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Figure 1.
Association of Alzheimer Disease Risk Genes With Brain Amyloidosis in the Pooled Sample
Association of Alzheimer Disease Risk Genes With Brain Amyloidosis in the Pooled Sample

Images were visualized using P < .01 (uncorrected) and cluster size (k) of 50 voxels. Scale indicates T values.

Figure 2.
Association of Alzheimer Disease Risk Genes With Brain Amyloidosis in the Normal Control, Mild Cognitive Impairment, and Dementia Groups
Association of Alzheimer Disease Risk Genes With Brain Amyloidosis in the Normal Control, Mild Cognitive Impairment, and Dementia Groups

Images were visualized using P < .01 (uncorrected) and cluster size (k) of 50 voxels. Scale indicates T values.

Figure 3.
β-Coefficient Maps of the Main Association of FERMT2 and Its Interaction With Diagnosis and the Association of FERMT2 Within Each Diagnostic Group
β-Coefficient Maps of the Main Association of FERMT2 and Its Interaction With Diagnosis and the Association of FERMT2 Within Each Diagnostic Group

Main association of FERMT2 with brain amyloidosis (A), its interaction with diagnosis (B), and the association of FERMT2 with brain amyloidosis in each diagnostic group (C) displayed using Statistical Parametric Mapping 8.

Table 1.  
Demographic Characteristics and Distribution of Genotypes
Demographic Characteristics and Distribution of Genotypes
Table 2.  
FWE- and FDR-Corrected Cluster Analyses and Within-Cluster Peak Effectsa
FWE- and FDR-Corrected Cluster Analyses and Within-Cluster Peak Effectsa
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Original Investigation
March 2018

Associations of the Top 20 Alzheimer Disease Risk Variants With Brain Amyloidosis

Author Affiliations
  • 1Department of Neurology, School of Medicine, Indiana University, Indianapolis
  • 2Department of Radiology and Imaging Sciences, Center for Neuroimaging, School of Medicine, Indiana University, Indianapolis
  • 3Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis
  • 4Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California
  • 5Department of Medicine Statistics Core, David Geffen School of Medicine at UCLA, Los Angeles, California
JAMA Neurol. 2018;75(3):328-341. doi:10.1001/jamaneurol.2017.4198
Key Points

Question  Which of the recently validated Alzheimer disease genetic risk variants are associated with brain amyloidosis?

Findings  In this study of 977 individuals from the Alzheimer’s Disease Neuroimaging Initiative, the adenosine triphosphate–binding cassette subfamily A member 7 gene had the strongest association with brain amyloidosis after apolipoprotein E ε4. The fermitin family homologue 2 gene had a stage-dependent association with brain amyloidosis, which was most pronounced in the mild cognitive impairment stage.

Conclusions  This study found an association of AD risk variants with brain amyloidosis.

Abstract

Importance  Late-onset Alzheimer disease (AD) is highly heritable. Genome-wide association studies have identified more than 20 AD risk genes. The precise mechanism through which many of these genes are associated with AD remains unknown.

Objective  To investigate the association of the top 20 AD risk variants with brain amyloidosis.

Design, Setting, and Participants  This study analyzed the genetic and florbetapir F 18 data from 322 cognitively normal control individuals, 496 individuals with mild cognitive impairment, and 159 individuals with AD dementia who had genome-wide association studies and 18F-florbetapir positron emission tomographic data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), a prospective, observational, multisite tertiary center clinical and biomarker study. This ongoing study began in 2005.

Main Outcomes and Measures  The study tested the association of AD risk allele carrier status (exposure) with florbetapir mean standard uptake value ratio (outcome) using stepwise multivariable linear regression while controlling for age, sex, and apolipoprotein E ε4 genotype. The study also reports on an exploratory 3-dimensional stepwise regression model using an unbiased voxelwise approach in Statistical Parametric Mapping 8 with cluster and significance thresholds at 50 voxels and uncorrected P < .01.

Results  This study included 977 participants (mean [SD] age, 74 [7.5] years; 535 [54.8%] male and 442 [45.2%] female) from the ADNI-1, ADNI-2, and ADNI–Grand Opportunity. The adenosine triphosphate–binding cassette subfamily A member 7 (ABCA7) gene had the strongest association with amyloid deposition (χ2 = 8.38, false discovery rate–corrected P < .001), after apolioprotein E ε4. Significant associations were found between ABCA7 in the asymptomatic and early symptomatic disease stages, suggesting an association with rapid amyloid accumulation. The fermitin family homolog 2 (FERMT2) gene had a stage-dependent association with brain amyloidosis (FERMT2 × diagnosis χ2 = 3.53, false discovery rate–corrected P = .05), which was most pronounced in the mild cognitive impairment stage.

Conclusions and Relevance  This study found an association of several AD risk variants with brain amyloidosis. The data also suggest that AD genes might differentially regulate AD pathologic findings across the disease stages.

Introduction

Sporadic Alzheimer disease (AD) is 70% to 80% heritable.1,2 The strongest genetic risk factor for AD is the apolipoprotein E (APOE) gene (OMIM 107741). The APOE ε4 allele carries the greatest risk through the reduction of β-amyloid (Aβ) clearance.3-5APOE ε4 carriers have a significantly higher prevalence of Pittsburgh compound B uptake than noncarriers across all disease stages,6 including presymptomatic amyloidosis in cognitively normal control individuals.7 Peripheral blood apoE protein levels correlate with amyloid positron emission tomography (PET) binding.8,9 These data indicate that imaging phenotypes can provide meaningful information related to gene function and pathophysiologic findings.

Previous large-scale genome-wide association studies (GWASs)10-15 have identified and validated 20 novel AD genetic risk loci. Few of these loci are in or near genes associated with Aβ aggregation and clearance and are thought to influence amyloid deposition.15,16 For the remainder, the precise disease-associated mechanism remains unknown.

Several imaging genetics studies16-20 have reported associations of some of the AD risk genes with brain amyloidosis or neurodegeneration. Phosphatidylinositol-binding clathrin assembly protein (PICALM) (OMIM 603025) rs3851179, bridging integrator 1 (BIN1) (OMIM 601248) rs7561528, complement component receptor 1 (CR1) rs1408077 (OMIM 120620), adenosine triphosphate–binding cassette subfamily A member 7 (ABCA7) (OMIM 605414) rs3764650, and membrane-spanning 4-domains, subfamily A, member 6a (MS4A6A) (OMIM 606548) rs610932 are associated with cortical and hippocampal atrophy.21,22ABCA7rs3764650 and rs3752246; BIN1rs744373; CR1rs6701713, rs3818361, and rs6656401; and clusterin (CLU) rs3818361 (OMIM 185430) are associated with amyloid deposition. Although these studies enrich the imaging genetics field, they also have significant shortcomings. Many of these research studies have focused on a single variant19 or a few variants16-18,22-25 while ignoring the complex polygenic disease background. In addition, all analyses of gene-endophenotype associations to date have largely used averaged phenotypic records across all disease stages. Such an approach is justified if the risk variant has a static or conserved effect during the disease course. However, considering the complicated and constantly evolving disease pathophysiologic process with early amyloid deposition, later onset of neuronal degeneration, and variable degree of inflammation, we considered stage-dependent genetic associations. Furthermore, improved understanding of the polygenetic risk factors for AD could enable personalized risk assessment, whereas an in-depth characterization of disease-associated mechanism could lead to new therapeutic avenues.

