKGROUND

Fear of firework noises and other loud, sudden noises (noise reactivity) is a significant problem for many dogs and may have a negative effect on both welfare and, in severe cases, the life expectancy of dogs. A wide range of behavior traits, including fear-related behaviors, have high heritability estimates in dogs. The aim of this study was to estimate genomic heritability for fear of fireworks and loud noises in dogs.

RESULTS

A genomic heritability estimate was performed based on genome-wide SNPs from standard poodles with records of fear of fireworks and noise reactivity. The study was based on questionnaires answered by owners, who also volunteered to return a cheek swab from their dog for DNA analyses. SNP-based heritability was estimated to be 0.28 for firework fear and 0.16 for noise reactivity. We also identified an interesting region on chromosome 17 that was weakly associated with both traits.

CONCLUSIONS

We have estimated low to medium genomic heritabilities for fear of fireworks and noise reactivity in standard poodles. We have also identified an interesting region on chromosome 17, which harbors genes that have been shown to be involved in different psychiatric traits with anxiety components in humans. The region was associated with both traits; however, the association was weak and need further verification from other studies.

Genetic analysis of a large Han Chinese family line with schizoaffective psychosis

To locate the specific susceptibility genes of a high incidence of schizoaffective disease (SAD) with autonomic dominant inheritance, we recruited a family group from Henan Province with a high incidence of SAD, including 19 individuals sampled from five generations. We used a genome-wide high-density SNP chip to perform genotype detection. The LINKAGE package and MENDEL programs were used for. The two-point and multipoint analyses were calculated by Merlin and SimWalk2 software to obtain the nonparametric linkage (NPL) value, corresponding P value, and parameter linkage limit of detection (LOD) value. Genome-wide linkage analysis yielded a significant linkage signal located on the short arm of chromosome 19. In the dominant genetic model, the LOD of the multipoint parametric analysis was 2.5, and the nonparametric analysis was 19.4 (P < 0.00001). Further haploid genotype analysis localized the candidate region in the 19p13.3-13.2 region, beginning at rs178414 and ending at rs11668751 with a physical length of approximately 4.9 Mb. We believe that the genes responsible for SAD are in this region.

Current State of Modeling Human Psychiatric Disorders Using Zebrafish

Psychiatric disorders are highly prevalent brain pathologies that represent an urgent, unmet biomedical problem. Since reliable clinical diagnoses are essential for the treatment of psychiatric disorders, their animal models with robust, relevant behavioral and physiological endpoints become necessary. Zebrafish (Danio rerio) display well-defined, complex behaviors in major neurobehavioral domains which are evolutionarily conserved and strikingly parallel to those seen in rodents and humans. Although zebrafish are increasingly often used to model psychiatric disorders, there are also multiple challenges with such models as well. The field may therefore benefit from a balanced, disease-oriented discussion that considers the clinical prevalence, the pathological complexity, and societal importance of the disorders in question, and the extent of its detalization in zebrafish central nervous system (CNS) studies. Here, we critically discuss the use of zebrafish for modeling human psychiatric disorders in general, and highlight the topics for further in-depth consideration, in order to foster and (re)focus translational biological neuroscience research utilizing zebrafish. Recent developments in molecular biology research utilizing this model species have also been summarized here, collectively calling for a wider use of zebrafish in translational CNS disease modeling.

Why does the X chromosome lag behind autosomes in GWAS findings?

The X-chromosome is among the largest human chromosomes. It differs from autosomes by a number of important features including hemizygosity in males, an almost complete inactivation of one copy in females, and unique patterns of recombination. We used data from the Catalog of Published Genome Wide Association Studies to compare densities of the GWAS-detected SNPs on the X-chromosome and autosomes. The density of GWAS-detected SNPs on the X-chromosome is 6-fold lower compared to the density of the GWAS-detected SNPs on autosomes. Differences between the X-chromosome and autosomes cannot be explained by differences in the overall SNP density, lower X-chromosome coverage by genotyping platforms or low call rate of X-chromosomal SNPs. Similar differences in the density of GWAS-detected SNPs were found in female-only GWASs (e.g. ovarian cancer GWASs). We hypothesized that the lower density of GWAS-detected SNPs on the X-chromosome compared to autosomes is not a result of a methodological bias, e.g. differences in coverage or call rates, but has a real underlying biological reason-a lower density of functional SNPs on the X-chromosome versus autosomes. This hypothesis is supported by the observation that (i) the overall SNP density of X-chromosome is lower compared to the SNP density on autosomes and that (ii) the density of genic SNPs on the X-chromosome is lower compared to autosomes while densities of intergenic SNPs are similar.

The Joint Effect of Body Mass Index and Serum Lipid Levels on Incident Dementia among Community-Dwelling Older Adults

OBJECTIVES

This study aimed to explore the joint effect of body mass index (BMI) and serum lipids levels on incident dementia.

METHODS

We prospectively followed up with 1,627 dementia-free community residents aged ≥60 for 5.7 years on average. At baseline, weight, and height were measured, and total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were detected in serum. Demographic characteristics were collected through questionnaires. Dementia was based on consensus diagnosis of neurologists and neuropsychologists using DSM-IV criteria. Additive Cox proportional model was used to assess the exposure-response relationship between BMI and serum lipid levels and dementia risk. Interactions and further classifications of BMI and serum lipid levels were further presented by bivariate surface models and decision-tree models.

RESULTS

The joint effects of TC with BMI, TG with BMI, and LDL-C with BMI on the risk of incident dementia shared a similar pattern, different from their independent exposure-response curves. The joint effect of HDL-C with BMI showed an S-surface but without statistical significance. Participants with TC<5.4 mmol/L and BMI<21 kg/m2 (Hazard Ratio(HR) 1.93, 95% Confidence Interval (CI) 1.05-3.53), TC<5.4 mmol/L and BMI≥21 kg/m2 (HR 1.73, 95% CI 1.09-2.72), and TC≥5.4 mmol/L and BMI<21 kg/m2 (HR 4.02, 95% CI 2.10-7.71) were identified to have the increased risk of incident dementia compared to those with TC≥5.4 mmol/L and BMI≥21 kg/m2. Participants with TG<1.7 mmol/L and BMI<21 kg/m2 had an increased risk of incident dementia compared to those with TG≥1.7 mmol/L and BMI≥21 kg/m2 (HR 1.98, 95%CI 1.17-3.3). Participants with LDL-C≥3.3 mmol/L and BMI<21 kg/m2 were identified to have an increased risk of incident dementia compared to those with LDL-C≥3.3 mmol/L and BMI≥21 kg/m2 (HR 3.33, 95%CI 1.64-6.78).

