Common polygenic variation contributes to risk of schizophrenia and bipolar disorder

Genome-wide association studies (GWAS) and post-GWAS analyses of impulsivity: A systematic review

Impulsivity is related to a host of mental and behavioral problems. It is a complex construct with many different manifestations, most of which are heritable. The genetic compositions of these impulsivity manifestations, however, remain unclear. A number of genome-wide association studies (GWAS) and post-GWAS analyses have tried to address this issue. We conducted a systematic review of all GWAS and post-GWAS analyses of impulsivity published up to December 2023. Available data suggest that single nucleotide polymorphisms (SNPs) in more than a dozen of genes (e.g., CADM2, CTNNA2, GPM6B) are associated with different measures of impulsivity at genome-wide significant levels. Post-GWAS analyses further show that different measures of impulsivity are subject to different degrees of genetic influence, share few genetic variants, and have divergent genetic overlap with basic personality traits such as extroversion and neuroticism, cognitive ability, psychiatric disorders, substance use, and obesity. These findings shed light on controversies in the conceptualization and measurement of impulsivity, while providing new insights on the underlying mechanisms that yoke impulsivity to psychopathology.

Recapitulation of Perturbed Striatal Gene Expression Dynamics of Donors' Brains With Ventral Forebrain Organoids Derived From the Same Individuals With Schizophrenia

OBJECTIVE

Schizophrenia is a brain disorder that originates during neurodevelopment and has complex genetic and environmental etiologies. Despite decades of clinical evidence of altered striatal function in affected patients, studies examining its cellular and molecular mechanisms in humans are limited. To explore neurodevelopmental alterations in the striatum associated with schizophrenia, the authors established a method for the differentiation of induced pluripotent stem cells (iPSCs) into ventral forebrain organoids (VFOs).

METHODS

VFOs were generated from postmortem dural fibroblast-derived iPSCs of four individuals with schizophrenia and four neurotypical control individuals for whom postmortem caudate genotypes and transcriptomic data were profiled in the BrainSeq neurogenomics consortium. Individuals were selected such that the two groups had nonoverlapping schizophrenia polygenic risk scores (PRSs).

RESULTS

Single-cell RNA sequencing analyses of VFOs revealed differences in developmental trajectory between schizophrenia and control individuals in which inhibitory neuronal cells from the patients exhibited accelerated maturation. Furthermore, upregulated genes in inhibitory neurons in schizophrenia VFOs showed a significant overlap with upregulated genes in postmortem caudate tissue of individuals with schizophrenia compared with control individuals, including the donors of the iPSC cohort.

CONCLUSIONS

The findings suggest that striatal neurons derived from high-PRS individuals with schizophrenia carry abnormalities that originated during early brain development and that the VFO model can recapitulate disease-relevant cell type-specific neurodevelopmental phenotypes in a dish.

The interaction between early life complications and a polygenic risk score for schizophrenia is associated with brain activity during emotion processing in healthy participants

BACKGROUND

Previous evidence suggests that early life complications (ELCs) interact with polygenic risk for schizophrenia (SCZ) in increasing risk for the disease. However, no studies have investigated this interaction on neurobiological phenotypes. Among those, anomalous emotion-related brain activity has been reported in SCZ, even if evidence of its link with SCZ-related genetic risk is not solid. Indeed, it is possible this relationship is influenced by non-genetic risk factors. Thus, this study investigated the interaction between SCZ-related polygenic risk and ELCs on emotion-related brain activity.

METHODS

169 healthy participants (HP) in a discovery and 113 HP in a replication sample underwent functional magnetic resonance imaging (fMRI) during emotion processing, were categorized for history of ELCs and genome-wide genotyped. Polygenic risk scores (PRSs) were computed using SCZ-associated variants considering the most recent genome-wide association study. Furthermore, 75 patients with SCZ also underwent fMRI during emotion processing to verify consistency of their brain activity patterns with those associated with risk factors for SCZ in HP.

RESULTS

Results in the discovery and replication samples indicated no effect of PRSs, but an interaction between PRS and ELCs in left ventrolateral prefrontal cortex (VLPFC), where the greater the activity, the greater PRS only in presence of ELCs. Moreover, SCZ had greater VLPFC response than HP.

CONCLUSIONS

These results suggest that emotion-related VLPFC response lies in the path from genetic and non-genetic risk factors to the clinical presentation of SCZ, and may implicate an updated concept of intermediate phenotype considering early non-genetic factors of risk for SCZ.

Association between the HHEX polymorphism and delayed memory in first-episode schizophrenic patients

The hematopoietically-expressed homeobox gene (HHEX) played a critical role in regulating the immune system that the abnormality of which was involved in the psychopathology and cognitive deficits of psychiatric disorders. The aim of this study was to investigate the effect of HHEX rs1111875 polymorphism on the susceptibility and cognitive deficits of first-episode schizophrenic patients (FSP). We assessed cognitive function in 239 first-episode patients meeting DSM-IV for schizophrenia, and 368 healthy controls using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). The HHEX rs1111875 polymorphism was genotyped. Our results showed that the allelic and genotypic frequencies of HHEX rs1111875 polymorphism didn't differ between FSP and healthy controls (both p > 0.05) after adjusting for sex and age. Cognitive test scores in FSP were significantly lower than those in healthy controls on all scales (all p < 0.001) except for the visuospatial/constructional score (p > 0.05) after adjusting for covariates. There was a significant genotype (p < 0.05) rather than genotype × diagnosis (p > 0.05) effect on the delayed memory score after adjusting for covariates. The HHEX rs1111875 polymorphism was significantly associated with the delayed memory score in FSP (p < 0.05), but not in healthy controls (p > 0.05) after adjusting for covariates. Our findings supported that the HHEX rs1111875 polymorphism did not contribute to the susceptibility to FSP. However, this polymorphism might influence the delayed memory in FSP. Moreover, FSP had poorer cognitive function than healthy controls except for the visuospatial/constructional domain.

A polygenic score method boosted by non-additive models

Dominance heritability in complex traits has received increasing recognition. However, most polygenic score (PGS) approaches do not incorporate non-additive effects. Here, we present GenoBoost, a flexible PGS modeling framework capable of considering both additive and non-additive effects, specifically focusing on genetic dominance. Building on statistical boosting theory, we derive provably optimal GenoBoost scores and provide its efficient implementation for analyzing large-scale cohorts. We benchmark it against seven commonly used PGS methods and demonstrate its competitive predictive performance. GenoBoost is ranked the best for four traits and second-best for three traits among twelve tested disease outcomes in UK Biobank. We reveal that GenoBoost improves prediction for autoimmune diseases by incorporating non-additive effects localized in the MHC locus and, more broadly, works best in less polygenic traits. We further demonstrate that GenoBoost can infer the mode of genetic inheritance without requiring prior knowledge. For example, GenoBoost finds non-zero genetic dominance effects for 602 of 900 selected genetic variants, resulting in 2.5% improvements in predicting psoriasis cases. Lastly, we show that GenoBoost can prioritize genetic loci with genetic dominance not previously reported in the GWAS catalog. Our results highlight the increased accuracy and biological insights from incorporating non-additive effects in PGS models.

Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations

BACKGROUND

The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing.

OBJECTIVE AND RATIONALE

This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom.

SEARCH METHODS

We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant.

OUTCOMES

The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling.

WIDER IMPLICATIONS

The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.

Epigenetic signatures of social anxiety, panic disorders and stress experiences: Insights from genome-wide DNA methylation risk scores

Social anxiety disorder (SAD) and panic disorder (PD) are prevalent anxiety disorders characterized by a complex interplay of genetic and environmental factors. Both disorders share overlapping features and often coexist, despite displaying distinct characteristics. Childhood life adversity, overall stressful life events, and genetic factors contribute to the development of these disorders. DNA methylation, an epigenetic modification, has been implicated in the pathogenesis of these diseases. In this study, we investigated whether whole-genome DNA methylation risk scores (MRSs) for SAD risk, severity of social anxiety, childhood life adversity, PD risk, and overall stressful life events were associated with SAD or PD case‒control status. Preliminary epigenome-wide association studies (EWASs) for SAD risk, severity of social anxiety, and childhood life adversity were conducted in 66 SAD individuals and 77 healthy controls (HCs). Similarly, EWASs for PD risk and overall stressful life events were performed in 182 PD individuals and 81 HCs. MRSs were calculated from these EWASs. MRSs derived from the EWASs of SAD risk and severity of social anxiety were greater in PD patients than in HCs. Additionally, MRSs derived from the EWASs of overall stressful life events, particularly in PD individuals, were lower in SAD individuals than in HCs. In contrast, MRSs for childhood life adversity or PD risk were not significantly associated with PD or SAD case‒control status. These findings highlight the epigenetic features shared in both disorders and the distinctive epigenetic features related to social avoidance in SAD patients, helping to elucidate the epigenetic basis of these disorders.

Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes

Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.

Gene - maltreatment interplay in adult ADHD symptoms: main role of a gene-environment correlation effect in a Brazilian population longitudinal study

Childhood maltreatment correlates with attention-deficit/hyperactivity disorder (ADHD) in previous research. The interaction between ADHD genetic predisposition and maltreatment's impact on ADHD symptom risk remains unclear. We aimed to elucidate this relationship by examining the interplay between a polygenic score for ADHD (ADHD-PGS) and childhood maltreatment in predicting ADHD symptoms during young adulthood. Using data from the 2004 Pelotas (Brazil) birth cohort comprising 4231 participants, we analyzed gene-environment interaction (GxE) and correlation (rGE). We further explored rGE mechanisms through mediation models. ADHD symptoms were assessed at age 18 via self-report (Adult Self Report Scale - ASRS) and mother-reports (Strength and Difficulties Questionnaire - SDQ). The ADHD-PGS was derived from published ADHD GWAS meta-analysis. Physical and psychological child maltreatment was gauged using the Parent-Child Conflict Tactics Scale (CTSPC) at ages 6 and 11, with a mean score utilized as a variable. The ADHD-PGS exhibited associations with ADHD symptoms on both ASRS (β = 0.53; 95% CI: 0.03; 1.03, p = 0.036), and SDQ (β = 0.20; 95% CI: 0.08; 0.32, p = 0.001) scales. The total mean maltreatment score was associated with ADHD symptoms using both scales [(βASRS = 0.51; 95% CI: 0.26;0.77) and (βSDQ = 0.24; 95% CI: 0.18;0.29)]. The ADHD-PGS was associated with total mean maltreatment scores (β = 0.09; 95% CI: 0.01; 0.17; p = 0.030). Approximately 47% of the total effect of ADHD-PGS on maltreatment was mediated by ADHD symptoms at age 6. No evidence supported gene-environment interaction in predicting ADHD symptoms. Our findings underscore the significant roles of genetics and childhood maltreatment as predictors for ADHD symptoms in adulthood, while also indicating a potential evocative mechanism through gene-environment correlation.

Cerebral Amyloidosis in Individuals with Subjective Cognitive Decline: From Genetic Predisposition to Actual Cerebrospinal Fluid Measurements

The possible relationship between Subjective Cognitive Decline (SCD) and dementia needs further investigation. In the present study, we explored the association between specific biomarkers of Alzheimer's Disease (AD), amyloid-beta 42 (Aβ42) and Tau with the odds of SCD using data from two ongoing studies. In total, 849 cognitively normal (CN) individuals were included in our analyses. Among the participants, 107 had available results regarding cerebrospinal fluid (CSF) Aβ42 and Tau, while 742 had available genetic data to construct polygenic risk scores (PRSs) reflecting their genetic predisposition for CSF Aβ42 and plasma total Tau levels. The associations between AD biomarkers and SCD were tested using logistic regression models adjusted for possible confounders such as age, sex, education, depression, and baseline cognitive test scores. Abnormal values of CSF Aβ42 were related to 2.5-fold higher odds of SCD, while higher polygenic loading for Aβ42 was associated with 1.6-fold higher odds of SCD. CSF Tau, as well as polygenic loading for total Tau, were not associated with SCD. Thus, only cerebral amyloidosis appears to be related to SCD status, either in the form of polygenic risk or actual CSF measurements. The temporal sequence of amyloidosis being followed by tauopathy may partially explain our findings.

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

Dementias Platform UK: Bringing genetics into life

INTRODUCTION

The Dementias Platform UK (DPUK) Data Portal is a data repository bringing together a wide range of cohorts. Neurodegenerative dementias are a group of diseases with highly heterogeneous pathology and an overlapping genetic component that is poorly understood. The DPUK collection of independent cohorts can facilitate research in neurodegeneration by combining their genetic and phenotypic data.

METHODS

For genetic data processing, pipelines were generated to perform quality control analysis, genetic imputation, and polygenic risk score (PRS) derivation with six genome-wide association studies of neurodegenerative diseases. Pipelines were applied to five cohorts.

DISCUSSION

The data processing pipelines, research-ready imputed genetic data, and PRS scores are now available on the DPUK platform and can be accessed upon request though the DPUK application process. Harmonizing genome-wide data for multiple datasets increases scientific opportunity and allows the wider research community to access and process data at scale and pace.

Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries

We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs.

Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits

Hypertension affects more than one billion people worldwide. Here we identify 113 novel loci, reporting a total of 2,103 independent genetic signals (P < 5 × 10-8) from the largest single-stage blood pressure (BP) genome-wide association study to date (n = 1,028,980 European individuals). These associations explain more than 60% of single nucleotide polymorphism-based BP heritability. Comparing top versus bottom deciles of polygenic risk scores (PRSs) reveals clinically meaningful differences in BP (16.9 mmHg systolic BP, 95% CI, 15.5-18.2 mmHg, P = 2.22 × 10-126) and more than a sevenfold higher odds of hypertension risk (odds ratio, 7.33; 95% CI, 5.54-9.70; P = 4.13 × 10-44) in an independent dataset. Adding PRS into hypertension-prediction models increased the area under the receiver operating characteristic curve (AUROC) from 0.791 (95% CI, 0.781-0.801) to 0.826 (95% CI, 0.817-0.836, ∆AUROC, 0.035, P = 1.98 × 10-34). We compare the 2,103 loci results in non-European ancestries and show significant PRS associations in a large African-American sample. Secondary analyses implicate 500 genes previously unreported for BP. Our study highlights the role of increasingly large genomic studies for precision health research.

Neuroticism polygenic risk predicts conversion from mild cognitive impairment to Alzheimer's disease by impairing inferior parietal surface area

The high prevalence of conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) makes early prevention of AD extremely critical. Neuroticism, a heritable personality trait associated with mental health, has been considered a risk factor for conversion from aMCI to AD. However, whether the neuroticism genetic risk could predict the conversion of aMCI and its underlying neural mechanisms is unclear. Neuroticism polygenic risk score (N-PRS) was calculated in 278 aMCI patients with qualified genomic and neuroimaging data from ADNI. After 1-year follow-up, N-PRS in patients of aMCI-converted group was significantly greater than those in aMCI-stable group. Logistic and Cox survival regression revealed that N-PRS could significantly predict the early-stage conversion risk from aMCI to AD. These results were well replicated in an internal dataset and an independent external dataset of 933 aMCI patients from the UK Biobank. One sample Mendelian randomization analyses confirmed a potentially causal association from higher N-PRS to lower inferior parietal surface area to higher conversion risk of aMCI patients. These analyses indicated that neuroticism genetic risk may increase the conversion risk from aMCI to AD by impairing the inferior parietal structure.

The role of Toll-like receptors in neuropsychiatric disorders: Immunopathology, treatment, and management

Neuropsychiatric disorders denote a broad range of illnesses involving neurology and psychiatry. These disorders include depressive disorders, anxiety, schizophrenia, bipolar disorder, attention deficit hyperactivity disorder, autism spectrum disorders, headaches, and epilepsy. In addition to their main neuropathology that lies in the central nervous system (CNS), lately, studies have highlighted the role of immunity and neuroinflammation in neuropsychiatric disorders. Toll-like receptors (TLRs) are innate receptors that act as a bridge between the innate and adaptive immune systems via adaptor proteins (e.g., MYD88) and downstream elements; TLRs are classified into 13 families that are involved in normal function and illnesses of the CNS. TLRs expression affects the course of neuropsychiatric disorders, and is influenced during their pharmacotherapy; For example, the expression of multiple TLRs is normalized during the major depressive disorder pharmacotherapy. Here, the role of TLRs in neuroimmunology, treatment, and management of neuropsychiatric disorders is discussed. We recommend longitudinal studies to comparatively assess the cell-type-specific expression of TLRs during treatment, illness progression, and remission. Also, further research should explore molecular insights into TLRs regulation and related pathways.

