ACT indicates Adult Changes in Thought; ADC, Alzheimer Disease Center; ADNI, Alzheimer’s Disease Neuroimaging Initiative; GenADA, Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer’s Disease; MIRAGE, Multi-Institutional Research on Alzheimer Genetic Epidemiology; NIA-LOAD, National Institute on Aging–Late-Onset Alzheimer Disease; OHSU, Oregon Health & Science University; ROSMAP, Religious Orders Study/Memory and Aging Project; TGEN2, Translational Genomics Research Institute 2; UM/VU/MSSM, University of Miami/Vanderbilt University/Mount Sinai School of Medicine; UPITT, University of Pittsburgh; and WASHU, Washington University in St Louis.
eTable 1. Descriptive Statistics
eTable 2. Replication of Age-at-Onset (AAO) Associations of SNPs Most Significantly Associated With LOAD in Nine Genomic Regions and APOE
eTable 3. Risk-Weighted Burden Analysis Results for APOE and 9 LOAD Candidate Genes Excluding Three Datasets With Later Onset Age (ACT, OHSU, ROSMAP)
eTable 4. Risk-Weighted Burden Analysis Results for APOE and 9 LOAD Candidate Genes Excluding Two Family Datasets (MIRAGE, LOAD)
eAppendix. Genome-Wide Analysis of Age-at-Onset in the ADGC
eFigure. Genomic Inflation for Discovery Genome-Wide Association Analysis of AAO
eTable 5. Genome-Wide Association Results by Genomic Region for Age-at-Onset of LOAD
eTable 6. Strongest Genomewide Association Results for Age-at-Onset of LOAD Under Minimal Covariate Adjustment
eTable 7. Strongest Genomewide Association Results for Age-at-Onset of LOAD Adjusting for Dosage of APOE e4 Alleles and Sex
eTable 8. Burden Analysis Results for APOE and SNPs Associated With AAO at P<10-6 From 7 Genomic Regions
eTable 9. Risk-Weighted Burden Analysis Results for APOE and SNPs Associated With AAO at P<10-6 From 7 Genomic Regions
Adam C. Naj, Gyungah Jun, Christiane Reitz, Brian W. Kunkle, William Perry, Yo Son Park, Gary W. Beecham, Ruchita A. Rajbhandary, Kara L. Hamilton-Nelson, Li-San Wang, John S. K. Kauwe, Matthew J. Huentelman, Amanda J. Myers, Thomas D. Bird, Bradley F. Boeve, Clinton T. Baldwin, Gail P. Jarvik, Paul K. Crane, Ekaterina Rogaeva, M. Michael Barmada, F. Yesim Demirci, Carlos Cruchaga, Patricia L. Kramer, Nilufer Ertekin-Taner, John Hardy, Neill R. Graff-Radford, Robert C. Green, Eric B. Larson, Peter H. St. George-Hyslop, Joseph D. Buxbaum, Denis A. Evans, Julie A. Schneider, Kathryn L. Lunetta, M. Ilyas Kamboh, Andrew J. Saykin, Eric M. Reiman, Philip L. De Jager, David A. Bennett, John C. Morris, Thomas J. Montine, Alison M. Goate, Deborah Blacker, Debby W. Tsuang, Hakon Hakonarson, Walter A. Kukull, Tatiana M. Foroud, Eden R. Martin, Jonathan L. Haines, Richard P. Mayeux, Lindsay A. Farrer, Gerard D. Schellenberg, Margaret A. Pericak-Vance, . Effects of Multiple Genetic Loci on Age at Onset in Late-Onset Alzheimer DiseaseA Genome-Wide Association Study. JAMA Neurol. 2014;71(11):1394–1404. doi:10.1001/jamaneurol.2014.1491
Because APOE locus variants contribute to risk of late-onset Alzheimer disease (LOAD) and to differences in age at onset (AAO), it is important to know whether other established LOAD risk loci also affect AAO in affected participants.
To investigate the effects of known Alzheimer disease risk loci in modifying AAO and to estimate their cumulative effect on AAO variation using data from genome-wide association studies in the Alzheimer Disease Genetics Consortium.
Design, Setting, and Participants
The Alzheimer Disease Genetics Consortium comprises 14 case-control, prospective, and family-based data sets with data on 9162 participants of white race/ethnicity with Alzheimer disease occurring after age 60 years who also had complete AAO information, gathered between 1989 and 2011 at multiple sites by participating studies. Data on genotyped or imputed single-nucleotide polymorphisms most significantly associated with risk at 10 confirmed LOAD loci were examined in linear modeling of AAO, and individual data set results were combined using a random-effects, inverse variance–weighted meta-analysis approach to determine whether they contribute to variation in AAO. Aggregate effects of all risk loci on AAO were examined in a burden analysis using genotype scores weighted by risk effect sizes.
Main Outcomes and Measures
Age at disease onset abstracted from medical records among participants with LOAD diagnosed per standard criteria.
Analysis confirmed the association of APOE with earlier AAO (P = 3.3 × 10−96), with associations in CR1 (rs6701713, P = 7.2 × 10−4), BIN1 (rs7561528, P = 4.8 × 10−4), and PICALM (rs561655, P = 2.2 × 10−3) reaching statistical significance (P < .005). Risk alleles individually reduced AAO by 3 to 6 months. Burden analyses demonstrated that APOE contributes to 3.7% of the variation in AAO (R2 = 0.256) over baseline (R2 = 0.221), whereas the other 9 loci together contribute to 2.2% of the variation (R2 = 0.242).
