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Nat Genet. Author manuscript; available in PMC Nov 1, 2011.
Published in final edited form as:
Published online Apr 3, 2011. doi:  10.1038/ng.803
PMCID: PMC3084173
UKMSID: UKMS34702
Common variants in ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease
Paul Hollingworth,1,109 Denise Harold,1,109 Rebecca Sims,1,109 Amy Gerrish,1,109 Jean-Charles Lambert,2,3,4,109 Minerva M Carrasquillo,5,109 Richard Abraham,1 Marian L Hamshere,1 Jaspreet Singh Pahwa,1 Valentina Moskvina,1 Kimberley Dowzell,1 Nicola Jones,1 Alexandra Stretton,1 Charlene Thomas,1 Alex Richards,1 Dobril Ivanov,1 Caroline Widdowson,1 Jade Chapman,1 Simon Lovestone,6,7 John Powell,7 Petroula Proitsi,7 Michelle K Lupton,7 Carol Brayne,8 David C Rubinsztein,9 Michael Gill,10 Brian Lawlor,10 Aoibhinn Lynch,10 Kristelle S Brown,11 Peter A Passmore,12 David Craig,12 Bernadette McGuinness,12 Stephen Todd,12 Clive Holmes,13 David Mann,14 A David Smith,15 Helen Beaumont,15 Donald Warden,15 Gordon Wilcock,16 Seth Love,17 Patrick G Kehoe,17 Nigel M Hooper,18 Emma R. L. C. Vardy,14,18,19 John Hardy,20,21 Simon Mead,22 Nick C Fox,22 Martin Rossor,22 John Collinge,22 Wolfgang Maier,23,24 Frank Jessen,23 Britta Schürmann,23,26 Eckart Rüther,24,25,26 Reiner Heun,23,27 Heike Kölsch,23 Hendrik van den Bussche,28 Isabella Heuser,29 Johannes Kornhuber,30 Jens Wiltfang,31 Martin Dichgans,32,33 Lutz Frölich,34 Harald Hampel,35 Michael Hüll,36 John Gallacher,36 Dan Rujescu,35 Ina Giegling,35 Alison M Goate,37,38,39 John S K Kauwe,40 Carlos Cruchaga,37 Petra Nowotny,37 John C Morris,38 Kevin Mayo,37 Kristel Sleegers,41,42 Karolien Bettens,41,42 Sebastiaan Engelborghs,41,43 Peter P De Deyn,41,43 Christine Van Broeckhoven,41,42 Gill Livingston,44 Nicholas J Bass,44 Hugh Gurling,44 Andrew McQuillin,44 Rhian Gwilliam,45 Panagiotis Deloukas,45 Ammar Al-Chalabi,46 Christopher E Shaw,46 Magda Tsolaki,47 Andrew B Singleton,48 Rita Guerreiro,48 Thomas W Mühleisen,49,50 Markus M Nöthen,25,49,50 Susanne Moebus,51 Karl-Heinz Jöckel,51 Norman Klopp,52 H-Erich Wichmann,52,53,54 V Shane Pankratz,55 Sigrid B Sando,56,57 Jan O Aasly,56,57 Maria Barcikowska,58 Zbigniew K Wszolek,59 Dennis W Dickson,5 Neill R Graff-Radford,5,59 Ronald C Petersen,60,61 the Alzheimer’s Disease Neuroimaging Initiative,62 Cornelia M van Duijn,63,64 Monique MB Breteler,63,64 M Arfan Ikram,63,64 Anita L DeStefano,65,66 Annette L Fitzpatrick,67 Oscar Lopez,68,69 Lenore J Launer,70 Sudha Seshadri,66,71 CHARGE consortium, Claudine Berr,72 Dominique Campion,73 Jacques Epelbaum,74 Jean-François Dartigues,75 Christophe Tzourio,76 Annick Alpérovitch,76 Mark Lathrop,77,78 EADI1 consortium, Thomas M Feulner,79 Patricia Friedrich,79 Caterina Riehle,79 Michael Krawczak,80,81,82 Stefan Schreiber,81,82 Manuel Mayhaus,79 S Nicolhaus,82 Stefan Wagenpfeil,83 Stacy Steinberg,84 Hreinn Stefansson,84 Kari Stefansson,85 Jon Snædal,86 Sigurbjörn Björnsson,86 Palmi V. Jonsson,86 Vincent Chouraki,2,3,4 Benjamin Genier-Boley,2,3,4 Mikko Hiltunen,87 Hilkka Soininen,87 Onofre Combarros,88,89 Diana Zelenika,90 Marc Delepine,90 Maria J Bullido,89,91 Florence Pasquier,4,92 Ignacio Mateo,88,89 Ana Frank-Garcia,89,93 Elisa Porcellini,94 Olivier Hanon,95 Eliecer Coto,96 Victoria Alvarez,96 Paolo Bosco,97 Gabriele Siciliano,98 Michelangelo Mancuso,98 Francesco Panza,99 Vincenzo Solfrizzi,99 Benedetta Nacmias,100 Sandro Sorbi,100 Paola Bossù,101 Paola Piccardi,102 Beatrice Arosio,103 Giorgio Annoni,104 Davide Seripa,105 Alberto Pilotto,105 Elio Scarpini,106 Daniela Galimberti,106 Alexis Brice,107 Didier Hannequin,108 Federico Licastro,94 Lesley Jones,1 Peter A Holmans,1 Thorlakur Jonsson,84 Matthias Riemenschneider,79 Kevin Morgan,11 Steven G Younkin,5 Michael J Owen,1 Michael O’Donovan,1 Philippe Amouyel,2,3,4,92 and Julie Williams1
1Medical Research Council (MRC) Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Neurology, School of Medicine, Cardiff University, Cardiff, UK.