Heterogeneity analysis provides evidence for a genetically homogeneous subtype of bipolar-disorder

Bipolar Disorder (BD) is a complex disease. It is heterogeneous, both at the phenotypic and genetic level, although the extent and impact of this heterogeneity is not fully understood. One way to assess this heterogeneity is to look for patterns in the subphenotype data, identify a more phenotypically homogeneous set of subjects, and perform a genome-wide association-study (GWAS) and subsequent secondary analyses restricted to this homogeneous subset. Because of the variability in how phenotypic data was collected by the various BD studies over the years, homogenizing the phenotypic data is a challenging task, and so is replication. As members of the Psychiatric Genomics Consortium (PGC), we have access to the raw genotypes of 18,711 BD cases and 29,738 controls. This amount of data makes it possible for us to set aside the intricacies of phenotype and allow the genetic data itself to determine which subjects define a homogeneous genetic subgroup. In this paper, we leverage recent advances in heterogeneity analysis to look for distinct homogeneous genetic BD subgroups (or biclusters) that manifest the broad phenotype we think of as Bipolar Disorder. As our data was generated by 27 studies and genotyped on a variety of platforms (OMEX, Affymetrix, Illumina), we use a biclustering algorithm capable of covariate-correction. Covariate-correction is critical if we wish to distinguish disease-related signals from those which are a byproduct of ancestry, study or genotyping platform. We rely on the raw genotyped data and do not include any data generated through imputation. We first apply this covariate-corrected biclustering algorithm to a cohort of 2524 BD cases and 4106 controls from the Bipolar Disease Research Network (BDRN: OMEX). We find evidence of genetic heterogeneity delineating a statistically significant bicluster comprising a subset of BD cases which exhibits a disease-specific pattern of differential-expression across a subset of SNPs. This pattern replicates across the remaining data-sets collected by the PGC containing 5781/8289 (OMEX), 3581/7591 (Illumina), and 6825/9752(Affymetrix) cases/controls, respectively. This bicluster includes subjects diagnosed with bipolar type-I, as well as subjects diagnosed with bipolar type-II. However, the bicluster is enriched for bipolar type-I over type-II and may represent a collection of correlated genetic risk-factors. By investigating the bicluster-informed polygenic-risk-scoring (PRS), we find that the disease-specific pattern highlighted by the bicluster can be leveraged to eliminate noise from our GWAS analyses and improve not only risk prediction, particularly when using only a relatively small subset (e.g., ~ 1%) of the available SNPs, but also SNP replication. Though our primary focus is only the analysis of disease-related signal, we also identify replicable control-related heterogeneity. Covariate-corrected biclustering of raw genetic data appears to be a promising route for untangling heterogeneity and identifying replicable homogeneous genetic subtypes of complex disease. It may also prove useful in identifying protective effects within the control group. This approach circumvents some of the difficulties presented by subphenotype data collected by meta-analyses or 23 andMe, e.g., missingness, assessment variation, and reliance on self-report.

Analysis of schizophrenia-associated genetic markers in the HLA region as risk factors for tardive dyskinesia

OBJECTIVES

The pathology of Tardive Dyskinesia (TD) has yet to be fully understood, but there have been proposed hypotheses for the cause of this condition. Our team previously reported a possible association of TD with the Complement Component C4 gene in the HLA region. In this study, we explored the HLA region further by examining two previously identified schizophrenia-associated HLA-region single-nucleotide polymorphisms (SNPs), namely rs13194504 and rs210133.

METHODS

The SNPs rs13194504 and rs210133 were tested for association with the occurrence and severity of TD in a sample of 172 schizophrenia patients who were recruited for four studies from three different clinical sites in Canada and USA.

RESULTS

The rs13194504 AA genotype was associated with decreased severity for TD as measured by Abnormal Involuntary Movement Scale (AIMS) scores (p = 0.047) but not for TD occurrence. SNP rs210133 was not significantly associated with either TD occurrence or AIMS scores.

CONCLUSION

Our findings suggest that the rs13194504 AA genotype may play a role in TD severity, while SNP rs210133 may not have a major role in the risk or severity of TD.

Flexible scaffold-based cheminformatics approach for polypharmacological drug design

Effective treatments for complex central nervous system (CNS) disorders require drugs with polypharmacology and multifunctionality, yet designing such drugs remains a challenge. Here, we present a flexible scaffold-based cheminformatics approach (FSCA) for the rational design of polypharmacological drugs. FSCA involves fitting a flexible scaffold to different receptors using different binding poses, as exemplified by IHCH-7179, which adopted a "bending-down" binding pose at 5-HT2AR to act as an antagonist and a "stretching-up" binding pose at 5-HT1AR to function as an agonist. IHCH-7179 demonstrated promising results in alleviating cognitive deficits and psychoactive symptoms in mice by blocking 5-HT2AR for psychoactive symptoms and activating 5-HT1AR to alleviate cognitive deficits. By analyzing aminergic receptor structures, we identified two featured motifs, the "agonist filter" and "conformation shaper," which determine ligand binding pose and predict activity at aminergic receptors. With these motifs, FSCA can be applied to the design of polypharmacological ligands at other receptors.

Genome-wide association analysis of eggshell color of an F2 generation population reveals candidate genes in chickens

Eggshell color is an important visual characteristic that affects consumer preferences for eggs. Eggshell color, which has moderate to high heritability, can be effectively enhanced through molecular marker selection. Various studies have been conducted on eggshell color at specific time points. However, few longitudinal data are available on eggshell color. Therefore, the objective of this study was to investigate eggshell color using the Commission International de L'Eclairage L*a*b* system with multiple measurements at different ages (age at the first egg and at 32, 36, 40, 44, 48, 52, 56, 60, 66, and 72 weeks) within the same individuals from an F2 resource population produced by crossing White Leghorn and Dongxiang Blue chicken. Using an Affymetrix 600 single nucleotide polymorphism (SNP) array, we estimated the genetic parameters of the eggshell color trait, performed genome-wide association studies (GWASs), and screened for the potential candidate genes. The results showed that pink-shelled eggs displayed a significant negative correlation between L* values and both a* and b* values. Genetic heritability based on SNPs showed that the heritability of L*, a*, and b* values ranged from 0.32 to 0.82 for pink-shelled eggs, indicating a moderate to high level of genetic control. The genetic correlations at each time point were mostly above 0.5. The major-effect regions affecting the pink eggshell color were identified in the 10.3-13.0 Mb interval on Gallus gallus chromosome 20, and candidate genes were selected, including SLC35C2, PCIF1, and SLC12A5. Minor effect polygenic regions were identified on chromosomes 1, 6, 9, 12, and 15, revealing 11 candidate genes, including MTMR3 and SLC35E4. Members of the solute carrier family play an important role in influencing eggshell color. Overall, our findings provide valuable insights into the phenotypic and genetic aspects underlying the variation in eggshell color. Using GWAS analysis, we identified multiple quantitative trait loci (QTLs) for pink eggshell color, including a major QTL on chromosome 20. Genetic variants associated with eggshell color may be used in genomic breeding programs.

Clinical associations with a polygenic predisposition to benign lower white blood cell counts

Polygenic variation unrelated to disease contributes to interindividual variation in baseline white blood cell (WBC) counts, but its clinical significance is uncharacterized. We investigated the clinical consequences of a genetic predisposition toward lower WBC counts among 89,559 biobank participants from tertiary care centers using a polygenic score for WBC count (PGSWBC) comprising single nucleotide polymorphisms not associated with disease. A predisposition to lower WBC counts was associated with a decreased risk of identifying pathology on a bone marrow biopsy performed for a low WBC count (odds-ratio = 0.55 per standard deviation increase in PGSWBC [95%CI, 0.30-0.94], p = 0.04), an increased risk of leukopenia (a low WBC count) when treated with a chemotherapeutic (n = 1724, hazard ratio [HR] = 0.78 [0.69-0.88], p = 4.0 × 10-5) or immunosuppressant (n = 354, HR = 0.61 [0.38-0.99], p = 0.04). A predisposition to benign lower WBC counts was associated with an increased risk of discontinuing azathioprine treatment (n = 1,466, HR = 0.62 [0.44-0.87], p = 0.006). Collectively, these findings suggest that there are genetically predisposed individuals who are susceptible to escalations or alterations in clinical care that may be harmful or of little benefit.

Brain and Blood Transcriptome-Wide Association Studies Identify Five Novel Genes Associated with Alzheimer's Disease

INTRODUCTION

Transcriptome-wide Association Studies (TWAS) extend genome-wide association studies (GWAS) by integrating genetically-regulated gene expression models. We performed the most powerful AD-TWAS to date, using summary statistics from cis -eQTL meta-analyses and the largest clinically-adjudicated Alzheimer's Disease (AD) GWAS.

