Background/Aims: The Alzheimer’s Disease Sequencing Project (ADSP) aims to identify novel genes influencing Alzheimer’s disease (AD). Variants within genes known to cause dementias other than AD have previously been associated with AD risk. We describe evidence of co-segregation and associations between variants in dementia genes and clinically diagnosed AD within the ADSP. Methods: We summarize the properties of known pathogenic variants within dementia genes, describe the co-segregation of variants annotated as “pathogenic” in ClinVar and new candidates observed in ADSP families, and test for associations between rare variants in dementia genes in the ADSP case-control study. The participants were clinically evaluated for AD, and they represent European, Caribbean Hispanic, and isolate Dutch populations. Results/Conclusions: Pathogenic variants in dementia genes were predominantly rare and conserved coding changes. Pathogenic variants within ARSA, CSF1R, and GRN were observed, and candidate variants in GRN and CHMP2B were nominated in ADSP families. An independent case-control study provided evidence of an association between variants in TREM2, APOE, ARSA, CSF1R, PSEN1, and MAPT and risk of AD. Variants in genes which cause dementing disorders may influence the clinical diagnosis of AD in a small proportion of cases within the ADSP.

Alzheimer’s disease (AD; MIM: 104300) is genetically heterogeneous, and shares phenotypic characteristics with other dementias. Autosomal dominant early-onset AD is caused by rare variants in APP [1], PSEN1 [2], and PSEN2 [3, 4]. Common variants in dozens of genes are associated with late-onset AD [5-7]. The APOE ε4 allele has the strongest and most consistent evidence for an association with an increased risk of both sporadic and familial AD [6, 7], and has been independently associated with cognitive impairment [8, 9] and other dementing disorders [10, 11]. Variants within genes causing other dementing disorders have also been associated with AD risk, including MAPT [12-14], PRNP [15], GRN [14], and TREM2 [16, 17]. Patients with variants in these genes can meet the clinical diagnostic criteria for AD, such as observed in GRN [18, 19] and frontotemporal lobar degeneration (MIM: 607485) [20-22], MAPT [23-26] and frontotemporal dementia (FTD; MIM: 600274) [23], and PRNP and prion diseases [27, 28]. It can be challenging to differentiate AD from other causes of dementia using clinical criteria alone [29, 30], while even the defining neuropathological features of AD may be observed in patients with other dementias and in cognitively normal controls [15, 31]. These shared features suggest shared etiologies across dementing disorders.

It is possible that pathogenic variants in dementia genes may explain the genetic cause of dementia among carriers within the Alzheimer’s Disease Sequencing Project (ADSP). Targeted sequencing of the early-onset AD genes, GRN, and MAPT among persons diagnosed with AD has identified known pathogenic variants in PSEN1 [32-34], PSEN2 [35], and GRN [33], and candidate variants in APP [33, 34], PSEN1 [34-36], PSEN2 [34, 36], and both MAPT and GRN [32-34, 36]. When family data are available, these candidate variants rarely explain the cause of AD throughout a given family [33], possibly due to genetic heterogeneity or incomplete penetrance. Alternatively, the candidate variants may represent neutral variation and have no relationship to AD risk.

Known pathogenic variants in dementia genes may provide useful characteristics for filtering candidate variants for AD. For Mendelian traits, it is common to prioritize rare, coding changes [37] predicted to be deleterious by a bioinformatic algorithm such as the Combined Annotation Dependent Depletion (CADD) score [38] or a measure of conservation like the Genomic Evolutionary Rate Profiling (GERP) score [39, 40]. It is not clear that the same selective pressures behind these assumptions hold for complex and late-onset traits like AD [39, 41]. It would hasten the discovery of novel AD variants if bioinformatic scores could accurately predict the pathogenicity of such a variant.

We hypothesize that variants within 35 dementia-related genes (Table 1) might be associated with the risk of a clinical diagnosis of AD. We describe variants reported to be pathogenic in dementia genes and define criteria for prioritizing candidate variants. We identify these variants, and candidate variants sharing their characteristics, in families sequenced by the ADSP. Finally, we summarize the evidence of an association between AD and variants within dementia genes from the ADSP case-control study. The results suggest that variants within genes causing dementias distinct from AD may yet play a role in clinically diagnosed AD.

Table 1.

Genes whose variants cause dementing disorders

Genes whose variants cause dementing disorders
Genes whose variants cause dementing disorders

Subjects and Samples


The ADSP has generated whole genome sequencing (WGS) data from members of multiplex AD families and whole exome sequencing (WES) data for a large case-control cohort [42]. Briefly, WGS data were collected on 582 individuals from 111 multiplex AD families of European or Caribbean Hispanic ancestry, favoring families with multiple cases across generations and relatively few copies of the APOE ε4 allele, as described elsewhere [42]. Among these 582 individuals, 498 were clinically diagnosed with probable or definite AD (∼11% neuropathologically confirmed AD) [42-44]. These genomes represent families with European-American (NC, UM, and UP prefixes), Caribbean Hispanic (CU prefix), and Dutch (ERF prefix) ancestry. These families were ascertained from multiple sites, including contributors to the Alzheimer’s Disease Genetics Consortium (ADGC), and the neurology working group of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. The WES case-control study prioritized individuals with European ancestry by their low estimated risk of AD, and neuropathologically confirmed controls when available [42]. A balanced number of 5,107 cases (∼38% neuropathologically confirmed AD) and 4,976 controls were selected for WES. These data are available from the database of Genotypes and Phenotypes (dbGaP; study accession No. phs000572.v7.p4). All subjects have provided informed consent, and this study was approved by the institutional review boards of the participating institutions.