CONCLUSIONS

Our study showed that low BMI combined with low or high levels of serum lipids may increase the risk of dementia among older adults. This finding suggests the potential impacts of these two metabolic indexes on the risk of dementia.

Genomics in Treatment Development

The Human Genome Project mapped the 3 billion base pairs in the human genome, which ushered in a new generation of genomically focused treatment development. While this has been very successful in other areas, neuroscience has been largely devoid of such developments. This is in large part because there are very few neurological or mental health conditions that are related to single-gene variants. While developments in pharmacogenomics have been somewhat successful, the use of genetic information in practice has to do with drug metabolism and adverse reactions. Studies of drug metabolism related to genetic variations are an important part of drug development. However, outside of cancer biology, the actual translation of genomic information into novel therapies has been limited. Epigenetics, which relates in part to the effects of the environment on DNA, is a promising newer area of relevance to CNS disorders. The environment can induce chemical modifications of DNA (e.g., cytosine methylation), which can be induced by the environment and may represent either shorter- or longer-term changes. Given the importance of environmental influences on CNS disorders, epigenetics may identify important treatment targets in the future.

Current Status of Therapeutic Drug Monitoring in Mental Health Treatment: A Review

Therapeutic drug monitoring (TDM) receives growing interest in different psychiatric clinical settings (emergency, inpatient, and outpatient services). Despite its usefulness, TDM remains underemployed in mental health. This is partly due to the need for evidence about the relationship between drug serum concentration and efficacy and tolerability, both in the general population and even more in subpopulations with atypical pharmacokinetics. This work aims at reviewing the scientific literature published after 2017, when the most recent guidelines about the use of TDM in mental health were written. We found 164 pertinent records that we included in the review. Some promising studies highlighted the possibility of correlating early drug serum concentration and clinical efficacy and safety, especially for antipsychotics, potentially enabling clinicians to make decisions on early laboratory findings and not proceeding by trial and error. About populations with pharmacokinetic peculiarities, the latest studies confirmed very common alterations in drug blood levels in pregnant women, generally with a progressive decrease over pregnancy and a very relevant dose-adjusted concentration increase in the elderly. For adolescents also, several drugs result in having different dose-related concentration values compared to adults. These findings stress the recommendation to use TDM in these populations to ensure a safe and effective treatment. Moreover, the integration of TDM with pharmacogenetic analyses may allow clinicians to adopt precise treatments, addressing therapy on an individual pharmacometabolic basis. Mini-invasive TDM procedures that may be easily performed at home or in a point-of-care are very promising and may represent a turning point toward an extensive real-world TDM application. Although the highlighted recent evidence, research efforts have to be carried on: further studies, especially prospective and fixed-dose, are needed to replicate present findings and provide clearer knowledge on relationships between dose, serum concentration, and efficacy/safety.

Identification of New Genes and Genetic Variant Loci Associated with Breast Muscle Development in the Mini-Cobb F2 Chicken Population Using a Genome-Wide Association Study

Native chicken has become a favorite choice for consumers in many Asian countries recently, not only for its potential nutritional value but also for its deep ties to local food culture. However, low growth performance and limited meat production restrict their economic potential. Conducting a genome-wide association study (GWAS) for chicken-breast muscle development will help identify loci or candidate genes for different traits and potentially provide new insight into this phenotype in chickens and other species. To improve native chicken growth performance, especially breast muscle development, we performed a GWAS to explore the potential genetic mechanisms of breast muscle development in an F2 population constructed by reciprocal crosses between a fast-growing broiler chicken (Cobb500) and a slow-growing native chicken (Daweishan mini chicken). The results showed that 11 SNPs, which exceeded the 10% genome significance level (p = 1.79 × 10-8) were considered associated with breast muscle development traits, where six SNPS, NC_006126.5: g.3138376T>G, NC_006126.5: g.3138452A>G, NC_006088.5: g.73837197A>G, NC_006088.5: g.159574275A>G, NC_006089.5: g.80832197A>G, and NC_006127.5: g.48759869G>T was first identified in this study. In total, 13 genes near the SNPs were chosen as candidate genes, and none of them had previously been studied for their role in breast muscle development. After grouping the F2 population according to partial SNPs, significant differences in breast muscle weight were found among different genotypes (p < 0.05), and the expression levels of ALOX5AP, USPL1, CHRNA9, and EFNA5 among candidate genes were also significantly different (p < 0.05). The results of this study will contribute to the future exploration of the potential genetic mechanisms of breast muscle development in domestic chickens and also support the expansion of the market for native chicken in the world.

Unsupervised Machine Learning to Identify Depressive Subtypes

OBJECTIVES

This study evaluated an unsupervised machine learning method, latent Dirichlet allocation (LDA), as a method for identifying subtypes of depression within symptom data.

METHODS

Data from 18,314 depressed patients were used to create LDA models. The outcomes included future emergency presentations, crisis events, and behavioral problems. One model was chosen for further analysis based upon its potential as a clinically meaningful construct. The associations between patient groups created with the final LDA model and outcomes were tested. These steps were repeated with a commonly-used latent variable model to provide additional context to the LDA results.

RESULTS

Five subtypes were identified using the final LDA model. Prior to the outcome analysis, the subtypes were labeled based upon the symptom distributions they produced: psychotic, severe, mild, agitated, and anergic-apathetic. The patient groups largely aligned with the outcome data. For example, the psychotic and severe subgroups were more likely to have emergency presentations (odds ratio [OR] = 1.29; 95% confidence interval [CI], 1.17-1.43 and OR = 1.16; 95% CI, 1.05-1.29, respectively), whereas these outcomes were less likely in the mild subgroup (OR = 0.86; 95% CI, 0.78-0.94). We found that the LDA subtypes were characterized by clusters of unique symptoms. This contrasted with the latent variable model subtypes, which were largely stratified by severity.

