Psychosis is a complex and heterogeneous neuropsychiatric condition characterized by a loss of contact with reality, whose symptoms can include delusions and hallucinations. Episodic psychosis and altered cognitive function are major features of schizophrenia, a severe neurodevelopmental disorder that contributes significantly to the global burden of disease (Whiteford et al., 2013). Schizophrenia is highly heritable (Hilker et al., 2018; Sullivan et al., 2003) and recent genetic studies have indicated a complex polygenic architecture involving hundreds of genetic variants that individually confer a minimal increase on the overall risk of developing the disorder (Purcell et al., 2009). Large-scale genome-wide association studies (GWAS) have identified approximately 160 regions of the genome harboring common variants robustly associated with the diagnosis of schizophrenia, with evidence for a substantial polygenic component in signals that individually fall below genome-wide levels of significance (Pardiñas et al., 2018; Ripke et al., 2014). As the majority of schizophrenia-associated variants do not directly index coding changes affecting protein structure, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is dysregulated (Maurano et al., 2012).

A major hypothesis is that GWAS variants predominantly act to influence the regulation of gene expression. This hypothesis is supported by an enrichment of schizophrenia-associated variants in core regulatory domains (e.g., active promotors and enhancers) (Hannon et al., 2019a). As a consequence, there has been growing interest in the role of epigenetic variation in the molecular etiology of schizophrenia. DNA methylation is the best-characterized epigenetic modification, acting to influence gene expression via disruption of transcription factor binding and recruitment of methyl-binding proteins that initiate chromatin compaction and gene silencing. Despite being traditionally regarded as a mechanism of transcriptional repression, DNA methylation is actually associated with both increased and decreased gene expression (Wagner et al., 2014) and other genomic functions including alternative splicing and promoter usage (Maunakea et al., 2010). We previously demonstrated how DNA methylation is under local genetic control (Hannon et al., 2018a; Hannon et al., 2016b), identifying an enrichment of DNA methylation quantitative trait loci (mQTL) among genomic regions associated with schizophrenia (Hannon et al., 2016b). Furthermore, we have used mQTL associations to identify discrete sites of regulatory variation associated with schizophrenia risk variants implicating specific genes within these regions (Hannon et al., 2016a; Hannon et al., 2018a; Hannon et al., 2016b; Hannon et al., 2017). Of note, epigenetic variation induced by environmental exposures has been hypothesized as another mechanism by which non-genetic factors can affect risk for neuropsychiatric disorders including schizophrenia (Dempster et al., 2013).

The development of standardized assays for quantifying DNA methylation at specific sites across the genome has enabled the systematic analysis of associations between methylomic variation and environmental exposures or diseases (Murphy and Mill, 2014). Because DNA methylation is a dynamic process, these epigenome-wide association studies (EWAS) are more complex to design and interpret than GWAS (Mill and Heijmans, 2013; Rakyan et al., 2011; Relton and Davey Smith, 2010). As for observational epidemiological studies of exposures and outcomes, a number of potentially important confounding factors (e.g., tissue or cell type, age, sex, lifestyle exposures, medication, and disorder-associated exposures) that can directly influence DNA methylation need to be considered along with the possibility of reverse causation. Despite these difficulties, recent studies have identified schizophrenia-associated DNA methylation differences in analyses of post-mortem brain tissue (Jaffe et al., 2016; Pidsley et al., 2014; Viana et al., 2017; Wockner et al., 2014), and also detected disease-associated variation in peripheral blood samples from both schizophrenia-discordant monozygotic twin pairs (Dempster et al., 2011) and clinically ascertained case-control cohorts (Aberg et al., 2014; Hannon et al., 2016a; Kinoshita et al., 2014). We previously reported an EWAS of variable DNA methylation associated with schizophrenia in >1700 individuals, meta-analyzing data from three independent cohorts and identifying methylomic biomarkers of disease (Hannon et al., 2016a). Together these data support a role for differential DNA methylation in the molecular etiology of schizophrenia, although it is not clear whether disease-associated methylation differences are themselves secondary to the disorder itself or a result of other schizophrenia-associated factors.

