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[Importance] About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts., [Objective] To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples., [Design, Setting, and Participants] Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10 501) and individuals with non-TRS (n = 20 325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G])., [Main Outcomes and Measures] GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition., [Results] The study included a total of 85 490 participants (48 635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P = .001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P = .04)., [Conclusions and Relevance] In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance., This work was supported by Medical Research Council Centre grant MR/L010305/1, Medical Research Council Program grant MR/P005748/1, and Medical Research Council Project grants MR/L011794/1 and MC_PC_17212 to Cardiff University and by the National Centre for Mental Health, funded by the Welsh Government through Health and Care Research Wales. This work acknowledges the support of the Supercomputing Wales project, which is partially funded by the European Regional Development Fund via the Welsh Government. Dr Pardiñas was supported by an Academy of Medical Sciences Springboard Award (SBF005\1083). Dr Andreassen was supported by the Research Council of Norway (grants 283798, 262656, 248980, 273291, 248828, 248778, and 223273); KG Jebsen Stiftelsen, South-East Norway Health Authority, and the European Union’s Horizon 2020 Research and Innovation Programme (grant 847776). Dr Ajnakina was supported by an National Institute for Health Research postdoctoral fellowship (PDF-2018-11-ST2-020). Dr Joyce was supported by the University College London Hospitals/UCL University College London Biomedical Research Centre. Dr Kowalec received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement (793530) from the government of Canada Banting postdoctoral fellowship programme and the University of Manitoba. Dr Sullivan was supported by the Swedish Research Council (Vetenskapsrådet, D0886501), the European Union’s Horizon 2020 programme (COSYN, 610307) and the US National Institute of Mental Health (U01 MH109528 and R01 MH077139). The Psychiatric Genomics Consortium was partly supported by the National Institute Of Mental Health (grants R01MH124873). The Sweden Schizophrenia Study was supported by the National Institute Of Mental Health (grant R01MH077139). The STRATA consortium was supported by a Stratified Medicine Programme grant to Dr MacCabe from the Medical Research Council (grant MR/L011794/1), which funded the research and supported Drs Pardiñas, Smart, Kassoumeri, Murray, Walters, and MacCabe. Dr Smart was supported by a Collaboration for Leadership in Applied Health Research and Care South London at King’s College Hospital National Health Service Foundation Trust. The AESOP (US) cohort was funded by the UK Medical Research Council (grant G0500817). The Belfast (UK) cohort was funded by the Research and Development Office of Northern Ireland. The Bologna (Italy) cohort was funded by the European Community’s Seventh Framework program (HEALTH-F2-2010–241909, project EU-GEI). The Genetics and Psychosis project (London, UK) cohort was funded by the UK National Institute of Health Research Specialist Biomedical Research Centre for Mental Health, South London and the Maudsley National Health Service Mental Health Foundation Trust (SLAM) and the Institute of Psychiatry, Psychology, and Neuroscience at King’s College London; Psychiatry Research Trust; Maudsley Charity Research Fund; and the European Community’s Seventh Framework program (HEALTH-F2-2009-241909, project EU-GEI). The Lausanne (Switzerland) cohort was funded by the Swiss National Science Foundation (grants 320030_135736/1, 320030-120686, 324730-144064, 320030-173211, and 171804); the National Center of Competence in Research Synaptic Bases of Mental Diseases from the Swiss National Science Foundation (grant 51AU40_125759); and Fondation Alamaya. The Oslo (Norway) cohort was funded by the Research Council of Norway (grant 223273/F50, under the Centers of Excellence funding scheme, 300309, 283798) and the South-Eastern Norway Regional Health Authority (grants 2006233, 2006258, 2011085, 2014102, 2015088, and 2017-112). The Paris (France) cohort was funded by European Community’s Seventh Framework program (HEALTH-F2-2010–241909, project EU-GEI). The Prague (Czech Republic) cohort was funded by the Ministry of Health of the Czech Republic (grant NU20-04-00393). The Santander (Spain) cohort was funded by the following grants to Dr Crespo-Facorro: Instituto de Salud Carlos III (grants FIS00/3095, PI020499, PI050427, and PI060507), Plan Nacional de Drogas Research (grant 2005-Orden sco/3246/2004), SENY Fundatio Research (grant 2005-0308007), Fundacion Marques de Valdecilla (grant A/02/07, API07/011) and Ministry of Economy and Competitiveness and the European Fund for Regional Development (grants SAF2016-76046-R and SAF2013-46292-R). The West London (UK) cohort was funded by The Wellcome Trust (grants 042025, 052247, and 064607).

Schizophrenia is a highly polygenic disorder with important contributions from both common and rare risk alleles. We analyzed exome sequencing data for de novo variants (DNVs) in a new sample of 613 schizophrenia trios and combined this with published data to give a total of 3,444 trios. In this new data, loss-of-function (LoF) DNVs were significantly enriched among 3,471 LoF-intolerant genes, which supports previous findings. In the full dataset, genes associated with neurodevelopmental disorders (n = 159) were significantly enriched for LoF DNVs. Within these neurodevelopmental disorder genes, SLC6A1, which encodes a γ-aminobutyric acid transporter, was associated with missense-damaging DNVs. In 1,122 trios for which genome-wide common variant data were available, schizophrenia and bipolar disorder polygenic risk were significantly overtransmitted to probands. Probands carrying LoF or deletion DNVs in LoF-intolerant or neurodevelopmental disorder genes had significantly less overtransmission of schizophrenia polygenic risk than did non-carriers, which provides a second robust line of evidence that these DNVs increase liability to schizophrenia.

Background\ud The value of the nosological distinction between non-affective and affective psychosis has frequently been challenged. We aimed to investigate the transdiagnostic dimensional structure and associated characteristics of psychopathology at First Episode Psychosis (FEP). Regardless of diagnostic categories, we expected that positive symptoms occurred more frequently in ethnic minority groups and in more densely populated environments, and that negative symptoms were associated with indices of neurodevelopmental impairment.\ud \ud Method\ud This study included 2182 FEP individuals recruited across six countries, as part of the EUropean network of national schizophrenia networks studying Gene–Environment Interactions (EU-GEI) study. Symptom ratings were analysed using multidimensional item response modelling in Mplus to estimate five theory-based models of psychosis. We used multiple regression models to examine demographic and context factors associated with symptom dimensions.\ud \ud Results\ud A bifactor model, composed of one general factor and five specific dimensions of positive, negative, disorganization, manic and depressive symptoms, best-represented associations among ratings of psychotic symptoms. Positive symptoms were more common in ethnic minority groups. Urbanicity was associated with a higher score on the general factor. Men presented with more negative and less depressive symptoms than women. Early age-at-first-contact with psychiatric services was associated with higher scores on negative, disorganized, and manic symptom dimensions.\ud \ud Conclusions\ud Our results suggest that the bifactor model of psychopathology holds across diagnostic categories of non-affective and affective psychosis at FEP, and demographic and context determinants map onto general and specific symptom dimensions. These findings have implications for tailoring symptom-specific treatments and inform research into the mood-psychosis spectrum.

Background:\udRare copy number variants (CNVs) are associated with risk of neurodevelopmental disorders characterised by varying degrees of cognitive impairment, including schizophrenia, autism spectrum disorder and intellectual disability. However, the effects of many individual CNVs in carriers without neurodevelopmental disorders are not yet fully understood, and little is known about the effects of reciprocal copy number changes of known pathogenic loci.\ud\udAims\udWe aimed to analyse the effect of CNV carrier status on cognitive performance and measures of occupational and social outcomes in unaffected individuals from the UK Biobank.\ud\udMethod:\udWe called CNVs in the full UK Biobank sample and analysed data from 420 247 individuals who passed CNV quality control, reported White British or Irish ancestry and were not diagnosed with neurodevelopmental disorders. We analysed 33 pathogenic CNVs, including their reciprocal deletions/duplications, for association with seven cognitive tests and four general measures of functioning: academic qualifications, occupation, household income and Townsend Deprivation Index.\ud\udResults:\udMost CNVs (24 out of 33) were associated with reduced performance on at least one cognitive test or measure of functioning. The changes on the cognitive tests were modest (average reduction of 0.13 s.d.) but varied markedly between CNVs. All 12 schizophrenia-associated CNVs were associated with significant impairments on measures of functioning.\ud\udConclusions\udCNVs implicated in neurodevelopmental disorders, including schizophrenia, are associated with cognitive deficits, even among unaffected individuals. These deficits may be subtle but CNV carriers have significant disadvantages in educational attainment and ability to earn income in adult life.

BACKGROUND: The 'jumping to conclusions' (JTC) bias is associated with both psychosis and general cognition but their relationship is unclear. In this study, we set out to clarify the relationship between the JTC bias, IQ, psychosis and polygenic liability to schizophrenia and IQ.; METHODS: A total of 817 first episode psychosis patients and 1294 population-based controls completed assessments of general intelligence (IQ), and JTC, and provided blood or saliva samples from which we extracted DNA and computed polygenic risk scores for IQ and schizophrenia.; RESULTS: The estimated proportion of the total effect of case/control differences on JTC mediated by IQ was 79%. Schizophrenia polygenic risk score was non-significantly associated with a higher number of beads drawn (B = 0.47, 95% CI -0.21 to 1.16, p = 0.17); whereas IQ PRS (B = 0.51, 95% CI 0.25-0.76, p < 0.001) significantly predicted the number of beads drawn, and was thus associated with reduced JTC bias. The JTC was more strongly associated with the higher level of psychotic-like experiences (PLEs) in controls, including after controlling for IQ (B = -1.7, 95% CI -2.8 to -0.5, p = 0.006), but did not relate to delusions in patients.; CONCLUSIONS: Our findings suggest that the JTC reasoning bias in psychosis might not be a specific cognitive deficit but rather a manifestation or consequence, of general cognitive impairment. Whereas, in the general population, the JTC bias is related to PLEs, independent of IQ. The work has the potential to inform interventions targeting cognitive biases in early psychosis.

