Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression

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Abstract

Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association (GWA) meta-analysis based in 135,458 cases and 344,901 control, We identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression, and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relations of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine and define the basis of major depression and imply a continuous measure of risk underlies the clinical phenotype.

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Model-based Analysis of ChIP-Seq (MACS)

Yong Zhang, Tao Liu, Clifford Meyer … (2008)

Background The determination of the 'cistrome', the genome-wide set of in vivo cis-elements bound by trans-factors [1], is necessary to determine the genes that are directly regulated by those trans-factors. Chromatin immunoprecipitation (ChIP) [2] coupled with genome tiling microarrays (ChIP-chip) [3,4] and sequencing (ChIP-Seq) [5-8] have become popular techniques to identify cistromes. Although early ChIP-Seq efforts were limited by sequencing throughput and cost [2,9], tremendous progress has been achieved in the past year in the development of next generation massively parallel sequencing. Tens of millions of short tags (25-50 bases) can now be simultaneously sequenced at less than 1% the cost of traditional Sanger sequencing methods. Technologies such as Illumina's Solexa or Applied Biosystems' SOLiD™ have made ChIP-Seq a practical and potentially superior alternative to ChIP-chip [5,8]. While providing several advantages over ChIP-chip, such as less starting material, lower cost, and higher peak resolution, ChIP-Seq also poses challenges (or opportunities) in the analysis of data. First, ChIP-Seq tags represent only the ends of the ChIP fragments, instead of precise protein-DNA binding sites. Although tag strand information and the approximate distance to the precise binding site could help improve peak resolution, a good tag to site distance estimate is often unknown to the user. Second, ChIP-Seq data exhibit regional biases along the genome due to sequencing and mapping biases, chromatin structure and genome copy number variations [10]. These biases could be modeled if matching control samples are sequenced deeply enough. However, among the four recently published ChIP-Seq studies [5-8], one did not have a control sample [5] and only one of the three with control samples systematically used them to guide peak finding [8]. That method requires peaks to contain significantly enriched tags in the ChIP sample relative to the control, although a small ChIP peak region often contains too few control tags to robustly estimate the background biases. Here, we present Model-based Analysis of ChIP-Seq data, MACS, which addresses these issues and gives robust and high resolution ChIP-Seq peak predictions. We conducted ChIP-Seq of FoxA1 (hepatocyte nuclear factor 3α) in MCF7 cells for comparison with FoxA1 ChIP-chip [1] and identification of features unique to each platform. When applied to three human ChIP-Seq datasets to identify binding sites of FoxA1 in MCF7 cells, NRSF (neuron-restrictive silencer factor) in Jurkat T cells [8], and CTCF (CCCTC-binding factor) in CD4+ T cells [5] (summarized in Table S1 in Additional data file 1), MACS gives results superior to those produced by other published ChIP-Seq peak finding algorithms [8,11,12]. Results Modeling the shift size of ChIP-Seq tags ChIP-Seq tags represent the ends of fragments in a ChIP-DNA library and are often shifted towards the 3' direction to better represent the precise protein-DNA interaction site. The size of the shift is, however, often unknown to the experimenter. Since ChIP-DNA fragments are equally likely to be sequenced from both ends, the tag density around a true binding site should show a bimodal enrichment pattern, with Watson strand tags enriched upstream of binding and Crick strand tags enriched downstream. MACS takes advantage of this bimodal pattern to empirically model the shifting size to better locate the precise binding sites. Given a sonication size (bandwidth) and a high-confidence fold-enrichment (mfold), MACS slides 2bandwidth windows across the genome to find regions with tags more than mfold enriched relative to a random tag genome distribution. MACS randomly samples 1,000 of these high-quality peaks, separates their Watson and Crick tags, and aligns them by the midpoint between their Watson and Crick tag centers (Figure 1a) if the Watson tag center is to the left of the Crick tag center. The distance between the modes of the Watson and Crick peaks in the alignment is defined as 'd', and MACS shifts all the tags by d/2 toward the 3' ends to the most likely protein-DNA interaction sites. Figure 1 MACS model for FoxA1 ChIP-Seq. (a,b) The 5' ends of strand-separated tags from a random sample of 1,000 model peaks, aligned by the center of their Watson and Crick peaks (a) and by the FKHR motif (b). (c) The tag count in ChIP versus control in 10 kb windows across the genome. Each dot represents a 10 kb window; red dots are windows containing ChIP peaks and black dots are windows containing control peaks used for FDR calculation. (d) Tag density profile in control samples around FoxA1 ChIP-Seq peaks. (e,f) MACS improves the motif occurrence in the identified peak centers (e) and the spatial resolution (f) for FoxA1 ChIP-Seq through tag shifting and λlocal. Peaks are ranked by p-value. The motif occurrence is calculated as the percentage of peaks with the FKHR motif within 50 bp of the peak summit. The spatial resolution is calculated as the average distance from the summit to the nearest FKHR motif. Peaks with no FKHR motif within 150 bp of the peak summit are removed from the spatial resolution calculation. When applied to FoxA1 ChIP-Seq, which was sequenced with 3.9 million uniquely mapped tags, MACS estimates the d to be only 126 bp (Figure 1a; suggesting a tag shift size of 63 bp), despite a sonication size (bandwidth) of around 500 bp and Solexa size-selection of around 200 bp. Since the FKHR motif sequence dictates the precise FoxA1 binding location, the true distribution of d could be estimated by aligning the tags by the FKHR motif (122 bp; Figure 1b), which gives a similar result to the MACS model. When applied to NRSF and CTCF ChIP-Seq, MACS also estimates a reasonable d solely from the tag distribution: for NRSF ChIP-Seq the MACS model estimated d as 96 bp compared to the motif estimate of 70 bp; applied to CTCF ChIP-Seq data the MACS model estimated a d of 76 bp compared to the motif estimate of 62 bp. Peak detection For experiments with a control, MACS linearly scales the total control tag count to be the same as the total ChIP tag count. Sometimes the same tag can be sequenced repeatedly, more times than expected from a random genome-wide tag distribution. Such tags might arise from biases during ChIP-DNA amplification and sequencing library preparation, and are likely to add noise to the final peak calls. Therefore, MACS removes duplicate tags in excess of what is warranted by the sequencing depth (binomial distribution p-value 2). Among the remaining 34.6% ChIP-Seq unique peaks, 1,045 (13.3%) were not tiled or only partially tiled on the arrays due to the array design. Therefore, only 21.4% of ChIP-Seq peaks are indeed specific to the sequencing platform. Furthermore, ChIP-chip targets with higher fold-enrichments are more likely to be reproducibly detected by ChIP-Seq with a higher tag count (Figure 3b). Meanwhile, although the signals of array probes at the ChIP-Seq specific peak regions are below the peak-calling cutoff, they show moderate signal enrichments that are significantly higher than the genomic background (Wilcoxon p-value 2) and ChIP-Seq (MACS; FDR <1%). Shown are the numbers of regions detected by both platforms (that is, having at least 1 bp in common) or unique to each platform. (b) The distributions of ChIP-Seq tag number and ChIP-chip MATscore [13] for FoxA1 binding sites identified by both platforms. (c) MATscore distributions of FoxA1 ChIP-chip at ChIP-Seq/chip overlapping peaks, ChIP-Seq unique peaks, and genome background. For each peak, the mean MATscore for all probes within the 300 bp region centered at the ChIP-Seq peak summit is used. Genome background is based on MATscores of all array probes in the FoxA1 ChIP-chip data. (d) Width distributions of FoxA1 ChIP-Seq/chip overlapping peaks and ChIP-Seq unique peaks at different fold-enrichments (less than 25, 25 to 50, and larger than 50). (e) Spatial resolution for FoxA1 ChIP-chip and ChIP-Seq peaks. The Wilcoxon test was used to calculate the p-values for (d) and (e). (f) Motif occurrence within the central 200 bp regions for FoxA1 ChIP-Seq/chip overlapping peaks and platform unique peaks. Error bars showing standard deviation were calculated from random sampling of 500 peaks ten times for each category. Background motif occurrences are based on 100,000 randomly selected 200 bp regions in the human genome, excluding