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Genome-wide by Environment Interaction Study of Stressful Life Events and Hospital-Treated Depression in the iPSYCH2012 Sample

  • Nis P. Suppli
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Mental Health Center Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
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  • Klaus K. Andersen
    Affiliations
    Statistics and Pharmacoepidemiology, Danish Cancer Society Research Center, Copenhagen, Denmark
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  • Esben Agerbo
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    National Centre for Register-based Research, Department of Economics, Aarhus University, Aarhus, Denmark

    Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
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  • Veera M. Rajagopal
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Department of Biomedicine, Aarhus University, Aarhus, Denmark

    Center for Integrative Sequencing, Aarhus University, Aarhus, Denmark

    Center for Genomics and Personalized Medicine, Aarhus, Denmark
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  • Vivek Appadurai
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Institute of Biological Psychiatry, Copenhagen Mental Health Services, Copenhagen, Denmark
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  • Jonathan R.I. Coleman
    Affiliations
    Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom

    National Institute for Health Research Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
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  • Gerome Breen
    Affiliations
    Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom

    National Institute for Health Research Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
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  • Jonas Bybjerg-Grauholm
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Department for Congenital Disorders, Statens Serum Institute, Copenhagen, Denmark
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  • Marie Bækvad-Hansen
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Department for Congenital Disorders, Statens Serum Institute, Copenhagen, Denmark
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  • Carsten B. Pedersen
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    National Centre for Register-based Research, Department of Economics, Aarhus University, Aarhus, Denmark

    Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
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  • Marianne G. Pedersen
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    National Centre for Register-based Research, Department of Economics, Aarhus University, Aarhus, Denmark

    Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
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  • Wesley K. Thompson
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Division of Biostatistics, Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, California
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  • Trine Munk-Olsen
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    National Centre for Register-based Research, Department of Economics, Aarhus University, Aarhus, Denmark

    Department of Clinical Medicine, University of Southern Denmark, Odense, Denmark
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  • Michael E. Benros
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Mental Health Center Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark

    Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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  • Thomas D. Als
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Department of Biomedicine, Aarhus University, Aarhus, Denmark

    Center for Integrative Sequencing, Aarhus University, Aarhus, Denmark

    Center for Genomics and Personalized Medicine, Aarhus, Denmark
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  • Jakob Grove
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Department of Biomedicine, Aarhus University, Aarhus, Denmark

    Center for Integrative Sequencing, Aarhus University, Aarhus, Denmark

    Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark

    Center for Genomics and Personalized Medicine, Aarhus, Denmark
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  • Thomas Werge
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Institute of Biological Psychiatry, Copenhagen Mental Health Services, Copenhagen, Denmark
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  • Anders D. Børglum
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Department of Biomedicine, Aarhus University, Aarhus, Denmark

    Center for Integrative Sequencing, Aarhus University, Aarhus, Denmark

    Center for Genomics and Personalized Medicine, Aarhus, Denmark
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  • David M. Hougaard
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Department for Congenital Disorders, Statens Serum Institute, Copenhagen, Denmark
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  • Ole Mors
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Psychosis Research Unit, Aarhus University Hospital–Psychiatry, Aarhus
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  • Merete Nordentoft
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Mental Health Center Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
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  • Preben B. Mortensen
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    National Centre for Register-based Research, Department of Economics, Aarhus University, Aarhus, Denmark

    Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
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  • Katherine L. Musliner
    Correspondence
    Address correspondence to Katherine L. Musliner, Ph.D.
    Affiliations
    Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    National Centre for Register-based Research, Department of Economics, Aarhus University, Aarhus, Denmark

    Department of Clinical Medicine, Aarhus University, Aarhus, Denmark

    Department of Affective Disorders, Aarhus University Hospital–Psychiatry, Aarhus, Denmark
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Open AccessPublished:November 09, 2021DOI:https://doi.org/10.1016/j.bpsgos.2021.11.003

      Abstract

      Background

      Researchers have long investigated a hypothesized interaction between genetic risk and stressful life events in the etiology of depression, but studies on the topic have yielded inconsistent results.

      Methods

      We conducted a genome-wide by environment interaction study (GWEIS) in 18,532 patients with depression from hospital-based settings and 20,184 population controls. All individuals were drawn from the iPSYCH2012 case-cohort study, a nationally representative sample identified from Danish national registers. Information on stressful life events including family disruption, serious medical illness, death of a first-degree relative, parental disability, and child maltreatment was identified from the registers and operationalized as a time-varying count variable. Hazard ratios for main and interaction effects were estimated using Cox regressions weighted to accommodate the case-cohort design. Our replication sample included 22,880 depression cases and 50,378 controls from the UK Biobank.

      Results

      The GWEIS in the iPSYCH2012 sample yielded three novel, genome-wide–significant (p < 5 × 10−8) loci located in the ABCC1 gene (rs56076205, p = 3.7 × 10−10), the AKAP6 gene (rs3784187, p = 1.2 × 10−8), and near the MFSD1 gene (rs340315, p = 4.5 × 10−8). No hits replicated in the UK Biobank (rs56076205: p = .87; rs3784187: p = .93; rs340315: p = .71).

      Conclusions

      In this large, population-based GWEIS, we did not find any replicable hits for interaction. Future gene-by-stress research in depression should focus on establishing even larger collaborative GWEISs to attain sufficient power.

