Polygenic Risk Scores for Subtyping of Schizophrenia.

Components and Correlates of Family Burden in Schizophrenia

Many psychiatric disorders share genetic liabilities, but whether these shared liabilities can be utilized to classify and differentiate psychiatric disorders remains unclear. In this study, we use polygenic risk scores (PRSs) of 42 traits comorbid with schizophrenia (SCZ), bipolar disorder (BIP), and major depressive disorder (MDD) to evaluate their utilities. We found that combining target specific PRS with PRSs of comorbid traits can improve the classification of the target disorders. Importantly, without inclusion of PRSs from targeted disorders, we can still classify SCZ (accuracy 0.710 ± 0.008, AUC 0.789 ± 0.011), BIP (accuracy 0.782 ± 0.006, AUC 0.852 ± 0.004), and MDD (accuracy 0.753 ± 0.019, AUC 0.822 ± 0.010). Furthermore, PRSs from comorbid traits alone can effectively differentiate unaffected controls and patients with SCZ, BIP, and MDD (accuracy 0.861 ± 0.003, AUC 0.961 ± 0.041). Our results demonstrate that shared liabilities can be used effectively to improve the classification and differentiation of these disorders. The finding that PRSs from comorbid traits alone can classify and differentiate SCZ, BIP and MDD reasonably well implies that a majority of the risk variants composing target PRSs are shared with comorbid traits. Overall, our results suggest that a data-driven approach may be feasible to classify and differentiate these disorders.

Recent GWASs have demonstrated that comorbid disorders share genetic liabilities. But whether and how these shared liabilities can be used for the classification and differentiation of comorbid disorders remains unclear. In this study, we use polygenic risk scores (PRSs) estimated from 42 comorbid traits and the deep neural networks (DNN) architecture to classify and differentiate schizophrenia (SCZ), bipolar disorder (BIP) and major depressive disorder (MDD). Multiple PRSs were obtained for individuals from the schizophrenia (SCZ) (cases = 6,317, controls = 7,240), bipolar disorder (BIP) (cases = 2,634, controls 4,425) and major depressive disorder (MDD) (cases = 1,704, controls = 3,357) datasets, and classification models were constructed with and without the inclusion of PRSs of the target (SCZ, BIP or MDD). Models with the inclusion of target PRSs performed well as expected. Surprisingly, we found that SCZ could be classified with only the PRSs from 35 comorbid traits (not including the target SCZ and directly related traits) (accuracy 0.760 ± 0.007, AUC 0.843 ± 0.005). Similar results were obtained for BIP (33 traits, accuracy 0.768 ± 0.007, AUC 0.848 ± 0.009), and MDD (36 traits, accuracy 0.794 ± 0.010, AUC 0.869 ± 0.004). Furthermore, these PRSs from comorbid traits alone could effectively differentiate unaffected controls, SCZ, BIP, and MDD patients (average categorical accuracy 0.861 ± 0.003, average AUC 0.961 ± 0.041). These results suggest that the shared liabilities from comorbid traits alone may be sufficient to classify SCZ, BIP and MDD. More importantly, these results imply that a data-driven and objective diagnosis and differentiation of SCZ, BIP and MDD may be feasible.

Polygenic signal for symptom dimensions and cognitive performance in patients with chronic schizophrenia

Although displaying genetic correlations, psychiatric disorders are clinically defined as categorical entities as they each have distinguishing clinical features and may involve different treatments. Identifying differential genetic variations between these disorders may reveal how the disorders differ biologically and help to guide more personalized treatment. Here we presented a statistical framework and comprehensive analysis to identify genetic markers differentially associated with various psychiatric disorders/traits based on GWAS summary statistics, covering 18 psychiatric traits/disorders and 26 comparisons. We also conducted comprehensive analysis to unravel the genes, pathways and SNP functional categories involved, and the cell types and tissues implicated. We also assessed how well one could distinguish between psychiatric disorders by polygenic risk scores (PRS). SNP-based heritabilities (h2snp) were significantly larger than zero for most comparisons. Based on current GWAS data, PRS have mostly modest power to distinguish between psychiatric disorders. For example, we estimated that AUC for distinguishing schizophrenia from major depressive disorder (MDD), bipolar disorder (BPD) from MDD and schizophrenia from BPD were 0.694, 0.602 and 0.618, respectively, while the maximum AUC (based on h2snp) were 0.763, 0.749 and 0.726, respectively. We also uncovered differences in each pair of studied traits in terms of their differences in genetic correlation with comorbid traits. For example, clinically defined MDD appeared to more strongly genetically correlated with other psychiatric disorders and heart disease, when compared to non-clinically defined depression in UK Biobank. Our findings highlight genetic differences between psychiatric disorders and the mechanisms involved. PRS may help differential diagnosis of selected psychiatric disorders in the future with larger GWAS samples.