We report a comprehensive analysis of the associations of all well-validated AD risk variants with brain amyloidosis. Our goal was to establish their relative contribution to the amyloid burden. We hypothesized that our multivariable analytic approach would help us more accurately model the probability distribution of our imaging outcome measure and that we would detect several genetic variants in addition to APOE ε4 that are associated with brain amyloidosis. In addition, we hypothesized that we might also find stage-dependent associations with amyloid accumulation.

Methods
Participants

Data used in this study were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). The ADNI is a longitudinal study with approximately 50 sites across the United States and Canada that was launched in 2003 (http://adni.loni.usc.edu). The goal of the ADNI is to track the progression of AD by using clinical and cognitive tests, magnetic resonance imaging (MRI), fludeoxyglucose PET, amyloid PET, cerebrospinal fluid, and blood biomarkers. The institutional review boards of all sites participating in the ADNI provided review and approval of the ADNI data collection protocol.

The clinical description of the ADNI cohort has been previously published.26-28 Diagnosis of AD was based on the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association criteria.29-31 Individuals with AD dementia were required to have Mini-Mental State Examination (MMSE)32 scores between 20 and 26 and a Clinical Dementia Rating (CDR) score of 0.5 to 1 at baseline.33 Qualifying individuals with mild cognitive impairment (MCI) had memory concerns but no significant functional impairment, scored between 24 and 30 on the MMSE, had a global CDR score of 0.5, had a CDR memory score of 0.5 or greater, and had objective memory impairment on the Wechsler Memory Scale–Logical Memory II test.34 The controls had MMSE scores between 24 and 30, had a global CDR score of 0, and did not meet criteria for MCI and AD. Individuals were excluded if they refused or were unable to undergo MRI; had other neurologic disorders, active depression, a history of psychiatric diagnosis, a history of alcohol or other substance dependence within the past 2 years; had less than 6 years of education; or were not fluent in English or Spanish. The full list of inclusion and exclusion criteria can be accessed on pages 23 to 29 of the online ADNI protocol (http://adni.loni.usc.edu/wp-content/uploads/2010/09/ADNI_GeneralProceduresManual.pdf). Written informed consent was obtained from all participants, and all data were deidentified.

Gene Variant Selection and Imputation

The ADNI-1 participants were genotyped using the Illumina Human610-Quad BeadChip array (Illumina Inc), whereas the ADNI-2 and the ADNI–Grand Opportunity (GO) participants were genotyped using the Illumina HumanOmniExpress BeadChip (Illumina Inc) according to the manufacturer’s protocol. We focused on the 20 well-established AD risk genes identified and validated in the largest AD GWASs to date.10-15 In addition to the variants reported in these articles, we included all other variants that were previously associated with brain amyloidosis16-19 (eTable 1 in the Supplement), which yielded a total of 36 variants.

Missing genotypes (eTable 2 in the Supplement) were imputed using MACH and minimac in a 2-stage procedure using the 1000 Genomes project pilot data as a reference panel. Minimac yielded the posterior probabilities of the imputed genotypes at ungenotyped marker loci for each individual. The threshold to accept each imputed genotype was set at r2 = 0.30.35

Nine genes were represented by more than 1 single-nucleotide polymorphism (SNP). Because linkage disequilibrium (LD) introduces colinearity bias, we performed LD analyses followed by Cohen κ statistics (eFigure 1 and eTable 3 in the Supplement). When choosing between 2 variants with significant overlap (high LD and high κ), we retained the variant with least data missingness. Our final number of variants was thus reduced to 27. ABCA7, BIN1, CLU, CR1, ephrin receptor EphA1 (EPHA1) (OMIM 179610), and sortilin-related receptor (SORL1) (OMIM 602005) were represented with more than 1 variant in the analyses (eTable 3 in the Supplement).

Allele frequencies for each gene variant were assessed. Genotypes were collapsed when the minor allele homozygote frequency was less than 2% as follows: ABCA7rs3764650 GG/GT vs TT, Cass scaffolding protein family member 4 (CASS4) (HGNC 15878) rs7274581 CC/TC vs TT, CLUrs9331949 AG/GG vs AA, desmoglein 2 (DSG2) (OMIM 125671) rs8093731 TT/TC vs CC, fermitin family homologue 2 FERMT2 (OMIM 607746) rs17125944 CC/TC vs TT, and SORL1rs112183431 CC/TC vs TT. The remaining variants were coded by minor allele dosage.

Florbetapir F 18 PET Data Acquisition Protocol and Analyses

The florbetapir F 18 PET acquisition and preprocessing protocols are available at http://www.adni-info.org. In our main analyses, we used the mean whole-`brain standard uptake volume ratios (SUVRs) from University of California, Berkeley downloaded from the ADNI database (http://adni.loni.usc.edu). This variable was obtained by averaging the SUVRs obtained using whole cerebellum as the reference region across the frontal, anterior-posterior cingulate, lateral-parietal, and lateral-temporal gray matter regions.36 The University of California, Berkeley, protocols for 18F-florbetapir preprocessing, coregistration, and normalization have been previously described.36

To visualize the regional pattern of associations in 3 dimensions, we downloaded all preprocessed 18F-florbetapir data from the Laboratory of Neuroimaging Image Data Archive (https://ida.loni.usc.edu). We aligned the images to the corresponding MRI from the same visit, normalized to MNI space using measures obtained from the MRI spatial transformation and intensity normalized to the intensity of the whole cerebellum reference region to create SUVR images, as previously described.37

Statistical Analysis
R Statistical Analyses

Clinical and demographic characteristics (age, sex, educational level, MMSE, APOE ε4 genotype, and diagnosis) for each variant were compared using t tests or χ2 tests with 2-sided P values as appropriate. Stepwise multivariable linear regression models with all 27 AD risk variants were performed first in the pooled sample and second in each diagnostic category using amyloid PET mean SUVR as the outcome measure. An additional model in the pooled sample using only amyloid-positive individuals (SUVR>1.17) is available in the eResults in the Supplement. All regression models included age, sex, and APOE ε4 genotype as covariates. The regression model for the pooled sample was also corrected for diagnosis. The decision to exclude variables was based on the Akaike information criterion critical P value threshold of .16.38 Because we included only previously validated candidate genes, our significance threshold was set at P < .05. Correction for false discovery rate (FDR) was applied.

Analyses in Imaging Space

All imaging analyses were performed in an exploratory fashion. To explore the spatial distribution of the associations, we reproduced the final stepwise regression models using voxelwise regression in Statistical Parametric Mapping 8 (SPM8; Wellcome Department of Cognitive Neuroscience). The SPM8 models included all variants retained in the R statistical models (including those that were retained based on the Akaike information criterion) covaried for age, sex, and APOE ε4 genotype. The pooled model also included diagnosis as a covariate. Because of the exploratory nature of our secondary results, we allowed a less stringent visualization threshold: voxelwise threshold of P < .01 (uncorrected) with a minimum cluster size (k) of 50 voxels. We also computed familywise error (FWE) and FDR-corrected cluster and peak statistics as appropriate.

