Comparison of the heritability of schizophrenia and endophenotypes in the COGS-1 family study

Association of neurotransmitter pathway polygenic risk with specific symptom profiles in psychosis

A primary goal of psychiatry is to better understand the pathways that link genetic risk to psychiatric symptoms. Here, we tested association of diagnosis and endophenotypes with overall and neurotransmitter pathway-specific polygenic risk in patients with early-stage psychosis. Subjects included 205 demographically diverse cases with a psychotic disorder who underwent comprehensive psychiatric and neurological phenotyping and 115 matched controls. Following genotyping, we calculated polygenic scores (PGSs) for schizophrenia (SZ) and bipolar disorder (BP) using Psychiatric Genomics Consortium GWAS summary statistics. To test if overall genetic risk can be partitioned into affected neurotransmitter pathways, we calculated pathway PGSs (pPGSs) for SZ risk affecting each of four major neurotransmitter systems: glutamate, GABA, dopamine, and serotonin. Psychosis subjects had elevated SZ PGS versus controls; cases with SZ or BP diagnoses had stronger SZ or BP risk, respectively. There was no significant association within psychosis cases between individual symptom measures and overall PGS. However, neurotransmitter-specific pPGSs were moderately associated with specific endophenotypes; notably, glutamate was associated with SZ diagnosis and with deficits in cognitive control during task-based fMRI, while dopamine was associated with global functioning. Finally, unbiased endophenotype-driven clustering identified three diagnostically mixed case groups that separated on primary deficits of positive symptoms, negative symptoms, global functioning, and cognitive control. All clusters showed strong genome-wide risk. Cluster 2, characterized by deficits in cognitive control and negative symptoms, additionally showed specific risk concentrated in glutamatergic and GABAergic pathways. Due to the intensive characterization of our subjects, the present study was limited to a relatively small cohort. As such, results should be followed up with additional research at the population and mechanism level. Our study suggests pathway-based PGS analysis may be a powerful path forward to study genetic mechanisms driving psychiatric endophenotypes.

Environmental pollution and extreme weather conditions: insights into the effect on mental health

Environmental pollution exposures, including air, soil, water, light, and noise pollution, are critical issues that may implicate adverse mental health outcomes. Extreme weather conditions, such as hurricanes, floods, wildfires, and droughts, may also cause long-term severe concerns. However, the knowledge about possible psychiatric disorders associated with these exposures is currently not well disseminated. In this review, we aim to summarize the current knowledge on the impact of environmental pollution and extreme weather conditions on mental health, focusing on anxiety spectrum disorders, autism spectrum disorders, schizophrenia, and depression. In air pollution studies, increased concentrations of PM2.5, NO2, and SO2 were the most strongly associated with the exacerbation of anxiety, schizophrenia, and depression symptoms. We provide an overview of the suggested underlying pathomechanisms involved. We highlight that the pathogenesis of environmental pollution-related diseases is multifactorial, including increased oxidative stress, systematic inflammation, disruption of the blood-brain barrier, and epigenetic dysregulation. Light pollution and noise pollution were correlated with an increased risk of neurodegenerative disorders, particularly Alzheimer's disease. Moreover, the impact of soil and water pollution is discussed. Such compounds as crude oil, heavy metals, natural gas, agro-chemicals (pesticides, herbicides, and fertilizers), polycyclic or polynuclear aromatic hydrocarbons (PAH), solvents, lead (Pb), and asbestos were associated with detrimental impact on mental health. Extreme weather conditions were linked to depression and anxiety spectrum disorders, namely PTSD. Several policy recommendations and awareness campaigns should be implemented, advocating for the advancement of high-quality urbanization, the mitigation of environmental pollution, and, consequently, the enhancement of residents' mental health.

Association of neurotransmitter pathway polygenic risk with specific symptom profiles in psychosis

A primary goal of psychiatry is to better understand the pathways that link genetic risk to psychiatric symptoms. Here, we tested association of diagnosis and endophenotypes with overall and neurotransmitter pathway-specific polygenic risk in patients with early-stage psychosis. Subjects included 206 demographically diverse cases with a psychotic disorder who underwent comprehensive psychiatric and neurological phenotyping and 115 matched controls. Following genotyping, we calculated polygenic scores (PGSs) for schizophrenia (SZ) and bipolar disorder (BP) using Psychiatric Genomics Consortium GWAS summary statistics. To test if overall genetic risk can be partitioned into affected neurotransmitter pathways, we calculated pathway PGSs (pPGSs) for SZ risk affecting each of four major neurotransmitter systems: glutamate, GABA, dopamine, and serotonin. Psychosis subjects had elevated SZ PGS versus controls; cases with SZ or BP diagnoses had stronger SZ or BP risk, respectively. There was no significant association within psychosis cases between individual symptom measures and overall PGS. However, neurotransmitter-specific pPGSs were moderately associated with specific endophenotypes; notably, glutamate was associated with SZ diagnosis and with deficits in cognitive control during task-based fMRI, while dopamine was associated with global functioning. Finally, unbiased endophenotype-driven clustering identified three diagnostically mixed case groups that separated on primary deficits of positive symptoms, negative symptoms, global functioning, and cognitive control. All clusters showed strong genome-wide risk. Cluster 2, characterized by deficits in cognitive control and negative symptoms, additionally showed specific risk concentrated in glutamatergic and GABAergic pathways. Due to the intensive characterization of our subjects, the present study was limited to a relatively small cohort. As such, results should be followed up with additional research at the population and mechanism level. Our study suggests pathway-based PGS analysis may be a powerful path forward to study genetic mechanisms driving psychiatric endophenotypes.