Conclusions and Relevance
We confirmed an association of APOE (OMIM 107741) variants with AAO among affected participants with LOAD and observed novel associations of CR1 (OMIM 120620), BIN1 (OMIM 601248), and PICALM (OMIM 603025) with AAO. In contrast to earlier hypothetical modeling, we show that the combined effects of Alzheimer disease risk variants on AAO are on the scale of, but do not exceed, the APOE effect. While the aggregate effects of risk loci on AAO may be significant, additional genetic contributions to AAO are individually likely to be small.
Alzheimer disease (AD) (OMIM 104300) affects more than 13% of individuals 65 years and older, and its prevalence increases with age, occurring in less than 1% of those 65 years and younger and in up to 40% of the population after age 90 years.1- 4 While genetic studies5,6 of late-onset AD (LOAD) confirmed at least 10 loci contributing to risk of disease, including APOE, PICALM, CLU, CR1, BIN1, CD2AP, EPHA1, MS4A4A, CD33, and ABCA7, genes modifying age at onset (AAO) of LOAD have not been widely studied. Earlier linkage and candidate gene studies identified a few loci possibly underlying variation of AAO (eg, GSTO1),7 but only variation in the APOE region has been consistently confirmed.8- 12
A multitude of studies have attempted to identify susceptibility genes for AAO in AD. The first study13 to identify a genetic association with AAO showed a lower mean AAO among affected participants with AD for each additional copy of the ε4 allele at the APOE locus on chromosome 19q (84.3 years for 0 copy, 75.5 years for 1 copy, and 68.4 years for 2 copies), a finding that has since been replicated.14 Subsequent genome-wide linkage scans examining AAO in patients with AD and unaffected family members (using age at study entry) found suggestive evidence of linkage on chromosome 19 to APOE (logarithm of odds [LOD], 3.28),15 which was confirmed in later investigations.16 Multiple studies identified other suggestive linkage signals on chromosomes 4q, 8q, 1q, 6p, 7q, 15, and 19p16- 18 in families of white race/ethnicity and on chromosomes 5q, 7q, 14q, and 17q19 in Caribbean Hispanics, although the specific loci driving these linkage signals remain unknown. More recently, an AAO genome-wide association study20 (GWAS) in 2222 AD patients of white race/ethnicity confirmed an association at APOE and found strong evidence of association (P = 5.0 × 10−7) on chromosome 4q31.3 in the DCHS2 gene.
The lack of overlap in the regions identified across these studies may have resulted from differences in the approaches applied such as varied strategies for censoring unaffected pedigree members and differences in covariates adjusted for in analyses. Reduced statistical power from the limited availability of extended families for analysis may also have contributed to the differences in findings between these early linkage and association studies. The high variability in approaches and findings highlights the need for a more comprehensive approach to identify genetic risk factors that may influence LOAD AAO, as well as LOAD risk directly. To date, variants in the 10 confirmed LOAD risk loci have not been examined for their possible influence on AAO among affected participants with LOAD.
Using data from 9162 affected participants with LOAD from a GWAS of LOAD by the Alzheimer Disease Genetics Consortium (ADGC),6 we examined whether variants most significantly associated with LOAD risk in 10 LOAD loci are also associated with differences in AAO among affected participants with LOAD. Furthermore, we used a genetic burden analysis approach to determine the proportion of variation in AAO accounted for by variants in these established LOAD risk genes.
The ADGC comprises 14 case-control, prospective, and family-based data sets with data on 9162 participants of white race/ethnicity with AD occurring after age 60 years who also had complete AAO information, gathered between September 1989 and January 2011 at multiple sites by participating studies. The ADGC received approval for analysis and use of data from the University of Pennsylvania Institutional Review Board. Participants’ written or oral consents were obtained by their originating studies. A detailed description of ascertainment and the collection of genotype and phenotype data in the individual data sets of the ADGC is available elsewhere.6 Briefly, individuals in each data set (eTable 1 in the Supplement) were genotyped using commercially available GWAS high-density single-nucleotide polymorphism (SNP) genotyping microarrays (Illumina or Affymetrix). All individuals with LOAD met National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer’s Disease and Related Disorders Association criteria for definite, probable, or possible LOAD,21 and AAO of LOAD, which was abstracted from medical records for most individuals, was defined as the age when LOAD-related symptoms manifested, as reported by the individual or by an informant. Age at ascertainment was substituted for data sets lacking AAO information (Washington University in St Louis and Alzheimer’s Disease Neuroimaging Initiative) (eTable 1 in the Supplement). Unaffected individuals and affected participants with LOAD lacking AAO information, those with an AAO or age at death younger than 60 years, and individuals of nonwhite race/ethnicity with European ancestry were excluded from the association analyses.
Individuals were excluded if Affymetrix chip genotypes were called for less than 95% of SNPs or if Illumina chip genotypes were called for less than 98% of SNPs. In addition, samples were excluded if reported sex differed from genetic sex by X-chromosome analysis (PLINK; http://pngu.mgh.harvard.edu/purcell/plink/).22 Samples were dropped from family data sets if reported relationships differed from relatedness from identity by descent (IBD) estimation (using PREST; http://fisher.utstat.toronto.edu/sun/Software/Prest/).23 If samples were duplicated in different data sets, only one sample per duplicate pair was kept in the analysis. After exclusions, data on 9162 affected participants remained for subsequent analyses.
After sample quality control, genotyped SNPs were excluded from the analysis if their minor allele frequencies (MAFs) were less than 0.02 for Affymetrix chips or less than 0.01 for Illumina chips or if the SNPs were observed to be out of Hardy-Weinberg equilibrium with P < 10−6. Imputed SNPs were excluded if the quality score (“Info” from IMPUTE2; http://mathgen.stats.ox.ac.uk/impute/impute_v2.html)24 was less than 0.50. Genome-wide genotype imputation was performed in each cohort using IMPUTE2 software24 with all available reference haplotypes from 1000 Genomes (December 2010 release; http://www.1000genomes.org/announcements/june-2011-data-release-2011-06-23). Imputation quality was assessed using the Info statistic, and only SNPs imputed with an Info of 0.50 or higher were included in the analysis. The 10 SNPs examined herein were among the common set of SNPs produced in imputation.