The ADSP sequencing data were generated at Baylor University, the Broad Institute, and Washington University. The sequencing, variant calling, and quality control (QC) methods are detailed elsewhere [45]. The read data were aligned to the GRCh37-Lite reference genome using the Burrows-Wheeler Aligner (BWA, v0.6.2) [46]. Variants were called using both the Genome Analysis Tool Kit (GATK)-HaplotypeCaller [47-49] and Atlas V2 [50] pipelines. The QC pipeline was built upon the CHARGE consortium’s QC protocol [51]. Discrepancies between the GATK and Atlas V2 calls were reconciled by the ADSP QC Working Group to create the “consensus” data set. This QC protocol involved the development of scripts in the Python, Perl, and R (v2.15 and v3.1.1) languages, as well as the software programs PLINK (v1.07 and v1.9) [52] and PedCheck (v1.2) [53]. APOE genotypes were provided by the contributing centers; the necessary genotypes did not pass QC in the WGS data set.

Single nucleotide variants (SNVs) within the canonical transcripts of the dementia genes were extracted from the consensus-called WGS data dated May 18, 2015 (Table 1) [45]. The ADSP Annotation Working Group provided consistent annotation of all variants (WGS v1 annotation files) [54]. The genomic context and consequence of variants was provided by the Ensembl Variant Effect Predictor tool (VEP v80) [55, 56] and SeattleSeq Annotation 138 (, including allele frequencies from the Exome Sequencing Project (ESP) [57], 1000 Genomes Project [58], and Exome Aggregation Consortium (ExAC) [59]. The variant-specific metrics of predicted pathogenicity or severity included SIFT [60], PolyPhen-2 [61], GERP [62], and CADD [38] scores.

The variant inclusion criteria, analysis parameters, and association test results from the ADSP WES data set are described elsewhere [63]. Subject-level QC identified unrelated samples with minimal missing genotype data and either European or Caribbean Hispanic ancestry (5,740 cases and 5,096 controls). SNVs and insertions/deletions were extracted from the Atlas V2 genotype call set, and included in association testing if they passed QC, were predicted to cause a moderate (e.g., missense) or high (e.g., stop-gain) impact consequence, and were rare (minor allele frequency [MAF] < 5%).

Center for Precision Diagnostics Sample

The Center for Precision Diagnostics (CPD) at the University of Washington provided targeted sequencing data for 48 neuropathologically confirmed controls with self-reported European ancestry (50% female). The 48 controls represent cognitively normal elderly adults enrolled in AD centers who did not meet neuropathological criteria for AD or Parkinson’s disease upon their death at the age of ≥54 years [64]. The CPD used a targeted capture approach to sequence a panel of genes known to cause neurodegenerative disorders, including the dementia panel listed in Table 1 (disorders named in online Supplemental Table 1; for all online suppl. material, see, with > 99% of the targeted coding regions and canonical splice sites sequenced to > 20× coverage. Current information about this panel and the sequencing methods is available online ( DNA fragments were captured using the Exome v1.0 (Integrated DNA Technologies) system, paired-end sequencing was performed using Illumina technologies, including the Rapid Run v2.0 chemistry and a HiSeq 2500 sequencer. Reads were aligned to the hg19 reference genome using the BWA, and variant calling was performed with the GATK. This research was approved by the Veterans Affairs Puget Sound institutional review board (MIRB #00088).

Analysis Methods

We focused our analyses on the 35 genes listed in Table 1, which represent the dementia gene panel provided by the CPD, a CLIA-certified laboratory for diagnostic genetic testing. Variants within these genes were downloaded from ClinVar (December 22, 2015) and converted from build GRCh38 sequence positions to GRCh37 using the LiftOver tool from the UCSC Genome Browser [65, 66]. We restricted the analyses to “consequential” variants, i.e., those annotated as pathogenic, likely pathogenic, risk variant, or protective in ClinVar, and used their reported consequences (e.g., missense). Consequential variants observed in the ADSP were evaluated by literature review on a variant-by-variant basis, including the AD & FTD Mutation Database [67] (, Online Mendelian Inheritance in Man [68, 69] (OMIM;, and AlzForum [70] ( databases. SNVs within the dementia genes were extracted from the 1000 Genomes phase 3 data (release v1.3; and the CPD controls using VCFtools (v0.1.14) [71]. SNVs within dementia genes extracted from ClinVar, 1000 Genomes, and CPD data were annotated with VEP (v84), ANNOVAR [72], and GEMINI (v0.18.0) [73]. Annotation included allele frequencies in the ESP, 1000 Genomes Project, and ExAC, and PolyPhen-2 (HDIV), SIFT, GERP (++RS), and CADD (Phred-scaled) scores [74]. Variants with PolyPhen-2 scores ≥0.957, SIFT scores < 0.05, or CADD scores (Phred scaled) ≥15 were predicted to be deleterious/pathogenic, while those with GERP scores ≥3 were considered conserved [75, 76].