Results

The study population was composed of participants from the ADNI-1, ADNI-2, and ADNI-GO stages39 and consisted of 322 controls, 496 individuals with MCI, and 159 individuals with AD who had available GWAS and 18F-florbetapir PET data (mean [SD] age, 74 [7.5] years; 535 [54.8%] male and 442 [45.2%] female). Group comparisons of demographic characteristics and distribution of the genotypes that were retained in the regression models are given in Table 1. APOE ε4 had significant associations with brain amyloidosis (eFigure 2 in the Supplement). There were no significant differences in age, sex, educational level, MMSE score, and APOE ε4 distribution between carriers and noncarriers or by allele dosage for any of the genotypes except for zinc finger CW-type and PWWP domain containing 1 (ZCWPW1) (HGNC 23486) for which risk allele homozygotes were less educated (P = .02).

Pooled Sample

In the pooled sample, the stepwise linear regression model achieved an R2 of 0.35 (95% CI, 0.33-0.37; P < .001). ABCA7rs37522462 = 8.38, FDR-corrected P < .001), EPHA1rs117711452 = 4.08, FDR-corrected P = .03), and PICALMrs38511792 = 3.67, FDR-corrected P = .04) were significantly associated with mean SUVR in the pooled sample. Other associations were as follows: ZCPWPW1rs14766792 = 2.74, FDR-corrected P = .08), FERMT2rs171259442 = 3.63, FDR-corrected P = .08), and protein tyrosine-kinase 2β (PTK2B) rs28834970 (OMIM 601212) (χ2 = 2.52, FDR-corrected P = .01). ABCA7rs3764650 and CLUrs11136000 were included in the model based on the Akaike selection criterion. A reduced model that included only age, sex, educational level, and APOE ε4 achieved a reduced R2 of 0.31 (95% CI, 0.29-0.33). The between-model difference in R2 and reduced R2 was 0.038 (95% CI, 0.029-0.047). Figure 1 and Figure 2 show these associations and Table 2 gives FWE- and FDR-corrected cluster-level results and within-cluster peak associations for genetic variants identified in our models.

Interaction Analyses

To further test for the presence of a stage-specific association, we conducted a linear regression analysis in the pooled sample including interaction terms. FERMT2 was the only variant that had a significant interaction (FERMT2 × diagnosis χ2 = 3.53, FDR-corrected P = .05). The effect sizes for the remaining genes remained unchanged. Figure 3 shows the β-coefficient maps of the main effect size of FERMT2 and its interaction with diagnosis as well as the FERMT2 effect size within each diagnostic group.

Exploratory Analyses Within Diagnostic Groups

In the control group, the model achieved an R2 of 0.17 (95% CI, 0.14-0.21; P < .001; reduced R2 = 0.14; 95% CI, 0.11-0.17; R2–reduced R2 difference = 0.032; 95% CI, 0.015-0.05). Significant associations were seen for PICALMrs38511792 = 3.56, FDR-corrected P = .04). The association for ABCA7rs3764650 was χ2 = 3.16 (FDR-corrected P = .09). ABCA7rs3752246 was included in the model based on the Akaike selection criterion.

In the MCI group, the model achieved an R2 of 0.3 (95% CI, 0.27-0.32; P < .001; reduced R2 = 0.24; 95% CI, 0.21-0.27; R2–reduced R2 difference = 0.058; 95% CI, 0.042-0.074). ABCA7rs37522462 = 7.22, FDR-corrected P = .002), EPHA1rs117711452 = 3.74, FDR-corrected P = .03), FERMT2rs171259442 = 10.38, FDR-corrected P = .002), and SORL1rs11314972 = 3.66, FDR-corrected P = .03) were significantly associated with mean SUVR. The association for ABCA7rs3764650 was χ2 = 2.9 (FDR-corrected P = .09).

In the dementia group, the model achieved an R2 of 0.35 (95% CI, 0.29-0.41; P < .0001; reduced R2 = 0.22; 95% CI, 0.16-0.28; R2–reduced R2 difference = 0.13; 95% CI, 0.09-0.17). Other associations were as follows: EPHA1rs117711452 = 5.05, FDR-corrected P = .01), ZCWPW1rs14766792 = 3.79, FDR-corrected P = .04), DSG2rs80937312 = 3.27, FDR-corrected P = .08), CLUrs93319492 = 4.09, FDR-corrected P = .058), and SORL1rs11314972 = 2.51, FDR-corrected P = .08).

Figure 1 and Figure 2 present exploratory visualization of these associations, and Table 2 presents the FWE- and FDR-corrected cluster-level results and within-cluster peak associations for genetic variants identified in our models.

Discussion

Improved understanding of the polygenetic risk factors that are associated with AD could enable personalized risk assessment. To our knowledge, this is the first comprehensive analysis of the association of the top 20 AD risk variants with brain amyloidosis. We were able to confirm the previously reported association between ABCA7 and brain amyloidosis as described by Shulman et al16 and Hughes et al.18 Our study found that after APOE ε4, ABCA7 has the strongest association with amyloid deposition. We were unable to confirm the reported associations of CR120 likely because the associations previously reported were determined using a univariable approach. It is plausible that the previously reported CR1 association is better accounted for by other AD-related genes, which were not part of the original analysis. We also found evidence of a stage-dependent gene association of FERMT2 with brain amyloidosis. This is, to our knowledge, the first report of such an association.

Several genes had associations with brain amyloidosis. ABCA7 encodes a 2146–amino acid ABC family transporter protein.40 The ABC protein family is responsible for the transport of a variety of molecules across cellular membranes, primarily lipids. ABCA7 is expressed in nervous tissue, with the highest expression in microglia.41 Loss of function of ABCA7 was associated with increased β-secretase cleavage of amyloid precursor protein (APP), leading to higher levels of Aβ in vitro and in vivo.42 A previous ADNI study43 analyzed the associations of 15 ABCA7 loci with cerebrospinal fluid Aβ and florbetapir SUVR. Three variants (rs3752242, rs3752240, and rs4147912) were significantly associated with brain amyloidosis but not with brain atrophy. One of these 3 SNPs (rs3752242) is in LD with ABCA7rs3752246, lending support to our findings. Further evidence of the role of ABCA7 in AD comes from a study44 that reported one rare missense variant (rs72973581; minor allele frequency of 4.3%) to confer a significant protection against AD. In a previous publication,45 a late but profound effect of ABCA7 was found on neurodegeneration. Individuals with AD dementia had significant associations of ABCA7rs3752246 with gray matter density throughout the brain. Individuals with MCI and controls did not have such an association.

CLU encodes for clusterin, an extracellular chaperone protein that consists of 427 amino acids. CLU is highly expressed in neurons and ependymal cells.46 It seems to be involved in a variety of processes throughout the body, including synaptic maintenance and programmed cell death.47,48 Under physiologic conditions, clusterin reduces aggregation and promotes clearance of Aβ.49CLU is highly expressed in the hippocampi in patients with AD and Pick disease.50 Clusterin protein levels are also elevated in AD, and its pattern of distribution correlates positively with that of Aβ42 and Aβ40 in postmortem tissue.51

DSG2 encodes a cell adhesion desmosome cadherin protein. DSG2 binds plaque proteins and intermediate filaments and seems to play a role in inflammation.52 Although this gene was reported to be associated with AD risk, a mechanistic explanation of this association has not yet been elucidated. DSG2 is expressed in epithelial-derived tissues, such as epithelial cell lines,53 epithelial malignant tumors,54 and the brain, especially the corpus callosum region.55-57 We found an association with amyloid deposition later in the disease course, indicating a late modulatory effect on amyloid deposition.