We performed association analysis on individual data sets assuming an additive model on log-transformed AAO with covariate adjustment for population substructure. For cases from case-control data sets, linear regression was performed in PLINK,22 while for analysis of cases from family data sets (used only in the primary analysis of risk variants), generalized estimating equations with a linear model as implemented in a statistical package (R; http://www.r-project.org/)25 were used. To account for the effects of population substructure, we performed a principal components analysis on affected participants within each data set (using EIGENSTRAT; http://genepath.med.harvard.edu/~reich/EIGENSTRAT.htm)26 on a subset of 21 109 SNPs common to all genotyping platforms. The first 3 principal components from the analysis were incorporated in our minimal model for covariate adjustment. We also performed analyses conditioning on the major AAO-modifying effects of APOE through an extended model of covariate adjustment that included sex and the number of APOE ε4 alleles (0, 1, or 2). Results from individual data sets were combined in the meta-analysis using inverse variance weighting (as implemented in METAL; http://www.sph.umich.edu/csg/abecasis/metal/),27 applying a genomic control to each data set. With this set of 9162 affected participants, for 10 focused independent hypothesis tests, we expected to have greater than 80% power to detect loci at α = 0.05 with as little effect as 5 months’ difference in AAO per allelic copy for very common variants (MAF, 0.30), and greater than 80% power to detect 8 months’ difference in AAO per allelic copy for variants of modest or low frequency (MAF, 0.10).28
Because of limitations in the availability of genotyped replication data sets with similar AAO phenotypes and ascertainment, we performed a discovery genome-wide association meta-analysis among 6143 cases in 10 ADGC case-control data sets to determine whether SNPs with weak or no LOAD risk associations may contribute to differences in AAO, as well as to assess the genetic burden attributable to these variants. Methods, results, and a brief summary are provided in the eAppendix, eFigure, and eTables 5-9 in the Supplement. Replication data on affected participants from 6 new ADGC data sets (described in the Methods subsection of the eAppendix in the Supplement) were also examined.
In addition to association meta-analysis, we performed several genetic burden analyses to determine the percentage contribution of LOAD susceptibility SNPs in 10 LOAD candidate genes to variation in AAO. Risk-weighted genetic burden analyses of AAO linearly modeled locus-specific effects as the product of the meta-analysis–estimated LOAD risk (across-study change in AAO for each copy of the minor allele) and the dosage of the minor allele (scale, 0-2; estimated from genotype-specific imputation probabilities) and were implemented in analyses of risk variants. Additional covariate adjustment in the burden model included covariates for population substructure from principal components analysis and data set–specific effects. We also performed a score-based genetic burden analysis of AAO using a risk genotype score derived from summing dosages of the risk alleles at the 10 LOAD risk loci examined.
Descriptive characteristics of the individual ADGC data sets are summarized in eTable 1 in the Supplement. There were more female affected participants (5480 [59.8%]) than male affected participants. The mean (SD) AAO was 74.3 (7.6) years for the entire group. Several data sets had later ages at onset (Figure). Two of these were population-based cohorts of aging and memory loss, Religious Orders Study/Memory and Aging Project (mean [SD] AAO, 85.6 [6.3] years) and Adult Changes in Thought (mean [SD] AAO, 83.9 [4.8] years). A third was a case-control data set, Oregon Health & Science University, which intentionally ascertained individuals with later AAO (mean [SD] AAO, 86.1 [5.5] years). While data from these studies did not largely change the patterns of association observed (data not shown) in association testing, we performed several subanalyses to assess their effect on the genetic burden analyses as described below.
We confirmed an association of the APOE ε4 allele with lower AAO, with each additional copy of the ε4 allele reducing AAO by 2.45 years (β = −2.45, P = 3.3 × 10−96). Examining the variants most strongly associated with LOAD in 9 genomic regions with genome-wide statistically significant associations in our GWAS of LOAD risk (Table 1),6 we observed that several LOAD risk loci also demonstrated statistically significant associations (P < .005) with AAO, including rs6701713 in CR1 (P = 7.2 × 10−4), rs7561528 in BIN1 (P = 4.8 × 10−4), and rs561655 in PICALM (P = 2.2 × 10−3). Both rs6701713 in CR1 and rs7561528 in BIN1 demonstrated a reduced AAO for each copy of the risk variant, with each copy of the risk allele A at rs6701713 (MAF, 0.24) advancing AAO by approximately 5 months (β = −0.41; 95% CI, −0.65 to −0.17) and with each copy of the risk allele A at rs7561528 (MAF, 0.37) advancing AAO by slightly less than 4 months (β = −0.31; 95% CI, −0.52 to −0.09). In contrast, each copy of the more common risk allele A (frequency, 0.62) at rs561655 in the PICALM gene corresponded with earlier onset by approximately 4 months (β = −0.33; 95% CI, −0.55 to 0.12). These patterns of association remained largely unchanged after adjustment for APOE ε4 allele dosage and sex for the CR1 variant (rs6701713; β = −0.41; 95% CI, −0.69 to −0.12; P = 4.9 × 10−3) and for the BIN1 variant (rs7561528; β = −0.32; 95% CI, −0.57 to −0.08; P = 9.9 × 10−3). While the size and direction of the association remained the same as in the minimally adjusted model, the association of the PICALM variant demonstrated only marginal significance after this additional adjustment (rs561655; β = 0.32; 95% CI, 0.07-0.57; P = .011). Investigation of AAO associations in the vicinity of these AD risk variants revealed no substantially different associations among nearby variants. Directions of variant effects were concordant between AD risk and AAO; all variants that increase risk also lower AAO. We examined these associations in a limited replication data set of 1978 cases from 6 newly available ADGC case-control data sets (described in the eAppendix in the Supplement). Although similar directionality of effects on AAO were observed for all the LOAD risk variants (eTable 2 in the Supplement), other than APOE, none of the AAO associations of CR1, BIN1, and PICALM variants in the replication data set of less than 2000 affected participants were nominally significant (P < .05). Power with these data are limited in a data set of 1978, and for a variant of MAF of 0.20, there is 80% power to detect at a difference in AAO of about 10 months at α = 0.05, whereas for a variant of MAF of 0.30, 80% power can detect a 9-month AAO difference.
