Schizophrenia Polygenic Risk Scores Research Articles

Schizophrenia Imaging Signatures and Their Associations With Cognition, Psychopathology, and Genetics in the General Population.

The prevalence and significance of schizophrenia-related phenotypes at the population level is debated in the literature. Here, the authors assessed whether two recently reported neuroanatomical signatures of schizophrenia-signature 1, with widespread reduction of gray matter volume, and signature 2, with increased striatal volume-could be replicated in an independent schizophrenia sample, and investigated whether expression of these signatures can be detected at the population level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. This cross-sectional study used an independent schizophrenia-control sample (N=347; ages 16-57 years) for replication of imaging signatures, and then examined two independent population-level data sets: typically developing youths and youths with psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort (N=359; ages 16-23 years) and adults in the UK Biobank study (N=836; ages 44-50 years). The authors quantified signature expression using support-vector machine learning and compared cognition, psychopathology, and polygenic risk between signatures. Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youths with psychosis spectrum symptoms than in typically developing youths, whereas signature 2 frequency was similar in the two groups. In both youths and adults, signature 1 was associated with worse cognitive performance than signature 2. Compared with adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. The authors successfully replicated two neuroanatomical signatures of schizophrenia and describe their prevalence in population-based samples of youths and adults. They further demonstrated distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.

Peripheral lymphocyte signaling pathway deficiencies predict treatment response in first-onset drug-naïve schizophrenia

Despite being a major cause of disability worldwide, the pathophysiology of schizophrenia and molecular basis of treatment response heterogeneity continue to be unresolved. Recent evidence suggests that multiple aspects of pathophysiology, including genetic risk factors, converge on key cell signaling pathways and that exploration of peripheral blood cells might represent a practical window into cell signaling alterations in the disease state. We employed multiplexed phospho-specific flow cytometry to examine cell signaling epitope expression in peripheral blood mononuclear cell (PBMC) subtypes in drug-naïve schizophrenia patients (n = 49) relative to controls (n = 61) and relate these changes to serum immune response proteins, schizophrenia polygenic risk scores and clinical effects of treatment, including drug response and side effects, over the longitudinal course of antipsychotic treatment. This revealed both previously characterized (Akt1) and novel cell signaling epitopes (IRF-7 (pS477/pS479), CrkL (pY207), Stat3 (pS727), Stat3 (pY705) and Stat5 (pY694)) across PBMC subtypes which were associated with schizophrenia at disease onset, and correlated with type I interferon-related serum molecules CD40 and CXCL11. Alterations in Akt1 and IRF-7 (pS477/pS479) were additionally associated with polygenic risk of schizophrenia. Finally, changes in Akt1, IRF-7 (pS477/pS479) and Stat3 (pS727) predicted development of metabolic and cardiovascular side effects following antipsychotic treatment, while IRF-7 (pS477/pS479) and Stat3 (pS727) predicted early improvements in general psychopathology scores measured using the Brief Psychiatric Rating Scale (BPRS). These findings suggest that peripheral blood cells can provide an accessible surrogate model for intracellular signaling alterations in schizophrenia and have the potential to stratify subgroups of patients with different clinical outcomes or a greater risk of developing metabolic and cardiovascular side effects following antipsychotic therapy.

Facial Emotion Recognition in Psychosis and Associations With Polygenic Risk for Schizophrenia: Findings From the Multi-Center EU-GEI Case–Control Study

Background and HypothesisFacial Emotion Recognition is a key domain of social cognition associated with psychotic disorders as a candidate intermediate phenotype. In this study, we set out to investigate global and specific facial emotion recognition deficits in first-episode psychosis, and whether polygenic liability to psychotic disorders is associated with facial emotion recognition.Study Design828 First Episode Psychosis (FEP) patients and 1308 population-based controls completed assessments of the Degraded Facial Affect Recognition Task (DFAR) and a subsample of 524 FEP and 899 controls provided blood or saliva samples from which we extracted DNA, performed genotyping and computed polygenic risk scores for schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MD).Study ResultsA worse ability to globally recognize facial emotion expressions was found in patients compared with controls [B= −1.5 (0.6), 95% CI −2.7 to −0.3], with evidence for stronger effects on negative emotions (fear [B = −3.3 (1.1), 95% CI −5.3 to −1.2] and anger [B = −2.3 (1.1), 95% CI −4.6 to −0.1]) than on happiness [B = 0.3 (0.7), 95% CI −1 to 1.7]. Pooling all participants, and controlling for confounds including case/control status, facial anger recognition was associated significantly with Schizophrenia Polygenic Risk Score (SZ PRS) [B = −3.5 (1.7), 95% CI −6.9 to −0.2].ConclusionsPsychosis is associated with impaired recognition of fear and anger, and higher SZ PRS is associated with worse facial anger recognition. Our findings provide evidence that facial emotion recognition of anger might play a role as an intermediate phenotype for psychosis.