EPHA1 encodes a 976–amino acid protein that belongs to the EPH family of receptor tyrosine kinases.58EPHA1 plays a role in contact-dependent signaling and nervous system development.59-62EPHA1 is highly expressed in the cerebral cortex and hippocampus.63 A previous analysis64 of ADNI-1 data reported that EPHA1rs11771145 is associated with less brain atrophy and higher cerebral metabolic rate in MCI. Analyses of the cognitively normal imaging subcohort of the Ginkgo Evaluation of Memory study implicated another EPHA1 allele (rs11767557), which is in LD with ours, to have a negative effect on brain amyloidosis.18

FERMT2 encodes for a 680–amino acid scaffolding extracellular matrix protein that plays a role in cell adhesions.65,66FERMT2 is expressed in the brain (http://www.proteinatlas.org/ENSG00000073712-FERMT2/tissue). FERMT2 is upregulated in atherosclerotic plaques, suggesting a possible role in inflammation and leukocyte extravasation.67FERMT2 is a coactivator of β3-integrin68—a microglial and reactive astrocyte marker that plays a role in poststroke brain tissue recovery.69,70FERMT2 has also been associated with a cognitive decline in AD71 and modifies tau neurotoxicity in a Drosophila model.72

PICALM encodes a 652–amino acid protein that binds to clathrin’s heavy chain and assists in vesicle assembly and endocytosis.73PICALM was recently identified as a risk gene for late-onset AD.74 PICALM colocalizes with APP. PICALM knockdown resulted in a reduction in the amount of APP internalized and a reduction in Aβ generation.75 In a previous study,76PICALM was found to modulate the clearance of tau and thus autophagy. PICALM has been associated with brain changes in AD. Morgen et al77 reported a negative association with prefrontal brain volume and working memory, whereas Biffi et al78 found associations with hippocampal amygdalar and white matter lesion volume, as well as with entorhinal, parahippocampal, and temporal pole cortical thickness.

SORL1 encodes a 2186–amino acid protein from the low-density lipoprotein receptor family.79 SORL1 readily binds APOE and lipoprotein lipase and localizes to both the Golgi apparatus and the plasma membrane, where it likely mediates endocytosis.80SORL1 plays a role in APP trafficking and recycling.81SORL1 is downregulated in lymphoblasts and cortical pyramidal neurons of patients with AD.82 The neuronal SORL1 protein level determines cognitive decline and conversion from MCI to AD.83 The protein level also correlates with the levels of the APP soluble products that result from β-secretase cleavage.84 An SNP in LD with our variant (rs1133174) has also been linked to brain atrophy in AD.85

The ZCWPW1 gene codes for a 648–amino acid protein. ZCWPW1 is considered to be a risk gene for late-onset AD.86 Its proposed mechanism of action is through epigenetic regulation of gene expression.87-89

Strengths and Limitations

Several strengths and limitations of our study warrant discussion. One of the major strengths lies in the careful clinical, biomarker, and genetic characterization of all individuals enrolled in the ADNI. The ADNI protocol uses unified subject assessment, standardization of all imaging, biofluid and DNA and RNA data collection and processing, and meticulous data quality control across all study sites. Another strength of the study is the fairly large sample size that allowed us to achieve enough statistical power to test the associations of 27 AD-associated risk variants using a polygenic model.

A major limitation of our study is that we only report cross-sectional analyses; thus, we cannot make definitive conclusions regarding genetic effects on amyloid deposition over time. From our cross-sectional observations across the disease continuum, we drew conclusions about early vs late genetic influences on brain amyloidosis that will need to be further tested using a longitudinal design, which is what we plan to do next. Another limitation of our work is that the sample size was not big enough to allow us to test for gene-gene and gene-environment interactions. Last but not least, the ADNI uses rigorous exclusion criteria typical of clinical trials, rendering the ADNI cohort not representative of the general population, which may negatively affect the generalizability of our results. Thus, our next steps will be to validate our findings in a large, independent, longitudinal cohort.

Conclusions

We found an association of genetic variants with brain amyloidosis, the salient pathognomonic feature of AD. Four of the genetic variants reported here, ABCA7, CLU, EPHA1, and SORL1, have been previously linked to the amyloidogenic AD pathways. To our knowledge, we are the first to report a stage-specific association for a genetic variant (ie, FERMT2).

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Article Information

Accepted for Publication: October 19, 2017.

Corresponding Author: Liana G. Apostolova, MD, MSc, Department of Neurology, School of Medicine, Indiana University, 355 W 16th St, Ste 4700, Indianapolis, IN 46202 (lapostol@iu.edu).

Published Online: January 16, 2018. doi:10.1001/jamaneurol.2017.4198

Author Contributions: Dr Apostolova had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Apostolova, Saykin.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Apostolova, Stage, Goukasian, Grotts, Nho.

Critical revision of the manuscript for important intellectual content: Apostolova, Risacher, Duran, Stage, Goukasian, West, Do, Wilhalme, Nho, Phillips, Elashoff, Saykin.

Statistical analysis: Apostolova, Risacher, Duran, Stage, Goukasian, West, Do, Grotts, Wilhalme, Nho, Phillips, Elashoff.

Obtained funding: Apostolova, Saykin.

Administrative, technical, or material support: Apostolova, Risacher, Stage, Do, Phillips, Saykin.

Study supervision: Apostolova.

Conflict of Interest Disclosures: Dr Apostolova reported serving on an advisory board for Eli Lilly and Company and on the speaker’s bureau for Piramal and Eli Lilly and Company and receiving research support from GE Healthcare. Dr Saykin reported receiving research support from Eli Lilly and Company and AVID Radiopharmaceuticals. No other disclosures were reported.

Funding/Support: The design and conduct of the study, data analysis and interpretation, and preparation, review, and approval of the manuscript were funded by grants R01 AG040770 (Dr Apostolova), NIA K02 AG048240 (Dr Apostolova), NIA P30 AG010133 (Dr Saykin), and NIA K01 AG049050 (Dr Risacher) from the National Institute on Aging, the Alzheimer’s Association (Dr Risacher), the Easton Consortium for Alzheimer’s Drug Discovery and Biomarker Development (Dr Apostolova), the Indiana University Strategic Research Initiative (Dr Apostolova), the Indiana University Physician Science Initiative (Dr Apostolova), and the Indiana Clinical and Translational Science Institute (Dr Risacher). Data collection and sharing for this project were funded by grant U01 AG024904 from the National Institutes of Health and award W81XWH-12-2-0012 from the US Department of Defense to the Alzhiemer’s Disease Neuroimaging Initiative (ADNI). The ADNI is funded by the National Institute on Aging, by the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association, Alzheimer’s Drug Discovery Foundation, Araclon Biotech, BioClinica Inc, Biogen, Bristol-Myers Squibb Company, CereSpir Inc, Cogstate, Eisai Inc, Elan Pharmaceuticals Inc, Eli Lilly and Company, EuroImmun, F. Hoffmann-La Roche Ltd and its affiliated company Genentech Inc, Fujirebio, GE Healthcare, IXICO Ltd, Janssen Alzheimer Immunotherapy Research & Development LLC, Johnson & Johnson Pharmaceutical Research & Development LLC, Lumosity, Lundbeck, Merck & Co Inc, Meso Scale Diagnostics LLC, NeuroRx Research, Neurotrack Technologies, Novartis Pharmaceuticals Corporation, Pfizer Inc, Piramal Imaging, Servier, Takeda Pharmaceutical Company, and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California.