Depression is more frequent among individuals exposed to traumatic events. Both trauma exposure and depression are heritable. However, the relationship between these traits, including the role of genetic risk factors, is complex and poorly understood. When modelling trauma exposure as an environmental influence on depression, both gene-environment correlations and gene-environment interactions have been observed. The UK Biobank concurrently assessed Major Depressive Disorder (MDD) and self-reported lifetime exposure to traumatic events in 126,522 genotyped individuals of European ancestry. We contrasted genetic influences on MDD stratified by reported trauma exposure (final sample size range: 24,094-92,957). The SNP-based heritability of MDD with reported trauma exposure (24%) was greater than MDD without reported trauma exposure (12%). Simulations showed that this is not confounded by the strong, positive genetic correlation observed between MDD and reported trauma exposure. We also observed that the genetic correlation between MDD and waist circumference was only significant in individuals reporting trauma exposure (r g = 0.24, p = 1.8 × 10 -7 versus r g = -0.05, p = 0.39 in individuals not reporting trauma exposure, difference p = 2.3 × 10 -4 ). Our results suggest that the genetic contribution to MDD is greater when reported trauma is present, and that a complex relationship exists between reported trauma exposure, body composition, and MDD.

Many common illnesses, for reasons that have not been identified, differentially affect men and women. For instance, the autoimmune diseases systemic lupus erythematosus (SLE) and Sjögren’s syndrome affect nine times more women than men1, whereas schizophrenia affects men with greater frequency and severity relative to women2. All three illnesses have their strongest common genetic associations in the major histocompatibility complex (MHC) locus, an association that in SLE and Sjögren’s syndrome has long been thought to arise from alleles of the human leukocyte antigen (HLA) genes at that locus3–6. Here we show that variation of the complement component 4 (C4) genes C4A and C4B, which are also at the MHC locus and have been linked to increased risk for schizophrenia7, generates 7-fold variation in risk for SLE and 16-fold variation in risk for Sjögren’s syndrome among individuals with common C4 genotypes, with C4A protecting more strongly than C4B in both illnesses. The same alleles that increase risk for schizophrenia greatly reduce risk for SLE and Sjögren’s syndrome. In all three illnesses, C4 alleles act more strongly in men than in women: common combinations of C4A and C4B generated 14-fold variation in risk for SLE, 31-fold variation in risk for Sjögren’s syndrome, and 1.7-fold variation in schizophrenia risk among men (versus 6-fold, 15-fold and 1.26-fold variation in risk among women, respectively). At a protein level, both C4 and its effector C3 were present at higher levels in cerebrospinal fluid and plasma8,9 in men than in women among adults aged between 20 and 50 years, corresponding to the ages of differential disease vulnerability. Sex differences in complement protein levels may help to explain the more potent effects of C4 alleles in men, women’s greater risk of SLE and Sjögren’s syndrome and men’s greater vulnerability to schizophrenia. These results implicate the complement system as a source of sexual dimorphism in vulnerability to diverse illnesses.

Publisher's version (útgefin grein), Depression is a leading cause of worldwide disability but there remains considerable uncertainty regarding its neural and behavioural associations. Here, using non-overlapping Psychiatric Genomics Consortium (PGC) datasets as a reference, we estimate polygenic risk scores for depression (depression-PRS) in a discovery (N = 10,674) and replication (N = 11,214) imaging sample from UK Biobank. We report 77 traits that are significantly associated with depression-PRS, in both discovery and replication analyses. Mendelian Randomisation analysis supports a potential causal effect of liability to depression on brain white matter microstructure (β: 0.125 to 0.868, pFDR, The present study is supported by a Wellcome Trust Strategic Award “Stratifying Resilience and Depression Longitudinally” (STRADL) (Reference 104036/Z/14/Z) and MRC Mental Health Data Pathfinder Award (Reference MC_PC_17209). Data acquisition and analyses were conducted using the UK Biobank Resource under approved project #4844. Participation of the UK Biobank subjects is gratefully appreciated. We also thank UK Biobank team for collecting and preparing data for analyses. Funding from the Biotechnology and Biological Sciences Research Council (BBSRC) and Medical Research Council (MRC) is gratefully acknowledged. The PGC has received major funding from the US National Institute of Mental Health (5 U01MH109528-03). We thank the research participants and employees of 23andMe for supporting this study. X.S. receives support from China Scholarship Council (No. 201506040037). H.C.W. is supported by a JMAS SIM fellowship from the Royal College of Physicians of Edinburgh and by an ESAT College Fellowship from the University of Edinburgh. D.M.H. is supported by a Sir Henry Wellcome Postdoctoral Fellowship (Reference 213674/Z/18/Z) and a 2018 NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (Reference 27404). W.D.H. is supported by a grant from Age UK (Disconnected Mind Project). A.M.M. is supported by the Sackler Trust. I.J.D. is a participant in UK Biobank.

The EUGEI project was supported by the grant agreement HEALTH-F2-2010-241909 from the European Community’s Seventh Framework Programme. The authors are grateful to the patients and their families for participating in the project. They also thank all research personnel involved in the GROUP project, in particular J. van Baaren, E. Veermans, G. Driessen, T. Driesen, E. van’t Hag and J. de Nijs. Bart PF Rutten was funded by a VIDI award number 91718336 from the Netherlands Scientific Organisation., Pries, L.-K., Lage-Castellanos, A., Delespaul, P., Kenis, G., Luykx, J.J., Lin, B.D., Richards, A.L., Akdede, B., Binbay, T., Altinyazar, V., Yalinçetin, B., Gümüş-Akay, G., Cihan, B., Soygür, H., Ulaş, H., Cankurtaran, E.Ş., Kaymak, S.U., Mihaljevic, M.M., Petrovic, S.A., Mirjanic, T., Bernardo, M., Cabrera, B., Bobes, J., Saiz, P.A., García-Portilla, M.P., Sanjuan, J., Aguilar, E.J., Santos, J.L., Jiménez-López, E., Arrojo, M., Carracedo, A., López, G., González-Peñas, J., Parellada, M., Maric, N.P., Atbaşoğlu, C., Ucok, A., Alptekin, K., Saka, M.C., Arango, C., O'Donovan, M., Rutten, B.P.F., van Os, J., Guloksuz, S.

Background Adult ADHD has been assumed to be a continuation of childhood‐onset ADHD. However, recent studies have identified individuals with ADHD in adulthood who have not had ADHD in childhood. Whether or not these individuals have a ‘typical’ neurodevelopmental profile is not clear. Methods We tested two explanations for the emergence of apparent late‐onset ADHD symptomatology using the ALSPAC epidemiological cohort, by grouping individuals according to their scores on the Strengths and Difficulties Questionnaire (SDQ) hyperactivity subscale at ages 12 and 17 years. First, we tested whether some of those with apparent late‐onset ADHD symptoms had been potentially misclassified on the basis of earlier SDQ hyperactivity scores (ages 7, 8 and 9 years) or of subthreshold symptoms at age 12 years. Second, we investigated the possibility that those with ‘genuine’ late‐onset ADHD symptoms had a delayed manifestation of the same liability that underlies childhood‐onset symptoms, by investigating whether they had a similar profile of neurodevelopmental impairments (in the domains of autistic symptomatology, language, reading, spelling, executive functioning and IQ) as those with typical childhood‐onset ADHD. Results N = 56/75 (75%) of those with apparent late‐onset ADHD had had high ADHD scores at least one point in childhood, suggesting that they may have been misclassified on the basis of their score at age 12 years. The remaining 19 individuals (25%) with genuine late‐onset ADHD symptoms did not show a profile of neurodevelopmental impairment typically seen in ADHD, instead showing similar levels of autistic symptoms, language skills, executive functioning ability and IQ to those without ADHD symptoms. The only exceptions were that this group showed reading and spelling problems at age 9 years. Conclusions Our work suggests that this small number of individuals with genuine late‐onset symptoms may not be most appropriately considered as having a typical neurodevelopmental disorder.

Importance: Although the usefulness of polygenic risk scores as a measure of genetic liability for major depression (MD) has been established, their association with depression in the general population remains relatively unexplored.Objective: To evaluate whether polygenic risk scores for MD, bipolar disorder (BD), and schizophrenia (SZ) are associated with depression in the general population and explore whether these polygenic liabilities are associated with heterogeneity in terms of age at onset and severity at the initial depression diagnosis.Design, Setting, and Participants: Participants were drawn from the Danish iPSYCH2012 case-cohort study, a representative sample drawn from the population of Denmark born between May 1, 1981, and December 31, 2005. The hazard of depression was estimated using Cox regressions modified to accommodate the case-cohort design. Case-only analyses were conducted using linear and multinomial regressions. The data analysis was conducted from February 2017 to June 2018.Exposures: Polygenic risk scores for MD, BD, and SZ trained using the most recent genome-wide association study results from the Psychiatric Genomics Consortium.Main Outcomes and Measures: The main outcome was first depressive episode (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10] code F32) treated in hospital-based psychiatric care. Severity at the initial diagnosis was measured using the ICD-10 code severity specifications (mild, moderate, severe without psychosis, and severe with psychosis) and treatment setting (inpatient, outpatient, and emergency).Results: Of 34 573 participants aged 10 to 31 years at censoring, 68% of those with depression were female compared with 48.9% of participants without depression. Each SD increase in polygenic liability for MD, BD, and SZ was associated with 30% (hazard ratio [HR], 1.30; 95% CI, 1.27-1.33), 5% (HR, 1.05; 95% CI, 1.02-1.07), and 12% (HR, 1.12; 95% CI, 1.09-1.15) increases in the hazard of depression, respectively. Among cases, a higher polygenic liability for BD was associated with earlier depression onset (β = -.07; SE = .02; P = .002).Conclusions and Relevance: Polygenic liability for MD is associated with first depression in the general population, which supports the idea that these scores tap into an underlying liability for developing the disorder. The fact that polygenic risk for BD and polygenic risk for SZ also were associated with depression is consistent with prior evidence that these disorders share some common genetic overlap. Variations in polygenic liability may contribute slightly to heterogeneity in clinical presentation, but these associations appear minimal.

Background: \ud Studies involving clinically recruited samples show that genetic liability to schizophrenia overlaps with that for several psychiatric disorders including bipolar disorder, major depression and, in a population study, anxiety disorder and negative symptoms in adolescence.\ud \ud Aims: \ud We examined whether, at a population level, association between schizophrenia liability and anxiety disorders continues into adulthood, for specific anxiety disorders and as a group. We explored in an epidemiologically based cohort the nature of adult psychopathology sharing liability to schizophrenia.\ud \ud Method: \ud Schizophrenia polygenic risk scores (PRSs) were calculated for 590 European-descent individuals from the Christchurch Health and Development Study. Logistic regression was used to examine associations between schizophrenia PRS and four anxiety disorders (social phobia, specific phobia, panic disorder and generalised anxiety disorder), schizophrenia/schizophreniform disorder, manic/hypomanic episode, alcohol dependence, major depression, and – using linear regression – total number of anxiety disorders. A novel population-level association with hypomania was tested in a UK birth cohort (Avon Longitudinal Study of Parents and Children).\ud \ud Results: \ud Schizophrenia PRS was associated with total number of anxiety disorders and with generalised anxiety disorder and panic disorder. We show a novel population-level association between schizophrenia PRS and manic/hypomanic episode.\ud \ud Conclusions: \ud The relationship between schizophrenia liability and anxiety disorders is not restricted to psychopathology in adolescence but is present in adulthood and specifically linked to generalised anxiety disorder and panic disorder. We suggest that the association between schizophrenia liability and hypomanic/manic episodes found in clinical samples may not be due to bias.\ud \ud Declarations of interest: \ud None.