regions in genome assembly gaps (containing 'N'). Comparing the difference between ChIP-chip and ChIP-Seq peaks, we find that the average peak width from ChIP-chip is twice as large as that from ChIP-Seq. The average distance from peak summit to motif is significantly smaller in ChIP-Seq than ChIP-chip (Figure 3e), demonstrating the superior resolution of ChIP-Seq. Under the same 1% FDR cutoff, the FKHR motif occurrence within the central 200 bp from ChIP-chip or ChIP-Seq specific peaks is comparable with that from the overlapping peaks (Figure 3f). This suggests that most of the platform-specific peaks are genuine binding sites. A comparison between NRSF ChIP-Seq and ChIP-chip (Figure S3 in Additional data file 1) yields similar results, although the overlapping peaks for NRSF are of much better quality than the platform-specific peaks. Discussion ChIP-Seq users are often curious as to whether they have sequenced deep enough to saturate all the binding sites. In principle, sequencing saturation should be dependent on the fold-enrichment, since higher-fold peaks are saturated earlier than lower-fold ones. In addition, due to different cost and throughput considerations, different users might be interested in recovering sites at different fold-enrichment cutoffs. Therefore, MACS produces a saturation table to report, at different fold-enrichments, the proportion of sites that could still be detected when using 90% to 20% of the tags. Such tables produced for FoxA1 (3.9 million tags) and NRSF (2.2 million tags) ChIP-Seq data sets (Figure S4 in Additional data file 1; CTCF does not have a control to robustly estimate fold-enrichment) show that while peaks with over 60-fold enrichment have been saturated, deeper sequencing could still recover more sites less than 40-fold enriched relative to the chromatin input DNA. As sequencing technologies improve their throughput, researchers are gradually increasing their sequencing depth, so this question could be revisited in the future. For now, we leave it up to individual users to make an informed decision on whether to sequence more based on the saturation at different fold-enrichment levels. The d modeled by MACS suggests that some short read sequencers such as Solexa may preferentially sequence shorter fragments in a ChIP-DNA pool. This may contribute to the superior resolution observed in ChIP-Seq data, especially for activating transcription and epigenetic factors in open chromatin. However, for repressive factors targeting relatively compact chromatin, the target regions might be harder to sonicate into the soluble extract. Furthermore, in the resulting ChIP-DNA, the true targets may tend to be longer than the background DNA in open chromatin, making them unfavorable for size-selection and sequencing. This implies that epigenetic markers of closed chromatin may be harder to ChIP, and even harder to ChIP-Seq. To assess this potential bias, examining the histone mark ChIP-Seq results from Mikkelsen et al. [7], we find that while the ChIP-Seq efficiency of the active mark H3K4me3 remains high as pluripotent cells differentiate, that of repressive marks H3K27me3 and H3K9me3 becomes lower with differentiation (Table S2 in Additional data file 1), even though it is likely that there are more targets for these repressive marks as cells differentiate. We caution ChIP-Seq users to adopt measures to compensate for this bias when ChIPing repressive marks, such as more vigorous sonication, size-selecting slightly bigger fragments for library preparation, or sonicating the ChIP-DNA further between decrosslinking and library preparation. MACS calculates the FDR based on the number of peaks from control over ChIP that are called at the same p-value cutoff. This FDR estimate is more robust than calculating the FDR from randomizing tags along the genome. However, we notice that when tag counts from ChIP and controls are not balanced, the sample with more tags often gives more peaks even though MACS normalizes the total tag counts between the two samples (Figure S5 in Additional data file 1). While we await more available ChIP-Seq data with deeper coverage to understand and overcome this bias, we suggest to ChIP-Seq users that if they sequence more ChIP tags than controls, the FDR estimate of their ChIP peaks might be overly optimistic. Conclusion As developments in sequencing technology popularize ChIP-Seq, we propose a novel algorithm, MACS, for its data analysis. MACS offers four important utilities for predicting protein-DNA interaction sites from