Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Meta-Analysis
, 50 (5), 668-681

Genome-wide Association Analyses Identify 44 Risk Variants and Refine the Genetic Architecture of Major Depression

Naomi R Wray  1   2 Stephan Ripke  3   4   5 Manuel Mattheisen  6   7   8   9 Maciej Trzaskowski  10 Enda M Byrne  10 Abdel Abdellaoui  11 Mark J Adams  12 Esben Agerbo  8   13   14 Tracy M Air  15 Till M F Andlauer  16   17 Silviu-Alin Bacanu  18 Marie Bækvad-Hansen  8   19 Aartjan F T Beekman  20 Tim B Bigdeli  18   21 Elisabeth B Binder  16   22 Douglas R H Blackwood  12 Julien Bryois  23 Henriette N Buttenschøn  7   8   24 Jonas Bybjerg-Grauholm  8   19 Na Cai  25   26 Enrique Castelao  27 Jane Hvarregaard Christensen  6   7   8 Toni-Kim Clarke  12 Jonathan I R Coleman  28 Lucía Colodro-Conde  29 Baptiste Couvy-Duchesne  30   31 Nick Craddock  32 Gregory E Crawford  33   34 Cheynna A Crowley  35 Hassan S Dashti  3   36 Gail Davies  37 Ian J Deary  37 Franziska Degenhardt  38   39 Eske M Derks  29 Nese Direk  40   41 Conor V Dolan  11 Erin C Dunn  42   43   44 Thalia C Eley  28 Nicholas Eriksson  45 Valentina Escott-Price  46 Farnush Hassan Farhadi Kiadeh  47 Hilary K Finucane  48   49 Andreas J Forstner  38   39   50   51 Josef Frank  52 Héléna A Gaspar  28 Michael Gill  53 Paola Giusti-Rodríguez  54 Fernando S Goes  55 Scott D Gordon  56 Jakob Grove  6   7   8   57 Lynsey S Hall  12   58 Eilis Hannon  59 Christine Søholm Hansen  8   19 Thomas F Hansen  60   61   62 Stefan Herms  38   39   51 Ian B Hickie  63 Per Hoffmann  38   39   51 Georg Homuth  64 Carsten Horn  65 Jouke-Jan Hottenga  11 David M Hougaard  8   19 Ming Hu  66 Craig L Hyde  67 Marcus Ising  68 Rick Jansen  20 Fulai Jin  69   70 Eric Jorgenson  71 James A Knowles  72 Isaac S Kohane  73   74   75 Julia Kraft  5 Warren W Kretzschmar  76 Jesper Krogh  77 Zoltán Kutalik  78   79 Jacqueline M Lane  3   36   80 Yihan Li  76 Yun Li  35   54 Penelope A Lind  29 Xiaoxiao Liu  70 Leina Lu  70 Donald J MacIntyre  81   82 Dean F MacKinnon  55 Robert M Maier  30 Wolfgang Maier  83 Jonathan Marchini  84 Hamdi Mbarek  11 Patrick McGrath  85 Peter McGuffin  28 Sarah E Medland  29 Divya Mehta  30   86 Christel M Middeldorp  11   87   88 Evelin Mihailov  89 Yuri Milaneschi  20 Lili Milani  89 Jonathan Mill  59 Francis M Mondimore  55 Grant W Montgomery  10 Sara Mostafavi  90   91 Niamh Mullins  28 Matthias Nauck  92   93 Bernard Ng  91 Michel G Nivard  11 Dale R Nyholt  94 Paul F O'Reilly  28 Hogni Oskarsson  95 Michael J Owen  96 Jodie N Painter  29 Carsten Bøcker Pedersen  8   13   14 Marianne Giørtz Pedersen  8   13   14 Roseann E Peterson  18   97 Erik Pettersson  23 Wouter J Peyrot  20 Giorgio Pistis  27 Danielle Posthuma  98   99 Shaun M Purcell  100 Jorge A Quiroz  101 Per Qvist  6   7   8 John P Rice  102 Brien P Riley  18 Margarita Rivera  28   103 Saira Saeed Mirza  41 Richa Saxena  3   36   80 Robert Schoevers  104 Eva C Schulte  105   106 Ling Shen  71 Jianxin Shi  107 Stanley I Shyn  108 Engilbert Sigurdsson  109 Grant B C Sinnamon  110 Johannes H Smit  20 Daniel J Smith  111 Hreinn Stefansson  112 Stacy Steinberg  112 Craig A Stockmeier  113 Fabian Streit  52 Jana Strohmaier  52 Katherine E Tansey  114 Henning Teismann  115 Alexander Teumer  116 Wesley Thompson  8   61   117   118 Pippa A Thomson  119 Thorgeir E Thorgeirsson  112 Chao Tian  45 Matthew Traylor  120 Jens Treutlein  52 Vassily Trubetskoy  5 André G Uitterlinden  121 Daniel Umbricht  122 Sandra Van der Auwera  123 Albert M van Hemert  124 Alexander Viktorin  23 Peter M Visscher  10   30 Yunpeng Wang  8   61   117 Bradley T Webb  125 Shantel Marie Weinsheimer  8   61 Jürgen Wellmann  115 Gonneke Willemsen  11 Stephanie H Witt  52 Yang Wu  10 Hualin S Xi  126 Jian Yang  10   30 Futao Zhang  10 eQTLGen23andMeVolker Arolt  127 Bernhard T Baune  15 Klaus Berger  115 Dorret I Boomsma  11 Sven Cichon  38   51   128   129 Udo Dannlowski  127 E C J de Geus  11   130 J Raymond DePaulo  55 Enrico Domenici  131 Katharina Domschke  132 Tõnu Esko  3   89 Hans J Grabe  123 Steven P Hamilton  133 Caroline Hayward  134 Andrew C Heath  102 David A Hinds  45 Kenneth S Kendler  18 Stefan Kloiber  68   135   136 Glyn Lewis  137 Qingqin S Li  138 Susanne Lucae  68 Pamela F A Madden  102 Patrik K Magnusson  23 Nicholas G Martin  56 Andrew M McIntosh  12   37 Andres Metspalu  89   139 Ole Mors  8   140 Preben Bo Mortensen  7   8   13   14 Bertram Müller-Myhsok  16   17   141 Merete Nordentoft  8   142 Markus M Nöthen  38   39 Michael C O'Donovan  96 Sara A Paciga  143 Nancy L Pedersen  23 Brenda W J H Penninx  20 Roy H Perlis  43   144 David J Porteous  119 James B Potash  145 Martin Preisig  27 Marcella Rietschel  52 Catherine Schaefer  71 Thomas G Schulze  52   55   106   146   147 Jordan W Smoller  42   43   44 Kari Stefansson  112   148 Henning Tiemeier  41   149   150 Rudolf Uher  151 Henry Völzke  116 Myrna M Weissman  85   152 Thomas Werge  8   61   153 Ashley R Winslow  154   155 Cathryn M Lewis  28   156 Douglas F Levinson  157 Gerome Breen  28   158 Anders D Børglum  6   7   8 Patrick F Sullivan  159   160   161 Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium
Affiliations
Meta-Analysis