Publisher's version (útgefin grein), Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike’s information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease risk., We want to thank Susanne Sauer and Maik Ködel for their technical assistance and Jessica Keverne for language editing. We thank all mothers who took part in the on-going PREDO study. We are grateful to all the families in Norway who participate in the on-going MoBa cohort study. We thank the Drakenstein Child Health Study staff, and the clinical and administrative staff of the Western Cape Government Department of Health at Paarl Hospital and at the clinics for support of the Study. We also thank our collaborators and students. Finally, we thank all mothers and children enroled in the Drakenstein Child Health Study. We thank the research participants and employees of 23andMe, Inc. for their contribution to this study. This work was supported by the Academy of Finland (E.K., H.L., K.R., and J.L.); University of Helsinki Research Funds (J.L., M.L.P., and H.L.), British Heart Foundation (RMR); Tommy’s (RMR); European Commission (EK, KR, Horizon 2020 Award SC1–2016-RTD-733280 RECAP); NorFace DIAL (E.K., KR PremLife); Foundation for Pediatric Research (E.K.); Juho Vainio Foundation (E.K.); Novo Nordisk Foundation (E.K.); Signe and Ane Gyllenberg Foundation (E.K., K.R.); Sigrid Jusélius Foundation (E.K.); Finnish Medical Foundation (H.L.); Jane and Aatos Erkko Foundation (H.L.); Päivikki and Sakari Sohlberg Foundation (H.L., P.M.V.); the Clinical Graduate school in Pediatrics and Obstetrics/Gynaecology in University of Helsinki (P.M.V.). The Norwegian Mother and Child Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research, NIH/NIEHS (contract no N01-ES-75558), NIH/NINDS (grant no.1 UO1 NS 047537–01 and grant no.2 UO1 NS 047537–06A1). For this work, MoBa 1 and 2 were supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES-49019) and the Norwegian Research Council/BIOBANK (grant no 221097). This work was also partly supported by the Research Council of Norway through its Centres of Excellence funding scheme, project number 262700. The Drakenstein Child Health Study is supported by the Bill and Melinda Gates Foundation (OPP 1017641); with additional support for this work from the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health (NICHD) under Award Number R21HD085849; and the Fogarty International Center (FIC). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Additional support for H.J.Z., D.J.S. and N.K., and for research reported in this publication was by the South African Medical Research Council (SAMRC); N.K. receives support from the SAMRC under a Self-Initiated Research Grant. The views and opinions expressed are those of the authors and do not necessarily represent the official views of the SAMRC. This work was also funded by the German Federal Ministry of Education and Research through the Research Consortium Integrated Network IntegraMent (grant 01ZX1314H) under the auspices of the e:Med Programme (NSM). The UCI cohort was supported by a European Research Area Network (ERA Net) Neuron grant (01EW1407A, CB) and National Institutes of Health grant (R01 HD-060628, CB) as well as NIH grant R01 MH-105538 (PDW). This work was also funded by the Canadian Institute for Advanced Research, Child and Brain Development Program, Toronto, ON, Canada (KJOD).

BACKGROUND: Major depressive disorder is a clinically heterogeneous psychiatric disorder with a polygenic architecture. Genome-wide association studies have identified a number of risk-associated variants across the genome and have reported growing evidence of NETRIN1 pathway involvement. Stratifying disease risk by genetic variation within the NETRIN1 pathway may provide important routes for identification of disease mechanisms by focusing on a specific process, excluding heterogeneous risk-associated variation in other pathways. Here, we sought to investigate whether major depressive disorder polygenic risk scores derived from the NETRIN1 signaling pathway (NETRIN1-PRSs) and the whole genome, excluding NETRIN1 pathway genes (genomic-PRSs), were associated with white matter microstructure.METHODS: We used two diffusion tensor imaging measures, fractional anisotropy (FA) and mean diffusivity (MD), in the most up-to-date UK Biobank neuroimaging data release (FA: n = 6401; MD: n = 6390).RESULTS: We found significantly lower FA in the superior longitudinal fasciculus (β = -.035, pcorrected = .029) and significantly higher MD in a global measure of thalamic radiations (β = .029, pcorrected = .021), as well as higher MD in the superior (β = .034, pcorrected = .039) and inferior (β = .029, pcorrected = .043) longitudinal fasciculus and in the anterior (β = .025, pcorrected = .046) and superior (β = .027, pcorrected = .043) thalamic radiation associated with NETRIN1-PRS. Genomic-PRS was also associated with lower FA and higher MD in several tracts.CONCLUSIONS: Our findings indicate that variation in the NETRIN1 signaling pathway may confer risk for major depressive disorder through effects on a number of white matter tracts.

Background Genetic influences on gene expression in the human fetal brain plausibly impact upon a variety of postnatal brain-related traits, including susceptibility to neuropsychiatric disorders. However, to date, there have been no studies that have mapped genome-wide expression quantitative trait loci (eQTL) specifically in the human prenatal brain. Results We performed deep RNA sequencing and genome-wide genotyping on a unique collection of 120 human brains from the second trimester of gestation to provide the first eQTL dataset derived exclusively from the human fetal brain. We identify high confidence cis-acting eQTL at the individual transcript as well as whole gene level, including many mapping to a common inversion polymorphism on chromosome 17q21. Fetal brain eQTL are enriched among risk variants for postnatal conditions including attention deficit hyperactivity disorder, schizophrenia, and bipolar disorder. We further identify changes in gene expression within the prenatal brain that potentially mediate risk for neuropsychiatric traits, including increased expression of C4A in association with genetic risk for schizophrenia, increased expression of LRRC57 in association with genetic risk for bipolar disorder, and altered expression of multiple genes within the chromosome 17q21 inversion in association with variants influencing the personality trait of neuroticism. Conclusions We have mapped eQTL operating in the human fetal brain, providing evidence that these confer risk to certain neuropsychiatric disorders, and identifying gene expression changes that potentially mediate susceptibility to these conditions. Electronic supplementary material The online version of this article (10.1186/s13059-018-1567-1) contains supplementary material, which is available to authorized users.

Key Points Question Do neuropsychiatric disorder genetic risk variants influence developmental trajectories of depression in youth? Findings In this population-based study including 7543 adolescents, distinct depression trajectory classes were identified. A later-adolescence–onset class (17.3% of the sample) showed a typical depression trajectory and was associated with major depressive disorder risk alleles, and an early-adolescence–onset class (9.0%) showed clinically significant symptoms at age 12 years and was associated with schizophrenia and attention-deficit hyperactivity disorder genetic risk, childhood attention-deficit hyperactivity disorder, and neurodevelopmental traits. Meaning Depression in youth is heterogeneous; findings are consistent with emerging evidence for a neurodevelopmental component to some cases of depression and that this component is more likely when onset is very early., Importance Depression often first manifests in adolescence. Thereafter, individual trajectories vary substantially, but it is not known what shapes depression trajectories in youth. Adult studies suggest that genetic risk for schizophrenia, a psychiatric disorder with a neurodevelopmental component, may contribute to an earlier onset of depression. Objective To test the hypothesis that there are distinct trajectories of depressive symptoms and that genetic liability for neurodevelopmental psychiatric disorders (eg, schizophrenia, attention deficit/hyperactivity disorder [ADHD]), as well as for major depressive disorder (MDD), contribute to early-onset depression. Design, Setting, and Participants The Avon Longitudinal Study of Parents and Children is an ongoing, prospective, longitudinal, population-based cohort that has been collecting data since September 6, 1990, including data on 7543 adolescents with depressive symptoms at multiple time points. The present study was conducted between November 10, 2017, and August 14, 2018. Main Outcomes and Measures Trajectories based on self-reported depressive symptoms dichotomized by the clinical cutpoint; MDD, schizophrenia, and ADHD polygenic risk score (PRS) were predictors. Results In 7543 adolescents with depression data on more than 1 assessment point between a mean (SD) age of 10.64 (0.25) years and 18.65 (0.49) years (3568 [47.3%] male; 3975 [52.7%] female), 3 trajectory classes were identified: persistently low (73.7%), later-adolescence onset (17.3%), and early-adolescence onset (9.0%). The later-adolescence–onset class was associated with MDD genetic risk only (MDD PRS: odds ratio [OR], 1.27; 95% CI, 1.09-1.48; P = .003). The early-adolescence–onset class was also associated with MDD genetic risk (MDD PRS: OR, 1.24; 95% CI, 1.06-1.46; P = .007) but additionally with genetic risk for neurodevelopmental disorders (schizophrenia PRS: OR, 1.22; 95% CI, 1.04-1.43; P = .01; ADHD PRS: OR, 1.32; 95% CI, 1.13-1.54; P, This population-based study examines the association between genetic risk variants for neuropsychiatric disorders and the age at onset and trajectories of depression in children.