We examined the genetic burden of APOE and the LOAD risk variants in the 9 genomic regions on variation in AAO (Table 2) in the 14 ADGC data sets with complete AAO data. In our baseline model, 22.1% of the variation in AAO (R2 = 0.221) was accounted for by population substructure and study-specific effects. The independent contributions of dosage of the APOE ε4 allele to the genetic burden was roughly 3.7% of AAO variation (R2 = 0.256), while the cumulative effect of the 9 LOAD risk variants was 2.2% (R2 = 0.242), together accounting for approximately 5.6% of genetic variation in AAO (R2 = 0.277). Excluding study-specific effects, APOE accounts for 4.8% of the remaining variation, and the 9 LOAD risk variants account for another 2.8%, for a combined contribution of 7.2% of the variation of AAO. Variant effects in burden modeling were consistent with the association results for individual variants described above.
To determine whether ascertainment differences may have influenced the amount of variation in AAO attributable to LOAD risk variants, we examined the effects of the 3 data sets with much later average AAO (Adult Changes in Thought, Oregon Health & Science University, and Religious Orders Study/Memory and Aging Project) and the 2 family-based data sets (National Institute on Aging–LOAD and Multi-Institutional Research on Alzheimer Genetic Epidemiology) on genetic burden analyses. In analyses that excluded the data sets with later average AAO (eTable 3 in the Supplement), we found that these data sets account for much of the data set–specific AAO variation, reducing the effect of data set on AAO variation from just over 22% to 2.5% (R2 = 0.0251). In these analyses, after excluding data set–specific effects, the percentage variation attributable to APOE was slightly higher at 4.3% (R2 = 0.0434), the effect attributable to the 9 LOAD risk variants was similar to before at 1.1% (R2 = 0.0367), and the combined contribution of both was observed to be 5.5% (R2 = 0.0799). Removal of the family data sets (eTable 4 in the Supplement) did not appreciably change the variation attributable to study-specific effects (R2 = 0.225), nor did it substantially change the relative effects of APOE and the 9 LOAD risk variants on AAO variation.
To determine the aggregate effect of risk alleles from the 10 LOAD loci, we also tested the association of a risk genotype score derived from summed unweighted dosages of the risk alleles at the 10 LOAD risk loci examined (Table 3). We observed that, for each risk allele copy at these 9 LOAD risk loci, there was a lower AAO of 1.8 months (β = −0.15; 95% CI, −0.24 to −0.07; P = 2.7 × 10−4). Including APOE ε4 dosage, the combined effect of the 9 LOAD risk loci and APOE ε4 corresponded to a lower AAO of 4.2 months (β = −0.35; 95% CI, −0.43 to −0.27; P = 1.0 × 10−17) for each LOAD risk allele copy. Examining only the variants at CR1, BIN1, and PICALM that showed significant lowering of AAO, AAO is still lower for each risk copy (4.9 months) (β = −0.41; 95% CI, −0.54 to −0.27; P = 1.9 × 10−9) and more so when APOE ε4 is included in the score (10.1 months) (β = −0.84; 95% CI, −0.96 to −0.73; P = 8.4 × 10−44).
Our analysis of more than 9000 affected participants having LOAD with AAO information is the largest genetic study of LOAD AAO to date. Examining AAO associations at LOAD risk loci, we confirmed the association of APOE region variation with AAO and found additional strong associations with AAO among variants at 3 of the other 9 established risk loci (CR1, BIN1, and PICALM). Burden analysis demonstrated that the cumulative variation explained by SNPs at 9 LOAD risk loci was about one-third as much as the percentage variation in AAO from APOE. A risk genotype score analysis found that, in aggregate, each additional risk allele at the major LOAD loci lowers AAO by as much as 10 months per copy, emphasizing that the aggregate effect of these risk loci may lead to much earlier onset for some affected participants with LOAD.
The APOE ε4 allele was observed to have a smaller effect on phenotype variation in AAO herein (3%-4%) than in some previous investigations (7%-9%).29 This may be owing to differences in study design; for instance, all previous estimates were made in pedigrees enriched in cases and often the APOE ε4 allele, whereas most affected participants examined herein were unrelated (only 2302 of 9162 affected participants [25.1%] were from family data sets). However, this deflation is consistent with several recent findings: 2 recent analyses using GWAS data found that the APOE ε4 allele contributed to 4%30 and 6%31 of the phenotype variation in LOAD risk, with which APOE ε4 is more strongly associated than AAO.
In addition to confirming the predominance of the effect of APOE on AAO, we showed that the cumulative effects of risk loci associated with AAO may have an effect of similar scale on AAO. In our secondary analysis of genome-wide association, cumulative effects on the genetic burden of SNPs associated with AAO but with little or no effect on LOAD risk accounted for more variation in AAO compared with the non-APOE risk variants (2.2% vs 1.1%) but were still dwarfed by the effects of APOE on variation in AAO (approximately 4%).