      Keywords

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      ). Research examining the interaction between polygenic risk scores and SLEs has also yielded inconsistent results, with some finding evidence for interaction (
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      ).
      The hypothesis-driven (i.e., candidate gene) approach for identifying specific variants associated with a given outcome has not been successful in psychiatric research (
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      ). This has led to the embrace of the genome-wide-association study (GWAS) as a method for identifying variants associated with psychiatric disorders in a theoretically agnostic fashion. In a GWAS, single nucleotide polymorphisms (SNPs) in sufficient linkage disequilibrium to tag the entire genome are tested for association with the outcome of interest. Significance is evaluated based on an adjusted alpha level to avoid false positive results. This method has been highly successful in psychiatric genetics and has led to the identification of over 100 variants associated with major depression at the genome-wide–significant alpha level (
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      To our knowledge, four prior studies have used this theoretically agnostic, genome-wide approach to evaluate whether individual genetic variants interact with SLEs as risk factors for depressive symptoms measured using symptoms scales including the Beck Depression Inventory, the General Health Questionnaire, and the Centers for Epidemiological Studies Depression Scale. Dunn et al. (
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      ) conducted a genome-wide by environment interaction study (GWEIS) of depressive symptoms in a sample of 7179 African American and 3138 Hispanic/Latina women. They identified one genome-wide–significant SNP in the African American sample near the CEP350 gene (rs4652467, p = 4.10 × 10−10); however, this association did not replicate. Ikeda et al. (
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      ) conducted a GWEIS of depressive symptoms and SLEs in 1088 individuals recruited from among employees of the Fujita Health University Hospital in Japan. The authors reported a significant interaction for a SNP near the BMP2 gene (rs10485715, p = 8.2 × 10−9); however, no attempts were made to replicate this result. Otowa et al. (
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      ) conducted a GWEIS of depressive symptoms and SLEs in 320 Japanese individuals, with no genome-wide–significant results. Most recently, Arnau-Soler et al. (
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      ) conducted GWEISs of depressive symptoms and SLEs in 4919 Europeans from the Generation Scotland cohort and 99,057 Europeans from the UK Biobank. The authors found two SNPs significant for interaction at the genome-wide level in the Generation Scotland sample: one near the PIWIL4 gene (p = 4.95 × 10−9) and one intronic to the ZCCHC2 gene (p = 1.46 × 10−8). They found no genome-wide–significant hits in the UK Biobank, and the significant hits from the Generation Scotland Sample did not replicate in the UK biobank.
      Most of these GWEISs had sample sizes that most likely left them underpowered to detect significant interaction results. In addition, the outcome of all of these studies was depressive symptoms, rather than clinically defined major depression. Although depressive symptoms are highly genetically correlated with major depressive disorder (
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      Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression.
      ), they nevertheless are a distinct outcome with, potentially, distinct associations with individual SNPs. Furthermore, all of these studies relied, out of necessity, on measures of SLEs that were retrospective and therefore potentially subject to recall bias (
      • Baldwin J.R.
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      ). Finally, prior GWEISs were not able to account for the time-dependent nature of both SLEs and depression. SLEs can occur at multiple points during the lifespan, and analytic strategies that fail to account for this can potentially be subject to bias. GWAS has traditionally used logistic regressions to calculate odds ratios for the associations between individual SNPs and the odds of being a case. However, this approach does not measure risk for developing the disorder, which is arguably more useful from a clinical and public health standpoint (
      • Murray G.K.
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      ). A different methodological approach is therefore needed to determine the associations between individual SNPs and risk for developing major depression, as well as potential interactions between SNPs and SLEs as risk factors for developing major depression.
      Our aim in this study was to examine interactions between individual SNPs and a time-dependent, prospective measure of SLEs as risk factors for major depression in the general population. To accomplish this, we used data from the iPSYCH2012 case-cohort sample—a population-based cohort of individuals born in Denmark that includes information on psychiatric diagnoses from hospital-based settings. In addition, we also conducted a GWAS of major depression using survival analysis, rather than logistic regression, as the underlying statistical methodology to examine the associations between individual SNPs and risk for developing major depression in the general population.