2Inserm U744, F-59019 Lille, France.
3Institut Pasteur de Lille, F-59019, Lille, France.
4Université de Lille Nord de France, F-59000 Lille, France.
5Department of Neuroscience, Mayo Clinic College of Medicine, Jacksonville, Florida, USA.
6National Institute for Health Research Biomedical Research Centre for Mental Health at the South London and Maudsley National Health Service Foundation Trust and Institute of Psychiatry, Kings College, London, UK.
7Department of Neuroscience, Institute of Psychiatry, Kings College, London, UK.
8Institute of Public Health, University of Cambridge, Cambridge, UK.
9Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK.
10Mercer’s Institute for Research on Aging, St. James Hospital and Trinity College, Dublin, Ireland.
11Institute of Genetics, Queen’s Medical Centre, University of Nottingham, Nottingham, UK.
12Ageing Group, Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, UK.
13Division of Clinical Neurosciences, School of Medicine, University of Southampton, Southampton, UK.
14Neurodegeneration and Mental Health Research Group, School of Community Based Medicine, University of Manchester, Hope Hospital, Stott Lane, Salford, Manchester, UK.
15Oxford Project to Investigate Memory and Ageing (OPTIMA), University of Oxford, John Radcliffe Hospital, Oxford, UK.
16Nuffield Department of Clinical Medicine, Medical Sciences Division. University of Oxford, Headington, Oxford. OX3 7BN. UK.
17Dementia Research Group, University of Bristol Institute of Clinical Neurosciences, Frenchay Hospital, Bristol, UK.
18Institute of Molecular and Cellular Biology, Faculty of Biological Sciences, LIGHT Laboratories, University of Leeds, LS2 9JT, UK
19Cerebral Function Unit, Salford Royal NHS Trust, Stott Lane, Salford, M6 8HD, UK.
20Department of Molecular Neuroscience, Institute of Neurology, London, UK.
21Reta Lilla Weston Laboratories, Institute of Neurology, London, UK.
22Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.
23Department of Psychiatry, University of Bonn, Bonn, Germany.
24German Centre for Neurodegenerative Diseases, Bonn, Bonn, Germany.
25Institute for Molecular Psychiatry, University of Bonn, Bonn, Germany.
26Department of Psychiatry, University of Göttingen, Germany.
27Department of Psychiatry, Royal Derby Hospital, Derby, DE22 3WQ, UK.
28Institute of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Germany.
29Department of Psychiatry, Charité Berlin, Berlin, Germany.
30Department of Psychiatry and Psychotherapy, University of Erlangen-Nuremberg, Germany.
31Landschaftsverband Rheinland-Hospital Essen, Department of Psychiatry and Psychotherapy, University Duisburg-Essen, Essen, Germany.
32Department of Neurology, Klinikum der Universität München, Munich, Germany.
33Institute for Stroke and Dementia Research, Klinikum der Universität München, Munich, Germany.
34Department of Geriatric Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
35Department of Psychiatry, Psychsomatic Medicine and Psychotherapy, Johann Wolfgang Goethe-University, Frankfurt, Germany.
36Department of Primary Care and Public Health, School of Medicine, Cardiff University, Cardiff, UK.
37Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri, USA.
38Department of Neurology, Washington University School of Medicine, St Louis, Missouri, USA.
39Department of Genetics, Washington University School of Medicine, St Louis, Missouri, USA.
40Department of Biology, Brigham Young University, Provo, Utah, USA.
41Institute Born-Bunge and University of Antwerp, Antwerpen, Belgium.
42Neurodegenerative Brain Diseases group, Department of Molecular Genetics, VIB, Antwerpen, Belgium.
43Memory Clinic and Department of Neurology, ZNA Middelheim, Antwerpen, Belgium.