Role of the 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.

Group Information: A complete listing of Alzheimer’s Disease Neuroimaging Initiative (ADNI) investigators is as follows: ADNI I, GO, II, and III Leadership and Infrastructure: Principal Investigator: Michael W. Weiner, MD, University of California, San Francisco; ATRI PI and Director of Coordinating Center Clinical Core: Paul Aisen, MD, University of Southern California; Executive Committee: Michael Weiner, MD, University of California, San Francisco; Paul Aisen, MD, University of Southern California; Ronald Petersen, MD, PhD, Mayo Clinic, Rochester; Clifford R. Jack, Jr, MD, Mayo Clinic, Rochester; William Jagust, MD, University of California, Berkeley; John Q. Trojanowki, MD, PhD, University of Pennsylvania; Arthur W. Toga, PhD, University of Southern California; Laurel Beckett, PhD, University of California, Davis; Robert C. Green, MD, MPH, Brigham and Women’s Hospital/Harvard Medical School; Andrew J. Saykin, PsyD, Indiana University; John Morris, MD, Washington University, St Louis; Leslie M. Shaw, University of Pennsylvania; ADNI External Advisory Board: Zaven Khachaturian, PhD, Prevent Alzheimer’s Disease 2020 (chair); Greg Sorensen, MD, Siemens; Maria Carrillo, PhD, Alzheimer’s Association; Lew Kuller, MD, University of Pittsburgh; Marc Raichle, MD, Washington University, St Louis; Steven Paul, MD, Cornell University; Peter Davies, MD, Albert Einstein College of Medicine of Yeshiva University; Howard Fillit, MD, AD Drug Discovery Foundation; Franz Hefti, PhD, Acumen Pharmaceuticals; David Holtzman, MD, Washington University, St Louis; M. Marcel Mesulam, MD, Northwestern University; William Potter, MD, National Institute of Mental Health; Peter Snyder, PhD, Brown University; ADNI 3 Private Partner Scientific Board: Veronika Logovinsky, MD, PhD, Eli Lilly (chair); Data and Publications Committee: Robert C. Green, MD, MPH, Brigham and Women’s Hospital/Harvard Medical School; (chair); Resource Allocation Review Committee: Tom Montine, MD, PhD, University of Washington (chair); Clinical Core Leaders: Ronald Petersen, MD, PhD, Mayo Clinic, Rochester (core principal investigator); Paul Aisen, MD, University of Southern California; Clinical Informatics and Operations: Gustavo Jimenez, MBS, University of Southern California; Michael Donohue, PhD, University of Southern California; Devon Gessert, BS, University of Southern California; Kelly Harless, BA, University of Southern California; Jennifer Salazar, MBS, University of Southern California; Yuliana Cabrera, BS, University of Southern California; Sarah Walter, MSc, University of Southern California; Lindsey Hergesheimer, BS, University of Southern California; Biostatistics Core Leaders and Key Personnel: Laurel Beckett, PhD, University of California, Davis (core principal investigator); Danielle Harvey, PhD, University of California, Davis; Michael Donohue, PhD, University of California, San Diego; MRI Core Leaders and Key Personnel: Clifford R. Jack, Jr, MD, Mayo Clinic, Rochester (core principal investigator); Matthew Bernstein, PhD, Mayo Clinic, Rochester; Nick Fox, MD, University of London; Paul Thompson, PhD, UCLA School of Medicine; Norbert Schuff, PhD, University of California, San Francisco, MRI; Charles DeCarli, MD, University of California, Davis; Bret Borowski, RT, Mayo Clinic; Jeff Gunter, PhD, Mayo Clinic; Matt Senjem, MS, Mayo Clinic; Prashanthi Vemuri, PhD, Mayo Clinic; David Jones, MD, Mayo Clinic; Kejal Kantarci, Mayo Clinic; Chad Ward Mayo Clinic; PET Core Leaders and Key Personnel: William Jagust, MD, University of California, Berkeley (core principal investigator); Robert A. Koeppe, PhD, University of Michigan; Norm Foster, MD, University of Utah; Eric M. Reiman, MD, Banner Alzheimer’s Institute; Kewei Chen, PhD, Banner Alzheimer’s Institute; Chet Mathis, MD, University of Pittsburgh; Susan Landau, PhD, University of California, Berkeley; Neuropathology Core Leaders: John C. Morris, MD, Washington University, St Louis; Nigel J. Cairns, PhD, FRCPath, Washington University, St Louis; Erin Franklin, MS, CCRP Washington University, St Louis; Lisa Taylor-Reinwald, BA, HTL, Washington University, St Louis (past investigator); Biomarkers Core Leaders and Key Personnel: Leslie M. Shaw, PhD, University of Pennsylvania School of Medicine; John Q. Trojanowki, MD, PhD, University of Pennsylvania School of Medicine; Virginia Lee, PhD, MBA, University of Pennsylvania School of Medicine; Magdalena Korecka, PhD, University of Pennsylvania School of Medicine; Michal Figurski, PhD, University of Pennsylvania School of Medicine; Informatics Core Leaders and Key Personnel: Arthur W. Toga, PhD, University of Southern California (core principal investigator); Karen Crawford, University of Southern California; Scott Neu, PhD, University of Southern California; Genetics Core Leaders and Key Personnel: Andrew J. Saykin, PsyD, Indiana University; Tatiana M. Foroud, PhD, Indiana University; Steven Potkin, MD, University of California, Irvine; Li Shen, PhD, Indiana University; Kelley Faber, MS, CCRC, Indiana University; Sungeun Kim, PhD, Indiana University; Kwangsik Nho, PhD, Indiana University; Initial Concept Planning & Development: Michael W. Weiner, MD, University of California, San Francisco; Lean Thal, MD, University of California, San Diego; Zaven Khachaturian, PhD, Prevent Alzheimer’s Disease 2020; Early Project Proposal Development: Leon Thal, MD, University of California, San Diego; Neil Buckholtz, National Institute on Aging; Michael W. Weiner, MD, University of California, San Francisco; Peter J. Snyder, PhD, Brown University; William Potter, MD, National Institute of Mental Health; Steven Paul, MD, Cornell University; Marilyn Albert, PhD, Johns Hopkins University; Richard Frank, MD, PhD, Richard Frank Consulting; Zaven Khachaturian, PhD, Prevent Alzheimer’s Disease 2020; National Institute on Aging: John Hsiao, MD, National Institute on Aging; Investigators by Site Oregon Health & Science University: Joseph Quinn, MD; Lisa C. Silbert, MD; Betty Lind, BS; Jeffrey A. Kaye, MD (past investigator); Raina Carter, BA (past investigator); Sara Dolen, BS (past investigator); University of Southern California: Lon S. Schneider, MD; Sonia Pawluczyk, MD; Mauricio Becerra, BS; Liberty Teodoro, RN; Bryan M. Spann, DO, PhD (past