Associations between psychiatric polygenic risk scores and general and specific psychopathology symptoms in childhood and adolescence between and within dizygotic twin pairs.

Although polygenic risk scores (PRS) predict psychiatric problems, these associations might be attributable to indirect pathways including population stratification, assortative mating, or dynastic effects (mediation via parental environments). The goal of this study was to examine whether PRS-psychiatric symptom associations were attributable to indirect versus direct pathways. The sample consisted of 3,907 dizygotic (DZ) twin pairs. In childhood, their parents rated them on 98 symptoms. In adolescence (n = 2,393 DZ pairs), both the parents and the twins rated themselves on 20 symptoms. We extracted one general and seven specific factors from the childhood data, and one general and three specific factors from the adolescent data. We then regressed each general factor model onto ten psychiatric PRS simultaneously. We first conducted the regressions between individuals (β) and then within DZ twin pairs (βw ), which controls for indirect pathways. In childhood, the PRS for ADHD predicted general psychopathology (β = 0.09, 95% CI: [0.06, 0.12]; βw = 0.07 [0.01, 0.12]). Furthermore, the PRS for ADHD predicted specific inattention (β = 0.04 [0.00, 0.08]; βw = 0.09 [0.01, 0.17]) and specific hyperactivity (β = 0.07 [0.04, 0.11]; βw = 0.09 [0.01, 0.16]); the PRS for schizophrenia predicted specific learning (β = 0.08 [0.03, 0.13]; βw = 0.19 [0.08, 0.30]) and specific inattention problems (β = 0.05 [0.01, 0.09]; βw = 0.10 [0.02, 0.19]); and the PRS for neuroticism predicted specific anxiety (β = 0.06 [0.02, 0.10]; βw = 0.06 [0.00, 0.12]). Overall, the PRS-general factor associations were similar between individuals and within twin pairs, whereas the PRS-specific factors associations amplified by 84% within pairs. This implies that PRS-psychiatric symptom associations did not appear attributable to indirect pathways such as population stratification, assortative mating, or mediation via parental environments. Rather, genetics appeared to directly influence symptomatology.

Systems-Level Analysis of Genetic Variants Reveals Functional and Spatiotemporal Context in Treatment-resistant Schizophrenia.

Treatment-resistant schizophrenia (TRS) occurs in one-third of the patients, but the molecular determinants of poor antipsychotic response remain unclear. We compared genetic data of patients with TRS (n = 63) with non-TRS (n = 111) by polygenic risk scores (PRS) calculated by PRSice software using PGC2_SCZ (Psychiatric Genomics Consortium - Schizophrenia) data. TRS criteria followed the International Psychopharmacology Algorithm Project SCZ algorithm. Statistical clustering and functional enrichment analyses of genes harboring TRS-linked variants were performed. Individuals on the top three deciles of schizophrenia PRS distribution exhibited higher odds of being refractory to antipsychotics than those on the bottom three deciles. Clusters of interacting variant-harboring genes were identified among the association signals. They are upregulated in the dorsolateral prefrontal, orbitofrontal, temporal, and inferior parietal areas during adolescence and early adulthood. Similar gene modules were found using transcriptional data from the same brain regions in individuals with schizophrenia. Genes were enriched among markers of cortical interneurons and somatosensory pyramidal cells. Finally, the enrichment of the clustered genes in drug-response expression signatures revealed compounds that could be employed to identify novel antipsychotic targets. In conclusion, we identified variant-harboring genes that may predispose SCZ patients to poor antipsychotic response and found statistically enriched clusters which provided functional and spatiotemporal context for TRS, suggesting that genotypic variation may converge to biological alterations at the interplay between actin dynamics and synaptic organization.