Psychosocial stress, interpersonal sensitivity, and social withdrawal in clinical high risk for psychosis: a systematic review

Stress has repeatedly been implicated in the onset and exacerbation of positive symptoms of psychosis. Increasing interest is growing for the role of psychosocial stress in the development of psychosis symptoms in individuals at Clinical High Risk (CHR) for psychosis. A systematic review was therefore conducted to summarize the existing evidence base regarding psychosocial stress, interpersonal sensitivity, and social withdrawal in individuals at CHR for psychosis. An electronic search of Ovid (PsychINFO, EMBASE, MEDLINE, and GLOBAL HEALTH) was conducted until February 2022. Studies that examined psychosocial stress in CHR were included. Twenty-nine studies were eligible for inclusion. Psychosocial stress, interpersonal sensitivity, and social withdrawal were higher in CHR individuals compared to healthy controls and there was some evidence of their association with positive symptoms of psychosis. Two types of psychosocial stressors were found to occur more frequently with CHR status, namely daily stressors, and early and recent trauma, while significant life events did not appear to be significant. Greater exposure to psychosocial stress, emotional abuse, and perceived discrimination significantly increased risk of transition to psychosis in CHR. No studies examined the role of interpersonal sensitivity on transition to psychosis in CHR. This systematic review provides evidence for the association of trauma, daily stressors, social withdrawal, and interpersonal sensitivity with CHR status. Further studies investigating the impact of psychosocial stress on psychosis symptom expression in individuals at CHR and its effects on transition to psychosis are therefore warranted.

Critical review of the evidence base regarding theories conceptualising the aetiology of psychosis

A critical review of literature related to the aetiology of psychosis was conducted with specific emphasis on genetics. It was found that, although many published articles were retrieved via database searches, the format of the information was disparate in presentation leading to unnecessary inconsistences. This suggests the need for insightful collaboration by authors and standardisation of published articles to prevent academic and specialism barriers remaining as a discouragement to non-specialists wishing to access this information.

Computerized Assessment of Psychosis Risk

Early detection and intervention with young people at clinical high risk (CHR) for psychosis is critical for prevention efforts focused on altering the trajectory of psychosis. Early CHR research largely focused on validating clinical interviews for detecting at-risk individuals; however, this approach has limitations related to: (1) specificity (i.e., only 20% of CHR individuals convert to psychosis) and (2) the expertise and training needed to administer these interviews is limited. The purpose of our study is to develop the computerized assessment of psychosis risk (CAPR) battery, consisting of behavioral tasks that require minimal training to administer, can be administered online, and are tied to the neurobiological systems and computational mechanisms implicated in psychosis. The aims of our study are as follows: (1A) to develop a psychosis-risk calculator through the application of machine learning (ML) methods to the measures from the CAPR battery, (1B) evaluate group differences on the risk calculator score and test the hypothesis that the risk calculator score of the CHR group will differ from help-seeking and healthy controls, (1C) evaluate how baseline CAPR battery performance relates to symptomatic outcome two years later (i.e., conversion and symptomatic worsening). These aims will be explored in 500 CHR participants, 500 help-seeking individuals, and 500 healthy controls across the study sites. This project will provide a next-generation CHR battery, tied to illness mechanisms and powered by cutting-edge computational methods that can be used to facilitate the earliest possible detection of psychosis risk.

Functional Connectivity Combined With a Machine Learning Algorithm Can Classify High-Risk First-Degree Relatives of Patients With Schizophrenia and Identify Correlates of Cognitive Impairments

Schizophrenia (SCZ) is an inherited disease, with the familial risk being among the most important factors when evaluating an individual's risk for SCZ. However, robust imaging biomarkers for the disease that can be used for diagnosis and determination of the prognosis are lacking. Here, we explore the potential of functional connectivity (FC) for use as a biomarker for the early detection of high-risk first-degree relatives (FDRs). Thirty-eight first-episode SCZ patients, 38 healthy controls (HCs), and 33 FDRs were scanned using resting-state functional magnetic resonance imaging. The subjects' brains were parcellated into 200 regions using the Craddock atlas, and the FC between each pair of regions was used as a classification feature. Multivariate pattern analysis using leave-one-out cross-validation achieved a correct classification rate of 88.15% [sensitivity 84.06%, specificity 92.18%, and area under the receiver operating characteristic curve (AUC) 0.93] for differentiating SCZ patients from HCs. FC located within the default mode, frontal-parietal, auditory, and sensorimotor networks contributed mostly to the accurate classification. The FC patterns of each FDR were input into each classification model as test data to obtain a corresponding prediction label (a total of 76 individual classification scores), and the averaged individual classification score was then used as a robust measure to characterize whether each FDR showed an SCZ-type or HC-type FC pattern. A significant negative correlation was found between the average classification scores of the FDRs and their semantic fluency scores. These findings suggest that FC combined with a machine learning algorithm could help to predict whether FDRs are likely to show an SCZ-specific or HC-specific FC pattern.

Genome-Wide Association Studies of Schizophrenia and Bipolar Disorder in a Diverse Cohort of US Veterans

BACKGROUND

Schizophrenia (SCZ) and bipolar disorder (BIP) are debilitating neuropsychiatric disorders, collectively affecting 2% of the world's population. Recognizing the major impact of these psychiatric disorders on the psychosocial function of more than 200 000 US Veterans, the Department of Veterans Affairs (VA) recently completed genotyping of more than 8000 veterans with SCZ and BIP in the Cooperative Studies Program (CSP) #572.