© 2017 American Medical Association. All rights reserved.IMPORTANCE The association between major depressive disorder (MDD) and obesitymay stem from shared immunometabolic mechanisms particularly evident in MDD with atypical features, characterized by increased appetite and/or weight (A/W) during an active episode. OBJECTIVE To determine whether subgroups of patients with MDD stratified according to the A/W criterion had a different degree of genetic overlap with obesity-related traits (body mass index [BMI] and levels of C-reactive protein [CRP] and leptin). DESIGN, SETTING, AND PATIENTS This multicenter study assembled genome-wide genotypic and phenotypic measures from 14 data sets of the Psychiatric Genomics Consortium. Data sets were drawn from case-control, cohort, and population-based studies, including 26 628 participants with established psychiatric diagnoses and genome-wide genotype data. Data on BMI were available for 15 237 participants. Data were retrieved and analyzed from September 28, 2015, through May 20, 2017. MAIN OUTCOMES AND MEASURES Lifetime DSM-IV MDDwas diagnosed using structured diagnostic instruments. Patients with MDD were stratified into subgroups according to change in the DSM-IV A/W symptoms as decreased or increased. RESULTS Data included 11 837 participants with MDD and 14 791 control individuals, for a total of 26 628 participants (59.1% female and 40.9%male). Among participants with MDD, 5347 (45.2%) were classified in the decreased A/W and 1871 (15.8%) in the increased A/W subgroups. Common genetic variants explained approximately 10% of the heritability in the 2 subgroups. The increased A/W subgroup showed a strong and positive genetic correlation (SE) with BMI (0.53 [0.15]; P = 6.3 × 10-4), whereas the decreased A/W subgroup showed an inverse correlation (-0.28 [0.14]; P = .06). Furthermore, the decreased A/W subgroup had a higher polygenic risk for increased BMI (odds ratio [OR], 1.18; 95%CI, 1.12-1.25; P = 1.6 × 10-10) and levels of CRP (OR, 1.08; 95%CI, 1.02-1.13; P = 7.3 × 10-3) and leptin (OR, 1.09; 95%CI, 1.06-1.12; P = 1.7 × 10-3). CONCLUSIONS AND RELEVANCE The phenotypic associations between atypical depressive symptoms and obesity-related traits may arise from shared pathophysiologic mechanisms in patients with MDD. Development of treatments effectively targeting immunometabolic dysregulations may benefit patients with depression and obesity, both syndromes with important disability.

Polygenic risk scores, based on risk variants identified in genome-wide-association-studies (GWAS), explain a considerable portion of the heritability for schizophrenia (SZ) and bipolar disorder (BD). However, little is known about the combined effects of these variants, although polygenic neuroimaging has developed into a powerful tool of translational neuroscience. In this study, we used genome wide significant SZ risk variants to test the predictive capacity of the polygenic model and explored potential associations with white matter volume, a key candidate in imaging phenotype for psychotic disorders. By calculating the combined additive schizophrenia risk of seven SNPs (significant hits from a recent schizophrenia GWAS study), we show that increased additive genetic risk for SZ was associated with reduced white matter volume in a group of participants (n = 94) consisting of healthy individuals, SZ first-degree relatives, SZ patients and BD patients. This effect was also seen in a second independent sample of healthy individuals (n = 89). We suggest that a moderate portion of variance (~4%) of white matter volume can be explained by the seven hits from the recent schizophrenia GWAS. These results provide evidence for associations between cumulative genetic risk for schizophrenia and intermediate neuroimaging phenotypes in models of psychosis. Our work contributes to a growing body of literature suggesting that polygenic risk may help to explain white matter alterations associated with familial risk for psychosis., Highlights • We demonstrate that compared to controls, white matter volume is reduced in SZ relatives, SZ patients and BD patients. • Seven schizophrenia risk SNPs are associated with reduced white matter volume across the psychosis spectrum. • Increased schizophrenia risk is associated with reduced white matter volume

M. Kivimäki työryhmäjäsen. The contribution of rare and low-frequency variants to human traits is largely unexplored. Here we describe insights from sequencing whole genomes (low read depth, 7x) or exomes (high read depth, 80x) of nearly 10,000 individuals from population-based and disease collections. In extensively phenotyped cohorts we characterize over 24 million novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with levels of triglycerides (APOB), adiponectin (ADIPOQ) and low-density lipoprotein cholesterol (LDLR and RGAG1) from single-marker and rare variant aggregation tests. We describe population structure and functional annotation of rare and low-frequency variants, use the data to estimate the benefits of sequencing for association studies, and summarize lessons from disease-specific collections. Finally, we make available an extensive resource, including individual-level genetic and phenotypic data and web-based tools to facilitate the exploration of association results.