METHODS

We implemented the OTTERS TWAS pipeline, leveraging cis -eQTL data from cortical brain tissue (MetaBrain; N=2,683) and blood (eQTLGen; N=31,684) to predict gene expression, then applied these models to AD-GWAS data (Cases=21,982; Controls=44,944).

RESULTS

We identified and validated five novel gene associations in cortical brain tissue ( PRKAG1 , C3orf62 , LYSMD4 , ZNF439 , SLC11A2 ) and six genes proximal to known AD-related GWAS loci (Blood: MYBPC3 ; Brain: MTCH2 , CYB561 , MADD , PSMA5 , ANXA11 ). Further, using causal eQTL fine-mapping, we generated sparse models that retained the strength of the AD-TWAS association for MTCH2 , MADD , ZNF439 , CYB561 , and MYBPC3 .

DISCUSSION

Our comprehensive AD-TWAS discovered new gene associations and provided insights into the functional relevance of previously associated variants.

Unravelling the genetic basis of Schizophrenia

Neuronal development is a highly regulated mechanism that is central to organismal function in animals. In humans, disruptions to this process can lead to a range of neurodevelopmental phenotypes, including Schizophrenia (SCZ). SCZ has a significant genetic component, whereby an individual with an SCZ affected family member is eight times more likely to develop the disease than someone with no family history of SCZ. By examining a combination of genomic, transcriptomic and epigenomic datasets, large-scale 'omics' studies aim to delineate the relationship between genetic variation and abnormal cellular activity in the SCZ brain. Herein, we provide a brief overview of some of the key omics methods currently being used in SCZ research, including RNA-seq, the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and high-throughput chromosome conformation capture (3C) approaches (e.g., Hi-C), as well as single-cell/nuclei iterations of these methods. We also discuss how these techniques are being employed to further our understanding of the genetic basis of SCZ, and to identify associated molecular pathways, biomarkers, and candidate drug targets.

Evaluating and improving health equity and fairness of polygenic scores

Polygenic scores (PGSs) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single-nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWASs, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum (JLS). In the simulation settings we explore, JLS provides more accurate PGSs compared to other methods, especially when measured in terms of fairness. In analyses of UK Biobank data, JLS was computationally more efficient but slightly less accurate than a Bayesian comparator, SDPRX. Like all PGS methods, JLS requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how JLS can help mitigate fairness-related harms that might result from the use of PGSs in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWASs for different ancestries, JLS is an effective approach for enhancing portability and reducing predictive bias.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Shared genetic basis and causality between schizophrenia and inflammatory bowel disease: evidence from a comprehensive genetic analysis

BACKGROUND

The comorbidity between schizophrenia (SCZ) and inflammatory bowel disease (IBD) observed in epidemiological studies is partially attributed to genetic overlap, but the magnitude of shared genetic components and the causality relationship between them remains unclear.

METHODS

By leveraging large-scale genome-wide association study (GWAS) summary statistics for SCZ, IBD, ulcerative colitis (UC), and Crohn's disease (CD), we conducted a comprehensive genetic pleiotropic analysis to uncover shared loci, genes, or biological processes between SCZ and each of IBD, UC, and CD, independently. Univariable and multivariable Mendelian randomization (MR) analyses were applied to assess the causality across these two disorders.

RESULTS

SCZ genetically correlated with IBD (rg = 0.14, p = 3.65 × 10−9), UC (rg = 0.15, p = 4.88 × 10−8), and CD (rg = 0.12, p = 2.27 × 10−6), all surpassed the Bonferroni correction. Cross-trait meta-analysis identified 64, 52, and 66 significantly independent loci associated with SCZ and IBD, UC, and CD, respectively. Follow-up gene-based analysis found 11 novel pleiotropic genes (KAT5, RABEP1, ELP5, CSNK1G1, etc) in all joint phenotypes. Co-expression and pathway enrichment analysis illustrated those novel genes were mainly involved in core immune-related signal transduction and cerebral disorder-related pathways. In univariable MR, genetic predisposition to SCZ was associated with an increased risk of IBD (OR 1.11, 95% CI 1.07–1.15, p = 1.85 × 10−6). Multivariable MR indicated a causal effect of genetic liability to SCZ on IBD risk independent of Actinobacteria (OR 1.11, 95% CI 1.06–1.16, p = 1.34 × 10−6) or BMI (OR 1.11, 95% CI 1.04–1.18, p = 1.84 × 10−3).

CONCLUSIONS

We confirmed a shared genetic basis, pleiotropic loci/genes, and causal relationship between SCZ and IBD, providing novel insights into the biological mechanism and therapeutic targets underlying these two disorders.

MIR137 polygenic risk for schizophrenia and ephrin-regulated pathway: Role in lateral ventricles and corpus callosum volume

Background/Objective. Enlarged lateral ventricle (LV) volume and decreased volume in the corpus callosum (CC) are hallmarks of schizophrenia (SZ). We previously showed an inverse correlation between LV and CC volumes in SZ, with global functioning decreasing with increased LV volume. This study investigates the relationship between LV volume, CC abnormalities, and the microRNA MIR137 and its regulated genes in SZ, because of MIR137's essential role in neurodevelopment. Methods. Participants were 1224 SZ probands and 1466 unaffected controls from the GENUS Consortium. Brain MRI scans, genotype, and clinical data were harmonized across cohorts and employed in the analyses. Results. Increased LV volumes and decreased CC central, mid-anterior, and mid-posterior volumes were observed in SZ probands. The MIR137-regulated ephrin pathway was significantly associated with CC:LV ratio, explaining a significant proportion (3.42 %) of CC:LV variance, and more than for LV and CC separately. Other pathways explained variance in either CC or LV, but not both. CC:LV ratio was also positively correlated with Global Assessment of Functioning, supporting previous subsample findings. SNP-based heritability estimates were higher for CC central:LV ratio (0.79) compared to CC or LV separately. Discussion. Our results indicate that the CC:LV ratio is highly heritable, influenced in part by variation in the MIR137-regulated ephrin pathway. Findings suggest that the CC:LV ratio may be a risk indicator in SZ that correlates with global functioning.

Impact of genetic, sociodemographic, and clinical features on antidepressant treatment trajectories in the perinatal period

Pregnant women on antidepressants must balance potential fetal harm with the relapse risk. While various clinical and sociodemographic factors are known to influence treatment decisions, the impact of genetic factors remains unexplored. We conducted a cohort study among 2,316 women with diagnosed affective disorders who had redeemed antidepressant prescriptions six months before pregnancy, identified from the Danish Integrated Psychiatric Research study. We calculated polygenic risk scores (PGSs) for major depression (MDD), bipolar disorder (BD), and schizophrenia (SCZ) using individual-level genetic data and summary statistics from genome-wide association studies. We retrieved data on sociodemographic and clinical features from national registers. Applying group-based trajectory modeling, we identified four treatment trajectories across pregnancy and postpartum: Continuers (38.2 %), early discontinuers (22.7 %), late discontinuers (23.8 %), and interrupters (15.3 %). All three PGSs were not associated with treatment trajectories; for instance, the relative risk ratio for continuers versus early discontinuers was 0.93 (95 % CI: 0.81-1.06), 0.98 (0.84-1.13), 1.09 (0.95-1.27) for per 1-SD increase in PGS for MDD, BD, and SCZ, respectively. Sociodemographic factors were generally not associated with treatment trajectories, except for the association between primiparity and continuing antidepressant use. Women who received ≥2 classes or a higher dose of antidepressants had a higher probability of being late discontinuers, interrupters, and continuers. The likelihood of continuing antidepressants or restarting antidepressants postpartum increased with the previous antidepressant treatment duration. Our findings indicate that continued antidepressant use during pregnancy is influenced by the severity of the disease rather than genetic predisposition as measured by PGSs.

Polygenic risk scores mediating functioning outcomes through cognitive and clinical features in youth at family risk and controls

Schizophrenia and bipolar disorder exhibit substantial clinical overlap, particularly in individuals at familial high risk, who frequently present sub-threshold symptoms before the onset of illness. Severe mental disorders are highly polygenic traits, but their impact on the stages preceding the manifestation of mental disorders remains relatively unexplored. Our study aimed to examine the influence of polygenic risk scores (PRS) on sub-clinical outcomes over a 2-year period in youth at familial high risk for schizophrenia and bipolar disorder and controls. The sample included 222 children and adolescents, comprising offspring of parents with schizophrenia (n = 38), bipolar disorder (n = 80), and community controls (n = 104). We calculated PRS for psychiatric disorders, neuroticism and cognition using the PRS-CS method. Linear mixed-effects models were employed to investigate the association between PRS and cognition, symptom severity and functioning. Mediation analyses were conducted to explore whether clinical features acted as intermediaries in the impact of PRS on functioning outcomes. SZoff exhibited elevated PRS for schizophrenia. In the entire sample, PRS for depression, neuroticism, and cognitive traits showed associations with sub-clinical features. The effect of PRS for neuroticism and general intelligence on functioning outcomes were mediated by cognition and symptoms severity, respectively. This study delves into the interplay among genetics, the emergence of sub-clinical symptoms and functioning outcomes, providing novel evidence on mechanisms underpinning the continuum from sub-threshold features to the onset of mental disorders. The findings underscore the interplay of genetics, cognition, and clinical features, providing insights for personalized early interventions.

