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Clozapine is the most effective antipsychotic for patients with treatment-resistant schizophrenia. However, response is highly variable and possible genetic underpinnings of this variability remain unknown. Here, we performed polygenic risk score (PRS) analyses to estimate the amount of variance in symptom severity among clozapine-treated patients explained by PRSs (R2) and examined the association between symptom severity and genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activity. Genome-wide association (GWA) analyses were performed to explore loci associated with symptom severity. A multicenter cohort of 804 patients (after quality control N = 684) with schizophrenia spectrum disorder treated with clozapine were cross-sectionally assessed using the Positive and Negative Syndrome Scale and/or the Clinical Global Impression-Severity (CGI-S) scale. GWA and PRS regression analyses were conducted. Genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activities were calculated. Schizophrenia-PRS was most significantly and positively associated with low symptom severity (p = 1.03 × 10−3; R2 = 1.85). Cross-disorder-PRS was also positively associated with lower CGI-S score (p = 0.01; R2 = 0.81). Compared to the lowest tertile, patients in the highest schizophrenia-PRS tertile had 1.94 times (p = 6.84×10−4) increased probability of low symptom severity. Higher genotype-predicted CYP2C19 enzyme activity was independently associated with lower symptom severity (p = 8.44×10−3). While no locus surpassed the genome-wide significance threshold, rs1923778 within NFIB showed a suggestive association (p = 3.78×10−7) with symptom severity. We show that high schizophrenia-PRS and genotype-predicted CYP2C19 enzyme activity are independently associated with lower symptom severity among individuals treated with clozapine. Our findings open avenues for future pharmacogenomic projects investigating the potential of PRS and genotype-predicted CYP-activity in schizophrenia.

The European Community’s Seventh Framework Programme under grant agreement No. HEALTH-F2-2010-241909 (EUGEI); The Spanish sample was supported by the Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/024) (...), Velthorst, E., Mollon, J., Murray, R.M., de Haan, L., Germeys, I.M., Glahn, D.C., Arango, C., van der Ven, E., Di Forti, M., Bernardo, M., Guloksuz, S., Delespaul, P., Mezquida, G., Amoretti, S., Bobes, J., Saiz, P.A., García-Portilla, M.P., Santos, J.L., Jiménez-López, E., Sanjuan, J., Aguilar, E.J., Arrojo, M., Carracedo, A., López, G., González-Peñas, J., Parellada, M., Atbaşoğlu, C., Saka, M.C., Üçok, A., Alptekin, K., Akdede, B., Binbay, T., Altınyazar, V., Ulaş, H., Yalınçetin, B., Gümüş-Akay, G., Beyaz, B.C., Soygür, H., Cankurtaran, E.Ş., Kaymak, S.U., Maric, N.P., Mihaljevic, M.M., Petrovic, S.A., Mirjanic, T., Del-Ben, C.M., Ferraro, L., Gayer-Anderson, C., Jones, P.B., Jongsma, H.E., Kirkbride, J.B., La Cascia, C., Lasalvia, A., Tosato, S., Llorca, P.-M., Menezes, P.R., Morgan, C., Quattrone, D., Menchetti, M., Selten, J.-P., Szöke, A., Tarricone, I., Tortelli, A., McGuire, P., Valmaggia, L., Kempton, M.J., van der Gaag, M., Riecher-Rössler, A., Bressan, R.A., Barrantes-Vidal, N., Nelson, B., McGorry, P., Pantelis, C., Krebs, M.-O., Ruhrmann, S., Sachs, G., Rutten, B.P.F., van Os, J., Alizadeh, B.Z., van Amelsvoort, T., Bartels-Velthuis, A.A., Bruggeman, R., van Beveren, N.J., Luykx, J.J., Cahn, W., Simons, C.J.P., Kahn, R.S., Schirmbeck, F., van Winkel, R., Calem, M., Tognin, S., Modinos, G., Pisani, S., Kraan, T.C., van Dam, D.S., Burger, N., Amminger, G.P., Politis, A., Goodall, J., Borgwardt, S., Studerus, E., Gadelha, A., Brietzke, E., Asevedo, G., Asevedo, E., Zugman, A., Domínguez-Martínez, T., Monsonet, M., Cristóbal-Narváez, P., Racioppi, A., Kwapil, T.R., Kazes, M., Daban, C., Bourgin, J., Gay, O., Mam-Lam-Fook, C., Nordholm, D., Rander, L., Krakauer, K., Glenthøj, L.B., Glenthøj, B., Gebhard, D., Arnhold, J., Klosterkötter, J., Lasser, I., Winklbaur, B., Reichenberg, A., EU-GEI High Risk Study