ChIP-Seq. First, MACS improves the spatial resolution of the predicted sites by empirically modeling the distance d and shifting tags by d/2. Second, MACS uses a dynamic λlocal parameter to capture local biases in the genome and improves the robustness and specificity of the prediction. It is worth noting that in addition to ChIP-Seq, λlocal can potentially be applied to other high throughput sequencing applications, such as copy number variation and digital gene expression, to capture regional biases and estimate robust fold-enrichment. Third, MACS can be applied to ChIP-Seq experiments without controls, and to those with controls with improved performance. Last but not least, MACS is easy to use and provides detailed information for each peak, such as genome coordinates, p-value, FDR, fold_enrichment, and summit (peak center). Materials and methods Dataset ChIP-Seq data for three factors, NRSF, CTCF, and FoxA1, were used in this study. ChIP-chip and ChIP-Seq (2.2 million ChIP and 2.8 million control uniquely mapped reads, simplified as 'tags') data for NRSF in Jurkat T cells were obtained from Gene Expression Omnibus (GSM210637) and Johnson et al. [8], respectively. ChIP-Seq (2.9 million ChIP tags) data for CTCF in CD4+ T cells were derived from Barski et al. [5]. ChIP-chip data for FoxA1 and controls in MCF7 cells were previously published [1], and their corresponding ChIP-Seq data were generated specifically for this study. Around 3 ng FoxA1 ChIP DNA and 3 ng control DNA were used for library preparation, each consisting of an equimolar mixture of DNA from three independent experiments. Libraries were prepared as described in [8] using a PCR preamplification step and size selection for DNA fragments between 150 and 400 bp. FoxA1 ChIP and control DNA were each sequenced with two lanes by the Illumina/Solexa 1G Genome Analyzer, and yielded 3.9 million and 5.2 million uniquely mapped tags, respectively. Software implementation MACS is implemented in Python and freely available with an open source Artistic License at [16]. It runs from the command line and takes the following parameters: -t for treatment file (ChIP tags, this is the ONLY required parameter for MACS) and -c for control file containing mapped tags; --format for input file format in BED or ELAND (output) format (default BED); --name for name of the run (for example, FoxA1, default NA); --gsize for mappable genome size to calculate λBG from tag count (default 2.7G bp, approximately the mappable human genome size); --tsize for tag size (default 25); --bw for bandwidth, which is half of the estimated sonication size (default 300); --pvalue for p-value cutoff to call peaks (default 1e-5); --mfold for high-confidence fold-enrichment to find model peaks for MACS modeling (default 32); --diag for generating the table to evaluate sequence saturation (default off). In addition, the user has the option to shift tags by an arbitrary number (--shiftsize) without the MACS model (--nomodel), to use a global lambda (--nolambda) to call peaks, and to show debugging and warning messages (--verbose). If a user has replicate files for ChIP or control, it is recommended to concatenate all replicates into one input file. The output includes one BED file containing the peak chromosome coordinates, and one xls file containing the genome coordinates, summit, p-value, fold_enrichment and FDR (if control is available) of each peak. For FoxA1 ChIP-Seq in MCF7 cells with 3.9 million and 5.2 million ChIP and control tags, respectively, it takes MACS 15 seconds to model the ChIP-DNA size distribution and less than 3 minutes to detect peaks on a 2 GHz CPU Linux computer with 2 GB of RAM. Figure S6 in Additional data file 1 illustrates the whole process with a flow chart. Abbreviations ChIP, chromatin immunoprecipitation; CTCF, CCCTC-binding factor; FDR, false discovery rate; FoxA1, hepatocyte nuclear factor 3α; MACS, Model-based Analysis of ChIP-Seq data; NRSF, neuron-restrictive silencer factor. Authors' contributions XSL, WL and YZ conceived the project and wrote the paper. YZ, TL and CAM designed the algorithm, performed the research and implemented the software. JE, DSJ, BEB, CN, RMM and MB performed FoxA1 ChIP-Seq experiments and contributed to ideas. All authors read and approved the final manuscript. Additional data files The following additional data are available. Additional data file 1 contains supporting Figures S1-S6, and supporting Tables S1 and S2. Supplementary Material Additional data file 1 Figures S1-S6, and Tables S1 and S2. Click here for file