CONCLUSIONS

This study suggests that LDA can surface clinically meaningful, qualitative subtypes. Future work could be incorporated into studies concerning the biological bases of depression, thereby contributing to the development of new psychiatric therapeutics.

GeneCup: mining PubMed and GWAS catalog for gene-keyword relationships

Interpreting and integrating results from omics studies typically requires a comprehensive and time consuming survey of extant literature. GeneCup is a literature mining web service that retrieves sentences containing user-provided gene symbols and keywords from PubMed abstracts. The keywords are organized into an ontology and can be extended to include results from human genome-wide association studies. We provide a drug addiction keyword ontology that contains over 300 keywords as an example. The literature search is conducted by querying the PubMed server using a programming interface, which is followed by retrieving abstracts from a local copy of the PubMed archive. The main results presented to the user are sentences where gene symbol and keywords co-occur. These sentences are presented through an interactive graphical interface or as tables. All results are linked to the original abstract in PubMed. In addition, a convolutional neural network is employed to distinguish sentences describing systemic stress from those describing cellular stress. The automated and comprehensive search strategy provided by GeneCup facilitates the integration of new discoveries from omic studies with existing literature. GeneCup is free and open source software. The source code of GeneCup and the link to a running instance is available at https://github.com/hakangunturkun/GeneCup.

The shallow cognitive map hypothesis: A hippocampal framework for thought disorder in schizophrenia

Memories are not formed in isolation. They are associated and organized into relational knowledge structures that allow coherent thought. Failure to express such coherent thought is a key hallmark of Schizophrenia. Here we explore the hypothesis that thought disorder arises from disorganized Hippocampal cognitive maps. In doing so, we combine insights from two key lines of investigation, one concerning the neural signatures of cognitive mapping, and another that seeks to understand lower-level cellular mechanisms of cognition within a dynamical systems framework. Specifically, we propose that multiple distinct pathological pathways converge on the shallowing of Hippocampal attractors, giving rise to disorganized Hippocampal cognitive maps and driving conceptual disorganization. We discuss the available evidence at the computational, behavioural, network, and cellular levels. We also outline testable predictions from this framework, including how it could unify major chemical and psychological theories of schizophrenia and how it can provide a rationale for understanding the aetiology and treatment of the disease.

SNP characteristics and validation success in genome wide association studies

Genome wide association studies (GWASs) have identified tens of thousands of single nucleotide polymorphisms (SNPs) associated with human diseases and characteristics. A significant fraction of GWAS findings can be false positives. The gold standard for true positives is an independent validation. The goal of this study was to identify SNP features associated with validation success. Summary statistics from the Catalog of Published GWASs were used in the analysis. Since our goal was an analysis of reproducibility, we focused on the diseases/phenotypes targeted by at least 10 GWASs. GWASs were arranged in discovery-validation pairs based on the time of publication, with the discovery GWAS published before validation. We used four definitions of the validation success that differ by stringency. Associations of SNP features with validation success were consistent across the definitions. The strongest predictor of SNP validation was the level of statistical significance in the discovery GWAS. The magnitude of the effect size was associated with validation success in a non-linear manner. SNPs with risk allele frequencies in the range 30-70% showed a higher validation success rate compared to rarer or more common SNPs. Missense, 5'UTR, stop gained, and SNPs located in transcription factor binding sites had a higher validation success rate compared to intergenic, intronic and synonymous SNPs. There was a positive association between validation success and the level of evolutionary conservation of the sites. In addition, validation success was higher when discovery and validation GWASs targeted the same ethnicity. All predictors of validation success remained significant in a multivariate logistic regression model indicating their independent contribution. To conclude, we identified SNP features predicting validation success of GWAS hits. These features can be used to select SNPs for validation and downstream functional studies.

Genome-Wide Analysis of Disordered Eating Behavior in the Mexican Population

Alterations in eating behavior characterized eating disorders (ED). The genetic factors shared between ED diagnoses have been underexplored. The present study performed a genome-wide association study in individuals with disordered eating behaviors in the Mexican population, blood methylation quantitative trait loci (blood-meQTL), summary data-based Mendelian randomization (SMR) analysis, and in silico function prediction by different algorithms. The analysis included a total of 1803 individuals. We performed a genome-wide association study and blood-meQTL analysis by logistic and linear regression. In addition, we analyzed in silico functional variant prediction, phenome-wide, and multi-tissue expression quantitative trait loci. The genome-wide association study identified 44 single-nucleotide polymorphisms (SNP) associated at a nominal value and seven blood-meQTL at a genome-wide threshold. The SNPs show enrichment in genome-wide associations of the metabolic and immunologic domains. In the in silico analysis, the SNP rs10419198 (p-value = 4.85 × 10-5) located on an enhancer mark could change the expression of PRR12 in blood, adipocytes, and brain areas that regulate food intake. Additionally, we found an association of DNA methylation levels of SETBP1 (p-value = 6.76 × 10-4) and SEMG1 (p-value = 5.73 × 10-4) by SMR analysis. The present study supports the previous associations of genetic variation in the metabolic domain with ED.

The Utility of Research Domain Criteria in Diagnosis and Management of Dual Disorders: A Mini-Review

The Research Domain Criteria (RDoC) initiative has been considered a comprehensive alternative classification framework for understanding neuropsychiatric ailments, as opposed to the longstanding, traditional DSM framework. Where the DSM categorizes neuropsychiatric disorders as each being distinct and diagnostically defined by the presence of specified symptoms, RDoC provides a multidimensional conceptualization of psychiatric disorders with neurobiological roots. By taking a multidimensional approach, RDoC overcomes two major constraints of the DSM framework: that is, that the DSM is categorical in its approach to psychiatric disorders to the point of understating the intersectionality between concomitant disorders, and that the DSM focuses mainly on clinical features. RDoC seems to better account for the intersection between dual disorders and considers a range of factors, from the more microscopic (e.g., genetics or molecular functions) to the more macroscopic (e.g., environmental influences). The multidimensional approach of RDoC is particularly appealing in the context of dual disorders. Dual disorders refers to a concurrent psychiatric disorder with an addiction disorder. RDoC accounts for the fact that there is often overlap in symptoms across and bidirectional influence between various disorders. However, to date, there is limited research into the clinical utility of RDoC, and less so in the context of the clinical management of dual disorders. In this Mini-Review, we discuss how RDoC differs from the DSM, what outcomes have been reported in utilizing RDoC clinically, the utility of RDoC for the diagnosis, management, and monitoring of psychopathology, and the limitations of RDoC as well as avenues for future research.

