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Alzheimer’s Disease Research

What Has Guided Research So Far and Why It Is High Time for a Paradigm Shift

• © 2023
• Christian Behl 0

Institute of Pathobiochemistry, University Medical Center of the Johannes Gutenberg University, Mainz, Germany

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• Aims to answer why—after more than 100 years of Alzheimer's research—there is still no convincing therapy available
• Informs on leading perspectives and key developments of Alzheimer's research from its beginnings up until today
• Promotes a paradigm shift in Alzheimer's Disease research and a greater openness towards new disease hypotheses

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About this book

This book highlights the key phases and central findings of Alzheimer’s Disease research since the introduction of the label ‘Alzheimer’s Disease’ in 1910. The author, Christian Behl, puts dementia research in the context of the respective zeitgeist and summarizes the paths that have led to the currently available Alzheimer’s drugs. As the reader is taken through the major developments in Alzheimer's Disease research, particularly over the past thirty years, Behl poses critical questions: Why are the exact causes of Alzheimer's Disease still in the dark, despite all the immense, worldwide research efforts in academia as well as in the pharmaceutical industry? Why has the majority of an entire research field kept focusing on a single hypothesis that establishes the deposition of the amyloid beta peptide in the brain as the key trigger of Alzheimer's pathology, even though this concept has still not been convincingly proven in the clinics? Are there other hypotheses that might explainthe pathogenesis of this complex brain disease, and if so, why were these perspectives not adequately followed?

In this book, Behl tries to answer these questions. Starting with the historical background, the author illustrates the long and arduous research journey, its numerous setbacks, and the many alternative explanations for the disease, which have started gaining increasing attention and acceptance in the Alzheimer’s research community only more recently. 

With his deep dive into the history and progression of this research, including the most recent developments, Behl explains why he believes that it is high time to promote a paradigm shift in Alzheimer’s Disease research.

• Alzheimer Clinics
• Alzheimer Therapy
• Alzheimer's Disease
• Amyloid Plaques
• Amyloid-Cascade-Hypothesis
• Agenda Setting
• Aternative Hypotheses
• Risk Factors

Table of contents (21 chapters)

Front matter, introduction.

Christian Behl

The Psychiatrist and Pathologist Aloysius Alzheimer and His Seminal Findings

Alzheimer’s disease research after 1945: the recommencement, alzheimer’s research goes deeper: ultrastructural electron microscopy studies, focus on neurochemistry led to the cholinergic hypothesis of alzheimer’s disease, the glutamatergic hypothesis of alzheimer’s disease, biochemistry and genetics point out a prime suspect for causing alzheimer’s disease, getting to the bottom of it: amyloid beta peptide is derived from a larger precursor, step by step toward an amyloid beta peptide-based hypothesis of alzheimer’s disease, concerns about the amyloid cascade hypothesis and reappraisals, ignorance or conspiracy or just an amyloid firewall that blocks alternative ideas, in the slip stream of amyloid: the tau and tangle hypothesis, focus on tauopathies and beyond, alzheimer’s research gains momentum and spreads out, the amyloid cascade hypothesis has to deliver, finally, beyond app , psen1 , psen2 , and apoe : what else does the genome tell us, alternative hypotheses and observations that were somehow lost on the way, is the persistence of the amyloid cascade hypothesis a result of constant confirmation bias, driving forces of alzheimer’s research directions.

“The depth of facts and explanatory figures will enlighten experts in Alzheimer’s disease (AD) research, students, and journalists. We know of no other work that reaches the scope of scholarship of this volume.” (George Perry and Rudolph Castellani, Journal of Alzheimer's Disease, June 20, 2024)

“This book ... represents a tour de force of almost 700 pages written by a leading contributor to the field over the last three decades, Christian Behl. ... The text is extremely thoroughly referenced, and the author describes his literature research process in detail.” (Anthony J. Turner, Journal of Neurochemistry, October 14, 2023)

Authors and Affiliations

About the author.

Christian Behl is Professor of Pathobiochemistry and Director of the Institute of Pathobiochemistry at the University Medical Center of the Johannes Gutenberg University Mainz, Germany. He has been closely following Alzheimer’s Disease research since the early 1990’s, when he first got involved into the field himself during his time at the Salk Institute for Biological Studies, La Jolla, USA. He stayed active in the field all through his research station at the Max Planck Institute of Psychiatry, Munich, Germany, and later in Mainz. There his current research (in Mainz) focuses on the cellular degradation mechanism autophagy in the context of neurodegeneration and aging. For quite some time Behl has been an active advocate for widening the focus of Alzheimer’s Disease research to improve the understanding of this complex, age-related brain disorder. Behl is member of several scientific boards, including the German Alzheimer Foundation.

Bibliographic Information

Book Title : Alzheimer’s Disease Research

Book Subtitle : What Has Guided Research So Far and Why It Is High Time for a Paradigm Shift

Authors : Christian Behl

DOI : https://doi.org/10.1007/978-3-031-31570-1

Publisher : Springer Cham

eBook Packages : Biomedical and Life Sciences , Biomedical and Life Sciences (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

Hardcover ISBN : 978-3-031-31569-5 Published: 14 July 2023

Softcover ISBN : 978-3-031-31572-5 Published: 15 July 2024

eBook ISBN : 978-3-031-31570-1 Published: 13 July 2023

Edition Number : 1

Number of Pages : XXV, 652

Number of Illustrations : 9 b/w illustrations, 107 illustrations in colour

Topics : Neurosciences , Neurology , Physiology , Cognitive Psychology , Neurosciences , Neurosciences

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• Published: 04 April 2022

New insights into the genetic etiology of Alzheimer’s disease and related dementias

• Céline Bellenguez   ORCID: orcid.org/0000-0002-1240-7874 1   na1 ,
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• Jonathan Vogelgsang   ORCID: orcid.org/0000-0001-9326-8193 242 , 243 ,
• Martin Vyhnalek 179 , 180 ,
• Michael Wagner   ORCID: orcid.org/0000-0003-2589-6440 7 , 9 ,
• David Wallon   ORCID: orcid.org/0000-0002-2634-7198 244 ,
• Li-San Wang 15 ,
• Ruiqi Wang 20 , 21 ,
• Leonie Weinhold 230 ,
• Jens Wiltfang   ORCID: orcid.org/0000-0003-1492-5330 242 , 245 , 246 ,
• Gill Windle 184 ,
• Bob Woods   ORCID: orcid.org/0000-0002-6781-651X 184 ,
• Mary Yannakoulia   ORCID: orcid.org/0000-0003-2171-7337 247 ,
• Habil Zare   ORCID: orcid.org/0000-0001-5902-6238 130 ,
• Yi Zhao 15 ,
• Xiaoling Zhang   ORCID: orcid.org/0000-0001-8237-1857 248 ,
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• Lindsay A. Farrer   ORCID: orcid.org/0000-0001-5533-4225 20 , 87 , 109 ,
• Bruce M. Psaty   ORCID: orcid.org/0000-0002-7278-2190 22 , 84 , 250 ,
• Mohsen Ghanbari   ORCID: orcid.org/0000-0002-9476-7143 12 ,
• Towfique Raj   ORCID: orcid.org/0000-0002-9355-5704 103 , 104 , 105 , 160 ,
• Perminder Sachdev   ORCID: orcid.org/0000-0002-9595-3220 34 ,
• Karen Mather   ORCID: orcid.org/0000-0003-4143-8941 34 ,
• Frank Jessen 7 , 9 ,
• M. Arfan Ikram   ORCID: orcid.org/0000-0003-0372-8585 12 ,
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• Magda Tsolaki   ORCID: orcid.org/0000-0002-2072-8010 35 , 36 ,
• Margaret A. Pericak-Vance   ORCID: orcid.org/0000-0001-7283-8804 152 ,
• Philippe Amouyel   ORCID: orcid.org/0000-0001-9088-234X 1 ,
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• Ruth Frikke-Schmidt   ORCID: orcid.org/0000-0003-4084-5027 198 , 211 ,
• Jordi Clarimon 11 , 29 ,
• Jean-François Deleuze 17 ,
• Giacomina Rossi 120 ,
• Sudha Seshadri   ORCID: orcid.org/0000-0001-6135-2622 21 , 109 , 130 ,
• Ole A. Andreassen   ORCID: orcid.org/0000-0002-4461-3568 46 ,
• Martin Ingelsson 25 ,
• Mikko Hiltunen 19   na2 ,
• Kristel Sleegers   ORCID: orcid.org/0000-0002-0283-2332 2 , 3 , 4   na2 ,
• Gerard D. Schellenberg 15   na2 ,
• Cornelia M. van Duijn   ORCID: orcid.org/0000-0002-2374-9204 12 , 13   na2 ,
• Rebecca Sims   ORCID: orcid.org/0000-0002-3885-1199 16   na2 ,
• Wiesje M. van der Flier   ORCID: orcid.org/0000-0001-8766-6224 5   na2 ,
• Agustín Ruiz 10 , 11   na2 ,
• Alfredo Ramirez   ORCID: orcid.org/0000-0003-4991-763X 7 , 8 , 9 , 130 , 251   na2 &
• Jean-Charles Lambert   ORCID: orcid.org/0000-0003-0829-7817 1   na2  

Nature Genetics volume  54 ,  pages 412–436 ( 2022 ) Cite this article

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• Alzheimer's disease
• Genome-wide association studies

Characterization of the genetic landscape of Alzheimer’s disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/‘proxy’ AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele.

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AD is the most common form of dementia. The heritability is high, estimated to be between 60% and 80% 1 . This strong genetic component provides an opportunity to determine the pathophysiological processes in AD and to identify new biological features, new prognostic/diagnostic markers and new therapeutic targets through translational genomics. Characterizing the genetic risk factors in AD is therefore a major objective; with the advent of high-throughput genomic techniques, a large number of putative AD-associated loci/genes have been reported 2 . However, much of the underlying heritability remains unexplained. Hence, increasing the sample size of genome-wide association studies (GWASs) is an obvious solution that has already been used to characterize new genetic risk factors in other common, complex diseases (e.g., diabetes).

GWAS meta-analysis

The European Alzheimer & Dementia Biobank (EADB) consortium brings together the various European GWAS consortia already working on AD. A new dataset of 20,464 clinically diagnosed AD cases and 22,244 controls has been collated from 15 European countries. The EADB GWAS results were meta-analyzed with a proxy-AD GWASs of the UK Biobank (UKBB) dataset. The UKBB’s proxy-AD designation is based on questionnaire data in which individuals are asked whether their parents had dementia. This method has been used successfully in the past 3 but is less specific than a clinical or pathological diagnosis of AD; hence, we will refer to these cases as proxy AD and related dementia (proxy-ADD). EADB stage I (GWAS meta-analysis) was based on 39,106 clinically diagnosed AD cases, 46,828 proxy-ADD cases (as defined in the Supplementary Note ), 401,577 controls (Supplementary Tables 1 and 2 ) and 21,101,114 variants that passed our quality control (Fig. 1 ; see Supplementary Fig. 1 for the quantile–quantile plot and genomic inflation factors). We selected all variants with a P value below 1 × 10 −5 in stage I. We defined nonoverlapping regions around these variants, excluded the region corresponding to APOE and examined the remaining variants in a large follow-up sample that included AD cases and controls from the ADGC, FinnGen and CHARGE consortia (stage II; 25,392 AD cases and 276,086 controls). A signal was considered as significant on the genome-wide level if it (1) was nominally associated ( P  ≤ 0.05) in stage II, (2) had the same direction of association in the stage I and II analyses and (3) was associated with the ADD risk with P  ≤ 5 × 10 −8 in the stage I and stage II meta-analysis. Furthermore, we applied a PLINK clumping procedure 4 to define potential independent hits within the stage I results ( Methods ). After validation by conditional analyses ( Supplementary Note and Supplementary Tables 3 and 4 ), this approach enabled us to define 39 signals in 33 loci already known to be associated with the risk of developing ADD 3 , 5 , 6 , 7 , 8 , 9 , 10 and identify 42 loci defined as new at the time of analysis (Tables 1 and 2 , Supplementary Table 5 and Supplementary Figs. 2 – 29 ). Of the 42 new loci, 17 had P  ≤ 5 × 10 −8 in stage I and 25 were associated with P  ≤ 5 × 10 −8 after follow-up (stage I and stage II meta-analysis, including the ADGC, CHARGE and FinnGen data). We also identified 6 loci with P  ≤ 5 × 10 −8 in the stage I and stage II analysis but with P  > 0.05 in stage II (Supplementary Table 6 ). It is noteworthy that the magnitude of the associations in stage I did not change substantially if we restricted the analysis to clinically diagnosed AD cases (Supplementary Table 7 and Supplementary Fig. 30 ). Similarly, none of the signals observed appeared to be especially driven by the UKBB data (Supplementary Table 7 and Supplementary Figs. 2 – 29 ). Nine of these loci ( APP , CCDC6 , GRN , LILRB2 , NCK2 , TNIP1 , TMEM106B , TSPAN14 and SHARPIN ) were recently reported in three articles using part of the GWAS data included in our study 11 , 12 , 13 . We also generated a detailed analysis of the human leukocyte antigen ( HLA ) locus on the basis of the clinically diagnosed AD cases (Supplementary Tables 8 and 9 , Supplementary Figs. 31 and 32 and Supplementary Note ).

P values are two-sided raw P values derived from a fixed-effect meta-analysis. Variants with a P value below 1 × 10 −36 are not shown. Loci with a genome-wide significant signal are annotated (known loci in black and new loci in red). Variants in new loci are highlighted in red. The red dotted line represents the genome-wide significance level ( P  = 5 × 10 −8 ), and the black dotted line represents the suggestive significance level ( P  = 1 × 10 −5 ).

