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Nat Genet. Author manuscript; available in PMC 2011 November 1.
Published in final edited form as:
Nat Genet. 2011 May; 43(5): 436–441.
Published online 2011 April 3. doi:  10.1038/ng.801
PMCID: PMC3090745
NIHMSID: NIHMS287699
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Common variants in MS4A4/MS4A6E, CD2uAP, CD33, and EPHA1 are associated with late-onset Alzheimer’s disease
Adam C Naj,1,115 Gyungah Jun,2,3,4,115 Gary W Beecham,1,5 Li-San Wang,6 Badri Narayan Vardarajan,3 Jacqueline Buros,3 Paul J Gallins,1 Joseph D Buxbaum,7,8,9 Gail P Jarvik,10,11 Paul K Crane,12 Eric B Larson,13 Thomas D Bird,14 Bradley F Boeve,15 Neill R Graff-Radford,16,17 Philip L De Jager,18,19 Denis Evans,20 Julie A Schneider,21,22 Minerva M Carrasquillo,16 Nilufer Ertekin-Taner,16,17 Steven G Younkin,16 Carlos Cruchaga,23 John SK Kauwe,24 Petra Nowotny,23 Patricia Kramer,25,26 John Hardy,27 Matthew J Huentelman,28 Amanda J Myers,29 Michael M Barmada,30 F. Yesim Demirci,30 Clinton T Baldwin,3 Robert C Green,3,31,32 Ekaterina Rogaeva,33 Peter St George-Hyslop,33,34 Steven E Arnold,35 Robert Barber,36 Thomas Beach,37 Eileen H Bigio,38 James D Bowen,39 Adam Boxer,40 James R Burke,41 Nigel J Cairns,42 Chris S Carlson,43 Regina M Carney,44 Steven L Carroll,45 Helena C Chui,46 David G Clark,47 Jason Corneveaux,28 Carl W Cotman,48 Jeffrey L Cummings,49 Charles DeCarli,50 Steven T DeKosky,51 Ramon Diaz-Arrastia,52 Malcolm Dick,48 Dennis W Dickson,16 William G Ellis,53 Kelley M Faber,54 Kenneth B Fallon,45 Martin R Farlow,55 Steven Ferris,56 Matthew P Frosch,57 Douglas R Galasko,58 Mary Ganguli,59 Marla Gearing,60,61 Daniel H Geschwind,62 Bernardino Ghetti,63 John R Gilbert,1,5 Sid Gilman,64 Bruno Giordani,65 Jonathan D Glass,66 John H Growdon,67 Ronald L Hamilton,68 Lindy E Harrell,47 Elizabeth Head,69 Lawrence S Honig,70 Christine M Hulette,71 Bradley T Hyman,67 Gregory A Jicha,72 Lee-Way Jin,53 Nancy Johnson,73 Jason Karlawish,74 Anna Karydas,40 Jeffrey A Kaye,26,75 Ronald Kim,76 Edward H Koo,58 Neil W Kowall,31,77 James J Lah,66 Allan I Levey,66 Andrew P Lieberman,78 Oscar L Lopez,79 Wendy J Mack,80 Daniel C Marson,47 Frank Martiniuk,81 Deborah C Mash,82 Eliezer Masliah,58,83 Wayne C McCormick,12 Susan M McCurry,84 Andrew N McDavid,43 Ann C McKee,31,77 Marsel Mesulam,85,86 Bruce L Miller,40 Carol A Miller,87 Joshua W Miller,53 Joseph E Parisi,88,89 Daniel P Perl,90 Elaine Peskind,91 Ronald C Petersen,15 Wayne W Poon,48 Joseph F Quinn,26 Ruchita A Rajbhandary,1 Murray Raskind,91 Barry Reisberg,56,92 John M Ringman,49 Erik D Roberson,47 Roger N Rosenberg,52 Mary Sano,8 Lon S Schneider,46,93 William Seeley,40 Michael L Shelanski,94 Michael A Slifer,1,5 Charles D Smith,72 Joshua A Sonnen,95 Salvatore Spina,63 Robert A Stern,31 Rudolph E Tanzi,67 John Q Trojanowski,6 Juan C Troncoso,96 Vivianna M Van Deerlin,6 Harry V Vinters,49,97 Jean Paul Vonsattel,98 Sandra Weintraub,85,86 Kathleen A Welsh-Bohmer,41,99 Jennifer Williamson,70 Randall L Woltjer,100 Laura B Cantwell,6 Beth A Dombroski,6 Duane Beekly,101 Kathryn L Lunetta,2 Eden R Martin,1,5 M. Ilyas Kamboh,30,79 Andrew J Saykin,54,102 Eric M Reiman,28,103,104,105 David A Bennett,22,106 John C Morris,42,107 Thomas J Montine,95 Alison M Goate,23 Deborah Blacker,108,109 Debby W Tsuang,91 Hakon Hakonarson,110 Walter A Kukull,111 Tatiana M Foroud,54 Jonathan L Haines,112,113 Richard Mayeux,70,114 Margaret A Pericak-Vance,1,5 Lindsay A Farrer,2,3,4,31,32 and Gerard D Schellenberg6
1The John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida, USA.