Gene-based tests were performed using SKAT-O [77] separately for the European-ancestry and Caribbean Hispanic subjects, then meta-analyzed using skatOMeta [78, 79]. Only the results of the meta-analyses under models 0 (covariates include sequencing center and population-specific principal components) and 2 (covariates include those of model 0 plus age, sex, principal components, and dosage of APOE ε2 and ε4 alleles) are presented herein [63]. The results from an intermediate model, i.e., model 1, were excluded for simplicity. The summary statistics were generated in R (v3.2.5).

Characteristics of Known Consequential SNVs in Dementia Genes

The consequential SNVs in dementia genes reported in ClinVar were overwhelmingly rare missense variants with evidence of conservation and predicted pathogenicity. Although start/stop and splice site variants were represented (Fig. 1), all reported consequential SNVs in 9 dementia genes were missense: EIF2B3, EIF2B5, MAPT, PDGFB, PDGFRB, PSEN1, PSEN2, SLC25A12, and VCP. In contrast, most of the consequential SNVs in GRN were either start/stop or splice site variants. Most consequential SNVs reported in ClinVar were rare, with 492/496 (99.2%) of the SNVs with MAFs < 0.001 in independent reference populations. Among the 64 consequential SNVs observed in the reference data sets, 60 had MAFs < 0.001. Three of the remaining 4 SNVs were in APOE: rs7412 and rs429358 define the ε2 and ε4 alleles provided by the ADSP contributors and are associated with protection or risk of AD [80], while rs769455 had an MAF < 1% in reference populations and was observed 4 times as often in European-American AD cases versus controls [80]. Homozygotes for the remaining variant, rs5848 (MAF = 0.42 in the 1000 Genomes Project), have an increased risk of both FTD [81] and AD [82].

Fig. 1.

Distribution of consequential ClinVar single nucleotide variants across dementia genes. x axis: ratio of pathogenic/likely pathogenic/risk variant/protective variants meeting the inclusion criteria. Black, missense variants; white, start or stop gain or loss or frameshift; dark gray, splice donor/acceptor/region variant; light gray, 3′ UTR variant.

Fig. 1.

Distribution of consequential ClinVar single nucleotide variants across dementia genes. x axis: ratio of pathogenic/likely pathogenic/risk variant/protective variants meeting the inclusion criteria. Black, missense variants; white, start or stop gain or loss or frameshift; dark gray, splice donor/acceptor/region variant; light gray, 3′ UTR variant.

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Most consequential SNVs reported to ClinVar within the dementia genes were predicted to be pathogenic or conserved, unlike the SNVs observed in the reference or control data sets (Fig. 2). Where annotation was available, the observed distribution of CADD and GERP scores better discriminated between the consequential ClinVar variants and the reference data sets (Fig. 2). Most consequential SNVs had high GERP or CADD scores: 91% of the consequential SNVs had GERP scores ≥3, and 84% had CADD scores (Phred scaled) ≥15.

Fig. 2.

Percent of single nucleotide variants (SNVs) passing pathogenic/deleterious/conserved thresholds across the SNV data sets. PolyPhen-2: 143/496 SNVs are missing data; SIFT scores: 116/496 SNVs are missing data; Phred-scaled CADD scores: 85/496 are missing data; GERP scores: 85/496 SNVs are missing data. Black, all consequential variants in the 1000 Genomes Project; dark gray, consequential variants in the 1000 Genomes Project with a minor allele frequency < 0.001 in the 1000 Genomes, Exome Sequencing Project, and Exome Aggregation Consortium data sets; white, consequential variants reported in ClinVar; light gray, consequential variants in Center for Precision Diagnostics controls.

Fig. 2.

Percent of single nucleotide variants (SNVs) passing pathogenic/deleterious/conserved thresholds across the SNV data sets. PolyPhen-2: 143/496 SNVs are missing data; SIFT scores: 116/496 SNVs are missing data; Phred-scaled CADD scores: 85/496 are missing data; GERP scores: 85/496 SNVs are missing data. Black, all consequential variants in the 1000 Genomes Project; dark gray, consequential variants in the 1000 Genomes Project with a minor allele frequency < 0.001 in the 1000 Genomes, Exome Sequencing Project, and Exome Aggregation Consortium data sets; white, consequential variants reported in ClinVar; light gray, consequential variants in Center for Precision Diagnostics controls.

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Pathogenic ClinVar SNVs Observed in the ADSP Family WGS Data

Within the ADSP family WGS data, 7 SNVs within 6 dementia genes were annotated as pathogenic in ClinVar (Table 2). Four of the SNVs cause recessive disorders, but they were only observed in heterozygotes (rs28940893, rs80358257, rs113994049, and rs147313927) and are therefore unlikely to cause their corresponding dementias in these heterozygotes. The remaining 3 SNVs in GRN, CSF1R, and ARSA could potentially influence the dementia phenotype in their carriers.

Table 2.

Pathogenic and candidate SNVs observed in the ADSP family WGS data

Pathogenic and candidate SNVs observed in the ADSP family WGS data
Pathogenic and candidate SNVs observed in the ADSP family WGS data

Dominant variants in GRN cause FTD (Table 1). However, the homozygous alternate genotype at GRN variant rs5848 is associated with an increased risk of both FTD (OR = 3.18) [81] and AD (OR = 1.31 [82] and 1.386 [83]). Within the ADSP WGS data, 65 homozygotes (45 cases) were observed within the 35 families. These subjects met the clinical criteria for either probable or definite AD, and are therefore not likely to have FTD, although comprehensive imaging data for these subjects are not available to formally exclude FTD pathology.