Mental health advocates and policy makers are increasingly attuned to the importance of the recovery concept, and psychiatrists and neuroscientists increasingly emphasize the medical model and neurobiological mechanisms in relation to schizophrenia. Studies have shown that people with schizophrenia are tremendously heterogeneous in each domain of recovery, and the various domains of recovery are themselves relatively independent from one another. Studies have also shown that current interventions are effective for specific dimensions of the illness and functions, are usually ameliorative rather than curative, and are effective only for a proportion of patients. Hence, the authors suggest defining recovery in terms of improvements in specific domains rather than globally -- for example, "recovery of cognitive functioning" or "recovery of vocational functioning" -- to signify improvements in specific areas. This definition realistically emphasizes states of relative and partial recovery that patients can achieve in response to treatment. The emphasis on a range of improvements in specific areas should allow clinicians to communicate more clearly regarding the current findings and goals of treatment. The article also examines current research on various aspects of recovery, including the effects of treatment on pathophysiology, symptoms, cognitive impairments, quality of life, and self-agency. An operational definition of recovery allows for bridging hope and recovery with important advances in the science of the brain. Future clinical and neuroscience research and service development should emphasize measures of recovery as outcomes for people with schizophrenia.

The initial goal of genome-wide association studies (GWASs) ofpsychiatricdisorderswas to identify individual genetic variants that predispose to illness. Along the way, another application of GWAS data was discovered1: the polygenic risk score (PRS). The concept is simple. We start with all single-nucleotide polymorphisms (SNPs) assessed in a training sample. In this issue of JAMA Psychiatry, Jones et al2 report findings using the second Psychiatric Genomics Consortium schizophrenia GWAS as that sample.3 TheseSNPsarecleanedbyeliminatingones thatare toorare or too highly correlated with their nearest neighbor. The PRS canbeexaminedacross a rangeofPvalue thresholds, as in the first application of thismethod,1 or, as in the study by Jones et al,2 to a single a priori threshold, such as P = .05. For simplicity, imagine that, after cleaning,wehad 1million SNPs distributedacross thegenome inour training sample.Wewould then take the50000SNPs thatmost significantlydiscriminatecases and controls andnote the allele (theA, T, C, orGweall learned about in basic genetics) associatedwith disease risk and its effect size.We then takeour target sample—in this case, thewellknown Avon Longitudinal Study of Parents and Children, a population-based cohort from western England. For each individual, we examine their GWAS data and, for each of our 50000SNPs,determinewhether theyhave0, 1, or 2of the risk alleles. That 0 to 2 score is thenmultiplied by the effect size in the training sample (the logarithm of its odds ratio for schizophrenia) and summed.The total score represents an individual’s PRS for schizophrenia. In this case, because the training set wasso large—andhencerathergoodatseparatingout truefrom false-positive signals—the schizophrenia PRS had, compared withmany other applications of PRSs, a reasonable aggregate effect on adult samples, accounting for approximately 7% of schizophrenia case-control variation on the liability scale. What is soexcitingabout thePRS is that, tomeasure it, you onlyneedDNA(andagood trainingset).Youdonotneed twins or adoptees. You do not need to interview relatives. However, 2 prominent caveats are noteworthy. First, the PRS only reflects thevariationcapturedby the individual commonSNPs used for theGWAS. ThePRSwill not reflect rare SNPs or variation arising from genomic abnormalities (eg, duplications or deletions). Second, thePRS is anaggregatemeasureof risk and does not point to specific variants or any underlying biology. Conceptually, the PRS is therefore similar to the latent genetic variance that psychiatric geneticists have long estimated using twin and adoption designs. Unlike these latent measures, the PRS is assessed fromDNAandnot from resemblance between relatives. However, the PRS is not as predictive because the liability assessed using twin studies indexes all kinds of genomic variants. The PRS also does not have the methodologic concerns of twin studies (eg, equal environment assumption), although it does have several issues of its own that are beyond the scope of this review. What did Jones et al2 find? On a reasonably large sample of adolescents (3676 to 5444 participants), they tried to predict the following from their schizophrenia PRS: (1) positive psychotic experiences, (2) negative symptoms, (3) anxietydisorders, and (4) depression. They found that the schizophrenia PRS significantly predicted negative symptoms and anxiety disorders but not positive psychotic experiences or depression. The effect sizes of their 2 significant results were modest at approximately 1.2 per SD. Therefore, an individual in the top 2.5% of the schizophrenia PRSwould have roughly a 45% increased risk for being in the top decile of negative symptomsorhaving 1 ormore anxietydisorders.What ismost interesting about themethod used in this study is that it provides a new approach to understanding how the genetic risk for schizophreniamanifests itself in adolescence—a question that formed the focus of several high-risk studies of schizophrenia launched a generation or more ago. Let us first focus on the prediction of the 2 key schizophrenia dimensions of positive and negative symptoms. As Jones et al2 point out, their findings closely mirror those reported by Fanous et al4 in adult schizophrenia samples that theschizophreniaPRSsignificantlypredicteddisorganizedand negative symptomsbutnot positive symptoms.Of critical importance, the studyby Joneset al2wasnotperformed inadults with schizophrenia but in a general population of adolescents. The significanceof isolatedpsychotic symptoms ingeneral population samples remains controversial and especially so among adolescents. To their credit, Jones et al2 used an interview-based measure that attempted to confirm the veracity of the symptom,which is likely tobemuchmorevalid than questionnaire-basedmeasures.However, their thresholdwas low, with only 1 confirmed symptom. Recent studies have raised questions about the specificity of isolated psychotic symptoms, suggesting that theymightbeabetter indicator for a broad vulnerability to psychopathology rather than a specific index of schizophrenia.5 The most intriguing result in this study was the relationship between the schizophrenia PRS and negative symptoms. The negative symptom scale used (the Community Assessment of Psychic Experiences [CAPE]) is heterogeneous Related article page 221 Opinion