Polygenic Risk for Schizophrenia Has Sex-Specific Effects on Brain Activity during Memory Processing in Healthy Individuals.

Genetic risk for schizophrenia has a negative impact on memory and other cognitive abilities in unaffected individuals, and it was recently shown that this effect is specific to males. Using functional MRI, we investigated the effect of a polygenic risk score (PRS) for schizophrenia on brain activation during working memory and episodic memory in 351 unaffected participants (167 males and 184 females, 25–95 years), and specifically tested if any effect of PRS on brain activation is sex-specific. Schizophrenia PRS was significantly associated with decreased brain activation in the left dorsolateral prefrontal cortex (DLPFC) during working-memory manipulation and in the bilateral superior parietal lobule (SPL) during episodic-memory encoding and retrieval. A significant interaction effect between sex and PRS was seen in the bilateral SPL during episodic-memory encoding and retrieval, and sex-stratified analyses showed that the effect of PRS on SPL activation was male-specific. These results confirm previous findings of DLPFC inefficiency in schizophrenia, and highlight the SPL as another important genetic intermediate phenotype of the disease. The observed sex differences suggest that the previously shown male-specific effect of schizophrenia PRS on cognition translates into an additional corresponding effect on brain functioning.

Familial aggregation and shared genetic loading for major psychiatric disorders and type 2 diabetes.

Psychiatric disorders, such as schizophrenia (SCZ), major depressive disorder (MDD) and bipolar disorder (BPD), are highly comorbid with type 2 diabetes. However, the mechanisms underlying such comorbidity are understudied. This study explored the familial aggregation of common psychiatric disorders and type 2 diabetes by testing family history association, and investigated the shared genetic loading between them by testing the polygenic risk score (PRS) association. A total of 105,184 participants were recruited from the Taiwan Biobank, and genome-wide genotyping data were available for 95,238 participants. The Psychiatric Genomics Consortium-derived PRS for SCZ, MDD and BPD was calculated. Logistic regression was used to estimate the OR with CIs between a family history of SCZ/MDD/BPD and a family history of type 2 diabetes, and between the PRS and the risk of type 2 diabetes. A family history of type 2 diabetes was associated with a family history of SCZ (OR 1.23, 95% CI 1.08, 1.40), MDD (OR 1.19, 95% CI 1.13, 1.26) and BPD (OR 1.26, 95% CI 1.15, 1.39). Compared with paternal type 2 diabetes, maternal type 2 diabetes was associated with a higher risk of a family history of SCZ. SCZ PRS was negatively associated with type 2 diabetes in women (OR 0.92, 95% CI 0.88, 0.97), but not in men; the effect of SCZ PRS reduced after adjusting for BMI. MDD PRS was positively associated with type 2 diabetes (OR 1.04, 95% CI 1.00, 1.07); the effect of MDD PRS reduced after adjusting for BMI or smoking. BPD PRS was not associated with type 2 diabetes. The comorbidity of type 2 diabetes with psychiatric disorders may be explained by shared familial factors. The shared polygenic loading between MDD and type 2 diabetes implies not only pleiotropy but also a shared genetic aetiology for the mechanism behind the comorbidity. The negative correlation between polygenic loading for SCZ and type 2 diabetes implies the role of environmental factors.

Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score.

Background: Prior studies have separately demonstrated that magnetic resonance imaging (MRI) and schizophrenia polygenic risk score (PRS) are predictive of antipsychotic medication treatment outcomes in schizophrenia. However, it remains unclear whether MRI combined with PRS can provide superior prognostic performance. Besides, the relative importance of these measures in predictions is not investigated. Methods: We collected 57 patients with schizophrenia, all of which had baseline MRI and genotype data. All these patients received approximately 6 weeks of antipsychotic medication treatment. Psychotic symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS) at baseline and follow-up. We divided these patients into responders (N = 20) or non-responders (N = 37) based on whether their percentages of PANSS total reduction were above or below 50%. Nine categories of MRI measures and PRSs with 145 different p-value thresholding ranges were calculated. We trained machine learning classifiers with these baseline predictors to identify whether a patient was a responder or non-responder. Results: The extreme gradient boosting (XGBoost) technique was applied to build binary classifiers. Using a leave-one-out cross-validation scheme, we achieved an accuracy of 86% with all MRI and PRS features. Other metrics were also estimated, including sensitivity (85%), specificity (86%), F1-score (81%), and area under the receiver operating characteristic curve (0.86). We found excluding a single feature category of gray matter volume (GMV), amplitude of low-frequency fluctuation (ALFF), and surface curvature could lead to a maximum accuracy drop of 10.5%. These three categories contributed more than half of the top 10 important features. Besides, removing PRS features caused a modest accuracy drop (8.8%), which was not the least decrease (1.8%) among all feature categories. Conclusions: Our classifier using both MRI and PRS features was stable and not biased to predicting either responder or non-responder. Combining with MRI measures, PRS could provide certain extra predictive power of antipsychotic medication treatment outcomes in schizophrenia. PRS exhibited medium importance in predictions, lower than GMV, ALFF, and surface curvature, but higher than measures of cortical thickness, cortical volume, and surface sulcal depth. Our findings inform the contributions of PRS in predictions of treatment outcomes in schizophrenia.