44Department of Mental Health Sciences, University College London, UK.
45The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
46MRC Centre for Neurodegeneration Research, Department of Clinical Neuroscience, King’s College London, Institute of Psychiatry, London, UK.
47Third Department of Neurology, Aristotle University of Thessaloniki, Thessaloniki, Greece.
48Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA.
49Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany.
50Institute of Human Genetics, University of Bonn, Bonn, Germany.
51Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany.
52Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
53Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany.
54Klinikum Grosshadern, Munich, Germany.
55Division of Biomedical Statistics and Informatics, Mayo Clinic and Mayo Foundation, Rochester, Minnesota, USA.
56Department of Neurology, St. Olav’s Hospital, Edvard Griegs Gate 8, 7006 Trondheim, Norway.
57Department of Neuroscience, Norwegian University of Science and Technology, NTNU, 7491 Trondheim, Norway.
58Department of Neurodegenerative Disorders, Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland.
59Department of Neurology, Mayo Clinic College of Medicine, Jacksonville, FL 32224, USA.
60Department of Neurology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
61Mayo Alzheimer Disease Research Center, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
62Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu\ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. ADNI investigators include (complete listing available at www.loni.ucla.edu\ADNI\Collaboration\ADNI_Authorship_list.pdf).
63Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
64Netherlands Consortium for Healthy Aging, The Netherlands.
65Departments of Neurology and Biostatistics, Boston University School of Medicine, Boston, Massachussets, USA.
66The National Heart Lung and Blood Institute’s Framingham Heart Study, Framingham, Massachussets, USA.
67Department of Epidemiology, University of Washington, Seattle, Washington, USA.
68Department of Neurology, The Alzheimer’s Disease Research Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
69Department of Psychiatry, The Alzheimer’s Disease Research Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
70Neuroepidemiology Section, Laboratory of Epidemiology, Demography and Biometry (LJL), National Institute on Aging, Washington DC, USA.
71Department of Neurology, Boston University School of Medicine, Boston, Massachussets, USA.
72Inserm U888, Hôpital La Colombière, Montpellier, France.
73Inserm U614, Faculté de Médecine-Pharmacie de Rouen, Rouen, France.
74UMR 894, Inserm Faculté de Médecine, Université Paris Descartes, Paris, France.
75Inserm U897, Victor Segalen University, Bordeaux, France.
76Inserm U708, Paris, France.
77Centre National de Genotypage, Institut Genomique, Commissariat à l’énergie Atomique, Evry, France.
78Fondation Jean Dausset- CEPH, Paris, France.
79Department of Psychiatry and Psychotherapy, Universitätsklinikum des Saarlandes, Universität des Saarlandes, Germany.
80Institute of Medical Informatics and Statistics, Christian-Albrechts-University, Kiel, Germany.
81Biobank Popgen, Institute of Experimental Medicine, Section of Epidemiology, Christian-Albrechts University, Kiel, Germany
82Institute for Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany.
83Inst. of Medical Statistics and Epidemiology; Klinikum Rechts der Isar, TU-München, Germany.
84deCODE Genetics, Reykjavik, Iceland.
85deCODE Genetics and University of Iceland, Faculty of Medicine, Reykjavik, Iceland.
86Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
87Department of Neurology, University of Eastern Finland and Kuopio University Hospital, 70211, Kuopio, Finland.
88Neurology Service, “Marqués de Valdecilla” University Hospital (University of Cantabria), Santander, Spain.
89CIBERNED, “Marqués de Valdecilla” University Hospital (University of Cantabria), Santander, Spain.
90Centre National de Genotypage, Institut Genomique, Commissariat à l’énergie Atomique, Evry, France.
91Centro de Biologia Molecular Severo Ochoa (CSIC-UAM, Universidad Autonoma, Campus de Cantoblanco, S-28049, Madrid, Spain.
92Centre Hospitalier Régional Universitaire de Lille, Lille, France.
93Servicio de Neurologia, Hospital Universitario La Paz (UAM) 28034 Madrid, Spain.
94Department of Experimental Pathology, School of Medicine, University of Bologna, Italy.
95Departement de Geriatrie, CHU de Dijon, F-21000, Dijon, France.
96Genetic Molecular Unit, Hospital Universitario Central de Asturias, 33006-Oviedo, Spain.
97IRCCS Oasi Maria SS, 94018 Troina , Italy
98Department of Neuroscience, Neurological Clinic, University of Pisa, I-56100, Italy.
99Department of Geriatrics, Center for Aging Brain, Memory Unit, University of Bari, Policlinico, 70124 Bari , Italy.
100Department of Neurological and Psychiatric Sciences, University of Florence, 50134 Florence, Italy.
101Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Roma, Italy.