2Department of Biostatistics, Boston University, Boston, Massachusetts, USA.
3Department of Medicine (Genetics Program), Boston University, Boston, Massachusetts, USA.
4Department of Ophthalmology, Boston University, Boston, Massachusetts, USA.
5Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, Florida, USA.
6Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA.
7Department of Neuroscience, Mount Sinai School of Medicine, New York, New York, USA.
8Department of Psychiatry, Mount Sinai School of Medicine, New York, New York, USA.
9Departments of Genetics and Genomic Sciences, Mount Sinai School of Medicine+C120, New York, New York, USA.
10Department of Genome Sciences, University of Washington, Seattle, Washington, USA.
11Department of Medicine (Medical Genetics), University of Washington, Seattle, Washington, USA.
12Department of Medicine, University of Washington, Seattle, Washington, USA.
13Group Health Research Institute, Seattle, Washington, USA.
14Department of Neurology, University of Washington, Seattle, Washington, USA.
15Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA.
16Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA.
17Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA.
18Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
19Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA.
20Rush Institute for Healthy Aging, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois, USA.
21Department of Pathology (Neuropathology), Rush University Medical Center, Chicago, Illinois, USA.
22Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA.
23Department of Psychiatry and Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University School of Medicine, St. Louis, Missouri, USA.
24Department of Biology, Brigham Young University, Provo, Utah, USA.
25Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, Oregon, USA.
26Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA.
27Institute of Neurology, University College London, Queen Square, London, UK.
28Neurogenomics Division, Translational Genomics Research Institute, Phoenix, Arizona, USA.
29Department of Psychiatry & Behavioral Sciences, University of Miami, Miami, Florida, USA.
30Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
31Department of Neurology, Boston University, Boston, Massachusetts, USA.
32Department of Epidemiology, Boston University, Boston, Massachusetts, USA.
33Tanz Centre for Research in Neurodegenerative Disease, University of Toronto, Toronto, Ontario, Canada.
34Cambridge Institute for Medical Research and Department of Clinical Neurosciences, University of Cambridge, Cambridge, Massachusetts, UK.
35Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA.
36Department of Pharmacology and Neuroscience, University of Texas Southwestern, Fort Worth, Texas, USA.
37Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Phoenix, Arizona, USA.
38Department of Pathology, Northwestern University, Chicago, Illinois, USA.
39Swedish Medical Center, Seattle, Washington, USA.
40Department of Neurology, University of California San Francisco, San Fransisco, California, USA.
41Department of Medicine, Duke University, Durham, North Carolina, USA.
42Department of Pathology and Immunology, Washington University, St. Louis, Missouri, USA.
43Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
44Department of Psychiatry, Vanderbilt University, Nashville, Tennessee, USA.
45Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
46Department of Neurology, University of Southern California, Los Angeles, California, USA.
47Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
48Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine, California, USA.
49Department of Neurology, University of California Los Angeles, Los Angeles, California, USA.
50Department of Neurology, University of California Davis, Sacramento, California, USA.
51University of Virginia School of Medicine, Charlottesville, Virginia, USA.
52Department of Neurology, University of Texas Southwestern, Dallas, Texas, USA.
53Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento, California, USA.
54Department of Medical and Molecular Genetics, Indiana University, Indianapolis, Indiana, USA.
55Department of Neurology, Indiana University, Indianapolis, Indiana, USA.
56Department of Psychiatry, New York University, New York, New York, USA.
57C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, Charlestown, Massachusetts, USA.
58Department of Neurosciences, University of California San Diego, La Jolla, California, USA.
59Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
60Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia, USA.
61Emory Alzheimer's Disease Center, Emory University, Atlanta, Georgia, USA.
62Neurogenetics Program, University of California Los Angeles, Los Angeles, California, USA.
63Department of Pathology and Laboratory Medicine, Indiana University, Indianapolis, Indiana, USA.
64Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA.
65Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA.
66Department of Neurology, Emory University, Atlanta, Georgia, USA.
67Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, USA.
68Department of Pathology (Neuropathology), University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
69Department of Molecular and Biomedical Pharmacology, University of California Irvine, Irvine, California, USA.
70Taub Institute on Alzheimer's Disease and the Aging Brain, Department of Neurology, Columbia University, New York, New York, USA.
71Department of Pathology, Duke University, Durham, North Carolina, USA.
72Department of Neurology, University of Kentucky, Lexington, Kentucky, USA.
73Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, Illinois, USA.
74Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA.
75Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA.
76Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine, California, USA.
77Department of Pathology, Boston University, Boston, Massachusetts, USA.
78Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.
79University of Pittsburgh Alheimer's Disease Research Center, Pittsburgh, Pennsylvania, USA.
80Department of Preventive Medicine, University of Southern California, Los Angeles, California, USA.
81Department of Medicine - Pulmonary, New York University, New York, New York, USA.
82Department of Neurology, University of Miami, Miami, Florida, USA.
83Department of Pathology, University of California San Diego, La Jolla, California, USA.
84School of Nursing Northwest Research Group on Aging, University of Washington, Seattle, Washington, USA.
85Alzheimer's Disease Center, Northwestern University, Chicago, Illinois, USA.
86Cognitive Neurology, Northwestern University, Chicago, Illinois, USA.
87Department of Pathology, University of Southern California, Los Angeles, California, USA.
88Department of Anatomic Pathology, Mayo Clinic, Rochester, Minnesota, USA.
89Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA.
90Department of Pathology, Mount Sinai School of Medicine, New York, New York, USA.
91Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA.
92Alzheimer's Disease Center, New York University, New York, New York, USA.
93Department of Psychiatry, University of Southern California, Los Angeles, California, USA.
94Department of Pathology, Columbia University, New York, New York, USA.
95Department of Pathology, University of Washington, Seattle, Washington, USA.
96Department of Pathology, Johns Hopkins University, Baltimore, Maryland, USA.
97Department of Pathology & Laboratory Medicine, University of California Los Angeles, Los Angeles, California, USA.
98Taub Institute Department of Pathology, Columbia University, New York, New York, USA.
99Department of Psychiatry & Behavioral Sciences, Duke University, Durham, North Carolina, USA.
100Department of Pathology, Oregon Health & Science University, Portland, Oregon, USA.
101National Alzheimer's Coordinating Center, University of Washington, Seattle, Washington, USA.
102Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA.
103Department of Psychiatry, University of Arizona, Phoenix, Arizona, USA.
104Arizona Alzheimer’s Consortium, Phoenix, Arizona, USA.
105Banner Alzheimer's Institute, Phoenix, Arizona, USA.
106Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA.
107Department of Neurology, Washington University, St. Louis, Missouri, USA.
108Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.
109Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, USA.
110Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
111Department of Epidemiology, University of Washington, Seattle, Washington, USA.
112Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA.
113Vanderbilit Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, USA.
114Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA.
Address correspondence to: Gerard D. Schellenberg, Ph.D., Department of Pathology and Laboratory Medicine University of Pennsylvania School of Medicine Room 609B Stellar-Chance Laboratories, 422 Curie Boulevard, Philadelphia, PA 19104-6100, Phone office: (215) 746-4580, FAX: (215) 898-9969, gerardsc/at/mail.med.upenn.edu
115These authors contributed equally to this work.