Hair cortisol concentration (HCC) is a promising measure of long-term hypothalamus-pituitary-adrenal (HPA) axis activity. Previous research has suggested an association between HCC and psychological variables, and initial studies of inter-individual variance in HCC have implicated genetic factors. However, whether HCC and psychological variables share genetic risk factors remains unclear. The aims of the present twin study were to: (i) assess the heritability of HCC; (ii) estimate the phenotypic and genetic correlation between HPA axis activity and the psychological variables perceived stress, depressive symptoms, and neuroticism; using formal genetic twin models and molecular genetic methods, i.e. polygenic risk scores (PRS). HCC was measured in 671 adolescents and young adults. These included 115 monozygotic and 183 dizygotic twin-pairs. For 432 subjects PRS scores for plasma cortisol, major depression, and neuroticism were calculated using data from large genome wide association studies. The twin model revealed a heritability for HCC of 72%. No significant phenotypic or genetic correlation was found between HCC and the three psychological variables of interest. PRS did not explain variance in HCC. The present data suggest that HCC is highly heritable. However, the data do not support a strong biological link between HCC and any of the investigated psychological variables. Consortia CORtisolNETwork (CORNET) Consortium Jennifer L. Bolton21 Caroline Hayward23 Nese Direk24,25 Anna Anderson21 Jennifer Huffman23 James F. Wilson26 Harry Campbell26 Igor Rudan26 Alan Wright23 Nicholas Hastie23 Sarah H. Wild26 Fleur P. Velders24 Albert Hofman24 Andre G. Uitterlinden24,27 Jari Lahti28 Katri Räikkönen28 Eero Kajantie29 Elisabeth Widen30 Aarno Palotie30,31 Johan G. Eriksson29,32,33,34,35 Marika Kaakinen36 Marjo-Riitta Järvelin36,37,38,39 Nicholas J. Timpson40 George Davey Smith40 Susan M. Ring41 David M. Evans40 Beate St Pourcain41 Toshiko Tanaka42 Yuri Milaneschi42,43 Stefania Bandinelli44 Luigi Ferrucci42 Pim van der Harst45,46,47 Judith GM Rosmalen48 Stephen JL Bakker49 Niek Verweij45 Robin PF Dullaart49 Anubha Mahajan50 Cecilia M. Lindgren50 Andrew Morris50 Lars Lind51 Erik Ingelsson51 Laura N. Anderson52 Craig E. Pennell53 Stephen J. Lye52 Stephen G. Matthews54 Joel Eriksson55 Dan Mellstrom55 Claes Ohlsson55 Jackie F. Price26 Mark WJ Strachan21 Rebecca M. Reynolds21 Henning Tiemeier24,56,57 Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium (PGC) Stephan Ripke58,59,60 Manuel Mattheisen61,62,63 Abdel Abdellaoui64 Mark J. Adams65 Esben Agerbo66,8,67 Tracy M. Air68 Till FM Andlauer69,70 Silviu-Alin Bacanu71 Marie Bækvad-Hansen67,72 Aartjan TF Beekman43 David A. Bennett73 Klaus Berger74 Tim B. Bigdeli71,11 Jonas Bybjerg-Grauholm67,72 Enda M. Byrne5 Na Cai75 Enrique Castelao76 Toni-Kim Clarke65 Jonathan RI Coleman77 Converge Consortium78 Nick Craddock79 Udo Dannlowski80,81 Gareth Davies82 Gail Davies83 Eco. J. C. de Geus64,84 Philip De Jager85 Ian J. Deary83 Franziska Degenhardt12,13 Erin C. Dunn86,87,88 Erik A. Ehli82 Thalia C. Eley77 Valentina Escott-Price89 Tõnu Esko58,90,91,92 Hilary K. Finucane93,94 Michael Gill95 Scott D. Gordon96 Jakob Grove61,62,67,97 Lynsey S. Hall65,98 Thomas F. Hansen99,100 Christine Søholm Hansen67,72 Thomas F. Hansen101 Andrew C. Heath102 Anjali K. Henders5 Stefan Herms12,13,15 Per Hoffmann12,13,15 Georg Homuth103 Carsten Horn104 Jouke- Jan Hottenga64 David Hougaard67,72 Hailiang Huang59,86,105 Marcus Ising106 Rick Jansen43 Eric Jorgenson107 Stefan Kloiber108,109 James A Knowles110 Warren W. Kretzschmar50 Jesper Krogh111 Zoltán Kutalik112,113 Maren Lang3 Glyn Lewis114 Yihan Li50 Donald J. MacIntyre115,116 Pamela AF Madden102 Jonathan Marchine117 Hamdi Mbarek118,64 Peter McGuffin77 Divya Mehta119 Andres Metspalu92,120 Christel M. Middeldorp64 Evelin Mihailov92,121 Lili Milani92 Grant W. Montgomery122 Sara Mostafavi123,124 Niamh Mullins77 Matthias Nauck125,126 Bernard Ng124 Merete Nordentoft67,127 Dale R. Nyholt128 Michael C. O’Donovan129 Paul F. O’Reilly77 Hogni Oskarsson130 Michael J. Owen129 Sara A. Paciga131 Carsten Bøcker Pedersen66,67,8 Marianne Giørtz Pedersen66,67,8 Nancy L. Pedersen132 Michele L. Pergadia133 Roseann E. Peterson11,71 Erik Pettersson134 Wouter J. Peyrot43 David J. Porteous135 Danielle Posthuma136,137 James B. Potash138 Jorge A. Quiroz139 John P. Rice102 Brien P. Riley71 Margarita Rivera77,140 Douglas M. Ruderfer141 Saira Saeed Mirza24 Robert Schoevers142 Ling Shen107 Jianxin Shi143 Engilbert Sigurdsson144 Grant CB Sinnamon145 Johannes H. Smit43 Daniel J. Smith146 Jordan W. Smoller86 Hreinn Stephansson147 Stacy Steinberg147 Jana Strohmaier3 Katherine E. Tansey148 Alexander Teumer149 Wesley Thompson100,135,150,151,152 Pippa A. Thomson135 Thorgeir E. Thorgeirsson147 Jens Treutlein3 Maciej Trzaskowski153 Daniel Umbricht154 Sandra van der Auwera155 Gerard van Grootheest43 Albert M. van Hemert156 Alexander Viktorin132 Henry Völzke149 Yunpeng Wang67,100,151 Bradley T. Webb157 Myrna M. Weissman158,159 Jürgen Wellmann74 Gonneke Willemsen64 Hualin S. Xi160 Bernhard T. Baune68 Douglas H. R. Blackwood65 Dorret I. Boomsma64 Anders D. Børglum61,62,67 Henriette N. Buttenschøn62,67,161 Sven Cichon12,162,163,164 Enrico Domenici165 Jonathan Flint50,166 Hans J. Grabe155 Steven P. Hamilton167 Kenneth S. Kendler71 Qingqin S. Li168 Susanne Lucae106 Patrik K. Magnusson132 Andrew M. McIntosh65,83 Ole Mors67,169 Preben Bo Mortensen62,8,67 Bertram Müller-Myhsok69,70,170 Brenda WJH Penninx43 Roy H. Perlis87,171 Martin Preisig76 Catherine Schaefer107 Jordan W. Smoller87,88 Kari Stephansson147 Henning Tiemeier24,56,57 Rudolf Uher172 Thomas Werge100,150,173 Ashley R. Winslow174,175 Gerome Breen77,176 Douglas F. Levinson177 Cathryn M. Lewis77,178 Naomi R. Wray5,153 Patrick F. Sullivan134,179,180 23MRC Human Genetics Unit, Institute for Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK. 24Department of Epidemiology, Erasmus Medical Centre, Rotterdam, Netherlands. 25Psychiatry, Dokuz Eylul University School Of Medicine, Izmir, TR, Turkey. 26Centre for Population Health Sciences, Institute for Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH8 9AG, UK. 27Internal Medicine, Erasmus MC, Rotterdam, NL, Netherlands. 28Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland. 29National Institute for Health and Welfare, Helsinki, Finland. 30Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. 31Department of Medical Genetics, University of Helsinki and University Central Hospital, Helsinki, Finland. 32Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland. 33Helsinki University Central Hospital, Unit of General Practice, Helsinki, Finland. 34Folkhalsan Research Centre, Helsinki, Finland. 35Vasa Central Hospital, Vasa, Finland. 36Institute of Health Sciences and Biocenter Oulu, University of Oulu, Oulu, Finland. 37Department of Children and Yond People and Families, National Institute for Health and elfare, Oulu, Finland. 38Department of Epidemiology and Biostatistics, MRC-HPA Centre for Environment and Health, Imperial College London, London, UK. 39Unit of Primary Care, Oulu University Hospital, Oulu, Finland. 40MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Bristol, UK. 41School of Social and Community Medicine, University of Bristol, Bristol, UK. 42Longitudinal Studies Section, Clinical Research Branch, National Institute on Aging, Baltimore, MD, USA. 43Department of Psychiatry, VU University Medical Center/GGZ inGeest, Amsterdam, Netherlands. 44Geriatric Unit, ASF, Florence, Italy. 45University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, Netherlands. 46University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, Netherlands. 47Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, Netherlands. 48University of Groningen, University Medical Center Groningen, Interdisciplinary Center for Psychiatric Epidemiology, Groningen, Netherlands. 49University of Groningen, University Medical Center Groningen, Department of Internal Medicine, Groningen, Netherlands. 50Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. 51Department of Medical Sciences, Uppsala University, Uppsala, Sweden. 52Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada. 53School of Women’s and Infant’s Health, The University of Western Australia, Crawley, Australia. 54Department of Physiology, University of Toronto, Toronto, Ontario, Canada. 55Center for Bone and Arthritis Research, Institute of Medicin, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. 56Child and Adolescent Psychiatry, Erasmus MC, Rotterdam, Netherlands. 57Psychiatry, Erasmus MC, Rotterdam, Netherlands. 58Medical and Population Genetics, Broad Institute, Cambridge, USA. 59Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, USA. 60Department of Psychiatry and Psychotherapy, Universitätsmedizin Berlin Campus Charité Mitte, Berlin, Germany. 61Department of Biomedicine, Aarhus University, Aarhus, Denmark. 62iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark. 63iSPYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark. 64Dept of Biological Psychology, VU University Amsterdam, Amsterdam, Netherlands. 65Division of Psychiatry, University of Edinburgh, Edinburgh, UK. 66Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark. 67iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark. 68Discipline of Psychiatry, University of Adelaide, Adelaide, Australia. 69Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany. 70Munich Cluster for Systems Neurology (SyNergy), Munich, Germany. 71Department of Psychiatry, Virginia Commonwealth University, Richmond, USA. 72Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark. 73Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, USA. 74Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, UK. 75Human Genetics, Wellcome Trust Sanger Institute, Cambridge, UK. 76Department of Psychiatry, University Hospital of Lausanne, Prilly, Switzerland. 77MRC Social Genetic and Developmental Psychiatry Centre, King’s College London, London, UK. 78University of Oxford, Oxford, UK. 79Psychological Medicine, Cardiff University, Cardiff, UK. 80Department of Psychiatry, University of Marburg, Marburg, Germany. 81Department of Psychiatry, University of Münster, Münster, Germany. 82Avera Institute for Human Genetics, Sioux Falls, USA. 83Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK. 84EMGO+ Institute, VU University Medical Center, Amsterdam, Netherlands. 85Neurology, Brigham and Women’s Hospital, Boston, USA. 86Stanley Center for Psychiatric Research, Broad Institute, Cambridge, USA. 87Department of Psychiatry, Massachusetts General Hospital, Boston, USA. 88Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Massachusetts General Hospital, Boston, USA. 89Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK. 90Division of Endocrinology, Children’s Hospital Boston, Boston, USA. 91Department of Genetics, Harvard Medical School, Boston, USA. 92Estonian Genome Center, University of Tartu, Tartu, Estonia. 93Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA. 94Department of Mathematics, Massachusetts Institute of Technology, Cambridge, USA. 95Department of Psychiatry, Trinity College Dublin, Dublin, Ireland. 96Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Australia. 97Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark. 98Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK. 99Danish Headache Centre, Department of Neurology, Rigshospitalet Glostrup, Glostrup, Denmark. 100Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Capital Region of Denmark, Roskilde, Denmark. 101iPSYCH, The Lundbeck Foundation Initiative for Psychiatric Research, Copenhagen, Denmark. 102Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, USA. 103Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Germany. 104Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland. 105Department of Medicine, Harvard Medical School, Boston, USA. 106Max Planck Institute of Psychiatry, Munich, Germany. 107Division of Research, Kaiser Permanente Northern California, Oakland, USA. 108Centre for Addiction and Mental Health, Toronto, Canada. 109Department of Psychiatry, University of Toronto, Toronto, Canada. 110Psychiatry & The Behavioral Sciences, University of Southern California, Los Angeles, USA. 111Department of Endocrinology at Herlev University Hospital, University of Copenhagen, Copenhagen, Denmark. 112Swiss Institute of Bioinformatics, Lausanne, Switzerland. 113Institute of Social and Preventive Medicine (IUMSP), Lausanne University Hospital, Lausanne, Switzerland. 114Division of Psychiatry, University College London, London, UK. 115Mental Health NHS 24, Glasgow, UK. 116Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK. 117Statistics, University of Oxford, Oxford, UK. 118EMGO+ Institute for Health and Care Research, Amsterdam, Netherlands. 119School of Psychology and Counseling, Queensland University of Technology, Brisbane, Australia. 120Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia. 121Estonian Biocentre, Tartu, Estonia. 122Institute for Molecular Biology, University of Queensland, Brisbane, Australia. 123Medical Genetics, University of British Columbia, Vancouver, Canada. 124Statistics, University of British Columbia, Vancouver, Canada. 125DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Matthias Nauck, Greifswald, Germany. 126Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany. 127Mental Health Centre Copenhagen, Copenhagen Universtity Hospital, Copenhagen, Denmark. 128Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia. 129MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK. 130Humus, Reykjavik, Iceland. 131Human Genetics and Computational Biomedicine, Pfizer Global Research and Development, Groton, USA. 132Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 133Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, USA. 134Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 135Medical Genetics Section, CGEM, IGMM, University of Edinburgh, Edinburgh, UK. 136Complex Trait Genetics, VU University Amsterdam, Amsterdam, Netherlands. 137Clinical Genetics, VU University Medical Center, Amsterdam, Netherlands. 138Psychiatry, University of Iowa, Iowa City, USA. 139Solid GT, Boston, USA. 140Department of Biochemistry and Molecular Biology II, Institute of Neurosciences, Center for Biomedical Research, University of Granada, Granada, Spain. 141Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA. 142Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. 143Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, USA. 144Faculty of Medicine, Department of Psychiatry, School of Health Sciences, University of Iceland, Reykjavik, Iceland. 145School of Medicine and Dentistry, James Cook University, Townsville, Australia. 146Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK. 147deCODE Genetics/Amgen, Reykjavik, Iceland. 148College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK. 149Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany. 150iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark. 151KG Jebsen Centre for Psychosis Research, Norway Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway. 152Department of Psychiatry, University of California, San Diego, San Diego, USA. 153Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia. 154Roche Pharmaceutical Research and Early Development, Neuroscience, Ophthalmology and Rare Diseases Discovery & Translational Medicine Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland. 155Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany. 156Department of Psychiatry, Leiden University Medical Center, Leiden, Netherlands. 157Virginia Institute of Psychiatric & Behavioral Genetics, Virginia Commonwealth University, Richmond, USA. 158Psychiatry, Columbia University College of Physicians and Surgeons, New York, USA. 159Division of Epidemiology, New York State Psychiatric Institute, New York, USA. 160Computational Sciences Center of Emphasis, Pfizer Global Research and Development, Cambridge, USA. 161Department of Clinical Medicine, Translational Neuropsychiatry Unit, Aarhus University, Aarhus, Denmark. 162Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany. 163Department of Biomedicine, University of Basel, Basel, CH, Switzerland. 164Division of Medical Genetics, University of Basel, Basel, CH, Switzerland. 165Centre for Integrative Biology, Università degli Studi di Trento, Trento, Italy. 166Psychiatry, University of California Los Angeles, Los Angeles, USA. 167Psychiatry, Kaiser Permanente Northern California, San Francisco, USA. 168Neuroscience Therapeutic Area, Janssen Research and Development, LLC, Titusville, USA. 169Psychosis Research Unit, Aarhus University Hospital, Risskov, Aarhus, Denmark. 170Institute of Translational Medicine, University of Liverpool, Liverpool, UK. 171Psychiatry, Harvard Medical School, Boston, USA. 172Psychiatry, Dalhousie University, Halifax, Canada. 173Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. 174Human Genetics and Computational Biomedicine, Pfizer Global Research and Development, Cambridge, USA. 175Orphan Disease Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA. 176NIHR BRC for Mental Health, King’s College London, London, UK. 177Psychiatry & Behavioral Sciences, Stanford University, Stanford, USA. 178Department of Medical & Molecular Genetics, King’s College London, London, UK. 179Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, USA. 180Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, USA.