Transcriptomic profiling as biological markers of depression - A pilot study in unipolar and bipolar women

OBJECTIVES

A significant challenge in psychiatry is the differential diagnosis of depressive episodes in the course of mood disorders. Gene expression profiling may provide an opportunity for such distinguishment.

METHODS

We studied differentially expressed genes in women with a depressive episode in the course of unipolar depression (UD) (n = 24) and bipolar disorder types I (BDI) (n = 13) and II (BDII) (n = 19), and healthy controls (n = 15).

RESULTS

Different types of depression varied in the number and type of up or down-regulated genes. The pathway analysis showed: in UD, up-regulated rheumatoid arthritis pathway (including ITGB2, CXCL8, TEK, TLR4 genes), and down-regulated taste transduction pathway (TAS2R10, TAS2R46, TAS2R14, TAS2R43, TAS2R45, TAS2R19, TAS2R13, TAS2R20, GNG13); in BDI, eight down-regulated pathways: glutamatergic synapse, retrograde endocannabinoid signalling, axon guidance, calcium signalling, nicotine addiction, PI3K-Akt signalling, drug metabolism - cytochrome P450, and morphine addiction; in BDII, up-regulated osteoclast differentiation and Notch signalling pathway, and down-regulated type I diabetes mellitus pathway. Distinct expression markers analysis uncovered the unique for UD, up-regulated bladder cancer pathway (HBEGF and CXCL8 genes).

CONCLUSIONS

This pilot study suggests a probability of differentiating depression in the course of UD, BDI, and II, based on transcriptomic profiling.

Multi-Modal Data Analysis for Alzheimer's Disease Diagnosis: An Ensemble Model Using Imagery and Genetic Features

Alzheimer's disease (AD) is a devastating neurological disorder primarily affecting the elderly. An estimated 6.2 million Americans age 65 and older are suffering from Alzheimer's dementia today. Brain magnetic resonance imaging (MRI) is widely used for the clinical diagnosis of AD. In the meanwhile, medical researchers have identified 40 risk locus using single-nucleotide polymorphisms (SNPs) information from Genome-wide association study (GWAS) in the past decades. However, existing studies usually treat MRI and GWAS separately. For instance, convolutional neural networks are often trained using MRI for AD diagnosis. GWAS and SNPs are frequently used to identify genomic traits. In this study, we propose a multi-modal AD diagnosis neural network that uses both MRIs and SNPs. The proposed method demonstrates a novel way to use GWAS findings by directly including SNPs in predictive models. We test the proposed methods on the Alzheimer's Disease Neuroimaging Initiative dataset. The evaluation results show that the proposed method improves the model performance on AD diagnosis and achieves 93.5% AUC and 96.1% AP, respectively, when patients have both MRI and SNP data. We believe this work brings exciting new insights to GWAS applications and sheds light on future research directions.

d-Amino Acids and pLG72 in Alzheimer's Disease and Schizophrenia

Numerous studies over the last several years have shown that d-amino acids, especially d-serine, have been related to brain and neurological disorders. Acknowledged neurological functions of d-amino acids include neurotransmission and learning and memory functions through modulating N-methyl-d-aspartate type glutamate receptors (NMDARs). Aberrant d-amino acids level and polymorphisms of genes related to d-amino acids metabolism are associated with neurodegenerative brain conditions. This review summarizes the roles of d-amino acids and pLG72, also known as d-amino acid oxidase activator, on two neurodegenerative disorders, schizophrenia and Alzheimer’s disease (AD). The scope includes the changes in d-amino acids levels, gene polymorphisms of G72 genomics, and the role of pLG72 on NMDARs and mitochondria in schizophrenia and AD. The clinical diagnostic value of d-amino acids and pLG72 and the therapeutic importance are also reviewed.

Sex specific effect of gut microbiota on the risk of psychiatric disorders: A Mendelian randomisation study and PRS analysis using UK Biobank cohort

OBJECTIVE

The relationships between gut microbiota and brain-related diseases/traits remains not fully understood.

METHOD

A two-stage study was performed to investigate the relationships between gut microbiota and brain-related diseases/traits, and evaluate the potential sex specific effects of gut microbiota. In discovery stage, we systematically scanned the relationships between 515 brain-related diseases/traits and gut microbiota through two-sample Mendelian randomisation analysis. Using ∼500,000 individuals derived from the UK Biobank, polygenetic risk scoring (PRS) analysis was performed to validate the associations detected in discovery stage. To evaluate the potential sex-specific effect of gut microbiota on brain-related disorders, PRS analysis was conducted in female and male, respectively.

RESULTS

After systematically scanning diseases or traits, 41 of the 515 brain-related diseases/traits were identified to be associated with gut microbiota, such as Neuroticism score (P_2-MR = 0.0018), worrier/anxious feelings (P_2-MR = 0.0013), Suffer from 'nerves' (P_2-MR = 0.0062) and Nervous feelings (P_2-MR = 0.0158). 5 of 41 brain-related diseases or traits were successfully validated in UK Biobank, such as Neuroticism score (P_UK = 0.0024, P_UK-female = 0.0063, P_UK-male = 0.1142), Nervous feelings (P_UK = 0.0043, P_UK-female = 0.0115, P_UK-male = 0.1670) and Worrier/anxious feelings (P_UK = 0.0166, P_UK-female = 0.0196, P_UK-male = 0.2930).

CONCLUSION

Our results suggest that gut microbiota contributed more to brain-related diseases or traits in females than in males.Key pointsA two-stage study was performed to investigate the relationships between gut microbiota and brain-related diseases/traits.Using the individuals derived from the UK Biobank, polygenetic risk scoring analysis was performed to validate the associations detected in the discovery stage.Our results suggest that gut microbiota contributed more to brain-related diseases or traits in females than in males.

Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies

Large-scale, case-control genome-wide association studies (GWASs) have revealed genetic variations associated with diverse neurological and psychiatric disorders. Recent advances in neuroimaging and genomic databases of large healthy and diseased cohorts have empowered studies to characterize effects of the discovered genetic factors on brain structure and function, implicating neural pathways and genetic mechanisms in the underlying biology. However, the unprecedented scale and complexity of the imaging and genomic data requires new advanced biomedical data science tools to manage, process and analyze the data. In this work, we introduce Neuroimaging PheWAS (phenome-wide association study): a web-based system for searching over a wide variety of brain-wide imaging phenotypes to discover true system-level gene-brain relationships using a unified genotype-to-phenotype strategy. This design features a user-friendly graphical user interface (GUI) for anonymous data uploading, study definition and management, and interactive result visualizations as well as a cloud-based computational infrastructure and multiple state-of-art methods for statistical association analysis and multiple comparison correction. We demonstrated the potential of Neuroimaging PheWAS with a case study analyzing the influences of the apolipoprotein E (APOE) gene on various brain morphological properties across the brain in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Benchmark tests were performed to evaluate the system's performance using data from UK Biobank. The Neuroimaging PheWAS system is freely available. It simplifies the execution of PheWAS on neuroimaging data and provides an opportunity for imaging genetics studies to elucidate routes at play for specific genetic variants on diseases in the context of detailed imaging phenotypic data.

Genetic Studies of Mental Illness: Are Children Being Left Behind?

Understanding the genetic architecture of psychiatric disorders is paramount to linking psychopathologies to their genetic underpinnings. In turn, this knowledge can inform strategies for identifying high-risk individuals, early intervention, and development of personalized treatment approaches.^1,2 Over the past 2 decades, owing to lowering per capita costs and relative ease of analysis, a plethora of studies have used single nucleotide polymorphism genotyping and genome-wide association studies (GWASs) to unravel common and rare risk loci underlying psychiatric disorders and their endophenotypes.³ In contrast to the single allele focus of classical Mendelian inheritance, mental illnesses are often polygenic in nature with multiple common genetic variants, each contributing a small, but meaningful added risk. By interrogating the entire genome, GWASs have allowed the functional assessment of promising candidate genes in in vivo as well as in vitro models of psychiatric disease. Further, these findings have spawned the approach of calculating polygenic risk scores, a promising strategy for inferring genetic susceptibility to the development of psychopathology by taking into account the polygenic structure of psychiatric disorders.

Cultural Evolution of Genetic Heritability

Behavioral genetics and cultural evolution have both revolutionized our understanding of human behavior-largely independent of each other. Here we reconcile these two fields under a dual inheritance framework, offering a more nuanced understanding of the interaction between genes and culture. Going beyond typical analyses of gene-environment interactions, we describe the cultural dynamics that shape these interactions by shaping the environment and population structure. A cultural evolutionary approach can explain, for example, how factors such as rates of innovation and diffusion, density of cultural sub-groups, and tolerance for behavioral diversity impact heritability estimates, thus yielding predictions for different social contexts. Moreover, when cumulative culture functionally overlaps with genes, genetic effects become masked, unmasked, or even reversed, and the causal effects of an identified gene become confounded with features of the cultural environment. The manner of confounding is specific to a particular society at a particular time, but a WEIRD (Western, educated, industrialized, rich, democratic) sampling problem obscures this boundedness. Cultural evolutionary dynamics are typically missing from models of gene-to-phenotype causality, hindering generalizability of genetic effects across societies and across time. We lay out a reconciled framework and use it to predict the ways in which heritability should differ between societies, between socioeconomic levels and other groupings within some societies but not others, and over the life course. An integrated cultural evolutionary behavioral genetic approach cuts through the nature-nurture debate and helps resolve controversies in topics such as IQ.

Single-Nucleotide Polymorphisms Associated with the Senescence-Accelerated Phenotype of OXYS Rats: A Focus on Alzheimer's Disease-Like and Age-Related-Macular-Degeneration-Like Pathologies

Alzheimer's disease (AD) and age-related macular degeneration (AMD) are two complex incurable neurodegenerative disorders the common pathogenesis of which is actively discussed. There are overlapping risk factors and molecular mechanisms of the two diseases; at the same time, there are arguments in favor of the notion that susceptibility to each of these diseases is associated with a distinct genetic background. Here we identified single-nucleotide polymorphisms (SNPs) that are specific for senescence-accelerated OXYS rats, which simulate key characteristics of both sporadic AD and AMD. Transcriptomes of the hippocampus, prefrontal cortex, and retina (data of RNA-Seq) were analyzed. We detected SNPs in genes Rims2, AABR07072639.2, Lemd2, and AABR07045405.1, which thus can express significantly truncated proteins lacking functionally important domains. Additionally, 33 mutations in genes-which are related to various metabolic and signaling pathways-cause nonsynonymous amino acid substitutions presumably leading to disturbances in protein structure or functions. Some of the genes carrying these SNPs are associated with aging, neurodegenerative, and mental diseases. Thus, we revealed the SNPs can lead to abnormalities in protein structure or functions and affect the development of the senescence-accelerated phenotype of OXYS rats. Our data are consistent with the latest results of genome-wide association studies that highlight the importance of multiple pathways for the pathogenesis of AD and AMD. Identified SNPs can serve as promising research objects for further studies on the molecular mechanisms underlying this particular rat model as well as for the prediction of potential biomarkers of AD and AMD.

Activation of endogenous retroviruses during brain development causes an inflammatory response

Endogenous retroviruses (ERVs) make up a large fraction of mammalian genomes and are thought to contribute to human disease, including brain disorders. In the brain, aberrant activation of ERVs is a potential trigger for an inflammatory response, but mechanistic insight into this phenomenon remains lacking. Using CRISPR/Cas9‐based gene disruption of the epigenetic co‐repressor protein Trim28, we found a dynamic H3K9me3‐dependent regulation of ERVs in proliferating neural progenitor cells (NPCs), but not in adult neurons. In vivo deletion of Trim28 in cortical NPCs during mouse brain development resulted in viable offspring expressing high levels of ERVs in excitatory neurons in the adult brain. Neuronal ERV expression was linked to activated microglia and the presence of ERV‐derived proteins in aggregate‐like structures. This study demonstrates that brain development is a critical period for the silencing of ERVs and provides causal in vivo evidence demonstrating that transcriptional activation of ERV in neurons results in an inflammatory response.