102Lab of Molecular Genetics, Section of Clinical Pharmacology, Department of Neuroscience, University of Cagliari, Italy.
103Department of Internal Medicine, Università degli Studi di Milano, Fondazione IRCCS, Ospedale Maggiore, Mangiagalli e Regina Elena, Milan Italy.
104Department of Clinical Medicine and Prevention, University of Milano-Bicocca, Monza Italy.
105Geriatric Unit & Gerontology-Geriatric Research Laboratory, Department of Medical Science, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo , I-71013, Italy.
106Dept. of Neurological Sciences, University of Milan, 35 via F. Sforza, Milan, 20122, Italy.
107Inserm, UMR_S679, Hopital de la salpétirère, 75651 Paris, France.
108Inserm U614, Faculté de Médecine-Pharmacie de Rouen, F-76183, Rouen, France.
Corresponding authors
109These authors contributed equally to this work.
Author Contributions J. Williams directed this study-assisted by M.J.O. and M.O.D and also helped by P.H, R.S., A.G., R.A., L.J. and D.Harold. J. Williams, P.H. and D.H. took primary responsibility for drafting the manuscript assisted by R.S., A.G., R.A., M.O. D and M.J.O. All authors contributed to the sample collection, sample preparation, genotyping and/or conduct of the GWAS upon which this study is based. J. Williams, R.A., P.H., R.S., A.G., C.W., J.Chapman, K.D., N.J., , A.S., C. Thomas, S. Lovestone, J.P., P.Priotsi., M.K.L., C.Brayne, D.C.R., M.G., B.L., A.L., K. Morgan, K.S.B., P.A.P., D.Craig, B.M., S.T., C.H., D.M., A.D.S., S. Love, P.G.K., J.H., S. Mead, N.C.F., M.Rossor, J.Collinge., W.M., F.J., B.S., E.R., R.H., H.K, H.v.d.B., I.H., J.K., J. Wiltfang, M.Dichgans, L.F., H.H., M.Hüll, J.G., A.M.G., D.R., I.G., J.S.K.K., C.C., P.N., J.C.M., K. Mayo, K.Sleegers, K.B., S.E. P.P.D., C.v.B.,G.L., N.J.B., H.G., A.M., M.T., T.W.M., M.M.N., S.Moebus, K.J., N.K. and H.W. contributed towards clinical sample collection, ascertainment, diagnosis and preparation of samples from the independent GERAD2 sample genotyped as part of this study. R.S., D.Harold A.G., D.R. and I.G. were responsible for procedures related to genotyping the GERAD2 sample. V.C., B.G., M. Hiltunen, O.C., D.Z., M. Delepine, M.J.B., F.Pasquier, I.M., A.F., E.P., O.H., E. Coto, V.A., P. Bosco, G.S., M. Mancuso, F. Panza, B.N., S.Sorbi, P.Bossu, P.Piccardi, B.A., G.A., D.S., E.S., D.G., A.B., D. Hannequin, F.L., H. Soinine, J.C.L. and P.A. were responsible for sample collection, sample preparation, genotyping and analysis of the EADI2 Sample. S.S, A.L.D, O.L, L.L as well as M.A.I, C.M.v.D., M.M.B.B. contributed clinical and genotypic data to the CHARGE GWAS. J.C.L and P.A. contributed clinical and genotypic data. M.M.C. played a leading role, along with H.B., D.W., G.W., N.M.H., E.V., S.B.D., J.O.A., M.B., Z.K.W., D.W.D., N.R.G.R. P.C.P., K. Morgan and S.G.Y. in sample collection, sample preparation, genotyping and analysis of the Mayo2 Sample. M. Riemenschneider, T.F., P.F., C.R., M.K., S. Schreiber, M. Mayhaus, S.N. and S.W. were responsible for sample collection, conduct and analysis of the AD-IG GWAS. S. Steinberg, T.J., H. Stefansson, K. Stefansson, J.S., S.B. and P.V.J were responsible for sample collection, conduct and analysis of the deCODE GWAS. D.Harold and P.H. completed statistical quality control and produced association statistics, under the supervision of J. Williams and P.A.H. All authors discussed the results and approved the manuscript.