Lymphoblast transcriptome analysis in 22q11.2 deletion syndrome individuals with schizophrenia-spectrum disorder

OBJECTIVES

22q11.2 deletion is the most prominent risk factor for schizophrenia (SZ). The aim of the present study was to identify unique transcriptome profile for 22q11.2 deletion syndrome (DS)-related SZ-spectrum disorder (SZ-SD).

METHODS

We performed RNA-Seq screening in lymphoblasts collected from 20 individuals with 22q11.2DS (10 men and 10 women, four of each sex with SZ-SD and six with no psychotic disorders (Np)).

RESULTS

Sex effect in RNA-Seq descriptive analysis led to separating the analyses between men and women. In women, only one differentially expressed gene (DEG), HLA-DQA2, was associated with SZ-SD. In men, 48 DEGs (adjp < 0.05) were found to be associated with SZ-SD. Ingenuity pathway analysis of top 85 DEGs (p < 4.66E - 04) indicated significant enrichment for immune-inflammatory response (IIR) and neuro-inflammatory signalling pathways. Additionally, NFATC2, IFNG, IFN-alpha, STAT1 and IL-4 were identified as upstream regulators. Co-expression network analysis revealed the contribution of endoplasmic reticulum protein processing and N-Glycan biosynthesis. These findings indicate dysregulation of IIR and post-translational protein modification processes in individuals with 22q11.2DS-related SZ-SD.

CONCLUSIONS

Candidate pathways and upstream regulators may serve as novel biomarkers and treatment targets for SZ. Future transcriptome studies, including larger samples and proteomic analysis, are needed to substantiate our findings.

Proteome-wide association studies using summary proteomic data identified 23 risk genes of Alzheimer's disease

Characterizing the genetic mechanisms underlying Alzheimer's disease (AD) dementia is crucial for developing new therapeutics. Proteome-wide association study (PWAS) integrating proteomics data with genome-wide association study (GWAS) summary data was shown as a powerful tool for detecting risk genes. The identified PWAS risk genes can be interpretated as having genetic effects mediated through the genetically regulated protein abundances. Existing PWAS analyses of AD often rely on the availability of individual-level proteomics and genetics data of a reference cohort. Leveraging summary-level protein quantitative trait loci (pQTL) reference data of multiple relevant tissues is expected to improve PWAS findings for studying AD. Here, we applied our recently developed OTTERS tool to conduct PWAS of AD dementia, by leveraging summary-level pQTL data of brain, cerebrospinal fluid (CSF), and plasma tissues, and multiple statistical methods. For each target protein, imputation models of the protein abundance with genetic predictors were trained from summary-level pQTL data, estimating a set of pQTL weights for considered genetic predictors. PWAS p-values were obtained by integrating GWAS summary data of AD dementia with estimated pQTL weights. PWAS p-values from multiple statistical methods were combined by the aggregated Cauchy association test to yield one omnibus PWAS p-value for the target protein. We identified significant PWAS risk genes through omnibus PWAS p-values and analyzed their protein-protein interactions using STRING. Their potential causal effects were assessed by the probabilistic Mendelian randomization (PMR-Egger). As a result, we identified a total of 23 significant PWAS risk genes for AD dementia in brain, CSF, and plasma tissues, including 7 novel findings. We showed that 15 of these risk genes were interconnected within a protein-protein interaction network involving the well-known AD risk gene of APOE and 5 novel findings, and enriched in immune functions and lipids pathways including positive regulation of immune system process, positive regulation of macrophage proliferation, humoral immune response, and high-density lipoprotein particle clearance. Existing biological evidence was found to relate our novel findings with AD. We validated the mediated causal effects of 14 risk genes (60.8%). In conclusion, we identified both known and novel PWAS risk genes, providing novel insights into the genetic mechanisms in brain, CSF, and plasma tissues, and targeted therapeutics development of AD dementia. Our study also demonstrated the effectiveness of integrating public available summary-level pQTL data with GWAS summary data for mapping risk genes of complex human diseases.

Using Genetics to Inform Interventions Related to Sodium and Potassium in Hypertension

BACKGROUND

Hypertension is a key risk factor for major adverse cardiovascular events but remains difficult to treat in many individuals. Dietary interventions are an effective approach to lower blood pressure (BP) but are not equally effective across all individuals. BP is heritable, and genetics may be a useful tool to overcome treatment response heterogeneity. We investigated whether the genetics of BP could be used to identify individuals with hypertension who may receive a particular benefit from lowering sodium intake and boosting potassium levels.

METHODS

In this observational genetic study, we leveraged cross-sectional data from up to 296 475 genotyped individuals drawn from the UK Biobank cohort for whom BP and urinary electrolytes (sodium and potassium), biomarkers of sodium and potassium intake, were measured. Biologically directed genetic scores for BP were constructed specifically among pathways related to sodium and potassium biology (pharmagenic enrichment scores), as well as unannotated genome-wide scores (conventional polygenic scores). We then tested whether there was a gene-by-environment interaction between urinary electrolytes and these genetic scores on BP.

RESULTS

Genetic risk and urinary electrolytes both independently correlated with BP. However, urinary sodium was associated with a larger BP increase among individuals with higher genetic risk in sodium- and potassium-related pathways than in those with comparatively lower genetic risk. For example, each SD in urinary sodium was associated with a 1.47-mm Hg increase in systolic BP for those in the top 10% of the distribution of genetic risk in sodium and potassium transport pathways versus a 0.97-mm Hg systolic BP increase in the lowest 10% (P=1.95×10-3). This interaction with urinary sodium remained when considering estimated glomerular filtration rate and indexing sodium to urinary creatinine. There was no strong evidence of an interaction between urinary sodium and a standard genome-wide polygenic score of BP.

CONCLUSIONS

The data suggest that genetic risk in sodium and potassium pathways could be used in a precision medicine model to direct interventions more specifically in the management of hypertension. Intervention studies are warranted.

Shared genetic mechanisms underlying association between sleep disturbances and depressive symptoms

OBJECTIVES

Polygenic scores (PGS) for sleep disturbances and depressive symptoms in an epidemiological cohort were contrasted. The overlap between genes assigned to variants that compose the PGS predictions was tested to explore the shared genetic bases of sleep problems and depressive symptoms.

METHODS

PGS analysis was performed on the São Paulo Epidemiologic Sleep Study (EPISONO, N = 1042), an adult epidemiological sample. A genome wide association study (GWAS) for depression grounded the PGS calculations for Beck Depression Index (BDI), while insomnia GWAS based the PGS for Insomnia Severity Index (ISI) and Pittsburg Sleep Quality Index (PSQI). Pearson's correlation was applied to contrast PGS and clinical scores. Fisher's Exact and Benjamin-Hochberg tests were used to verify the overlaps between PGS-associated genes and the pathways enriched among their intersections.

RESULTS

All PGS models were significant when individuals were divided as cases or controls according to BDI (R2 = 1.2%, p = 0.00026), PSQI (R2 = 3.3%, p = 0.007) and ISI (R2 = 3.4%, p = 0.021) scales. When clinical scales were used as continuous variables, the PGS models for BDI (R2 = 1.5%, p = 0.0004) and PSQI scores (R2 = 3.3%, p = 0.0057) reached statistical significance. PSQI and BDI scores were correlated, and the same observation was applied to their PGS. Genes assigned to variants that compose the best-fit PGS predictions for sleep quality and depressive symptoms were significantly overlapped. Pathways enriched among the intersect genes are related to synapse function.

CONCLUSIONS

The genetic bases of sleep quality and depressive symptoms are correlated; their implicated genes are significantly overlapped and converge on neural pathways. This data suggests that sleep complaints accompanying depressive symptoms are not secondary issues, but part of the core mental illness.