Use of multiple polygenic risk scores for distinguishing schizophrenia-spectrum disorder and affective psychosis categories in a first-episode sample; the EU-GEI study.

Schizophrenia (SZ), bipolar disorder (BD) and depression (D) run in families. This susceptibility is partly due to hundreds or thousands of common genetic variants, each conferring a fractional risk. The cumulative effects of the associated variants can be summarised as a polygenic risk score (PRS). Using data from the EUropean Network of national schizophrenia networks studying Gene-Environment Interactions (EU-GEI) first episode case-control study, we aimed to test whether PRSs for three major psychiatric disorders (SZ, BD, D) and for intelligent quotient (IQ) as a neurodevelopmental proxy, can discriminate affective psychosis (AP) from schizophrenia-spectrum disorder (SSD). Participants (842 cases, 1284 controls) from 16 European EU-GEI sites were successfully genotyped following standard quality control procedures. The sample was stratified based on genomic ancestry and analyses were done only on the subsample representing the European population (573 cases, 1005 controls). Using PRS for SZ, BD, D, and IQ built from the latest available summary statistics, we performed simple or multinomial logistic regression models adjusted for 10 principal components for the different clinical comparisons. In case-control comparisons PRS-SZ, PRS-BD and PRS-D distributed differentially across psychotic subcategories. In case-case comparisons, both PRS-SZ [odds ratio (OR) = 0.7, 95% confidence interval (CI) 0.54-0.92] and PRS-D (OR = 1.31, 95% CI 1.06-1.61) differentiated AP from SSD; and within AP categories, only PRS-SZ differentiated BD from psychotic depression (OR = 2.14, 95% CI 1.23-3.74). Combining PRS for severe psychiatric disorders in prediction models for psychosis phenotypes can increase discriminative ability and improve our understanding of these phenotypes. Our results point towards the potential usefulness of PRSs in specific populations such as high-risk or early psychosis phases.

Polygenic risk, familial liability and stress reactivity in psychosis: an experience sampling study.

There is evidence for a polygenic contribution to psychosis. One targetable mechanism through which polygenic variation may impact on individuals and interact with the social environment is stress sensitization, characterized by elevated reactivity to minor stressors in daily life. The current study aimed to investigate whether stress reactivity is modified by polygenic risk score for schizophrenia (PRS) in cases with enduring non-affective psychotic disorder, first-degree relatives of cases, and controls. We used the experience sampling method to assess minor stressors, negative affect, positive affect and psychotic experiences in 96 cases, 79 first-degree relatives, i.e. siblings, and 73 controls at wave 3 of the Dutch Genetic Risk and Outcome of Psychosis (GROUP) study. Genome-wide data were collected at baseline to calculate PRS. We found that associations of momentary stress with psychotic experiences, but not with negative and positive affect, were modified by PRS and group (all pFWE<0.001). In contrast to our hypotheses, siblings with high PRS reported less intense psychotic experiences in response to momentary stress compared to siblings with low PRS. No differences in magnitude of these associations were observed in cases with high v. low level of PRS. By contrast, controls with high PRS showed more intense psychotic experiences in response to stress compared to those with low PRS. This tentatively suggests that polygenic risk may operate in different ways than previously assumed and amplify reactivity to stress in unaffected individuals but operate as a resilience factor in relatives by attenuating their stress reactivity.

Effects of polygenic risk of schizophrenia on interhemispheric callosal white matter integrity and frontotemporal functional connectivity in first-episode schizophrenia.