Variants at microRNA-137 (MIR137), one of the most strongly associated schizophrenia risk loci identified to date, have been associated with poorer cognitive performance. As microRNA-137 is known to regulate the expression of ~1900 other genes, including several that are independently associated with schizophrenia, we tested whether this gene set was also associated with variation in cognitive performance. Our analysis was based on an empirically derived list of genes whose expression was altered by manipulation of MIR137 expression. This list was cross-referenced with genome-wide schizophrenia association data to construct individual polygenic scores. We then tested, in a sample of 808 patients and 192 controls, whether these risk scores were associated with altered performance on cognitive functions known to be affected in schizophrenia. A subgroup of healthy participants also underwent functional imaging during memory (n=108) and face processing tasks (n=83). Increased polygenic risk within the empirically derived miR-137 regulated gene score was associated with significantly lower performance on intelligence quotient, working memory and episodic memory. These effects were observed most clearly at a polygenic threshold of P=0.05, although significant results were observed at all three thresholds analyzed. This association was found independently for the gene set as a whole, excluding the schizophrenia-associated MIR137 SNP itself. Analysis of the spatial working memory fMRI task further suggested that increased risk score (thresholded at P=10−5) was significantly associated with increased activation of the right inferior occipital gyrus. In conclusion, these data are consistent with emerging evidence that MIR137 associated risk for schizophrenia may relate to its broader downstream genetic effects.

It is well known that inbreeding increases the risk of recessive monogenic diseases, but it is less certain whether it contributes to the etiology of complex diseases such as schizophrenia. One way to estimate the effects of inbreeding is to examine the association between disease diagnosis and genome-wide autozygosity estimated using runs of homozygosity (ROH) in genome-wide single nucleotide polymorphism arrays. Using data for schizophrenia from the Psychiatric Genomics Consortium (n = 21,868), Keller et al. (2012) estimated that the odds of developing schizophrenia increased by approximately 17% for every additional percent of the genome that is autozygous (β = 16.1, CI(β) = [6.93, 25.7], Z = 3.44, p = 0.0006). Here we describe replication results from 22 independent schizophrenia case-control datasets from the Psychiatric Genomics Consortium (n = 39,830). Using the same ROH calling thresholds and procedures as Keller et al. (2012), we were unable to replicate the significant association between ROH burden and schizophrenia in the independent PGC phase II data, although the effect was in the predicted direction, and the combined (original + replication) dataset yielded an attenuated but significant relationship between Froh and schizophrenia (β = 4.86,CI(β) = [0.90,8.83],Z = 2.40,p = 0.02). Since Keller et al. (2012), several studies reported inconsistent association of ROH burden with complex traits, particularly in case-control data. These conflicting results might suggest that the effects of autozygosity are confounded by various factors, such as socioeconomic status, education, urbanicity, and religiosity, which may be associated with both real inbreeding and the outcome measures of interest., Author Summary It is well known that mating between relatives increases the risk that a child will have a rare recessive genetic disease, but there has also been increasing interest and inconsistent findings on whether inbreeding is a risk factor for common, complex psychiatric disorders such as schizophrenia. The best powered study to date investigating this theory predicted that the odds of developing schizophrenia increase by approximately 17% for every additional percent of the genome that shows evidence of inbreeding. In this replication, we used genome-wide single nucleotide polymorphism data from 18,562 schizophrenia cases and 21,268 controls to quantify the degree to which they were inbred and to test the hypothesis that schizophrenia cases show higher mean levels of inbreeding. Contrary to the original study, we did not find evidence for distant inbreeding to play a role in schizophrenia risk. There are various confounding factors that could explain the discrepancy in results from the original study and our replication, and this should serve as a cautionary note–careful attention should be paid to issues like ascertainment when using the data from genome-wide case-control association studies for secondary analyses for which the data may not have originally been intended.

Attention-deficit hyperactivity disorder (ADHD) is a complex polygenic disorder. This study aimed to discover common and rare DNA variants associated with ADHD in a large homogeneous Han Chinese ADHD case-control sample. The sample comprised 1,040 cases and 963 controls. All cases met DSM-IV ADHD diagnostic criteria. We used the Affymetrix6.0 array to assay both single nucleotide polymorphisms (SNPs) and copy number variants (CNVs). Genome-wide association analyses were performed using PLINK. SNP-heritability and SNP-genetic correlations with ADHD in Caucasians were estimated with genome-wide complex trait analysis (GCTA). Pathway analyses were performed using the Interval enRICHment Test (INRICH), the Disease Association Protein-Protein Link Evaluator (DAPPLE), and the Genomic Regions Enrichment of Annotations Tool (GREAT). We did not find genome-wide significance for single SNPs but did find an increased burden of large, rare CNVs in the ADHD sample (P=0.038). SNP-heritability was estimated to be 0.42 (standard error, 0.13, P=0.0017) and the SNP-genetic correlation with European Ancestry ADHD samples was 0.39 (SE 0.15, P=0.0072). The INRICH, DAPPLE, and GREAT analyses implicated several gene ontology cellular components, including neuron projections and synaptic components, which are consistent with a neurodevelopmental pathophysiology for ADHD. This study suggested the genetic architecture of ADHD comprises both common and rare variants. Some common causal variants are likely to be shared between Han Chinese and Caucasians. Complex neurodevelopmental networks may underlie ADHD's etiology. (c) 2013 Wiley Periodicals, Inc. Refereed/Peer-reviewed

Progress has recently been made in understanding the genetic basis of schizophrenia and other psychiatric disorders. Longitudinal studies are complicated by participant dropout, which could be related to the presence of psychiatric problems and associated genetic risk. We tested whether common genetic variants implicated in schizophrenia were associated with study nonparticipation among 7,867 children and 7,850 mothers from the Avon Longitudinal Study of Parents and Children (ALSPAC; 1991–2007), a longitudinal population cohort study. Higher polygenic risk scores for schizophrenia were consistently associated with noncompletion of questionnaires by study mothers and children and nonattendance at data collection throughout childhood and adolescence (ages 1–15 years). These associations persisted after adjustment for other potential correlates of nonparticipation. Results suggest that persons at higher genetic risk for schizophrenia are likely to be underrepresented in cohort studies, which will underestimate risk of this and related psychiatric, cognitive, and behavioral phenotypes in the population. Statistical power to detect associations with these phenotypes will be reduced, while analyses of schizophrenia-related phenotypes as outcomes may be biased by the nonrandom missingness of these phenotypes, even if multiple imputation is used. Similarly, in complete-case analyses, collider bias may affect associations between genetic risk and other factors associated with missingness.

Compared with the general population, an excess of rare copy number variants (CNVs) has been identified in people with schizophrenia. Females with neurodevelopmental disorders and in the general population have been reported to carry more large, rare CNVs than males. Given that many schizophrenia datasets do not have equal gender ratios in cases and controls, sex differences in CNV burden might have impacted on estimates of case-related CNV burden and also on associations to individual loci. In a sample of 13,276 cases and 17,863 controls, we observed a small but significant excess of large (≥500 Kb), rare (

Cognitive impairment is a core feature of schizophrenia but there is limited understanding of the genetic relationship between cognition in the general population and schizophrenia. We examine how common variants associated with schizophrenia en masse contribute to childhood cognitive ability in a population-based sample, and the extent to which common genetic variants associated with childhood cognition explain variation in schizophrenia. Schizophrenia polygenic risk scores were derived from the Psychiatric Genomics Consortium (n = 69 516) and tested for association with IQ, attention, processing speed, working memory, problem solving, and social cognition in over 5000 children aged 8 from the Avon Longitudinal Study of Parents and Children birth cohort. Polygenic scores for these cognitive domains were tested for association with schizophrenia in a large UK schizophrenia sample (n = 11 853). Bivariate genome-wide complex trait analysis (GCTA) estimated the amount of shared genetic factors between schizophrenia and cognitive domains. Schizophrenia polygenic risk score was associated with lower performance IQ (P = .001) and lower full IQ (P = .013). Polygenic score for performance IQ was associated with increased risk for schizophrenia (P = 3.56E-04). Bivariate GCTA revealed moderate genetic correlation between schizophrenia and both performance IQ (r G = −.379, P = 6.62E-05) and full IQ (r G = −.202, P = 5.00E-03), with approximately 14% of the genetic component of schizophrenia shared with that for performance IQ. Our results support the presence of shared common genetic factors between schizophrenia and childhood cognitive ability. We observe a genetic relationship between schizophrenia and performance IQ but not verbal IQ or other cognitive variables, which may have implications for studies utilizing cognitive endophenotypes for psychosis.

Duplications at 15q11.2-q13.3 overlapping the Prader-Willi/Angelman syndrome (PWS/AS) region have been associated with developmental delay (DD), autism spectrum disorder (ASD) and schizophrenia (SZ). Due to presence of imprinted genes within the region, the parental origin of these duplications may be key to the pathogenicity. Duplications of maternal origin are associated with disease, whereas the pathogenicity of paternal ones is unclear. To clarify the role of maternal and paternal duplications, we conducted the largest and most detailed study to date of parental origin of 15q11.2-q13.3 interstitial duplications in DD, ASD and SZ cohorts. We show, for the first time, that paternal duplications lead to an increased risk of developing DD/ASD/multiple congenital anomalies (MCA), but do not appear to increase risk for SZ. The importance of the epigenetic status of 15q11.2-q13.3 duplications was further underlined by analysis of a number of families, in which the duplication was paternally derived in the mother, who was unaffected, whereas her offspring, who inherited a maternally derived duplication, suffered from psychotic illness. Interestingly, the most consistent clinical characteristics of SZ patients with 15q11.2-q13.3 duplications were learning or developmental problems, found in 76% of carriers. Despite their lower pathogenicity, paternal duplications are less frequent in the general population with a general population prevalence of 0.0033% compared to 0.0069% for maternal duplications. This may be due to lower fecundity of male carriers and differential survival of embryos, something echoed in the findings that both types of duplications are de novo in just over 50% of cases. Isodicentric chromosome 15 (idic15) or interstitial triplications were not observed in SZ patients or in controls. Overall, this study refines the distinct roles of maternal and paternal interstitial duplications at 15q11.2-q13.3, underlining the critical importance of maternally expressed imprinted genes in the contribution of Copy Number Variants (CNVs) at this interval to the incidence of psychotic illness. This work will have tangible benefits for patients with 15q11.2-q13.3 duplications by aiding genetic counseling., Author Summary The genetic interval 15q11.2-q13.3 on human chromosome 15 contains several so-called “imprinted genes” which are subject to epigenetic marking leading to activity from only one parental copy. This is in contrast to non-imprinted genes, whose activity is independent of their parent-of-origin. Deletions affecting the 15q11.2-q13.3 interval cause Prader-Willi and Angelman syndromes (PWS/AS), depending on whether the deletions are paternally or maternally derived respectively. Duplications at the PWS/AS interval region may also lead to neurodevelopmental disorders, including developmental delay (DD), autism spectrum disorder (ASD) and schizophrenia (SZ). Due to presence of imprinted genes within the region, the parental origin of these duplications may be key to the pathogenicity. We show, for the first time, that paternal duplications lead to an increased risk of developing DD/ASD/multiple congenital anomalies (MCA) but, unlike maternal duplication, do not appear to increase risk for SZ. This study refines the distinct roles of maternal and paternal duplications at 15q11.2-q13.3, underlining the critical importance of maternally active imprinted genes in the contribution to the incidence of psychotic illness. This work will have tangible benefits for patients with 15q11.2-q13.3 duplications by aiding genetic counseling.