We sought to identify new susceptibility loci for Alzheimer’s disease (AD) through a staged association study (GERAD+) and by testing suggestive loci reported by the Alzheimer’s Disease Genetic Consortium (ADGC). First, we undertook a combined analysis of four genome-wide association datasets (Stage 1) and identified 10 novel variants with P≤1×10−5. These were tested for association in an independent sample (Stage 2). Three SNPs at two loci replicated and showed evidence for association in a further sample (Stage 3). Meta-analyses of all data provide compelling evidence that ABCA7 (meta-P 4.5×10−17; including ADGC meta-P=5.0×10−21) and the MS4A gene cluster (rs610932, meta-P=1.8×10−14; including ADGC meta-P=1.2×10−16; rs670139, meta-P=1.4×10−9; including ADGC meta-P=1.1×10−10) are novel susceptibility loci for AD. Second, we observed independent evidence for association for three suggestive loci reported by the ADGC GWAS, which when combined shows genome-wide significance: CD2AP (GERAD+ P=8.0×10−4; including ADGC meta-P=8.6×10−9), CD33 (GERAD+ P=2.2×10−4; including ADGC meta-P=1.6×10−9) and EPHA1 (GERAD+ P=3.4×10−4; including ADGC meta-P=6.0×10−10). These findings support five novel susceptibility genes for AD.
Alzheimer’s disease (AD) is the most common form of dementia, with both environmental and genetic factors contributing to risk. AD is genetically complex and shows heritability up to 79%1. Rare variants in three genes (APP, PSEN1 & PSEN2)1 cause disease in a minority of cases, but until recently the Apolipoprotein E gene (APOE), was the only gene known to increase disease risk for the common form of AD with late-onset2. In 2009 we published a genome-wide association study (GWAS) of AD in a sample designated GERAD1 (Genetic and Environmental Risk in AD Consortium 1), which identified two new genome-wide significant susceptibility loci: clusterin (CLU: P=8.5×10−10) and phosphatidylinositol-binding clathrin assembly protein gene (PICALM: P=1.3×10−9). We also observed more variants with P-values<1×10−5 than were expected by chance (P=7.5×10−6)3. These included variants in the complement receptor 1 (CR1) gene, the bridging integrator 1 (BIN1) gene and the membrane-spanning 4A gene cluster (MS4A gene cluster). A second independent AD GWAS by Lambert and colleagues4 using the EADI1 sample (European Alzheimer’s Disease Initiative 1) showed genome-wide significant evidence for association with CLU (P=7.5×10−9) and CR1 (P=3.7×10−9), and support for PICALM (P=3×10−3). Combined analysis of the GERAD1 and EADI1 data yield highly significant support for all three loci (CLU meta-P=6.7×10−16, PICALM meta-P=6.3×10−9, CR1 meta-P=3.2×10−12). The associations in CLU, PICALM and CRI have since been replicated in several independent datasets5-8, shown trends in another9 and relationships with neurodegenerative processes underlying disease10. In addition, members of this consortium have since reported genome-wide significant association for BIN1 (P=1.6×10−11) and support for ephrin receptor A1 (EPHA1; P=1.7×10−6)11..
This study sought to identify new common susceptibility variants for AD by first undertaking a three-stage association study based upon predominantly European samples (GERAD+, see Figure 1) and second, by testing these samples for loci showing suggestive evidence for association in the American Alzheimer’s Disease Genetics Consortium (ADGC) GWAS12.
Figure 1
Figure 1
GERAD+ study design.
The first stage of this study comprised a meta-analysis of four AD GWAS datasets (6688 cases, 13685 controls), including: GERAD13, EADI14, Translational Genomics Research Institute (TGEN1)13 and Alzheimer’s Disease Neuroimaging Initiative (ADNI)14. Single nucleotide polymorphisms (SNPs) which remained significant at P≤1×10−5 were then tested for replication in the second stage of this study, comprising 4896 cases and 4903 controls including genotyping of the GERAD2 sample and in silico replication in the deCODE and German Alzheimer’s disease Integrated Genome Research Network (AD-IG) GWAS datasets. In Stage 3, novel SNPs showing significant evidence of replication in Stage 2 were then tested for association in a sample comprising 8286 cases and 21258 controls, which included new genotyping in the EADI24 and Mayo2 samples, and in silico replication in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) sample11. Sample descriptions and characteristics can be found in the Supplementary Note and Supplementary Table 1.
In Stage 1 we identified 61 SNPs associated with AD at P≤1×10−5 following meta-analysis of 496763 SNPs in the GERAD1, TGEN1, ADNI and EADI1 (see Supplementary Table 2 and the Supplementary Note). Ten SNPs at novel loci and two at previously identified susceptibility loci that surpassed the P≤1×10−5 threshold, were selected for further analysis (see below). One SNP, rs610932 (Stage 1 P=1.8×10−8) at the MS4A (membrane spanning 4A) gene cluster, surpassed the threshold (P<5.0×10−8)15 for genome-wide significance. We also observed strong evidence for association at ABCA7 (ATP-binding cassette, sub-family A, member 7; rs3764650; Stage 1 P=2.6×10−7).