Ensemble learning for integrative prediction of genetic values with genomic variants

BACKGROUND

Whole genome variants offer sufficient information for genetic prediction of human disease risk, and prediction of animal and plant breeding values. Many sophisticated statistical methods have been developed for enhancing the predictive ability. However, each method has its own advantages and disadvantages, so far, no one method can beat others.

RESULTS

We herein propose an Ensemble Learning method for Prediction of Genetic Values (ELPGV), which assembles predictions from several basic methods such as GBLUP, BayesA, BayesB and BayesCπ, to produce more accurate predictions. We validated ELPGV with a variety of well-known datasets and a serious of simulated datasets. All revealed that ELPGV was able to significantly enhance the predictive ability than any basic methods, for instance, the comparison p-value of ELPGV over basic methods were varied from 4.853E-118 to 9.640E-20 for WTCCC dataset.

CONCLUSIONS

ELPGV is able to integrate the merit of each method together to produce significantly higher predictive ability than any basic methods and it is simple to implement, fast to run, without using genotype data. is promising for wide application in genetic predictions.

The complement system in neurodegenerative diseases

Complement is an important component of innate immune defence against pathogens and crucial for efficient immune complex disposal. These core protective activities are dependent in large part on properly regulated complement-mediated inflammation. Dysregulated complement activation, often driven by persistence of activating triggers, is a cause of pathological inflammation in numerous diseases, including neurological diseases. Increasingly, this has become apparent not only in well-recognized neuroinflammatory diseases like multiple sclerosis but also in neurodegenerative and neuropsychiatric diseases where inflammation was previously either ignored or dismissed as a secondary event. There is now a large and rapidly growing body of evidence implicating complement in neurological diseases that cannot be comprehensively addressed in a brief review. Here, we will focus on neurodegenerative diseases, including not only the 'classical' neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease, but also two other neurological diseases where neurodegeneration is a neglected feature and complement is implicated, namely, schizophrenia, a neurodevelopmental disorder with many mechanistic features of neurodegeneration, and multiple sclerosis, a demyelinating disorder where neurodegeneration is a major cause of progressive decline. We will discuss the evidence implicating complement as a driver of pathology in these diverse diseases and address briefly the potential and pitfalls of anti-complement drug therapy for neurodegenerative diseases.

Scaled and Efficient Derivation of Loss of Function Alleles in Risk Genes for Neurodevelopmental and Psychiatric Disorders in Human iPSC

Translating genetic findings for neurodevelopmental and psychiatric disorders (NPD) into actionable disease biology would benefit from large-scale and unbiased functional studies of NPD genes. Leveraging the cytosine base editing (CBE) system, here we developed a pipeline for clonal loss-of-function (LoF) allele mutagenesis in human induced pluripotent stem cells (hiPSCs) by introducing premature stop-codons (iSTOP) that lead to mRNA nonsense-mediated-decay (NMD) or protein truncation. We tested the pipeline for 23 NPD genes on 3 hiPSC lines and achieved highly reproducible, efficient iSTOP editing in 22 NPD genes. Using RNAseq, we confirmed their pluripotency, absence of chromosomal abnormalities, and NMD. Interestingly, for three schizophrenia risk genes (SETD1A, TRIO, CUL1), despite the high efficiency of base editing, we only obtained heterozygous LoF alleles, suggesting their essential roles for cell growth. We replicated the reported neural phenotypes of SHANK3-haploinsufficiency and found CUL1-LoF reduced neurite branches and synaptic puncta density. This iSTOP pipeline enables a scaled and efficient LoF mutagenesis of NPD genes, yielding an invaluable shareable resource.

Exploring functional dysconnectivity in schizophrenia: alterations in eigenvector centrality mapping and insights into related genes from transcriptional profiles

Schizophrenia is a mental health disorder characterized by functional dysconnectivity. Eigenvector centrality mapping (ECM) has been employed to investigate alterations in functional connectivity in schizophrenia, yet the results lack consistency, and the genetic mechanisms underlying these changes remain unclear. In this study, whole-brain voxel-wise ECM analyses were conducted on resting-state functional magnetic resonance imaging data. A cohort of 91 patients with schizophrenia and 91 matched healthy controls were included during the discovery stage. Additionally, in the replication stage, 153 individuals with schizophrenia and 182 healthy individuals participated. Subsequently, a comprehensive analysis was performed using an independent transcriptional database derived from six postmortem healthy adult brains to explore potential genetic factors influencing the observed functional dysconnectivity, and to investigate the roles of identified genes in neural processes and pathways. The results revealed significant and reliable alterations in the ECM across multiple brain regions in schizophrenia. Specifically, there was a significant decrease in ECM in the bilateral superior and middle temporal gyrus, and an increase in the bilateral thalamus in both the discovery and replication stages. Furthermore, transcriptional analysis revealed 420 genes whose expression patterns were related to changes in ECM, and these genes were enriched mainly in biological processes associated with synaptic signaling and transmission. Together, this study enhances our knowledge of the neural processes and pathways involved in schizophrenia, shedding light on the genetic factors that may be linked to functional dysconnectivity in this disorder.

Methylome-wide and meQTL analysis helps to distinguish treatment response from non-response and pathogenesis markers in schizophrenia

Schizophrenia is a complex condition with entwined genetic and epigenetic risk factors, posing a challenge to disentangle the intermixed pathological and therapeutic epigenetic signatures. To resolve this, we performed 850K methylome-wide and 700K genome-wide studies on the same set of schizophrenia patients by stratifying them into responders, non-responders, and drug-naïve patients. The key genes that signified the response were followed up using real-time gene expression studies to understand the effect of antipsychotics at the gene transcription level. The study primarily implicates hypermethylation in therapeutic response and hypomethylation in the drug-non-responsive state. Several differentially methylated sites and regions colocalized with the schizophrenia genome-wide association study (GWAS) risk genes and variants, supporting the convoluted gene-environment association. Gene ontology and protein-protein interaction (PPI) network analyses revealed distinct patterns that differentiated the treatment response from drug resistance. The study highlights the strong involvement of several processes related to nervous system development, cell adhesion, and signaling in the antipsychotic response. The ability of antipsychotic medications to alter the pathology by modulating gene expression or methylation patterns is evident from the general increase in the gene expression of response markers and histone modifiers and the decrease in class II human leukocyte antigen (HLA) genes following treatment with varying concentrations of medications like clozapine, olanzapine, risperidone, and haloperidol. The study indicates a directional overlap of methylation markers between pathogenesis and therapeutic response, thereby suggesting a careful distinction of methylation markers of pathogenesis from treatment response. In addition, there is a need to understand the trade-off between genetic and epigenetic observations. It is suggested that methylomic changes brought about by drugs need careful evaluation for their positive effects on pathogenesis, course of disease progression, symptom severity, side effects, and refractoriness.

Genetic risk for Alzheimer's disease and adherence to the Mediterranean diet: results from the HELIAD study

Obejctives: The aim of the current study was to investigate whether genetic risk factors may moderate the association between adherence to the Mediterranean diet and AD incidence.Mehtods: The sample was drawn from the HELIAD study, a longitudinal study with a follow-up interval of 3 years. In total 537 older adults without dementia or AD at baseline were included. Adherence to the Mediterranean diet was assessed at baseline and AD diagnosis was determined at both visits. A Polygenic Index for late onset AD (PGI-AD) was constructed. Cox proportional hazard models adjusted for age, sex, education, baseline Global cognition score and APOE e-4 genotype were employed to evaluate the association between PGI-AD and Mediterranean diet with AD incidence. Next, we examined the association between adherence to the Mediterranean diet and AD risk over time across participants stratified by low and high PGI-AD.Results: Twenty-eight participants developed AD at follow-up. In fully adjusted models both the PGI-AD and the adherence to the Mediterranean diet were associated with AD risk (p < 0.05 for both). In the low PGI-AD group, those with a low adherence had a 10-fold higher risk of developing AD per year of follow-up, than did the participants with a high adherence to the Mediterranean diet (p = 0.011), whereas no such association was found for participants in the high PGI-AD group.Discussion: The association of Mediterranean diet with AD risk is more prominent in the group of older adults with a low polygenic risk for developing AD. Our findings suggest that genetic risk factors should be taken into account when planning interventions aiming to improve cognitive health.