Genetic overlap with other neurodegenerative diseases

We tested the association of the lead variants within our new loci with the risk of developing other neurodegenerative diseases or AD-related disorders (Supplementary Fig. 33 and Supplementary Tables 10 – 12 ). We also performed more precise colocalization analyses (using Coloc R package, https://cran.r-project.org/web/packages/coloc/index.html ) for five loci known to be associated with Parkinson’s disease ( IDUA and CTSB ), types of frontotemporal dementia ( TMEM106B and GRN ) and amyotrophic lateral sclerosis ( TNIP1 ) (Supplementary Tables 13 and 14 ). The IDUA signal for Parkinson’s disease was independent of the signal in ADD (coloc posterior probability (PP)3 = 99.9%), but we were not able to determine whether the CTSB signals colocalized. The TMEM106B and GRN signals in frontotemporal lobar degeneration with TAR DNA-binding protein (TDP-43) inclusions (frontotemporal lobar degeneration TDP) probably share causal variants with ADD (coloc PP4 = 99.8% and coloc PP4 = 80.1%, respectively). Lastly, we were not able to determine whether the TNIP1 signals colocalized for ADD and amyotrophic lateral sclerosis.

Pathway analyses

Next, we sought to perform a pathway enrichment analysis on the stage I association results to gain better biological understanding of this newly expanded genetic landscape for ADD. Ninety-three gene sets were still statistically significant after correction for multiple testing ( q  ≤ 0.05; Methods and Supplementary Table 15 ). As described previously, the most significant gene sets are related to amyloid and tau 5 ; other significant gene sets are related to lipids, endocytosis and immunity (including macrophage and microglial cell activation). When restricting this analysis to the meta-analysis based on the clinically diagnosed AD cases, 54 gene sets were significant ( q  ≤ 0.05). Of these 54 gene sets, 33 reached q  ≤ 0.05 in the stage I analysis and all reached P  ≤ 0.05. This indicates that the inclusion of proxy-ADD cases does not cause disease-relevant biological information to be missed and underlines the additional power of this type of analysis.

We next performed a single-cell expression enrichment analysis by using the average gene expression per nucleus (Av. Exp.) data in the human Allen Brain Atlas (49,495 nuclei from 8 human brains). Only the microglial expression reached a high level of significance ( P  = 1.7 × 10 −8 ; Supplementary Table 16 ); greater expression corresponded to a more significant association with ADD. After adjusting for microglial Av. Exp., the remaining associations became nonsignificant; this indicates that microglial Av. Exp. drives all the other cell-type associations. These results were observed whatever the brain region studied (Supplementary Table 16 ). A similar result was observed using a mouse single-cell dataset 14 (Supplementary Table 17 and Supplementary Note ).

Lastly, we looked at whether the relationship between an elevated microglia Av. Exp. and a genetic association with the ADD risk was specific to particular biological processes (Supplementary Table 18 ) by analyzing the interaction between microglia Av. Exp. and pathway membership in MAGMA 15 . Of the five most significant interaction signals ( q  ≤ 10 −3 ), two were directly associated with endocytosis processes ( GO:0006898 and GO:0031623 ); this suggested a functional relationship between microglia and endocytosis, which is known to be involved in phagocytosis (Supplementary Table 18 ). It is noteworthy that we also detected an interaction between GO:1902991 (regulation of amyloid precursor protein (APP) catabolic process) and the gene expression level in microglia ( q  = 1.4 × 10 −3 ; Supplementary Table 18 ). Even though these data suggest a functional relationship between microglia and APP/amyloid beta (Aβ) peptide pathways, this observation reinforces the likely involvement of microglial endocytosis in AD, a mechanism that is also strongly involved in APP metabolism 16 . Of note, there are overall similarities in the interaction effects of human and mouse microglia expression with genes in biological pathways of relevance to the AD genetic risk (Supplementary Table 18 and Supplementary Note ).

Gene prioritization

We next attempted to identify the genes most likely to be responsible for the association signal with ADD at each new locus. To this end, we studied the downstream effects of ADD-associated variants on molecular phenotypes (i.e., expression, splicing, protein expression, methylation and histone acetylation) in various cis -quantitative trait locus ( cis -QTL) catalogues from AD-relevant tissues, cell types and brain regions. We investigated the genetic colocalization between association signals for the ADD risk and those for the molecular phenotypes and the association between the ADD risk and these phenotypes by integrating cis -QTL information into our ADD GWAS. Moreover, we considered the lead variant annotation (the allele frequency, protein-altering effects and nearest protein-coding gene) and a genome-wide, high-content short interfering RNA screen for APP metabolism 17 . Based on this evidence, we developed a systematic gene prioritization strategy that yielded a total weighted score of between 0 and 100 for each gene (Supplementary Fig. 34 and Supplementary Note ). This score was used to compare and prioritize genes in the new loci within 1 Mb upstream and 1 Mb downstream of the lead variants. Genes either were ranked as tier 1 (greater likelihood of being the causal risk gene responsible for the ADD signal) or tier 2 (lower likelihood and the absence of a minimum level of evidence as a causal risk gene) or were not ranked.

From all newly identified loci, this gene prioritization yielded 31 tier 1 genes and 24 tier 2. The 55 prioritized genes, the details of the analyses and the supporting evidence are summarized in Fig. 2a and the Supplementary Note (Supplementary Tables 19 – 30 and Supplementary Figs. 35 – 45 ). Among the 31 tier 1 genes, we observed that 25 of these genes were the only prioritized gene in their respective locus. For the remaining 6 tier 1 genes, we also found tier 2 genes in their respective locus. We also identified five loci containing several tier 2 prioritized genes. In one of these loci, locus 39 (L39), the tier 2 prioritized gene LILRB2 had strong additional support from published literature ( Supplementary Note ). In five loci, our prioritization score did not identify sufficient molecular evidence to prioritize genes with exception of being the nearest gene (L10, L12, L13, L14 and L32). Finally, we excluded the complex IGH cluster (L27) from gene prioritization analyses due to genomic complexity of the telomeric locus as a consequence of known fusion events 18 .

a , Summary of weighted scores for each evidence category for the prioritized genes in the 42 new genome-wide-significant loci. Using our gene prioritization method, we considered the genes within 1 Mb of each new lead variant and prioritized a total of 55 genes in 42 new loci at two different confidence levels (31 tier 1 genes and 24 tier 2 genes). The leftmost squares indicate the new locus index number. The different types of evidence are colored according to the seven different domains to which they belonged. Weighted scores for each evidence category are rescaled to a 0–100 scale, and the proportions of mean human brain cell-type-specific expression for each gene are also rescaled to a 0–100 scale; darker colors represent higher scores or higher expression proportions. Tier 1 genes are shown in dark green, and tier 2 genes are shown in light green. Only tier 1 and tier 2 genes are shown for each locus. Supplementary Fig. 35 shows full results. MAFs and CADD (v1.6) PHRED scores for rare and/or protein-altering rare variants are labeled in white within the respective squares. b , Pathway enrichment analysis based on the tier 1 gene list. Only the ten strongest associations (according to STRING software) are presented here. coloc, colocalization; eQTL, expression QTL; eTWAS, expression transcriptome-wide association study; GO, Gene Ontology; haQTL, histone acetylation QTL; Mon. Mac., monocytes and macrophages; sTWAS, splicing transcriptome-wide association study; m/haQTL, methylation/histone acetylation QTL; sQTL, splicing QTL; FDR, false discovery rate.

We highlight two examples, L18 and L23. In L18, the lead variant, rs76928645 (MAF = 10%), is intergenic and is located more than 100 kb downstream or upstream of the two nearest protein-coding genes ( SEC61G and EGFR , respectively). Our gene prioritization analyses suggested that EGFR was the only risk gene (Fig. 3 ). We found that both the lead variant ( rs76928645 ) and the other nearby variants in linkage disequilibrium (LD) are significant expression QTLs (eQTLs) for regulating EGFR expression downstream. The eQTL signals in brain strongly colocalized with the GWAS signal (with eQTL coloc PP4s of 98.3% in the temporal cortex (TCX) and 99.5% in the dorsolateral prefrontal cortex (DLPFC)). Accordingly, the fine-mapped expression transcriptome-wide association study (eTWAS) associations (Fine-mapping Of CaUsal gene Sets (FOCUS) posterior inclusion probability (PIP) = 1; eTWAS P  = 6.9 × 10 −9 , eTWAS Z  = + 5.8 in the TCX; eTWAS P  = 3.1 × 10 −11 , eTWAS Z  = + 6.6 in the DLPFC) indicated that genetic downregulation of EGFR expression is associated with a lower ADD risk (Fig. 3 ; Supplementary Tables 22 , 24 and 26 ; and Supplementary Figs. 36a , 39 and 41 ).

a , The regional plot of the new SEC61G locus (L18) shows the EADB GWAS stage I ( n  = 487,511) ADD association signal within 200 kb of the intergenic lead variant, rs76928645 (the two closest protein-coding genes, SEC61G and EGFR , are more than 100 kb from the lead variant), together with the eQTLs in the same region identified for SEC61G and EGFR expression separately in the TCX (MayoRNAseq TCX eQTL catalog based on n  = 259 individuals). The rs7692864 lead variant is shown in purple, and LD r 2 values (calculated for the EADB Trans-Omics for Precision Medicine (TOPMed) dataset ( n  = 42,140) with respect to the lead variant) are indicated on a color scale. y axis, −log 10 for the GWAS or eQTL P value; x axis, hg38 genomic position on chromosome 7. b , Colocalization between the EGFR eQTL signal (MayoRNAseq TCX, n  = 259 individuals) and the EADB GWAS stage I ( n  = 487,511) signal (eQTL coloc PP4 = 98.3%); with the significant eTWAS association (eTWAS P  = 6.9 × 10 −9 and eTWAS Z  = 5.8) and fine-mapped (FOCUS PIP = 1) eTWAS association in the same catalog. y axis, eQTL −log 10 ( P ) value; x axis, GWAS −log 10 ( P ) value. LD r 2 values and color scales are as in a . c , The eQTL violin plot shows a significant association (eQTL P  = 3 × 10 −18 ) between the rs76928645 lead variant genotype and EGFR expression in the TCX (MayoRNAseq TCX, n  = 259 individuals), where the protective allele T is associated with lower EGFR expression (eQTL β, −0.39). Each data point represents a sample whose normalized EGFR expression value is indicated on the y axis, and the rs76928645 genotype information is indicated on the x axis. The lower and upper hinges of the boxes represent the first and third quantiles, the whiskers extend 1.5 times the interquartile range from the hinges and the line represents the median.

In L23, we observed numerous eQTL-GWAS and methylation QTL (mQTL)-GWAS hits for TSPAN14 that support the hypothesis that increased brain expression of TSPAN14 is associated with increased ADD risk. We also identified several splice junctions in TSPAN14 whose genetic regulation signals in lymphoblastoid cell lines (LCLs) and brain colocalized with the ADD association signal. These splice junctions were also associated with ADD risk (Fig. 4 , Supplementary Tables 22 – 28 and Supplementary Figs. 36 – 42 and 44c ). As three of these splice junctions were related to new complex cryptic splicing events that were predicted to result in two cryptic exons not previously described in known TSPAN14 transcripts (based on GENCODE v38), we designed a long-read single-molecule (Nanopore) sequencing experiment ( Supplementary Note ) to validate these cryptic exons on a total of 93 complementary DNA (cDNA) samples derived from LCLs, frontal cortex and hippocampus and consequently validated those cryptic exons (Fig. 4 ). All three of the validated cryptic splicing events occur within the ADAM10-interacting domain of TSPAN14. Cryptic exon 1 is at least 45 bp long, and cryptic exon 2 is 118 bp long.

a , Splicing QTL (sQTL)-GWAS integration results. Known TSPAN14 transcripts (GENCODE v38; green, coding sequences; gray, noncoding) plotted with −log 10 ( P ) for (1) EADB GWAS stage I ( n  = 487,511) signal (black), (2) sQTL signal for chr10:80509471–80510106 junction (supporting cryptic exon 1) in the EADB Belgian LCL sQTL catalog ( n  = 70 individuals, blue) and (3) sQTL signal for chr10:80512269–80512719 junction in the MayoRNAseq TCX sQTL catalog ( n  = 259 individuals, red); hg38 genomic position is shown above. LCL and brain-based sQTL coloc and sTWAS analyses associate ADD risk with these junctions that suggest cryptic splicing within ADAM10-interacting domain of TSPAN14 (magenta), which was predicted to result in two cryptic exons. b , Long-read sequencing validation of TSPAN14 cryptic exons. Nanopore sequencing results ( Supplementary Note ) in the zoomed-in region of chr10:80506973–80516400 (cumulative coverage in log 10 scale). Pooled LCL cDNA sample sequenced for cDNA Amplicon2 shown in blue. cDNA Amplicon1 was sequenced on biologically independent hippocampal (HPC; n  = 16, red), frontal cortex (FC; n  = 18, pink) and LCL ( n  = 59, orange) cDNA samples. Green, canonical exons (8–12); dotted black lines, canonical splicing; blue, cryptic exon 1 (>45 bp); red, cryptic exon 2 (118 bp). All annotated junctions use canonical splice donor (GT) and acceptor (AG) sites. c , d , sQTL-GWAS colocalization plots for chr10:80509471–80510106 (supporting cryptic exon 1) in the EADB Belgian LCL sQTL catalog ( n  = 70 individuals) ( c ) and chr10:80512269–80512719 (supporting cryptic exon 2) in the MayoRNAseq TCX sQTL catalog ( n  = 259 individuals) ( d ). sQTL signals for the two junctions colocalize with ADD signal (PP4s of 98.8% and 97.4%, respectively), and sTWAS associates with increased preference for the cryptic splicing with decreased ADD risk (sTWAS P  = 6.28 × 10 −12 and 1.6 × 10 −13 , sTWAS Z  = −6.9 and −7.4, respectively). y axis, sQTL −log 10 ( P ); x axis, EADB GWAS stage I −log 10 ( P ). LD r 2 values calculated within EADB-TOPMed dataset ( n  = 42,140) based on the lead variant rs6586028 (purple) are indicated on a color scale.