Genome-wide Association Analyses Identify 44 Risk Variants and Refine the Genetic Architecture of Major Depression

Naomi R Wray et al. Nat Genet.

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 meta-analysis based in 135,458 cases and 344,901 controls and 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 relationships 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 the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.

Conflict of interest statement

Competing Financial Interests

Aartjan TF Beekman: Speakers bureaus of Lundbeck and GlaxoSmithKline. Greg Crawford: Co-founder of Element Genomics. Enrico Domenici: Employee of Hoffmann-La Roche at the time this study was conducted, consultant to Roche and Pierre-Fabre. Nicholas Eriksson: Employed by 23andMe, Inc. and owns stock in 23andMe, Inc. David Hinds: Employee of and own stock options in 23andMe, Inc. Sara Paciga: Employee of Pfizer, Inc. Craig L Hyde: Employee of Pfizer, Inc. Ashley R Winslow: Former employee and stockholder of Pfizer, Inc. Jorge A Quiroz: Employee of Hoffmann-La Roche at the time this study was conducted. Hreinn Stefansson: Employee of deCODE Genetics/AMGEN. Kari Stefansson: Employee of deCODE Genetics/AMGEN. Stacy Steinberg: Employee of deCODE Genetics/AMGEN. Patrick F Sullivan: Scientific advisory board for Pfizer Inc and an advisory committee for Lundbeck. Thorgeir E Thorgeirsson: Employee of deCODE Genetics/AMGEN. Chao Tian: Employee of and own stock options in 23andMe, Inc.