      Methods and Materials

      Study Design and Sample

      Data were drawn from the iPSYCH2012 study, which has a case-cohort design (
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      The iPSYCH2012 case-cohort sample: New directions for unravelling genetic and environmental architectures of severe mental disorders.
      ). In this design, the study sample is nested within a larger base population and includes all cases from the full cohort but only a subset of noncases (
      • Barlow W.E.
      • Ichikawa L.
      • Rosner D.
      • Izumi S.
      Analysis of case-cohort designs.
      ). This reduces the cost and burden associated with collecting biological specimens (in the case of iPSYCH, DNA for genetic analysis). The subset used as the comparison group is typically a random sample of individuals drawn from the full cohort (i.e., the subcohort). Because it is random, some cases will by chance be selected as part of the subcohort. The great benefit of this design over a nested case-control design is that it enables the unbiased calculation of risk and hazard ratios, as in a cohort study (
      • Self S.G.
      • Prentice R.L.
      Asymptotic distribution theory and efficiency results for case-cohort studies.
      ). Because not all noncases from the full cohort are included, this design can be more efficient and cost-effective than a cohort study, particularly when the collection of biological samples is involved (
      • Barlow W.E.
      • Ichikawa L.
      • Rosner D.
      • Izumi S.
      Analysis of case-cohort designs.
      ,
      • Self S.G.
      • Prentice R.L.
      Asymptotic distribution theory and efficiency results for case-cohort studies.
      ,
      • Prentice R.L.
      A case-cohort design for epidemiologic cohort studies and disease prevention trials.
      ). For a detailed overview on case-cohort designs, see Barlow et al. (
      • Barlow W.E.
      • Ichikawa L.
      • Rosner D.
      • Izumi S.
      Analysis of case-cohort designs.
      ), and for a brief tutorial, see Musliner et al. (
      • Musliner K.L.
      • Mortensen P.B.
      • McGrath J.J.
      • Suppli N.P.
      • Hougaard D.M.
      • Bybjerg-Grauholm J.
      • et al.
      Association of polygenic liabilities for major depression, bipolar disorder, and schizophrenia with risk for depression in the Danish population.
      ) (Supplement).
      The iPSYCH2012 case-cohort sample includes a subcohort of 30,000 individuals (i.e., the subcohort) selected randomly from the base population of all individuals born in Denmark between 1981 and 2005 who survived to their first birthday and had known mothers (n = 1,472,762). To this random sample all additional cases from the base population (n = 56,189) were added, i.e., individuals who received a diagnosis of affective disorder, schizophrenia, autism, or attention-deficit/hyperactivity disorder between 1994 and 2012 in inpatient, outpatient, or emergency room settings in Danish psychiatric hospitals. Records of psychiatric diagnoses are stored in the Danish Psychiatric Central Research Register (
      • Mors O.
      • Perto G.P.
      • Mortensen P.B.
      The Danish Psychiatric Central Research Register.
      ). Around 4% of individuals in the subcohort (n = 1188) also received one of the above psychiatric diagnoses, bringing the total number of individuals with a psychiatric diagnosis to 57,377. Biological material for DNA analysis was linked to information from national population-based registers using the unique, personal identification number assigned to all Danish citizens and legal residents since 1968 by the Danish Civil Registration System (
      • Pedersen C.B.
      The Danish Civil Registration System.
      ). The Danish Civil Registration System also includes parents’ personal identification numbers, allowing establishment of all known first-degree relatives (parents, siblings, half-siblings, and offspring).
      For this study, we selected all individuals in the iPSYCH2012 subcohort and the remaining patients with depression (ICD-10 codes F32–F33) from the full cohort 1) who were of European ancestry based on principal component analysis, 2) who were successfully genotyped, and 3) for whom follow-up data starting at 10 years of age was available. We also removed at random 1 person from each pair of relatives (second degree or closer, πˆ > 0.2). The final study sample included 38,716 individuals: 20,563 individuals from the subcohort (of whom 379 had a depression diagnosis) and 18,153 additional individuals from the full cohort with a depression diagnosis (total number of patients with depression = 18,532).

      Measures

      Stressful Life Events

      SLEs included death of a parent, sibling, or child; serious medical illness in the individual or one of their first-degree relatives; family disruption owing to divorce or separation; parental disability; and child maltreatment. SLE variables were obtained from Danish national population-based registers (
      • Mors O.
      • Perto G.P.
      • Mortensen P.B.
      The Danish Psychiatric Central Research Register.
      ,
      • Lynge E.
      • Sandegaard J.L.
      • Rebolj M.
      The Danish National Patient Register.
      ,
      • Petersson F.
      • Baadsgaard M.
      • Thygesen L.C.
      Danish registers on personal labour market affiliation.
      ). A detailed description of how each SLE was measured is shown in Table S1. Dahl et al. (
      • Dahl S.K.
      • Larsen J.T.
      • Petersen L.
      • Ubbesen M.B.
      • Mortensen P.B.
      • Munk-Olsen T.
      • et al.
      Early adversity and risk for moderate to severe unipolar depressive disorder in adolescence and adulthood: A register-based study of 978,647 individuals.
      ) examined these events in the Danish registers and found that all were associated with depression risk individually, and that the number of SLEs was associated with depression in a dose-response fashion (
      • Dahl S.K.
      • Larsen J.T.
      • Petersen L.
      • Ubbesen M.B.
      • Mortensen P.B.
      • Munk-Olsen T.
      • et al.
      Early adversity and risk for moderate to severe unipolar depressive disorder in adolescence and adulthood: A register-based study of 978,647 individuals.
      ). Information on SLEs was combined into a time-varying count variable, such that individuals contributed person-time to the analyses within whichever category of SLE that they were in at that time, and switched to contribute person-time within a different SLE category when they experienced a subsequent SLE.

      Genetic Data

      DNA was obtained from blood spots collected at birth as part of routine clinical screening and stored in the Danish Newborn Screening Biobank (
      • Norgaard-Pedersen B.
      • Hougaard D.M.
      Storage policies and use of the Danish Newborn Screening Biobank.
      ). Bloodspots were located for 80,422 (93%) members of the iPSYCH2012 sample. Samples were genotyped at the Broad Institute of Harvard and MIT (Cambridge, MA) in 23 waves using the Infinium PsychChip v1.0 array (Illumina). Quality control and imputation were performed using the RICOPILI pipeline (
      • Lam M.
      • Awasthi S.
      • Watson H.J.
      • Goldstein J.
      • Panagiotaropoulou G.
      • Trubetskoy V.
      • et al.
      RICOPILI: Rapid Imputation for COnsortias PIpeLIne.
      ). The filtering process excluded variants with call frequency <0.98 or a Hardy-Weinberg equilibrium p value <1 × 10−6. Ninety percent (n = 77,639) of the sample passed quality control.