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A global reference for human genetic variation

Lachlan Coin, Robert Garry, Oleksyk Taras (2017)

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

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The Molecular Signatures Database (MSigDB) hallmark gene set collection.

Arthur Liberzon, Chet Birger, Helga Thorvaldsdóttir … (2015)

The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.

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Author and article information

Journal

Journal ID (nlm-journal-id): 9216904

Journal ID (pubmed-jr-id): 2419

Journal ID (nlm-ta): Nat Genet

Journal ID (iso-abbrev): Nat. Genet.

Title: Nature genetics

ISSN (Print): 1061-4036

ISSN (Electronic): 1546-1718

Publication date Nihms-submitted: 17 February 2018

Publication date (Electronic): 26 April 2018

Publication date (Print): May 2018

Publication date PMC-release: 26 October 2018

Volume: 50

Issue: 5

Pages: 668-681

Affiliations

[1 ]Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, AU

[2 ]Queensland Brain Institute, The University of Queensland, Brisbane, QLD, AU

[3 ]Medical and Population Genetics, Broad Institute, Cambridge, MA, US

[4 ]Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, US

[5 ]Department of Psychiatry and Psychotherapy, Universitätsmedizin Berlin Campus Charité Mitte, Berlin, DE

[6 ]Department of Biomedicine, Aarhus University, Aarhus, DK

[7 ]iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, DK

[8 ]iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research,, DK

[9 ]Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, SE

[10 ]Dept of Biological Psychology & EMGO+ Institute for Health and Care Research, Vrije Universiteit Amsterdam, Amsterdam, NL

[11 ]Division of Psychiatry, University of Edinburgh, Edinburgh, GB

[12 ]Centre for Integrated Register-based Research, Aarhus University, Aarhus, DK

[13 ]National Centre for Register-Based Research, Aarhus University, Aarhus, DK

[14 ]Discipline of Psychiatry, University of Adelaide, Adelaide, SA, AU

[15 ]Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, DE

[16 ]Munich Cluster for Systems Neurology (SyNergy), Munich, DE

[17 ]Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, US

[18 ]Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, DK

[19 ]Department of Psychiatry, Vrije Universiteit Medical Center and GGZ inGeest, Amsterdam, NL

[20 ]Virginia Institute for Psychiatric and Behavior Genetics, Richmond, VA, US

[21 ]Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, US

[22 ]Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE

[23 ]Department of Clinical Medicine, Translational Neuropsychiatry Unit, Aarhus University, Aarhus, DK

[24 ]Statistical genomics and systems genetics, European Bioinformatics Institute (EMBL-EBI), Cambridge, GB

[25 ]Human Genetics, Wellcome Trust Sanger Institute, Cambridge, GB

[26 ]Department of Psychiatry, University Hospital of Lausanne, Prilly, Vaud, CH

[27 ]MRC Social Genetic and Developmental Psychiatry Centre, King’s College London, London, GB

[28 ]Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Herston, QLD, AU

[29 ]Centre for Advanced Imaging, The University of Queensland, Saint Lucia, QLD, AU

[30 ]Queensland Brain Institute, The University of Queensland, Saint Lucia, QLD, AU

[31 ]Psychological Medicine, Cardiff University, Cardiff, GB

[32 ]Center for Genomic and Computational Biology, Duke University, Durham, NC, US

[33 ]Department of Pediatrics, Division of Medical Genetics, Duke University, Durham, NC, US

[34 ]Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, US

[35 ]Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

[36 ]Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, GB

[37 ]Institute of Human Genetics, University of Bonn, Bonn, DE

[38 ]Life&Brain Center, Department of Genomics, University of Bonn, Bonn, DE

[39 ]Psychiatry, Dokuz Eylul University School of Medicine, Izmir, TR

[40 ]Epidemiology, Erasmus MC, Rotterdam, Zuid-Holland, NL

[41 ]Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, US

[42 ]Department of Psychiatry, Massachusetts General Hospital, Boston, MA, US

[43 ]Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Massachusetts General Hospital, Boston, MA, US

[44 ]Research, 23andMe, Inc., Mountain View, CA, US

[45 ]Neuroscience and Mental Health, Cardiff University, Cardiff, GB

[46 ]Bioinformatics, University of British Columbia, Vancouver, BC, CA

[47 ]Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, US

[48 ]Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, US

[49 ]Department of Psychiatry (UPK), University of Basel, Basel, CH

[50 ]Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, CH

[51 ]Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Baden-Württemberg, DE

[52 ]Department of Psychiatry, Trinity College Dublin, Dublin, IE

[53 ]Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, US

[54 ]Psychiatry & Behavioral Sciences, Johns Hopkins University, Baltimore, MD, US

[55 ]Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, AU

[56 ]Bioinformatics Research Centre, Aarhus University, Aarhus, DK

[57 ]Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, GB

[58 ]University of Exeter Medical School, Exeter, UK

[59 ]Danish Headache Centre, Department of Neurology, Rigshospitalet, Glostrup, DK

[60 ]Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Capital Region of Denmark, Copenhagen, DK

[61 ]iPSYCH, The Lundbeck Foundation Initiative for Psychiatric Research, Copenhagen, DK