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Abstract
The Alzheimer Disease Genetics Consortium (ADGC) performed a genome-wide association study (GWAS) of late-onset Alzheimer disease (LOAD) using a 3 stage design consisting of a discovery stage (Stage 1) and two replication stages (Stages 2 and 3). Both joint and meta-analysis analysis approaches were used. We obtained genome-wide significant results at MS4A4A [rs4938933; Stages 1+2, meta-analysis (PM) = 1.7 × 10−9, joint analysis (PJ) = 1.7 × 10−9; Stages 1–3, PM = 8.2 × 10−12], CD2AP (rs9349407; Stages 1–3, PM = 8.6 × 10−9), EPHA1 (rs11767557; Stages 1–3 PM = 6.0 × 10−10), and CD33 (rs3865444; Stages 1–3, PM = 1.6 × 10−9). We confirmed that CR1 (rs6701713; PM = 4.6×10−10, PJ = 5.2×10−11), CLU (rs1532278; PM = 8.3 × 10−8, PJ = 1.9×10−8), BIN1 (rs7561528; PM = 4.0×10−14; PJ = 5.2×10−14), and PICALM (rs561655; PM = 7.0 × 10−11, PJ = 1.0×10−10) but not EXOC3L2 are LOAD risk loci1–3.
 
Alzheimer Disease (AD) is a neurodegenerative disorder affecting more than 13% of individuals aged 65 years and older and 30%–50% aged 80 years and older4–5. Early work identified mutations in APP, PSEN1, and PSEN2 that cause early-onset autosomal dominant AD6–9 and variants in APOE that affect LOAD susceptibility10. A recent GWAS identified CR1, CLU, PICALM, and BIN1 as LOAD susceptibility loci1–3. However, because LOAD heritability estimates are high (h2 ≈ 60–80%)11, much of the genetic contribution remains unknown.
To identify genetic variants associated with risk for AD, the ADGC assembled a discovery dataset [Stage 1; 8,309 LOAD cases, 7,366 cognitively normal controls (CNEs)] using data from eight cohorts and a ninth newly assembled cohort from the 29 NIA-funded Alzheimer Disease Centers (ADCs) (Supplementary Tables 1 and 2, Supplementary Note) with data coordinated by the National Alzheimer Coordinating Center (NACC) and samples coordinated by the National Cell Repository for Alzheimer Disease (NCRAD). For the Stage 2 replication, we used four additional datasets and additional samples from the ADCs (3,531 LOAD cases, 3,565 CNEs). The Stage 3 replication used the results of association analyses provided by three other consortia (Hollingworth et al.12; 7,650 LOAD cases, 25,839 mixed-age controls). For Stages 1 and 2, we used both a meta-analysis (M) approach that integrates results from association analyses of individual datasets; and a joint analysis (J) approach where genotype data from each study are pooled. The latter method has improved power over meta-analysis in the absence of between-study heterogeneity13 and more direct correction for confounding sampling bias14. We were limited to meta-analysis for Stage 3.
Because cohorts were genotyped using different platforms, we used imputation to generate a common set of 2,324,889 SNPs. We applied uniform stringent quality control measures to all datasets to remove low-quality and redundant samples and problematic SNPs (Supplementary Tables 3, 4, and Online Methods). We performed association analysis assuming an additive model on the log odds ratio scale with adjustment for population substructure using logistic regression for case-control data and generalized estimating equations (GEE) with a logistic model for family data. Results from individual datasets were combined in the meta-analysis using the inverse variance method, applying a genomic control to each dataset. The joint analysis was performed using GEE and incorporated terms to adjust for population substructure and site-specific effects (Online Methods). For both approaches, we also examined an extended model of covariate adjustment that adjusted for age (age at onset or death in cases; age at exam or death in controls), sex, and number of APOE ε4 alleles (0, 1, or 2). Genomic inflation factors (λ) for both the discovery meta-analysis and the joint analysis and extended models were less than 1.05, indicating that there was not substantial inflation of the test statistics (Supplementary Table 3, Supplementary Figure 1). Association findings from meta-analysis and joint analysis were comparable.
In Stage 1, the strongest signal was from the APOE region (e.g., rs4420638, PM =1.1 × 10−266, PJ =1.3 × 10−253; Supplementary Table 5). Excluding the APOE region, SNPs at nine distinct loci yielded a PM or PJ ≤ 10−6 (Table 1; all SNPs with P < 10−4 are in Supplementary Table 5). SNPs from these nine loci were carried forward to Stage 2. Five of these had not previously been associated with LOAD at a genome-wide significance level of P ≤ 5.0 × 10−8 (MS4A, EPHA1, CD33, ARID5B, and CD2AP). Because Hollingworth et al.12 identified SNPs at ABCA7 as a novel LOAD locus, we included ABCA7 region SNPs in Stage 2 and provided the results to Hollingworth et al.12. For all loci in Table 1, we did not detect evidence for effect heterogeneity (Supplementary Fig. 2). One novel locus (MS4A) was significant in the Stage 1+2 analysis. Four other loci approached but did not reach genome-wide significance in the Stage 1+2 analyses and were carried forward to Stage 3. For three of these (CD33, EPHA1, and CD2AP), Stage 3 analysis strengthened evidence for association. However, Stages 2 and 3 results did not support Stage 1 results for ARID5B 2 (Table 2).