METHODS

We performed genome-wide association studies (GWAS) in CSP #572 and benchmarked the predictive value of polygenic risk scores (PRS) constructed from published findings. We combined our results with available summary statistics from several recent GWAS, realizing the largest and most diverse studies of these disorders to date.

RESULTS

Our primary GWAS uncovered new associations between CHD7 variants and SCZ, and novel BIP associations with variants in Sortilin Related VPS10 Domain Containing Receptor 3 (SORCS3) and downstream of PCDH11X. Combining our results with published summary statistics for SCZ yielded 39 novel susceptibility loci including CRHR1, and we identified 10 additional findings for BIP (28 326 cases and 90 570 controls). PRS trained on published GWAS were significantly associated with case-control status among European American (P < 10-30) and African American (P < .0005) participants in CSP #572.

CONCLUSIONS

We have demonstrated that published findings for SCZ and BIP are robustly generalizable to a diverse cohort of US veterans. Leveraging available summary statistics from GWAS of global populations, we report 52 new susceptibility loci and improved fine-mapping resolution for dozens of previously reported associations.

Enhancing Psychosis Risk Prediction Through Computational Cognitive Neuroscience

Research suggests that early identification and intervention with individuals at clinical high risk (CHR) for psychosis may be able to improve the course of illness. The first generation of studies suggested that the identification of CHR through the use of specialized interviews evaluating attenuated psychosis symptoms is a promising strategy for exploring mechanisms associated with illness progression, etiology, and identifying new treatment targets. The next generation of research on psychosis risk must address two major limitations: (1) interview methods have limited specificity, as recent estimates indicate that only 15%-30% of individuals identified as CHR convert to psychosis and (2) the expertise needed to make CHR diagnosis is only accessible in a handful of academic centers. Here, we introduce a new approach to CHR assessment that has the potential to increase accessibility and positive predictive value. Recent advances in clinical and computational cognitive neuroscience have generated new behavioral measures that assay the cognitive mechanisms and neural systems that underlie the positive, negative, and disorganization symptoms that are characteristic of psychotic disorders. We hypothesize that measures tied to symptom generation will lead to enhanced sensitivity and specificity relative to interview methods and the cognitive intermediate phenotype measures that have been studied to date that are typically indicators of trait vulnerability and, therefore, have a high false positive rate for conversion to psychosis. These new behavioral measures have the potential to be implemented on the internet and at minimal expense, thereby increasing accessibility of assessments.

A trans-ethnic two-stage polygenetic scoring analysis detects genetic correlation between osteoporosis and schizophrenia

BACKGROUNDS

To explore the genetic correlation between schizophrenia (SCZ) and osteoporosis (OP).

DESIGN, SETTING, PARTICIPANTS, MEASUREMENTS

We conducted a trans-ethnic two-stage genetic correlation analysis of OP and SCZ, totally invoking 2286 Caucasia subjects in discovery stage and 4124 Chinese subjects in replication stage. The bone mineral density (BMD) and bone area values of ulna & radius, hip and spine were measured using Hologic 4500W dual energy X-ray absorptiometry machine. SCZ was diagnosed according to DSM-IV criteria. For the genome-wide association study (GWAS) of Caucasian OP, Chinese OP and Chinese SCZ, SNP genotyping was performed using Affymetrix SNP 6.0 array. For the GWAS of Caucasian SCZ, SNP genotyping was conducted using the Affymetrix 5.0 array, Affymetrix 6.0 array and Illumina 550 K array. Polygenetic risk scoring (PRS) analysis was conducted by PRSice software. Also, Linkage disequilibrium score regression (LD Score regression) analysis was performed to evaluate the genetic correlation between OP and SCZ. Multi-trait analysis of GWAS (MTAG) was performed to detect novel candidate genes for osteoporosis and SCZ.

RESULTS

In the Caucasia discovery samples, significant genetic correlations were observed for ulna & radius BMD vs. SCZ (P value = 0.010), ulna & radius area vs. SCZ (P value = 0.031). In the Chinese replication samples, we observed significant correlation for ulna & radius area vs. SCZ (P value = 0.019). In addition, LD Score regression also identified significant genetic correlations between SCZ and bone phenotypes in Caucasian and Chinese sample respectively. MTAG analysis identified several novel candidate genes, such as CTNNA2 (MTAG P value = 2.24 × 10-6) for SCZ and FADS2 (MTAG P value = 2.66 × 10-7) for osteoporosis.

CONCLUSIONS

Our study results support the overlapped genetic basis for osteoporosis and SCZ, and provide novel clues for elucidating the biological mechanism of increased osteoporosis risk in SCZ patients.

The Relationship Between Polygenic Risk Scores and Cognition in Schizophrenia

BACKGROUND

Cognitive impairment is a clinically important feature of schizophrenia. Polygenic risk score (PRS) methods have demonstrated genetic overlap between schizophrenia, bipolar disorder (BD), major depressive disorder (MDD), educational attainment (EA), and IQ, but very few studies have examined associations between these PRS and cognitive phenotypes within schizophrenia cases.

METHODS

We combined genetic and cognitive data in 3034 schizophrenia cases from 11 samples using the general intelligence factor g as the primary measure of cognition. We used linear regression to examine the association between cognition and PRS for EA, IQ, schizophrenia, BD, and MDD. The results were then meta-analyzed across all samples. A genome-wide association studies (GWAS) of cognition was conducted in schizophrenia cases.

RESULTS

PRS for both population IQ (P = 4.39 × 10-28) and EA (P = 1.27 × 10-26) were positively correlated with cognition in those with schizophrenia. In contrast, there was no association between cognition in schizophrenia cases and PRS for schizophrenia (P = .39), BD (P = .51), or MDD (P = .49). No individual variant approached genome-wide significance in the GWAS.