Dominant variants in CSF1R cause hereditary diffuse leukoencephalopathy with spheroids (MIM: 221820), a type of dementia which can mimic AD [84] (Table 1). A pathogenic [84, 85] SNV in CSF1R, rs281860278, was observed in a member of family UM0304F diagnosed with probable AD at the age of 72 years. However, rs281860278 does not segregate with dementia in this family; this SNV was not observed in the carrier’s sequenced relatives, including a sibling diagnosed with definite AD at the age of 73 years, a sibling diagnosed with probable AD at the age of 75 years, and a niece at risk of AD at the age of 55 years. External sequencing data revealed that the SNV was observed in 2 additional siblings who were not clinically diagnosed with AD at the time of their deaths at the ages of 91 and 94 years.

The homozygous genotypes for each of 2 SNVs, rs6151429 (Table 2) and ARSA missense variant rs2071421 [86], lead to arylsulfatase A pseudodeficiency [86-89], which can cause metachromatic leukodystrophy (MIM: 250100) if inherited with another ARSA missense variant [90, 91]. Homozygotes for both rs6151429 and rs2071421 were observed in 3 ADSP families (CU0044F, CU0049F, and NC0205F), although none carried an additional ARSA missense variant. WGS data were available for 11 individuals diagnosed with probable AD and 1 individual at risk of AD within family CU0044F. Within family CU0044F, 2 siblings diagnosed with probable AD at the ages of 71 and 86 years were homozygous for both rs6151429 and rs2071421. These 2 siblings are the offspring of a consanguineous marriage between avuncular relatives, suggesting they inherited both copies of the ARSA SNVs identical by descent. External sequencing data revealed that 0/3 additional at-risk members of CU0044F were homozygous for rs6151429 and rs2071421. WGS data were available for 6 individuals diagnosed with probable AD and 1 at-risk individual in family CU0049F. The only individual homozygous for both rs6151429 and rs2071421 from family CU0049F was the individual at risk of developing AD at the age of 60 years. This at-risk individual is the offspring of parents who were diagnosed with probable AD, with no sequencing data available. Within family NC0205F, WGS data were available for 1 individual diagnosed with definite AD, 1 diagnosed with probable AD, 1 diagnosed with possible AD, and 1 individual with an unknown phenotype. The 2 homozygotes observed in family NC0205F include a subject diagnosed with definite AD at the age of 75 years and a sibling diagnosed with possible AD at the age of 80 years, but not their cousins at risk of AD or diagnosed with possible AD at the age of 70 years. Most homozygotes for the 2 ARSA SNVs were clinically diagnosed with AD, but they do not represent all relatives diagnosed with AD within their families. External autopsy and imaging data were unavailable for any individual homozygous for both rs6151429 and rs2071421.

Rare SNVs Observed in the ADSP Family WGS Data That Share Properties with Consequential ClinVar SNVs

Two SNVs in the ADSP WGS family data share characteristics with known pathogenic variants in the dementia genes: a reference MAF < 0.001, a CADD score (Phred scaled) ≥15, and a GERP score ≥3 (Table 2). The GRN missense SNV rs141111290 has previously been associated with AD [34] and is observed in ADSP families CU0012F and CU0042F. Within family CU0012F, rs141111290 carriers include a subject diagnosed with probable AD at the age of 73 years and a sibling diagnosed with possible AD at the age of 58 years, but not their siblings diagnosed with probable AD at the ages of 61 and 63 years. Within family CU0042F, the rs141111290 heterozygote was at risk of AD as of the age of 69 years, but the SNV was not observed in their sequenced relatives diagnosed with either probable (age 86 and 91 years) or possible (age 73 years) AD. In contrast, the CHMP2B SNV rs149380040 was observed in both sequenced cases from family CU0021F, diagnosed with probable AD at the ages of 77 and 83 years. This SNV causes a p.Ser194Leu change in the canonical transcript of CHMP2B and falls outside the conserved snf7 domain containing variants thought to cause FTD [92]. Although other variants in CHMP2B cause FTD, the ADSP cases carrying the p.Ser194Leu variant met the clinical criteria for probable AD and did not show clinical evidence of FTD.

Gene-Based Association Testing in the ADSP Case-Control Exome Data

Gene-based tests of rare variants revealed evidence of an association between 6 dementia genes (APOE, ARSA, CSF1R, MAPT, PSEN1, and TREM2) and risk of AD in the ADSP case-control analysis (p < 0.05; Table 3). TREM2 was significantly associated with AD under model 0 after controlling for the number of dementia genes tested (p = 1.82E–11). For each of these genes, the frequency of carrying at least one rare variant allele with a predicted moderate or high impact was low (Table 3), suggesting that rare variants in these genes may play a small but important role in the risk of clinically diagnosed AD.

Table 3.