Table 1
Table 1
Genome-wide Association Results for LOAD in the ADGC Stage 1 and Stage 2 datasets
Table 2
Table 2
Meta-Analysis of Stage 1+2 with Stage 3 (CHARGE/GERAD/EADI1 Consortia 2) GWAS Results
Stage 1+2 analysis identified the MS4A gene cluster as a novel LOAD locus (PM = 1.7 × 10−9, PJ = 1.7 × 10−9)(Table 1, Fig. 1A). The minor allele (MAF = 0.39) was protective with identical odds ratios (ORs) from both meta-analysis and joint analysis (ORM and ORJ = 0.88, 95% CI: 0.85–0.92). In the Stage 1+2 analysis, other SNPs gave smaller P values when compared to discovery SNP rs4938933, with the most significant SNP being rs4939338 (PM = 2.6 × 10−11, PJ = 4.6 × 10−11; ORM and ORJ = 0.87, 95% CI: 0.84–0.91) (Supplementary Table 5). In the accompanying manuscript12, genome-wide significant results were also obtained at the MS4A locus (rs670139, PM = 5.0 × 10−12) using an independent sample. In a combined analysis of ADGC results and those from Hollingworth et al.12, the evidence for this locus at rs4938933 increased to PM = 8.2 × 10−12 (Table 3: ORM = 0.89, 95% CI: 0.87–0.92; Fig. 1A).
Figure 1
Figure 1
Regional association plots from the three-stage meta-analysis with LOAD. PM values for association are shown for: (A) MS4A gene cluster, (B) CD2AP, (C) EPHA1, and (D) CD33. For each locus, the genomic position (NCBI Build 37.1) is plotted on the X-axis (more ...)
SNPs in the CD2AP locus also met our Stage 1 criteria for additional analysis (Fig. 1B). Stage 2 data modestly strengthened this association, but the results did not reach genome-wide significance. Stage 3 analysis yielded a genome-wide significance result for rs9349407 (PM = 8.6 × 10−9), identifying CD2AP as a novel LOAD locus. The minor allele (MAF = 0.27) at this SNP increased risk for LOAD (ORM = 1.11, 95% CI: 1.07–1.15) (Table 2, Fig. 1B).
Another locus studied further in Stages 2 and 3 centered on EPHA1. Previous work provided suggestive evidence that this is a LOAD risk locus, although the associations did not reach genome-wide significance (P = 1.7 × 10−6)2. Here, results from Stages 1 and 2 for SNP rs11767557, located in the promoter region of EPHA1, reached genome-wide significance in the joint analysis. The addition of Stage 3 results increased evidence for association (PM = 6.0 × 10−10, Table 2, Fig. 1C). The minor allele (MAF = 0.19) for this SNP is protective (ORM = 0.90, 95% CI: 0.86–0.93). We observed no evidence for heterogeneity at this locus (Supplementary Fig. 2D, heterogeneity P = 0.58).
In Stages 1 and 2, strong evidence for association was also obtained for SNPs in CD33, a gene located approximately 6Mb from APOE, but the results did not reach genome-wide significance. The addition of Stage 3 data confirmed that CD33 is a LOAD risk locus (rs3865444; Stages 1–3, PM = 1.6 × 10−9). The minor allele (MAF = 0.30) is protective (ORM = 0.91, 95% CI: 0.88–0.93; Tables 1,​,2,2, Fig. 1D). A single SNP (rs3826656) in the 5’ region of CD33, was previously reported as an AD-related locus using a family-based approach as genome-wide significant (P = 6.6 × 10−6) 15. We were unable to replicate this finding (PM = 0.73; PJ = 0.39, Stage 1 analysis for rs3826656). Though rs3826656 is only 1,348 bp from our top SNP (rs3865444), these 2 sites display only weak LD (r2 = 0.13).