When selecting SNPs for testing in Stage 2, we excluded known susceptibility loci that had previously been tested in GERAD2 and limited analysis of BIN1 and CR1, which had not been tested in GERAD2, to the most significant SNPs at each locus (See Supplementary Table 2). Following pruning for linkage disequilibrium, twelve SNPs were taken forward for replication in Stage 2 (10 excluding BIN1 and CR1).
Five of the twelve SNPs tested in Stage 2 showed significant evidence for replication using a Bonferroni adjusted threshold for significance of P=4.2×10−3 (see Table 1 and Supplementary Table 3). In addition to SNPs at BIN1 and CR1, one SNP within ABCA7 (rs3764650, Stage 2 P=1.9×10−5) and two SNPS at the MS4A gene cluster (rs610932, stage 2 P=1.6×10−3; rs670139 Stage 2 P=1.1×10−3) showed evidence of replication in Stage 2. The three SNPs implicating novel risk loci were tested for association in the Stage 3 sample and showed further evidence of replication (rs3764650, Stage 3 P=2.9×10−7; rs610932, Stage 3 P=2.1×10−5; rs670139, Stage 3 P=3.2×10−3; see Table 1 and Supplementary Table 3).
Table 1
Table 1
Results of the GERAD+ study.
We conducted an inverse variance weighted meta-analysis of data from Stages 1, 2 and 3 (See Table 1 and Supplementary Table 3). This provided strong evidence for association with rs3764650 at ABCA7 (meta-P=4.5×10−17) and two SNPs at the MS4A gene cluster: rs610932 (meta-P=1.8×10−14) and rs670139 (meta-P=1.4×10−9). When combining GERAD+ and ADGC results (after removing overlapping samples) ABCA7 has a P-value of 5.0×10−21 (OR=1.22). The two SNPs at the MS4A gene cluster, rs610932 and rs670139, showed P-values of 1.2×10−16 (OR=0.91) and 1.1×10−10 (OR=1.08), respectively, in the combined analysis of GERAD+ and ADGC results. It is noteworthy that the most significant ADGC SNP at the MS4A locus is in LD with our top SNP (rs4938933 with rs610932 r2=0.62, D’=0.86), thus both datasets may be detecting the same underlying signal.
This study also provides additional independent support for association with CR1 (Stage 2 P=1.4×10−3) and BIN1 (Stage 2 P=3.8×10−5; see Table 1 for meta-analysis.) We did not observe interaction between APOE and the novel variants identified in this study, indeed we did not find evidence of epistasis between any of the genome-wide significant variants identified to date (ABCA7, MS4A, BIN1, CR1, PICALM, CLU or APOE) (see Supplementary Table 4a). Likewise, adjusting for the presence of at least one APOE ε4 allele had little effect on the results of analysis of the three novel variants (see Supplementary Table 4b). We also found no evidence for association between these loci and age at onset of AD (rs3764650: P=0.17; rs670139: P=0.38; rs610932: P=0.95; rs744373: P=0.87; rs3818361: P=0.58).
This study therefore shows strong statistical support for two novel AD risk loci, which replicate over a number of independent case-control samples. The first of these is the ATP-binding cassette, sub-family A, member 7 (ABCA7) locus (Figure 2A). The associated marker is rs3764650, which is located in intron 13. This SNP was the only variant in the gene that passed our Stage 1 criterion, which is not unexpected given the low levels of linkage disequilibrium (LD) between this SNP and others included in the GWAS. However, in a preliminary attempt to identify an associated functional variant at the ABCA7 locus, we genotyped the GERAD2 sample for rs3752246, a non-synonymous SNP in exon 32 of the gene, which showed the highest LD with rs3764650 out of all HapMap ABCA7 coding variants based on r2 (r2=0.36, D’=0.89). This variant (which was not genotyped in Stage 1) was also associated with AD (GERAD2 P=1×10−3, OR=1.17). Rs3752246 encodes a glycine to alanine substitution at position 1527 of the protein (accession number NP_061985.2) which is predicted to be a benign change16, and is unlikely to be the relevant functional variant. We used data from two published expression quantitative trait loci (eQTL) datasets (derived from lymphoblastoid cell lines17 and brain18) to determine if rs3764650 is associated with the expression of ABCA7. However, no association was observed (see Supplementary Table 5). Further work will be required to identify the causal variant(s) at this locus.
Figure 2
Figure 2
Schematic of the associated variants reported in reference to (A) the ABCA7 gene and (B) chromosomal region chr11:59.81Mb-60.1Mb harboring members of the MS4A gene cluster. Chromosome positions are shown at the top of the schematics (UCSC Feb 2009). Gene (more ...)