Lastly, we used STRING v11 (ref. 19 ) to analyze protein–protein interaction for (1) previously known AD genes from GWASs, (2) our prioritized new genes (tier 1 in Fig. 2a and Supplementary Table 20 ) and (3) a combination of the two ( Supplementary Note ). The largest networks contained 14, 8 and 30 proteins, respectively (Supplementary Fig. 46 ). These networks were larger than would be expected by chance (respectively, P  < 2 × 10 −5 , P  = 2.8 × 10 −3 and P  < 2 × 10 −5 based on comparison with 50,000 randomly simulated protein lists matched for the number of proteins and the total number of interactions for each protein). Notably, the number of interactions between our prioritized genes and previously known genes is also significantly greater than would be expected ( P  < 1 × 10 −4 ), indicating that the newly prioritized genes are biologically relevant in AD. No such enrichment ( P  = 0.88) was observed for the remaining genes in the new loci, again highlighting the value of our prioritization approach.

We next performed a pathway enrichment analysis of the tier 1 genes using STRING. We found that several gene sets linked to the immune system remained statistically significant after correction for multiple testing (Fig. 2b and Supplementary Table 31 ), especially regulation of the tumor necrosis factor (TNF)-mediated signaling pathway ( GO:0010803 ). We report the potential genetic implication of the linear ubiquitin chain assembly complex (LUBAC), which is a major regulator of the aforementioned signaling pathway 20 . Two of the LUBAC’s three complements are encoded by the new tier 1 prioritized genes SHARPIN and RBCK1 , and the complex’s function is directly regulated by OTULIN (also a new tier 1 prioritized gene).

We next looked at whether the genetic ADD burden (as measured by a genetic risk score (GRS)) generated from our genome-wide significant variants ( n  = 83, excluding APOE ; Supplementary Table 32 ) might influence the rate of conversion to AD in (1) individuals from several prospective, population-based cohorts and (2) patients with mild cognitive impairment (MCI) in prospective memory clinic studies (Supplementary Table 33 ). We used Cox regression models to assess the association after adjustment for age at baseline, sex, the number of APOE-ε4 and APOE-ε2 alleles, and genetic principal components (PCs).

In population-based cohorts with clinically diagnosed AD cases, the GRS was significantly associated with conversion to AD; this was shown in a fixed-effect meta-analysis (hazard ratio (HR) (95%CI) per average risk allele = 1.076 (1.064–1.088), P  = 9.2 × 10 −40 ; Fig. 5 and Supplementary Table 34 ). Likewise, the GRS was significantly associated with AD conversion in patients with MCI (HR = 1.056 (1.040–1.072), P  = 2.8 × 10 −12 ; Fig. 5 and Supplementary Table 35 ). Furthermore, we found that the GRS association increased significantly when the new variants discovered in the present study were added to the previously described variants (Supplementary Table 36 ) for both population-based studies (HR = 1.052 (1.037–1.068), P  = 1.5 × 10 −11 ) and MCI cohorts (HR = 1.034 (1.013–1.055), P  = 1.4 × 10 −3 ).

a , b , Meta-analysis results of the association between the GRS and the risk of progression to AD in population-based cohorts ( n  = 17,545 independent samples) ( a ) and MCI cohorts ( n  = 4,114 independent samples) ( b ). Data are presented as HR together with 95% CIs derived from Cox regression analyses for each individual cohort. HRs indicate the effect of the GRS as the increment in the AD risk associated with each additional average risk allele in the GRS. Null hypothesis testing is based on a meta-analysis of individual cohort effects using fixed effects (FE) and random effects (RE) models. Resulting HRs and 95% CIs and the respective Z test and associated two-sided P value are shown at the bottom of the figure. Heterogeneity between cohorts is indicated by the I2 index together with the respective Cochran’s Q statistic (distributed as χ² statistic), associated degrees of freedom and P value. 3C, Three-City Study; AgeCoDe, German study on aging cognition and dementia; AMC, additional, independent memory clinic cohort from Fundacio ACE; DCN, German Dementia Competence Network study; FACE, Fundacio ACE memory clinic cohort; FHS, Framingham Heart Study; HAN, BALTAZAR multicenter prospective memory clinic study; MAS, Sydney Memory and Ageing Study; RS1, Rotterdam Study first cohort; RS2, Rotterdam Study second cohort; VITA, Vienna Transdanube Aging study; UAN, memory clinic cohort from the Hospital Network Antwerp; UHA, University of Halle memory clinic cohort; ZIM, Heidelberg/Mannheim memory clinic sample.

Importantly, the results of our meta-analysis suggest that the risk of conversion to AD rises with the number of risk alleles from non-APOE risk variants in the GRS by 1.9-fold in population-based cohorts (HR = 1.93 (1.75–2.13); Fig. 5 ) and 1.6-fold in MCI cohorts (HR = 1.63 (1.42–1.87); Fig. 6 ) on top of effects of age and the APOE ε4 allele. These observations result from the comparison of hypothetical individuals with a GRS value at the first decile of the distribution versus those with a GRS value at the ninth decile (Fig. 6 ). With regard to APOE , carrying an additional APOE-ε4 allele was associated with a slightly higher increase in the AD risk in population-based cohorts (HR = 2.19 (2.03–2.37)) and MCI cohorts (HR = 1.90 (1.73–2.07)). There was no interaction between the GRS and the number of APOE -ε4 alleles (Supplementary Table 37 ).

a , b , Representative plots of the progression to AD over 10 years in the population-based 3C study ( a ) and the progression from MCI to AD over 5 years in the Fundació ACE cohort ( b ). The figures show the probabilities of conversion (survival probabilities) to AD ( y axes) for a hypothetical participant with average covariates (mean values for age and PCs, and the mode for sex and APOE ) and a GRS at the first (lowest) decile (in blue) or a GRS at the ninth (highest) decile (red). The shaded regions correspond to the 95% CI.

In an MCI cohort setting, this effect of the GRS corresponds to a median AD conversion probability within 3 years of 21.9% in patients with a GRS below the first decile (range, 4.1–34.9%) and 37.5% (range, 10.8–56.2%) in patients with a GRS above the ninth decile. There was a consistent increase in probability between these deciles in all cohorts (median (range), 13.8% (6.6–25.0%); Supplementary Table 38 ).

To better define the GRS discriminative ability regarding AD conversion, we assessed the improvements in three indices of predictive performance after adding the GRS to a Cox model containing age, sex, PCs and the number of APOE -ε4 and APOE -ε2 alleles as covariates (Supplementary Tables 34 and 35 ). We found a small but consistent increase in the discrimination between AD converters and nonconverters, as indicated by the concordance index (C-index) in population-based cohorts (Δ 5years -C-index fixed-effects  = 0.002 (0.0004–0.004)) and MCI cohorts (Δ 3years -C-index fixed-effects  = 0.007 (0.001–0.012)). This finding was further supported by small-to-moderate increases in the continuous NRI (net reclassification improvement) index in population-based cohorts (NRI 5year-fixed-effects  = 0.248 (0.159–0.336)) and MCI cohorts (NRI 3year-fixed-effects  = 0.232 (0.140–0.325)); this indicates that the risk assignment is more appropriate to individuals when the GRS is taken into account 21 . Furthermore, an increase in the index of prediction accuracy (IPA) was observed in all of the population-based cohorts (average Δ 5years -IPA fixed-effects  = 0.29% (0.23%–0.35%)) and all but one of the MCI cohorts (average Δ 3years -IPA fixed-effects  = 1.53% (1.31%–1.76%)), indicating an overall improvement in predictive performance. As expected, the amount of improvement in this index varied greatly from one cohort to another, given its dependency on incidence rates. The value of adding the new genetic variants was emphasized by the fact that effect sizes (as measured by the indices of predictive ability) were lower when only previously known AD risk variants were included in the GRS (Supplementary Table 39 ).

The results were similar when we (1) computed indices for other follow-up time points, (2) applied a random effects meta-analysis, (3) considered conversion to all-cause-dementia as the outcome and (4) excluded the Framingham Heart Study (FHS), as it was part of the stage II of the GWAS from which ORs for PRS computation were extracted (Supplementary Tables 34 – 44 and Supplementary Fig. 47 ).

Our meta-analysis combined a large, new case–control study with previous GWASs. We identified 75 independent loci for ADD; 33 had been reported previously, and 42 correspond to new signals at the time of this analysis. The prioritized genes and their potential impact on the pathophysiology of AD are described in the Supplementary Note .

Our pathway enrichment analyses removed ambiguities concerning the involvement of tau-binding proteins and APP/Aβ peptide metabolism in late-onset AD processes at a much higher level than had been described previously 5 . It is noteworthy that new genetic risk factors are often first evaluated in the context of known pathways; many new research approaches were developed to systematically characterize putative links among APP metabolism, tau function and ADD genetic risk factors 22 , 23 . This approach can lead to circular reasoning and thus artificial enrichment in specific processes. However, we implicate ADAM17 , a gene whose protein product is known to carry α-secretase activity as ADAM10 (ref. 24 ). This observation suggests that the nonamyloidogenic pathway for APP metabolism might be deregulated in AD. In addition to APP , we also identified six highly plausible prioritized (tier 1) genes ( ICA1L , DGKQ , ICA1 , DOC2A , WDR81 and LIME1 ) that are likely to modulate the metabolism of APP.

These pathway enrichment analyses also confirmed the involvement of innate immunity and microglial activation in ADD (Supplementary Table 15 ). Our single-cell expression enrichment analysis also highlighted genes expressed in microglia (Supplementary Tables 16 and 17 ). Indeed, three of our prioritized (tier 1) genes ( RHOH , BLNK and SIGLEC11 ) and two of our tier 2 genes ( LILRB2 and RASGE1FC ) appeared to be mainly expressed in microglia (>90% relative to the total expression summed across cell types; Fig. 2a and Supplementary Table 45 ). Importantly, SIGLEC11 and LILRB2 have already been linked to Aβ peptides/amyloid plaques 25 , 26 .

Here, we also provide genetic evidence of the LUBAC’s potential implication in ADD. Two of the LUBAC’s three complements are encoded by SHARPIN and RBCK1 , and the LUBAC is regulated by OTULIN; all three genes were found to be high-confidence, prioritized risk genes in our study. The LUBAC is the only E3 ligase known to form linear ubiquitin chains de novo through ubiquitin’s N-terminal methionine. The complex has mostly been studied in the context of inflammation, innate immunity and defense against intracellular pathogens. For instance, the LUBAC is reportedly essential for NLRP3 inflammasome activation 27 and thus acts as a key innate immune regulator 28 . In turn, the NLRP3 inflammasome is essential for the development and progression of Aβ pathology in mice 29 and may drive tau pathology through Aβ-induced microglial activation 30 . The LUBAC is also reportedly involved in autophagy, and linear ubiquitin chain modifications of TDP-43-positive neuronal cytoplasmic inclusions have been described as potential inducers of autophagic clearance 31 . Lastly, the LUBAC has been studied as a regulator of TNF-α signaling in particular 20 .

Interestingly, the TNF-α signaling pathway was also flagged by other genetic findings in our study (Supplementary Fig. 48 ). For example, ADAM17 (also known as TNF-α-converting enzyme) is of pivotal importance in the activation of TNF-α signaling 32 . For TNIP1 , its gene product (TNF-α-induced protein 3-interacting protein 1) is involved in the inhibition of the TNF-α signaling pathway and nuclear factor κB activation/translocation 33 . Additional signal related to TNF-α is the one found at SPPL2A (one of the 33 confirmed loci). The protein encoded by SPPL2A is involved in noncanonical shedding of TNF-α 34 , and PGRN has been described as a TNF receptor ligand and an antagonist of TNF-α signaling 35 . Several lines of evidence had linked the inhibition of TNF-α signaling with reduction of both Aβ and tau pathologies in vivo 36 , 37 . Although a potential inflammatory connection has been suggested for TNF-α through the activation of NLRP3 inflammasome 38 , the TNF-α signaling pathway is also involved in many other brain physiological functions (e.g., synaptic plasticity in neurons) and pathophysiological processes (e.g., synapse loss) in the brain 39 . Furthermore, the involvement of the TNF-α signaling pathway and the LUBAC might be important in cell types other than microglia in AD. It is important to note that six of our prioritized (tier 1) genes ( ICA1L , EGFR , RITA1 , MYO15A , LIME1 and APP ) are expressed at a low level in microglia (<10%, relative to the total expression summed across cell types; Supplementary Table 45 ), emphasizing that ADD results from complex crosstalk between different cell types in the brain 23 , 40 . It is also noteworthy that the EGFR pathway is known to interact with the TNF-α signaling pathway 41 , which suggests interplay between the two signaling pathways during the ADD development.

A better understanding of the etiology of ADD might also result from the observation that the risks of developing ADD and frontotemporal dementia are associated with the same causal variants in GRN and TMEM106B . This association might be due to the misclassification of clinical diagnosis of AD and the presence of proxy-ADD cases in the UKBB. However, GRN and TMEM106B have also been linked to brain health and many other neurodegenerative diseases. For instance, GRN and TMEM106B are reportedly potential genetic risk factors for differential aging in the cerebral cortex 42 and cognitive impairment in amyotrophic lateral sclerosis 43 and Parkinson’s disease 44 , 45 . Lastly, both GRN and TMEM106B have already been associated with neuropathological features of AD 46 , 47 , 48 . Taken as a whole, these data may thus emphasize a potential continuum between neurodegenerative diseases in which common pathological mechanisms are driven by GRN and TMEM106B . Interestingly, both GRN and TMEM106B are reported to be involved in defective endosome/lysosome trafficking/function 49 , 50 , a defect that is also observed in AD.

By applying a GRS derived from all the genome-wide-significant variants discovered in this study, we identified an association with the risk of incident AD in prospective population-based cohorts and with the risk of progression over time from MCI to AD (Fig. 5 and Supplementary Table 33 ). In patients with MCI, previous associations of AD risk with a GRS built on previously known genetic AD risk variants has been inconsistent 51 . It is important to note that the GRS has an impact on the AD risk in addition to that of age and that the GRS’s effect is independent of APOE status. With a view to translating genetic findings into preventive measures and personalized medicine, we also sought to provide the GRS’s added value for risk prediction by calculating the discriminative capacity through three different indices. Overall, the indices suggested that the effect size for the association between the GRS and AD was small but significant. Despite this modest effect, the inclusion of the GRS into the predictive model consistently improved the assignment of the risk of progression, as expressed by the net reclassification improvement (NRI) index 21 . Importantly, the cumulative improvements in risk prediction (due to inclusion of the new variants in the GRS) led to a 1.6- to 1.9-fold increase in the AD risk from the lowest to the highest decile, in addition to the effects of age and APOE status. We also showed that in addition to known risk variants, the new risk variants identified in the present study are significantly associated with progression to AD. The results of future GWASs are expected to further improve AD-risk prediction. Hence, the GRS will help to sharpen the threshold that differentiates between people at risk of progressing to dementia and those who are not.