Hollingworth et al 12 report highly significant evidence for the association of an ABCA7 SNP rs3764650 with LOAD (PM = 4.5 × 10−17) that included data from our study. In our Stage 1+2 analysis, we obtained suggestive evidence for association with ABCA7 SNP rs3752246 (PM = 5.8 × 10−7, and PJ = 5.0 × 10−7), which is a missense variant (G1527A) that may alter the function of the ABCA7 protein (see Supplementary Table 6 for functional SNPs in LD with SNPs yielding PM or PJ < 10−4).
Our Stage 1+2 analyses also confirmed the association of previously reported loci (BIN1, CR1, CLU, and PICALM) with LOAD (Table 1). For each locus, supporting evidence was P ≤ 5.0 × 10−8 in one or both types of analysis.
We also examined SNPs with statistically significant GWAS results reported by others (GAB216, PCDH11X17, GOLM118, and MTHFD1L 19, Supplementary Table 7). Stage 1 data were used except for PCDH11X where Stage 1+2 data were used because Affymetrix platforms do not contain the appropriate SNP. Only SNPs in the APOE, CR1, PICALM, and BIN1 loci demonstrated P < 10−6. For MTHFD1L19, at rs11754661 (previously reported P = 4.7 × 10−8) we obtained modest independent association evidence (ORM = 1.16, 95% CI: 1.04–1.29, PM = 0.006; ORJ = 1.19, 95% CI: 1.08–1.32, PJ = 7.5 × 10−4). For the remaining sites, only nominal evidence (P < 0.05) or no evidence was obtained. For the GAB2 locus16 at rs10793294 (previously reported P = 1.60 × 10−7), we obtained nominal statistical significance results (PM = 0.017; PJ = 0.029). The association for rs5984894 in the PCDH11X locus17 (previously reported P = 3.9 × 10−12), did not replicate (PM = 0.89, PJ = 0.26). Likewise, findings at GOLM118 for rs10868366 (previously reported P = 2.40 × 10−4) did not replicate (PM = 0.71; PJ = 0.62). Another gene consistently implicated in LOAD is SORL120 where at rs3781835 (previously reported P = 0.006), we obtained modest evidence for association (ORM = 0.72, 95% CI: 0.60–0.86, PM = 2.9 × 10−4; ORJ = 0.78, 95% CI: 0.59–0.86; PJ = 3.8 × 10−4).
We examined the influence of the APOE ε4 allele on the loci in Table 1, stratified by and in interactions with APOE ε4 allele carrier status. After adjustment, all loci had similar effect sizes to the unadjusted analyses with some showing a modest reduction in statistical significance. We previously reported evidence for a PICALM-APOE21 interaction using a dataset that largely overlaps with the Stage 1 dataset used here. However, using the Stage 1+2 data, we do not replicate this finding or see evidence of SNP-APOE interactions with Table 1 loci (data not shown).
Previous work reported an association between LOAD and chromosome 19 SNP rs597668, located 7.2 kb proximal to EXOC3L2 and 296 kb distal of APOE 2. While we did observe a signal for this SNP (Stage 1, PM = 1.5 × 10−9; PJ = 7.7 × 10−10) and other SNPs in the EXOC2L3-MARK4 region, evidence was completely extinguished for all SNPs after adjustment for APOE (Online Methods, Supplementary Table 8), suggesting that signal in this region is from APOE.
Our observation of genome-wide significant associations at MS4A4A, CD2AP, EPHA1, and CD33 extend our understanding of the genetic architecture of LOAD and confirm the emerging consensus that common genetic variation plays a significant role in the etiology of LOAD. With our findings and those by Hollingsworth et al.12, there are now ten LOAD susceptibility loci (APOE, CR1, CLU, PICALM, BIN1, EPHA1, MS4A, CD33, CD2AP, and ABCA7). Examining the amount of genetic effect attributable to these candidate genes, the most strongly associated SNPs at each locus other than APOE demonstrated population attributable fractions (PAFs) between 2.72–5.97% (Supplemental Table 9), with a cumulative PAF for non-APOE loci estimated to be as much as 35%; however, these estimates may vary widely between studies22, and the actual effect sizes are likely to be much smaller than those estimated here because of the ‘winner’s curse’. Also the results do not account for interaction among loci, and are not derived from appropriate population-based samples.