[62 ]Brain and Mind Centre, University of Sydney, Sydney, NSW, AU

[63 ]Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Mecklenburg-Vorpommern, DE

[64 ]Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, CH

[65 ]Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, US

[66 ]Statistics, Pfizer Global Research and Development, Groton, CT, US

[67 ]Max Planck Institute of Psychiatry, Munich, DE

[68 ]Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, US

[69 ]Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, US

[70 ]Division of Research, Kaiser Permanente Northern California, Oakland, CA, US

[71 ]Psychiatry & The Behavioral Sciences, University of Southern California, Los Angeles, CA, US

[72 ]Informatics Program, Boston Children’s Hospital, Boston, MA, US

[73 ]Department of Medicine, Brigham and Women’s Hospital, Boston, MA, US

[74 ]Department of Biomedical Informatics, Harvard Medical School, Boston, MA, US

[75 ]Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, GB

[76 ]Department of Endocrinology at Herlev University Hospital, University of Copenhagen, Copenhagen, DK

[77 ]Swiss Institute of Bioinformatics, Lausanne, VD, CH

[78 ]Institute of Social and Preventive Medicine (IUMSP), University Hospital of Lausanne, Lausanne, VD, CH

[79 ]Dept of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA

[80 ]Mental Health, NHS 24, Glasgow, GB

[81 ]Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, GB

[82 ]Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, DE

[83 ]Statistics, University of Oxford, Oxford, GB

[84 ]Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, US

[85 ]School of Psychology and Counseling, Queensland University of Technology, Brisbane, QLD, AU

[86 ]Child and Youth Mental Health Service, Children’s Health Queensland Hospital and Health Service, South Brisbane, QLD, AU

[87 ]Child Health Research Centre, University of Queensland, Brisbane, QLD, AU

[88 ]Estonian Genome Center, University of Tartu, Tartu, EE

[89 ]Medical Genetics, University of British Columbia, Vancouver, BC, CA

[90 ]Statistics, University of British Columbia, Vancouver, BC, CA

[91 ]DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, University Medicine Greifswald, Greifswald, Mecklenburg-Vorpommern, DE

[92 ]Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Mecklenburg-Vorpommern, DE

[93 ]Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, AU

[94 ]Humus, Reykjavik, IS

[95 ]MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, GB

[96 ]Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US

[97 ]Complex Trait Genetics, Vrije Universiteit Amsterdam, Amsterdam, NL

[98 ]Clinical Genetics, Vrije Universiteit Medical Center, Amsterdam, NL

[99 ]Department of Psychiatry, Brigham and Women’s Hospital, Boston, MA, US

[100 ]Solid Biosciences, Boston, MA, US

[101 ]Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, MO, US

[102 ]Department of Biochemistry and Molecular Biology II, Institute of Neurosciences, Center for Biomedical Research, University of Granada, Granada, ES

[103 ]Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, NL

[104 ]Department of Psychiatry and Psychotherapy, Medical Center of the University of Munich, Campus Innenstadt, Munich, DE

[105 ]Institute of Psychiatric Phenomics and Genomics (IPPG), Medical Center of the University of Munich, Campus Innenstadt, Munich, DE

[106 ]Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, US

[107 ]Behavioral Health Services, Kaiser Permanente Washington, Seattle, WA, US

[108 ]Faculty of Medicine, Department of Psychiatry, University of Iceland, Reykjavik, IS

[109 ]School of Medicine and Dentistry, James Cook University, Townsville, QLD, AU

[110 ]Institute of Health and Wellbeing, University of Glasgow, Glasgow, GB

[111 ]deCODE Genetics / Amgen, Reykjavik, IS

[112 ]Psychiatry & Human Behavior, University of Mississippi Medical Center, Jackson, MS, US

[113 ]College of Biomedical and Life Sciences, Cardiff University, Cardiff, GB

[114 ]Institute of Epidemiology and Social Medicine, University of Münster, Münster, Nordrhein-Westfalen, DE

[115 ]Institute for Community Medicine, University Medicine Greifswald, Greifswald, Mecklenburg-Vorpommern, DE

[116 ]KG Jebsen Centre for Psychosis Research, Norway Division of Mental Health and Addiction, Oslo University Hospital, Oslo, NO

[117 ]Department of Psychiatry, University of California, San Diego, San Diego, CA, US