A recent study estimated that fewer than 100 causal common variants may explain the entire AD risk 52 ; if that estimate is correct, then our study might have already characterized a large proportion of this genetic component due to common variants. However, several reasons strongly underscore the need for additional efforts to fully characterize the still-missing AD genetic component. First, it is probable that additional, yet-unknown loci bear common variants modulating the risk for AD. Second, identification of rare variants with very low frequencies is a major challenge for genetic studies, because available samples with sequencing data in AD are underpowered. Notably, almost all the genes with rare variants associated with AD risk also present common variants associated with AD risk (i.e., TREM2 , SORL1 , ABCA7 , ABCA1 , PLCγ2 and ADAM10 ) 53 . Third, gene–gene and gene–environment interactions have not yet been studied in detail. Hence, by increasing the GWAS sample size and improving imputation panels, conventional and (above all) more complex analyses will have more statistical power and should enable the characterization of associations with rare/structural variants. Lastly, higher-powered GWASs of multiancestry populations will be particularly welcome for characterizing potential new genetic risk factors, improving fine-mapping approaches and developing specific GRSs (because GRSs developed with European-ancestry populations are known to be less effective with other ancestries).

In conclusion, we have validated 33 previous loci, doubled the total number of genetic loci associated with the ADD risk, expanded our current knowledge of the pathophysiology of ADD, identified new opportunities for the development of GRSs and gene-specific treatments and opened up a pathway to translational genomics and personalized medicine.

All of our stage I meta-analysis samples came from the following consortia/datasets: EADB, GR@ACE, EADI, GERAD/PERADES, DemGene, Bonn, the Rotterdam study, the CCHS study, NxC and the UKBB. In the UKBB, individuals who did not report dementia or any family history of dementia were used as controls; the analysis included 2,447 diagnosed cases, 46,828 proxy cases of dementia and 338,440 controls. All individuals included in stage I are of European ancestry; demographic data on these case–control studies are summarized in Supplementary Table 1 , and more detailed descriptions are available in the Supplementary Note . Stage II samples are from the ADGC, CHARGE and FinnGen consortia (Supplementary Table 1 and Supplementary Note ) and are described in detail elsewhere 5 , 6 , 9 , 10 , 54 , 55 , 56 . Written informed consent was obtained from study participants or, for those with substantial cognitive impairment, a caregiver, legal guardian or other proxy. Study protocols for all cohorts were reviewed and approved by the appropriate institutional review boards.

Quality control and imputation

A standard quality control was performed on variants and samples from all datasets individually. The samples were then imputed with the TOPMed reference panel 57 , 58 . The Haplotype Reference Consortium (HRC) panel 59 was also used for some datasets (Supplementary Table 2 ). For the UKBB, we used the provided imputed data generated from a combination of the 1000 Genomes, HRC and UK10K reference panels ( Supplementary Note ).

Stage I analyses

Tests of the association between clinical or proxy-ADD status and autosomal genetic variants were conducted separately in each dataset by using logistic regression and an additive genetic model, as implemented in SNPTEST 2.5.4-beta3 (ref. 60 ) or PLINK v1.90 (ref. 4 ). However, a logistic mixed model (as implemented in SAIGE v0.36.4 (ref. 61 )) was considered for the UKBB data. We analyzed the genotype probabilities in SNPTEST (using the newml method) and dosages in PLINK and SAIGE. Analyses were adjusted for PCs and genotyping centers, when necessary (Supplementary Table 2 ). For the UKBB dataset, only variants with a MAF above 0.01% and a minor allele count (MAC) above 3 were analyzed, and effect sizes and standard errors were corrected by a factor of two, because proxy cases were analyzed 7 . This approach is appropriate for variants with a moderate-to-high frequency and a small effect size. For all datasets, we filtered out duplicated variants and variants with (1) missing data on the effect size, standard error or P value; (2) an absolute effect size above 5; (3) an imputation quality below 0.3; and (4) a value below 20 for the product of the MAC and the imputation quality (MAC-info score). For datasets not imputed with the TOPMed reference panel, we also excluded (1) variants for which conversion of position or alleles from the GRCh37 assembly to the GRCh38 assembly was not possible or problematic or (2) variants with very large difference of frequency between the TOPMed reference panel and the reference panels used to perform imputation.

Results were then combined across studies in a fixed-effect meta-analysis with an inverse-variance weighted approach, as implemented in METAL v2011-03-25 software 62 . We filtered out (1) variants with a heterogeneity P value below 5 × 10 −8 , (2) variants analyzed in less than 20% of the total number of cases and (3) variants with frequency amplitude above 0.4 (defined as the difference between the maximum and minimum frequencies across all the studies). We also excluded variants not analyzed in the EADB-TOPMed dataset.

The genomic inflation factor lambda was computed with the GenABEL 1.8-0 R package 63 and a median approach after exclusion of the APOE region (44–46 Mb on chromosome 19 in GRCh38). The LD score regression intercept was computed with LDSC v1.0.1 software using the ‘baselineLD’ LD scores built from 1000 Genomes phase 3 (ref. 64 ). The analysis was restricted to HapMap 3 variants and excluded multiallelic variants, variants without an rs ID and variants in the APOE region.

Definition of associated loci

A region of ±500 kb was defined around each variant with a stage I P value below 1 × 10 −5 . These regions were then merged (using bedtools v2.27.0 software; https://bedtools.readthedocs.io/en/latest/ ) to define nonoverlapping regions. The region corresponding to the APOE locus was excluded. We then used the PLINK clumping procedure to define independent hits in each region. An iterative clumping procedure was applied to all variants with a stage I P value below 1 × 10 −5 , starting with the variant with the lowest P value (referred to as the index variant). Variants with a stage I P value below 1 × 10 −5 , located within 500 kb of this index variant and in LD with the index variant ( r 2 above 0.001) were assigned to the index variant’s clump. The clumping procedure was then applied until all the variants had been clumped. LD in the EADB-TOPMed dataset was computed using high-quality (probability ≥0.8) imputed genotypes.

Stage II analyses

Variants with a stage I P value below 1 × 10 −5 were followed up ( Supplementary Note ). Results were combined across all stage I and II studies in a fixed-effect meta-analysis with an inverse variance weighted approach, as implemented in METAL. In each clump, we then reported the variants with positive follow-up results (i.e., the same direction of effect in stage I and stage II, and a stage II P value below 0.05) and the lowest P value in the meta-analysis. Those variants were considered to be associated at the genome-wide significance level if they had a P value below 5 × 10 −8 in the stage I and II meta-analysis. However, we excluded the chr6:32657066:G:A variant, because its frequency amplitude was high.

Pathway analysis

A total of 10,271 gene sets were considered for analysis ( Supplementary Note ). Gene set enrichment analyses were performed in MAGMA v1.08 (refs. 65 , 66 ), with correction for the number of variants in each gene, LD between variants and LD between genes. LD was computed from the EADB-TOPMed dataset using high-quality (probability ≥0.9) imputed genotypes. The measure of pathway enrichment was the MAGMA ‘competitive’ test (in which the association statistic for genes in the pathway is compared with those for all other protein-coding genes), as recommended by De Leeuw et al. 67 . We used the ‘mean’ test statistic, which uses the sum of −log(variant P value) across all genes. The primary analysis assigned variants to genes if they lay within the gene boundaries, although a secondary analysis used a window of 35 kb upstream and 10 kb downstream to assign variants to genes (as in Kunkle et al. 5 ). The primary analysis included all variants with an imputation quality above 0.8. We used q values 68 to account for multiple testing.

Expression in various cell types

The expression of genes was assigned to specific cell classes of the adult brain, as described previously 69 . Briefly, middle temporal gyrus single-nucleus transcriptomes from the Allen Brain Atlas dataset (49,555 total nuclei derived from 8 human tissue donors aged 24–66 years) were used to annotate and select six main cell classes using Seurat 3.1.1 (ref. 70 ): glutamatergic neurons, GABAergic neurons, astrocytes, oligodendrocytes, microglia and endothelial cells. Enrichment analyses were performed by using the mean gene expression per nucleus for each cell type relative to the total expression summed across cell types as a quantitative covariate in a MAGMA gene property analysis.

Functional interpretation of GWAS signals and gene prioritization

To prioritize candidate genes in the new loci, we systematically searched for evidence for these genes in seven different domains: (1) variant annotation, (2) eQTL-GWAS integration, (3) sQTL-GWAS integration, (4) protein QTL (pQTL)-GWAS integration, (5) mQTL-GWAS integration, (6) histone acetylation QTL (haQTL)-GWAS integration and (7) APP metabolism. On the basis of this evidence, we then defined a gene prioritization score of between 0 and 100 for each candidate gene (Supplementary Fig. 34 ). Detailed information on the domains, categories (e.g., the tissue or cell type for QTL-GWAS integration domains) and subcategories (for the type of evidence) is given in Supplementary Table 19 . A brief summary of how evidence was assessed in each domain is provided below, together with a detailed description of the gene prioritization strategy.

Candidate genes

We considered protein-coding candidate genes within a ±1-Mb window of the new lead variants. The genes in overlapping loci (i.e., L28, L30 and L37) were assigned to their respective loci based on proximity to the lead variants, and the distal genes were not considered for gene prioritization in the investigated loci. Moreover, we did not perform gene prioritization in the complex IGH gene cluster locus (L27), as this telomeric region contains complex splicing events (spanning a high number of IGH genes) that probably result from known fusion events 18 .

The variant annotation domain

In this domain, we determined whether the candidate gene was the nearest protein-coding gene to the lead variant and/or whether the lead variant was a rare variant (MAF < 1%) and/or protein-altering variant of the investigated candidate gene.

Molecular QTL–GWAS integration domains

To study the downstream effects of new ADD-associated variants on molecular phenotypes (i.e., expression, splicing, protein expression, methylation and histone acetylation) in various AD-relevant tissues, cell types and brain regions, molecular cis -QTL information (i.e., the genetic variants that regulate these molecular phenotypes) was integrated with the stage I ADD GWAS results in genetic colocalization analyses, TWASs and a genetically driven DNA methylation scan. These molecular QTLs include eQTLs, sQTLs, pQTLs, mQTLs and haQTLs. We mapped and prepared eQTL/sQTL catalogs in AD-relevant bulk brain regions from AMP-AD cohorts 71 , 72 , 73 , 74 and in LCLs from the EADB Belgian cohort. We used additional eQTL/sQTL information in AD-relevant bulk brain regions from GTEx 75 and microglia from the MiGA study 76 . Furthermore, eQTLs in monocytes and macrophages from various datasets 77 , 78 , 79 , 80 , 81 , 82 (as prepared by eQTL Catalogue 83 ) were included in the analyses. Data on pQTLs 84 , mQTLs 85 and haQTLs 85 were available for DLPFC. Using each molecular QTL catalogue, the effect of the lead variants was queried and significant associations were reported. Moreover, genetic colocalization studies were conducted by comparing ADD association signals with the eQTL/sQTL signals from AMP-AD bulk brain, MiGA microglia and EADB LCL cohorts. We also conducted eTWASs and splicing TWASs (sTWAS) of the ADD risk, along with fine mapping of the eTWAS results. To this end, we trained functional expression and splicing reference panels based on the AMP-AD bulk brain and EADB LCL cohorts, and we leveraged precalculated reference panel weights 86 for the GTEx dataset 75 in tissues and cells of interest. Lastly, for the mQTL-GWAS integration domain, we also tested for associations between ADD and genetically driven DNA methylation (MetaMeth analysis) in blood (with blood–brain methylation correlation estimates obtained from BECon 87 ) using the procedures described by Freytag et al. 88 and Barbeira et al. 89 . A detailed description of the datasets and methods used for each of these analyses is given in the Supplementary Note .

APP metabolism domain

We assessed the functional impact of gene underexpression on APP metabolism for all candidate genes based on a genome-wide high-content short interfering RNA screen 17 ( Supplementary Note ).

Gene prioritization score

We computed a gene prioritization score for each candidate gene as the weighted sum of the evidence identified in the seven domains. We specified a weight for each type of evidence, as detailed in Supplementary Table 19 . For the molecular QTL-GWAS integration domains, we gave more weight to replicated hits (i.e., evidence in several datasets) than to single hits. We also gave more weight to hits observed in brain (the bulk brain and microglia datasets) than to hits observed in other tissues/cell types (LCLs, monocytes, macrophages and blood). To avoid score inflation, several specific rules were applied: (1) for the results of sQTL- and mQTL-based analyses, multiple splice junctions or CpGs annotated for the same genes were aggregated prior to weighting due to correlated data; (2) if we observed a fine-mapped eTWAS association for a gene, its other significant (but not fine-mapped) eTWAS associations were not considered; (3) for genes having several significant CpGs (prior to aggregation) in MetaMeth analyses, the associated CpGs with a low (<75% percentile) blood–brain methylation correlation estimate were not considered if the gene also had associated CpGs with a high (≥75% percentile) blood–brain methylation correlation estimate.