Gene-based analysis of rare SNVs in the ADSP exome data

Gene-based analysis of rare SNVs in the ADSP exome data
Gene-based analysis of rare SNVs in the ADSP exome data

We provided evidence that SNVs within AD genes (APOE [93], PSEN1 [2, 94], and TREM2 [16, 17, 95]), genes causing dementias which may mimic AD (MAPT [12-14, 23-25] and CSF1R [96-99]), and a gene causing distinct dementias (ARSA) possibly influence the phenotype for a small number of cases of AD in the ADSP. Detailed phenotype and pathologic data are necessary to determine what role, if any, these SNVs play in AD, which are unavailable for most carriers of these SNVs in the ADSP data set. However, a few of these SNVs have strong evidence in the literature supporting their pathogenic role in dementia, including rs5848 in GRN [82, 83] and rs141111290 in CHMP2B [34], which have previously demonstrated strong statistical associations with the risk of AD.

Among the genes causing non-AD dementias, pathogenic SNVs in both ARSA and CSF1R were observed in the family-based ADSP WGS data set and gene-based association testing in the large case-control cohort. CSF1R is differentially expressed in mouse models of AD [100, 101] and surrounding Aβ plaques in human cases of AD [102]. Inhibiting CSF1R in mouse models of AD ameliorates memory loss and synaptic degeneration [103]. That inhibition can be done pharmacologically [103], suggesting CSF1R may be a promising drug target for AD. The connection between ARSA and AD is more tenuous. ARSA is downregulated in sex-specific analyses of cells derived from sporadic AD patients [104]. Furthermore, the pathogenic SNV observed in the ADSP WGS data set, rs6151429, was observed in 34% of postmortem brain samples from AD cases, which is much higher than the frequency in related populations [105]. These findings are intriguing, though the potential relationship between ARSA and AD requires further study before conclusions may be drawn.

Descriptive analyses of consequential SNVs in ClinVar have revealed several patterns which may help identify novel variants driving dementing disorders in these genes. Our results suggest that coding changes in dementia genes with low frequencies (MAF < 0.001) in reference populations and high CADD and/or GERP scores should be prioritized when nominating candidate variants for these disorders. Strict application of these filters to the ADSP WGS data prioritized an additional missense variant in GRN previously associated with AD risk [34] and a missense variant in CHMP2B observed in both sequenced cases diagnosed with probable AD in a single pedigree. Further analysis of each of these SNVs is required to evaluate their potential contribution to AD risk.

Variants in known dementia genes do not appear to explain the AD phenotype in the majority of the ADSP. However, rare pathogenic SNVs, or those sharing similar properties, may influence the phenotype in 1% of the subjects within the ADSP family WGS data set diagnosed with either definite or probable AD; this percentage increases to 11% when the common GRN risk variant is included. Where observed, SNVs annotated as “pathogenic” by ClinVar in known dementia genes would explain no more than half of the AD cases in a single family, consistent with the known genetic heterogeneity of AD [5, 94]. This suggests that novel AD genes remain to be uncovered in the ADSP sequence data set.