      Analyses

      Main and interaction effects for the associations between individual SNPs, SLEs, and depression were estimated using a series of Cox regressions. Owing to undersampling of noncases in a case-cohort design, weights must be applied to obtain accurate estimates (
      • Barlow W.E.
      • Ichikawa L.
      • Rosner D.
      • Izumi S.
      Analysis of case-cohort designs.
      ). These weights ensure that only members of the random subcohort contribute person-time to the survival analyses, while cases outside the cohort enter the analyses a moment before their time of failure. For this study, we used the weighting method proposed by Prentice (
      • Prentice R.L.
      A case-cohort design for epidemiologic cohort studies and disease prevention trials.
      ), in which members of the subcohort (including cases) receive a weight of 1, and depression cases outside the subcohort receive a weight of 0 before their failure date and 1 when they enter the risk set in which they themselves fail. This method has been shown to produce estimates that most closely resemble those obtained from the full cohort (
      • Onland-Moret N.C.
      • van der A.D.L.
      • van der Schouw Y.T.
      • Buschers W.
      • Elias S.G.
      • van Gils C.H.
      • et al.
      Analysis of case-cohort data: A comparison of different methods.
      ).
      Persons in the study sample were followed from 10 years of age until first depression diagnosis, death, emigration, or December 31, 2012, whichever came first. The underlying time metric was age in days. The time-dependent SLE count variable was analyzed as a continuous variable. All analyses were adjusted for sex, birth year, and the first 5 ancestral principal components. Wald statistics were used to test for interaction. Analyses were conducted in R (version 3.1.2; R Foundation for Statistical Computing). Regional visualizations of results from GWEIS analyses were plotted with LocusZoom (
      • Pruim R.J.
      • Welch R.P.
      • Sanna S.
      • Teslovich T.M.
      • Chines P.S.
      • Gliedt T.P.
      • et al.
      LocusZoom: Regional visualization of genome-wide association scan results.
      ).
      There are approximately 11 million directly genotyped and imputed SNPs available for members of the iPSYCH2012 sample. However, according to Danish law, some register-based data are available only at dedicated servers at Statistics Denmark. Because this study includes variables that can only be accessed through these servers, we were required to conduct the analyses in a Windows environment (Microsoft Corp.), which created some computational challenges that made it impossible to run our GWAS and GWEIS analysis in the full set of 11 million SNPs. To get around these challenges, we conducted our GWEIS of SLEs and depression in two stages: first, we selected a subset of SNPs in which minor allele frequency (MAF) was >0.01 and missing rate was <0.1. From there, we conducted linkage disequilibrium pruning with various r2 thresholds and found that an r2 value of 0.7 left us with 496,162 high-quality SNPs distributed across the genome. These SNPs were then uploaded onto the Statistics Denmark servers and merged with the register-based data for GWAS and GWEIS analysis. Based on the GWEIS analysis using these 496,162 SNPs, we identified all SNPs with interaction p values below p = 1 × 10−5. We then went back to the original sample of 11 million SNPs and identified all additional SNPs located 500 kb upstream or downstream of these SNPs and uploaded them onto the server at Statistics Denmark. This enabled a second stage of analysis in which there was dense coverage of the areas with suggestive evidence for interaction. For this second stage, statistical significance was evaluated at the genome-wide–significant α level of p < 5 × 10−8. Given the actual number of SNPs included in our GWEIS and the fact that the second stage of SNP selection specifically aimed to increase coverage of specific genomic areas, we posit p < 5 × 10−8 to be a conservative threshold.

      Replication Attempt

      We attempted to replicate our top findings in a case-control sample of depression drawn from the UK Biobank (
      • Bycroft C.
      • Freeman C.
      • Petkova D.
      • Band G.
      • Elliott L.T.
      • Sharp K.
      • et al.
      The UK Biobank resource with deep phenotyping and genomic data.
      ). The UK Biobank includes more than 500,000 persons 40–69 years of age at recruitment and holds a variety of biological measurements, lifestyle indicators, and biomarkers, including genome-wide genotype data on all participants. The current replication analyses were based on a sample of 73,258 genetically unrelated persons of European ancestry (22,880 depression cases and 50,378 controls) for whom SNP data as well as information on trauma exposure were available (
      • Coleman J.R.I.
      • Peyrot W.J.
      • Purves K.L.
      • Davis K.A.S.
      • Rayner C.
      • Choi S.W.
      • et al.
      Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank.
      ). Lifetime depression was assessed with questions from the Composite International Diagnostic Interview. Trauma exposure was operationalized as a dichotomous variable based on self-report of severe trauma experiences in childhood and adulthood. For detailed information on the replication sample, see Coleman et al. (
      • Coleman J.R.I.
      • Peyrot W.J.
      • Purves K.L.
      • Davis K.A.S.
      • Rayner C.
      • Choi S.W.
      • et al.
      Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank.
      ) (Supplement). We tested for interaction between the dichotomous trauma exposure and all available SNPs located within ±500 kb of the most significant SNP from each of the three genome-wide–significant loci identified in the iPSYCH2012 GWEIS. In total, 7745 SNPs were tested for interaction using PLINK2a (
      • Chang C.C.
      • Chow C.C.
      • Tellier L.C.
      • Vattikuti S.
      • Purcell S.M.
      • Lee J.J.
      Second-generation PLINK: Rising to the challenge of larger and richer datasets.
      ). We assessed the number of independent loci tested for interaction at varying r2 (0.1–0.5) and differently sized windows (250–3000 kb) yielding 443 to 1252 independent loci (see Table S2).