investigator); University of California, San Diego: James Brewer, MD, PhD; Helen Vanderswag, RN; Adam Fleisher, MD (past investigator); University of Michigan: Jaimie Ziolkowski, MA, BS, TLL; Judith L. Heidebrink, MD, M; Joanne L. Lord, LPN, BA, CCRC (past investigator); Mayo Clinic, Rochester: Ronald Petersen, MD, PhD; Sara S. Mason, RN; Colleen S. Albers, RN; David Knopman, MD; Kris Johnson, RN (past investigator); Baylor College of Medicine: Javier Villanueva-Meyer, MD; Valory Pavlik, PhD; Nathaniel Pacini, MA; Ashley Lamb, MA; Joseph S. Kass, MD, LD, FAAN; Rachelle S. Doody, MD, PhD (past investigator); Victoria Shibley, MS (past investigator); Munir Chowdhury, MBBS, MS (past investigator); Susan Rountree, MD (past investigator); Mimi Dang, MD (past investigator); Columbia University Medical Center: Yaakov Stern, PhD; Lawrence S. Honig, MD, PhD; Karen L. Bell, MD; Randy Yeh, MD. Washington University, St Louis: Beau Ances, MD, PhD, MSc; John C. Morris, MD; David Winkfield, BS; Maria Carroll, RN, MSN, GCNS-BC; Angela Oliver, RN, BSN, MSG; Mary L. Creech, RN, MSW (past investigator); Mark A. Mintun, MD (past investigator); Stacy Schneider, APRN, BC, GNP (past investigator); University of Alabama, Birmingham: Daniel Marson, JD, PhD; David Geldmacher, MD; Marissa Natelson Love, MD; Randall Griffith, PhD, ABPP (past investigator); David Clark, MD (past investigator); John Brockington, MD (past investigator); Mount Sinai School of Medicine: Hillel Grossman, MD; Effie Mitsis, PhD (past investigator); Rush University Medical Center: Raj C. Shah, MD; Melissa Lamar, PhD; Patricia Samuels; Wien Center: Ranjan Duara, MD; Maria T. Greig-Custo, MD; Rosemarie Rodriguez, PhD; Johns Hopkins University: Marilyn Albert, PhD; Chiadi Onyike, MD; Daniel D’Agostino II, BS; Stephanie Kielb, BS (past investigator); New York University: Martin Sadowski, MD, PhD; Mohammed O. Sheikh, MD; Jamika Singleton-Garvin, CCRP; Anaztasia Ulysse Mrunalini Gaikwad; Duke University Medical Center: P. Murali Doraiswamy, MBBS, FRCP; Jeffrey R. Petrella, MD; Olga James, MD; Salvador Borges-Neto, MD; Terence Z. Wong, MD (past investigator); Edward Coleman (past investigator); University of Pennsylvania: Jason H. Karlawish, MD; David A. Wolk, MD; Sanjeev Vaishnavi, MD; Christopher M. Clark, MD (past investigator); Steven E. Arnold, MD (past investigator); University of Kentucky: Charles D. Smith, MD; Greg Jicha, MD; Peter Hardy, PhD; Riham El Khouli, MD; Elizabeth Oates, MD; Gary Conrad, MD; University of Pittsburgh: Oscar L. Lopez, MD; MaryAnn Oakley, MA; Donna M. Simpson, CRNP, MPH; University of Rochester Medical Center: Anton P. Porsteinsson, MD; Kim Martin, RN; Nancy Kowalksi, MS, RNC; Melanie Keltz, RN; Bonnie S. Goldstein, MS, NP (past investigator); Kelly M. Makino, BS (past investigator); M. Saleem Ismail, MD (past investigator); Connie Brand, RN (past investigator); University of California Irvine IMIND: Gaby Thai, MD; Aimee Pierce, MD; Beatriz Yanez, RN; Elizabeth Sosa, PhD; Megan Witbracht, PhD; University of Texas Southwestern Medical School: Kyle Womack, MD; Dana Mathews, MD, PhD; Mary Quiceno, MD; Emory University: Allan I. Levey, MD, PhD; James J. Lah, MD, PhD; Janet S. Cellar, DNP, PMHCNS-BC University of Kansas, Medical Center: Jeffrey M. Burns, MD; Russell H. Swerdlow, MD; William M. Brooks, PhD; University of California, Los Angeles: Ellen Woo, PhD; Daniel H.S. Silverman, MD, PhD; Edmond Teng, MD, PhD; Sarah Kremen, MD; Liana Apostolova, MD (past investigator); Kathleen Tingus, PhD (past investigator); Po H. Lu, PsyD (past investigator); George Bartzokis, MD (past investigator); Mayo Clinic, Jacksonville: Neill R Graff-Radford, MBBCH, FRCP (London) Francine Parfitt, MSH, CCRC Kim Poki-Walker, BA; Indiana University: Martin R. Farlow, MD; Ann Marie Hake, MD; Brandy R. Matthews, MD (past investigator); Jared R. Brosch, MD; Scott Herring, RN, CCRC Yale University School of Medicine: Christopher H. van Dyck, MD; Richard E. Carson, PhD; Pradeep Varma, MD; McGill University, Montreal-Jewish General Hospital: Howard Chertkow, MD; Howard Bergman, MD; Chris Hosein, MEd, Sunnybrook Health Sciences, Ontario: Sandra Black, MD, FRCPC Bojana Stefanovic, PhD; Chris (Chinthaka) Heyn, BSC, PhD, MD, FRCPC U.B.C. Clinic for AD & Related Disorders: Ging-Yuek Robin Hsiung, MD, MHSc, FRCPC Benita Mudge, BS; Vesna Sossi, PhD; Howard Feldman, MD, FRCPC (past investigator); Michele Assaly, MA (past investigator); Cognitive Neurology - St. Joseph's, Ontario: Elizabeth Finger, MD; Stephen Pasternack, MD, PhD; William Pavlosky, MD; Irina Rachinsky, MD (past investigator); Dick Drost, PhD (past investigator); Andrew Kertesz, MD (past investigator); Cleveland Clinic Lou Ruvo Center for Brain Health: Charles Bernick, MD, MPH; Donna Munic, PhD; Northwestern University: Marek-Marsel Mesulam, MD; Emily Rogalski, PhD; Kristine Lipowski, MA; Sandra Weintraub, PhD; Borna Bonakdarpour, MD; Diana Kerwin, MD (past investigator); Chuang-Kuo Wu, MD, PhD (past investigator); Nancy Johnson, PhD (past investigator); Premiere Research Inst (Palm Beach Neurology): Carl Sadowsky, MD; Teresa Villena, MD; Georgetown University Medical Center: Raymond Scott Turner, MD, PhD; Kathleen Johnson, NP Brigid Reynolds, NP Brigham and Women's Hospital: Reisa A. Sperling, MD; Keith A. Johnson, MD; Gad A. Marshall, MD; Stanford University: Jerome Yesavage, MD; Joy L. Taylor, PhD; Steven Chao, MD, PhD; Barton Lane, MD (past investigator); Allyson Rosen, PhD (past investigator); Jared Tinklenberg, MD (past investigator); Banner Sun Health Research Institute: Edward Zamrini, MD; Christine M. Belden, PsyD; Sherye A. Sirrel, CCRC Boston University: Neil Kowall, MD; Ronald Killiany, PhD; Andrew E. Budson, MD; Alexander Norbash, MD (past investigator); Patricia Lynn Johnson, BA (past investigator); Howard University: Thomas O. Obisesan, MD, MPH; Ntekim E. Oyonumo, MD, PhD; Joanne Allard, PhD; Olu Ogunlana, BPharm; Case Western Reserve University: Alan Lerner, MD; Paula Ogrocki, PhD; Curtis Tatsuoka, PhD; Parianne Fatica, BA, CCRC; University of California, Davis – Sacramento: Evan Fletcher, PhD; Pauline Maillard, PhD; John Olichney, MD; Charles DeCarli, MD; Owen Carmichael, PhD (past investigator); Neurological Care of CNY: Smita Kittur, MD (past investigator); Parkwood Institute: Michael Borrie, MB ChB; T-Y Lee, PhD; Rob