OBJECTIVE: This study aimed to assess the heterogeneity and stability of cognition in patients with a non-affective psychotic disorder and their unaffected siblings. In addition, we aimed to predict the cognitive subtypes of siblings by their probands. METHOD: Assessments were conducted at baseline, 3 and 6 years in 1119 patients, 1059 siblings and 586 controls from the Genetic Risk and Outcome of Psychosis (GROUP) study. Group-based trajectory modeling was applied to identify trajectories and clustered multinomial logistic regression analysis was used for prediction modeling. A composite score of eight neurocognitive tests was used to measure cognitive performance. RESULTS: Five stable cognitive trajectories ranging from severely altered to high cognitive performance were identified in patients. Likewise, four stable trajectories ranging from moderately altered to high performance were found in siblings. Siblings had a higher risk of cognitive alteration when patients' alteration was mild (OR = 2.21), moderate (OR = 5.70), and severe (OR = 10.07) compared with patients with intact cognitive function. The familial correlation coefficient between pairs of index patients and their siblings was 0.27 (P = 0.003). CONCLUSIONS: The cognitive profiles identified in the current study might be suitable as endophenotypes and could be used in future genetic studies and predicting functional and clinical outcomes. ispartof: Acta Psychiatrica Scandinavica vol:138 issue:6 pages:591-604 ispartof: location:United States status: published

Obsessive–compulsive symptoms (OCS) are frequently reported in patients with schizophrenia and have been associated with subjective distress and higher impairment. Recent studies suggest fluctuation in co-occurring OCS and associations with the course of psychotic symptoms. Current evidence is limited by few studies with long assessments intervals and a sole focus on between-subject comparisons. The aim of this study was to specifically investigate co-variation of symptom domains over time within individuals. Patients with a psychotic disorder (n = 56) and un-affected siblings (n = 49) completed monthly assessments of clinical and subclinical symptoms over 6 months. Mixed-model multilevel analyses examined the variability and relationship between OCS and positive, negative, and depressive symptoms on the between- and within-subject level. Symptom domains were associated across subjects and assessment times, in patients and siblings, with the strongest association between OCS and (subclinical) positive symptoms. Within-subjects, substantial variability and co-variation of all symptom domains was found. Particularly, between-subject differences in positive symptoms and within-subject change in depressive symptoms predicted subsequent OCS in patients 1 months later. This is the first prospective study disaggregating between and within-subject associations between co-occurring OCS and symptom cluster of psychosis. Differences on these two levels suggest different underlying mechanisms. The association between depressive symptoms and subsequent increase/decrease of OCS within patients may have important treatment implications. Electronic supplementary material The online version of this article (10.1007/s00406-018-0884-4) contains supplementary material, which is available to authorized users.

Exposures constitute a dense network of the environment: exposome. Here, we argue for embracing the exposome paradigm to investigate the sum of nongenetic "risk" and show how predictive modeling approaches can be used to construct an exposome score (ES; an aggregated score of exposures) for schizophrenia. The training dataset consisted of patients with schizophrenia and controls, whereas the independent validation dataset consisted of patients, their unaffected siblings, and controls. Binary exposures were cannabis use, hearing impairment, winter birth, bullying, and emotional, physical, and sexual abuse along with physical and emotional neglect. We applied logistic regression (LR), Gaussian Naive Bayes (GNB), the least absolute shrinkage and selection operator (LASSO), and Ridge penalized classification models to the training dataset. ESs, the sum of weighted exposures based on coefficients from each model, were calculated in the validation dataset. In addition, we estimated ES based on meta-analyses and a simple sum score of exposures. Accuracy, sensitivity, specificity, area under the receiver operating characteristic, and Nagelkerke's R-2 were compared. The ESMeta-analyses performed the worst, whereas the sum score and the ESGNB were worse than the ESLR that performed similar to the ESLASSO and ESRIDGE. The ESLR distinguished patients from controls (odds ratio [OR] = 1.94, P < .001), patients from siblings (OR = 1.58, P < .001), and siblings from controls (OR = 1.21, P= .001). An increase in ESLR was associated with a gradient increase of schizophrenia risk. In reference to the remaining fractions, the ESLR at top 30%, 20%, and 10% of the control distribution yielded ORs of 3.72, 3.74, and 4.77, respectively. Our findings demonstrate that predictive modeling approaches can be harnessed to evaluate the exposome.

ObjectiveImmune-mediated inflammatory diseases (IMIDs) are heterogeneous and complex conditions with overlapping clinical symptoms and elevated familial aggregation, which suggests the existence of a shared genetic component. In order to identify this genetic background in a systematic fashion, we performed the first cross-disease genome-wide meta-analysis in systemic seropositive rheumatic diseases, namely, systemic sclerosis, systemic lupus erythematosus, rheumatoid arthritis and idiopathic inflammatory myopathies.MethodsWe meta-analysed ~6.5 million single nucleotide polymorphisms in 11 678 cases and 19 704 non-affected controls of European descent populations. The functional roles of the associated variants were interrogated using publicly available databases.ResultsOur analysis revealed five shared genome-wide significant independent loci that had not been previously associated with these diseases: NAB1, KPNA4-ARL14, DGQK, LIMK1 and PRR12. All of these loci are related with immune processes such as interferon and epidermal growth factor signalling, response to methotrexate, cytoskeleton dynamics and coagulation cascade. Remarkably, several of the associated loci are known key players in autoimmunity, which supports the validity of our results. All the associated variants showed significant functional enrichment in DNase hypersensitivity sites, chromatin states and histone marks in relevant immune cells, including shared expression quantitative trait loci. Additionally, our results were significantly enriched in drugs that are being tested for the treatment of the diseases under study.ConclusionsWe have identified shared new risk loci with functional value across diseases and pinpoint new potential candidate loci that could be further investigated. Our results highlight the potential of drug repositioning among related systemic seropositive rheumatic IMIDs.