Gene prioritization strategy

After obtaining a total weighted score per gene, we ranked genes per locus according to their prioritization scores and compared the relative score differences between the highest ranked gene and other genes in the investigated locus. If this relative difference was at least 20% and the gene prioritization score for the highest ranked gene was ≥4, then we classified this gene as a tier 1 prioritized gene in the investigated locus (i.e., a greater likelihood of being the true risk gene responsible for the ADD signal). If this absolute threshold was not met, then the highest ranked gene was classified as a tier 2 prioritized gene (i.e., a lower level of confidence and absence of the minimum level of evidence for a true risk gene). Furthermore, other genes in a locus harboring a tier 1 gene were classified as tier 2 prioritized genes if the relative score difference versus the highest ranked (tier 1) gene was between 20% and 50%. Lastly, when the relative score difference between the highest ranked gene and other genes in the same locus was <20%, then both the highest ranked gene and all genes with a score difference <20% were classified as tier 2 prioritized genes in the investigated locus; based on the current evidence, it is difficult to prioritize two or more similarly scored genes. The gene prioritization strategy is summarized in Supplementary Fig. 34 . Detailed descriptions and discussions of prioritized genes and tier levels in each investigated new locus can be found in the Supplementary Note .

GRS analysis

Eight longitudinal MCI cohorts and seven population-based studies were included in the analysis and are fully described in the Supplementary Note and Supplementary Table 33 . The GRSs were calculated as previously described 90 . Briefly, we considered variants with genome-wide significant evidence of association with ADD in our study. We did not include any APOE variants in the GRS. Variants were directly genotyped or imputed ( R ² ≥ 0.3). Imputation was performed using the HRC panel 59 for subcohorts from the Rotterdam study and the TOPMed panel for the other cohorts 57 . For HRC-imputed data, LD proxies were considered for variants that were not available in this reference panel. The GRS was calculated as the weighted average of the number of risk-increasing alleles for each variant, using dosages. Weights were based on the respective log(OR) obtained in stage II. The GRS was then multiplied by the number of included variants. Thus, the HR measured the effect of carrying one additional average risk allele.

To assess whether the new variants in this study contribute to the risk of conversion to AD (in addition to known AD genes), we calculated two GRSs: one based solely on variants known before this study (GRS known , n  = 39; Table 1 ) and another based on variants identified in the present study (GRS novel , n  = 44; Table 2 ). These GRSs were calculated in the same way as the GRS encompassing all the variants.

The association between the GRS and the risk of progression to dementia in individuals from population-based cohorts or patients with MCI from memory clinics was tested statistically using Cox proportional hazards models. The models were adjusted for age, sex, the first four PCs (to correct for potential population stratification) and the number of APOE -ε4 and APOE - ε2 alleles (assuming an additive effect). In the FHS study, the generation was used as an additional covariate. In the 3C study, the analysis was adjusted for age, sex, the number of APOE alleles, the two first PCs and center. The PCs used were generated for each cohort, using the same variants as in the case/control study’s PC analysis. The number of APOE -ε4 alleles was obtained from direct genotyping or, if missing, the genotypes (with probability >0.8) derived from the TOPMed imputations. The interaction between the GRS and the number of APOE -ε4 alleles was tested on the multiplicative scale. In the primary analysis, conversion to AD was used as the outcome (conversions to non-AD dementias were coded as being censored at time of conversion), but analyses were repeated using all-cause dementia as the outcome.

To quantify the effect size of the potential association between the GRS and conversion to dementia regarding predictive performance, we computed three different indices measuring different aspects of the predictive performance of the GRS in our prospective, longitudinal cohort studies 91 : the continuous version of the C-index, 92 , 93 the continuous NRI 94 and IPA 95 ( Supplementary Note ). For all indices, we provide point estimates and 95% CIs.

In the main analysis, indices were computed at the time point for which all cohorts in a specific setting (i.e., population-based studies or memory clinics, respectively) provided follow-up observations (that is 5 years for population-based cohorts and 3 years for MCI cohorts). In a sensitivity analysis, indices for longer or shorter follow-up periods were also derived (that is 3 years and 10 years for population-based cohorts and 5 years for MCI cohorts). Standard errors for indices were derived by non-parametric bootstrapping with 1,000 samples.

To determine the average effect of the GRS across the various cohorts examined, individual cohort results were subjected to both inverse-variance weighted meta-analyses (primary analyses) and random effects meta-analysis ( Supplementary Note ). To facilitate comparisons of results for different time points, cohorts with longer follow-up periods were meta-analyzed separately. Furthermore, two memory clinic cohorts with a limited sample size ( N  < 50) were excluded to assess their impact on the final meta-analysis results. Meta-analyses were performed using the ‘metafor’ (3.0.2) R package 96 .

To further illustrate the clinical relevance of the GRS, we pooled computed GRSs across four population-based cohorts (3C, AgeCoDe, VITA and MAS) and computed deciles of the GRS distribution for use as a common reference for all cohorts. We then computed the increase in risk when augmenting the GRS value from the first decile (GRS = 50.76) to the ninth decile (GRS = 59.74) of the distribution. To represent this risk increase in the HR, we rescaled the HR derived from our meta-analyses results using the equation \(e^{\log\left( {\rm HR} \right) \ast \left( {{\rm{GRS9th}}_{\rm{decile}}} - {{\rm{GRS1st}}_{\rm{decile}}} \right)}\) . Importantly, this approach yields exactly the same results as transforming the GRS so that a one unit increment corresponds to the increase from the lowest decile to the highest decile.

Furthermore, we approximated the probability of conversion to AD at 3 and 5 years in memory clinic patients with MCI by using Cox models implemented in the ‘PredictCox’ function from the ‘riskRegression’ (2020.12.8) R package 97 . We did not derive AD conversion probabilities for two cohorts with very small sample sizes ( N  < 50). Predicted AD conversion probabilities were derived and averaged for all patients in each of the groups formed by the decile of the GRS distribution in each cohort. The difference between the groups with the highest and lowest GRSs was computed in each cohort. We report the median (range) results in each group formed by the GRS deciles.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data Availability

Genome-wide summary statistics have been deposited to the European Bioinformatics Institute GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ) under accession no. GCST90027158.

The significant eQTLs/sQTLs mapped and eTWAS/sTWAS functional reference panel weights generated for this study (in AD-relevant bulk brain regions from AMP-AD cohorts and in LCLs from the EADB Belgian cohort) are publicly available at https://doi.org/10.5281/zenodo.5745927 and https://doi.org/10.5281/zenodo.5745929 .

Anonymized aligned reads of the amplicon-based long-read Nanopore cDNA sequencing experiment conducted for the TSPAN14 splicing analysis are available through the European Nucleotide Archive under accession PRJEB49234 .

Moreover, the following data used in the gene prioritization are publicly available:

AMP-AD rnaSeqReprocessing Study ( https://www.synapse.org/#!Synapse:syn9702085 );

MayoRNAseq whole-genome sequencing variant call formats (WGS VCFs) ( https://www.synapse.org/#!Synapse:syn11724002 );

ROSMAP WGS VCFs ( https://www.synapse.org/#!Synapse:syn11724057 );

MSBB WGS VCFs ( https://www.synapse.org/#!Synapse:syn11723899 );

eQTLGen ( https://www.eqtlgen.org/ );

eQTL Catalogue database ( https://www.ebi.ac.uk/eqtl/ );

Brain xQTL serve ( http://mostafavilab.stat.ubc.ca/xqtl/ );

GTEx v8 eQTL and sQTL catalogs ( https://www.gtexportal.org/ );

GTEx v8 expression and splicing prediction models ( http://predictdb.org/ );

MiGA eQTLs ( https://doi.org/10.5281/zenodo.4118605 );

MiGA sQTLs ( https://doi.org/10.5281/zenodo.4118403 );

MiGA meta-analysis ( https://doi.org/10.5281/zenodo.4118676 ); and

Wingo et al. 84 pQTL data ( https://www.synapse.org/#!Synapse:syn23627957 ).

Code availability

We used publicly available software for all analyses. The software are listed in the Supplementary Note with their appropriate citations and/or URLs.

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Acknowledgements

We thank the many study participants, researchers and staff for collecting and contributing to the data, the high-performance computing service at the University of Lille and the staff at CEA-CNRGH for their help with sample preparation and genotyping and excellent technical assistance. We thank Antonio Pardinas for his help. We thank the Netherlands Brain Bank. This research was conducted using the UKBB resource (application number 61054). This work was funded by a grant (EADB) from the EU Joint Programme – Neurodegenerative Disease Research. INSERM UMR1167 is also funded by the INSERM, Institut Pasteur de Lille, Lille Métropole Communauté Urbaine and French government’s LABEX DISTALZ program (development of innovative strategies for a transdisciplinary approach to AD). Full consortium acknowledgements and funding are in the Supplementary Note .

Author information

These authors contributed equally: Céline Bellenguez, Fhari Kuçukali, Iris Jansen, Luca Kleineidam, Sonia Moreno-Grau, Najaf Amin, Adam Naj and Rafael Campos-Martin.

These authors jointly supervised this work: Mikko Hiltunen, Kristel Sleegers, Gerard Schellenberg, Cornelia van Duijn, Rebecca Sims, Wiesje van der Flier, Agustin Ruiz, Alfredo Ramirez and Jean-Charles Lambert.

Authors and Affiliations

Université de Lille, INSERM, CHU Lille, Institut Pasteur Lille, U1167-RID-AGE, Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, Lille, France

Céline Bellenguez, Benjamin Grenier-Boley, Vincent Damotte, Marcos R. Costa, Julien Chapuis, R. Pineda-Sánchez, Nathalie Fievet, Hieab Adams, Philippe Amouyel & Jean-Charles Lambert

Complex Genetics of Alzheimer’s Disease Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium

Fahri Küçükali, Christine Van Broeckhoven, Jasper Van Dongen & Kristel Sleegers

Laboratory of Neurogenetics, Institute Born - Bunge, Antwerp, Belgium

Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium

Fahri Küçükali, Jasper Van Dongen & Kristel Sleegers

Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands

Iris E. Jansen, Sven J. van der Lee, Henne Holstege, Marc Hulsman, Yolande A. L. Pijnenburg, Philip Scheltens, Niccolo Tesí & Wiesje M. van der Flier

Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije University, Amsterdam, the Netherlands

Iris E. Jansen, Danielle Posthuma & Tim Lu

Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany

Luca Kleineidam, Victor Andrade, Michael T. Heneka, Wolfgang Maier, Anja Schneider, Michael Wagner, Kayenat Parveen, Frank Jessen & Alfredo Ramirez

Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, University of Cologne, Medical Faculty, Cologne, Germany

Luca Kleineidam, Rafael Campos-Martin, Victor Andrade, Maria Carolina Dalmasso, Klaus Fließbach & Alfredo Ramirez

German Center for Neurodegenerative Diseases (DZNE Bonn), Bonn, Germany

Luca Kleineidam, Klaus Fließbach, Michael T. Heneka, Wolfgang Maier, Matthias Schmid, Anja Schneider, Annika Spottke, Michael Wagner, Henning Boecker, André Lacour, Christine Herold, Tim Becker, Ying Wu, Yanbing Wang, Frank Jessen & Alfredo Ramirez

Research Center and Memory Clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain

Sonia Moreno-Grau, Itziar de Rojas, Pablo Garcia-Gonzalez, Carla Abdelnour, Emilio Alarcón-Martín, Montserrat Alegret, Mercè Boada, Miguel Calero, Ana Espinosa, Pablo García-González, Isabel Hernández, Marta Marquié, Laura Montrreal, Adelina Orellana, Gemma Ortega, Alba Pérez-Cordón, Raquel Puerta, Natalia Roberto, Maitée Rosende-Roca, Ángela Sanabria, Oscar Sotolongo-Grau, Juan Pablo Tartan, Lluís Tárraga, Sergi Valero, Ana Mauleón, Ana Pancho, Anna Gailhajenet, Asunción Lafuente, Elvira Martín, Esther Pelejà, Liliana Vargas, Mar Buendia, Marina Guitart, Mariona Moreno, Marta Ibarria, Nuria Aguilera, Pilar Cañabate, Silvia Preckler, Susana Diego, Nuria Aguilera, Amanda Cano, Pilar Cañabate, Raúl Nuñez-Llaves, Clàudia Olivé, Ester Pelejá & Agustín Ruiz

CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain

Sonia Moreno-Grau, Itziar de Rojas, Pablo Garcia-Gonzalez, Carla Abdelnour, Daniel Alcolea, Montserrat Alegret, Rafael Blesa, Mercè Boada, Dolores Buiza-Rueda, Laura Cervera-Carles, Ana Espinosa, Juan Fortea, María J. Bullido, Ana Frank-García, Jose Maria García-Alberca, Isabel Hernández, Carmen Lage, Alberto Lleó, Adolfo Lopez de Munain, Marta Marquié, Angel Martín Montes, Miguel Medina, Pablo Mir, Fermin Moreno, Adelina Orellana, Gemma Ortega, Jordi Pérez-Tur, Alberto Rábano, Eloy Rodriguez-Rodriguez, Maitée Rosende-Roca, Ángela Sanabria, Pascual Sánchez Juan, Lluís Tárraga, Sergi Valero, Miren Zulaica, Ad Adarmes-Gómez, D. Macias-García, F. Carrillo, Isabel Sastre Merlín, L. Garrote-Espina, M. Carrion-Claro, Ma Labrador, Mt Periñán, P. Gómez-Garre, R. Escuela, R. Vigo-Ortega, S. Jesús, Nuria Aguilera, Pilar Cañabate, Astrid D. Adarmes-Gómez, Fátima Carrillo, Mario Carrión-Claro, Rocío Escuela, Lorena Garrote-Espina, Pilar Gómez-Garre, Silvia Jesús, Miguel Angel Labrador Espinosa, Sara López-García, Daniel Macias-García, María Teresa Periñán-Tocino, Rocío Pineda-Sánchez, Isabel Sastre, Rosario Vigo-Ortega, Jordi Clarimon & Agustín Ruiz

Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands

Najaf Amin, Amber Yaqub, Ivana Prokic, Shahzad Ahmad, Hata Comic, Tavia Evans, Maria Knol, William Kremen, Gena Roshchupkin, Dina Vojinovic, Mohsen Ghanbari, M. Arfan Ikram & Cornelia M. van Duijn

Nuffield Department of Population Health, Oxford University, Oxford, UK

Najaf Amin & Cornelia M. van Duijn

Department of Biostatistics, Epidemiology, and Informatics, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