Bartha, PhD; University of Wisconsin: Sterling Johnson, PhD; Sanjay Asthana, MD; Cynthia M. Carlsson, MD, MS; Banner Alzheimer's Institute: Pierre Tariot, MD; Anna Burke, MD; Joel Hetelle, BS; Kathryn DeMarco, BS; Nadira Trncic, MD, PhD, CCRC (past investigator); Adam Fleisher, MD (past investigator); Stephanie Reeder, BA (past investigator); Dent Neurologic Institute: Vernice Bates, MD; Horacio Capote, MD; Michelle Rainka, PharmD, CCRP; Ohio State University: Douglas W. Scharre, MD; Maria Kataki, MD, PhD; Rawan Tarawneh, MD; Albany Medical College: Earl A. Zimmerman, MD; Dzintra Celmins, MD; David Hart, MD; Hartford Hospital, Olin Neuropsychiatry Research Center: Godfrey D. Pearlson, MD; Karen Blank, MD; Karen Anderson, RN; Dartmouth-Hitchcock Medical Center: Laura A. Flashman, PhD; Marc Seltzer, MD; Mary L. Hynes, RN, MPH; Robert B. Santulli, MD (past investigator); Wake Forest University Health Sciences: Kaycee M. Sink, MD, MAS; Mia Yang, MD; Akiva Mintz, MD, PhD; Rhode Island Hospital: Brian R. Ott, MD; Geoffrey Tremont, PhD; Lori A. Daiello, Pharm.D, ScM; Butler Hospital: Stephen Salloway, MD, MS; Paul Malloy, PhD; Stephen Correia, PhD; Athena Lee, PhD; UC San Francisco: Howard J. Rosen, MD; Bruce L. Miller, MD; David Perry, MD; Medical University South Carolina: Jacobo Mintzer, MD, MBA; Kenneth Spicer, MD, PhD; David Bachman, MD; St. Joseph’s Health Care: Elizabeth Finger, MD; Stephen Pasternak, MD; Irina Rachinsky, MD; John Rogers, MD; Andrew Kertesz, MD (past investigator); Dick Drost, MD (past investigator); Nathan Kline Institute: Nunzio Pomara, MD; Raymundo Hernando, MD; Antero Sarrael, MD; University of Iowa College of Medicine: Delwyn D. Miller, PharmD, MD; Karen Ekstam Smith, RN; Hristina Koleva, MD; Ki Won Nam, MD; Hyungsub Shim, MD; Susan K. Schultz, MD (past investigator); Cornell University Norman Relkin, MD, PhD; Gloria Chiang, MD; Michael Lin, MD; Lisa Ravdin, PhD; University of South Florida: USF Health Byrd Alzheimer’s Institute: Amanda Smith, MD; Christi Leach, MD; Balebail Ashok Raj, MD (past investigator); Kristin Fargher, MD (past investigator); Courtney Bodge, PhD; ADNI Part A: Leadership and Infrastructure Principal Investigator: Michael W. Weiner, MD, University of California, San Francisco; ATRI Principal Investigator and Director of Coordinating Center Clinical Core: Paul Aisen, MD, University of Southern California; Executive Committee: Michael Weiner, MD, University of California, San Francisco; Paul Aisen, MD, University of Southern California; Ronald Petersen, MD, PhD, Mayo Clinic, Rochester; Robert C. Green, MD, MPH, Brigham and Women’s Hospital/Harvard Medical School; Danielle Harvey, PhD, University of California, Davis; Clifford R. Jack, Jr, MD, Mayo Clinic, Rochester; William Jagust, MD, University of California, Berkeley; John C. Morris, MD, Washington University, St Louis; Andrew J. Saykin, PsyD, Indiana University; Leslie M. Shaw, PhD, Perelman School of Medicine, University of Pennsylvania; Arthur W. Toga, PhD, University of Southern California; John Q. Trojanowki, MD, PhD, Perelman School of Medicine, University of Pennsylvania; Psychological Evaluation/PTSD Core: Thomas Neylan, MD, University of California, San Francisco; Traumatic Brain Injury/TBI Core: Jordan Grafman, PhD, Rehabilitation Institute of Chicago, Feinberg School of Medicine, Northwestern University; Data and Publication Committee (DPC): Robert C. Green, MD, MPH, Brigham and Women’s Hospital/Harvard Medical School (chair); Resource Allocation Review Committee: Tom Montine, MD, PhD, University of Washington (chair); Clinical Core Leaders: Michael Weiner MD (core principal investigator); Ronald Petersen, MD, PhD, Mayo Clinic, Rochester (core principal investigator); Paul Aisen, MD, University of Southern California; Clinical Informatics and Operations: Gustavo Jimenez, MBS, University of Southern California; Michael Donohue, PhD, University of Southern California; Devon Gessert, BS, University of Southern California; Kelly Harless, BA, University of Southern California; Jennifer Salazar, MBS, University of Southern California; Yuliana Cabrera, BS, University of Southern California; Sarah Walter, MSc, University of Southern California; Lindsey Hergesheimen, BS, University of Southern California; San Francisco Veterans Affairs Medical Center: Thomas Neylan, MD, University of California, San Francisco; Jacqueline Hayes, University of California, San Francisco; Shannon Finley, University of California, San Francisco; Biostatistics Core Leaders and Key Personnel: Danielle Harvey, PhD, University of California, Davis (core principal investigator); Michael Donohue, PhD, University of California, San Diego; MRI Core Leaders and Key Personnel: Clifford R. Jack, Jr, MD, Mayo Clinic, Rochester (core principal investigator); Matthew Bernstein, PhD, Mayo Clinic, Rochester; Bret Borowski, RT, Mayo Clinic; Jeff Gunter, PhD, Mayo Clinic; Matt Senjem, MS, Mayo Clinic; Kejal Kantarci, Mayo Clinic; Chad Ward, Mayo Clinic; PET Core Leaders and Key Personnel: William Jagust, MD, University of California, Berkeley (core principal investigator); Robert A. Koeppe, PhD, University of Michigan; Norm Foster, MD, University of Utah; Eric M. Reiman, MD, Banner Alzheimer’s Institute; Kewei Chen, PhD, Banner Alzheimer’s Institute; Susan Landau, PhD, University of California, Berkeley; Neuropathology Core Leaders: John C. Morris, MD, Washington University, St Louis; Nigel J. Cairns, PhD, FRCPath, Washington University, St Louis; Erin Householder, MS, Washington University, St Louis; Biomarkers Core Leaders and Key Personnel: Leslie M. Shaw, PhD, Perelman School of Medicine, University of Pennsylvania; John Q. Trojanowki, MD, PhD, Perelman School of Medicine, University of Pennsylvania; Virginia Lee, PhD, MBA, Perelman School of Medicine, University of Pennsylvania; Magdalena Korecka, PhD, Perelman School of Medicine, University of Pennsylvania; Michal Figurski, PhD, Perelman School of Medicine, University of Pennsylvania; Informatics Core Leaders and Key Personnel: Arthur W. Toga, PhD, University of Southern California (core principal investigator); Karen Crawford, University of Southern California; Scott Neu, PhD, University of Southern California; Genetics Core Leaders and Key Personnel: Andrew J. Saykin, PsyD, Indiana University; Tatiana M. Foroud, PhD, Indiana