Figures

Fig. 1
Fig. 1. Results of GWA meta-analysis of seven cohorts for major depression
(a) Relation between adding cohorts and number of genome-wide significant genomic regions (before the rigorous vetting used to define the final 44 regions). Beginning with the largest cohort (#1 on the x-axis), added the next largest cohort (#2) until all cohorts were included (#7). The number next to each point shows the total effective sample size equivalent to sample size where the numbers of cases and controls are equal. (b) Association test quantile-quantile plot showing a marked departure from a null model of no associations (y-axis truncated 10−12). (c) Manhattan plot with x-axis showing genomic position (chr1-chr22 plus chrX), and the y-axis showing statistical significance as –log10(P) t-statistic; threshold for significance accounting for multiple testing shown by horizontal line. Association test from meta-analysis of 135,458 major depression cases and 344,901 controls. The red line shows the genome-wide significance threshold (P=5×10−8).
Fig. 2
Fig. 2. Genetic risk score (GRS) prediction analyses into PGC29 MDD target samples
(a) Variance explained (liability scale) based on different discovery samples for three target samples: PGC29 (16,823 cases, 25,632 controls), iPSYCH (a nationally representative sample of 18,629 cases and 17,841 controls,) and a clinical cohort from Münster not included in the GWA analysis (845 MDD inpatient cases, 834 controls). PGC29-LOO: Target sample is one of the PGC29 samples, with discovery sample the remaining 28 PGC29 samples, hence, leave-one-out. (b) Odds ratios of major depression per GRS decile relative to the first decile for iPSYCH and PGC29 target samples. (c) Odds ratios of major depression in GRS standard deviation (SD): 3,950 early onset vs 3,950 late onset cases earlier age at onset; 4,958 severe vs 3,976 moderate cases defined by count of endorsed MDD symptom criteria; 5,574 cases recurrent MDD vs 12,968 single episode cases; severity defined as chronic/unremitting MDD 610 “Stage IV” cases vs 499 “Stage II” or 332 first-episode MDD used the NESDA sample from PGC29. Error bars represent 95% confidence intervals. Logistic regression association test p-values in the target sample for GRS generated from SNPs with p-value < 0.05 in the discovery sample.
Fig. 3
Fig. 3. Comparisons of the major depression GWA meta-analysis
(a) Enrichment in bulk tissue mRNA-seq from GTEx; t-statistic, sample sizes in GTEx range from N=75–564. Threshold for significance accounting for multiple testing shown by vertical line. (b) Major depression results and enrichment in three major brain cell types; t-statistic; threshold for significance accounting for multiple testing shown by horizontal line. Sample sizes vary as these data are aggregated from many different sources. (c) Partitioned LDSC to evaluate enrichment of the major depression GWA findings in over 50 functional genomic annotations (Supplementary Table 8); enrichment statistic; threshold for significance accounting for multiple testing given by horizontal dashed line. Sample sizes vary as these data are aggregated from many different sources.
Fig. 4
Fig. 4
Generative topographic mapping of the 19 significant pathway results. The average position of each pathway on the map is represented by a point. The map is colored by the –log10(P) obtained using MAGMA. The X and Y coordinates result from a kernel generative topographic mapping algorithm (GTM) that reduces high dimensional gene sets to a two-dimensional scatterplot by accounting for gene overlap between gene sets. Each point represents a gene set. Nearby points are more similar in gene overlap than more distant points. The color surrounding each point (gene set) indicates significance per the scale on the right. The significant pathways (Supplementary Table 11) fall into nine main clusters as described in the text.

Similar articles

See all similar articles

Cited by 224 PubMed Central articles

See all "Cited by" articles

References

    1. Kessler RC, Bromet EJ. The epidemiology of depression across cultures. Annu Rev Public Health. 2013;34:119–38. - PMC - PubMed
    1. Judd LL. The clinical course of unipolar major depressive disorders. Archives of general psychiatry. 1997;54:989–91. - PubMed
    1. Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet. 2006;367:1747–57. - PubMed
    1. Wittchen HU, et al. The size and burden of mental disorders and other disorders of the brain in Europe 2010. European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology. 2011;21:655–79. - PubMed
    1. Ferrari AJ, et al. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med. 2013;10:e1001547. - PMC - PubMed

Publication types

Grant support

LinkOut - more resources

Feedback