Second, we implicate the membrane-spanning 4A (MS4A) gene cluster (Figure 2B). The association spans an LD block of 293 kb (chr11: 59,814,28760,107,105) and includes 6 of 16 known genes comprising the membrane-spanning 4-domains, subfamily A (MS4A). These are MS4A2, MS4A3, MS4A4A, MS4A4E, MS4A6A and MS4A6E. The associated SNPs are found in the 3′ UTR of MS4A6A (rs610932) and the intergenic region between MS4A4E and MS4A6A (rs670139). rs610932 shows nominally significant association with expression levels of MS4A6A in cerebellum and temporal cortex (0.01<P<0.05; see Supplementary Table 5), but not in frontal cortex, pons, or lymphoblastoid cell lines. The non-synonymous SNP that is most strongly associated with the genome-wide significant variants is rs2304933. This SNP was analyzed in Stage 1 but showed weaker evidence for association (P=0.006) than the genome-wide significant variant at this locus in the same sample.
We also sought to follow up four additional loci showing suggestive evidence for association with AD (1×10−6>=P>5×10−8) from the ADGC GWAS12. These loci included CD33, EPHA1, CD2AP and ARID5B. It should be noted that evidence for suggestive association with EPHA1 and CD33 has been reported previously. Members of this collaboration were the first to report EPHA1 as showing suggestive evidence of association with AD (rs11771145, P=1.7×10−6; LD with ADGC SNP rs11767557: r2 = 0.28, D’=0.75)11, which included GERAD1 and EADI1 samples reported on here. Similarly, Bertram and colleagues were the first to show suggestive evidence for CD33 (rs3826656, P=4.0×10−6; LD with ADGC SNP rs3865444: r2 = 0.13, D’=1.0)19.
We combined data from the GERAD+ dataset comprising GERAD1, EADI1, deCODE and AD-IG GWAS datasets (up to 6992 cases and 13472 controls) using inverse variance meta-analysis. The TGEN1, ADNI and Mayo1 datasets were included in the ADGC discovery set and were thus excluded from these particular analyses. We observed support for association with CD2AP (rs9349407, P=8.0×10−4, OR=1.11), CD33 (rs3865444, P=2.2×10−4, OR=0.89) and EPHA1 (rs11767557, P=3.4×10−4, OR=0.90).
When these data were combined with ADGC we observed genome-wide evidence for association with AD (rs9349407, GERAD+ & ADGC meta-P=8.6×10−9, OR=1.11; rs3865444, GERAD+ & ADGC meta-P=1.6×10−9, OR=0.91; rs11767557, GERAD+ & ADGC meta-P=6.0×10−10, OR=0.90). We observed nominally significant evidence of association with ARID5B (rs2588969, P=3.3×10−2, OR=1.06), however the direction of effect was opposite to that reported by ADGC12, and was not significant overall (GERAD+ & ADGC meta-P=3.6×10−1, OR=0.99). See Table 2 for results and Supplementary Table 6 for results of additional SNPs at these loci.
Table 2
Table 2
Results of the combined analysis of the ADGC and GERAD+ consortia.
Taken together, these results show compelling evidence for an additional five novel AD susceptibility loci. ABCA7 encodes an ATP-binding cassette (ABC) transporter. The ABC transporter superfamily has roles in transporting a wide range of substrates across cell membranes20 ABCA7 is highly expressed in brain, particularly in hippocampal CA1 neurons21 and in microglia22. ABCA7 is involved in the efflux of lipids from cells to lipoprotein particles. Notably, the main lipoproteins in brain are APOE followed by CLU. Although no evidence for epistasitic interactions between the three genetic loci was observed (see Supplementary Table 4a), however, this is not a prerequisite for biological interaction between these molecules. In addition, ABCA7 has been shown to regulate APP processing and inhibit β-amyloid secretion in cultured cells overexpressing APP23. ABCA7 also modulates phagocytosis of apoptotic cells by macrophages mediated through the C1q complement receptor protein on the apoptotic cell surface23. ABCA7 is an orthologue of C. elegans ced-7, the product of which is known to clear apoptotic cells and the high levels of expression of ABCA7 in microglia are consistent with such a role.
The genes in the MS4A cluster on chromosome 11 have a common genomic structure with all other members of the family, including transmembrane domains indicating that they are likely to be part of a family of cell surface proteins24. MS4A2 encodes the beta subunit of high affinity IgE receptors25. The remaining genes in the LD block have no known specific functions. CD33 is a member of the sialic-acid-binding immunoglobulin-like lectins (Siglec) family which are thought to promote cell-cell interactions and regulate functions of cells in the innate and adaptive immune systems26. Most members of the Siglec family, including CD33, act as endocytic receptors, mediating endocytosis through a mechanism independent of clathrin27. CD2AP (CD2-associated protein) is a scaffold/adaptor protein28 which associates with cortactin, a protein also involved in the regulation of receptor mediated endocytosis29. It is striking that these two new susceptibility genes for AD, and the recently established susceptibility genes PICALM and BIN1 are all implicated in cell-cell communication and transduction of molecules across the membrane. EPHA1 is a member of the ephrin receptor subfamily. Ephrins and Eph receptors are membrane bound proteins which play roles in cell and axon guidance30 and in synaptic development and plasticity31. However EphA1 is expressed mainly in epithelial tissues32 where it regulates cell morphology and motility33. Additional roles in apoptosis34 and inflammation35 have also been proposed.