University; Steven Potkin, MD, University of California, Irvine; Li Shen, PhD, Indiana University; Kelley Faber, MS, CCRC, Indiana University; Sungeun Kim, PhD, Indiana University; Kwangsik Nho, PhD, Indiana University; Initial Concept Planning & Development: Michael W. Weiner, MD, University of California, San Francisco; Karl Friedl, US Department of Defense (retired); Part B: Investigators by Site: University of Southern California: Lon S. Schneider, MD, MS; Sonia Pawluczyk, MD; Mauricio Becerra; University of California, San Diego: James Brewer, MD, PhD; Helen Vanderswag, RN; Columbia University Medical Center: Yaakov Stern, PhD; Lawrence S. Honig, MD, PhD; Karen L. Bell, MD; Rush University Medical Center: Debra Fleischman, PhD; Konstantinos Arfanakis, PhD; Raj C. Shah, MD; Wien Center: Ranjan Duara, MD (principal investigator); Daniel Varon, MD (co–principal investigator); Maria T. Greig (HP coordinator); Duke University Medical Center: P. Murali Doraiswamy, MBBS; Jeffrey R. Petrella, MD; Olga James, MD; University of Rochester Medical Center: Anton P. Porsteinsson, MD (director); Bonnie Goldstein, MS, NP (coordinator); Kimberly S. Martin, RN; University of California, Irvine: Steven G. Potkin, MD; Adrian Preda, MD; Dana Nguyen, PhD; Medical University South Carolina: Jacobo Mintzer, MD, MBA; Dino Massoglia, MD, PhD, Olga Brawman-Mintzer, MD; Premiere Research Inst (Palm Beach Neurology): Carl Sadowsky, MD; Walter Martinez, MD; Teresa Villena, MD; University of California, San Francisco: William Jagust MD; Susan Landau, PhD; Howard Rosen, MD; David Perry; Georgetown University Medical Center: Raymond Scott Turner, MD, PhD; Kelly Behan Brigid Reynolds, NP; Brigham and Women's Hospital: Reisa A. Sperling, MD; Keith A. Johnson, MD; Gad Marshall, MD; Banner Sun Health Research Institute: Marwan N. Sabbagh, MD; Sandra A. Jacobson, MD; Sherye A. Sirrel, MS, CCRC; Howard University: Thomas O. Obisesan, MD, MPH; Saba Wolday, MSc; Joanne Allard, PhD; University of Wisconsin: Sterling C. Johnson, Ph.D. J. Jay Fruehling, MA; Sandra Harding, MS; University of Washington: Elaine R. Peskind, MD; Eric C. Petrie, MD, MS; Gail Li, MD, PhD; Stanford University: Jerome A. Yesavage, MD; Joy L. Taylor, PhD; Ansgar J. Furst, PhD; Steven Chao, MD; Cornell University: Norman Relkin, MD, PhD, Gloria Chiang, MD; Lisa Ravdin, PhD; ADNI Depression Part A: Leadership and Infrastructure Principal Investigator: Scott Mackin, PhD, University of California, San Francisco; ATRI PI and Director of Coordinating Center Clinical Core: Paul Aisen, MD, University of Southern California; Rema Raman, PhD, University of Southern California; Executive Committee: Scott Mackin, PhD, University of California, San Francisco; Michael Weiner, MD, University of California, San Francisco; Paul Aisen, MD, University of Southern California; Rema Raman, PhD, University of Southern California; Clifford R. Jack, Jr, MD, Mayo Clinic, Rochester; Susan Landau, PhD, University of California, Berkeley; Andrew J. Saykin, PsyD, Indiana University; Arthur W. Toga, PhD, University of Southern California; Charles DeCarli, MD, University of California, Davis; Robert A. Koeppe, PhD, University of Michigan; Data and Publication Committee (DPC): Robert C. Green, MD, MPH, Brigham and Women’s Hospital/Harvard Medical School (chair); Erin Drake, MA, Brigham and Women’s Hospital/Harvard Medical School (director); Clinical Core Leaders: Michael Weiner, MD (core principal investigator); Paul Aisen, MD, University of Southern California; Rema Raman, PhD, University of Southern California; Mike Donohue, PhD, University of Southern California; Clinical Informatics, Operations and Regulatory Affairs: Gustavo Jimenez, MBS, University of Southern California; Devon Gessert, BS, University of Southern California; Kelly Harless, BA, University of Southern California; Jennifer Salazar, MBS, University of Southern California; Yuliana Cabrera, BS, University of Southern California; Sarah Walter, MSc, University of Southern California; Lindsey Hergesheimer, BS, University of Southern California; Elizabeth Shaffer, BS; Psychiatry Site Leaders and Key Personnel: Scott Mackin, PhD, University of California, San Francisco; Craig Nelson, MD, University of California, San Francisco; David Bickford, BA, University of California, San Francisco; Meryl Butters, PhD, University of Pittsburgh; Michelle Zmuda, MA, University of Pittsburgh; MRI Core Leaders and Key Personnel: Clifford R. Jack, Jr, MD, Mayo Clinic, Rochester (core principal investigator); Matthew Bernstein, PhD, Mayo Clinic, Rochester; Bret Borowski, RT, Mayo Clinic, Rochester; Jeff Gunter, PhD, Mayo Clinic, Rochester; Matt Senjem, MS, Mayo Clinic, Rochester; Kejal Kantarci, MD, Mayo Clinic, Rochester; Chad Ward, BA, Mayo Clinic, Rochester; Denise Reyes, BS, Mayo Clinic, Rochester; PET Core Leaders and Key Personnel: Robert A. Koeppe, PhD, University of Michigan Susan Landau, PhD, University of California, Berkeley; Informatics Core Leaders and Key Personnel: Arthur W. Toga, PhD, University of Southern California (core principal investigator); Karen Crawford, University of Southern California; S Neu, PhD, University of Southern California; Genetics Core Leaders and Key Personnel: Andrew J. Saykin, PsyD, Indiana University; Tatiana M. Foroud, PhD, Indiana University; Kelley M. Faber, MS, CCRC, Indiana University; Kwangsik Nho, PhD, Indiana University; Kelly N. Nudelman, Indiana University; Part B: Investigators by Site: University of California, San Francisco: Scott Mackin, PhD; Howard Rosen, MD; Craig Nelson, MD; David Bickford, BA; Yiu Ho Au, BA; Kelly Scherer, BS; Daniel Catalinotto, BA; Samuel Stark, BA; Elise Ong, BA; Dariella Fernandez, BA; University of Pittsburgh: Meryl Butters, PhD; Michelle Zmuda, MA; Oscar L. Lopez, MD; MaryAnn Oakley, MA; Donna M. Simpson, CRNP, MPH.

Additional Information: Data used in preparation of this article were obtained from the ADNI database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. The ADNI Imaging Core contributed to the image preprocessing, the members of the ADNI Biomarker Core performed the cerebrospinal fluid biomarker analyses, and the investigators at the University of Pittsburgh performed the Pittsburgh compound B standard uptake value ratio analyses.

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