CONCLUSIONS

Cognition in schizophrenia cases is more strongly associated with PRS that index cognitive traits in the general population than PRS for neuropsychiatric disorders. This suggests the mechanisms of cognitive variation within schizophrenia are at least partly independent from those that predispose to schizophrenia diagnosis itself. Our findings indicate that this cognitive variation arises at least in part due to genetic factors shared with cognitive performance in populations and is not solely due to illness or treatment-related factors, although our findings are consistent with important contributions from these factors.

A Cross-Species Systems Genetics Analysis Links APBB1IP as a Candidate for Schizophrenia and Prepulse Inhibition

Background: Prepulse inhibition (PPI) of the startle response is a highly conserved form of sensorimotor gating, disruption of which is found in schizophrenia patients and their unaffected first-degree relatives. PPI can be measured in many species, and shows considerable phenotypic variation between and within rodent models. This makes PPI a useful endophenotype. Genome-wide association studies (GWAS) have been carried out to identify genetic variants underlying schizophrenia, and these suggest that schizophrenia is highly polygenic. GWAS have been unable to account for the high heritability of schizophrenia seen in family studies, partly because of the low power of GWAS due to multiple comparisons. By contrast, complementary mouse model linkage studies often have high statistical power to detect variants for behavioral traits but lower resolution, producing loci that include tens or hundreds of genes. To capitalize on the advantages of both GWAS and genetic mouse models, our study uses a cross-species approach to identify novel genes associated with PPI regulation, which thus may contribute to the PPI deficits seen in schizophrenia.

Results: Using experimental data from the recombinant inbred (RI) mouse panel BXD, we identified two significant loci affecting PPI. These genomic regions contain genetic variants which influence PPI in mice and are therefore candidates that may be influencing aspects of schizophrenia in humans. We next investigated these regions in whole-genome data from the Psychiatric Genomics Consortium (PGC) schizophrenia GWAS and identify one novel candidate gene (ABPP1IP) that was significantly associated with PPI in mice and risk of schizophrenia in humans. A systems genetics approach demonstrates that APBB1IP coexpresses with several other genes related to schizophrenia in several brain regions. Gene coexpression and enrichment analysis shows clear links between APBB1IP and the immune system.

Conclusion: The combination of human GWAS and mouse quantitative trait loci (QTL) from some of the largest study systems available has enabled us to identify a novel gene, APBB1IP, which influences schizophrenia in humans and PPI in mice.

Are familial liability for schizophrenia and obstetric complications independently associated with risk of psychotic illness, after adjusting for other environmental stressors in childhood?

OBJECTIVE

The interplay between genetic and environmental factors on risk for psychotic illness remains poorly understood. The aim of this study was to estimate independent and combined effects of familial liability for schizophrenia and exposure to obstetric complications on risk for developing psychotic illness, covarying with exposure to other environmental stressors.

METHODS

This whole-population birth cohort study used record linkage across Western Australian statewide data collections (midwives, psychiatric, hospital admissions, child protection, mortality) to identify liveborn offspring (n = 1046) born 1980-1995 to mothers with schizophrenia, comparing them to offspring of mothers with no recorded psychiatric history (n = 298,370).

RESULTS

Both maternal schizophrenia and pregnancy complications were each significantly associated with psychotic illness in offspring, with no interaction. Non-obstetric environmental stressors significantly associated with psychotic illness in offspring included the following: being Indigenous; having a mother who was not in a partnered relationship; episodes of disrupted parenting due to hospitalisation of mother, father or child; abuse in childhood; and living in areas of greatest socioeconomic disadvantage and with elevated rates of violent crime. Adjustment for these other environmental stressors reduced the hazard ratio for maternal schizophrenia substantially (from hazard ratio: 5.7, confidence interval: 4.5-7.2 to hazard ratio: 3.5, confidence interval: 2.8-4.4), but not the estimate for pregnancy complications (hazard ratio: 1.1, confidence interval: 1.0-1.2). The population attributable fraction for maternal schizophrenia was 1.4 and for pregnancy complications was 2.1.

CONCLUSION

Our finding of a substantial decrease in risk of psychotic illness associated with familial liability for psychosis following adjustment for other environmental stressors highlights potentially modifiable risk factors on the trajectory to psychotic illness and suggests that interventions that reduce or manage exposure to these risks may be protective, despite a genetic liability.

Sensorimotor gating deficits in "two-hit" models of schizophrenia risk factors

Genetic and environmental models of neuropsychiatric disease have grown exponentially over the last 20years. One measure that is often used to evaluate the translational relevance of these models to human neuropsychiatric disease is prepulse inhibition of startle (PPI), an operational measure of sensorimotor gating. Deficient PPI characterizes several neuropsychiatric disorders but has been most extensively studied in schizophrenia. It has become a useful tool in translational neuropharmacological and molecular genetics studies because it can be measured across species using almost the same experimental parameters. Although initial studies of PPI in rodents were pharmacological because of the robust predictive validity of PPI for antipsychotic efficacy, more recently, PPI has become standard common behavioral measures used in genetic and neurodevelopmental models of schizophrenia. Here we review "two hit" models of schizophrenia and discuss the utility of PPI as a tool in phenotyping these models of relevant risk factors. In the review, we consider approaches to rodent models of genetic and neurodevelopmental risk factors and selectively review "two hit" models of gene×environment and environment×environment interactions in which PPI has been measured.