Neurobiological underpinnings of cognitive subtypes in psychoses: A cross-diagnostic cluster analysis

Schizophrenia and bipolar disorder include patients with different characteristics, which may hamper the definition of biomarkers. One of the dimensions with greater heterogeneity among these patients is cognition. Recent studies support the identification of different patients' subgroups along the cognitive domain using cluster analysis. Our aim was to validate clusters defined on the basis of patients' cognitive status and to assess its relation with demographic, clinical and biological measurements. We hypothesized that subgroups characterized by different cognitive profiles would show differences in an array of biological data. Cognitive data from 198 patients (127 with chronic schizophrenia, 42 first episodes of schizophrenia and 29 bipolar patients) were analyzed by a K-means cluster approach and were compared on several clinical and biological variables. We also included 155 healthy controls for further comparisons. A two-cluster solution was selected, including a severely impaired group and a moderately impaired group. The severely impaired group was associated with higher illness duration and symptoms scores, lower thalamus and hippocampus volume, lower frontal connectivity and basal hypersynchrony in comparison to controls and the moderately impaired group. Moreover, both patients' groups showed lower cortical thickness and smaller functional connectivity modulation than healthy controls. This study supports the existence of different cognitive subgroups within the psychoses with different neurobiological underpinnings.

Leveraging large genomic datasets to illuminate the pathobiology of autism spectrum disorders

"Big data" approaches in the form of large-scale human genomic studies have led to striking advances in autism spectrum disorder (ASD) genetics. Similar to many other psychiatric syndromes, advances in genotyping technology, allowing for inexpensive genome-wide assays, has confirmed the contribution of polygenic inheritance involving common alleles of small effect, a handful of which have now been definitively identified. However, the past decade of gene discovery in ASD has been most notable for the application, in large family-based cohorts, of high-density microarray studies of submicroscopic chromosomal structure as well as high-throughput DNA sequencing-leading to the identification of an increasingly long list of risk regions and genes disrupted by rare, de novo germline mutations of large effect. This genomic architecture offers particular advantages for the illumination of biological mechanisms but also presents distinctive challenges. While the tremendous locus heterogeneity and functional pleiotropy associated with the more than 100 identified ASD-risk genes and regions is daunting, a growing armamentarium of comprehensive, large, foundational -omics databases, across species and capturing developmental trajectories, are increasingly contributing to a deeper understanding of ASD pathology.

Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome

Discovering drugs that efficiently treat brain diseases has been challenging. Genetic variants that modulate the expression of potential drug targets can be utilized to assess the efficacy of therapeutic interventions. We therefore employed Mendelian Randomization (MR) on gene expression measured in brain tissue to identify drug targets involved in neurological and psychiatric diseases. We conducted a two-sample MR using cis-acting brain-derived expression quantitative trait loci (eQTLs) from the Accelerating Medicines Partnership for Alzheimer's Disease consortium (AMP-AD) and the CommonMind Consortium (CMC) meta-analysis study (n = 1,286) as genetic instruments to predict the effects of 7,137 genes on 12 neurological and psychiatric disorders. We conducted Bayesian colocalization analysis on the top MR findings (using P<6x10-7 as evidence threshold, Bonferroni-corrected for 80,557 MR tests) to confirm sharing of the same causal variants between gene expression and trait in each genomic region. We then intersected the colocalized genes with known monogenic disease genes recorded in Online Mendelian Inheritance in Man (OMIM) and with genes annotated as drug targets in the Open Targets platform to identify promising drug targets. 80 eQTLs showed MR evidence of a causal effect, from which we prioritised 47 genes based on colocalization with the trait. We causally linked the expression of 23 genes with schizophrenia and a single gene each with anorexia, bipolar disorder and major depressive disorder within the psychiatric diseases and 9 genes with Alzheimer's disease, 6 genes with Parkinson's disease, 4 genes with multiple sclerosis and two genes with amyotrophic lateral sclerosis within the neurological diseases we tested. From these we identified five genes (ACE, GPNMB, KCNQ5, RERE and SUOX) as attractive drug targets that may warrant follow-up in functional studies and clinical trials, demonstrating the value of this study design for discovering drug targets in neuropsychiatric diseases.

Cutting-edge genetics in obsessive-compulsive disorder

This article reviews recent advances in the genetics of obsessive-compulsive disorder (OCD). We cover work on the following: genome-wide association studies, whole-exome sequencing studies, copy number variation studies, gene expression, polygenic risk scores, gene–environment interaction, experimental animal systems, human cell models, imaging genetics, pharmacogenetics, and studies of endophenotypes. Findings from this work underscore the notion that the genetic architecture of OCD is highly complex and shared with other neuropsychiatric disorders. Also, the latest evidence points to the participation of gene networks involved in synaptic transmission, neurodevelopment, and the immune and inflammatory systems in this disorder. We conclude by highlighting that further study of the genetic architecture of OCD, a great part of which remains to be elucidated, could benefit the development of diagnostic and therapeutic approaches based on the biological basis of the disorder. Studies to date revealed that OCD is not a simple homogeneous entity, but rather that the underlying biological pathways are variable and heterogenous. We can expect that translation from bench to bedside, through continuous effort and collaborative work, will ultimately transform our understanding of what causes OCD and thus how best to treat it.