We thank the patients and their families, whose help and participation made this work possible. Members of the ADGC and CHARGE consortium contributed samples to the ADSP, including Erin Abner, PhD; Hieab H. Adams, MSc; Larry D Adams, BA; Perrie Adams, PhD; Alyssa Aguirre, LCSW; Marilyn S. Albert, PhD; Roger L. Albin, MD; Lisa Alvarez, MS; Liana G. Apostolova, MD; Sanjay Asthana, MD; Craig S. Atwood, PhD; Sanford Auerbach, MD; Gayle Ayres, DO; Robert C. Barber, PhD; Lisa L. Barnes, PhD; Sandra Barral, PhD; Thomas G. Beach, MD, PhD; James T. Becker, PhD; Gary W. Beecham, PhD; Duane Beekly, BS; Alexa S. Beiser, PhD; Thomas Benke, MD; David A. Bennett, MD; Sarah Bertelsen, MA, JD; John Bertelson, MD; Eileen H. Bigio, MD; Thomas D. Bird, MD; Deborah Blacker, MD; Bradley F. Boeve, MD; James D. Bowen, MD; Adam Boxer, MD, PhD; James B. Brewer, MD, PhD; Adam Brickman, PhD; Sarah Brisebois, BAAS; James R. Burke, MD, PhD; Jeffrey M. Burns, MD, MS; Joseph D. Buxbaum, PhD; Nigel J. Cairns, PhD, FRCPath; Chuanhai Cao, PhD; Cynthia M. Carlsson, MD; Regina M Carney, MD; Minerva M. Carrasquillo, PhD; Richard J. Caselli, MD; Katrina Celis, MD; Scott Chasse, PhD; Vincent Chouraki, MD, PhD; Helena C. Chui, MD; Jaeyoon Chung, MS; Hata Comic, MD; Paul K. Crane, MD, MPH; Carlos Cruchaga, PhD; Michael L. Cuccaro, PhD; Munro Culum, PhD; L. Adrienne Cupples, PhD; Peter Dal-Bianco, MD; Eveleen Darby, MA/MS; Philip L. De Jager, MD, PhD; Mony de Leon, EdD; Charles DeCarli, MD; John C. DeToledo, MD; Malcolm Dick, PhD; Dennis W. Dickson, MD; John H. Dougherty, MD; Ranjan Duara, MD; Nilufer Ertekin-Taner, MD, PhD; Denis A. Evans, MD; Kelley M. Faber, MS; Thomas J. Fairchild, PhD; David W. Fardo, PhD; Martin R. Farlow, MD; Lindsay A. Farrer, PhD; Steven Ferris, PhD; Annette L. Fitzpatrick, PhD; Tatiana M. Foroud, PhD; Robert Friedland, MD; Matthew P. Frosch, MD, PhD; Douglas R. Galasko, MD; Marla Gearing, PhD; David S. Geldmacher, MD; Peter St. George-Hyslop, MD, FRCP; Daniel H. Geschwind, MD, PhD; Bernardino Ghetti, MD; Carey Gleason, PhD; Rodney C.P. Go, PhD; Alison M. Goate, D.Phil; Teresa Gomez-Isla, PhD; Rebecca Gottesman, MD, PhD; Thomas Grabowski, MD; Neill R. Graff-Radford, MD, MBBCh; Robert C. Green, MD; Patrick Griffith, MD; John H. Growdon, MD; Harry E. Gwirtsman, MD; Jonathan L. Haines, PhD; James R. Hall, PhD; Kara L. Hamilton-Nelson, MPH; John Hart, MD; Michelle Hernandez, CRC; Jayanadra J. Himali, PhD; Edith Hofer, PhD; Albert Hofman, MD, PhD; Lawrence S. Honig, MD, PhD; Ryan M. Huebinger, PhD; Matthew J. Huentelman, PhD; Christine M. Hulette, MD; Bradley T. Hyman, MD, PhD; Kamran Ikram, MD, PhD; Gail P. Jarvik, MD, PhD; James Jaworski, MPH; Suman Jayadev, MD; Lee-Way Jin, MD, PhD; Kim Johnson, PhysD; Leigh Johnson, PhD; Sterling Johnson, PhD; WT Longstreth Jr., MD; Gyungah R. Jun, PhD; M. Ilyas Kamboh, PhD; Anna Karydas, BA; Jeffrey A. Kaye, MD; C. Dirk Keene, MD, PhD; Aisha Khaleeq, MD; Ronald Kim, MD; Janice Knebl, DO; David Knopman, MD; Olena Korvatska, PhD; Peter J. Koudstaal, MD, PhD; Neil W. Kowall, MD; Joel H. Kramer, PsyD; Walter A. Kukull, PhD; Lewis H. Kuller, MD; Brian W. Kunkle, PhD, MPH; Alexander Kurz, MD; Laura J. Lacritz, PhD; Frank M. LaFerla, PhD; James J. Lah, MD, PhD; Rafael Lantigua, MD; Eric B. Larson, MD, MPH; W. William Lee, PhD; Allan I. Levey, MD, PhD; Ge Li, MD, PhD; Andrew P. Lieberman, MD, PhD; Richard B. Lipton, MD; Mark Logue, PhD; Oscar L. Lopez, MD; Kathryn L. Lunetta, PhD; Constantine G. Lyketsos, MD, MHS; Douglas Mains, DrPH; Jennifer Manly, PhD; Daniel C. Marson, JD, PhD; Eden R. Martin, PhD; Paul Massman, PhD; Richard Mayeux, MD; Wayne C. McCormick, MD, MPH; Susan M. McCurry, PhD; Ann C. McKee, MD; Martin Medrano, MD; Marsel Mesulam, MD; Bruce L. Miller, MD; Carol A. Miller, MD; Abhay Moghekar, MBBS; John C. Morris, MD; Thomas H. Mosley, PhD; Shubhabrata Mukherjee, PhD; Trung Nguyen, MD/PhD; Sid O’Bryant, PhD; Thomas Obisesan, MD; John M. Olichney, MD; Marcia Ory, PhD/MPH; Ruth Ottman, PhD; Raymond Palmer, PhD; Joseph E. Parisi, MD; Henry L. Paulson, MD, PhD; Valory Pavlik, PhD; David Paydarfar, MD; Victoria Perez, CRC; Margaret A. Pericak-Vance, MD, PhD; Elaine Peskind, MD; Ronald C. Petersen, MD, PhD; Helen Petrovitch, MD; Aimee Pierce, MD; Marsha Polk, MMEd; Wayne W. Poon, PhD; Luigi Puglielli, MD, PhD; Mary Quiceno, MD; Joseph F. Quinn, MD; Ashok Raj, MD; Farid Rajabli, PhD; Gerhard Ransmayr, MD; Murray Raskind, MD; Wendy Raskind, MD, PhD; Eric M. Reiman, MD; Barry Reisberg, MD; Joan S. Reisch, PhD; Christiane Reitz, MD; Dolly Reyes-Dumeyer, BS; Katie Rose Richmire, BA; Robert A. Rissman, PhD; Fernando Rivadeneira, MD, PhD; Erik D. Roberson, MD, PhD; Monica Rodriguear, MA; Ekaterina Rogaeva, PhD; Howard J. Rosen, MD; Roger N. Rosenberg, MD; Jerome I. Rotter, MD, FACP, FACMG; Donald R. Royall, MD; Yasaman Saba, BSc; Marwan Sabbagh, MD; A. Dessa Sadovnick, PhD; Martin Sadowski, MD, PhD; Mark A. Sager, MD; David P. Salmon, PhD; Mary Sano, PhD; Andrew J. Saykin, PsyD; Daniel Schaid, MD; Gerard D. Schellenberg, PhD; Michael Schmidt, PhD; Julie A. Schneider, MD; Lon S. Schneider, MD; Nicole Schupf, PhD; Bill Scott, PhD; William W. Seeley, MD; Scott Small, MD; Amanda G. Smith, MD; Janet Smith, BS; Robert A. Stern, PhD; Yaakov Stern, PhD; Alan Stevens, PhD; Robert A. Sweet, MD; Russell H. Swerdlow, MD; Rudolph E. Tanzi, PhD; Linda Teri, PhD; Jeffrey L. Tilson, PhD; Sarah E. Tomaszewski Farias, PhD; Giuseppe Tosto, MD; John Q. Trojanowski, MD, PhD; Juan C. Troncoso, MD; Magda Tsolaki, MD, PhD; Debby W. Tsuang, MD; Andre G. Uitterlinden, PhD; Vivianna M. Van Deerlin, MD, PhD; Linda J. Van Eldik, PhD; Jeffery M. Vance, MD, PhD; Badri Vardarajan, PhD; Harry V. Vinters, MD; Dina Voijnovic, MD, MS; Jean Paul Vonsattel, MD; Jen Chyong Wang, PhD; Sandra Weintraub, PhD; Kathleen A. Welsh-Bohmer, PhD; Shawn Westaway, PhD; Charles C. White, PhD; April Wiechmann, PhD; Kirk C. Wilhelmsen, MD/PhD; Benjamin Williams, MD/PhD; Henrick Wilms, MD/PhD; B. Gwen Windham, MD, MHS; Thomas S. Wingo, MD; Thomas Wisniewski, MD; David A. Wolk, PhD; Frank J. Wolters, MD, MSc; Randall L. Woltjer, MD, PhD; Martin Woon, PhD; Steven G. Younkin, MD, PhD; Chang-En Yu, PhD; Lei Yu, PhD.