Recent Advances in the Genetics of Schizophrenia

The last decade brought tremendous progress in the field of schizophrenia genetics. As a result of extensive collaborations and multiple technological advances, we now recognize many types of genetic variants that increase the risk. These include large copy number variants, rare coding inherited and de novο variants, and over 100 loci harboring common risk variants. While the type and contribution to the risk vary among genetic variants, there is concordance in the functions of genes they implicate, such as those whose RNA binds the fragile X-related protein FMRP and members of the activity-regulated cytoskeletal complex involved in learning and memory. Gene expression studies add important information on the biology of the disease and recapitulate the same functional gene groups. Studies of alternative phenotypes help us widen our understanding of the genetic architecture of mental function and dysfunction, how diseases overlap not only with each other but also with non-disease phenotypes. The challenge is to apply this new knowledge to prevention and treatment and help patients. The data generated so far and emerging technologies, including new methods in cell engineering, offer significant promise that in the next decade we will unlock the translational potential of these significant discoveries.

The Relationship between Cytokines and Verbal Memory in Individuals with Schizophrenia and Their Unaffected Siblings

BACKGROUND

Verbal memory impairment may be considered an endophenotype in schizophrenia (SZ), also affecting the siblings of SZ subjects. Furthermore, the immune-inflammatory system response has an important modulatory effect on brain processes, especially on memory circuits.

OBJECTIVE

Investigating the relationship between TNF-α and IL-6 and memory performance in patients with SZ, their unaffected siblings (SB) and healthy controls (HC).

METHODS

35 subjects with SZ, 36 SB, and 47 HC underwent a neurocognitive assessment for verbal memory by means of the revised Hopkins Verbal Learning Test (HVLT-R) in addition to serum cytokines analyses.

RESULTS

SZ patients performed worse in HVLT-R than SB and HC, but SB and HC were not different. Regarding the biomarker levels, we found significant results of TNF-α for both groups. However, we did not find differences between groups after multiple-comparisons analysis. There were no significant correlations between episodic verbal memory, TNF-α, and IL-6.

CONCLUSION

The results are compatible with the hypothesis that deficits in verbal memory of individuals with SZ could be secondary to inadequate functioning of cognitive processing areas, such as proactive cognitive control.

Three Approaches to Understanding and Classifying Mental Disorder: ICD-11, DSM-5, and the National Institute of Mental Health’s Research Domain Criteria (RDoC)

The diagnosis of mental disorder initially appears relatively straightforward: Patients present with symptoms or visible signs of illness; health professionals make diagnoses based primarily on these symptoms and signs; and they prescribe medication, psychotherapy, or both, accordingly. However, despite a dramatic expansion of knowledge about mental disorders during the past half century, understanding of their components and processes remains rudimentary. We provide histories and descriptions of three systems with different purposes relevant to understanding and classifying mental disorder. Two major diagnostic manuals—the International Classification of Diseases and the Diagnostic and Statistical Manual of Mental Disorders—provide classification systems relevant to public health, clinical diagnosis, service provision, and specific research applications, the former internationally and the latter primarily for the United States. In contrast, the National Institute of Mental Health’s Research Domain Criteria provides a framework that emphasizes integration of basic behavioral and neuroscience research to deepen the understanding of mental disorder. We identify four key issues that present challenges to understanding and classifying mental disorder: etiology, including the multiple causality of mental disorder; whether the relevant phenomena are discrete categories or dimensions; thresholds, which set the boundaries between disorder and nondisorder; and comorbidity, the fact that individuals with mental illness often meet diagnostic requirements for multiple conditions. We discuss how the three systems’ approaches to these key issues correspond or diverge as a result of their different histories, purposes, and constituencies. Although the systems have varying degrees of overlap and distinguishing features, they share the goal of reducing the burden of suffering due to mental disorder.

Prioritizing schizophrenia endophenotypes for future genetic studies: An example using data from the COGS-1 family study

Past studies describe numerous endophenotypes associated with schizophrenia (SZ), but many endophenotypes may overlap in information they provide, and few studies have investigated the utility of a multivariate index to improve discrimination between SZ and healthy community comparison subjects (CCS). We investigated 16 endophenotypes from the first phase of the Consortium on the Genetics of Schizophrenia, a large, multi-site family study, to determine whether a subset could distinguish SZ probands and CCS just as well as using all 16. Participants included 345 SZ probands and 517 CCS with a valid measure for at least one endophenotype. We used both logistic regression and random forest models to choose a subset of endophenotypes, adjusting for age, gender, smoking status, site, parent education, and the reading subtest of the Wide Range Achievement Test. As a sensitivity analysis, we re-fit models using multiple imputations to determine the effect of missing values. We identified four important endophenotypes: antisaccade, Continuous Performance Test-Identical Pairs 3-digit version, California Verbal Learning Test, and emotion identification. The logistic regression model that used just these four endophenotypes produced essentially the same results as the model that used all 16 (84% vs. 85% accuracy). While a subset of endophenotypes cannot replace clinical diagnosis nor encompass the complexity of the disease, it can aid in the design of future endophenotypic and genetic studies by reducing study cost and subject burden, simplifying sample enrichment, and improving the statistical power of locating those genetic regions associated with schizophrenia that may be the easiest to identify initially.

New insights into the endophenotypic status of cognition in bipolar disorder: genetic modelling study of twins and siblings

BACKGROUND

Twin studies have lacked statistical power to apply advanced genetic modelling techniques to the search for cognitive endophenotypes for bipolar disorder.

AIMS

To quantify the shared genetic variability between bipolar disorder and cognitive measures.

METHOD

Structural equation modelling was performed on cognitive data collected from 331 twins/siblings of varying genetic relatedness, disease status and concordance for bipolar disorder.