In this study we extend our previous analysis, quantifying DNA methylation across the genome in a total of 4483 participants from seven independent case-control cohorts including patients with schizophrenia or first-episode psychosis (FEP) (Figure 1). This represents the largest EWAS of schizophrenia and psychosis, and one of the largest case-control studies of DNA methylation for any disease phenotype. In each cohort, genomic DNA was isolated from whole blood, and DNA methylation was quantified across the genome using either the Illumina Infinium HumanMethylation450 microarray (‘450K array’) or the HumanMethylationEPIC microarray (‘EPIC array’) (see 'Materials and methods'). We implemented a stringent pipeline to meta-analyze EWAS results across datasets to identify associations between psychosis cases and variation in DNA methylation. We show how DNA methylation data can be leveraged to identify biological (e.g., differential cell counts) and environmental (e.g., smoking) factors associated with psychosis, and present evidence for molecular variation associated with clozapine exposure in patients with treatment-resistant schizophrenia (TRS).

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We report the most comprehensive study of methylomic variation associated with psychosis and schizophrenia, profiling DNA methylation across the genome in peripheral blood samples from 2379 cases and 2104 controls. We show how DNA methylation data can be leveraged to derive measures of blood cell counts and smoking that are associated with psychosis. Using a stringent pipeline to meta-analyze EWAS results across datasets, we identify novel DMPs associated with both psychosis and a more refined diagnosis of schizophrenia. Of note, we show evidence for the colocalization of genetic associations for schizophrenia and differential DNA methylation. Finally, we present evidence for differential methylation associated with TRS, potentially reflecting differences in DNA methylation associated with exposure to the atypical antipsychotic drug clozapine.

We identify robust psychosis-associated differences in cellular composition estimates derived from DNA methylation data, with cases having increased proportions of monocytes and granulocytes and decreased proportions of natural killer cells, CD4⁺ T-cells and CD8⁺ T-cells, compared to non-psychiatric controls. This analysis extends previous work based on a subset of these data, which reported a decrease in the proportion of natural killer cells and increase in the proportion of granulocytes in schizophrenia patients, with the large number of samples enabling us to identify additional associations with other cell types. We also confirm findings from an independent study of schizophrenia which reported significantly increased proportions of granulocytes and monocytes, and decreased proportions of CD8⁺ T-cells using estimates derived from DNA methylation data (Montano et al., 2016). Of note, because we can only derive proportion of cell types from whole blood DNA methylation data, and not actual counts, an increase in one or more cell types must be balanced by a decrease in one or more other cell types and an apparent change in the proportion of one specific cell type does not mean that the actual abundance of that cell type is altered. Despite this, the results from DNA methylation-derived cell proportions are consistent with previous studies based on empirical cell abundance measures which have reported increased monocyte counts (Beumer et al., 2012; Moody and Miller, 2018), increased neutrophil counts (Garcia-Rizo et al., 2019; Núñez et al., 2019), increased monocyte-to-lymphocyte ratio (Mazza et al., 2020; Steiner et al., 2019), and increased neutrophil-to-lymphocyte ratio (Karageorgiou et al., 2019; Mazza et al., 2020) in both schizophrenia and FEP patients compared to controls. Previous studies have also shown that higher neutrophil counts in schizophrenia patients correlate with a greater burden of positive symptoms (Núñez et al., 2019) suggesting that variations in the number of neutrophils is a potential marker of disease severity (Steiner et al., 2019). Our sub-analysis of TRS, which is associated with a higher number of positive symptoms (Bachmann et al., 2017), found that the increase in granulocytes was primarily driven by those with the more severe phenotype, supporting this hypothesis. Importantly, the differences we observe may actually reflect the effects of various antipsychotic medications that have been previously shown to influence cell proportions in blood (Steiner et al., 2019) or a recruitment bias whereby patients with low levels of granulocytes are not prescribed clozapine given the risk of agranulocytosis.