      Results

      Sample Characteristics

      Sample characteristics are shown in Table 1. Patients with depression inside and outside the population-based random subcohort showed similar characteristics. Sixty-nine percent of patients with depression and 49% of subcohort members were female. Mean age at first depression diagnosis was 19.6 years (19.7 years for patients outside the subcohort) (SD = 4.1 years inside the subcohort and 4.2 years outside the subcohort). SLEs were common—by 10 years of age, 48% of patients with depression (49% for patients outside the subcohort) and 39% of population-based control subjects had experienced at least one SLE.
      Table 1Sample Characteristics
      CharacteristicMD Cases Outside the Subcohort (n = 18,153)MD Cases Inside the Subcohort (n = 379)Noncases From the Subcohort (n = 20,184)
      Gender, n (%)
       Female12,430 (68.5%)263 (69.4%)9848 (48.8%)
       Male5723 (31.5%)116 (30.6%)10,336 (51.2%)
      Birth Cohort, n (%)
       1981–19855953 (32.8%)126 (33.3%)3585 (17.8%)
       1986–19906670 (36.7%)150 (39.6%)4570 (22.6%)
       1991–20025530 (30.5%)103 (27.2%)12,029 (59.6%)
      >1 SLE Before 10 Years of Age, n (%)8712 (48.0%)185 (48.8%)7857 (38.9%)
      Age at First MD Diagnosis, Years, Mean (SD)19.7 (4.2)19.6 (4.1)NA
      MD, major depression; NA, not applicable; SLE, stressful life event.

      GWAS Results

      Figure 1 shows results from GWASs examining the main effects of 496,162 SNPs on the hazard of depression (Figure 1A) and the hazard of experiencing at least one SLE (Figure 1B). The GWAS of the risk for developing depression yielded 1 genome-wide–significant hit (rs7700661, p = 1.99 × 10−8) and 52 hits in which p < 1 × 10−5 (Figure 1A). No individual SNPs had p values <1 × 10−5 for the main effect of SNPs on the hazard of SLEs (Figure 1B).
      Figure thumbnail gr1
      Figure 1Manhattan plots for main effects of 496,162 SNPs on risk for depression and SLEs in 18,532 patients with major depression and 20,184 population-based control subjects. (A) Main effects of 496,162 individual SNPs on risk for major depression diagnosis in hospital-based settings in Denmark from 1995 to 2012. (B) Main effects of 496,162 individual SNPs on risk for experiencing at least one SLE. SLE, stressful life event; SNP, single nucleotide polymorphism.