To access publisher full text version of this article. Please click on the hyperlink in Additional Links field. OBJECTIVE: Rare copy number variants have been implicated in different neurodevelopmental disorders, with the same copy number variants often increasing risk of more than one of these phenotypes. In a discovery sample of 22 schizophrenia patients with an early onset of illness (10-15 years of age), the authors observed in one patient a maternally derived 15q11-q13 duplication overlapping the Prader-Willi/Angelman syndrome critical region. This prompted investigation of the role of 15q11-q13 duplications in psychotic illness. METHOD: The authors scanned 7,582 patients with schizophrenia or schizoaffective disorder and 41,370 comparison subjects without known psychiatric illness for copy number variants at 15q11-q13 and determined the parental origin of duplications using methylation-sensitive Southern hybridization analysis. RESULTS: Duplications were found in four case patients and five comparison subjects. All four case patients had maternally derived duplications (0.05%), while only three of the five comparison duplications were maternally derived (0.007%), resulting in a significant excess of maternally derived duplications in case patients (odds ratio=7.3). This excess is compatible with earlier observations that risk for psychosis in people with Prader-Willi syndrome caused by maternal uniparental disomy is much higher than in those caused by deletion of the paternal chromosome. CONCLUSIONS: These findings suggest that the presence of two maternal copies of a fragment of chromosome 15q11.2-q13.1 that overlaps with the Prader-Willi/Angelman syndrome critical region may be a rare risk factor for schizophrenia and other psychoses. Given that maternal duplications of this region are among the most consistent cytogenetic observations in autism, the findings provide further support for a shared genetic etiology between autism and psychosis. Lundbeck Foundation R34-A3243 Danish National Advanced Technology Foundation 001-2009-2 Danish Council for Independent Research in Medical Sciences Danish Psychiatric Research Foundation European Union (EU) LSHM-CT-2006-037761 HEALTH-2007-2.2.1-10-223423 IMI-JU-NewMeds PIAP-GA-2008-218251 National Institutes of Health MH071425 National Genomic Network (NGFNplus) of the German Federal Ministry of Education and Research (BMBF Wellcome Trust, London Medical Research Council, London info:eu-repo/grantAgreement/EC/FP7/218251

Genetic associations involving both rare and common alleles have been reported for schizophrenia but there have been no systematic scans for rare recessive genotypes using fully phased trio data. Here, we use exome sequencing in 604 schizophrenia proband–parent trios to investigate the role of recessive (homozygous or compound heterozygous) nonsynonymous genotypes in the disorder. The burden of recessive genotypes was not significantly increased in probands at either a genome-wide level or in any individual gene after adjustment for multiple testing. At a system level, probands had an excess of nonsynonymous compound heterozygous genotypes (minor allele frequency, MAF ⩽1%) in voltage-gated sodium channels (VGSCs; eight in probands and none in parents, P=1.5 × 10−4). Previous findings of multiple de novo loss-of-function mutations in this gene family, particularly SCN2A, in autism and intellectual disability provide biological and genetic plausibility for this finding. Pointing further to the involvement of VGSCs in schizophrenia, we found that these genes were enriched for nonsynonymous mutations (MAF ⩽0.1%) in cases genotyped using an exome array, (5585 schizophrenia cases and 8103 controls), and that in the trios data, synaptic proteins interacting with VGSCs were also enriched for both compound heterozygosity (P=0.018) and de novo mutations (P=0.04). However, we were unable to replicate the specific association with compound heterozygosity at VGSCs in an independent sample of Taiwanese schizophrenia trios (N=614). We conclude that recessive genotypes do not appear to make a substantial contribution to schizophrenia at a genome-wide level. Although multiple lines of evidence, including several from this study, suggest that rare mutations in VGSCs contribute to the disorder, in the absence of replication of the original findings regarding compound heterozygosity, this conclusion requires evaluation in a larger sample of trios.

In this study, Proitsi and colleagues use a Mendelian randomization approach to dissect the causal nature of the association between circulating lipid levels and late onset Alzheimer's Disease (LOAD) and find that genetic predisposition to increased plasma cholesterol and triglyceride lipid levels is not associated with elevated LOAD risk. Please see later in the article for the Editors' Summary, Background Although altered lipid metabolism has been extensively implicated in the pathogenesis of Alzheimer disease (AD) through cell biological, epidemiological, and genetic studies, the molecular mechanisms linking cholesterol and AD pathology are still not well understood and contradictory results have been reported. We have used a Mendelian randomization approach to dissect the causal nature of the association between circulating lipid levels and late onset AD (LOAD) and test the hypothesis that genetically raised lipid levels increase the risk of LOAD. Methods and Findings We included 3,914 patients with LOAD, 1,675 older individuals without LOAD, and 4,989 individuals from the general population from six genome wide studies drawn from a white population (total n = 10,578). We constructed weighted genotype risk scores (GRSs) for four blood lipid phenotypes (high-density lipoprotein cholesterol [HDL-c], low-density lipoprotein cholesterol [LDL-c], triglycerides, and total cholesterol) using well-established SNPs in 157 loci for blood lipids reported by Willer and colleagues (2013). Both full GRSs using all SNPs associated with each trait at p, Editors' Summary Background Currently, about 44 million people worldwide have dementia, a group of brain disorders characterized by an irreversible decline in memory, communication, and other “cognitive” functions. Dementia mainly affects older people and, because people are living longer, experts estimate that more than 135 million people will have dementia by 2050. The commonest form of dementia is Alzheimer disease. In this type of dementia, protein clumps called plaques and neurofibrillary tangles form in the brain and cause its degeneration. The earliest sign of Alzheimer disease is usually increasing forgetfulness. As the disease progresses, affected individuals gradually lose their ability to deal with normal daily activities such as dressing. They may become anxious or aggressive or begin to wander. They may also eventually lose control of their bladder and of other physical functions. At present, there is no cure for Alzheimer disease although some of its symptoms can be managed with drugs. Most people with the disease are initially cared for at home by relatives and other unpaid carers, but many patients end their days in a care home or specialist nursing home. Why Was This Study Done? Several lines of evidence suggest that lipid metabolism (how the body handles cholesterol and other fats) is altered in patients whose Alzheimer disease develops after the age of 60 years (late onset Alzheimer disease, LOAD). In particular, epidemiological studies (observational investigations that examine the patterns and causes of disease in populations) have found an association between high amounts of cholesterol in the blood in midlife and the risk of LOAD. However, observational studies cannot prove that abnormal lipid metabolism (dyslipidemia) causes LOAD. People with dyslipidemia may share other characteristics that cause both dyslipidemia and LOAD (confounding) or LOAD might actually cause dyslipidemia (reverse causation). Here, the researchers use “Mendelian randomization” to examine whether lifetime changes in lipid metabolism caused by genes have a causal impact on LOAD risk. In Mendelian randomization, causality is inferred from associations between genetic variants that mimic the effect of a modifiable risk factor and the outcome of interest. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. So, if dyslipidemia causes LOAD, genetic variants that affect lipid metabolism should be associated with an altered risk of LOAD. What Did the Researchers Do and Find? The researchers investigated whether genetic predisposition to raised lipid levels increased the risk of LOAD in 10,578 participants (3,914 patients with LOAD, 1,675 elderly people without LOAD, and 4,989 population controls) using data collected in six genome wide studies looking for gene variants associated with Alzheimer disease. The researchers constructed a genotype risk score (GRS) for each participant using genetic risk markers for four types of blood lipids on the basis of the presence of single nucleotide polymorphisms (SNPs, a type of gene variant) in their DNA. When the researchers used statistical methods to investigate the association between the GRS and LOAD among all the study participants, they found no association between the GRS and LOAD. What Do These Findings Mean? These findings suggest that the genetic predisposition to raised blood levels of four types of lipid is not causally associated with LOAD risk. The accuracy of this finding may be affected by several limitations of this study, including the small proportion of lipid variance explained by the GRS and the validity of several assumptions that underlie all Mendelian randomization studies. Moreover, because all the participants in this study were white, these findings may not apply to people of other ethnic backgrounds. Given their findings, the researchers suggest that the observed epidemiological associations between abnormal lipid levels in the blood and variation in lipid levels for reasons other than genetics, or to LOAD risk could be secondary to variation in lipid levels for reasons other than genetics, or to LOAD, a possibility that can be investigated by studying blood lipid levels and other markers of lipid metabolism over time in large groups of patients with LOAD. Importantly, however, these findings provide new information about the role of lipids in LOAD development that may eventually lead to new therapeutic and public-health interventions for Alzheimer disease. Additional Information Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001713. The UK National Health Service Choices website provides information (including personal stories) about Alzheimer's disease The UK not-for-profit organization Alzheimer's Society provides information for patients and carers about dementia, including personal experiences of living with Alzheimer's disease The US not-for-profit organization Alzheimer's Association also provides information for patients and carers about dementia and personal stories about dementia Alzheimer's Disease International is the international federation of Alzheimer disease associations around the world; it provides links to individual associations, information about dementia, and links to World Alzheimer Reports MedlinePlus provides links to additional resources about Alzheimer's disease (in English and Spanish) Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)

Importance Despite Alzheimer disease (AD) and Parkinson disease (PD) being clinically distinct entities, there is a possibility of a pathological overlap, with some genome-wide association (GWA) studies suggesting that the 2 diseases represent a biological continuum. The application of GWA studies to idiopathic forms of AD and PD have identified a number of loci that contain genetic variants that increase the risk of these disorders. Objective To assess the genetic overlap between PD and AD by testing for the presence of potentially pleiotropic loci in 2 recent GWA studies of PD and AD. Design Combined GWA analysis. Setting Data sets from the United Kingdom, Germany, France, and the United States. Participants Thousands of patients with AD or PD and their controls. Main Outcomes and Measures Meta-analysis of GWA studies of AD and PD. Methods To identify evidence for potentially pleiotropic alleles that increased the risk for both PD and AD, we performed a combined PD-AD meta-analysis and compared the results with those obtained in the primary GWA studies. We also tested for a net effect of potentially polygenic alleles that were shared by both disorders by performing a polygenic score analysis. Finally, we also performed a gene-based association analysis that was aimed at detecting genes that harbor multiple disease-causing single-nucleotide polymorphisms, some of which confer a risk of PD and some a risk of AD. Results Detailed interrogation of the single-nucleotide polymorphism, polygenic, and gene-based analyses resulted in no significant evidence that supported the presence of loci that increase the risk of both PD and AD. Conclusions and Relevance Our findings therefore imply that loci that increase the risk of both PD and AD are not widespread and that the pathological overlap could instead be “downstream” of the primary susceptibility genes that increase the risk of each disease.