We also identified a highly significant increase in a DNA methylation-derived smoking score in patients with schizophrenia, replicating our previous finding (Hannon et al., 2016a). The smoking score captures multiple aspects of tobacco smoking behavior including both current smoking status and the quantity of cigarettes smoked; our results therefore reflect existing epidemiological evidence demonstrating that schizophrenia patients not only smoke more, but also smoke more heavily (de Leon et al., 2002; de Leon and Diaz, 2005; McClave et al., 2010). We also report an increased smoking score in patients with FEP, although not to the same extent as seen in schizophrenia, consistent with a meta-analysis reporting high levels of smoking in FEP (Myles et al., 2012). In the subset of treatment-resistant patients, we found that there was an additional increase in smoking score relative to schizophrenia cases prescribed alternative medications, supporting evidence for higher rates of smoking in TRS groups relative to treatment-responsive schizophrenia patients (Kennedy et al., 2014). These results not only highlight physiological (i.e., cell proportions) and environmental (i.e., smoking) differences associated with psychosis and schizophrenia and the utility of DNA methylation data for deriving these variables in epidemiological studies, but also highlight the importance of controlling for these differences as potential confounders in analyses of disease-associated DNA methylation differences.

Our EWAS, building on our previous analysis on a subset of the sample cohorts profiled here (Hannon et al., 2016a), identified 95 DMPs associated with psychosis that are robust to differences in measured smoking exposure and heterogeneity in blood cellular composition derived from DNA methylation data. Of note, we identified a dramatic increase in sites characterized by an increase in DNA methylation in patients. A key strength of our study is the inclusion of the full spectrum of schizophrenia diagnoses, from FEP through to treatment-resistant cases prescribed clozapine. While this may introduce heterogeneity into our primary analyses, we used a random effects meta-analysis to identify consistent effects across all cohorts and diagnostic subtypes. We also performed an additional analysis focused specifically on cases with a more refined diagnosis of schizophrenia excluding those with FEP, which identified over 1000 DMPs. A number of the top-ranked DMPs are annotated to genes that have a direct relevance to the etiology of schizophrenia, and GO analysis highlighted multiple pathways previously implicated in schizophrenia, including several related to the extracellular matrix (Berretta, 2012) and cell-cell adhesion (O'Dushlaine et al., 2011). Given the known genetic component to the etiology of schizophrenia, it is interesting that schizophrenia-associated DMPs were found to colocalize to several regions nominated by genetic association studies. Our results suggest that this analysis of a more specific phenotype in a smaller number of samples is potentially more powerful and that schizophrenia cases have a more discrete molecular phenotype that might reflect both etiological factors but also factors associated with a diagnosis of schizophrenia (e.g., medications, stress, etc.). The mean difference in DNA methylation between cases and controls for both psychosis and schizophrenia was small, consistent with other blood-based EWAS of schizophrenia (Montano et al., 2016) and complex traits (Hannon et al., 2018c; Hannon et al., 2019b; Marioni et al., 2018) in general. While individually they may be too small to have a strong predictive power as a biomarker, together they may have utility as a molecular classifier (Chen et al., 2020).

To explore whether schizophrenia-associated differences in DNA methylation are present before a formal diagnosis of schizophrenia, we also performed an EWAS of individuals with FEP. Strikingly, the majority of our schizophrenia-associated DMPs were found to have a consistent direction of effect in the EWAS of individuals with FEP. While this result is consistent with schizophrenia-associated differences being present prior to a formal diagnosis of schizophrenia, it is not sufficient to state that they are causal; they may still reflect some underlying environmental risk factors or be a consequence of having FEP (e.g., medication exposure or other psychiatric condition). Further work is needed to explore the extent to which the DMPs associated with psychosis and schizophrenia in this meta-analysis might have a causal role in disease.