      GWEIS Results

      The GWEIS analysis of 496,162 SNPs yielded 60 SNPs in which p < 1 × 10−5 (Table 2). After rerunning the GWEIS including all SNPs located within 500 kb of these 60 SNPs, three independent loci reached genome-wide significance (Figure 2). Hazard ratios for the three top hits are shown in Figure 3, and region plots are shown in Figure 4. The top hit, rs56076205 (p = 3.7 × 10−10), was located in an intron of the ABCC1 gene. Compared with homozygotes for the major allele, homozygotes for the minor allele (MAF = 0.07) had a hazard for depression >20 times greater than homozygotes for the major allele at 3 SLEs, and >500 times greater at 4+ SLEs (see Figure 3A). ABCC1 is known as a multidrug resistance protein and has a range of commonly used drugs as substrate (
      • Loscher W.
      • Potschka H.
      Drug resistance in brain diseases and the role of drug efflux transporters.
      ). Mice studies report a strong influence of ABCC1 on cerebral accumulation of amyloid-β (
      • Krohn M.
      • Lange C.
      • Hofrichter J.
      • Scheffler K.
      • Stenzel J.
      • Steffen J.
      • et al.
      Cerebral amyloid-beta proteostasis is regulated by the membrane transport protein ABCC1 in mice.
      ). The second hit, rs3784187 (p = 1.2 × 10−8), was located in an intron of the AKAP6 gene. For this SNP, homozygotes for the minor allele (MAF = 0.06) showed a negative interaction such that as SLEs increased, risk for depression decreased (see Figure 3B). The protein transcribed from the AKAP6 gene is involved in intracellular signaling in the protein kinase A pathway (
      • Uhlen M.
      • Fagerberg L.
      • Hallstrom B.M.
      • Lindskog C.
      • Oksvold P.
      • Mardinoglu A.
      • et al.
      Proteomics. Tissue-based map of the human proteome.
      ). In 2015, a meta-analysis from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium found a genome-wide–significant association between an SNP in the AKAP6 gene and general cognitive functioning (
      • Davies G.
      • Armstrong N.
      • Bis J.C.
      • Bressler J.
      • Chouraki V.
      • Giddaluru S.
      • et al.
      Genetic contributions to variation in general cognitive function: A meta-analysis of genome-wide association studies in the CHARGE consortium (N = 53949).
      ). The final hit, rs340315 (p = 4.5 × 10−8), was located near the MFSD1 gene. MFSD1 is a membrane-bound solute carrier present in a wide range of human tissues (
      • Uhlen M.
      • Fagerberg L.
      • Hallstrom B.M.
      • Lindskog C.
      • Oksvold P.
      • Mardinoglu A.
      • et al.
      Proteomics. Tissue-based map of the human proteome.
      ). A recent mice study reported MFSD1 to be abundant in the plasma membrane of neurons (
      • Perland E.
      • Hellsten S.V.
      • Lekholm E.
      • Eriksson M.M.
      • Arapi V.
      • Fredriksson R.
      The novel membrane-bound proteins MFSD1 and MFSD3 are putative SLC transporters affected by altered nutrient intake.
      ). Further, the study found alterations in gene expression in response to environmental stress. Homozygotes for the minor allele (MAF = 0.31) showed a similar pattern to the first hit, such that the hazard for depression was >3 times higher at 3 SLEs and >30 times higher at 4+ SLEs compared with homozygotes for the major allele (see Figure 3C).
      Table 2Sixty SNPs With p Values <1 × 105 for Interaction With SLEs on Risk for Major Depression Tested Among 496,162 SNPs in 18,532 Patients With Major Depression and 20,184 Population-Based Control Subjects
      ChromosomeLocation (bp)SNPInteraction, pMain Effect Depression, pMain Effect SLEs, pA1A2MAFGene Context
      116650609rs1493345071.05 × 10−6.0042.42AC0.02ARHGEF19---[FBXO42]--SZRD1
      121937317rs120830624.80 × 10−6.81.72TC0.05ALPL--[RAP1GAP]--USP48
      186628279rs1509606626.38 × 10−6.92.59AC0.02COL24A1-[]---ODF2L
      1153310297rs8214335.60 × 10−6.0014.56GA0.10PGLYRP3--[PGLYRP4]--S100A9
      1228022150rs1826709353.59 × 10−6.13.17GA0.01SNAP47--[PRSS38]--WNT9A
      1247711911chr1:2477119112.34 × 10−6.14.93TGTTCGTT0.17OR2C3--[GCSAML]--OR2G2
      231549959rs2074266.48 × 10−6.61.74CA0.35FADS1--[FADS2]--FADS3
      2125009457rs796532673.30 × 10−6.77.91AG0.02[CNTNAP5]---MTND5P22
      2150854878rs1492821575.78 × 10−6.07.43AG0.01MMADHC---[]---RND3
      2159027173rs108043906.21 × 10−6.10.47TC0.32UPP2--[]CCDC148
      2166023849rs621749515.51 × 10−6.19.97GA0.11SLC38A11---[SCN3A]--SCN2A
      320622558rs98466969.89 × 10−6.0001.69GC0.05SGOL1---[]
      322055173rs615533183.29 × 10−6.06.41AC0.04ZNF385D-AS2--[ZNF385D]---HMGB1P5
      377675638rs8766756.40 × 10−6.21.61CT0.50VDAC1P7---[ROBO2]---RP11-354H21.1
      3158545195rs67928274.51 × 10−6.04.85TG0.13RARRES1--[MFDS1]---IQCJ-SCHIP1
      3158551731rs617968094.34 × 10−6.11.98AG0.22MFSD1-[]---IQCJ-SCHIP1
      3158583584rs3402848.02 × 10−6.0001.86GA0.36MFSD1--[]--IQCJ-SCHIP1
      427574252rs750653096.72 × 10−6.08.23AG0.01RP11-415C15.2---[]--IGBP1P5
      459684939rs1165109336.43 × 10−6.01.74AG0.07RP11-577G20.2---[]
      469821738rs18410365.07 × 10−6.0031.21TG0.16UGT2A3-[]--UGT2B11
      4126117627rs131104722.16 × 10−7.13.60TC0.06ANKRD50---[]---FAT4
      4151061659rs727303617.41 × 10−6.09.99TC0.02RP11-423J7.1---[DCLK2]---LRBA
      4158141677rs285455625.47 × 10−6.03.69CT0.02GLRB--[GRIA2]---RP11-364P22.1