Members of the ADSP include: Shahzad Amad, PhD, MSc; Najaf Amin, PhD; Lucinda Antonacci-Fulton, MS; Elizabeth Appelbaum, BA; Eric Banks, PhD; Sandra Barral, PhD; Gary Beecham, PhD; Alexa Beiser, PhD; Michelle Bellair, MS; Jennifer E. Below, PhD; David A. Bennett, MD; Joshua C. Bis, PhD; Elizabeth E. Blue, PhD; Eric Boerwinkle, PhD; Jan Bressler, PhD; Lisa Brown, PhD; William S. Bush, PhD; Mariusz Butkiewicz, PhD; Laura Cantwell, MPH; Yuning Chen, MS; Micah Childress, AS; Seung Hoan Choi, PhD; Yi Fan Chou, MS; Jaeyoon Chung, MS; Carlos Cruchaga, PhD; Adrienne Cupples, PhD; Rebecca Cweibel, MA; Tyler Day, MS; Phillip L. De Jager, MD, PhD; Anita DeStefano, PhD; Huyen Dinh, BS; Harsha Doddapeneni, PhD; Michael Dorschner, PhD; Shannon Dugan-Perez, BA; Josée Dupuis, PhD; Adam English, PhD; Kelley Faber, MS; John Farrell, PhD; Lindsay Farrer, PhD; Michael Feolo, PhD; Myriam Fornage, PhD; Tatiana Foroud, PhD; Robert S. Fulton, PhD; Stacey Gabriel, PhD; Prabhakaran Gangadharan, MS; Richard A. Gibbs, PhD; Alison Goate, DPhil; Namrata Gupta, PhD; Jonathan Haines, PhD; Kara Hamilton-Nelson, MPH; Yi Han, PhD; Andrea R. Horimoto, MSc, PhD; Jianhong Hu, PhD; M. Afran Ikram, MD, PhD; James Jaworski, MPH; Joy Jayaseelan; Xueqiu Jian, PhD; Divya Kalra, MS; Manav Kapoor, PhD; Ziad Khan; Daniel C. Koboldt, MS; Viktoriya Korchina, BS; Brian Kunkle, PhD, MPH; Amanda Kuzma, PhD; Dan Lancour, BS; David E. Larson, PhD; Lenore J. Launer, PhD; Sandra Lee, PhD, MSN; Yuk Yee Leung, PhD; Han-Jen Lin, MS; Honghuang Lin, PhD; Ching Ti Liu, PhD; Xiaoming Liu, PhD; Xiuping Liu; Yue Liu; Kathryn L. Lunetta, PhD; Yiyi Ma, PhD; John Malamon, BSE; Edoardo Marcora, PhD; Eden Martin, PhD; Richard Mayeux, MD; Elisabeth Mlynarski, PhD; Thomas H. Mosley, PhD; Donna Muzny, MS; Rafael Nafikov, PhD; Adam Naj, PhD; Waleed Nasser, PhD; Alejandro Q. Nato Jr., PhD; Pat Navas, PhD; Hiep Nguyen, BS; Devanshi Patel, MS; Margaret Pericak-Vance, PhD; Bruce Psaty, MD, MPH, PhD; Liming Qu, MS; Farid Rajabli, PhD; Christiane Reitz, MD, PhD; Alan Renton, PhD; Dolly Reyes, BS; Kenneth Rice, PhD; Mohamad Saad, PhD; William Salerno, PhD; Jireh Santibanez, BS; Chloe Sarnowski, PhD; Claudia Satizabal, PhD; Gerard Schellenberg, PhD; Helena Schmidt, PhD; Michael Schmidt, PhD; Reinhold Schmidt, MD; Sudha Seshadri, MD; Evette Skinner; Sandra Smieszek, PhD; Harkirat Sohi, BS; Yeunjoo Song, PhD; Adam Stine, MS; Fangui Jenny Sun, PhD; Timothy Thornton, PhD; Giuseppe Tosto, MD, PhD; Debby W. Tsuang, MD; Otto Valladares, MS; Sven van der Lee, MD; Cornelia van Duijn, PhD; Ashley Vanderspek, MSc, PhD; Badri Vardarajan, PhD; Jason Waligorski, BS; Bowen Wang, MS; Weixin Wang, PhD; Li-San Wang, PhD; Simon White, MSc; Ellen Wijsman, PhD; Richard K. Wilson, PhD; Daniela Witten, PhD; Kim Worley, PhD; Li Charlie Xia, PhD; Nancy Zhang, PhD; Xiaoling Zhang, MD, PhD; Yi Zhao, MS; Yiming Zhu, MS.