Finally, we also report the first systematic analysis of individuals with TRS, identifying seven DMPs at which differential DNA methylation was significantly different in the subset of schizophrenia cases prescribed clozapine. These data are informative for the interpretation of our schizophrenia-associated differences, because a number of these DMPs are driven by the subset of patients on clozapine. Furthermore, a number of sites show opposite effects in our analyses of TRS vs. our analysis of schizophrenia, suggesting they might represent important differences between diagnostic groups. Because the prescription of clozapine is generally only undertaken in patients with TRS, we are unable to separate the effects of clozapine exposure from differences associated with a more severe sub-type of schizophrenia such as the influence of polypharmaceutical treatment.

Our results should be considered in light of a number of important limitations. First, our analyses were constrained by the technical limitations of the Illumina 450K and EPIC arrays, which assay only ~ 3% of CpG sites in the genome. Second, this is a cross-sectional study and it was not possible to distinguish cause from effect. It is possible, and indeed likely, for example, that the differences associated with both schizophrenia and TRS reflect the effects of medication exposure or other consequences of having schizophrenia, for example, living more stressful lives, poorer diet, and health. The importance of such confounding variables is demonstrated by our findings of differential smoking score and blood cell proportions derived directly from the DNA methylation data, although these examples also highlight the potential utility of such effects for molecular epidemiology. Third, although our aim was not to make inferences about mechanistic changes in the brain associated with psychosis, it is important to note that our study analyzed DNA methylation profiled in peripheral blood and therefore can provide only limited information about variation in the primary tissue associated with disease (Hannon et al., 2015). Although this limits mechanistic conclusions about the role of DNA methylation in schizophrenia, biomarkers, by definition, need to be measured in an easily accessible tissue and do not need to reflect the underlying pathogenic process. Furthermore, because most classifiers used to quantify variables such as smoking exposure and age have been trained in blood, this represents the optimal tissue in which to derive these measures. Of course, blood may also be an appropriate choice for investigating medication effects, particularly given the known effects on white blood cell counts associated with taking clozapine (Alvir et al., 1993). Fourth, while we have explored the potential effects of clozapine on DNA methylation by assessing a sub-group of individuals with TRS, this is just one of a range of antipsychotics schizophrenia and psychosis patients are prescribed. The fact that the TRS group show more extreme differences for many of the schizophrenia-associated DMPs suggests that the polypharmaceutical treatment regimens often prescribed to schizophrenia patients may produce specific DNA methylation signatures in patients, akin to the effect seen for smoking. Fifth, although we found no evidence for a significant interaction between sex and DNA methylation at DMPs associated with schizophrenia, it is possible that there are other DNA methylation differences associated with disease only in males or females. Finally, although we found some evidence that schizophrenia-associated DMPs colocalize to regions nominated by GWAS, the integration of our DNA methylation data with genetic data was beyond the scope of this analysis. Of note, we have previously used mQTL associations to identify discrete sites of regulatory variation associated with schizophrenia risk variants to prioritize specific genes within broad GWAS regions (Hannon et al., 2016a; Hannon et al., 2018a; Hannon et al., 2016b; Hannon et al., 2017), and future work will aim to further explore interactions between genetic and epigenetic risk factors.

In conclusion, our analysis of 4483 participants represents the largest study of blood-based DNA methylation in schizophrenia and psychosis yet performed, and one of the largest EWAS studies for any disease phenotype. Our study also includes the first within-case analysis of TRS yet performed, providing important molecular insights into genomic differences associated with poor outcome to standard therapeutic approaches. Our results highlight differences in measures of blood cellular composition and smoking behavior derived from methylomic data between not just cases and controls, but also between TRS patients prescribed clozapine and those prescribed alternative medications. We report widespread differences in DNA methylation in psychosis and schizophrenia, a subset of which are driven by the more severe treatment-resistant subset of patients. On a practical level, our study not only demonstrates the utility of DNA methylation data for deriving measures of specific physiological phenotypes (e.g., blood cell-type proportions) and environmental exposures (e.g., exposure to tobacco smoke) that can be used to identify epidemiological associations with health and disease, but also highlights the importance of properly controlling for these potential confounders in EWAS analyses. Our results are important because they suggest there are also clear molecular signatures of schizophrenia and psychosis that can be identified in whole blood DNA. Although it is unlikely these differences are mechanistically related to neuropathological changes in the brain, they may have utility as diagnostic and prognostic biomarkers in individuals with FEP and may potentially be used to differentiate individuals with TRS at an early stage of disease. Future work should aim to prospectively profile DNA methylation in individuals at risk for FEP and schizophrenia to explore how methylomic variation at baseline can predict outcome and the extent to which longitudinal changes at psychosis-associated DMPs map on to clinical trajectories.