      4186959486rs68187877.66 × 10−6.30.08CA0.40SORBS2--[]--TLR3
      533216242rs285665397.23 × 10−6.12.43TC0.15NPR3---[CTD-2066L21.3]---TARS
      5121067170rs77359966.14 × 10−6.16.75GA0.06RP11-510I6.3---[]---FTMT
      5178981060rs728225838.08 × 10−6.31.67TC0.04ADAMTS2---[RUFY1]--HNRNPH1
      615849887rs728234832.85 × 10−6.24.45AG0.01DTNBP1---[]---MYLIP
      6107310381rs94864844.53 × 10−6.30.53GA0.15QRSL1---[]--C6orf203
      721144220rs732775325.16 × 10−6.87.88GT0.01ABC5B-SP8---[]---SP4
      791011858rs732207659.71 × 10−6.51.57TC0.01FZD1---[RP11-115N4.1][RP11-142A5.1]---MTERF1
      832516140rs359554764.40 × 10−6.05.84CCAG0.47NRG1-IT3---[NRG1]---RP11-11N9.4
      856535514rs64740067.85 × 10−6.49.59CT0.40XKR4--[]--TMEM68
      8103203727rs41024003.89 × 10−6.49.43TC0.47NCALD--[]--RRM2B
      983000507rs78610301.56 × 10−7.0049.51TC0.50NPAP1P4-[]---RP11-117O7.2
      983023317rs107803946.22 × 10−6.0002.50GA0.32NPAP1P4--[]---RP11-117O7.2
      108286974rs17968672.85 × 10−7.0032.68AG0.06PRPF38AP1--[]--LINC00708
      1064266748rs109951784.87 × 10−7.00001.89AG0.45RTKN2---[ZNF365]---ADO
      10129586689rs19261813.86 × 10−6.17.34AC0.20FOXI2--[]--CLRN3
      1113920438rs618847778.35 × 10−6.12.54GA0.09FAR1---[]--SPON1
      1141939498rs1427994941.93 × 10−6.83.16TA0.01LRRC4C---[]--RP11-148I19.1
      1144032917rs1180083134.63 × 10−6.53.56TC0.03C11orf96--[]--ACCSL
      1144452139rs107690474.06 × 10−6.05.73AT0.50ALX4---[]--CD82
      1145881397rs1396704441.12 × 10−6.24.95AAG0.05SLC35C1--[CRY2]--MAPK8IP1
      11116604070rs1803535.53 × 10−6.45.88CT0.20AP000770.1--[]--BUD13
      11128996355rs79449391.48 × 10−6.02.71CT0.31TP53AIP1---[ARHGAP32]---BARX2
      12119758130rs1404379283.41 × 10−6.02.23CT0.02HSPB8--[]--CCDC60
      1351272084rs7974986.55 × 10−7.06.58AG0.08DLEU1-AS1---[DLEU1]--DLEU7
      13114591051rs95502664.89 × 10−6.01.28AG0.16GAS6--[]LINC00452
      1432860927rs19511851.60 × 10−6.08.94TC0.06ARHGAP5---[AKAP6][RP11-320M16.2]--RN7SL660P
      1535002935rs169595286.12 × 10−7.94.59GA0.11GOLGA8B---[]--GJD2
      166338673rs13444749.41 × 10−6.86.39GA0.12[RBFOX1][RB11-420N3.3]
      1616172008rs560762053.74 × 10−10.05.55TC0.07FOPNL---[ABCC1]--ABCC6
      1663680366rs124489303.17 × 10−7.09.86AC0.25RP11-368L12.1--[]--RP11-370P15.1
      1770291156rs19673045.85 × 10−6.67.11CA0.25SOX9---[]---SLC39A11
      1837425523rs20486473.52 × 10−6.03.48GC0.21RP11-244M2.1--[RP11-636021.1]---LINC01477
      1877985650rs1114470741.30 × 10−6.12.59TC0.02ADNP2--[PARD6G]
      1946785290rs1120879914.06 × 10−6.0005.60CT0.05IGFL1--[]--HIF3A
      2035329303rs622061506.36 × 10−6.01.35GA0.02SLA2--[NDRG3]--DSN1
      2131449079rs1171810454.86 × 10−6.03.98GT0.01GRIK1---[]--CLDN17
      The 492,162 included SNPs were selected according to the following criteria: MAF >0.01 and missing rate <0.1; subsequently, linkage disequilibrium pruning with an r2 value of 0.7 was implemented. The gene context column lists the SNP location within brackets. Most closely located genetic variants 500 kb upstream or downstream for the index SNP are listed as well with any genes prioritized over long intergenic noncoding RNA, pseudogenes, etc. Distance from the index to other listed variants is denoted by dashes: no dash indicates <1 kb, one dash indicates <10 kb, two dashes indicates <100 kb, and three dashes <500 kb.
      MAF, minor allele frequency; SLE, stressful life event; SNP, single nucleotide polymorphism.
      Figure thumbnail gr2
      Figure 2Manhattan plot of genome-wide by environment interaction analyses based on 18,532 patients with major depression and 20,184 population-based control subjects. The figure presents results of a GWEIS conducted in two stages. In stage 1, a GWEIS was conducted using 496,162 SNPs distributed across the genome. In stage 2, all SNPs located 500 kb up- or downstream from 60 SNPs with p values <10−5 in stage 1 were added to the analyses. The Manhattan plot shows results from both stages. GWEIS, genome-wide by environment interaction study; SNP, single nucleotide polymorphism.
      Figure thumbnail gr3
      Figure 3Interaction effects for stressful life events and top SNPs from 3 genome-wide–significant loci. Note. For each SNP, the HR for depression is plotted by number of stressful life events. Vertical bars represent 95% CI. Hazards were compared within each level of stressful life events with major allele homozygotes as reference. Wald statistics were used to test interactions, comparing linear trends for HR between genotypes. The small differences in the total number of observations are due to differences in the number of persons successfully genotyped for each SNP. Owing to the time-varying nature of the stressful life events variable, study participants could contribute person-time for different numbers of stressful life events. Therefore, the total number of observations exceeds the total number of participants in the study. HR, hazard ratio; SNP, single nucleotide polymorphism.
      Figure thumbnail gr4
      Figure 4Region plots for three top hits from a genome-wide by environment interaction study based on 18,532 patients with major depression and 20,184 population-based control subjects. The color of the dots indicates the linkage disequilibrium (r2) of SNPs with the top SNP of each loci. The r2 was based on the 1000 Genomes Project November 2014 European population. SNP, single nucleotide polymorphism.