The results of several previous studies have suggested potential associations of risk variants at these loci with AAO. A recent study10 using a small subset of the cases used in this study (Alzheimer Disease Center 1, 2, and 3 [n = 2569]) identified an association with a PICALM risk variant (rs3851179, P = .009). A study32 of the expression of the 10 LOAD risk genes in parietal lobe neurons from an autopsy series of AD brains demonstrated nominally significant evidence of an association between reduced BIN1 expression levels and earlier AAO (P = .041), as well as an association with a longer duration of disease. A study by Jones et al33 among persons with Down syndrome, which is typically associated with elevated AD risk at an earlier AAO, showed that risk variants in APOE (P = .014) and PICALM (P = .011) were correlated with lower AAO in patients with AD having Down syndrome.
Daw et al29 analyzed families with a high burden of AD and later AAO in a multiplex family data set and found evidence of at least 4 additional genes with major effects on variation in AAO as large as those of APOE. The lack of major AAO-modifying effects outside of APOE in our study is not consistent with the study by Daw et al and may reflect genetic heterogeneity of AAO genetics within LOAD or, more likely, may indicate the existence of large effect modifiers enriched in families with multiple affected members. APOE-related survival effects may have further complicated the identification of AAO-modifying genes. Furthermore, other genetic mechanisms, including the effects of rare variants, epigenetic modification, and gene-environment interactions, which have been reported to influence dementia risk and cognitive decline,34- 39 may also contribute to variation in AAO of AD. The identification of other genetic modifiers of AAO through studies of larger samples of affected participants with LOAD and studies using next-generation sequencing approaches, which can more thoroughly interrogate the genome, may yield additional genetic risk factors that influence AAO and provide new insights into the pathogenesis of LOAD.
We confirmed an association of APOE variants with AAO among affected participants with LOAD and observed novel associations of CR1, BIN1, and PICALM with AAO. In contrast to earlier hypothetical modeling, we show that the combined effects of AD risk variants on AAO are on the scale of, but do not exceed, the APOE effect. While the aggregate effects of risk loci on AAO may be significant, additional genetic contributions to AAO are individually likely to be small.
Accepted for Publication: May 5, 2014.
Corresponding Author: Margaret A. Pericak-Vance, PhD, John P. Hussman Institute for Human Genomics, University of Miami, 1501 NW 10th Ave, Biomedical Research Bldg, Rm 318, Miami, FL 33136 (firstname.lastname@example.org).
Published Online: September 8, 2014. doi:10.1001/jamaneurol.2014.1491.
The Alzheimer Disease Genetics Consortium includes Marilyn S. Albert, PhD; Roger L. Albin, MD; Liana G. Apostolova, MD; Steven E. Arnold, MD; Robert Barber, PhD; Lisa L. Barnes, PhD; Thomas G. Beach, MD, PhD; James T. Becker, PhD; Duane Beekly, BS; Eileen H. Bigio, MD; James D. Bowen, MD; Adam Boxer, MD, PhD; James R. Burke, MD, PhD; Nigel J. Cairns, PhD; Laura B. Cantwell, MPH; Chuanhai Cao, PhD; Chris S. Carlson; Regina M. Carney, MD; Minerva M. Carrasquillo, PhD; Steven L. Carroll, MD, PhD; Helena C. Chui, MD; David G. Clark, MD; Jason Corneveaux, BS; David H. Cribbs, PhD; Elizabeth A. Crocco, MD; Charles DeCarli, MD; Steven T. DeKosky, MD; Malcolm Dick, PhD; Dennis W. Dickson, MD; Ranjan Duara, MD; Kelley M. Faber, MS; Kenneth B. Fallon, MD; Martin R. Farlow, MD; Steven Ferris, PhD; Matthew P. Frosch, MD, PhD; Douglas R. Galasko, MD; Mary Ganguli, MD, MPH; Marla Gearing, PhD; Daniel H. Geschwind, MD, PhD; Bernardino Ghetti, MD; John R. Gilbert, PhD; Jonathan D. Glass, MD; John H. Growdon, MD; Ronald L. Hamilton, MD; Lindy E. Harrell, MD, PhD; Elizabeth Head, PhD; Lawrence S. Honig, MD, PhD; Christine M. Hulette, MD; Bradley T. Hyman, MD, PhD; Gregory A. Jicha, MD, PhD; Lee-Way Jin, MD, PhD; Anna Karydas, BA; Jeffrey A. Kaye, MD; Ronald Kim, MD; Edward H. Koo, MD; Neil W. Kowall, MD; Joel H. Kramer, PhD; Frank M. LaFerla, PhD; James J. Lah, Md, PhD; James B. Leverenz, MD; Allan I. Levey, MD, PhD; Ge Li, MD, PhD; Andrew P. Lieberman, MD, PhD; Chiao-Feng Lin, PhD; Oscar L. Lopez, Md; Constantine G. Lyketsos, MD, MHS; Wendy J. Mack, PhD; Frank Martiniuk, PhD; Deborah C. Mash, PhD; Eliezer Masliah, MD; Wayne C. McCormick; Susan M. McCurry, PhD; Andrew N. McDavid, BA; Ann C. McKee, MD; Marsel Mesulam, MD; Bruce L. Miller, MD; Carol A. Miller, MD; Joshua W. Miller, PhD; Jill R. Murrell, PhD; John M. Olichney, MD; Vernon S. Pankratz, PhD; Joseph E. Parisi, MD; Henry L. Paulson, MD, PhD; Elaine Peskind, MD; Ronald C. Petersen, MD, PhD; Aimee Pierce; Wayne W. Poon, PhD; Huntington Potter, PhD; Joseph F. Quinn, MD; Ashok Raj, MD; Murray Raskind, MD; Barry Reisberg, MD; John M. Ringman, MD, MS; Erik D. Roberson, MD, PhD; Howard J. Rosen, MD; Roger N. Rosenberg, MD; Mary Sano, PhD; Lon S. Schneider, MD; William W. Seeley, MD; Amanda G. Smith; Joshua A. Sonnen, MD; Salvatore Spina, MD; Robert A. Stern, PhD; Rudolph E. Tanzi, PhD; Tricia A. Thornton-Wells, PhD; John Q. Trojanowski, MD, PhD; Juan C. Troncoso, MD; Otto Valladares, MS; Vivianna M. Van Deerlin, MD, PhD; Linda J. Van Eldik, PhD; Badri N. Vardarajan, PhD; Harry V. Vinters, MD; Jean Paul Vonsattel, MD; Sandra Weintraub, PhD; Kathleen A. Welsh-Bohmer, PhD; Jennifer Williamson, MS, MPH; Sarah Wishnek; Randall L. Woltjer, MD, PhD; Clinton B. Wright, MD, MS; Steven G. Younkin, MD, PhD; Chang-En Yu, PhD; Lei Yu, PhD.