Polygenic risk for schizophrenia, social dispositions, and pace of epigenetic aging: Results from the Young Finns Study

Schizophrenia is often regarded as a disorder of premature aging. We investigated (a) whether polygenic risk for schizophrenia (PRSsch ) relates to pace of epigenetic aging and (b) whether personal dispositions toward active and emotionally close relationships protect against accelerated epigenetic aging in individuals with high PRSsch . The sample came from the population-based Young Finns Study (n = 1348). Epigenetic aging was measured with DNA methylation aging algorithms such as AgeAccelHannum , EEAAHannum , IEAAHannum , IEAAHorvath , AgeAccelHorvath , AgeAccelPheno , AgeAccelGrim , and DunedinPACE. A PRSsch was calculated using summary statistics from the most comprehensive genome-wide association study of schizophrenia to date. Social dispositions were assessed in terms of extraversion, sociability, reward dependence, cooperativeness, and attachment security. We found that PRSsch did not have a statistically significant effect on any studied indicator of epigenetic aging. Instead, PRSsch had a significant interaction with reward dependence (p = 0.001-0.004), cooperation (p = 0.009-0.020), extraversion (p = 0.019-0.041), sociability (p = 0.003-0.016), and attachment security (p = 0.007-0.014) in predicting AgeAccelHannum , EEAAHannum , or IEAAHannum . Specifically, participants with high PRSsch appeared to display accelerated epigenetic aging at higher (vs. lower) levels of extraversion, sociability, attachment security, reward dependence, and cooperativeness. A rather opposite pattern was evident for those with low PRSsch . No such interactions were evident when predicting the other indicators of epigenetic aging. In conclusion, against our hypothesis, frequent social interactions may relate to accelerated epigenetic aging in individuals at risk for psychosis. We speculate that this may be explained by social-cognitive impairments (perceiving social situations as overwhelming or excessively arousing) or ending up in less supportive or deviant social groups.

Assessing the association between global structural brain age and polygenic risk for schizophrenia in early adulthood: A recall-by-genotype study

Neuroimaging studies consistently show advanced brain age in schizophrenia, suggesting that brain structure is often 'older' than expected at a given chronological age. Whether advanced brain age is linked to genetic liability for schizophrenia remains unclear. In this pre-registered secondary data analysis, we utilised a recall-by-genotype approach applied to a population-based subsample from the Avon Longitudinal Study of Parents and Children to assess brain age differences between young adults aged 21-24 years with relatively high (n = 96) and low (n = 93) polygenic risk for schizophrenia (SCZ-PRS). A global index of brain age (or brain-predicted age) was estimated using a publicly available machine learning model previously trained on a combination of region-wise gray-matter measures, including cortical thickness, surface area and subcortical volumes derived from T1-weighted magnetic resonance imaging (MRI) scans. We found no difference in mean brain-PAD (the difference between brain-predicted age and chronological age) between the high- and low-SCZ-PRS groups, controlling for the effects of sex and age at time of scanning (b = -.21; 95% CI -2.00, 1.58; p = .82; Cohen's d = -.034; partial R2 = .00029). These findings do not support an association between SCZ-PRS and brain-PAD based on global age-related structural brain patterns, suggesting that brain age may not be a vulnerability marker of common genetic risk for SCZ. Future studies with larger samples and multimodal brain age measures could further investigate global or localised effects of SCZ-PRS.

Polygenic risk score comparator (PRScomp): Test population vs. worldwide populations

BACKGROUND

Polygenic risk scores (PRS) are a powerful tool for predicting an individual's genetic risk for complex diseases.

METHODS

We have developed a web service (PRScomp) as a user-friendly tool to evaluate PRS of the user own population and compare it with worldwide populations.

RESULTS

A disease/trait database has been constructed from GWAS Catalog summary statistics. Genotype data of test population is uploaded and merged with the reference dataset (1000 Genome Project and Human Genome Diversity Project) to obtain a file including the common SNPs. The user can select a disease/trait from the database and a curated set of risk markers is used to calculate summatory PRS. Distribution of z-scored PRS values is presented in publication-ready plots and text files that can be downloaded.

DISCUSSION

PRScomp can be useful for public health decision-making by identifying population-specific genetic risk factors and informing the development of targeted interventions for at-risk populations.

Genetic risk for hospitalization of African American patients with severe mental illness reveals HLA loci

Background

Mood disorders such as major depressive and bipolar disorders, along with posttraumatic stress disorder (PTSD), schizophrenia (SCZ), and other psychotic disorders, constitute serious mental illnesses (SMI) and often lead to inpatient psychiatric care for adults. Risk factors associated with increased hospitalization rate in SMI (H-SMI) are largely unknown but likely involve a combination of genetic, environmental, and socio-behavioral factors. We performed a genome-wide association study in an African American cohort to identify possible genes associated with hospitalization due to SMI (H-SMI).

Methods

Patients hospitalized for psychiatric disorders (H-SMI; n=690) were compared with demographically matched controls (n=4467). Quality control and imputation of genome-wide data were performed following the Psychiatric Genetic Consortium (PGC)-PTSD guidelines. Imputation of the Human Leukocyte Antigen (HLA) locus was performed using the HIBAG package.

Results

Genome-wide association analysis revealed a genome-wide significant association at 6p22.1 locus in the ubiquitin D (UBD/FAT10) gene (rs362514, p=9.43x10-9) and around the HLA locus. Heritability of H-SMI (14.6%) was comparable to other psychiatric disorders (4% to 45%). We observed a nominally significant association with 2 HLA alleles: HLA-A*23:01 (OR=1.04, p=2.3x10-3) and HLA-C*06:02 (OR=1.04, p=1.5x10-3). Two other genes (VSP13D and TSPAN9), possibly associated with immune response, were found to be associated with H-SMI using gene-based analyses.

Conclusion

We observed a strong association between H-SMI and a locus that has been consistently and strongly associated with SCZ in multiple studies (6p21.32-p22.1), possibly indicating an involvement of the immune system and the immune response in the development of severe transdiagnostic SMI.

Polygenic risk for major depression, attention deficit hyperactivity disorder, neuroticism, and schizophrenia are correlated with experience of intimate partner violence

Research has suggested that mental illness may be a risk factor for, as well as a sequela of, experiencing intimate partner violence (IPV). The association between IPV and mental illness may also be due in part to gene-environment correlations. Using polygenic risk scores for six psychiatric disorders - attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), neuroticism, and schizophrenia-and a combined measure of overall genetic risk for mental illness, we tested whether women's genetic risk for mental illness was associated with the experience of three types of intimate partner violence. In this cohort of women of European ancestry (N = 11,095), participants in the highest quintile of genetic risk for ADHD (OR range: 1.38-1.49), MDD (OR range: 1.28-1.43), neuroticism (OR range: (1.18-1.25), schizophrenia (OR range: 1.30-1.34), and overall genetic risk (OR range: 1.30-1.41) were at higher risk for experiencing more severe emotional and physical abuse, and, except schizophrenia, more severe sexual abuse, as well as more types of abuse and chronic abuse. In addition, participants in the highest quintile of genetic risk for neuroticism (OR = 1.43 95% CI: 1.18, 1.72), schizophrenia (OR = 1.33 95% CI: 1.10, 1.62), and the overall genetic risk (OR = 1.40 95% CI: 1.15, 1.71) were at higher risk for experiencing intimate partner intimidation and control. Participants in the highest quintile of genetic risk for ADHD, ASD, MDD, schizophrenia, and overall genetic risk, compared to the lowest quintile, were at increased risk for experiencing harassment from a partner (OR range: 1.22-1.92). No associations were found between genetic risk for BPD with IPV. A better understanding of the salience of the multiple possible pathways linking genetic risk for mental illness with risk for IPV may aid in preventing IPV victimization or re-victimization.

Patients with first-episode psychosis in northern Taiwan: neurocognitive performance and niacin response profile in comparison with schizophrenia patients of different familial loadings and relationship with clinical features

BACKGROUND

Examining patients with first-episode psychosis (FEP) provides opportunities to better understand the mechanism underlying these illnesses. By incorporating quantitative measures in FEP patients, we aimed to (1) determine the baseline distribution of clinical features; (2) examine the impairment magnitude of the quantitative measures by comparing with external controls and then the counterparts of schizophrenia patients of different familial loadings; and (3) evaluate whether these quantitative measures were associated with the baseline clinical features.

METHODS

Patients with FEP were recruited from one medical center, two regional psychiatric centers, and two private clinics in northern Taiwan with clinical features rated using the Positive and Negative Syndrome Scale (PANSS) and Personal and Social Performance (PSP) scale. Quantitative measurements included the Continuous Performance Test (CPT), Wisconsin Card Sorting Test (WCST), niacin response abnormality (NRA), and minor physical anomalies and craniofacial features (MPAs). To evaluate the relative performance of the quantitative measures in our FEP patients, four external comparison groups from previous studies were used, including three independent healthy controls for the CPT, WCST, and NRA, respectively, and one group of treatment-resistant schizophrenia patients for the MPAs. Additionally, patients from simplex families and patients from multiplex families were used to assess the magnitude of FEP patients' impairment on the CPT, WCST, and NRA.