Raw and processed data for the UCL, Aberdeen and Dublin cohorts are available through GEO accession numbers GSE84727, GSE80417, and GSE147221, respectively.

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Author details

1. Eilis Hannon

   University of Exeter Medical School, University of Exeter, Barrack Road, Exeter, United Kingdom

   Contribution

   Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6840-072X

2. Emma L Dempster

   University of Exeter Medical School, University of Exeter, Barrack Road, Exeter, United Kingdom

   Contribution

   Resources, Data curation, Formal analysis, Validation, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Georgina Mansell

   University of Exeter Medical School, University of Exeter, Barrack Road, Exeter, United Kingdom

   Contribution

   Data curation, Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2620-1786

4. Joe Burrage

   University of Exeter Medical School, University of Exeter, Barrack Road, Exeter, United Kingdom

   Contribution

   Resources, Data curation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

5. Nick Bass

   Division of Psychiatry, University College London, London, United Kingdom

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

6. Marc M Bohlken

   Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan, Utrecht, Netherlands

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

7. Aiden Corvin

   Department of Psychiatry and Neuropsychiatric Genetics Research Group, Trinity Translational Medicine Institute, Trinity College Dublin, St. James Hospital, Dublin, Ireland

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

8. Charles J Curtis

   1. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
   2. NIHR BioResource Centre Maudsley, South London and Maudsley NHS Foundation Trust (SLaM), King’s College London, London, United Kingdom

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

9. David Dempster

   Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

10. Marta Di Forti

    1. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
    2. South London and Maudsley NHS Mental Health Foundation Trust, London, United Kingdom
    3. National Institute for Health Research (NIHR), Mental Health Biomedical Research Centre, South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom

    Contribution

    Resources, Writing - review and editing

    Competing interests

    M Di Forti reports personal fees from Janssen, outside the submitted work.

11. Timothy G Dinan

    APC Microbiome Ireland, University College Cork, Cork, Ireland

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

12. Gary Donohoe

    Centre for Neuroimaging and Cognitive Genomics (NICOG), School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, Galway, Ireland

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

13. Fiona Gaughran

    1. Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
    2. National Psychosis Service, South London and Maudsley NHS Foundation Trust, London, United Kingdom

    Contribution

    Resources, Writing - review and editing

    Competing interests

    Fiona Gaughran has received honoraria from Lundbeck, Otsuka, and Sunovion. She has a family member with professional links to Lilly and GSK, including shares.

14. Michael Gill

    Department of Psychiatry and Neuropsychiatric Genetics Research Group, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, Ireland

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

15. Amy Gillespie

    1. Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
    2. Department of Psychiatry, Medical Sciences Division, University of Oxford, Oxford, United Kingdom

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

16. Cerisse Gunasinghe

    Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

17. Hilleke E Hulshoff

    Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

18. Christina M Hultman

    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

19. Viktoria Johansson

    1. Department of Medical Epidemiology and Biostatistics Sweden, Karolinska Institutet, Stockholm, Sweden
    2. Department of Clinical Neuroscience, Center for Psychiatry Research, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

20. René S Kahn

    1. Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
    2. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, United States

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

21. Jaakko Kaprio

    1. Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
    2. Department of Public Health, University of Helsinki, Helsinki, Finland

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3716-2455

22. Gunter Kenis

    Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

23. Kaarina Kowalec

    1. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
    2. College of Pharmacy, University of Manitoba, Winnipeg, Canada

    Contribution

    Resources, Writing - review and editing

    Competing interests

    Kaarina Kowalec has consulted with Emerald Lake Safety Ltd. (2017-2018) and has received speaker honoraria from Biogen/Fraser Health Multiple Sclerosis Clinic (2018). Both are unrelated to the work published here.