Working memory (WM) deficit in schizophrenia is thought to arise from a widespread neural inefficiency. However, we do not know if this deficit results from the illness-related genetic risk and influence the symptom burden in various domains, especially in patients who have an early onset illness. We used graph theory to examine the topology of the functional connectome in 99 subjects (27 early-onset schizophrenia (EOS), 24 asymptomatic siblings, and 48 healthy subjects) during an n-back task, and calculated their polygenic risk score (PRS) for susceptibility to schizophrenia. Linear regression analysis was used to test associations of the PRS, clinical symptoms, altered connectomic properties, and WM accuracy in EOS. Indices of small-worldness and segregation were elevated in EOS during the WM task compared with the other two groups; these connectomic aberrations correlated with increased PRS and negative symptoms. In patients with higher polygenic risk, WM performance was lower only when both the connectomic aberrations and the burden of negative symptoms were higher. Negative symptoms had a stronger moderating role in this relationship. Our findings suggest that the aberrant connectomic topology is a feature of WM task performance in schizophrenia; this relates to higher polygenic risk score as well as higher burden of negative symptoms. The deleterious effects of polygenic risk on cognition are played out via its effects on the functional connectome, as well as negative symptoms.

BackgroundIt remains unclear which biological mechanisms affect neurocognition in first episode psychosis (FEP) patients. There is minimal evidence from current literature suggesting an association between duration of untreated psychosis (DUP) or duration of untreated illness (DUI) and cognitive decline in FEP patients. It is still controversial whether genetic factors, such as polygenic risk score for schizophrenia, determine observed cognitive deficits. The study of interplay between DUP, DUI and genetic risk factors might be important to understand underlying pathways.MethodsNinety FEP patients where recruited during Athens First-Episode Psychosis Research study between 2015–2018. All participants provided inform consent. DUP for each patient was defined by NOS-DUP (Nottingham onset schedule: modified DUP version) assessment tool and DUI was determined using the symptom onset in schizophrenia (SOS) inventory. DNA was collected in order to create polygenic risk score (PGC) for schizophrenia for each individual using SNPs selected according to the significance of their association with the phenotype at nominal p-value thresholds of 0,05. WAIS-IV total score and subscales, i.e. Verbal Comprehension (VC), Perceptual Reasoning (PR), Working Memory (WM), Processing Speed (PS) and moreover index differences between these 4 subscales were applied as a measure of cognitive deficit.ResultsGeneralized linear model analysis, after adjustment for years of education and gender, found no significant main effect of DUP, DUI or PGC on any cognitive subscale. Furthermore, conducting an exploratory analysis for possible interactions between DUP/DUI and PGC (n=90), we found statistically significant findings of DUP x PGC (F=8,175, p=,005) and DUI x PGC (F=5,592, p=,021) for the cognitive variable of VC/WM difference. The interplay between DUP and PCG and DUI and PGC was associated with observed differences between VM and WM in our FEP sample.DiscussionOur preliminary results are consistent with recent literature suggesting that neurocognition is not determined by DUP, DUI or PGC for schizophrenia. Novel approach based on WAIS-IV indexes of interest allows to explore subtle differences between cognitive subdomains. Elucidating underlying interplay between genetic and disease-related mechanisms could be important to understand the core feature of cognitive deficit in FEP patients.