Affiliations of The Alzheimer Disease Genetics Consortium: John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida (Gilbert, Wishnek); Taub Institute on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University, New York, New York (Honig, Williamson); Department of Neurology, Columbia University, New York, New York (Vardarajan); The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, Florida (Gilbert); Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia (Cantwell, Lin, Trojanowski, Valladares, Van Deerlin); Neurogenomics Division, Translational Genomics Research Institute, Phoenix, Arizona (Corneveaux); Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, Florida (Carney, Crocco); Department of Neurology, Mayo Clinic, Rochester, Minnesota (Petersen); Department of Medicine, University of Washington, Seattle (McCormick, C. Yu); Department of Neurology, Oregon Health & Science University, Portland (Kaye, Quinn); Department of Neuroscience, Mayo Clinic, Jacksonville, Florida (Carrasquillo, Dickson, Younkin); Department of Psychiatry, Mount Sinai School of Medicine, New York, New York (Sano); Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois (Barnes, L. Yu); Alheimer Disease Research Center, University of Pittsburgh, Pittsburgh, Pennsylvania (Lopez); Department of Medical and Molecular Genetics, Indiana University, Indianapolis (Faber, Murrell); Department of Pathology and Immunology, Washington University in St Louis, St Louis, Missouri (Cairns); Department of Pathology, University of Washington, Seattle (Leverenz, Sonnen); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Li, Peskind, Raskind); Vanderbilit Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee (Thornton-Wells); Department of Neurology, Boston University, Boston, Massachusetts (Kowall, McKee, Stern); Department of Neurology, Johns Hopkins University, Baltimore, Maryland (Albert); Department of Neurology, University of Michigan, Ann Arbor (Albin, Paulson); Geriatric Research, Education and Clinical Center (GRECC), VA Ann Arbor Healthcare System (VAAAHS), Ann Arbor, Michigan (Albin); Michigan Alzheimer Disease Center, Ann Arbor (Albin); Department of Neurology, University of California Los Angeles, Los Angeles (Apostolova, Ringman, Vinters); Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia (Arnold); Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth (Barber); Department of Behavioral Sciences, Rush University Medical Center, Chicago, Illinois (Barnes); Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Phoenix, Arizona (Beach); Departments of Psychiatry, Neurology, and Psychology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Becker); National Alzheimer's Coordinating Center, University of Washington, Seattle (Beekly); Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois (Bigio); Cognitive Neurology and Alzheimer's Disease Center, Northwestern University, Chicago, Illinois (Bigio, Mesulam, Weintraub); Swedish Medical Center, Seattle, Washington (Bowen); Department of Neurology, University of California San Francisco, San Francisco (Boxer, Karydas, B. L. Miller, Rosen, Seeley); Department of Medicine, Duke University, Durham, North Carolina (Burke, Welsh-Bohmer); USF Health Byrd Alzheimer's Institute, University of South Florida, Tampa (Cao, Potter, Raj, Smith); Fred Hutchinson Cancer Research Center, Seattle, Washington (Carlson, McDavid); Department of Pathology, University of Alabama at Birmingham, Birmingham (Carroll, Fallon); Department of Neurology, University of Southern California, Los Angeles (Chui, Schneider); Department of Neurology, University of Alabama at Birmingham, Birmingham (Clark, Harrell, Roberson); Department of Neurology, University of California Irvine, Irvine (Cribbs, Pierce); Department of Neurology, University of California Davis, Sacramento (DeCarli, Olichney); University of Virginia School of Medicine, Charlottesville (DeKosky); Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine (Dick, Poon); Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, Florida (Duara); Department of Neurology, Indiana University, Indianapolis (Farlow); Department of Psychiatry, New York University, New York (Ferris, Reisberg); C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, Charlestown (Frosch); Department of Neurosciences, University of California San Diego, La Jolla (Galasko, Koo, Masliah); Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania (Ganguli); Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia (Gearing); Emory Alzheimer's Disease Center, Emory University, Atlanta, Georgia (Gearing); Neurogenetics Program, University of California Los Angeles, Los Angeles (Geschwind); Department of Pathology and Laboratory Medicine, Indiana University, Indianapolis (Ghetti, Murrell, Spina); Department of Neurology, Emory University, Atlanta, Georgia (Glass, Lah, Levey); Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston (Growdon, Hyman, Tanzi); Department of Pathology (Neuropathology), University of