A recent review of GWAS studies23 noted that risk alleles with small effect sizes (0.80 < OR < 1.2) likely exist for complex diseases such as LOAD but remain undetected, even with thousands of samples, because of insufficient power24. Our discovery dataset (Stage 1; 8,309 cases and 7,366 controls), was well-powered to detect associations exceeding the statistical significance threshold of P < 10−6 (Supplementary Table 9). If there are many loci of more modest effects, some, but not all, will likely be detected in any one study. This likely explains the genome-wide statistical significance for the ABCA7 locus in the accompanying manuscript12, which reaches only modest statistical significance in our dataset (rs3752246; PM = 1.0 × 10−5, PJ = 1.9 × 10−5). Finding additional LOAD loci will require larger studies with increased depth of genotyping to test for the effects of both common and rare variants.
Supplementary Material
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Acknowledgements
The National Institutes of Health, National Institute on Aging (NIH-NIA) supported this work through the following grants: ADGC, U01 AG032984, RC2 AG036528; NACC, U01 AG016976; NCRAD, U24 AG021886; NIA LOAD, U24 AG026395, U24 AG026390; Boston University, P30 AG013846, R01 HG02213, K24 AG027841, U01 AG10483, R01 CA129769, R01 MH080295, R01 AG009029, R01 AG017173, R01 AG025259; Columbia University, P50 AG008702, R37 AG015473; Duke University, P30 AG028377; Emory University, AG025688; Indiana University, P30 AG10133; Johns Hopkins University, P50 AG005146, R01 AG020688; Massachusetts General Hospital, P50 AG005134; Mayo Clinic, P50 AG016574; Mount Sinai School of Medicine, P50 AG005138, P01 AG002219; New York University, P30 AG08051, MO1RR00096, and UL1 RR029893; Northwestern University, P30 AG013854; Oregon Health & Science University, P30 AG008017, R01 AG026916; Rush University, P30 AG010161, R01 AG019085, R01 AG15819, R01 AG17917, R01 AG30146; University of Alabama at Birmingham, P50 AG016582, UL1RR02777; University of Arizona/TGEN, P30 AG019610, R01 AG031581, R01 NS059873; University of California, Davis, P30 AG010129; University of California, Irvine, P50 AG016573, P50, P50 AG016575, P50 AG016576, P50 AG016577; University of California, Los Angeles, P50 AG016570; University of California, San Diego, P50 AG005131; University of California, San Francisco, P50 AG023501, P01 AG019724; University of Kentucky, P30 AG028383; University of Michigan, P50 AG008671; University of Pennsylvania, P30 AG010124; University of Pittsburgh, P50 AG005133, AG030653; University of Southern California, P50 AG005142; University of Texas Southwestern, P30 AG012300; University of Miami, R01 AG027944, AG010491, AG027944, AG021547, AG019757; University of Washington, P50 AG005136, UO1 AG06781, UO1 HG004610; Vanderbilt University, R01 AG019085; and Washington University, P50 AG005681, P01 AG03991. ADNI Funding for ADNI is through the Northern California Institute for Research and Education by grants from Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., Alzheimer's Association, Alzheimer's Drug Discovery Foundation, the Dana Foundation, and by the National Institute of Biomedical Imaging and Bioengineering and NIA grants U01 AG024904, RC2 AG036535, K01 AG030514. We thank Creighton Phelps, Marcelle Morrison-Bogorad, and Marilyn Miller from NIA who are ex-officio ADGC members. Support was also from the Alzheimer’s Association (LAF, IIRG-08-89720; MP-V, IIRG-05-14147) and the Veterans Affairs Administration. P.S.G.-H. is supported by Wellcome Trust, Howard Hughes Medical Institute, and the Canadian Institute of Health Research.