Adam C. Naj, Jin Sha, Alessandra Chesi, Beth A. Dombroski, Jacob Haut, Pavel P. Kuksa, Chien-Yueh Lee, Edward B. Lee, Yuk Yee Leung, Mingyao Li, John Malamon, Liming Qu, John Q. Trojanowski, Otto Valladares & Vivianna M. Van Deerlin

Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

Adam C. Naj, Valentina Escott-Price, Pavel P. Kuksa, Chien-Yueh Lee, Otto Valladares, Li-San Wang, Yi Zhao & Gerard D. Schellenberg

MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Cardiff, UK

Peter A. Holmans, Catherine Bresner, Janet Harwood, Lauren Luckcuck, Rachel Marshall, Amy Williams, Charlene Thomas, Chloe Davies, William Nash, Kimberley Dowzell, Atahualpa Castillo Morales, Mateus Bernardo-Harrington, Julie Williams & Rebecca Sims

CEA, Centre National de Recherche en Génomique Humaine, Université Paris-Saclay, Evry, France

Anne Boland, Céline Besse, Delphine Daian, Bertrand Fin, Robert Olaso & Jean-François Deleuze

Section Genomics of Neurodegenerative Diseases and Aging, Department of Human Genetics Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands

Sven J. van der Lee, Henne Holstege, Marc Hulsman, Yiyi Ma & Niccolo Tesí

Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil

Marcos R. Costa & Mikko Hiltunen

Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland

Teemu Kuulasmaa, Alexa Beiser, Anita DeStefano, Kathryn L. Lunetta, Gina Peloso, Ruiqi Wang, Neil W. Kowall, Ann C. McKee, Jesse Mez, Robert A. Stern & Lindsay A. Farrer

Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA

Qiong Yang, Anita DeStefano, Lena Kilander, Malin Löwemark, Claudia L. Satizabal, Ruiqi Wang, Adrienne L. Cupples, Josée Dupuis, Shuo Li, Xuan Liu & Sudha Seshadri

Framingham Heart Study, Framingham, MA, USA

Qiong Yang, Oscar Lopez & Bruce M. Psaty

Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA

Joshua C. Bis & Alison E. Fohner

LACDR, Leiden, the Netherlands

Shahzad Ahmad

Department of Public Health and Carins Sciences/Geriatrics, Uppsala University, Uppsala, Sweden

Vilmantas Giedraitis & Martin Ingelsson

Centre of Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway

Dag Aarsland

Institute of Psychiatry, Psychology & Neuroscience, London, UK

Department of Surgery, Biochemistry and Molecular Biology, School of Medicine, University of Málaga, Málaga, Spain

Emilio Alarcón-Martín

Department of Neurology, II B Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain

Daniel Alcolea, Rafael Blesa, Laura Cervera-Carles, Juan Fortea, Alberto Lleó, Martin Rossor & Jordi Clarimon

Fundació Docència i Recerca MútuaTerrassa and Movement Disorders Unit, Department of Neurology, University Hospital MútuaTerrassa, Terrassa, Spain

Ignacio Alvarez, Mónica Diez-Fairen & Pau Pastor

Memory Disorders Unit, Department of Neurology, Hospital Universitari Mutua de Terrassa, Terrassa, Spain

Laboratorio de Genética, Hospital Universitario Central de Asturias, Oviedo, Spain

Victoria Álvarez & Irene Rosas Allende

Servicio de Neurología, Hospital Universitario Central de Asturias- Oviedo and Instituto de Investigación Biosanitaria del Principado de Asturias, Oviedo, Spain

Victoria Álvarez, Carmen Martínez Rodríguez, Manuel Menéndez-González & Irene Rosas Allende

Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia

Nicola J. Armstrong, Henry Brodaty, Anbupalam Thalamuthu, Perminder Sachdev & Karen Mather

First Department of Neurology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece

Anthoula Tsolaki, Tegos Thomas, Anna Anastasiou & Magda Tsolaki

Alzheimer Hellas, Thessaloniki, Greece

Anthoula Tsolaki, Tegos Thomas & Magda Tsolaki

Unidad de Demencias, Hospital Clínico Universitario Virgen de la Arrixaca, Murcia, Spain

Carmen Antúnez, Martirio Antequera, Agustina Legaz, Juan Marín-Muñoz, Begoña Martínez, Victoriana Martínez, Maria Pilar Vicente & Liliana Vivancos

School of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy

Ildebrando Appollonio, Elisa Conti, Lucio Tremolizzo, Carlo Ferrarese, Simona Andreoni, Gessica Sala & Chiara Paola Zoia

Neurology Unit, San Gerardo Hospital, Monza, Italy

Ildebrando Appollonio, Lucio Tremolizzo & Carlo Ferrarese

Fondazione IRCCS Ca’Granda, Ospedale Policlinico, Milan, Italy

Marina Arcaro, Daniela Galimberti & Elio Scarpini

Department of Laboratory Diagnostics, III Laboratory of Analysis, Brescia Hospital, Brescia, Italy

Silvana Archetti

Unitat Trastorns Cognitius, Hospital Universitari Santa Maria de Lleida, Lleida, Spain

Alfonso Arias Pastor, Raquel Huerto Vilas & Gerard Piñol-Ripoll

Institut de Recerca Biomedica de Lleida (IRBLLeida), Lleida, Spain

Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy

Beatrice Arosio

Geriatic Unit, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy

Beatrice Arosio, Simona Ciccone, Paolo Dionigi Rossi & Evelyn Ferri

NORMENT Centre, University of Oslo, Oslo, Norway

Lavinia Athanasiu, Srdjan Djurovic, Alexey A. Shadrin, Shahram Bahrami & Ole A. Andreassen

EA 4468, Université de Paris, APHP, Hôpital Broca, Paris, France

Henri Bailly, Emmanuelle Duron, Olivier Hanon & Jean-Sébastien Vidal

Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy

Nerisa Banaj, Gianfranco Spalletta, Francesca Assogna, Fabrizio Piras, Federica Piras, Valentina Ciullo, Jacob Shofany & Yi Zhao

Servei de Neurologia, Hospital Universitari i Politècnic La Fe, Valencia, Spain

Miquel Baquero & Juan Andrés Burguera

Taub Institute on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University, New York, NY, USA

Sandra Barral, Richard Mayeux, Nikolaos Scarmeas, Giuseppe Tosto, Badri N. Vardarajan, Sandra Barral, Lawrence S. Honig, Scott Small, Jean-Paul Vonsattel & Jennifer Williamson

Unit of Neurology, University of Parma and AOU, Parma, Italy

Sandra Barral & Marco Spallazzi

Clinic of Neurology, UH ‘Alexandrovska’, Medical University - Sofia, Sofia, Bulgaria

Sandra Barral, Shima Mehrabian, Latchezar Traykov & Diyana Belezhanska

Boston University and the NHLBI’s Framingham Heart Study, Boston, MA, USA

Alexa Beiser & Gina Peloso

CIEN Foundation/Queen Sofia Foundation Alzheimer Center, Madrid, Spain

Ana Belén Pastor, Miguel Calero, Teodoro del Ser, Miguel Medina & Alberto Rábano

Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA

Jennifer E. Below

Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA

Penelope Benchek, Jonathan L. Haines & Yeunjoo E. Song

Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA

Penelope Benchek, Michelle Grunin, Yeunjoo Song, Penelope Benchek, Will S. Bush, Jonathan L. Haines, Alan Lerner & Yeunjoo E. Song

Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

Luisa Benussi, Giuliano Binetti, Silvia Fostinelli & Roberta Ghidoni

Neuropsychiatry: Epidemiological and Clinical Research, PSNREC, Université de Montpellier, INSERM U1061, Montpellier, France

Claudine Berr

Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy

Valentina Bessi, Benedetta Nacmias & Sandro Sorbi

Azienda Ospedaliero-Universitaria Careggi, Florence, Italy

Valentina Bessi

MAC - Memory Clinic, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

Giuliano Binetti

Geriatrics Unit, Fondazione Policlinico A. Gemelli IRCCS, Rome, Italy

Alessandra Bizarro & Alessandra Lauria

Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA

Eric Boerwinkle, Jan Bressler & Chloé Sarnowski

Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

Eric Boerwinkle & Alessandro Padovani

Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy

Barbara Borroni, Innocenzo Rainero & Alberto Benussi

Department of Neuroscience “Rita Levi Montalcini”, University of Torino, Torino, Italy

Silvia Boschi, Alessandro Vacca & Fausto Roveta

Experimental Neuro-psychobiology Laboratory, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy

Paola Bossù & Sigrid B. Sando

Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway

Geir Bråthen, Ingvild Saltvedt & Sigrid B. Sando

Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway

Geir Bråthen & Myriam Fornage

School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA

Jan Bressler & Myriam Fornage

Dementia Centre for Research Collaboration, School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia

Henry Brodaty

Biosciences, School of Science and Technology, Nottingham Trent University, Nottingham, UK

Keeley J. Brookes

Centro de Neuropsiquiatría y Neurología de la Conducta (CENECON), Facultad de Medicina, Universidad de Buenos Aires (UBA), C.A.B.A., Buenos Aires, Argentina

Luis Ignacio Brusco & Carolina Muchnik

Departamento Ciencias Fisiológicas UAII, Facultad de Medicina, UBA, C.A.B.A., Buenos Aires, Argentina

Luis Ignacio Brusco

Hospital Interzonal General de Agudos Eva Perón, San Martín, Buenos Aires, Argentina

Department of Neurology, Erasmus MC, Rotterdam, the Netherlands

Dolores Buiza-Rueda, Merel Mol, Jeroen van Rooij, John van Swieten, Ad Adarmes-Gómez, D. Macias-García, F. Carrillo, L. Garrote-Espina, M. Carrion-Claro, Ma Labrador, Mt Periñán, P. Gómez-Garre, R. Escuela, R. Pineda-Sánchez, R. Vigo-Ortega & S. Jesús

Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig Maximilians Universität (LMU), Munich, Germany

Katharina Bûrger, Martin Dichgans, Michael Ewers, Daniel Janowitz & Rainer Malik

German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany

Katharina Bûrger & Michael Ewers

Faculty of Medical & Health Sciences, University of Auckland, Auckland, New Zealand

Vanessa Burholt

Wales Centre for Ageing & Dementia Research, Swansea University, Wales, New Zealand

Vanessa Burholt & Robert Perneczky

Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA

William S. Bush & Jonathan L. Haines

UFIEC, Instituto de Salud Carlos III, Madrid, Spain

Miguel Calero

Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA

Laura B. Cantwell, Amanda B. Kuzma & Bruce M. Psaty

INSERM, Bordeaux Population Health Research Center, UMR 1219, ISPED, CIC 1401-EC, Université de Bordeaux, Bordeaux, France

Geneviève Chene & Carole Dufouil

Pole Santé Publique, CHU de Bordeaux, Bordeaux, France

Medicine Biomedical Genetics Boston University School of Medicine, Boston, MA, USA

Jaeyoon Chung, Gyungah R. Jun & Lindsay A. Farrer

Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, USA

Michael L. Cuccaro, Jeffery Vance, Michael L. Cuccaro & John R. Gilbert

Grupo de Medicina Xenómica, Centro Nacional de Genotipado (CEGEN-PRB3-ISCIII), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

Ángel Carracedo, Inés Quintela, Maria Bjerke & Ellen De Roeck

Fundación Pública Galega de Medicina Xenómica- CIBERER-IDIS, University of Santiago de Compostela, Santiago de Compostela, Spain

Ángel Carracedo

Institute of Gerontology and Geriatrics, Department of Medicine and Surgery, University of Perugia, Perugia, Italy

Roberta Cecchetti, Patrizia Mecocci & Michela Scamosci

Department of Genetics and CNR-MAJ, Normandie University, UNIROUEN, INSERM U1245, CHU Rouen, Rouen, France

Camille Charbonnier, Gael Nicolas, Olivier Quenez & Dominique campion

Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA

Hung-Hsin Chen, Jennifer E. Below & Timothy Hohman

Unit of Clinical Pharmacology, University Hospital of Cagliari, Cagliari, Italy

Caterina Chillotti

Radboudumc Alzheimer Center, Department of Geriatrics, Radboud University Medical Center, Nijmegen, the Netherlands

Jurgen A. H. R. Claassen

Institute for Regenerative Medicine, University of Zürich, Schlieren, Switzerland

Christopher Clark & M. Marín

Unidad Clínica de Enfermedades Infecciosas y Microbiología, Hospital Universitario de Valme, Sevilla, Spain

Anaïs Corma-Gómez, Marta Fernández-Fuertes, Juan Macías, Juan A. Pineda, Luis M. Real & Juan Macias

Department of Neuroscience, Catholic University of Sacred Heart, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy

Emanuele Costantini, Antonio Daniele, Michela Orsini & Giordano Lacidogna

University of Bari, “A. Moro”, Bary, Italy

Carlo Custodero & Vincenzo Solfrizzi

School of Medicine, University of Thessaly, Larissa, Greece

Efthimios Dardiotis

Bordeaux Population Health Research Center, University Bordeaux, INSERM, Bordeaux, France

Jean-François Dartigues, Stéphanie Debette & Christophe Tzourio

Department of Neurology, University Medical Center Groningen, Groningen, the Netherlands

Peter Paul de Deyn

Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Katia de Paiva Lopes, Jack Humphrey, Joseph D. Buxbaum & Towfique Raj

Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Katia de Paiva Lopes, Jack Humphrey & Towfique Raj

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Katia de Paiva Lopes, Lot D. de Witte, Gijsje J. L. Snijders, Joseph D. Buxbaum & Mary Sano

Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital, Wuerzburg, Germany

Jürgen Deckert, Martin J. Herrmann & Thomas Polak

UKDRI@ Cardiff, School of Medicine, Cardiff University, Cardiff, UK

Nicola Denning, Valentina Escott-Price, Alun Meggy, Atahualpa Castillo Morales, Mateus Bernardo-Harrington & Julie Williams

Department of Neurology, Boston University School of Medicine, Boston, MA, USA

Anita DeStefano, Claudia L. Satizabal, Hugo J. Aparicio, Lindsay A. Farrer & Sudha Seshadri