STUDY OF NEUROLOGICAL SOFT SIGNS IN JAPANESE SCHIZOPHRENIC PATIENTS

The primary purpose of this article was to determine if cognitive abilities decline, remain unchanged, or modestly improve throughout the course of schizophrenic illness. Forty-two patients with a first hospitalization for schizophrenia or schizophreniform disorder and 16 normal comparison subjects had a battery of neuropsychological tests and a magnetic resonance imaging (MRI) brain scan at approximate yearly intervals for the first 2 to 5 years of illness. Summary rating scales for language, executive, memory, processing speed, and sensory-perceptual functions were constructed. Patients with schizophrenia scored 1 to 2 standard deviations below normal comparison subjects on neuropsychological test measures during the course of the study. Patients exhibited less improvement than comparison subjects on measures of verbal memory. In general, improvement in positive symptoms over the time interval was associated with improvement in cognition. No changes in regional brain measurements were correlated with cognitive change in the patient group. Patients with schizophrenia have considerable cognitive dysfunction in the first 4 to 5 years of illness, which is stable at a level of 1 to 2 standard deviations below that of comparison subjects. There is little evidence for deterioration of cognitive abilities over the first few years of illness, with the exception of verbal memory, which shows significantly less improvement in patients over time relative to that of comparison subjects.

Adding or Switching Antipsychotic Medications in Treatment-Refractory Schizophrenia

Association Between Polygenic Risk Score for Schizophrenia and Neurocognitive Measures in the Western Australian Family Study of Schizophrenia (Wafss)

Distinct structural alterations independently contributing to working memory deficits and symptomatology in paranoid schizophrenia

SummaryThe delineation of the symptoms of schizophrenia into positive and negative types has been an important trend in psychiatric research over the past decade. This approach reflects renewed interests in clinical description and in developing etiologic hypotheses. The responsivity of positive and negative symptoms to neuroleptic and other pharmacotherapies, an issue of considerable clinical and research importance, however, remain in controversy.We have observed that double-blind fluphenazine administration to 19 schizophrenic inpatients who had been maintained free from neuroleptic treatment for an extended period of time resulted in significant decreases in both positive as well as negative symptoms. The time course of symptom change differed, however, and the change in symptoms was not correlated, suggesting that the underlying pathophysiologies of positive and negative symptoms are only partially overlapping. The relative balance between positive and negative symptoms in individual patients, a putative schizophrenia trait characteristic, was found to be significantly altered by neuroleptic treatment, raising questions about the reliability of this classification approach.In longitudinal studies in which plasma levels of the dopamine metabolite, homovanillic acid (HVA), were measured, change in negative symptoms associated with neuroleptic withdrawal and treatment were correlated, respectively, with changes in levels of plasma HVA. These data further support a relationship between negative symptoms and dopaminergic function. In addition to neuroleptic-induced reductions in negative symptoms, the augmentation of neuroleptic antipsychotic effects by the triazolobenzodiazepine, alprazolam, includes improvement in negative and positive symptoms in responsive patients.These data including longitudinal studies of individual patients suggest that negative as well as positive symptoms respond to pharmacologic intervention. The clinical and research implications of these findings are discussed.