Schizophrenia is a severely debilitating psychiatric disorder with high heritability and polygenic architecture. A higher polygenic risk score for schizophrenia (SzPRS) has been associated with smaller gray matter volume, lower activation, and decreased functional connectivity (FC). However, the effect of polygenic inheritance on the brain white matter microstructure has only been sparsely reported. Eighty-four patients with first-episode schizophrenia (FES) patients and ninety-three healthy controls (HC) with genetics, diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI) data were included in our study. We investigated impaired white matter integrity as measured by fractional anisotropy (FA) in the FES group, further examined the effect of SzPRS on white matter FA and FC in the regions connected by SzPRS-related white matter tracts. Decreased FA was observed in FES in many commonly identified regions. Among these regions, we observed that in the FES group, but not the HC group, SzPRS was negatively associated with the mean FA in the genu and body of corpus callosum, right anterior corona radiata, and right superior corona radiata. Higher SzPRS was also associated with lower FCs between the left inferior frontal gyrus (IFG)-left inferior temporal gyrus (ITG), right IFG-left ITG, right IFG-left middle frontal gyrus (MFG), and right IFG-right MFG in the FES group. Higher polygenic risks are linked with disrupted white matter integrity and FC in patients with schizophrenia. These correlations are strongly driven by the interhemispheric callosal fibers and the connections between frontotemporal regions.

Genetic and psychosocial stressors have independent effects on the level of subclinical psychosis: findings from the multinational EU-GEI study.

Gene x environment (G×E) interactions, i.e. genetic modulation of the sensitivity to environmental factors and/or environmental control of the gene expression, have not been reliably established regarding aetiology of psychotic disorders. Moreover, recent studies have shown associations between the polygenic risk scores for schizophrenia (PRS-SZ) and some risk factors of psychotic disorders, challenging the traditional gene v. environment dichotomy. In the present article, we studied the role of GxE interaction between psychosocial stressors (childhood trauma, stressful life-events, self-reported discrimination experiences and low social capital) and the PRS-SZ on subclinical psychosis in a population-based sample. Data were drawn from the EUropean network of national schizophrenia networks studying Gene-Environment Interactions (EU-GEI) study, in which subjects without psychotic disorders were included in six countries. The sample was restricted to European descendant subjects (n = 706). Subclinical dimensions of psychosis (positive, negative, and depressive) were measured by the Community Assessment of Psychic Experiences (CAPE) scale. Associations between the PRS-SZ and the psychosocial stressors were tested. For each dimension, the interactions between genes and environment were assessed using linear models and comparing explained variances of 'Genetic' models (solely fitted with PRS-SZ), 'Environmental' models (solely fitted with each environmental stressor), 'Independent' models (with PRS-SZ and each environmental factor), and 'Interaction' models (Independent models plus an interaction term between the PRS-SZ and each environmental factor). Likelihood ration tests (LRT) compared the fit of the different models. There were no genes-environment associations. PRS-SZ was associated with positive dimensions (β = 0.092, R2 = 7.50%), and most psychosocial stressors were associated with all three subclinical psychotic dimensions (except social capital and positive dimension). Concerning the positive dimension, Independent models fitted better than Environmental and Genetic models. No significant GxE interaction was observed for any dimension. This study in subjects without psychotic disorders suggests that (i) the aetiological continuum hypothesis could concern particularly the positive dimension of subclinical psychosis, (ii) genetic and environmental factors have independent effects on the level of this positive dimension, (iii) and that interactions between genetic and individual environmental factors could not be identified in this sample.

Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia, Bipolar Disorder, and Depression in the VA Health Care System

Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia, Bipolar Disorder, and Depression in the VA Health Care System

Association of Etiological Factors for Hypomanic Symptoms, Bipolar Disorder, and Other Severe Mental Illnesses