Munich Cluster for Systems Neurology (SyNergy), Munich, Germany

Martin Dichgans

Klinikum rechts der Isar, Department of Psychiatry and Psychotherapy, Technical University of Munich, School of Medicine, Munich, Germany

Janine Diehl-Schmid, Oliver Goldhardt & Timo Grimmer

Institute of Cognitive Neurology and Dementia Research (IKND), Otto-Von-Guericke University, Magdeburg, Germany

Emrah Düzel, Nicola Salvadori & Elena Chipi

German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany

Emrah Düzel

Icelandic Heart Association, Kopovagur, Iceland

Gudny Eiriksdottir

Center for Neurosciences, Vrije Universiteit Brussel (VUB), Brussels, Belgium

Sebastiaan Engelborghs

Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium

Institute Born-Bunge, University of Antwerp, Antwerp, Belgium

Department of Neurology, UZ Brussel, Brussels, Belgium

Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN, USA

Kelley M. Faber, Tatiana M. Foroud & Andrew J. Saykin

Fondazione IRCCS, Istituto Neurologico Carlo Besta, Milan, Italy

Tagliavini Fabrizio, Giaccone Giorgio & Giacomina Rossi

Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Herlev, Denmark

Sune Fallgaard Nielsen & Børge G. Nordestgaard

Sanders-Brown Center on Aging, Department of Biostatistics, University of Kentucky, Lexington, KY, USA

David W. Fardo & Linda J. van Eldik

Centre for Memory Disturbances, Lab of Clinical Neurochemistry, Section of Neurology, University of Perugia, Perugia, Italy

Lucia Farotti & Lucilla Parnetti

University of Milan, Milan, Italy

Chiara Fenoglio, Daniela Galimberti, Elio Scarpini & Maria Serpente

Laboratory of Neurogenetics, Department of Internal Medicine, Texas Tech University Health Science Center, Lubbock, TX, USA

Raffaele Ferrari

Reta Lila Weston Research Laboratories, Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK

Raffaele Ferrari & John Hardy

Faculty of Medicine, University of Lisbon, Lisbon, Portugal

Catarina B. Ferreira & Alexandre de Mendonça

Department of Psychiatry, Social Medicine Center East- Donauspital, Vienna, Austria

Peter Fischer

Institute of Clinical Medicine, University of Oslo, Oslo, Norway

Tormod Fladby & Geir Selbæk

Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA

Bernard Fongang, Xueqiu Jian, Claudia L. Satizabal, Habil Zare, Maryam Bahadori, Monica Goss, Timothy Hughes, Debora Melo van Lent, Sudha Seshadri & Alfredo Ramirez

Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK

Nick C. Fox & Jonathan M. Schott

Unidad de Demencias, Servicio de Neurología y Neurofisiología. Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain

Emlio Franco-Macías, María Bernal Sánchez-Arjona, Silvia Rodrigo-Herrero, Marta Marín & Silvia Rodrigo

Instituto de Investigacion Sanitaria ‘Hospital la Paz’ (IdIPaz), Madrid, Spain

María J. Bullido, Ana Frank-García & Angel Martín Montes

Centro de Biología Molecular Severo Ochoa (UAM-CSIC), Madrid, Spain

María J. Bullido, Isabel Sastre Merlín & Isabel Sastre

Hospital Universitario la Paz, Madrid, Spain

Ana Frank-García & Angel Martín Montes

Department of Geriatric Psychiatry, Central Institute for Mental Health, Mannheim, University of Heidelberg, Heidelberg, Germany

Lutz Froelich & Lucrezia Hausner

Department of Genetics and Genomic Sciences, Ronald M. Loeb Center for Alzheimer’s Disease Icahn School of Medicine at Mount Sinai, New York, NY, USA

Brian Fulton-Howard & Alison M. Goate

Alzheimer Research Center & Memory Clinic, Andalusian Institute for Neuroscience, Málaga, Spain

Jose Maria García-Alberca, Silvia Mendoza, Saray Hevilla & Tamara Marín

Hospital Universitario Ramon y Cajal, IRYCIS, Madrid, Spain

Sebastian Garcia-Madrona, Guillermo Garcia-Ribas & María José Casajeros

Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria

Ina Giegling, Annette M. Hartmann & Dan Rujescu

Department of Biostatistics, Epidemiology, and Informatics Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Duber Gomez-Fonseca

CAEBI, Centro Andaluz de Estudios Bioinformáticos, Sevilla, Spain

Antonio González-Pérez & María Eugenia Sáez

Center for Alzheimer Research, Department NVS, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden

Caroline Graff, Charlotte Johansson, Anne Kinhult Ståhlbom, Abbe Ullgren, Charlotte Forsell & Håkan Thonberg

Unit for Hereditary Dementias, Karolinska University Hospital-Solna, Stockholm, Sweden

Caroline Graff, Charlotte Forsell & Håkan Thonberg

Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden

Giulia Grande, Erika J. Laukka & Goran Papenberg

Institute of Public Health, University of Cambridge, Cambridge, UK

Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Zurich, Switzerland

Edna Grünblatt

Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland

Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland

Icelandic Heart Association, Faculty of Medicine, University of Iceland, Reykjavik, Iceland

Vilmundur Gudnason

Human Genetics, School of Life Sciences, Life Sciences Building, University Park, University of Nottingham, Nottingham, UK

Tamar Guetta-Baranes

AI Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland

Annakaisa Haapasalo & Margaret A. Pericak-Vance

Department of Neurology, Medical School, University of Cyprus, Nicosia, Cyprus

Georgios Hadjigeorgiou & Brian W. Kunkle

The John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, USA

Kara L. Hamilton-Nelson, Eden R. Martin, Farid Rajabli, Susan Slifer, Gary W. Beecham, Michael L. Cuccaro, John R. Gilbert, Susan H. Slifer & Patrice L. Whitehead

GRC 21, Alzheimer Precision Medicine Initiative (APMI), Sorbonne University, AP-HP, Pitié-Salpêtrière Hospital, Paris, France

Harald Hampel

Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany

Stefanie Heilmann-Heimbach, Per Hoffmann & Markus M. Nöthen

Institute of Clinical Medicine, Neurology, University of Eastern, Kuopio, Finland

Seppo Helisalmi, Anne Koivisto, Jenni Lehtisalo, Hilkka Soininen & Alina Solomon

Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland

Seppo Helisalmi

Clinical and Experimental Science, Faculty of Medicine, University of Southampton, Southampton, UK

Clive Holmes

Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Jack Humphrey, Joseph D. Buxbaum & Towfique Raj

Department of Neurology, UMC Utrecht Brain Center, Utrecht, the Netherlands

Geert Jan Biessels

Biostatistics, University of Kentucky College of Public Health, Lexington, KY, USA

Yuriko Kastumata

Department of Biology, Brigham Young University, Provo, UT, USA

Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

Patrick G. Kehoe, Seth Love & Olivia Skrobot

Division of Clinical Geriatrics, Center for Alzheimer Research, Care Sciences and Society (NVS), Karolinska Institutet, Stockholm, Sweden

Miia Kivipelto, Francesca Mangialasche & Alina Solomon

Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland

Miia Kivipelto, Tiina Laatikainen & Jaakko Tuomilehto

Neuroepidemiology and Ageing Research Unit, School of Public Health, Imperial College London, London, UK

Miia Kivipelto

Research & Development, UnitStockholms Sjukhem, Stockholm, Sweden

Department of Neurology, Kuopio University Hospital, Kuopio, Finland

Anne Koivisto

Department of Neurosciences, University of Helsinki and Department of Geriatrics, Helsinki University Hospital, Helsinki, Finland

Department of Psychiatry and Psychotherapy, Universitätsklinikum Erlangen, and Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany

Johannes Kornhuber

Laboratory of Cognitive Neuroscience, School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece

Mary H. Kosmidis

Department of Epidemiology, University of Washington, Seattle, WA, USA

Walter A. Kukull

Neurology Service, Marqués de Valdecilla University Hospital (University of Cantabria and IDIVAL), Santander, Spain

Carmen Lage, Eloy Rodriguez-Rodriguez, Pascual Sánchez Juan & Sara López-García

Stockholm Gerontology Research Center, Stockholm, Sweden

Erika J. Laukka

Laboratory of Epidemiology, Demography, and Biometry, National Institute of Aging, The National Institutes of Health, Bethesda, MD, USA

Lenore Launer

Intramural Research Program/National Institute on Aging/National Institutes of Health, Bethesda, MD, USA

Lenore Launer & Jakub Hort

Public Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, Finland

Jenni Lehtisalo, Tiia Ngandu, Tiina Laatikainen, Jaakko Tuomilehto, Jaana Lindström & Markku Peltonen

Memory Clinic, Department of Neurology, Charles University, 2nd Faculty of Medicine and Motol University Hospital, Praha, Czechia

Ondrej Lerch, Martin Vyhnalek, Jan Laczo & Jakub Hort

International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czechia

Ondrej Lerch, Martin Vyhnalek & Jan Laczo

Departments of Neurology and Epidemiology, University of Washington, Seattle, WA, USA

William Longstreth Jr, Thomas D. Bird, Thomas J. Grabowski & Suman Jayadev

Department of Neurology, Hospital Universitario Donostia, OSAKIDETZA-Servicio Vasco de Salud, San Sebastian, Spain

Adolfo Lopez de Munain & Fermin Moreno

Department of Neurology, Columbia University, New York, NY, USA

Yiyi Ma, Dolly Reyes-Dumeyer, Giuseppe Tosto & Sandra Barral

School of Health Sciences, Bangor University, Bangor, UK

Catherine A. MacLeod, Gill Windle & Bob Woods

Institute of Neurology, Catholic University of the Sacred Heart, Rome, Italy

Carlo Masullo

Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA

Richard Mayeux, Christiane Reitz, Sandra Barral & Scott Small

MRC Prion Unit at UCL, UCL Institute of Prion Diseases, London, UK

Simon Mead, John Collinge & James Uphill

Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología. Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain

Pablo Mir, Astrid D. Adarmes-Gómez, Fátima Carrillo, Mario Carrión-Claro, Rocío Escuela, Lorena Garrote-Espina, Pilar Gómez-Garre, Silvia Jesús, Miguel Angel Labrador Espinosa, Daniel Macias-García, María Teresa Periñán-Tocino, Rocío Pineda-Sánchez & Rosario Vigo-Ortega

Institute for Urban Public Health, University Hospital of University Duisburg-Essen, Essen, Germany

Susanne Moebus

Neurological Tissue Bank of the Biobanc-Hospital Clinic-IDIBAPS, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain

Laura Molina-Porcel

Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Department, Hospital Clinic, Barcelona, Spain

Laboratory of Brain Aging and Neurodegeneration, FIL-CONICET, Buenos Aires, Argentina

Laura Morelli

Human Genetics, School of Life Sciences, University of Nottingham, Nottingham, UK

Kevin Morgan

Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA

Thomas Mosley

Laboratorio de Bioquímica Molecular, Facultad de Medicina, Instituto de Investigaciones Médicas A. Lanari, UBA, C.A.B.A, Buenos Aires, Argentina

Carolina Muchnik

Department of Medicine, University of Washington, Seattle, WA, USA

Shubhabrata Mukherjee, Paul K. Crane, Gail P. Jarvik, Wayne C. McCormick, Ellen M. Wijsman & Chang-En Yu

IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy

Benedetta Nacmias & Sandro Sorbi

Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark

Børge G. Nordestgaard, Anne Tybjærg-Hansen & Ruth Frikke-Schmidt

DIMEC, University of Parma, Parma, Italy

Caffarra Paolo

Resources and Research Memory Center (MRRC) of Distalz, LicendUniversity of Lille, INSERM, CHU Lille, UMR1172, Lille, France

Florence Pasquier, Vincent Deramerourt & Vincent Deramecourt

Institut de Biomedicina de València-CSIC CIBERNED, València, Spain

Jordi Pérez-Tur

Unitat Mixta de de Neurología y Genética, Institut d’Investigació Sanitària La Fe, València, Spain

US 41-UMS 2014-PLBS, bilille, Université de Lille, CNRS, INSERM, CHU Lille, Institut Pasteur de Lille, Lille, France

Pierre Pericard

Institute of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany

Oliver Peters

German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany

Oliver Peters & Josef Priller

Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy

Claudia Pisanu

CHUV, Old Age Psychiatry, Department of Psychiatry, Lausanne, Switzerland

Julius Popp

Old Age Psychiatry, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland

Department of Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland

Department of Neuropsychiatry and Laboratory of Molecular Psychiatry, Charité, Charitéplatz 1, Berlin, Germany

Josef Priller

Department of Clinical Biochemistry, Rigshospitalet, Copenhagen, Denmark

Jesper Qvist Thomassen, Anne Tybjærg-Hansen & Ruth Frikke-Schmidt

Department of Psychiatry & Neuropsychologie, Maastricht University, Alzheimer Center Limburg, Maastricht, the Netherlands

Inez Ramakers & Frans Verhey

Depatamento de Especialidades Quirúrgicas Bioquímica e Inmunología, Facultad de Medicina, Universidad de Málaga, Málaga, Spain

Luis M. Real

Delft Bioinformatics Lab, Delft University of Technology, Delft, the Netherlands

Marcel J. T. Reinders & Christiane Reitz

Taub Institute, Columbia University, New York, NY, USA

Christiane Reitz & Dolly Reyes-Dumeyer

Bioinformatics, College of Life Sciences, Brigham Young University, Provo, UT, USA

Perry Ridge

Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Leipzig, Germany

Steffi Riedel-Heller

Center of Mental Health, Clinic and Policlinic of Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Würzburg, Wuerzburg, Germany

Peter Riederer

Department of Research and Innovation, Helse Fonna, Haugesund Hospital, Haugesund, Norway

Arvid Rongve

Institute of Clinical Medicine (K1), The University of Bergen, Bergen, Norway

Departamento de Especialidades Quirúrgicas, Bioquímicas e Inmunología, School of Medicine, University of Málaga, Málaga, Spain

Jose Luis Royo

Department of Neuroscience and Mental Health, AOU Città della Salute e della Scienza di Torino, Torino, Italy