Genetic Basis of Aerobically Supported Voluntary Exercise: Results from a Selection Experiment with House Mice

The biological basis of exercise behavior is increasingly relevant for maintaining healthy lifestyles. Various quantitative genetic studies and selection experiments have conclusively demonstrated substantial heritability for exercise behavior in both humans and laboratory rodents. In the "High Runner" selection experiment, four replicate lines of Mus domesticus were bred for high voluntary wheel running (HR), along with four nonselected control (C) lines. After 61 generations, the genomes of 79 mice (9-10 from each line) were fully sequenced and single nucleotide polymorphisms (SNPs) were identified. We used nested ANOVA with MIVQUE estimation and other approaches to compare allele frequencies between the HR and C lines for both SNPs and haplotypes. Approximately 61 genomic regions, across all somatic chromosomes, showed evidence of differentiation; 12 of these regions were differentiated by all methods of analysis. Gene function was inferred largely using Panther gene ontology terms and KO phenotypes associated with genes of interest. Some of the differentiated genes are known to be associated with behavior/motivational systems and/or athletic ability, including Sorl1, Dach1, and Cdh10 Sorl1 is a sorting protein associated with cholinergic neuron morphology, vascular wound healing, and metabolism. Dach1 is associated with limb bud development and neural differentiation. Cdh10 is a calcium ion binding protein associated with phrenic neurons. Overall, these results indicate that selective breeding for high voluntary exercise has resulted in changes in allele frequencies for multiple genes associated with both motivation and ability for endurance exercise, providing candidate genes that may explain phenotypic changes observed in previous studies.

Donor genetic backgrounds contribute to the functional heterogeneity of stem cells and clinical outcomes

Stable and sustainable stem cell sources for stem cell‐based therapies are scarce and a key bottleneck for clinical applications. The regenerative potential of stem cells is usually attributed to several allogeneic or even autologous donor‐related factors. Genetic background and epigenetic variations in different individuals may significantly affect the functional heterogeneity of stem cells. Particularly, single‐nucleotide polymorphisms (SNPs) have been implicated in diseases with monogenetic or multifactorial and complex genetic etiologies. However, the possible effects of individual SNPs on donor stem cells remain far from fully elucidated. In this Perspective, we will discuss the roles played by donor genetic traits in the functional heterogeneity of induced pluripotent stem cells, mesenchymal stem cells, and hematopoietic stem cells and their implications for regenerative medicine and therapy.

Polygenic risk score as clinical utility in psychiatry: a clinical viewpoint

Genome-wide association studies (GWASs) have detected many susceptible variants for common diseases, including psychiatric disorders. However, because of the small effect size of each variant, clinical utility that aims for risk prediction and/or diagnostic assistance based on the individual "variants" is difficult to use. Therefore, to improve the statistical power, polygenic risk score (PRS) has been established and applied in the GWAS as a robust analytic tool. Although PRS has potential predictive ability, because of its current "insufficient" discriminative power at the individual level for clinical use, it remains limited solely in the research area, specifically in the psychiatric field. For a better understanding of the PRS, in this review, we (1) introduce the clinical features of psychiatric disorders, (2) summarize the recent GWAS/PRS findings in the psychiatric disorders, (3) evaluate the problems of PRS, and (4) propose its possible utility to apply PRS into the psychiatric clinical setting.

A TOMM40/APOE allele encoding APOE-E3 predicts high likelihood of late-onset Alzheimer's disease in autopsy cases

BACKGROUND

The APOE-ε4 allele is an established risk factor for Alzheimer's disease (AD). TOMM40 located adjacent to APOE has also been implicated in AD but reports of TOMM40 associations with AD that are independent of APOE-ε4 are at variance.

METHODS

We investigated associations of AD with haplotypes defined by three TOMM40 and two APOE single nucleotide polymorphisms in 73 and 71 autopsy cases with intermediate and high likelihood of AD (defined by BRAAK stages

RESULTS

We observed eight haplotypes with a frequency >0.02. The two haplotypes encoding APOE-E4 showed strong associations with AD that did not differ between intermediate and high likelihood AD. In contrast, a TOMM40 haplotype encoding APOE-E3 was identified as risk haplotype of high- (p = .0186), but not intermediate likelihood AD (p = .7530). Furthermore, the variant allele of rs2075650 located in intron 2 of TOMM40, increased the risk of high-, but not intermediate likelihood AD on the APOE-ε3/ε3 background (p = .0230).

CONCLUSION

The striking association of TOMM40 only with high likelihood AD may explain some contrasting results for TOMM40 in clinical studies and may reflect an association with more advanced disease and/or suggest a role of TOMM40 in the pathogenesis of neurofibrillary tangles.

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Key Words

• APOE
• Alzheimer’ disease
• TOMM40
• beta-amyloid
• genetics
• haplotypes
• neurofibrillary tangles

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MESH Headings

• Aged
• Aged, 80 and over
• Alzheimer Disease/genetics
• Alzheimer Disease/pathology
• Apolipoproteins E/genetics
• Female
• Gene Frequency
• Haplotypes
• Humans
• Male
• Membrane Transport Proteins/genetics
• Mitochondrial Precursor Protein Import Complex Proteins
• Polymorphism, Single Nucleotide

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Grants

• V 344 Austrian Science Fund FWF
• Austrian Science Fund
• Kurt und Senta Herrmann Stiftung

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Affiliation(s)

Selma M. Soyal Institute of Pharmacology and ToxicologyParacelsus Medical UniversitySalzburgAustria
Markus Kwik Institute of Pharmacology and ToxicologyParacelsus Medical UniversitySalzburgAustria
Ognian Kalev Division of NeuropathologyNeuromed Campus, Kepler University HospitalLinzAustria
Stefan Lenz Division of NeuropathologyNeuromed Campus, Kepler University HospitalLinzAustria
Greta Zara Institute of Pharmacology and ToxicologyParacelsus Medical UniversitySalzburgAustria
Peter Strasser Institute of Laboratory MedicineParacelsus Medical UniversitySalzburgAustria
Wolfgang Patsch Institute of Pharmacology and ToxicologyParacelsus Medical UniversitySalzburgAustria
Serge Weis Division of NeuropathologyNeuromed Campus, Kepler University HospitalLinzAustria

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Transposable Elements: A Common Feature of Neurodevelopmental and Neurodegenerative Disorders

The etiology of most neurological disorders is poorly understood and current treatments are largely ineffective. New ideas and concepts are therefore vitally important for future research in this area. This review explores the concept that dysregulation of transposable elements (TEs) contributes to the appearance and pathology of neurodevelopmental and neurodegenerative disorders. Despite TEs making up at least half of the human genome, they are vastly understudied in relation to brain disorders. However, recent advances in sequencing technologies and gene editing approaches are now starting to unravel the pathological role of TEs. Aberrant activation of TEs has been found in many neurological disorders; the resulting pathogenic effects, which include alterations of gene expression, neuroinflammation, and direct neurotoxicity, are starting to be resolved. An increased understanding of the relationship between TEs and pathological processes in the brain improves the potential for novel diagnostics and interventions for brain disorders.