RESULTS

Using a parsimonious AE model, verbal episodic and spatial working memory showed statistically significant genetic correlations with bipolar disorder (rg = |0.23|-|0.27|), which lost statistical significance after covarying for affective symptoms. Using an ACE model, IQ and visual-spatial learning showed statistically significant genetic correlations with bipolar disorder (rg = |0.51|-|1.00|), which remained significant after covarying for affective symptoms.

CONCLUSIONS

Verbal episodic and spatial working memory capture a modest fraction of the bipolar diathesis. IQ and visual-spatial learning may tap into genetic substrates of non-affective symptomatology in bipolar disorder.

From Linkage Studies to Epigenetics: What We Know and What We Need to Know in the Neurobiology of Schizophrenia

Schizophrenia is a complex psychiatric disorder characterized by the presence of positive, negative, and cognitive symptoms that lacks a unifying neuropathology. In the present paper, we will review the current understanding of molecular dysregulation in schizophrenia, including genetic and epigenetic studies. In relation to the latter, basic research suggests that normal cognition is regulated by epigenetic mechanisms and its dysfunction occurs upon epigenetic misregulation, providing new insights into missing heritability of complex psychiatric diseases, referring to the discrepancy between epidemiological heritability and the proportion of phenotypic variation explained by DNA sequence difference. In schizophrenia the absence of consistently replicated genetic effects together with evidence for lasting changes in gene expression after environmental exposures suggest a role of epigenetic mechanisms. In this review we will focus on epigenetic modifications as a key mechanism through which environmental factors interact with individual's genetic constitution to affect risk of psychotic conditions throughout life.

Gating Deficit Heritability and Correlation With Increased Clinical Severity in Schizophrenia Patients With Positive Family History

OBJECTIVE

The Consortium on the Genetics of Schizophrenia Family Study evaluated 12 primary and other supplementary neurocognitive and neurophysiological endophenotypes in schizophrenia probands and their families. Previous analyses of prepulse inhibition (PPI) and P50 gating measures in this sample revealed heritability estimates that were lower than expected based on earlier family studies. Here the authors investigated whether gating measures were more heritable in multiply affected families with a positive family history compared with families with only a single affected proband (singleton).

METHOD

A total of 296 nuclear families consisting of a schizophrenia proband, at least one unaffected sibling, and both parents underwent a comprehensive endophenotype and clinical characterization. The Family Interview for Genetic Studies was administered to all participants and used to obtain convergent psychiatric symptom information for additional first-degree relatives. Among the families, 97 were multiply affected, and 96 were singletons.

RESULTS

Both PPI and P50 gating displayed substantially increased heritability in the 97 multiply affected families (47% and 36%, respectively) compared with estimates derived from the entire sample of 296 families (29% and 20%, respectively). However, no evidence for heritability was observed for either measure in the 96 singleton families. Schizophrenia probands derived from the multiply affected families also displayed a significantly increased severity of clinical symptoms compared with those from singleton families.

CONCLUSIONS

PPI and P50 gating measures demonstrate substantially increased heritability in schizophrenia families with a higher genetic vulnerability for illness, providing further support for the commonality of genes underlying both schizophrenia and gating measures.

Genetic assessment of additional endophenotypes from the Consortium on the Genetics of Schizophrenia Family Study

The Consortium on the Genetics of Schizophrenia Family Study (COGS-1) has previously reported our efforts to characterize the genetic architecture of 12 primary endophenotypes for schizophrenia. We now report the characterization of 13 additional measures derived from the same endophenotype test paradigms in the COGS-1 families. Nine of the measures were found to discriminate between schizophrenia patients and controls, were significantly heritable (31 to 62%), and were sufficiently independent of previously assessed endophenotypes, demonstrating utility as additional endophenotypes. Genotyping via a custom array of 1536 SNPs from 94 candidate genes identified associations for CTNNA2, ERBB4, GRID1, GRID2, GRIK3, GRIK4, GRIN2B, NOS1AP, NRG1, and RELN across multiple endophenotypes. An experiment-wide p value of 0.003 suggested that the associations across all SNPs and endophenotypes collectively exceeded chance. Linkage analyses performed using a genome-wide SNP array further identified significant or suggestive linkage for six of the candidate endophenotypes, with several genes of interest located beneath the linkage peaks (e.g., CSMD1, DISC1, DLGAP2, GRIK2, GRIN3A, and SLC6A3). While the partial convergence of the association and linkage likely reflects differences in density of gene coverage provided by the distinct genotyping platforms, it is also likely an indication of the differential contribution of rare and common variants for some genes and methodological differences in detection ability. Still, many of the genes implicated by COGS through endophenotypes have been identified by independent studies of common, rare, and de novo variation in schizophrenia, all converging on a functional genetic network related to glutamatergic neurotransmission that warrants further investigation.

Re-visiting the nature and relationships between neurological signs and neurocognitive functions in first-episode schizophrenia: An invariance model across time

The present study examined different types of neurological signs in patients with first-episode schizophrenia and their relationships with neurocognitive functions. Both cross-sectional and longitudinal designs were adopted with the use of the abridged Cambridge Neurological Inventory which comprises items capturing motor coordination, sensory integration and disinhibition. A total of 157 patients with first-episode schizophrenia were assessed at baseline and 101 of them were re-assessed at six-month interval. A structural equation model (SEM) with invariance model across time was used for data analysis. The model fitted well with the data at baseline assessment, X^2(21) = 21.78, p = 0.413, NFI = 0.95, NNFI = 1.00, CFI = 1.00, IFI = 1.00, RMSEA = 0.015. Subsequent SEM analysis with invariance model at six-month interval also demonstrated the same stable pattern across time and showed strong measurement invariance and structure invariance across time. Our findings suggest that neurological signs capture more or less the same construct captured by conventional neurocognitive tests in patients with schizophrenia. The measurement and structure of these relationships appear to be stable over time.

