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Common variants in MS4A4/MS4A6E, CD2uAP, CD33, and EPHA1 are associated with late-onset Alzheimer’s disease

Common variants in MS4A4/MS4A6E, CD2uAP, CD33, and EPHA1 are associated with late-onset... 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 Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms 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, [email protected]. These authors contributed equally to this work. URLs. The Alzheimer Disease Genetics Consortium (ADGC), http://alois.med.upenn.edu/adgc/about/overview.html; ADNI database, (www.loni.ucla.edu/ADNI); ADNI investigators, http://www.loni.ucla.edu/ADNI/Collaboration/ADNI_Manuscript_Citations.pdf; APOE Genotyping kit from TIB MOLBIOL, http://www.roche-as.es/logs/LightMix %C2%AE_40-0445-16_ApoE-112-158_V080904.pdf; PLINK, http://pngu.mgh.harvard.edu/~purcell/plink/; PREST, http:// utstat.toronto.edu/sun/Software/Prest/; MACH, http://www.sph.umich.edu/csg/abecasis/mach/; EIGENSTRAT, http:// genepath.med.harvard.edu/~reich/EIGENSTRAT.htm; The R Project for Statistical Computing, http://www.r-project.org/; Package GWAF in R, http://cran.r-project.org/web/packages/GWAF/index.html; Package gee in R, http://cran.r-project.org/web/packages/gee/ index.html; UCSC Genome Browser, http://genome.ucsc.edu/; METAL, http://www.sph.umich.edu/csg/abecasis/Metal/; FUGUE, http://www.sph.umich.edu/csg/abecasis/fugue/. 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. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 2 analysis approaches were used. We obtained genome-wide significant results at MS4A4A −9 −9; [rs4938933; Stages 1+2, meta-analysis (P ) = 1.7 × 10 , joint analysis (P ) = 1.7 × 10 Stages M J −12 −9 1–3, P = 8.2 × 10 ], CD2AP (rs9349407; Stages 1–3, P = 8.6 × 10 ), EPHA1 (rs11767557; M M −10 −9 Stages 1–3 P = 6.0 × 10 ), and CD33 (rs3865444; Stages 1–3, P = 1.6 × 10 ). We M M −10 −11 confirmed that CR1 (rs6701713; P = 4.6×10 , P = 5.2×10 ), CLU (rs1532278; P = 8.3 × M J M −8 −8 −14 −14 10 , P = 1.9×10 ), BIN1 (rs7561528; P = 4.0×10 ; P = 5.2×10 ), and PICALM J M J −11 −10 1–3 (rs561655; P = 7.0 × 10 , P = 1.0×10 ) but not EXOC3L2 are LOAD risk loci . M J Alzheimer Disease (AD) is a neurodegenerative disorder affecting more than 13% of 4–5 individuals aged 65 years and older and 30%–50% aged 80 years and older . Early work identified mutations in APP, PSEN1, and PSEN2 that cause early-onset autosomal dominant 6–9 10 AD and variants in APOE that affect LOAD susceptibility . A recent GWAS identified 1–3 CR1, CLU, PICALM, and BIN1 as LOAD susceptibility loci . However, because LOAD 2 11 heritability estimates are high (h ≈ 60–80%) , 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. ; 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 heterogeneity and more direct correction for confounding sampling bias . 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 Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 3 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, P =1.1 × −266 −253 10 , P =1.3 × 10 ; Supplementary Table 5). Excluding the APOE region, SNPs at −6 −4 nine distinct loci yielded a P or P ≤ 10 (Table 1; all SNPs with P < 10 are in M J 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 −8 P ≤ 5.0 × 10 (MS4A, EPHA1, CD33, ARID5B, and CD2AP). Because Hollingworth et al. 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. . 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). Stage 1+2 analysis identified the MS4A gene cluster as a novel LOAD locus (P = 1.7 × −9 −9 10 , P = 1.7 × 10 )(Table 1, Fig. 1A). The minor allele (MAF = 0.39) was protective with identical odds ratios (ORs) from both meta-analysis and joint analysis (OR and OR = M J 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 (P −11 −11 = 2.6 × 10 , P = 4.6 × 10 ; OR and OR = 0.87, 95% CI: 0.84–0.91) (Supplementary J M J Table 5). In the accompanying manuscript , genome-wide significant results were also −12 obtained at the MS4A locus (rs670139, P = 5.0 × 10 ) using an independent sample. In a combined analysis of ADGC results and those from Hollingworth et al. , the evidence for −12 this locus at rs4938933 increased to P = 8.2 × 10 (Table 3: OR = 0.89, 95% CI: 0.87– M M 0.92; Fig. 1A). 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 −9 (P = 8.6 × 10 ), identifying CD2AP as a novel LOAD locus. The minor allele (MAF = 0.27) at this SNP increased risk for LOAD (OR = 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 −6 2 genome-wide significance (P = 1.7 × 10 ) . 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 (P = −10 6.0 × 10 , Table 2, Fig. 1C). The minor allele (MAF = 0.19) for this SNP is protective (OR = 0.90, 95% CI: 0.86–0.93). We observed no evidence for heterogeneity at this locus (Supplementary Fig. 2D, heterogeneity P = 0.58). Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 4 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 −9 (rs3865444; Stages 1–3, P = 1.6 × 10 ). The minor allele (MAF = 0.30) is protective (OR = 0.91, 95% CI: 0.88–0.93; Tables 1,2, Fig. 1D). A single SNP (rs3826656) in the 5’ region of CD33, was previously reported as an AD-related locus using a family-based −6 15 approach as genome-wide significant (P = 6.6 × 10 ) . We were unable to replicate this finding (P = 0.73; P = 0.39, Stage 1 analysis for rs3826656). Though rs3826656 is only M J 1,348 bp from our top SNP (rs3865444), these 2 sites display only weak LD (r = 0.13). Hollingworth et al report highly significant evidence for the association of an ABCA7 −17 SNP rs3764650 with LOAD (P = 4.5 × 10 ) that included data from our study. In our Stage 1+2 analysis, we obtained suggestive evidence for association with ABCA7 SNP −7 −7 rs3752246 (P = 5.8 × 10 , and P = 5.0 × 10 ), which is a missense variant (G1527A) M J that may alter the function of the ABCA7 protein (see Supplementary Table 6 for functional −4 SNPs in LD with SNPs yielding P or P < 10 ). M J 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 −8 ≤ 5.0 × 10 in one or both types of analysis. We also examined SNPs with statistically significant GWAS results reported by others 16 17 18 19 (GAB2 , PCDH11X , GOLM1 , and MTHFD1L , 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 −6 19 BIN1 loci demonstrated P < 10 . For MTHFD1L , at rs11754661 (previously reported P = −8 4.7 × 10 ) we obtained modest independent association evidence (OR = 1.16, 95% CI: −4 1.04–1.29, P = 0.006; OR = 1.19, 95% CI: 1.08–1.32, P = 7.5 × 10 ). For the remaining M J J sites, only nominal evidence (P < 0.05) or no evidence was obtained. For the GAB2 locus −7 at rs10793294 (previously reported P = 1.60 × 10 ), we obtained nominal statistical significance results (P = 0.017; P = 0.029). The association for rs5984894 in the M J 17 −12 PCDH11X locus (previously reported P = 3.9 × 10 ), did not replicate (P = 0.89, P = M J 18 −4 0.26). Likewise, findings at GOLM1 for rs10868366 (previously reported P = 2.40 × 10 ) did not replicate (P = 0.71; P = 0.62). Another gene consistently implicated in LOAD is M J SORL1 where at rs3781835 (previously reported P = 0.006), we obtained modest evidence −4 for association (OR = 0.72, 95% CI: 0.60–0.86, P = 2.9 × 10 ; OR = 0.78, 95% CI: M M J −4 0.59–0.86; P = 3.8 × 10 ). 