      Analysis of Top SNPs in UK Biobank

      None of the three top SNPs were statistically significant in the replication attempt using UK Biobank data (rs56076205, p = .87; rs3784187, p = .93; rs340315, p = .71). The most significant interactions involved the following SNPs: rs190869692 (p = 3.2 × 10-5) in the ABCC1 gene 38,653 bp upstream from the iPSYCH2012 hit in the same gene (r2 = 0.002, p = .58); rs111284027 (p = 9.4 × 10−5) in the ARHGAP5 gene 259,273 bp downstream from our hit in the AKAP6 gene (r2 = 0.003, p = .44); rs146472082 (p = 5.1 × 10−5) in the RARRES1 gene 155,569 bp downstream from our hit near the MFSD1 gene (r2 = 0.053, p = .0011) (see Figure S1). Thus, all three SNPs identified in the replication analyses represented independent loci from the three genome-wide–significant loci identified in the iPSYCH2012 GWEIS.

      Discussion

      In this study, we report results from the first comprehensive, population-based GWEIS investigating the interaction between individual SNPs and a time-varying measure of SLEs as risk factors for a diagnosis of depression treated in inpatient, outpatient, or emergency room settings. The GWEIS yielded genome-wide–significant effects in three independent loci located in the ABCC1, AKAP6, and MSFD1 genes, as well as 50 hits in which p < 1 × 10−5. We attempted to replicate our top hits in a large sample of depression cases and controls from the UK Biobank; however, none of the hits were significant in the replication sample. This suggests that the original hits were false positives. However, there are notable differences between iPSYCH2012 and UK Biobank in terms of sampling, measurement, and design. The fact that different statistical methods were used (survival analysis vs. logistic regression) could also have contributed. However, it is not straightforward to isolate the impact of the statistical method alone, because conducting a logistic regression in our own sample would require us to make substantial changes to the design and sample composition. Thus, it would be difficult to tell if any difference in the results was due to the different statistical method or to the different design. Ultimately, it remains a possibility that one or more of these hits might replicate in a sample in which the measurement, design, and analysis are more comparable; however, unless such evidence becomes available, these hits should not be considered robust.
      To our knowledge, this is the largest single-sample GWEIS conducted to date examining the interaction between individual variants and SLEs. Nevertheless, the presented analyses are still likely underpowered to detect most single-SNP gene-environment interactions (
      • Thomas D.
      Gene--environment-wide association studies: Emerging approaches.
      ). For years, GWASs were similarly underpowered to detect significant SNPs, until the development of large-scale international consortia allowed for the accumulation of enough samples to pass the inflection point for consistent findings (
      • Sullivan P.F.
      • Agrawal A.
      • Bulik C.M.
      • Andreassen O.A.
      • Borglum A.D.
      • Breen G.
      • et al.
      Psychiatric genomics: An update and an agenda.
      ). In comparison, the study of gene-environment interaction in psychiatric disorders has only begun to enter into its big data phase. The requirement for assessment of a complex, composite environment exposure in the large study populations necessary for studying interactions makes these studies challenging endeavors. Extrapolating from the history of GWASs in psychiatry, we believe that the inflection point for studies of gene-environment interaction will only be reached through international collaborations that combine studies with information on genetic variation and environment exposures.

      Methodological Considerations

      The following are additional methodological aspects of the study that should kept in mind when interpreting these results. First, the oldest depression cases in the iPSYCH2012 sample were diagnosed by 30 years of age. As such, they represent a cohort of early-onset depression cases, and therefore these results may not generalize to individuals who develop depression at older ages. Second, the depression cases in iPSYCH are all identified in hospital-based settings; therefore, these results may not generalize to individuals with untreated depression or individuals treated solely by their primary care doctors, who make up the majority of depression cases in Denmark (
      • Musliner K.L.
      • Liu X.
      • Gasse C.
      • Christensen K.S.
      • Wimberley T.
      • Munk-Olsen T.
      Incidence of medically treated depression in Denmark among individuals 15-44 years old: A comprehensive overview based on population registers.
      ). Third, although some of the SLEs included in this study are measured with high accuracy (e.g., death of a relative), others, particularly child maltreatment, are measured less accurately because they are based solely on register data. It is sadly very likely that some individuals in the sample experienced child maltreatment that was never recorded in the register, although the opposite (that individuals registered as having experienced child maltreatment did not experience it) is unlikely to be true. Fourth, we included a diverse range of stressful events in our study. Consequently, it is possible that some observed interactions relate to very specific types of SLEs. For example, it is plausible that risk for depression in relation to somatic disease is associated with the seriousness of the course of disease. Therefore, genetic variants associated with prognosis and/or treatment response could emerge as part of gene-environment interaction in the present study, e.g., ABCC1 has a range of anticancer and anti-HIV drugs as substrates, thus rendering somatic treatment less effective, thereby possibly increasing risk for depression.

      Conclusions

      In this population-based cohort of European ancestry, we identified three novel genetic loci that interacted with a time-varying measure of SLEs to predict hospital-treated depression at a genome-wide–significant level. However, none of these hits replicated in a large sample of depression cases and controls from the UK Biobank. Future gene-by-stress research in depression should focus on efforts to establish large collaborative GWEISs to generate sufficient statistical power to identify significant variants.

      Acknowledgments and Disclosures

      The iPSYCH project is funded by the Lundbeck Foundation (Grant Nos. R102-A9118, R155-2014-1724, and R248-2017-2003) and the universities and university hospitals of Aarhus and Copenhagen. KLM is funded by a postodoc fellowship from the Lundbeck Foundation (Grant No. R303-2018-3551). Genotyping of the iPSYCH2012 samples was supported by grants from the Lundbeck Foundation, the Stanley Foundation, the Simons Foundation (Grant No. SFARI 311789), and the National Institute of Mental Health (Grant No. 5U01MH094432-02). The Danish National Biobank resource is supported by the Novo Nordisk Foundation. The UK Biobank (Project ID 16577) represents independent research supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. High-performance computing facilities at the NIHR Biomedical Research Centre were funded with capital equipment grants from the Guy’s and St Thomas’ NHS Foundation Trust Charity (Grant No. TR130505) and Maudsley Charity (Grant No. 980).
      The authors gratefully acknowledge the Broad Institute for genotyping. Initial genetic analyses were performed on the GenomeDK high-performance computing facility supported by the Centre for Genomics and Personalized Medicine and Center for Integrative Sequencing, Aarhus University.
      A previous version of this article was published as a preprint on medRxiv: https://doi.org/10.1101/2021.09.03.21262452.
      TW has served as scientific advisor to H. Lundbeck A/S. GB has received consultancy and speaker fees from Eli Lilly, Otsuka, and Illumina. All other authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

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