International audience; BACKGROUND: The relationship between phoneme awareness, rapid automatized naming (RAN), verbal short-term/working memory (ST/WM) and diagnostic category is investigated in control and dyslexic children, and the extent to which this depends on orthographic complexity. METHODS: General cognitive, phonological and literacy skills were tested in 1,138 control and 1,114 dyslexic children speaking six different languages spanning a large range of orthographic complexity (Finnish, Hungarian, German, Dutch, French, English). RESULTS: Phoneme deletion and RAN were strong concurrent predictors of developmental dyslexia, while verbal ST/WM and general verbal abilities played a comparatively minor role. In logistic regression models, more participants were classified correctly when orthography was more complex. The impact of phoneme deletion and RAN-digits was stronger in complex than in less complex orthographies. CONCLUSIONS: Findings are largely consistent with the literature on predictors of dyslexia and literacy skills, while uniquely demonstrating how orthographic complexity exacerbates some symptoms of dyslexia.

GRB-associated binding protein 2 (GAB2) represents a compelling genome-wide association signal for late-onset Alzheimer's disease (LOAD) with reported odds ratios (ORs) ranging from 0.75-0.85. We tested eight GAB2 variants in four North American Caucasian case-control series (2,316 LOAD, 2,538 controls) for association with LOAD. Meta-analyses revealed ORs ranging from (0.61-1.20) with no significant association (all p>0.32). Four variants were hetergeneous across the populations (all p

We have conducted a genotyping study using a custom Illumina Infinium HD genotyping array, the ImmunoChip, in a new UK sample of 1218 bipolar disorder (BD) cases and 2913 controls that have not been used in any studies previously reported independently or in meta-analyses. The ImmunoChip was designed before the publication of the Psychiatric Genome-Wide Association Study Consortium Bipolar Disorder Working Group (PGC-BD) meta-analysis data. As such 3106 single-nucleotide polymorphisms (SNPs) with a P-value1 × 10(-3) from the BD meta-analysis by Ferreira et al. were genotyped. We report support for two of the three most strongly associated chromosomal regions in the Ferreira study, CACNA1C (rs1006737, P=4.09 × 10(-4)) and 15q14 (rs2172835, P=0.043) but not ANK3 (rs10994336, P=0.912). We have combined our ImmunoChip data (569 quasi-independent SNPs from the 3016 SNPs genotyped) with the recently published PGC-BD meta-analysis data, using either the PGC-BD combined discovery and replication data where available or just the discovery data where the SNP was not typed in a replication sample in PGC-BD. Our data provide support for two regions, at ODZ4 and CACNA1C, with prior evidence for genome-wide significant (GWS) association in PGC-BD meta-analysis. In addition, the combined analysis shows two novel GWS associations. First, rs7296288 (P=8.97 × 10(-9), odds ratio (OR)=0.9), an intergenic polymorphism on chromosome 12 located between RHEBL1 and DHH. Second, rs3818253 (P=3.88 × 10(-8), OR=1.16), an intronic SNP on chromosome 20q11.2 in the gene TRPC4AP, which lies in a high linkage disequilibrium region along with the genes GSS and MYH7B.

Background Attention-deficit/hyperactivity disorder (ADHD) can be viewed as the extreme end of traits in the general population. Epidemiological and twin studies suggest that ADHD frequently co-occurs with and shares genetic susceptibility with autism spectrum disorder (ASD) and ASD-related traits. The aims of this study were to determine whether a composite of common molecular genetic variants, previously found to be associated with clinically diagnosed ADHD, predicts ADHD and ASD-related traits in the general population. Methods Polygenic risk scores were calculated in the Avon Longitudinal Study of Parents and Children (ALSPAC) population sample (N = 8229) based on a discovery case-control genome-wide association study of childhood ADHD. Regression analyses were used to assess whether polygenic scores predicted ADHD traits and ASD-related measures (pragmatic language abilities and social cognition) in the ALSPAC sample. Polygenic scores were also compared in boys and girls endorsing any (rating ≥1) ADHD item (n = 3623). Results Polygenic risk for ADHD showed a positive association with ADHD traits (hyperactive-impulsive, p = .0039; inattentive, p = .037). Polygenic risk for ADHD was also negatively associated with pragmatic language abilities (p = .037) but not with social cognition (p = .43). In children with a rating ≥1 for ADHD traits, girls had a higher polygenic score than boys (p = .003). Conclusions These findings provide molecular genetic evidence that risk alleles for the categorical disorder of ADHD influence hyperactive-impulsive and attentional traits in the general population. The results further suggest that common genetic variation that contributes to ADHD diagnosis may also influence ASD-related traits, which at their extreme are a characteristic feature of ASD.

Background: Clozapine is uniquely effective in the management of treatment-resistant schizophrenia (TRS). However, a substantial proportion of patients discontinue treatment and this carries a poor prognosis. Methods: We investigated the risk factors, reasons and timing of clozapine discontinuation in a two-year retrospective cohort study of 316 patients with TRS receiving their first course of clozapine. Reasons for discontinuation of clozapine and duration of treatment were obtained from case notes and Cox regression was employed to test the association of baseline clinical factors with clozapine discontinuation.Results: A total of 142 (45%) patients discontinued clozapine within two years. By studying the reasons for discontinuations due to a patient decision, we found that adverse drug reactions (ADRs) accounted for over half of clozapine discontinuations. Sedation was the most common ADR cited as a reason for discontinuation and the risk of discontinuation due to ADRs was highest in the first few months of clozapine treatment. High levels of deprivation in the neighbourhood where the patient lived were associated with increased risk of clozapine discontinuation (HR = 2.12, 95% CI 1.30–3.47). Conclusions: Living in a deprived neighbourhood was strongly associated with clozapine discontinuation. Clinical management to reduce the burden of ADRs in the first few months of treatment may have a significant impact and help more patients experience the benefits of clozapine treatment.

BACKGROUND: Studies involving clinically recruited samples show that genetic liability to schizophrenia overlaps with that for several psychiatric disorders including bipolar disorder, major depression and, in a population study, anxiety disorder and negative symptoms in adolescence.AimsWe examined whether, at a population level, association between schizophrenia liability and anxiety disorders continues into adulthood, for specific anxiety disorders and as a group. We explored in an epidemiologically based cohort the nature of adult psychopathology sharing liability to schizophrenia. METHOD: Schizophrenia polygenic risk scores (PRSs) were calculated for 590 European-descent individuals from the Christchurch Health and Development Study. Logistic regression was used to examine associations between schizophrenia PRS and four anxiety disorders (social phobia, specific phobia, panic disorder and generalised anxiety disorder), schizophrenia/schizophreniform disorder, manic/hypomanic episode, alcohol dependence, major depression, and - using linear regression - total number of anxiety disorders. A novel population-level association with hypomania was tested in a UK birth cohort (Avon Longitudinal Study of Parents and Children). RESULTS: Schizophrenia PRS was associated with total number of anxiety disorders and with generalised anxiety disorder and panic disorder. We show a novel population-level association between schizophrenia PRS and manic/hypomanic episode. CONCLUSIONS: The relationship between schizophrenia liability and anxiety disorders is not restricted to psychopathology in adolescence but is present in adulthood and specifically linked to generalised anxiety disorder and panic disorder. We suggest that the association between schizophrenia liability and hypomanic/manic episodes found in clinical samples may not be due to bias.Declarations of interestNone.

This study was funded by the Medical Research Council, the European Community’s Seventh Framework Program grant [agreement HEALTH-F2-2009-241909 (Project EU-GEI)], São Paulo Research Foundation (grant 2012/0417-0), the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust and King’s College London, the NIHR BRC at University College London and the Wellcome Trust (grant 101272/Z/12/Z)., Tripoli, G., Quattrone, D., Ferraro, L., Gayer-Anderson, C., Rodriguez, V., La Cascia, C., La Barbera, D., Sartorio, C., Seminerio, F., Tarricone, I., Berardi, D., Szöke, A., Arango, C., Tortelli, A., Llorca, P.-M., De Haan, L., Velthorst, E., Bobes, J., Bernardo, M., Sanjuán, J., Santos, J.L., Arrojo, M., Del-Ben, C.M., Menezes, P.R., Selten, J.-P., Jones, P.B., Jongsma, H.E., Kirkbride, J.B., Lasalvia, A., Tosato, S., Richards, A., O'donovan, M., Rutten, B.P.F., Os, J.V., Morgan, C., Sham, P.C., Murray, R.M., Murray, G.K., Di Forti, M.

Genome‐wide association studies (GWASs) are highly effective at identifying common risk variants for schizophrenia. Rare risk variants are also important contributors to schizophrenia etiology but, with the exception of large copy number variants, are difficult to detect with GWAS. Exome and genome sequencing, which have accelerated the study of rare variants, are expensive so alternative methods are needed to aid detection of rare variants. Here we re‐analyze an Irish schizophrenia GWAS dataset (n = 3,473) by performing identity‐by‐descent (IBD) mapping followed by exome sequencing of individuals identified as sharing risk haplotypes to search for rare risk variants in coding regions. We identified 45 rare haplotypes (>1 cM) that were significantly more common in cases than controls. By exome sequencing 105 haplotype carriers, we investigated these haplotypes for functional coding variants that could be tested for association in independent GWAS samples. We identified one rare missense variant in PCNT but did not find statistical support for an association with schizophrenia in a replication analysis. However, IBD mapping can prioritize both individual samples and genomic regions for follow‐up analysis but genome rather than exome sequencing may be more effective at detecting risk variants on rare haplotypes. National Institutes of Health, Grant/Award Numbers: R01‐MH041953, R01‐MH083094; Science Foundation Ireland, Grant/Award Numbers: 08/IN.1/B1916, 12/IP/1359, 12/IP/1670; Wellcome Trust, Grant/Award Number: 085475/B/08/Z peer-reviewed