RESULTS

Among the 80 patients with FEP recruited in this study (58% female, mean age = 25.6 years, mean duration of untreated psychosis = 132 days), the clinical severity was mild to moderate (mean PANSS score = 67.3; mean PSP score = 61.8). Patients exhibited both neurocognitive and niacin response impairments (mean Z-scores: -1.24 for NRA, - 1.06 for undegraded d', - 0.70 for degraded d', - 0.32 for categories achieved, and 0.44 for perseverative errors) but did not show MPAs indicative of treatment resistance. Among these quantitative measures, three of the four neurocognitive indices were correlated with the baseline clinical features, whereas NRA did not show such correlation.

CONCLUSIONS

This FEP study of Taiwanese patients revealed the presence of neurocognitive performance and niacin response and their different relationships with clinical features, rendering this sample useful for future follow-up and incorporation of multiomics investigation.

SNVstory: inferring genetic ancestry from genome sequencing data

BACKGROUND

Genetic ancestry, inferred from genomic data, is a quantifiable biological parameter. While much of the human genome is identical across populations, it is estimated that as much as 0.4% of the genome can differ due to ancestry. This variation is primarily characterized by single nucleotide variants (SNVs), which are often unique to specific genetic populations. Knowledge of a patient's genetic ancestry can inform clinical decisions, from genetic testing and health screenings to medication dosages, based on ancestral disease predispositions. Nevertheless, the current reliance on self-reported ancestry can introduce subjectivity and exacerbate health disparities. While genomic sequencing data enables objective determination of a patient's genetic ancestry, existing approaches are limited to ancestry inference at the continental level.

RESULTS

To address this challenge, and create an objective, measurable metric of genetic ancestry we present SNVstory, a method built upon three independent machine learning models for accurately inferring the sub-continental ancestry of individuals. We also introduce a novel method for simulating individual samples from aggregate allele frequencies from known populations. SNVstory includes a feature-importance scheme, unique among open-source ancestral tools, which allows the user to track the ancestral signal broadcast by a given gene or locus. We successfully evaluated SNVstory using a clinical exome sequencing dataset, comparing self-reported ethnicity and race to our inferred genetic ancestry, and demonstrate the capability of the algorithm to estimate ancestry from 36 different populations with high accuracy.

CONCLUSIONS

SNVstory represents a significant advance in methods to assign genetic ancestry, opening the door to ancestry-informed care. SNVstory, an open-source model, is packaged as a Docker container for enhanced reliability and interoperability. It can be accessed from https://github.com/nch-igm/snvstory .

Processing speed mediates the relationship between DDR1 and psychosocial functioning in euthymic patients with bipolar disorder presenting psychotic symptoms

The DDR1 locus is associated with the diagnosis of schizophrenia and with processing speed in patients with schizophrenia and first-episode psychosis. Here, we investigated whether DDR1 variants are associated with bipolar disorder (BD) features. First, we performed a case‒control association study comparing DDR1 variants between patients with BD and healthy controls. Second, we performed linear regression analyses to assess the associations of DDR1 variants with neurocognitive domains and psychosocial functioning. Third, we conducted a mediation analysis to explore whether neurocognitive impairment mediated the association between DDR1 variants and psychosocial functioning in patients with BD. Finally, we studied the association between DDR1 variants and white matter microstructure. We did not find any statistically significant associations in the case‒control association study; however, we found that the combined genotypes rs1264323AA-rs2267641AC/CC were associated with worse neurocognitive performance in patients with BD with psychotic symptoms. In addition, the combined genotypes rs1264323AA-rs2267641AC/CC were associated with worse psychosocial functioning through processing speed. We did not find correlations between white matter microstructure abnormalities and the neurocognitive domains associated with the combined genotypes rs1264323AA-rs2267641AC/CC. Overall, the results suggest that DDR1 may be a marker of worse neurocognitive performance and psychosocial functioning in patients with BD, specifically those with psychotic symptoms.

Genetic influences on circulating retinol and its relationship to human health

Retinol is a fat-soluble vitamin that plays an essential role in many biological processes throughout the human lifespan. Here, we perform the largest genome-wide association study (GWAS) of retinol to date in up to 22,274 participants. We identify eight common variant loci associated with retinol, as well as a rare-variant signal. An integrative gene prioritisation pipeline supports novel retinol-associated genes outside of the main retinol transport complex (RBP4:TTR) related to lipid biology, energy homoeostasis, and endocrine signalling. Genetic proxies of circulating retinol were then used to estimate causal relationships with almost 20,000 clinical phenotypes via a phenome-wide Mendelian randomisation study (MR-pheWAS). The MR-pheWAS suggests that retinol may exert causal effects on inflammation, adiposity, ocular measures, the microbiome, and MRI-derived brain phenotypes, amongst several others. Conversely, circulating retinol may be causally influenced by factors including lipids and serum creatinine. Finally, we demonstrate how a retinol polygenic score could identify individuals more likely to fall outside of the normative range of circulating retinol for a given age. In summary, this study provides a comprehensive evaluation of the genetics of circulating retinol, as well as revealing traits which should be prioritised for further investigation with respect to retinol related therapies or nutritional intervention.

Polygenic effects on brain functional endophenotype for deficit and non-deficit schizophrenia

Deficit schizophrenia (DS) is a subtype of schizophrenia (SCZ). The polygenic effects on the neuroimaging alterations in DS still remain unknown. This study aims to calculate the polygenic risk scores for schizophrenia (PRS-SCZ) in DS, and further explores the potential associations with functional features of brain. PRS-SCZ was calculated according to the Whole Exome sequencing and Genome-wide association studies (GWAS). Resting-state fMRI, as well as biochemical features and neurocognitive data were obtained from 33 DS, 47 NDS and 41 HCs, and association studies of genetic risk with neuroimaging were performed in this sample. The analyses of amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo) and functional connectivity (FC) were performed to detect the functional alterations between DS and NDS. In addition, correlation analysis was used to investigate the relationships between functional features (ALFF, ReHo, FC) and PRS-SCZ. The PRS-SCZ of DS was significantly lower than that in NDS and HC. Compared to NDS, there was a significant increase in the ALFF of left inferior temporal gyrus (ITG.L) and left inferior frontal gyrus (IFG.L) and a significant decrease in the ALFF of right precuneus (PCUN.R) and ReHo of right middle frontal gyrus (MFG.R) in DS. FCs were widely changed between DS and NDS, mainly concentrated in default mode network, including ITG, PCUN and angular gyrus (ANG). Correlation analysis revealed that the ALFF of left ITG, the ReHo of right middle frontal gyrus, the FC value between insula and ANG, left ITG and right corpus callosum, left ITG and right PCUN, as well as the scores of Trail Making Test-B, were associated with PRS-SCZ in DS. The present study demonstrated the differential polygenic effects on functional changes of brain in DS and NDS, providing a potential neuroimaging-genetic perspective for the pathogenesis of schizophrenia.

Association of Blood Metabolomics Biomarkers with Brain Metabolites and Patient-Reported Outcomes as a New Approach in Individualized Diagnosis of Schizophrenia

Given its polygenic nature, there is a need for a personalized approach to schizophrenia. The aim of the study was to select laboratory biomarkers from blood, brain imaging, and clinical assessment, with an emphasis on patients' self-report questionnaires. Metabolomics studies of serum samples from 51 patients and 45 healthy volunteers, based on the liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS/MS), led to the identification of 3 biochemical indicators (cortisol, glutamate, lactate) of schizophrenia. These metabolites were sequentially correlated with laboratory tests results, imaging results, and clinical assessment outcomes, including patient self-report outcomes. The hierarchical cluster analysis on the principal components (HCPC) was performed to identify the most homogeneous clinical groups. Significant correlations were noted between blood lactates and 11 clinical and 10 neuroimaging parameters. The increase in lactate and cortisol were significantly associated with a decrease in immunological parameters, especially with the level of reactive lymphocytes. The strongest correlations with the level of blood lactate and cortisol were demonstrated by brain glutamate, N-acetylaspartate and the concentrations of glutamate and glutamine, creatine and phosphocreatine in the prefrontal cortex. Metabolomics studies and the search for associations with brain parameters and self-reported outcomes may provide new diagnostic evidence to specific schizophrenia phenotypes.