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-APOE 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). Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 5 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 . While we did observe a −9 −10 signal for this SNP (Stage 1, P = 1.5 × 10 ; P = 7.7 × 10 ) and other SNPs in the M J 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. , 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 studies , 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 studies 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 power . Our discovery dataset (Stage 1; 8,309 cases and 7,366 controls), was well-powered to detect associations exceeding the −6 statistical significance threshold of P < 10 (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 manuscript , which reaches only modest statistical significance in our −5 −5 dataset (rs3752246; P = 1.0 × 10 , P = 1.9 × 10 ). Finding additional LOAD loci will M J require larger studies with increased depth of genotyping to test for the effects of both common and rare variants. Supplementary Material Refer to Web version on PubMed Central for supplementary material. Authors 1,115 2,3,4,115 1,5 6 Adam C Naj , Gyungah Jun , Gary W Beecham , Li-San Wang , Badri 3 3 1 7,8,9 Narayan Vardarajan , Jacqueline Buros , Paul J Gallins , Joseph D Buxbaum , 10,11 12 13 14 Gail P Jarvik , Paul K Crane , Eric B Larson , Thomas D Bird , Bradley F 15 16,17 18,19 20 Boeve , Neill R Graff-Radford , Philip L De Jager , Denis Evans , Julie A 21,22 16 16,17 Schneider , Minerva M Carrasquillo , Nilufer Ertekin-Taner , Steven G 16 23 24 23 Younkin , Carlos Cruchaga , John SK Kauwe , Petra Nowotny , Patricia 25,26 27 28 29 Kramer , John Hardy , Matthew J Huentelman , Amanda J Myers , Michael Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 6 30 30 3 3,31,32 M Barmada , F. Yesim Demirci , Clinton T Baldwin , Robert C Green , 33 33,34 35 Ekaterina Rogaeva , Peter St George-Hyslop , Steven E Arnold , Robert 36 37 38 39 40 Barber , Thomas Beach , Eileen H Bigio , James D Bowen , Adam Boxer , 41 42 43 44 James R Burke , Nigel J Cairns , Chris S Carlson , Regina M Carney , Steven 45 46 47 28 L Carroll , Helena C Chui , David G Clark , Jason Corneveaux , Carl W 48 49 50 51 Cotman , Jeffrey L Cummings , Charles DeCarli , Steven T DeKosky , Ramon 52 48 16 53 Diaz-Arrastia , Malcolm Dick , Dennis W Dickson , William G Ellis , Kelley M 54 45 55 56 Faber , Kenneth B Fallon , Martin R Farlow , Steven Ferris , Matthew P 57 58 59 60,61 Frosch , Douglas R Galasko , Mary Ganguli , Marla Gearing , Daniel H 62 63 1,5 64 Geschwind , Bernardino Ghetti , John R Gilbert , Sid Gilman , Bruno 65 66 67 68 Giordani , Jonathan D Glass , John H Growdon , Ronald L Hamilton , Lindy E 47 69 70 71 Harrell , Elizabeth Head , Lawrence S Honig , Christine M Hulette , Bradley T 67 72 53 73 74 Hyman , Gregory A Jicha , Lee-Way Jin , Nancy Johnson , Jason Karlawish , 40 26,75 76 58 Anna Karydas , Jeffrey A Kaye , Ronald Kim , Edward H Koo , Neil W 31,77 66 66 78 Kowall , James J Lah , Allan I Levey , Andrew P Lieberman , Oscar L 79 80 47 81 Lopez , Wendy J Mack , Daniel C Marson , Frank Martiniuk , Deborah C 82 58,83 12 84 Mash , Eliezer Masliah , Wayne C McCormick , Susan M McCurry , Andrew 43 31,77 85,86 40 N McDavid , Ann C McKee , Marsel Mesulam , Bruce L Miller , Carol A 87 53 88,89 90 91 Miller , Joshua W Miller , Joseph E Parisi , Daniel P Perl , Elaine Peskind , 15 48 26 Ronald C Petersen , Wayne W Poon , Joseph F Quinn , Ruchita A 1 91 56,92 49 Rajbhandary , Murray Raskind , Barry Reisberg , John M Ringman , Erik D 47 52 8 46,93 Roberson , Roger N Rosenberg , Mary Sano , Lon S Schneider , William 40 94 1,5 72 Seeley , Michael L Shelanski , Michael A Slifer , Charles D Smith , Joshua A 95 63 31 67 Sonnen , Salvatore Spina , Robert A Stern , Rudolph E Tanzi , John Q 6 96 6 49,97 Trojanowski , Juan C Troncoso , Vivianna M Van Deerlin , Harry V Vinters , 98 85,86 41,99 Jean Paul Vonsattel , Sandra Weintraub , Kathleen A Welsh-Bohmer , 70 100 6 6 Jennifer Williamson , Randall L Woltjer , Laura B Cantwell , Beth A Dombroski , 101 2 1,5 30,79 Duane Beekly , Kathryn L Lunetta , Eden R Martin , M. Ilyas Kamboh , 54,102 28,103,104,105 22,106 Andrew J Saykin , Eric M Reiman , David A Bennett , John C 42,107 95 23 108,109 Morris , Thomas J Montine , Alison M Goate , Deborah Blacker , 91 110 111 Debby W Tsuang , Hakon Hakonarson , Walter A Kukull , Tatiana M 54 112,113 70,114 Foroud , Jonathan L Haines , Richard Mayeux , Margaret A Pericak- 1,5 2,3,4,31,32 6 Vance , Lindsay A Farrer , and Gerard D Schellenberg Affiliations The John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida, USA. Department of Biostatistics, Boston University, Boston, Massachusetts, USA. Department of Medicine (Genetics Program), Boston University, Boston, Massachusetts, USA. Department of Ophthalmology, Boston University, Boston, Massachusetts, USA. Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, Florida, USA. Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 7 Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. Department of Neuroscience, Mount Sinai School of Medicine, New York, New York, USA. Department of Psychiatry, Mount Sinai School of Medicine, New York, New York, USA. Departments of Genetics and Genomic Sciences, Mount Sinai School of Medicine +C120, New York, New York, USA. Department of Genome Sciences, University of Washington, Seattle, Washington, USA. Department of Medicine (Medical Genetics), University of Washington, Seattle, Washington, USA. Department of Medicine, University of Washington, Seattle, Washington, USA. Group Health Research Institute, Seattle, Washington, USA. Department of Neurology, University of Washington, Seattle, Washington, USA. Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA. Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA. Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA. Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA. Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA. Rush Institute for Healthy Aging, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois, USA. Department of Pathology (Neuropathology), Rush University Medical Center, Chicago, Illinois, USA. Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA. Department of Psychiatry and Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University School of Medicine, St. Louis, Missouri, USA. Department of Biology, Brigham Young University, Provo, Utah, USA. Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, Oregon, USA. Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA. Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 8 Institute of Neurology, University College London, Queen Square, London, UK. Neurogenomics Division, Translational Genomics Research Institute, Phoenix, Arizona, USA. Department of Psychiatry & Behavioral Sciences, University of Miami, Miami, Florida, USA. Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. Department of Neurology, Boston University, Boston, Massachusetts, USA. Department of Epidemiology, Boston University, Boston, Massachusetts, USA. Tanz Centre for Research in Neurodegenerative Disease, University of Toronto, Toronto, Ontario, Canada. Cambridge Institute for Medical Research and Department of Clinical Neurosciences, University of Cambridge, Cambridge, Massachusetts, UK. Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. Department of Pharmacology and Neuroscience, University of Texas Southwestern, Fort Worth, Texas, USA. Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Phoenix, Arizona, USA. Department of Pathology, Northwestern University, Chicago, Illinois, USA. Swedish Medical Center, Seattle, Washington, USA. Department of Neurology, University of California San Francisco, San Fransisco, California, USA. Department of Medicine, Duke University, Durham, North Carolina, USA. Department of Pathology and Immunology, Washington University, St. Louis, Missouri, USA. Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. Department of Psychiatry, Vanderbilt University, Nashville, Tennessee, USA. Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama, USA. Department of Neurology, University of Southern California, Los Angeles, California, USA. Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA. Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine, California, USA. Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 9 Department of Neurology, University of California Los Angeles, Los Angeles, California, USA. Department of Neurology, University of California Davis, Sacramento, California, USA. University of Virginia School of Medicine, Charlottesville, Virginia, USA. Department of Neurology, University of Texas Southwestern, Dallas, Texas, USA. Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento, California, USA. Department of Medical and Molecular Genetics, Indiana University, Indianapolis, Indiana, USA. Department of Neurology, Indiana University, Indianapolis, Indiana, USA. Department of Psychiatry, New York University, New York, New York, USA. C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, Charlestown, Massachusetts, USA. Department of Neurosciences, University of California San Diego, La Jolla, California, USA. Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia, USA. Emory Alzheimer's Disease Center, Emory University, Atlanta, Georgia, USA. Neurogenetics Program, University of California Los Angeles, Los Angeles, California, USA. Department of Pathology and Laboratory Medicine, Indiana University, Indianapolis, Indiana, USA. Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA. Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA. Department of Neurology, Emory University, Atlanta, Georgia, USA. Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, USA. Department of Pathology (Neuropathology), University of Pittsburgh, Pittsburgh, Pennsylvania, USA. Department of Molecular and Biomedical Pharmacology, University of California Irvine, Irvine, California, USA. Taub Institute on Alzheimer's Disease and the Aging Brain, Department of Neurology, Columbia University, New York, New York, USA. Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 10 Department of Pathology, Duke University, Durham, North Carolina, USA. Department of Neurology, University of Kentucky, Lexington, Kentucky, USA. Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, Illinois, USA. Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA. Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine, California, USA. Department of Pathology, Boston University, Boston, Massachusetts, USA. Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA. University of Pittsburgh Alheimer's Disease Research Center, Pittsburgh, Pennsylvania, USA. Department of Preventive Medicine, University of Southern California, Los Angeles, California, USA. Department of Medicine - Pulmonary, New York University, New York, New York, USA. Department of Neurology, University of Miami, Miami, Florida, USA. Department of Pathology, University of California San Diego, La Jolla, California, USA. School of Nursing Northwest Research Group on Aging, University of Washington, Seattle, Washington, USA. Alzheimer's Disease Center, Northwestern University, Chicago, Illinois, USA. Cognitive Neurology, Northwestern University, Chicago, Illinois, USA. Department of Pathology, University of Southern California, Los Angeles, California, USA. Department of Anatomic Pathology, Mayo Clinic, Rochester, Minnesota, USA. Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA. Department of Pathology, Mount Sinai School of Medicine, New York, New York, USA. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA. Alzheimer's Disease Center, New York University, New York, New York, USA. Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 11 Department of Psychiatry, University of Southern California, Los Angeles, California, USA. Department of Pathology, Columbia University, New York, New York, USA. Department of Pathology, University of Washington, Seattle, Washington, USA. Department of Pathology, Johns Hopkins University, Baltimore, Maryland, USA. Department of Pathology & Laboratory Medicine, University of California Los Angeles, Los Angeles, California, USA. Taub Institute Department of Pathology, Columbia University, New York, New York, USA. Department of Psychiatry & Behavioral Sciences, Duke University, Durham, North Carolina, USA. Department of Pathology, Oregon Health & Science University, Portland, Oregon, USA. National Alzheimer's Coordinating Center, University of Washington, Seattle, Washington, USA. Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA. Department of Psychiatry, University of Arizona, Phoenix, Arizona, USA. Arizona Alzheimer’s Consortium, Phoenix, Arizona, USA. Banner Alzheimer's Institute, Phoenix, Arizona, USA. Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA. Department of Neurology, Washington University, St. Louis, Missouri, USA. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, USA. Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. Department of Epidemiology, University of Washington, Seattle, Washington, USA. Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA. Vanderbilit Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, USA. Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA. Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 12 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. 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For the SNP with the lowest P-value at each locus in Stage 1 analyses, three P-values for association are shown: P meta-analysis of the ADGC Discovery (Stage 1) dataset (highlighted with a black diamond), P meta-analysis of the 1+2 Combined ADGC Discovery and Replication (Stages 1 + 2) datasets (highlighted with a blue diamond), and P meta-analysis of the combined ADGC dataset and the external 1+2+3 replication (Stages 1 + 2 + 3) datasets (highlighted with a red diamond). Computed estimates of linkage disequilibrium (r ) with the most significant SNP at each locus are 2 2 shown as an orange diamond for r ≥ 0.8, a yellow diamond for 0.5 ≤ r < 0.8, a grey 2 2 diamond for 0.2 ≤ r < 0.5, and a white diamond for r < 0.2. Genes in each region are indicated at the bottom of each panel. The length and the direction of the arrowhead represent the scaled size and the direction of the gene, respectively. Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 16 Nat Genet. Author manuscript; available in PMC 2011 November 01. Table 1 Genome-wide Association Results for LOAD in the ADGC Stage 1 and Stage 2 datasets −6 Association signals represent SNPs with the strongest associations within each locus demonstrating P ≤ 10 in the Stage 1 dataset or in/near previously reported genes, excluding the APOE region (Supplementary Table 5). ADGC Discovery (Stage 1) ADGC Replication (Stage 2) Combined Analysis (Stages 1+2) Nearest # SNP CH:MB MA MAF OR OR OR OR OR OR Gene SNPs M J M J M J P P P P P P M J M J M J (95% CI) (95% CI) (95% CI) (95% CI) (95% CI) (95% CI) 1.18 1.19 1.13 1.13 1.16 1.17 −8 −9 −10 −11 rs6701713 1:207.8 A 0.20 7 1.4×10 3.5×10 0.004 0.004 4.6×10 5.2×10 CR1 1.11–1.25 1.12–1.26 1.04–1.23 1.04–1.24 1.11–1.22 1.12–1.23 1.18 1.18 1.15 1.15 1.17 1.17 −11 −11 −4 −4 −14 −14 rs7561528 2:127.9 A 0.35 10 2.9×10 7.7×10 1.4×10 1.0×10 4.2×10 5.2×10 BIN1 1.13–1.24 1.12–1.24 1.07–1.24 1.07–1.24 1.13–1.22 1.12–1.22 1.14 1.14 1.07 1.08 1.12 1.12 −6 −6 −6 −6 rs9349407 6:47.5 CD2AP C 0.27 1 1.2×10 5.3×10 0.118 0.074 1.0×10 2.1×10 1.08–1.21 1.08–1.20 0.98–1.17 0.99–1.18 1.07–1.18 1.07–1.17 0.85 0.84 0.94 0.93 0.87 0.87 −8 −8 −7 −8 rs11767557 7:143.1 C 0.19 1 7.3×10 3.1×10 0.169 0.133 2.4×10 4.9×10 EPHA1 0.80–0.90 0.79–0.89 0.86–1.03 0.85–1.02 0.83–0.92 0.83–0.91 0.90 0.89 0.87 0.87 0.89 0.89 −5 −5 −4 −4 −8 −8 rs1532278 8:27.5 T 0.36 2 5.6×10 2.0×10 2.6×10 2.7×10 8.3×10 1.9×10 CLU 0.85–0.95 0.85–0.94 0.81–0.94 0.81–0.94 0.85–0.93 0.85–0.92 0.88 0.88 1.05 1.05 0.93 0.93 −6 −7 −4 rs2588969 10:63.6 ARID5B A 0.37 0 1.1×10 6.9×10 0.234 0.189 0.001 7.7×10 0.84–0.93 0.84–0.93 0.97–1.13 0.98–1.13 0.89–0.97 0.89–0.97 0.88 0.87 0.90 0.90 0.88 0.88 −8 −8 −9 −9 rs4938933 11:60.0 MS4A4A C 0.39 22 5.2×10 4.5×10 0.005 0.004 1.7×10 1.7×10 0.84–0.92 0.83–0.92 0.84–0.97 0.84–0.97 0.85–0.92 0.85–0.92 0.88 0.88 0.86 0.86 0.87 0.87 −7 −7 −5 −5 −11 −10 rs561655 11:85.8 G 0.34 36 1.2×10 4.6×10 8.4×10 3.7×10 7.0×10 1.0×10 PICALM 0.84–0.92 0.84–0.93 0.80–0.93 0.80–0.92 0.84–0.91 0.84–0.91 1.16 1.15 1.13 1.13 1.15 1.15 −5 −5 −7 −7 rs3752246 19:1.1 G 0.19 2 1.0×10 1.9×10 0.012 0.009 5.8×10 5.0×10 ABCA7 1.08–1.24 1.08–1.23 1.03–1.24 1.03–1.25 1.09–1.21 1.09–1.21 0.88 0.88 0.91 0.92 0.89 0.89 −7 −6 −7 −7 rs3865444 19:51.7 A 0.30 1 8.2×10 1.9×10 0.021 0.029 1.1×10 2.0×10 CD33 0.84–0.93 0.84–0.93 0.85–0.99 0.85–0.99 0.86–0.93 0.86–0.93 −6 CH:MB, chromosome:position (in mega base pairs, build 19); MA, minor allele; MAF, minor allele frequency; # SNPs, the number of SNPs for which P ≤ 1 × 10 in meta-analysis from the combined analysis in Stage 1+2; OR , odds ratio in meta-analysis; P , P-value in meta-analysis; OR , odds ratio in joint analysis; P , P-value in joint analysis. M M J J 3 1,3 1 2 Genes with previous case-control genome-wide statistically significant associations: CR1 , CLU , PICALM , BIN1 . Gene with a previous association not meeting genome-wide statistical significance: 2 15 EPHA1 . Family-based association study with reported genome-wide statistical significance: CD33 . −8 Genes with previously published case-control association signals at P ≤ 5.0 × 10 are denoted with * the case-control locus that did not meet this level of statistical significance is denoted with the locus previously reported in a family-based association study as genome-wide significant with 12 % locus identified in Hollingworth et al. with genome-wide significant evidence for association with. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 17 Nat Genet. Author manuscript; available in PMC 2011 November 01. Table 2 Meta-Analysis of Stage 1+2 with Stage 3 (CHARGE/GERAD/EADI1 Consortia ) GWAS Results Meta-analysis using an external replication case-control sample (Stage 3) for SNPs from novel loci at which associations did not exceed the genome-wide −8 statistical significance threshold (P = 5.0 × 10 ) in the ADGC meta-analysis (Stage 1+2). Results for MS4A are also included to show association results from the ADGC and accompanying manuscript . The external replication dataset did not include results from TGEN, ADNI, and MAYO cohorts (Supplementary Tables 1 and 2). Gene:SNP Cases Controls Total OR (95% CI) P OR (95% CI) P M M j J CD2AP: rs9349407 −6 −6 ADGC 11840 10931 22771 1.12 (1.07–1.18) 1.12 (1.07–1.17) 1.0 × 10 2.1 × 10 External 6922 18896 25818 1.09 (1.03–1.15) 0.002 - - −9 ADGC + External 18762 29827 48589 1.11 (1.07–1.15) - - 8.6 × 10 EPHA1: rs11767557 −7 −8 ADGC 11840 10931 22771 0.87 (0.83–0.92) 0.87 (0.83–0.91) 2.4 × 10 4.9 × 10 −4 External 6922 24666 31588 0.91 (0.87–0.96) 2.9 × 10 - - −10 ADGC + External 18762 35597 54359 0.90 (0.86–0.93) - - 6.0 × 10 ARID5B: rs2588969 −4 ADGC 11840 10931 22771 0.93 (0.89–0.97) 0.001 0.93 (0.89–0.97) 7.8 × 10 External 6922 18896 25818 1.06 (1.01–1.11) 0.018 - - ADGC + External 18762 29827 48589 0.99 (0.95–1.02) 0.362 - - MS4A4A: rs4938933 −9 −9 ADGC 11840 10931 22771 0.88 (0.85–0.92) 0.88 (0.85–0.92) 1.7 × 10 1.7 × 10 −4 External 6922 18896 25818 0.92 (0.88–0.97) - - 5.4 × 10 −12 ADGC + External 18762 29827 48589 0.89 (0.87–0.92) - - 8.2 × 10 CD33: rs3865444 −7 −7 ADGC 11840 10931 22771 0.89 (0.86–0.93) 0.89 (0.86–0.93) 1.1 × 10 2.0 × 10 External 6922 18896 25818 0.92 (0.88–0.97) 0.002 - - −9 ADGC + External 18762 29827 48589 0.91 (0.88–0.93) - - 1.6 × 10 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nature Genetics Pubmed Central

Common variants in MS4A4/MS4A6E, CD2uAP, CD33, and EPHA1 are associated with late-onset Alzheimer’s disease

Naj, Adam C; Jun, Gyungah; Beecham, Gary W; Wang, Li-San; Vardarajan, Badri Narayan; Buros, Jacqueline; Gallins, Paul J; Buxbaum, Joseph D; Jarvik, Gail P; Crane, Paul K; Larson, Eric B; Bird, Thomas D; Boeve, Bradley F; Graff-Radford, Neill R; De Jager, Philip L; Evans, Denis; Schneider, Julie A; Carrasquillo, Minerva M; Ertekin-Taner, Nilufer; Younkin, Steven G; Cruchaga, Carlos; Kauwe, John SK; Nowotny, Petra; Kramer, Patricia; Hardy, John; Huentelman, Matthew J; Myers, Amanda J; Barmada, Michael M; Demirci, F. Yesim; Baldwin, Clinton T; Green, Robert C; Rogaeva, Ekaterina; St George-Hyslop, Peter; Arnold, Steven E; Barber, Robert; Beach, Thomas; Bigio, Eileen H; Bowen, James D; Boxer, Adam; Burke, James R; Cairns, Nigel J; Carlson, Chris S; Carney, Regina M; Carroll, Steven L; Chui, Helena C; Clark, David G; Corneveaux, Jason; Cotman, Carl W; Cummings, Jeffrey L; DeCarli, Charles; DeKosky, Steven T; Diaz-Arrastia, Ramon; Dick, Malcolm; Dickson, Dennis W; Ellis, William G; Faber, Kelley M; Fallon, Kenneth B; Farlow, Martin R; Ferris, Steven; Frosch, Matthew P; Galasko, Douglas R; Ganguli, Mary; Gearing, Marla; Geschwind, Daniel H; Ghetti, Bernardino; Gilbert, John R; Gilman, Sid; Giordani, Bruno; Glass, Jonathan D; Growdon, John H; Hamilton, Ronald L; Harrell, Lindy E; Head, Elizabeth; Honig, Lawrence S; Hulette, Christine M; Hyman, Bradley T; Jicha, Gregory