Subsyndromal hypomanic symptoms are relatively common in the general population and are linked to the onset of bipolar disorder. Little is known about their etiology and whether this is shared with the etiology of bipolar disorder or other mental illnesses. To examine the genetic and environmental architecture of hypomanic symptoms in a nonclinical youth sample and compare estimates at varying severity levels and their association with diagnosed bipolar disorder. This cohort study used phenotypic and genetic data from the Child and Adolescent Twin Study in Sweden and included individuals with International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis of psychiatric disorders from national registries for residents of Sweden. Associations between hypomania and polygenic risk scores for bipolar disorder, major depressive disorder and schizophrenia were also investigated. Analysis began November 2018 and ended October 2021. Hypomanic symptoms were assessed using the parent-rated Mood Disorders Questionnaire when the twins were aged 18 years. Bipolar disorder diagnosis and/or lithium prescription were ascertained from national registries for residents of Sweden. Polygenic risk scores for psychiatric disorders were calculated using independent discovery genetic data. A total of 8568 twin pairs aged 18 years (9381 [54.7%] female) were included in the study. The hypomania heritability estimate was 59% (95% CI, 52%-64%) for male individuals and 29% (95% CI, 16%-44%) for female individuals. Unique environmental factors accounted for 41% (95% CI, 36%-47%) of the hypomania variance in male individuals and 45% (95% CI, 40%-50%) in female individuals. Shared environmental factors were only detected for female individuals and explained 26% (95% CI, 13%-38%) of the variance. The heritability estimates were fairly consistent across different hypomania severity groups. Moderate genetic (0.40; 95% CI, 0.21-0.58) and shared environmental (0.41; 95% CI, 0.03-0.75) correlations between hypomania and diagnosed bipolar disorder were found. Hypomania was significantly associated with the polygenic risk scores for schizophrenia (β = 0.08; SE = 0.026; P = .002) and major depressive disorder (β = 0.09; SE = 0.027; P = .001) but not bipolar disorder (β = 0.017; SE = 0.03; P = 0.57) (bipolar disorder I [β = 0.014; SE = 0.029; P = .64] or bipolar disorder II [β = 0.045; SE = 0.027; P = .10]). Higher heritability for hypomania was found for male compared with female individuals. The results highlight the shared etiologies between hypomanic symptoms, bipolar disorder, major depression, and schizophrenia in youths. Future research should focus on identifying specific shared genetic and environmental factors. These findings support a possible dimensional model of bipolar disorder, with hypomania representing a continuous trait underlying the disorder.

Polygenic risk scores across the extended psychosis spectrum

As early detection of symptoms in the subclinical to clinical psychosis spectrum may improve health outcomes, knowing the probabilistic susceptibility of developing a disorder could guide mitigation measures and clinical intervention. In this context, polygenic risk scores (PRSs) quantifying the additive effects of multiple common genetic variants hold the potential to predict complex diseases and index severity gradients. PRSs for schizophrenia (SZ) and bipolar disorder (BD) were computed using Bayesian regression and continuous shrinkage priors based on the latest SZ and BD genome-wide association studies (Psychiatric Genomics Consortium, third release). Eight well-phenotyped groups (n = 1580; 56% males) were assessed: control (n = 305), lower (n = 117) and higher (n = 113) schizotypy (both groups of healthy individuals), at-risk for psychosis (n = 120), BD type-I (n = 359), BD type-II (n = 96), schizoaffective disorder (n = 86), and SZ groups (n = 384). PRS differences were investigated for binary traits and the quantitative Positive and Negative Syndrome Scale. Both BD-PRS and SZ-PRS significantly differentiated controls from at-risk and clinical groups (Nagelkerke’s pseudo-R2: 1.3–7.7%), except for BD type-II for SZ-PRS. Out of 28 pairwise comparisons for SZ-PRS and BD-PRS, 9 and 12, respectively, reached the Bonferroni-corrected significance. BD-PRS differed between control and at-risk groups, but not between at-risk and BD type-I groups. There was no difference between controls and schizotypy. SZ-PRSs, but not BD-PRSs, were positively associated with transdiagnostic symptomology. Overall, PRSs support the continuum model across the psychosis spectrum at the genomic level with possible irregularities for schizotypy. The at-risk state demands heightened clinical attention and research addressing symptom course specifiers. Continued efforts are needed to refine the diagnostic and prognostic accuracy of PRSs in mental healthcare.

The benefit of diagnostic whole genome sequencing in schizophrenia and other psychotic disorders.