[118 ]Medical Genetics Section, CGEM, IGMM, University of Edinburgh, Edinburgh, GB

[119 ]Clinical Neurosciences, University of Cambridge, Cambridge, GB

[120 ]Internal Medicine, Erasmus MC, Rotterdam, Zuid-Holland, NL

[121 ]Roche Pharmaceutical Research and Early Development, Neuroscience, Ophthalmology and Rare Diseases Discovery & Translational Medicine Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, CH

[122 ]Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Mecklenburg-Vorpommern, DE

[123 ]Department of Psychiatry, Leiden University Medical Center, Leiden, NL

[124 ]Virginia Institute of Psychiatric & Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US

[125 ]Computational Sciences Center of Emphasis, Pfizer Global Research and Development, Cambridge, MA, US

[126 ]Institute for Molecular Bioscience; Queensland Brain Institute, The University of Queensland, Brisbane, QLD, AU

[127 ]Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, NL

[128 ]Department of Psychiatry, University of Münster, Münster, Nordrhein-Westfalen, DE

[129 ]Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, DE

[130 ]Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, CH

[131 ]Amsterdam Public Health Institute, Vrije Universiteit Medical Center, Amsterdam, NL

[132 ]Centre for Integrative Biology, Università degli Studi di Trento, Trento, Trentino-Alto Adige, IT

[133 ]Department of Psychiatry and Psychotherapy, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Rheinland-Pfalz, DE

[134 ]Psychiatry, Kaiser Permanente Northern California, San Francisco, CA, US

[135 ]Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, GB

[136 ]Centre for Addiction and Mental Health, Toronto, ON, CA

[137 ]Department of Psychiatry, University of Toronto, Toronto, ON, CA

[138 ]Division of Psychiatry, University College London, London, GB

[139 ]Neuroscience Therapeutic Area, Janssen Research and Development, LLC, Titusville, NJ, US

[140 ]Institute of Molecular and Cell Biology, University of Tartu, Tartu, EE

[141 ]Psychosis Research Unit, Aarhus University Hospital, Risskov, Aarhus, DK

[142 ]University of Liverpool, Liverpool, GB

[143 ]Mental Health Center Copenhagen, Copenhagen University Hospital, Copenhagen, DK

[144 ]Human Genetics and Computational Biomedicine, Pfizer Global Research and Development, Groton, CT, US

[145 ]Psychiatry, Harvard Medical School, Boston, MA, US

[146 ]Psychiatry, University of Iowa, Iowa City, IA, US

[147 ]Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, US

[148 ]Human Genetics Branch, NIMH Division of Intramural Research Programs, Bethesda, MD, US

[149 ]Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Niedersachsen, DE

[150 ]Faculty of Medicine, University of Iceland, Reykjavik, IS

[151 ]Child and Adolescent Psychiatry, Erasmus MC, Rotterdam, Zuid-Holland, NL

[152 ]Psychiatry, Erasmus MC, Rotterdam, Zuid-Holland, NL

[153 ]Psychiatry, Dalhousie University, Halifax, NS, CA

[154 ]Division of Epidemiology, New York State Psychiatric Institute, New York, NY, US

[155 ]Department of Clinical Medicine, University of Copenhagen, Copenhagen, DK

[156 ]Human Genetics and Computational Biomedicine, Pfizer Global Research and Development, Cambridge, MA, US

[157 ]Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US

[158 ]Department of Medical & Molecular Genetics, King’s College London, London, GB

[159 ]Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, US

[160 ]NIHR BRC for Mental Health, King’s College London, London, GB

[161 ]Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, US

Author notes

Correspond with: PF Sullivan ( pfsulliv@ 123456med.unc.edu ), Department of Genetics, CB#7264, University of North Carolina, Chapel Hill, NC, 27599-7264, USA. Voice, +919-966-3358. NR Wray ( naomi.wray@ 123456uq.edu.au ), Institute for Molecular Bioscience, Queensland Brain Institute, Brisbane, Australia. Voice, +61 7 334 66374

[†]

Equal contributions.

[*]

Co-last authors.

Article

Manuscript ID: NIHMS943355

DOI: 10.1038/s41588-018-0090-3

PMC ID: 5934326

PubMed ID: 29700475

SO-VID: f5343316-aa8b-4a76-9063-f118126f85d1

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Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression

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