Pittsburgh, Pittsburgh, Pennsylvania (Hamilton); Sanders-Brown Center on Aging, Department of Molecular and Biomedical Pharmacology, University of Kentucky, Lexington (Head); Department of Pathology, Duke University, Durham, North Carolina (Hulette); Sanders-Brown Center on Aging, Department Neurology, University of Kentucky, Lexington (Jicha); Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento (Jin, J. W. Miller); Department of Neurology, Portland Veterans Affairs Medical Center, Portland, Oregon (Kaye); Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine (Kim); Department of Pathology, Boston University, Boston, Massachusetts (Kowall, McKee); Department of Neuropsychology, University of California San Francisco, San Francisco (Kramer); Department of Neurobiology and Behavior, University of California Irvine, Irvine (LaFerla); Department of Pathology, University of Michigan, Ann Arbor (Lieberman); Department of Psychiatry, Johns Hopkins University, Baltimore, Maryland (Lyketsos); Department of Preventive Medicine, University of Southern California, Los Angeles (Mack); Department of Medicine - Pulmonary, New York University, New York (Martiniuk); Department of Neurology, University of Miami, Miami, Florida (Mash); Department of Pathology, University of California San Diego, La Jolla (Masliah); School of Nursing Northwest Research Group on Aging, University of Washington, Seattle (McCurry); Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, Illinois (Mesulam); Department of Pathology, University of Southern California, Los Angeles (C. A. Miller); Department of Biostatistics, Mayo Clinic, Rochester, Minnesota (Pankratz); Department of Anatomic Pathology, Mayo Clinic, Rochester, Minnesota (Parisi); Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Parisi); Alzheimer's Disease Center, New York University, New York (Reisberg); Department of Neurology, University of Texas Southwestern, Dallas (Rosenberg); Department of Psychiatry, University of Southern California, Los Angeles (Schneider); Department of Pathology, Johns Hopkins University, Baltimore, Maryland (Troncoso); Sanders-Brown Center on Aging, Department of Anatomy and Neurobiology, University of Kentucky, Lexington (Van Eldik); Department of Pathology & Laboratory Medicine, University of California Los Angeles, Los Angeles (Vinters); Taub Institute on Alzheimer's Disease and the Aging Brain, Department of Pathology, Columbia University, New York, New York (Vonsattel); Department of Psychiatry, Northwestern University Feinberg School of Medicine, Chicago, Illinois (Weintraub); Department of Psychiatry & Behavioral Sciences, Duke University, Durham, North Carolina (Welsh-Bohmer); Department of Pathology, Oregon Health & Science University, Portland (Woltjer); Evelyn F. McKnight Brain Institute, Department of Neurology, Miller School of Medicine, University of Miami, Miami, Florida (Wright).
Author Contributions: Drs Naj and Pericak-Vance had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Naj, Haines, Mayeux, Farrer, Schellenberg, Pericak-Vance.
Acquisition, analysis, or interpretation of data: Naj, Jun, Reitz, Kunkle, Perry, Park, Beecham, Rajbhandary, Hamilton-Nelson, Wang, Kramer, Ertekin-Taner, Hardy, Graff-Radford, Green, Larson, St. George-Hyslop, Buxbaum, Evans, Schneider, Kamboh, Saykin, Reiman, De Jager, Bennett, Morris, Montine, Goate, Blacker, Tsuang, Hakonarson, Kukull, Foroud, Haines, Mayeux, Farrer, Schellenberg, Pericak-Vance, Albert, Albin, Apostolova, Arnold, Barber, Barnes, Beach, Becker, Beekly, Bigio, Bowen, Boxer, Burke, Cairns, Cantwell, Cao, Carlson, Carney, Carrasquillo, Carrol, Chui, Clark, Corneveaux, Cribbs, Crocco, DeCarli, DeKosky, Dick, Dickson, Duara, Faber, Fallon, Farlow, Ferris, Frosch, Galasko, Ganguli, Gearing, Geschwind, Ghetti, Gilbert, Glass, Growdon, Hamilton, Harrell, Head, Honig, Hulette, Hyman, Jicha, Jin, Karydas, Kaye, Kim, Koo, Kowall, Kramer, LaFerla, Lah, Leverenz, Levey, Li, Lieberman, Lin, Lopez, Lyketsos, Mack, Martiniuk, Mash, Masliah, McCormick, McCurry, McDavid, McKee, Mesulam, B. L. Miller, C. A. Miller, J. W. Miller, Murrell, Olichney, Pankratz, Parisi, Paulson, Peskind, Petersen, Pierce, Poon, Potter, Quinn, Raj, Raskind, Reisberg, Ringman, Roberson, Rosen, Rosenberg, Sano, Schneider, Seeley, Smith, Sonnen, Spina, Stern, Tanzi, Thornton-Wells, Trojanowski, Troncoso, Valladares, Van Deerlin, Van Eldik, Vardarajan, Vinters, Vonsattel, Weintraub, Welsh-Bohmer, Williamson, Wishnek, Woltjer, Wright, Younkin, C-E. Yu, L. Yu.
Drafting of the manuscript: Naj, Pericak-Vance.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Naj, Kunkle, Perry, Park, Rajbhandary, Hamilton-Nelson, Pericak-Vance.
Obtained funding: Jarvik, Larson, St. George-Hyslop, Kamboh, Reiman, Bennett, Hakonarson, Haines, Farrer, Schellenberg, Pericak-Vance, Barnes, Beach, Leverenz.
Administrative, technical, or material support: Wang, Kauwe, Haines, Schellenberg, Geschwind.
Study supervision: Wang, Haines, Mayeux, Farrer, Schellenberg, Pericak-Vance.