Schizophrenia has a multifactorial etiology, involving a polygenic architecture. The potential benefit of whole genome sequencing (WGS) in schizophrenia and other psychotic disorders is not well studied. We investigated the yield of clinical WGS analysis in 251 families with a proband diagnosed with schizophrenia (N = 190), schizoaffective disorder (N = 49), or other conditions involving psychosis (N = 48). Participants were recruited in Israel and USA, mainly of Jewish, Arab, and other European ancestries. Trio (parents and proband) WGS was performed for 228 families (90.8%); in the other families, WGS included parents and at least two affected siblings. In the secondary analyses, we evaluated the contribution of rare variant enrichment in particular gene sets, and calculated polygenic risk score (PRS) for schizophrenia. For the primary outcome, diagnostic rate was 6.4%; we found clinically significant, single nucleotide variants (SNVs) or small insertions or deletions (indels) in 14 probands (5.6%), and copy number variants (CNVs) in 2 (0.8%). Significant enrichment of rare loss-of-function variants was observed in a gene set of top schizophrenia candidate genes in affected individuals, compared with population controls (N = 6,840). The PRS for schizophrenia was significantly increased in the affected individuals group, compared to their unaffected relatives. Last, we were also able to provide pharmacogenomics information based on CYP2D6 genotype data for most participants, and determine their antipsychotic metabolizer status. In conclusion, our findings suggest that WGS may have a role in the setting of both research and genetic counseling for individuals with schizophrenia and other psychotic disorders and their families.

Novel disease associations with schizophrenia genetic risk revealed in ~400,000 UK Biobank participants.

Schizophrenia is a serious mental disorder with considerable somatic and psychiatric morbidity. It is unclear whether comorbid health conditions predominantly arise due to shared genetic risk or consequent to having schizophrenia. To explore the contribution of genetic risk for schizophrenia, we analysed the effect of schizophrenia polygenic risk scores (PRS) on a broad range of health problems in 406 929 individuals with no schizophrenia diagnosis from the UK Biobank. Diagnoses were derived from linked health data including primary care, hospital inpatient records, and registers with information on cancer and deaths. Schizophrenia PRS were generated and tested for associations with general health conditions, 16 ICD10 main chapters, and 603 diseases using linear and logistic regressions. Higher schizophrenia PRS was significantly associated with poorer overall health ratings, more hospital inpatient diagnoses, and more unique illnesses. It was also significantly positively associated with 4 ICD10 chapters: mental disorders; respiratory diseases; digestive diseases; and pregnancy, childbirth and the puerperium, but negatively associated with musculoskeletal disorders. Thirty-one specific phenotypes were significantly associated with schizophrenia PRS, and the 19 novel findings include several musculoskeletal diseases, respiratory diseases, digestive diseases, varicose veins, pituitary hyperfunction, and other peripheral nerve disorders. These findings extend knowledge of the pleiotropic effect of genetic risk for schizophrenia and offer insight into how some conditions often comorbid with schizophrenia arise. Additional studies incorporating the genetic basis of hormone regulation and involvement of immune mechanisms in the pathophysiology of schizophrenia may further elucidate the biological mechanisms underlying schizophrenia and its comorbid conditions.

Association of Attention-Deficit/Hyperactivity Disorder and Depression Polygenic Scores with Lithium Response: A Consortium for Lithium Genetics Study

Response to lithium varies widely between individuals with bipolar disorder (BD). Polygenic risk scores (PRSs) can uncover pharmacogenomics effects and may help predict drug response. Patients (N = 2,510) with BD were assessed for long-term lithium response in the Consortium on Lithium Genetics using the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder score. PRSs for attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), and schizophrenia (SCZ) were computed using lassosum and in a model including all three PRSs and other covariates, and the PRS of ADHD (β = −0.14; 95% confidence interval [CI]: −0.24 to −0.03; p value = 0.010) and MDD (β = −0.16; 95% CI: −0.27 to −0.04; p value = 0.005) predicted worse quantitative lithium response. A higher SCZ PRS was associated with higher rates of medication nonadherence (OR = 1.61; 95% CI: 1.34–1.93; p value = 2e−7). This study indicates that genetic risk for ADHD and depression may influence lithium treatment response. Interestingly, a higher SCZ PRS was associated with poor adherence, which can negatively impact treatment response. Incorporating genetic risk of ADHD, depression, and SCZ in combination with clinical risk may lead to better clinical care for patients with BD.