Consortium on the Genetics of Schizophrenia (COGS) assessment of endophenotypes for schizophrenia: an introduction to this Special Issue of Schizophrenia Research

BACKGROUND

The COGS is a multi-site NIMH-sponsored investigation of the genetic basis of 12 primary and multiple secondary quantitative endophenotypes in schizophrenia.

METHODS

Since 2003, COGS has completed studies using a family-based ascertainment strategy (COGS-1), and a case-control ascertainment strategy (COGS-2) (cumulative "n">4000).

RESULTS

COGS-1 family study confirmed robust deficits in, and heritability of, these endophenotypes in schizophrenia, and provided evidence for a coherent genetic architecture underlying the risk for neurocognitive and neurophysiological deficits in this disorder. COGS-2 case-control findings, many reported herein, establish a foundation for fine genomic mapping and other analyses of these endophenotypes and risk genes for SZ. Several reports in this Special Issue compare findings of endophenotype deficits generated by fundamentally different COGS-1 vs. COGS-2 ascertainment strategies. Despite the expectation that family-based and case-control designs would establish demographically and potentially biologically distinct patient cohorts, findings generally revealed comparable patterns of endophenotype deficits across studies. The COGS-2 case-control design facilitated the accrual of a larger "n", permitting detailed analyses of factors moderating endophenotype performance. Some COGS-2 endophenotypes not assessed in COGS-1 are also reported, as is a new factor analytic strategy for identifying shared vs. unique factors among the COGS endophenotypes which can be used to develop composite variables with distinct genetic signatures.

DISCUSSION

The path to date of COGS-1 endophenotype and genetic findings, followed by replication and extension in COGS-2, establishes benchmarks for endophenotype deficits in SZ and their moderation by specific factors, and clear expectations for informative findings from upcoming COGS-2 genetic analyses.

The importance of endophenotypes in schizophrenia research

Endophenotypes provide a powerful neurobiological platform from which we can understand the genomic and neural substrates of schizophrenia and other common complex neuropsychiatric disorders. The Consortium on the Genetics of Schizophrenia (COGS) has conducted multisite studies on carefully selected key neurocognitive and neurophysiological endophenotypes in 300 families (COGS-1) and then in a follow up multisite case-control study of 2471 subjects (COGS-2). Endophenotypes are neurobiologically informed quantitative measures that show deficits in probands and their first degree relatives. They are more amenable to statistical analysis than are "fuzzy" qualitative clinical traits or confoundingly heterogeneous diagnostic categories. Endophenotypes are also viewed as uniquely informative in traditional diagnosis-based as well as emerging NIMH Research Domain (RDoC) contexts, offering a bridge between the two approaches to psychopathology classification and research. Endo- or intermediate phenotypes are heritable, and in the COGS-1 cohort their level of heritability is in the same range as is the heritability of schizophrenia itself, using the same statistical methods and subjects to assess both. Because we can demonstrate endophenotypes link to both gene networks and neural circuits on the one hand and also to real-life function, endophenotypes provide a critically important bridge for "connecting the dots" between genes, cells, circuits, information processing, neurocognition and functional impairment and personalized treatment selection in schizophrenia patients. By connecting schizophrenia risk genes with neurobiologically informed endophenotypes, and via the use of association, linkage, sequencing, stem cell and other strategies, we can provide our field with new neurobiologically informed information in our efforts to understand and treat schizophrenia. Evolving views, data and new analytic strategies about schizophrenia risk, pathology and treatment are described in this Viewpoint and in the accompanying Special Issue reports.

Validation of mismatch negativity and P3a for use in multi-site studies of schizophrenia: characterization of demographic, clinical, cognitive, and functional correlates in COGS-2

Mismatch negativity (MMN) and P3a are auditory event-related potential (ERP) components that show robust deficits in schizophrenia (SZ) patients and exhibit qualities of endophenotypes, including substantial heritability, test-retest reliability, and trait-like stability. These measures also fulfill criteria for use as cognition and function-linked biomarkers in outcome studies, but have not yet been validated for use in large-scale multi-site clinical studies. This study tested the feasibility of adding MMN and P3a to the ongoing Consortium on the Genetics of Schizophrenia (COGS) study. The extent to which demographic, clinical, cognitive, and functional characteristics contribute to variability in MMN and P3a amplitudes was also examined. Participants (HCS n=824, SZ n=966) underwent testing at 5 geographically distributed COGS laboratories. Valid ERP recordings were obtained from 91% of HCS and 91% of SZ patients. Highly significant MMN (d=0.96) and P3a (d=0.93) amplitude reductions were observed in SZ patients, comparable in magnitude to those observed in single-lab studies with no appreciable differences across laboratories. Demographic characteristics accounted for 26% and 18% of the variance in MMN and P3a amplitudes, respectively. Significant relationships were observed among demographically-adjusted MMN and P3a measures and medication status as well as several clinical, cognitive, and functional characteristics of the SZ patients. This study demonstrates that MMN and P3a ERP biomarkers can be feasibly used in multi-site clinical studies. As with many clinical tests of brain function, demographic factors contribute to MMN and P3a amplitudes and should be carefully considered in future biomarker-informed clinical studies.