24. James MacCabe

    Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom

    Contribution

    Resources, Writing - review and editing

    Competing interests

    James MacCabe has received research funding from H Lundbeck.

25. Colm McDonald

    Centre for Neuroimaging and Cognitive Genomics (NICOG), School of Medicine, National University of Ireland Galway, Galway, Ireland

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

26. Andrew McQuillin

    1. Division of Psychiatry, University College London, London, United Kingdom
    2. Division of Psychiatry, University College London, London, United Kingdom

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1567-2240

27. Derek W Morris

    Centre for Neuroimaging and Cognitive Genomics (NICOG), School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, Galway, Ireland

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

28. Kieran C Murphy

    Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

29. Colette J Mustard

    Division of Biomedical Sciences, Institute of Health Research and Innovation, University of the Highlands and Islands, Inverness, United Kingdom

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5834-2765

30. Igor Nenadic

    1. Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
    2. Department of Psychiatry and Psychotherapy, Philipps University Marburg/ Marburg University Hospital UKGM, Marburg, Germany

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

31. Michael C O'Donovan

    MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom

    Contribution

    Resources, Writing - review and editing

    Competing interests

    Michael C O'Donovan is supported by a collaborative research grant from Takeda Pharmaceuticals. Takeda played no part in the conception, design, implementation, or interpretation of this study.

32. Diego Quattrone

    1. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
    2. South London and Maudsley NHS Mental Health Foundation Trust, London, United Kingdom

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

33. Alexander L Richards

    MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

34. Bart PF Rutten

    Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9834-6346

35. David St Clair

    The Institute of Medical Sciences, Univeristy of Aberdeen, Aberdeen, United Kingdom

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

36. Sebastian Therman

    Department of Public Health Solutions, Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9407-4905

37. Timothea Toulopoulou

    Department of Psychology and National Magnetic Resonance Research Center (UMRAM), Aysel Sabuncu Brain Research Centre (ASBAM), Bilkent University, Ankara, Turkey

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

38. Jim Van Os

    Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

39. John L Waddington

    Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

40. Wellcome Trust Case Control Consortium (WTCCC)

41. CRESTAR consortium

42. Patrick Sullivan

    1. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
    2. Departments of Genetics and Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, United States

    Contribution

    Resources, Writing - review and editing

    Competing interests

    PF Sullivan reports the following potentially competing financial interests. Current: Lundbeck (advisory committee, grant recipient). Past three years: Pfizer (scientific advisory board).

43. Evangelos Vassos

    Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

44. Gerome Breen

    1. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
    2. NIHR BioResource Centre Maudsley, South London and Maudsley NHS Foundation Trust (SLaM), King’s College London, London, United Kingdom

    Contribution

    Resources, Funding acquisition, Writing - review and editing

    Competing interests

    No competing interests declared

45. David Andrew Collier

    Neuroscience Genetics, Eli Lilly and Company, Surrey, United Kingdom

    Contribution

    Resources, Funding acquisition, Writing - review and editing

    Competing interests

    David A Collier is a full time employee and stockholder of Eli Lilly and Company.

46. Robin M Murray

    Department of Psychosis Studies, Institute of Psychiatry, King’s College London, London, United Kingdom

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Competing interests

    Robin M Murray reports personal fees from Janssen, Lundbeck, Sunovion, Recordati and Otsuka

47. Leonard S Schalkwyk

    School of Life Sciences, University of Essex, Colchester, United Kingdom

    Contribution

    Conceptualization, Resources, Funding acquisition, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

48. Jonathan Mill

    University of Exeter Medical School, University of Exeter, Barrack Road, Exeter, United Kingdom

    Contribution

    Conceptualization, Supervision, Funding acquisition, Methodology, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    j.mill@exeter.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1115-3224

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