Footnotes
Author Contributions
Sample collection, phenotyping, and data management: J.D.Buxbaum, G.P.J., P.K.C., E.B.L., T.D.B., B.F.B., N.R.G., P.L.D., D.E., J.A.Schneider, M.M.C., N.E., S.G.Y., C.C., J.S.K.K., P.N., P.K., J.H., M.J.H., A.J.M., M.M.B., F.Y.D., C.T.B., R.C.G., E.R., P.S.G.-H., S.E.A., R.B., T.B., E.H.B., J.D.Bowen, A.B., J.R.B., N.J.C., C.S.C., S.L.C., H.C.C., D.G.C., J.C., C.W.C., J.L.C., C.D., S.T.D., R.D.-A., M.D., D.W.D., W.G.E., K.M.F., K.B.F., M.R.F., S.F., M.P.F., D.R.G., M.Ganguli, M.Gearing, D.H.G., B.Ghetti, J.R.G., S.G., B.Giordani, J.G., J.H.G., R.L.H., L.E.H., E.H., L.S.H., C.M.H., B.T.H., G.A.J., L.-W.J., N.J., J.K., A.K., J.A.K., R.K., E.H.K., N.W.K., J.J.L., A.I.L., A.P.L., O.L.L., W.J.M., D.C.Marson, F.M., D.C.Mash, E.M., W.C.M., S.M.M., A.N.M., A.C.M., M.M., B.L.M., C.A.M., J.W.M., J.E.P., D.P.P., E.P., R.C.P., W.W.P., J.F.Q., M.R., B.R., J.M.R., E.D.R., R.N.R., M.S., L.S.S., W.S., M.L.S., M.A.S., C.D.S., J.A.Sonnen, S.S., R.A.S., R.E.T., J.Q.T., J.C.T., V.M.V., H.V.V., J.P.V., S.W., K.A.W., J.W., R.L.W., L.B.C., B.A.D., D.Beekly, M.I.K., A.J.S., E.M.R., D.A.B., A.M.G., W.A.K., T.M.F., J.L.H., R.M., M.A.P., L.A.F. Study management and coordination: L.B.C., D.Beekly, D.A.B., J.C.M., T.J.M., A.M.G., D.Blacker, D.W.T., H.H., W.A.K., T.M.F., J.L.H., R.M., M.A.P., L.A.F., G.D.S. Statistical methods and analysis: A.C.N., G.J., G.W.B., L.-S.W., B.N.V., J.B., P.J.G., R.M.C., R.A.R., M.A.S., K.L.L., E.R.M., J.L.H., M.A.P., L.A.F. Interpretation of results: A.C.N., G.J., G.W.B., L.-S.W., B.N.V., J.B., P.J.G., R.A.R., M.A.S., K.L.L., E.R.M., M.I.K., A.J.S., E.M.R., D.A.B., J.C.M., T.J.M., A.M.G., D.Blacker, D.W.T., H.H., W.A.K., T.M.F., J.L.H., R.M., M.A.P., L.A.F., G.D.S. Manuscript writing group: A.C.N., G.J., G.W.B., L.-S.W., B.N.V., J.B., P.J.G., J.L.H., R.M., M.A.P., L.A.F., G.D.S. Study design: D.A.B., J.C.M., T.J.M., A.M.G., D.Blacker, D.W.T., H.H., W.A.K., T.M.F., J.L.H., R.M., M.A.P., L.A.F., G.D.S.
Competing Financial Interests
T.D.B. received licensing fees from and is on the speaker's bureau of Athena Diagnostics, Inc. M.R.F. receives research funding from BristolMyersSquibb Company, Danone Research, Elan Pharmaceuticals, Inc., Eli Lilly and Company, Novartis Pharmaceuticals Corporation, OctaPharma AG, Pfizer Inc., and Sonexa Therapeutics, Inc; Receives honoraria as scientific consultant from Accera, Inc., Astellas Pharma US Inc., Baxter, Bayer Pharmaceuticals Corporation, BristolMyersSquibb, Eisai Medical Research, Inc., GE Healthcare, Medavante, Medivation, Inc., Merck & Co., Inc., Novartis Pharmaceuticals Corp., Pfizer, Inc., Prana Biotechnology Ltd., QR Pharma., Inc., The sanofi-aventis Group, and Toyama Chemical Co., Ltd.; and is speaker for Eisai Medical Research, Inc., Forest Laboratories, Pfizer Inc. and Novartis Pharmaceuticals Corporation. A.M.G. has research funding from AstraZeneca, Pfizer and Genentech, and has received remuneration for giving talks at Pfizer and Genentech. R.C.P. is on the Safety Monitory Committee of Pfizer, Inc. (Wyeth) and a consultant to the Safety Monitoring Committee at Janssen Alzheimer's Immunotherapy Program (Elan), to Elan Pharmaceuticals, and to GE Healthcare. R.E.T. is a consultant to Eisai, Japan in the area of Alzheimer's genetics and a shareholder in, and consultant to Pathway Genomics, Inc, San Diego, CA.
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