The ascertainment and selection of controls was supported by grant 1I01BX000531 from the Department of Veterans Affairs and grants P30 AG008017, P30 AG028383, P30 AG010124, P30 AG010161, P50 NS053488, P50 AG005131, P50 NS062684, P50 AG005136, P50 AG005133, R01 NS048595, R01 NS065070, R01 AG010845, and U01 AG006781 from the National Institutes of Health (NIH).

For the Hispanic data: data collection for this project was supported by the Genetic Studies of Alzheimer’s Disease in Caribbean Hispanics (EFIGA) funded by the National Institute on Aging (NIA) and by the NIH (5R37AG015473 and RF1AG015473). We acknowledge the EFIGA study participants and the EFIGA research and support staff for their contributions to this study.

The ADSP is composed of two AD genetics consortia and three National Human Genome Research Institute (NHGRI)-funded Large Scale Sequencing and Analysis Centers (LSACs). The two AD genetics consortia are the ADGC, funded by NIA U01 AG032984, and the CHARGE consortium, funded by NIA R01 AG033193, the National Heart, Lung, and Blood Institute (NHLBI), other NIH institutes, and other foreign governmental and nongovernmental organizations. The Discovery Phase analysis of the sequencing data is supported through UF1AG047133 (to Drs. Schellenberg, Farrer, Pericak-Vance, Mayeux, and Haines); U01AG049505 to Dr. Seshadri; U01AG049506 to Dr. Boerwinkle; U01AG049507 to Dr. Wijsman; and U01AG049508 to Dr. Goate. The Discovery Extension Phase analysis is supported through U01AG052411 to Dr. Goate and U01AG052410 to Dr. Pericak-Vance. Data generation and harmonization in the Follow-Up Phases is supported through U54AG052427 (to Drs. Schellenberg and Wang).

The ADGC cohorts include: Adult Changes in Thought (ACT), the Alzheimer’s Disease Centers (ADCs), the Chicago Health and Aging Project (CHAP), the Memory and Aging Project (MAP), Mayo Clinic, Mayo Parkinson’s Disease controls, University of Miami, the Multi-Institutional Research in Alzheimer’s Genetic Epidemiology (MIRAGE) Study, the National Cell Repository for Alzheimer’s Disease (NCRAD), the NIA Late Onset of Alzheimer’s Disease (NIA-LOAD) Family Study, the Religious Orders Study (ROS), the Texas Alzheimer’s Research and Care Consortium (TARC), Vanderbilt University/Case Western Reserve University, the Washington Heights-Inwood Columbia Aging Project (WHICAP) and the Washington University Sequencing Project (WUSP), the Columbia University Hispanic-EFIGA, the University of Toronto, and Genetic Differences.

The CHARGE cohorts, with funding provided through 5RC2HL102419 and HL105756, include the following: the Atherosclerosis Risk in Communities (ARIC) Study, which is carried out as a collaborative study supported by NHLBI contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), the Austrian Stroke Prevention Study (ASPS), the Cardiovascular Health Study (CHS), the Erasmus Rucphen Family Study (ERF), the Framingham Heart Study (FHS), and the Rotterdam Study (RS). CHS research was supported through contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, and N01HC85086, as well as through grants U01HL080295 and U01HL130114 from the NHLBI, with an additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629, R01AG15928, and R01AG20098 from the NIA. A full list of principal CHS investigators and institutions can be found at The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

The three LSACs are: the Human Genome Sequencing Center at the Baylor College of Medicine (U54 HG003273), the Broad Institute Genome Center (U54HG003067), and the Washington University Genome Institute (U54HG003079).

The biological samples and associated phenotypic data used in primary data analyses were stored at the study investigators’ institutions, and at the NCRAD (U24AG021886) at Indiana University, funded by the NIA. The associated phenotypic data used in the primary and secondary data analyses were provided by the study investigators, the NIA-funded ADCs, and the National Alzheimer’s Coordinating Center (NACC; U01AG016976) and National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS; U24AG041689) at the University of Pennsylvania, funded by the NIA, as well as at the dbGaP funded by the NIH. This research was supported in part by the Intramural Research Program of the NIH, National Library of Medicine. Contributors to the genetic analysis data included study investigators on projects that were individually funded by the NIA and other NIH institutes, as well as by private US organizations or foreign governmental or non governmental organizations.

All authors declare no conflicts of interest.

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