Our study has generated strong statistical evidence that variants at ABCA7 and the MS4A gene cluster confer susceptibility to AD, which replicates over a number of independent case control samples. We also provide independent support for three loci showing suggestive evidence in a companion paper12, CD33, CD2AP and EPHA1,which when the data are combined show genome-wide levels of significance. Finally, we provide further evidence for BIN1 and CR1 loci as susceptibility loci. What is striking about our findings is the emerging consistency in putative function of the genes identified. Five of the recently identified AD susceptibility loci CLU, CR1, ABCA7, CD33 and EPHA1 have putative functions in the immune system; PICALM, BIN1, CD33, CD2AP are involved in processes at the cell membrane, including endocytosis and APOE, CLU and ABCA7 in lipid processing. It is conceivable that these processes would play strong roles in neurodegeneration and Aβ clearance from the brain. These findings therefore provide new impetus for focused studies aimed at understanding the pathogenesis of AD.
Figure 3
Figure 3
Forest plots showing association in the different datasets for SNPs at the ABCA7 (rs3764650) and MS4A (rs610932 & rs670139) loci.
Supplementary Material
Tables 1, 2, 3, 4a, 4b, 5, note, references, complete acknowledgements
Table 6
Table 7
Table 8
Online Methods
Acknowledgements
For complete acknowledgements please see the Supplementary Note. We thank the individuals and families who took part in this research and those who funded the groups who contributed to this study: Wellcome Trust; MRC (UK); ART; WAG; Mercer’s Institute for Research on Ageing; Alzheimer’s Society; Ulster Garden Villages; NI R&D Office; Royal College of Physicians; Dunhill Medical Trust; BRACE; US NIH, the Barnes Jewish Foundation; Charles and Joanne Knight Alzheimer’s Research Initiative; UCL Hospital/UCL Biomedical Centre; Lundbeck; German Federal Ministry of Education and Research Competence Network Dementia and Competence Network Degenerative Dementia; Alfried Krupp von Bohlen und Halbach-Stiftung; IRP of the NIA Department of Health and Human Services; University of Antwerp, Fund for Scientific Research-Flanders; Foundation for Alzheimer Research; Methusalem Excellence grant; Federal Science Policy Office Interuniversity Attraction Poles program; Mayo AD Research Center; NINDS; Robert and Clarice Smith Postdoctoral Fellowship and AD Research Program; Palumbo Professorship in AD Research; Carl Edward and Susan Bass Bolch Gift; Institut Pasteur de Lille; CNG; Fondation pour la Recherche Médicale Caisse; Nationale Maladie des Travailleurs Salariés, Direction Générale de la Sant; Institut de la Longévité; Agence Française de Sécurité Sanitaire des Produits de Santé; Aquitaine and Bourgogne Regional Councils; Fondation de France; French Ministry of Research/INSERM; Eisai; Health Research Council of the Academy of Finland; Nordic Centre of Excellence in Neurodegeneration; Italian Ministry of research; Carimonte Foundation; Italian ministry of Health; Fondazione Monzino; Ministerio de Educación y Ciencia the Ministerio de Sanidad y Consumo; Fundación Ramón Areces; National Institute of Biomedical Imaging Bioengineering; Abbott; AstraZeneca, Bayer Schering Pharma; Bristol-Myers Squibb; Elan; Genentech; GE; GlaxoSmithKline; Innogenetics; Johnson and Johnson; Eli Lilly; Medpace; Merck; Novartis; Pfizer; Hoffman-La Roche; Schering-Plough; Synarc; Wyeth; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; U.S. Food and Drug Administration; Foundation for the NIH Northern California Institute for Research and Education; Dana Foundation; German National Genome Research Network; German Ministry for Education and Research; NEI, NIDCD; Hjartavernd; Althingi; NHLBI; NIDDK; Robert Dawson Evans Endowment; Netherlands Organisation of Scientific Research; Netherlands Genomics Initiative; Erasmus Medical Center; Netherlands organization for scientific research; Netherlands Organization for the Health Research and Development; the Research Institute for Diseases in the Elderly; Ministry of Education, Culture and Science; Ministry for Health, Welfare and Sports; European Commission and the Municipality of Rotterdam.
Footnotes
Competing financial Interests The authors have applied for a patent based on the results of this research
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