A; Jin, Lee-Way; Johnson, Nancy; Karlawish, Jason; Karydas, Anna; Kaye, Jeffrey A; Kim, Ronald; Koo, Edward H; Kowall, Neil W; Lah, James J; Levey, Allan I; Lieberman, Andrew P; Lopez, Oscar L; Mack, Wendy J; Marson, Daniel C; Martiniuk, Frank; Mash, Deborah C; Masliah, Eliezer; McCormick, Wayne C; McCurry, Susan M; McDavid, Andrew N; McKee, Ann C; Mesulam, Marsel; Miller, Bruce L; Miller, Carol A; Miller, Joshua W; Parisi, Joseph E; Perl, Daniel P; Peskind, Elaine; Petersen, Ronald C; Poon, Wayne W; Quinn, Joseph F; Rajbhandary, Ruchita A; Raskind, Murray; Reisberg, Barry; Ringman, John M; Roberson, Erik D; Rosenberg, Roger N; Sano, Mary; Schneider, Lon S; Seeley, William; Shelanski, Michael L; Slifer, Michael A; Smith, Charles D; Sonnen, Joshua A; Spina, Salvatore; Stern, Robert A; Tanzi, Rudolph E; Trojanowski, John Q; Troncoso, Juan C; Deerlin, Vivianna M Van; Vinters, Harry V; Vonsattel, Jean Paul; Weintraub, Sandra; Welsh-Bohmer, Kathleen A; Williamson, Jennifer; Woltjer, Randall L; Cantwell, Laura B; Dombroski, Beth A; Beekly, Duane; Lunetta, Kathryn L; Martin, Eden R; Kamboh, M. Ilyas; Saykin, Andrew J; Reiman, Eric M; Bennett, David A; Morris, John C; Montine, Thomas J; Goate, Alison M; Blacker, Deborah; Tsuang, Debby W; Hakonarson, Hakon; Kukull, Walter A; Foroud, Tatiana M; Haines, Jonathan L; Mayeux, Richard; Pericak-Vance, Margaret A; Farrer, Lindsay A; Schellenberg, Gerard D
Nature Genetics , Volume 43 (5) – Apr 3, 2011
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1061-4036
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10.1038/ng.801
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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 Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms 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, [email protected]. These authors contributed equally to this work. URLs. The Alzheimer Disease Genetics Consortium (ADGC), http://alois.med.upenn.edu/adgc/about/overview.html; ADNI database, (www.loni.ucla.edu/ADNI); ADNI investigators, http://www.loni.ucla.edu/ADNI/Collaboration/ADNI_Manuscript_Citations.pdf; APOE Genotyping kit from TIB MOLBIOL, http://www.roche-as.es/logs/LightMix %C2%AE_40-0445-16_ApoE-112-158_V080904.pdf; PLINK, http://pngu.mgh.harvard.edu/~purcell/plink/; PREST, http:// utstat.toronto.edu/sun/Software/Prest/; MACH, http://www.sph.umich.edu/csg/abecasis/mach/; EIGENSTRAT, http:// genepath.med.harvard.edu/~reich/EIGENSTRAT.htm; The R Project for Statistical Computing, http://www.r-project.org/; Package GWAF in R, http://cran.r-project.org/web/packages/GWAF/index.html; Package gee in R, http://cran.r-project.org/web/packages/gee/ index.html; UCSC Genome Browser, http://genome.ucsc.edu/; METAL, http://www.sph.umich.edu/csg/abecasis/Metal/; FUGUE, http://www.sph.umich.edu/csg/abecasis/fugue/. 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. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 2 analysis approaches were used. We obtained genome-wide significant results at MS4A4A −9 −9; [rs4938933; Stages 1+2, meta-analysis (P ) = 1.7 × 10 , joint analysis (P ) = 1.7 × 10 Stages M J −12 −9 1–3, P = 8.2 × 10 ], CD2AP (rs9349407; Stages 1–3, P = 8.6 × 10 ), EPHA1 (rs11767557; M M −10 −9 Stages 1–3 P = 6.0 × 10 ), and CD33 (rs3865444; Stages 1–3, P = 1.6 × 10 ). We M M −10 −11 confirmed that CR1 (rs6701713; P = 4.6×10 , P = 5.2×10 ), CLU (rs1532278; P = 8.3 × M J M −8 −8 −14 −14 10 , P = 1.9×10 ), BIN1 (rs7561528; P = 4.0×10 ; P = 5.2×10 ), and PICALM J M J −11 −10 1–3 (rs561655; P = 7.0 × 10 , P = 1.0×10 ) but not EXOC3L2 are LOAD risk loci . M J Alzheimer Disease (AD) is a neurodegenerative disorder affecting more than 13% of 4–5 individuals aged 65 years and older and 30%–50% aged 80 years and older . Early work identified mutations in APP, PSEN1, and PSEN2 that cause early-onset autosomal dominant 6–9 10 AD and variants in APOE that affect LOAD susceptibility . A recent GWAS identified 1–3 CR1, CLU, PICALM, and BIN1 as LOAD susceptibility loci . However, because LOAD 2 11 heritability estimates are high (h ≈ 60–80%) , 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. ; 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 heterogeneity and more direct correction for confounding sampling bias . 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 Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 3 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, P =1.1 × −266 −253 10 , P =1.3 × 10 ; Supplementary Table 5). Excluding the APOE region, SNPs at −6 −4 nine distinct loci yielded a P or P ≤ 10 (Table 1; all SNPs with P < 10 are in M J 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 −8 P ≤ 5.0 × 10 (MS4A, EPHA1, CD33, ARID5B, and CD2AP). Because Hollingworth et al. 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. . 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). Stage 1+2 analysis identified the MS4A gene cluster as a novel LOAD locus (P = 1.7 × −9 −9 10 , P = 1.7 × 10 )(Table 1, Fig. 1A). The minor allele (MAF = 0.39) was protective with identical odds ratios (ORs) from both meta-analysis and joint analysis (OR and OR = M J 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 (P −11 −11 = 2.6 × 10 , P = 4.6 × 10 ; OR and OR = 0.87, 95% CI: 0.84–0.91) (Supplementary J M J Table 5). In the accompanying manuscript , genome-wide significant results were also −12 obtained at the MS4A locus (rs670139, P = 5.0 × 10 ) using an independent sample. In a combined analysis of ADGC results and those from Hollingworth et al. , the evidence for −12 this locus at rs4938933 increased to P = 8.2 × 10 (Table 3: OR = 0.89, 95% CI: 0.87– M M 0.92; Fig. 1A). 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 −9 (P = 8.6 × 10 ), identifying CD2AP as a novel LOAD locus. The minor allele (MAF = 0.27) at this SNP increased risk for LOAD (OR = 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 −6 2 genome-wide significance (P = 1.7 × 10 ) . 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 (P = −10 6.0 × 10 , Table 2, Fig. 1C). The minor allele (MAF = 0.19) for this SNP is protective (OR = 0.90, 95% CI: 0.86–0.93). We observed no evidence for heterogeneity at this locus (Supplementary Fig. 2D, heterogeneity P = 0.58). Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 4 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 −9 (rs3865444; Stages 1–3, P = 1.6 × 10 ). The minor allele (MAF = 0.30) is protective (OR = 0.91, 95% CI: 0.88–0.93; Tables 1,2, Fig. 1D). A single SNP (rs3826656) in the 5’ region of CD33, was previously reported as an AD-related locus using a family-based −6 15 approach as genome-wide significant (P = 6.6 × 10 ) . We were unable to replicate this finding (P = 0.73; P = 0.39, Stage 1 analysis for rs3826656). Though rs3826656 is only M J 1,348 bp from our top SNP (rs3865444), these 2 sites display only weak LD (r = 0.13). Hollingworth et al report highly significant evidence for the association of an ABCA7 −17 SNP rs3764650 with LOAD (P = 4.5 × 10 ) that included data from our study. In our Stage 1+2 analysis, we obtained suggestive evidence for association with ABCA7 SNP −7 −7 rs3752246 (P = 5.8 × 10 , and P = 5.0 × 10 ), which is a missense variant (G1527A) M J that may alter the function of the ABCA7 protein (see Supplementary Table 6 for functional −4 SNPs in LD with SNPs yielding P or P < 10 ). M J 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 −8 ≤ 5.0 × 10 in one or both types of analysis. We also examined SNPs with statistically significant GWAS results reported by others 16 17 18 19 (GAB2 , PCDH11X , GOLM1 , and MTHFD1L , 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 −6 19 BIN1 loci demonstrated P < 10 . For MTHFD1L , at rs11754661 (previously reported P = −8 4.7 × 10 ) we obtained modest independent association evidence (OR = 1.16, 95% CI: −4 1.04–1.29, P = 0.006; OR = 1.19, 95% CI: 1.08–1.32, P = 7.5 × 10 ). For the remaining M J J sites, only nominal evidence (P < 0.05) or no evidence was obtained. For the GAB2 locus −7 at rs10793294 (previously reported P = 1.60 × 10 ), we obtained nominal statistical significance results (P = 0.017; P = 0.029). The association for rs5984894 in the M J 17 −12 PCDH11X locus (previously reported P = 3.9 × 10 ), did not replicate (P = 0.89, P = M J 18 −4 0.26). Likewise, findings at GOLM1 for rs10868366 (previously reported P = 2.40 × 10 ) did not replicate (P = 0.71; P = 0.62). Another gene consistently implicated in LOAD is M J SORL1 where at rs3781835 (previously reported P = 0.006), we obtained modest evidence −4 for association (OR = 0.72, 95% CI: 0.60–0.86, P = 2.9 × 10 ; OR = 0.78, 95% CI: M M J −4 0.59–0.86; P = 3.8 × 10 ). 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-APOE 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). Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 5 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 . While we did observe a −9 −10 signal for this SNP (Stage 1, P = 1.5 × 10 ; P = 7.7 × 10 ) and other SNPs in the M J 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. , 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 studies , 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 studies 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 power . Our discovery dataset (Stage 1; 8,309 cases and 7,366 controls), was well-powered to detect associations exceeding the −6 statistical significance threshold of P < 10 (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 manuscript , which reaches only modest statistical significance in our −5 −5 dataset (rs3752246; P = 1.0 × 10 , P = 1.9 × 10 ). Finding additional LOAD loci will M J require larger studies with increased depth of genotyping to test for the effects of both common and rare variants. Supplementary Material Refer to Web version on PubMed Central for supplementary material. Authors 1,115 2,3,4,115 1,5 6 Adam C Naj , Gyungah Jun , Gary W Beecham , Li-San Wang , Badri 3 3 1 7,8,9 Narayan Vardarajan , Jacqueline Buros , Paul J Gallins , Joseph D Buxbaum , 10,11 12 13 14 Gail P Jarvik , Paul K Crane , Eric B Larson , Thomas D Bird , Bradley F 15 16,17 18,19 20 Boeve , Neill R Graff-Radford , Philip L De Jager , Denis Evans , Julie A 21,22 16 16,17 Schneider , Minerva M Carrasquillo , Nilufer Ertekin-Taner , Steven G 16 23 24 23 Younkin , Carlos Cruchaga , John SK Kauwe , Petra Nowotny , Patricia 25,26 27 28 29 Kramer , John Hardy , Matthew J Huentelman , Amanda J Myers , Michael Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 6 30 30 3 3,31,32 M Barmada , F. Yesim Demirci , Clinton T Baldwin , Robert C Green , 33 33,34 35 Ekaterina Rogaeva , Peter St George-Hyslop , Steven E Arnold , Robert 36 37 38 39 40 Barber , Thomas Beach , Eileen H Bigio , James D Bowen , Adam Boxer , 41 42 43 44 James R Burke , Nigel J Cairns , Chris S Carlson , Regina M Carney , Steven 45 46 47 28 L Carroll , Helena C Chui , David G Clark , Jason Corneveaux , Carl W 48 49 50 51 Cotman , Jeffrey L Cummings , Charles DeCarli , Steven T DeKosky , Ramon 52 48 16 53 Diaz-Arrastia , Malcolm Dick , Dennis W Dickson , William G Ellis , Kelley M 54 45 55 56 Faber , Kenneth B Fallon , Martin R Farlow , Steven Ferris , Matthew P 57 58 59 60,61 Frosch , Douglas R Galasko , Mary Ganguli , Marla Gearing , Daniel H 62 63 1,5 64 Geschwind , Bernardino Ghetti , John R Gilbert , Sid Gilman , Bruno 65 66 67 68 Giordani , Jonathan D Glass , John H Growdon , Ronald L Hamilton , Lindy E 47 69 70 71 Harrell , Elizabeth Head , Lawrence S Honig , Christine M Hulette , Bradley T 67 72 53 73 74 Hyman , Gregory A Jicha , Lee-Way Jin , Nancy Johnson , Jason Karlawish , 40 26,75 76 58 Anna Karydas , Jeffrey A Kaye , Ronald Kim , Edward H Koo , Neil W 31,77 66 66 78 Kowall , James J Lah , Allan I Levey , Andrew P Lieberman , Oscar L 79 80 47 81 Lopez , Wendy J Mack , Daniel C Marson , Frank Martiniuk , Deborah C 82 58,83 12 84 Mash , Eliezer Masliah , Wayne C McCormick , Susan M McCurry , Andrew 43 31,77 85,86 40 N McDavid , Ann C McKee , Marsel Mesulam , Bruce L Miller , Carol A 87 53 88,89 90 91 Miller , Joshua W Miller , Joseph E Parisi , Daniel P Perl , Elaine Peskind , 15 48 26 Ronald C Petersen , Wayne W Poon , Joseph F Quinn , Ruchita A 1 91 56,92 49 Rajbhandary , Murray Raskind , Barry Reisberg , John M Ringman , Erik D 47 52 8 46,93 Roberson , Roger N Rosenberg , Mary Sano , Lon S Schneider , William 40 94 1,5 72 Seeley , Michael L Shelanski , Michael A Slifer , Charles D Smith , Joshua A 95 63 31 67 Sonnen , Salvatore Spina , Robert A Stern , Rudolph E Tanzi , John Q 6 96 6 49,97 Trojanowski , Juan C Troncoso , Vivianna M Van Deerlin , Harry V Vinters , 98 85,86 41,99 Jean Paul Vonsattel , Sandra Weintraub , Kathleen A Welsh-Bohmer , 70 100 6 6 Jennifer Williamson , Randall L Woltjer , Laura B Cantwell , Beth A Dombroski , 101 2 1,5 30,79 Duane Beekly , Kathryn L Lunetta , Eden R Martin , M. Ilyas Kamboh , 54,102 28,103,104,105 22,106 Andrew J Saykin , Eric M Reiman , David A Bennett , John C 42,107 95 23 108,109 Morris , Thomas J Montine , Alison M Goate , Deborah Blacker , 91 110 111 Debby W Tsuang , Hakon Hakonarson , Walter A Kukull , Tatiana M 54 112,113 70,114 Foroud , Jonathan L Haines , Richard Mayeux , Margaret A Pericak- 1,5 2,3,4,31,32 6 Vance , Lindsay A Farrer , and Gerard D Schellenberg Affiliations The John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida, USA. Department of Biostatistics, Boston University, Boston, Massachusetts, USA. Department of Medicine (Genetics Program), Boston University, Boston, Massachusetts, USA. Department of Ophthalmology, Boston University, Boston, Massachusetts, USA. Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, Florida, USA. Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 7 Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. Department of Neuroscience, Mount Sinai School of Medicine, New York, New York, USA. Department of Psychiatry, Mount Sinai School of Medicine, New York, New York, USA. Departments of Genetics and Genomic Sciences, Mount Sinai School of Medicine +C120, New York, New York, USA. Department of Genome Sciences, University of Washington, Seattle, Washington, USA. Department of Medicine (Medical Genetics), University of Washington, Seattle, Washington, USA. Department of Medicine, University of Washington, Seattle, Washington, USA. Group Health Research Institute, Seattle, Washington, USA. Department of Neurology, University of Washington, Seattle, Washington, USA. Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA. Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA. Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA. Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA. Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA. Rush Institute for Healthy Aging, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois, USA. Department of Pathology (Neuropathology), Rush University Medical Center, Chicago, Illinois, USA. Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA. Department of Psychiatry and Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University School of Medicine, St. Louis, Missouri, USA. Department of Biology, Brigham Young University, Provo, Utah, USA. Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, Oregon, USA. Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA. Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 8 Institute of Neurology, University College London, Queen Square, London, UK. Neurogenomics Division, Translational Genomics Research Institute, Phoenix, Arizona, USA. Department of Psychiatry & Behavioral Sciences, University of Miami, Miami, Florida, USA. Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. Department of Neurology, Boston University, Boston, Massachusetts, USA. Department of Epidemiology, Boston University, Boston, Massachusetts, USA. Tanz Centre for Research in Neurodegenerative Disease, University of Toronto, Toronto, Ontario, Canada. Cambridge Institute for Medical Research and Department of Clinical Neurosciences, University of Cambridge, Cambridge, Massachusetts, UK. Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. Department of Pharmacology and Neuroscience, University of Texas Southwestern, Fort Worth, Texas, USA. Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Phoenix, Arizona, USA. Department of Pathology, Northwestern University, Chicago, Illinois, USA. Swedish Medical Center, Seattle, Washington, USA. Department of Neurology, University of California San Francisco, San Fransisco, California, USA. Department of Medicine, Duke University, Durham, North Carolina, USA. Department of Pathology and Immunology, Washington University, St. Louis, Missouri, USA. Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. Department of Psychiatry, Vanderbilt University, Nashville, Tennessee, USA. Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama, USA. Department of Neurology, University of Southern California, Los Angeles, California, USA. Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA. Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine, California, USA. Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 9 Department of Neurology, University of California Los Angeles, Los Angeles, California, USA. Department of Neurology, University of California Davis, Sacramento, California, USA. University of Virginia School of Medicine, Charlottesville, Virginia, USA. Department of Neurology, University of Texas Southwestern, Dallas, Texas, USA. Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento, California, USA. Department of Medical and Molecular Genetics, Indiana University, Indianapolis, Indiana, USA. Department of Neurology, Indiana University, Indianapolis, Indiana, USA. Department of Psychiatry, New York University, New York, New York, USA. C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, Charlestown, Massachusetts, USA. Department of Neurosciences, University of California San Diego, La Jolla, California, USA. Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia, USA. Emory Alzheimer's Disease Center, Emory University, Atlanta, Georgia, USA. Neurogenetics Program, University of California Los Angeles, Los Angeles, California, USA. Department of Pathology and Laboratory Medicine, Indiana University, Indianapolis, Indiana, USA. Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA. Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA. Department of Neurology, Emory University, Atlanta, Georgia, USA. Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, USA. Department of Pathology (Neuropathology), University of Pittsburgh, Pittsburgh, Pennsylvania, USA. Department of Molecular and Biomedical Pharmacology, University of California Irvine, Irvine, California, USA. Taub Institute on Alzheimer's Disease and the Aging Brain, Department of Neurology, Columbia University, New York, New York, USA. Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 10 Department of Pathology, Duke University, Durham, North Carolina, USA. Department of Neurology, University of Kentucky, Lexington, Kentucky, USA. Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, Illinois, USA. Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA. Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine, California, USA. Department of Pathology, Boston University, Boston, Massachusetts, USA. Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA. University of Pittsburgh Alheimer's Disease Research Center, Pittsburgh, Pennsylvania, USA. Department of Preventive Medicine, University of Southern California, Los Angeles, California, USA. Department of Medicine - Pulmonary, New York University, New York, New York, USA. Department of Neurology, University of Miami, Miami, Florida, USA. Department of Pathology, University of California San Diego, La Jolla, California, USA. School of Nursing Northwest Research Group on Aging, University of Washington, Seattle, Washington, USA. Alzheimer's Disease Center, Northwestern University, Chicago, Illinois, USA. Cognitive Neurology, Northwestern University, Chicago, Illinois, USA. Department of Pathology, University of Southern California, Los Angeles, California, USA. Department of Anatomic Pathology, Mayo Clinic, Rochester, Minnesota, USA. Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA. Department of Pathology, Mount Sinai School of Medicine, New York, New York, USA. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA. Alzheimer's Disease Center, New York University, New York, New York, USA. Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 11 Department of Psychiatry, University of Southern California, Los Angeles, California, USA. Department of Pathology, Columbia University, New York, New York, USA. Department of Pathology, University of Washington, Seattle, Washington, USA. Department of Pathology, Johns Hopkins University, Baltimore, Maryland, USA. Department of Pathology & Laboratory Medicine, University of California Los Angeles, Los Angeles, California, USA. Taub Institute Department of Pathology, Columbia University, New York, New York, USA. Department of Psychiatry & Behavioral Sciences, Duke University, Durham, North Carolina, USA. Department of Pathology, Oregon Health & Science University, Portland, Oregon, USA. National Alzheimer's Coordinating Center, University of Washington, Seattle, Washington, USA. Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA. Department of Psychiatry, University of Arizona, Phoenix, Arizona, USA. Arizona Alzheimer’s Consortium, Phoenix, Arizona, USA. Banner Alzheimer's Institute, Phoenix, Arizona, USA. Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA. Department of Neurology, Washington University, St. Louis, Missouri, USA. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, USA. Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. Department of Epidemiology, University of Washington, Seattle, Washington, USA. Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA. Vanderbilit Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, USA. Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA. Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 12 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. 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For the SNP with the lowest P-value at each locus in Stage 1 analyses, three P-values for association are shown: P meta-analysis of the ADGC Discovery (Stage 1) dataset (highlighted with a black diamond), P meta-analysis of the 1+2 Combined ADGC Discovery and Replication (Stages 1 + 2) datasets (highlighted with a blue diamond), and P meta-analysis of the combined ADGC dataset and the external 1+2+3 replication (Stages 1 + 2 + 3) datasets (highlighted with a red diamond). Computed estimates of linkage disequilibrium (r ) with the most significant SNP at each locus are 2 2 shown as an orange diamond for r ≥ 0.8, a yellow diamond for 0.5 ≤ r < 0.8, a grey 2 2 diamond for 0.2 ≤ r < 0.5, and a white diamond for r < 0.2. Genes in each region are indicated at the bottom of each panel. The length and the direction of the arrowhead represent the scaled size and the direction of the gene, respectively. Nat Genet. Author manuscript; available in PMC 2011 November 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 16 Nat Genet. Author manuscript; available in PMC 2011 November 01. Table 1 Genome-wide Association Results for LOAD in the ADGC Stage 1 and Stage 2 datasets −6 Association signals represent SNPs with the strongest associations within each locus demonstrating P ≤ 10 in the Stage 1 dataset or in/near previously reported genes, excluding the APOE region (Supplementary Table 5). ADGC Discovery (Stage 1) ADGC Replication (Stage 2) Combined Analysis (Stages 1+2) Nearest # SNP CH:MB MA MAF OR OR OR OR OR OR Gene SNPs M J M J M J P P P P P P M J M J M J (95% CI) (95% CI) (95% CI) (95% CI) (95% CI) (95% CI) 1.18 1.19 1.13 1.13 1.16 1.17 −8 −9 −10 −11 rs6701713 1:207.8 A 0.20 7 1.4×10 3.5×10 0.004 0.004 4.6×10 5.2×10 CR1 1.11–1.25 1.12–1.26 1.04–1.23 1.04–1.24 1.11–1.22 1.12–1.23 1.18 1.18 1.15 1.15 1.17 1.17 −11 −11 −4 −4 −14 −14 rs7561528 2:127.9 A 0.35 10 2.9×10 7.7×10 1.4×10 1.0×10 4.2×10 5.2×10 BIN1 1.13–1.24 1.12–1.24 1.07–1.24 1.07–1.24 1.13–1.22 1.12–1.22 1.14 1.14 1.07 1.08 1.12 1.12 −6 −6 −6 −6 rs9349407 6:47.5 CD2AP C 0.27 1 1.2×10 5.3×10 0.118 0.074 1.0×10 2.1×10 1.08–1.21 1.08–1.20 0.98–1.17 0.99–1.18 1.07–1.18 1.07–1.17 0.85 0.84 0.94 0.93 0.87 0.87 −8 −8 −7 −8 rs11767557 7:143.1 C 0.19 1 7.3×10 3.1×10 0.169 0.133 2.4×10 4.9×10 EPHA1 0.80–0.90 0.79–0.89 0.86–1.03 0.85–1.02 0.83–0.92 0.83–0.91 0.90 0.89 0.87 0.87 0.89 0.89 −5 −5 −4 −4 −8 −8 rs1532278 8:27.5 T 0.36 2 5.6×10 2.0×10 2.6×10 2.7×10 8.3×10 1.9×10 CLU 0.85–0.95 0.85–0.94 0.81–0.94 0.81–0.94 0.85–0.93 0.85–0.92 0.88 0.88 1.05 1.05 0.93 0.93 −6 −7 −4 rs2588969 10:63.6 ARID5B A 0.37 0 1.1×10 6.9×10 0.234 0.189 0.001 7.7×10 0.84–0.93 0.84–0.93 0.97–1.13 0.98–1.13 0.89–0.97 0.89–0.97 0.88 0.87 0.90 0.90 0.88 0.88 −8 −8 −9 −9 rs4938933 11:60.0 MS4A4A C 0.39 22 5.2×10 4.5×10 0.005 0.004 1.7×10 1.7×10 0.84–0.92 0.83–0.92 0.84–0.97 0.84–0.97 0.85–0.92 0.85–0.92 0.88 0.88 0.86 0.86 0.87 0.87 −7 −7 −5 −5 −11 −10 rs561655 11:85.8 G 0.34 36 1.2×10 4.6×10 8.4×10 3.7×10 7.0×10 1.0×10 PICALM 0.84–0.92 0.84–0.93 0.80–0.93 0.80–0.92 0.84–0.91 0.84–0.91 1.16 1.15 1.13 1.13 1.15 1.15 −5 −5 −7 −7 rs3752246 19:1.1 G 0.19 2 1.0×10 1.9×10 0.012 0.009 5.8×10 5.0×10 ABCA7 1.08–1.24 1.08–1.23 1.03–1.24 1.03–1.25 1.09–1.21 1.09–1.21 0.88 0.88 0.91 0.92 0.89 0.89 −7 −6 −7 −7 rs3865444 19:51.7 A 0.30 1 8.2×10 1.9×10 0.021 0.029 1.1×10 2.0×10 CD33 0.84–0.93 0.84–0.93 0.85–0.99 0.85–0.99 0.86–0.93 0.86–0.93 −6 CH:MB, chromosome:position (in mega base pairs, build 19); MA, minor allele; MAF, minor allele frequency; # SNPs, the number of SNPs for which P ≤ 1 × 10 in meta-analysis from the combined analysis in Stage 1+2; OR , odds ratio in meta-analysis; P , P-value in meta-analysis; OR , odds ratio in joint analysis; P , P-value in joint analysis. M M J J 3 1,3 1 2 Genes with previous case-control genome-wide statistically significant associations: CR1 , CLU , PICALM , BIN1 . Gene with a previous association not meeting genome-wide statistical significance: 2 15 EPHA1 . Family-based association study with reported genome-wide statistical significance: CD33 . −8 Genes with previously published case-control association signals at P ≤ 5.0 × 10 are denoted with * the case-control locus that did not meet this level of statistical significance is denoted with the locus previously reported in a family-based association study as genome-wide significant with 12 % locus identified in Hollingworth et al. with genome-wide significant evidence for association with. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Naj et al. Page 17 Nat Genet. Author manuscript; available in PMC 2011 November 01. Table 2 Meta-Analysis of Stage 1+2 with Stage 3 (CHARGE/GERAD/EADI1 Consortia ) GWAS Results Meta-analysis using an external replication case-control sample (Stage 3) for SNPs from novel loci at which associations did not exceed the genome-wide −8 statistical significance threshold (P = 5.0 × 10 ) in the ADGC meta-analysis (Stage 1+2). Results for MS4A are also included to show association results from the ADGC and accompanying manuscript . The external replication dataset did not include results from TGEN, ADNI, and MAYO cohorts (Supplementary Tables 1 and 2). Gene:SNP Cases Controls Total OR (95% CI) P OR (95% CI) P M M j J CD2AP: rs9349407 −6 −6 ADGC 11840 10931 22771 1.12 (1.07–1.18) 1.12 (1.07–1.17) 1.0 × 10 2.1 × 10 External 6922 18896 25818 1.09 (1.03–1.15) 0.002 - - −9 ADGC + External 18762 29827 48589 1.11 (1.07–1.15) - - 8.6 × 10 EPHA1: rs11767557 −7 −8 ADGC 11840 10931 22771 0.87 (0.83–0.92) 0.87 (0.83–0.91) 2.4 × 10 4.9 × 10 −4 External 6922 24666 31588 0.91 (0.87–0.96) 2.9 × 10 - - −10 ADGC + External 18762 35597 54359 0.90 (0.86–0.93) - - 6.0 × 10 ARID5B: rs2588969 −4 ADGC 11840 10931 22771 0.93 (0.89–0.97) 0.001 0.93 (0.89–0.97) 7.8 × 10 External 6922 18896 25818 1.06 (1.01–1.11) 0.018 - - ADGC + External 18762 29827 48589 0.99 (0.95–1.02) 0.362 - - MS4A4A: rs4938933 −9 −9 ADGC 11840 10931 22771 0.88 (0.85–0.92) 0.88 (0.85–0.92) 1.7 × 10 1.7 × 10 −4 External 6922 18896 25818 0.92 (0.88–0.97) - - 5.4 × 10 −12 ADGC + External 18762 29827 48589 0.89 (0.87–0.92) - - 8.2 × 10 CD33: rs3865444 −7 −7 ADGC 11840 10931 22771 0.89 (0.86–0.93) 0.89 (0.86–0.93) 1.1 × 10 2.0 × 10 External 6922 18896 25818 0.92 (0.88–0.97) 0.002 - - −9 ADGC + External 18762 29827 48589 0.91 (0.88–0.93) - - 1.6 × 10

Journal

Nature Genetics – Pubmed Central

Published: Apr 3, 2011

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