Conflict of Interest Disclosures: Dr Bird reported receiving licensing fees from and serving on the speaker’s bureau of Athena Diagnostics, Inc. Dr Goate reported receiving research funding from AstraZeneca, Pfizer, and Genentech and receiving remuneration for giving talks at Pfizer and Genentech. Dr Petersen reported being Chair, Data Monitoring Committee, at Pfizer, Chair, Data Monitoring Committee, at Janssen Alzheimer Immunotherapy, a Consultant at GE Healthcare, and a Consultant at Roche. No other disclosures were reported.
Funding/Support: The National Institute on Aging supported this work through the following grants: U01 AG032984 and RC2 AG036528 (Alzheimer Disease Genetics Consortium); U01 AG016976 (National Alzheimer’s Coordinating Center); U24 AG021886 (National Cell Repository for Alzheimer Disease); U24 AG026395, R01AG041797, and U24 AG026390 (National Institute on Aging–Late-Onset Alzheimer Disease); P30 AG019610 (Banner Sun Health Research Institute); P30 AG013846, U01 AG10483, R01 CA129769, R01 MH080295, R01 AG017173, R01 AG025259, and R01AG33193 (Boston University); P50 AG008702 and R37 AG015473 (Columbia University); P30 AG028377 and AG05128 (Duke University); AG025688 (Emory University); UO1 AG06781 and UO1 HG004610 (Group Health Research Institute); P30 AG10133 (Indiana University); P50 AG005146 and R01 AG020688 (The Johns Hopkins University); P50 AG005134 (Massachusetts General Hospital); P50 AG016574 (Mayo Clinic); P50 AG005138 and P01 AG002219 (Mount Sinai School of Medicine); P30 AG08051, MO1RR00096, UL1 RR029893, 5R01AG012101, 5R01AG022374, 5R01AG013616, 1RC2AG036502, and 1R01AG035137 (New York University); P30 AG013854 (Northwestern University); P30 AG008017 and R01 AG026916 (Oregon Health & Science University); P30 AG010161, R01 AG019085, R01 AG15819, R01 AG17917, and R01 AG30146 (Rush University); R01 NS059873 (Translational Genomics Research Institute); P50 AG016582 and UL1RR02777 (University of Alabama at Birmingham); R01 AG031581 (University of Arizona); P30 AG010129 (University of California, Davis); P50 AG016573, P50 AG016575, P50 AG016576, and P50 AG016577 (University of California, Irvine); P50 AG016570 (University of California, Los Angeles); P50 AG005131 (University of California, San Diego); P50 AG023501 and P01 AG019724 (University of California, San Francisco); P30 AG028383 and AG05144 (University of Kentucky); R01 AG027944, AG010491, AG027944, AG021547, and AG019757 (University of Miami); P50 AG008671 (University of Michigan); P30 AG010124 (University of Pennsylvania); P50 AG005133, AG030653, and AG041718 (University of Pittsburgh); P50 AG005142 (University of Southern California); P30 AG012300 (University of Texas Southwestern); P50 AG005136 (University of Washington); R01 AG019085 (Vanderbilt University); and P50 AG005681 and P01 AG03991 (Washington University in St Louis). The Kathleen Price Bryan Brain Bank at Duke University Medical Center is funded by grant NS39764 from the National Institute of Neurological Disorders and Stroke, by grant MH60451 from the National Institute of Mental Health, and by GlaxoSmithKline. Genotyping of the Translational Genomics Research Institute 2 cohort was supported by Kronos Science. The Translational Genomics Research Institute series was also funded by grant AG034504 from the National Institute on Aging (Dr Myers) and by the Alzheimer’s Foundation, Johnnie B. Byrd Sr Alzheimer’s Institute, Medical Research Council, and State of Arizona and includes samples from the following sites: Newcastle Brain Tissue Resource (funding via the Medical Research Council, local National Health Service trusts, and Newcastle University), Medical Research Council London Brain Bank for Neurodegenerative Diseases (funding via the Medical Research Council), South West Dementia Brain Bank (funding via numerous sources, including the Higher Education Funding Council for England, Alzheimer’s Research Trust, and BRACE [Bristol Research Into Alzheimer’s and Care of the Elderly], as well as North Bristol National Health Service Trust Research and Innovation Department and Dementias and Neurodegeneration), Netherlands Brain Bank (funding via numerous sources, including Stichting MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, and Internationale Stiching Alzheimer Onderzoek), Institut de Neuropatologia, Servei Anatomia Patologica, and Universitat de Barcelona. Funding for the Alzheimer’s Disease Neuroimaging Initiative is through the Northern California Institute for Research and Education by grants from Abbott, AstraZeneca, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson & Johnson, Eli Lilly and Company, Medpace, Inc, Merck and Co, Inc, Novartis AG, Pfizer, F. Hoffman–La Roche, Schering-Plough, Synarc, Inc, Alzheimer’s Association, Alzheimer’s Drug Discovery Foundation, and The Dana Foundation, as well as by the National Institute of Biomedical Imaging and Bioengineering and by grants U01 AG024904, RC2 AG036535, and K01 AG030514 from the National Institute on Aging. Support was also by grants IIRG-05-14147 and IIRG-08-89720 from the Alzheimer’s Association (Dr Pericak-Vance) and by the Biomedical Laboratory Research Program, Office of Research and Development, US Department of Veterans Affairs Administration. Dr St. George-Hyslop is supported by the Wellcome Trust, Howard Hughes Medical Institute, and Canadian Institute of Health Research.
Role of Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Additional Contributions: D. Stephen Snyder, PhD, and Marilyn Miller, PhD, from the National Institute on Aging contributed to this work as ex officio Alzheimer Disease Genetics Consortium members.
Correction: This article was corrected on November 10, 2014, to fix an error in the byline.