Factor structure and heritability of endophenotypes in schizophrenia: findings from the Consortium on the Genetics of Schizophrenia (COGS-1)

BACKGROUND

Although many endophenotypes for schizophrenia have been studied individually, few studies have examined the extent to which common neurocognitive and neurophysiological measures reflect shared versus unique endophenotypic factors. It may be possible to distill individual endophenotypes into composite measures that reflect dissociable, genetically informative elements.

METHODS

The first phase of the Consortium on the Genetics of Schizophrenia (COGS-1) is a multisite family study that collected neurocognitive and neurophysiological data between 2003 and 2008. For these analyses, participants included schizophrenia probands (n=83), their nonpsychotic siblings (n=151), and community comparison subjects (n=209) with complete data on a battery of 12 neurocognitive tests (assessing domains of working memory, declarative memory, vigilance, spatial ability, abstract reasoning, facial emotion processing, and motor speed) and 3 neurophysiological tasks reflecting inhibitory processing (P50 gating, prepulse inhibition and antisaccade tasks). Factor analyses were conducted on the measures for each subject group and across the entire sample. Heritability analyses of factors were performed using SOLAR.

RESULTS

Analyses yielded 5 distinct factors: 1) Episodic Memory, 2) Working Memory, 3) Perceptual Vigilance, 4) Visual Abstraction, and 5) Inhibitory Processing. Neurophysiological measures had low associations with these factors. The factor structure of endophenotypes was largely comparable across probands, siblings and controls. Significant heritability estimates for the factors ranged from 22% (Episodic Memory) to 39% (Visual Abstraction).

CONCLUSIONS

Neurocognitive measures reflect a meaningful amount of shared variance whereas the neurophysiological measures reflect largely unique contributions as endophenotypes for schizophrenia. Composite endophenotype measures may inform our neurobiological and genetic understanding of schizophrenia.

Neurocognitive performance in family-based and case-control studies of schizophrenia

BACKGROUND

Neurocognitive deficits in schizophrenia (SZ) are established and the Consortium on the Genetics of Schizophrenia (COGS) investigated such measures as endophenotypes in family-based (COGS-1) and case-control (COGS-2) studies. By requiring family participation, family-based sampling may result in samples that vary demographically and perform better on neurocognitive measures.

METHODS

The Penn computerized neurocognitive battery (CNB) evaluates accuracy and speed of performance for several domains and was administered across sites in COGS-1 and COGS-2. Most tests were included in both studies. COGS-1 included 328 patients with SZ and 497 healthy comparison subjects (HCS) and COGS-2 included 1195 patients and 1009 HCS.

RESULTS

Demographically, COGS-1 participants were younger, more educated, with more educated parents and higher estimated IQ compared to COGS-2 participants. After controlling for demographics, the two samples produced very similar performance profiles compared to their respective controls. As expected, performance was better and with smaller effect sizes compared to controls in COGS-1 relative to COGS-2. Better performance was most pronounced for spatial processing while emotion identification had large effect sizes for both accuracy and speed in both samples. Performance was positively correlated with functioning and negatively with negative and positive symptoms in both samples, but correlations were attenuated in COGS-2, especially with positive symptoms.

CONCLUSIONS

Patients ascertained through family-based design have more favorable demographics and better performance on some neurocognitive domains. Thus, studies that use case-control ascertainment may tap into populations with more severe forms of illness that are exposed to less favorable factors compared to those ascertained with family-based designs.

Joint Coupling of Awake EEG Frequency Activity and MRI Gray Matter Volumes in the Psychosis Dimension: A BSNIP Study

BACKGROUND

Many studies have examined either electroencephalogram (EEG) frequency activity or gray matter volumes (GMV) in various psychoses [including schizophrenia (SZ), schizoaffective (SZA), and psychotic bipolar disorder (PBP)]. Prior work demonstrated similar EEG and gray matter abnormalities in both SZ and PBP. Integrating EEG and GMV and jointly analyzing the combined data fully elucidates the linkage between the two and may provide better biomarker- or endophenotype-specificity for a particular illness. Joint exploratory investigations of EEG and GMV are scarce in the literature and the relationship between the two in psychosis is even less explored. We investigated a joint multivariate model to test whether the linear relationship or linkage between awake EEG (AEEG) frequency activity and GMV is abnormal across the psychosis dimension and if such effects are also present in first-degree relatives.

METHODS

We assessed 607 subjects comprising 264 probands [105 SZ, 72 SZA, and 87 PBP], 233 of their first degree relatives [82 SZ relatives (SZR), 71 SZA relatives (SZAR), and 80 PBP relatives (PBPR)], and 110 healthy comparison subjects (HC). All subjects underwent structural MRI (sMRI) and EEG scans. Frequency activity and voxel-based morphometric GMV were derived from EEG and sMRI data, respectively. Seven AEEG frequency and gray matter components were extracted using Joint independent component analysis (jICA). The loading coefficients (LC) were examined for group differences using analysis of covariance. Further, the LCs were correlated with psychopathology scores to identify relationship with clinical symptoms.

RESULTS

Joint ICA revealed a single component differentiating SZ from HC (p < 0.006), comprising increased posterior alpha activity associated with decreased volume in inferior parietal lobe, supramarginal, parahippocampal gyrus, middle frontal, inferior temporal gyri, and increased volume of uncus and culmen. No components were aberrant in either PBP or SZA or any relative group. No significant association was identified with clinical symptom measures.

CONCLUSION

Our data suggest that a joint EEG and GMV model yielded a biomarker specific to SZ, not abnormal in PBP or SZA. Alpha activity was related to both increased and decreased volume in different cortical structures. Additionally, the joint model failed to identify endophenotypes across psychotic disorders.