Protocol Across study: longitudinal transdiagnostic cognitive functioning, psychiatric symptoms, and biological parameters in patients with a psychiatric disorder

BACKGROUND

Patients with psychiatric disorders, such as major depressive disorder, schizophrenia or obsessive-compulsive disorder, often suffer from cognitive dysfunction. The nature of these dysfunctions and their relation with clinical symptoms and biological parameters is not yet clear. Traditionally, cognitive dysfunction is studied in patients with specific psychiatric disorders, disregarding the fact that cognitive deficits are shared across disorders. The Across study aims to investigate cognitive functioning and its relation with psychiatric symptoms and biological parameters transdiagnostically and longitudinally.

METHODS

The study recruits patients diagnosed with a variety of psychiatric disorders and has a longitudinal cohort design with an assessment at baseline and at one-year follow-up. The primary outcome measure is cognitive functioning. The secondary outcome measures include clinical symptoms, electroencephalographic, genetic and blood markers (e.g., fatty acids), and hair cortisol concentration levels.

DISCUSSION

The Across study provides an opportunity for a transdiagnostic, bottom-up, data-driven approach of investigating cognition in relation to symptoms and biological parameters longitudinally in patients with psychiatric disorders. The study may help to find new clusters of symptoms, biological markers, and cognitive dysfunctions that have better prognostic value than the current diagnostic categories. Furthermore, increased insight into the relationship among cognitive deficits, biological parameters, and psychiatric symptoms can lead to new treatment possibilities.

TRIAL REGISTRATION

Netherlands Trial Register (NTR): NL8170.

Recent Efforts to Dissect the Genetic Basis of Alcohol Use and Abuse

Alcohol use disorder (AUD) is defined by several symptom criteria, which can be dissected further at the genetic level. Over the past several years, our understanding of the genetic factors influencing alcohol use and abuse has progressed tremendously; numerous loci have been implicated in different aspects of alcohol use. Previously known associations with alcohol-metabolizing enzymes (ADH1B, ALDH2) have been replicated definitively. In addition, novel associations with loci containing the genes KLB, GCKR, CRHR1, and CADM2 have been reported. Downstream analyses have leveraged these genetic findings to reveal important relationships between alcohol use behaviors and both physical and mental health. AUD and aspects of alcohol misuse have been shown to overlap strongly with psychiatric disorders, whereas aspects of alcohol consumption have shown stronger links to metabolism. These results demonstrate that the genetic architecture of alcohol consumption only partially overlaps with the genetics of clinically defined AUD. We discuss the limitations of using quantitative measures of alcohol use as proxy measures for AUD, and we outline how future studies will require careful phenotype harmonization to properly capture the genetic liability to AUD.

High polygenic burden is associated with blood DNA methylation changes in individuals with suicidal behavior

Suicidal behavior is result of the interaction of several contributors, including genetic and environmental factors. The integration of approaches considering the polygenic component of suicidal behavior, such as polygenic risk scores (PRS) and DNA methylation is promising for improving our understanding of the complex interplay between genetic and environmental factors in this behavior. The aim of this study was the evaluation of DNA methylation differences between individuals with high and low genetic burden for suicidality. The present study was divided into two phases. In the first phase, genotyping with the Psycharray chip was performed in a discovery sample of 568 Mexican individuals, of which 149 had suicidal behavior (64 individuals with suicidal ideation, 50 with suicide attempt and 35 with completed suicide). Then, a PRS analysis based on summary statistics from the Psychiatric Genomic Consortium was performed in the discovery sample. In a second phase, we evaluated DNA methylation differences between individuals with high and low genetic burden for suicidality in a sub-sample of the discovery sample (target sample) of 94 subjects. We identified 153 differentially methylated sites between individuals with low and high-PRS. Among genes mapped to differentially methylated sites, we found genes involved in neurodevelopment (CHD7, RFX4, KCNA1, PLCB1, PITX1, NUMBL) and ATP binding (KIF7, NUBP2, KIF6, ATP8B1, ATP11A, CLCN7, MYLK, MAP2K5). Our results suggest that genetic variants might increase the predisposition to epigenetic variations in genes involved in neurodevelopment. This study highlights the possible implication of polygenic burden in the alteration of epigenetic changes in suicidal behavior.

Association of CHRNA5 Gene Variants with Crack Cocaine Addiction

Genome-wide studies provide increasing evidence of association of genetic variants with different behaviors. However, there is a growing need for replication and subsequent characterization of specific findings. In this sense, the CHRNA5 gene has been associated with nicotine (with genome-wide significance), alcohol and cocaine addictions. So far, this gene has not been evaluated in smoked (crack) cocaine. We aimed to analyze the influence of CHRNA5 variants in crack addiction susceptibility and severity. The sample includes 300 crack-addicted patients and 769 non-addicted individuals. The CHRNA5 SNPs evaluated were rs588765, rs16969968, and rs514743. Homozygosity for rs16969968 and rs588765 major alleles was nominally associated with a risk to crack addiction (GG, P = 0.032; CC, P = 0.036, respectively). Haplotype analyses reveal significant associations (rs588765|rs16969968|rs514743 p_{global-corrected} = 7.66 × 10^-5) and suggest a substantial role for rs16969968. These findings corroborate previous reports in cocaine addiction-in line with the expected effects of cocaine in the cholinergic system-and in the opposite direction of significant GWAS findings for nicotine addiction susceptibility. These results are strengthened by the first report of an association of rs588765 with crack addiction and by the haplotype findings. In summary, our study highlights the relevance of the α5 subunit on crack cocaine addiction, replicating previous results relating CHRNA5 with the genetics and pathophysiology of addiction of different drugs.