Association Between Genetic Risk for Psychiatric Disorders and the Probability of Living in Urban Settings

Urban residence has been highlighted as an environmental risk factor for schizophrenia and, to a lesser extent, several other psychiatric disorders. However, few studies have explored genetic effects on the choice of residence. To investigate whether individuals with genetic predisposition to a range of psychiatric disorders have an increased likelihood to live in urban areas. A cross-sectional retrospective cohort study including genotypes, address history, and geographic distribution of population density in the UK based on census data from 1931-2011 was conducted. Polygenic risk score (PRS) analyses, genome-wide association studies, genetic correlation, and 2-sample mendelian randomization analyses were applied to 385 793 UK Biobank participants with self-reported or general practitioner registration-based address history. The study was conducted from February 2018 to May 2021, and data analysis was performed from April 2018 to May 2021. Population density of residence at different ages and movement during the life span between urban and rural environments. In this cohort study of 385 793 unrelated UK Biobank participants (207 963 [54%] were women; age, 37-73 years; mean [SD], 56.7 [8] years), PRS analyses showed significant associations with higher population density across adult life (age 25 to >65 years) reaching highest significance at the 45- to 55-year age group for schizophrenia (88 people/km2; 95% CI, 65-98 people/km2), bipolar disorder (44 people/km2; 95% CI, 34-54 people/km2), anorexia nervosa (36 people/km2; 95% CI, 22-50 people/km2), and autism spectrum disorder (35 people/km2; 95% CI, 25-45 people/km2). The schizophrenia PRS was also significantly associated with higher birthplace population density (37 people/km2; 95% CI, 19-55 people/km2; P = 8 × 10-5). Attention-deficit/hyperactivity disorder PRS was significantly associated with reduced population density in adult life (-31 people/km2; 95% CI, -42 to -20 people/km2 at age 35-45 years). Individuals with higher PRS for schizophrenia, bipolar disorder, anorexia nervosa, and autism spectrum disorder and lower PRS for attention-deficit/hyperactivity disorder preferentially moved from rural environments to cities (difference in PRS with Tukey pairwise comparisons for schizophrenia: 0.05; 95% CI, 0.03 to 0.60; bipolar disorder: 0.10; 95% CI, 0.08 to 0.13; anorexia nervosa: 0.05; 95% CI, 0.03 to 0.07; autism spectrum disorder: 0.04; 95% CI 0.03 to 0.06; and attention-deficit/hyperactivity disorder: -0.09, 95% CI, -0.12 to -0.06). Genetic correlation results were largely consistent with PRS analyses, whereas mendelian randomization provided support for associations between schizophrenia and bipolar disorder and living in high population-density areas. These findings suggest that a high genetic risk for a variety of psychiatric disorders may affect an individual's choice of residence. This result supports the hypothesis of genetic selection of an individual's environment, which intersects the traditional gene-environment dichotomy.

Influence of polygenic risk scores for schizophrenia and resilience on the cognition of individuals at-risk for psychosis

Cognitive impairment is a core feature of schizophrenia which precedes the onset of full psychotic symptoms, even in the ultra-high-risk stage (UHR). Polygenic risk scores (PRS) can be computed for many psychiatric disorders and phenotyping traits, including scores for resilience. We explored the correlations between several PRS and neurocognition in UHR individuals. We included 107 UHR individuals; 29.9% of them converted to psychosis (UHR-C) while 57.0% did not (UHR-NC) during the 1-year follow-up. Cognitive performances were assessed with the Wechsler Adult Intelligence Scale estimating the Intelligence Quotient (IQ), the Trail Making Test, the verbal fluency, the Stroop test, and the Wisconsin card sorting test. Linear regression models were used to test their association with the PRS for schizophrenia, bipolar disorder, major depression, ADHD, cross-disorders, cognitive performance, intelligence, education attainment, and resilience to schizophrenia. UHR-C had a lower IQ than UHR-NC. The PRS for schizophrenia negatively correlated with IQ, while the PRS for cognitive performance and for resilience positively correlated with IQ. PRS for schizophrenia showed a significant correlation with working memory and processing speed indices. PRS for schizophrenia showed a higher effect on IQ in UHR-NC, and UHR-NC with high PRS for schizophrenia had a similar IQ as UHR-C. Conversely, UHR-C with a high PRS for resilience performed as well as UHR-NC. Our findings suggest that cognitive deficits may predate the onset of psychosis. The genetic architecture of schizophrenia seems to impacts the cognition in UHR-NC. Cognition is also mediated by PRS for resilience.