Elisa Rubino

Athens Association of Alzheimer’s Disease and Related Disorders, Athens, Greece

Paraskevi Sakka

Department of Geriatrics, St. Olav’s Hospital, Trondheim University Hospital, Trondheim, Norway

Ingvild Saltvedt

Department of Immunology, Hospital Universitario Doctor Negrín, Las Palmas de Gran Canaria, Las Palmas, Spain

Florentino Sanchez-Garcia

Neurology Department-Hospital Clínic, IDIBAPS, Universitat de Barcelona, Barcelona, Spain

Raquel Sánchez-Valle & Anna Antonell

First Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens, Medical School, Athens, Greece

Nikolaos Scarmeas

LVR-Hospital Essen, Department of Psychiatry and Psychotherapy, Medical Faculty, University of Duisburg-Essen, Essen, Germany

Norbert Scherbaum

Department of Primary Medical Care, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany

Martin Scherer & Hendrik van den Bussche

Institute of Medical Biometry, Informatics and Epidemiology, University Hospital of Bonn, Bonn, Germany

Matthias Schmid & Leonie Weinhold

Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway

Geir Selbæk & Ingun Ulstein

Laboratory for Advanced Hematological Diagnostics, Department of Hematology and Stem Cell Transplant, Vito Fazzi Hospital, Lecce, Italy

Davide Seripa

Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, CIBERDEM, Hospital Clínico San Carlos, Madrid, Spain

Manuel Serrano

Department of Neurology, University of Bonn, Bonn, Germany

Annika Spottke

Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy

Alessio Squassina & Maria Del Zompo

Department of Psychiatry, Namsos Hospital, Namsos, Norway

Eystein Stordal

Department of Internal Medicine and Biostatistics, Erasmus MC, Rotterdam, the Netherlands

Andre Uitterlinden

Neurodegenerative Brain Diseases Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium

Christine Van Broeckhoven

Department of Neurology, ErasmusMC, Rotterdam, the Netherlands

Aad van der Lugt & Jeroen van Rooij

Laboratory for Cognitive Neurology, Department of Neurosciences, University of Leuven, Leuven, Belgium

Rik Vandenberghe

Neurology Department, University Hospitals Leuven, Leuven, Belgium

Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen, Germany

Jonathan Vogelgsang & Jens Wiltfang

Department of Psychiatry, Harvard Medical School, McLean Hospital, Belmont, MA, USA

Jonathan Vogelgsang

Department of Neurology and CNR-MAJ, F 76000, Normandy Center for Genomic and Personalized Medicine, Normandie University, UNIROUEN, INSERM U1245, CHU Rouen, Rouen, France

David Wallon & Didier Hannequin

German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany

Jens Wiltfang

Medical Science Department, iBiMED, Aveiro, Portugal

Department of Nutrition and Diatetics, Harokopio University, Athens, Greece

Mary Yannakoulia & Costas Anastasiou

Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA

Xiaoling Zhang, Congcong Zhu, Clinton T. Baldwin, Jaeyoon Chung, John J. Farrell & Mark Logue

Neurosciences Area, Instituto Biodonostia, San Sebastian, Spain

Miren Zulaica

Department of Health Service, University of Washington, Seattle, WA, USA

Bruce M. Psaty

Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany

Alfredo Ramirez

Department of Clinical Biochemistry, Hematology and Immunology, Na Homolce Hospital, Prague, Czechia

Vaclav Matoska

Institute of Gerontology and Geriatrics, Department of Medicine, University of Perugia, Perugia, Italy

Patrizia Bastiani

Insitute of Biomedicine, University of Eastern Finland, Kuopio, Finland

Mari Takalo & Teemu Natunen

Center for Life Course Health Research, University of Oulu, Oulu, Finland

Riitta Antikainen & Timo Strandberg

Medical Research Center Oulu, Oulu University Hospital, Oulu, Finland

Riitta Antikainen

University of Helsinki and Helsinki University Hospital, Helsinki, Finland

Timo Strandberg

Division of Psychological Medicine and Clinial Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK

Richard Abraham, Paul Hollingworth, Lesley Jones, Michael C. O’Donovan, Michael J. Owen, Michael O’Donovan & Michael Owen

Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK

Ammar Al-Chalabi & Christopher E. Shaw

Division of Psychiatry, University College London, London, UK

Nicholas J. Bass, Gill Livingston, Andrew McQuillin & Nick Bass

Carol Brayne

Institute of Genetics, Queens Medical Centre, University of Nottingham, Nottingham, UK

Kristelle S. Brown & Kristelle Brown

Ageing Group, Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast, UK

David Craig & Peter Passmore

The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK

Pangiotis Deloukas, Rhian Gwilliam & Panagiotis Deloukas

Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK

Nick Fox, Amy Gerrish & Martin Rossor

Mercer’s Institute for Research on Ageing, St James’ Hospital, Dublin, Ireland

Michael Gill, Brian Lawlor & Aoibhinn Lynch

School of Biotechnology, Dublin City University, Dublin, Ireland

• Denise Harold

Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queens University, Belfast, UK

Jarret A. Johnston, Bernadette McGuinness, Peter Passmore & Janet A. Johnston

Department of Psychiatry, University of Oxford, Oxford, UK

Simon Lovestone

Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia

Michelle Lupton, John F. Powell, Petra Proitsi & John Powell

Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK

Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK

David Mann, Nigel M. Hooper & Stuart Pickering-Brown

Oxford Project to Investigate Memory and Ageing (OPTIMA), University of Oxford, Level 4, John Radcliffe Hospital, Oxford, UK

A. David Smith, Donald Warden & Gordon Wilcock

Department of Mental Health Sciences, University College London, London, UK

Hugh Gurling

Stephen Todd

Dementia Research Centre, UCL, London, UK

Catherine Mummery, Nathalie Ryan & Natalie S. Ryan

Servei de Neurologia Hospital Clínic, Universitari de València, Valencia, Spain

María Dolores Alonso

Department of Radiology, University Hospital Bonn, Bonn, Germany

Henning Boecker

German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany

Christoph Laske

Section for Dementia Research, Department of Psychiatry, Hertie Institute for Clinical Brain Research, Tübingen, Germany

Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany

Robert Perneczky

Service of Neurology, University Hospital Marqués de Valdecilla, IDIVAL, University of Cantabria, Santander, Spain

Carmen Lage & Sara López-García

Molecular Medicine Center, Department of Medical chemistry and biochemistry, Medical University of Sofia, Sofia, Bulgaria

Kalina Yonkova Mihova

Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Würzburg, Germany

Heike Weber

ENYS (Estudio en Neurociencias y Sistemas Complejos) CONICET- Hospital El Cruce “Nestor Kirchner”- UNAJ, Buenos Aires, Argentina

Silvia Kochen, Patricia Solis, Nancy Medel, Julieta Lisso, Zulma Sevillano, Daniel G. Politis, Valeria Cores & Carolina Cuesta

HIGA Eva Perón, Buenes Aires, Argentina

Daniel G. Politis, Valeria Cores & Carolina Cuesta

Neurología Clinica, Buenes Aires, Argentina

Cecilia Ortiz & Juan Ignacio Bacha

Dirección de Atención de Adultos Mayores del Min. Salud Desarrollo Social y Deportes de la Pcia. de Mendoza, Mendoza, Argentina

Mario Rios & Aldo Saenz

Laboratorio de Genética Forense del Ministerio Público de la Pcia de La Pampa, La Pampa, Argentina

Mariana Sanchez Abalos

Fundacion Sinapsis, Santa Rosa, Argentina

Eduardo Kohler

Hospital Dr. Lucio Molas, Santa Rosa; Fundacion Ayuda Enfermo Renal y Alta Complejidad (FERNAC), Santa Rosa, Argentina

Dana Lis Palacio, Ignacio Etchepareborda & Matias Kohler

Laboratory of Brain Aging and Neurodegeneration (FIL), Buneos Aires, Argentina

Gisela Novack, Federico Ariel Prestia, Pablo Galeano & Eduardo M. Castaño

Sandra Germani, Carlos Reyes Toso, Matias Rojo, Carlos Ingino, Carlos Mangone & Natividad Olivar

Cambridge Institute for Medical Research and UK Dementia Research Institute, University of Cambridge, Cambridge, UK

David C. Rubinsztein

German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany

Stefan Teipel

María Teresa Martínez-Larrad

Grupo de Medicina Xen´omica, Centro Nacional de Genotipado (CEGEN-PRB3-ISCIII), Universidad de Santiago de Compostela, Santiago de Compostela, Spain

Olalla Maroñas

Servei de Neurologia, Hospital Clínic Universitari de València, Valencia, Spain

Department of Neurology, Hospital Universitario Son Espases, Palma, Spain

Guillermo Amer-Ferrer & Salvadora Manzanares

BIOMICs, País Vasco; Centro de Investigación Lascaray, Universidad del País Vasco UPV/EHU, Vitoria-Gasteiz, Spain

Marian Martinez de Pancorbo

Fundación para la Formación e Investigación Sanitarias de la Región de Murcia, Palma, Spain

Salvadora Manzanares

Centro de Investigación y Terapias Avanzadas, Fundación CITA-Alzheimer, San Sebastian, Spain

Pablo Martínez-Lage Álvarez

Navarrabiomed, Pamplona, Spain

Maite Mendioroz Iriarte

Hospital Universitario La Princesa, Madrid, Spain

Diego Real de Asúa

UMR 7179 CNRS/MNHN, Brunoy, France

• Jacques Epelbaum

Sorbonne Université, Paris Brain Institute, APHP, INSERM, CNRS, Paris, France

Alexis Brice

Department of Neurology, Institute of Memory and Alzheimer’s Disease (IM2A), Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l’Hôpital, Paris, France

Bruno Dubois

Institute of Genetics, Queen’s Medical Centre, University of Nottingham, Nottingham, UK

James Turton & Jenny Lord

Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK

Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK

Elizabeth Fisher, Jason D. Warren, Rita Guerreiro & Robert Clarke

Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

Reinhard Heun & Angela Hodges

Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany

Heike Kölsch & Britta Schürmann

Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

Yogen Patel

Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

Yoav Ben-Shlomo

Division of Clinical Neurosciences, School of Medicine, University of Southampton, Southampton, UK

Rebecca Sussams

Somerset Partnership NHS Trust, Somerset, UK

Nick Warner

Institute of Primary Care and Public Health, Cardiff University, University Hospital of Wales, Cardiff, UK

Anthony Bayer

Department of Psychiatry and Psychotherapy, Charité University Medicine, Berlin, Germany

Isabella Heuser

Cologne Center for Genomics, University of Cologne, Cologne, Germany

Dmitriy Drichel

Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Munich, Germany

Norman Klopp & H-Erich Wichmann

Department of Psychiatry and Psychotherapy, University Hospital, Saarland, Germany

Manuel Mayhaus, Matthias Riemenschneider, Sabrina Pinchler, Thomas Feulner & Wei Gu

Department of Psychiatry, University of Freiburg, Freiburg, Germany

Michael Hüll

Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany

Lutz Frölich

Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany

Karl-Heinz Jöckel

Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway

• Shahram Bahrami

Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway

Ingunn Bosnes

Department of Psychiatry, Hospital Namsos, Nord-Trøndelag Health Trust, Namsos, Norway

Department of Neurology, Akershus University Hospital, Lørenskog, Norway

Centre for Old Age Psychiatry Research, Innlandet Hospital Trust, Ottestad, Norway

Sverre Bergh

Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland

Aarno Palotie, Mark Daly, Jaakko Kaprio, Samuli Ripatti, Nina Mars, Mitja Kurki, Juha Karjalainen, Aki Havulinna, Anu Jalanko, Priit Palta, Pietro della Briotta Parolo, Susanna Lemmelä, Jarmo Harju, Arto Lehisto, Andrea Ganna, Vincent Llorens, Hannele Laivuori, Sina Rüeger, Mari E. Niemi, Taru Tukiainen, Mary Pat Reeve, Henrike Heyne, Tuomo Kiiskinen, Jiwoo Lee, Kristin Tsuo, Amanda Elliott, Risto Kajanne, Mervi Aavikko, Manuel González Jiménez, Pietro della Briotta Parola, Arto Lehistö, Mari Kaunisto, Elina Kilpeläinen, Timo P. Sipilä, Georg Brein, Ghazal Awaisa, Anastasia Shcherban, Kati Donner & Kalle Pärn

AbbVie, Chicago, IL, USA

Howard Jacob, Jeff Waring, Bridget Riley-Gillis, Adam Ziemann, Jeffrey Waring, Sahar Esmaeeli, Nizar Smaoui, Anne Lehtonen, Bob Georgantas, Graham Heap, Fedik Rahimov, Apinya Lertratanakul, Relja Popovic, Justin Wade Davis & Danjuma Quarless

Astra Zeneca, Cambridge, UK

Athena Matakidou, David Close, Ben Challis, Slavé Petrovski & Eleonor Wigmore

Biogen, Cambridge, MA, USA

Heiko Runz, Sally John, Jimmy Liu, Susan Eaton, Sanni Lahdenperä, Stephanie Loomis, Chia-Yen Chen, Paola Bronson, Ellen Tsai & Yunfeng Huang

Celgene, Summit, NJ, USA

Robert Plenge, Shameek Biswas, Janet van Adelsberg, Keith Usiskin, Marla Hochfeld, Steven Greenberg, Joseph Maranville, Elmutaz Shaikho & Elhaj Mohammed

Genentech, San Francisco, CA, USA

Mark McCarthy, Julie Hunkapiller, John Michon, Geoff Kerchner, Natalie Bowers, Edmond Teng, John Eicher, Danny Oh, Hubert Chen, Andrew Peterson, Jennifer Schutzman, Erich Strauss, Hao Chen, David Choy, Diana Chang, Tushar Bhangale & Sarah Pendergrass

GlaxoSmithKline, Brentford, UK

Meg Ehm, Dawn Waterworth, Fanli Xu, David Pulford, Linda McCarthy, Jorge Esparza Gordillo, Jo Betts, Soumitra Ghosh, Diptee Kulkarni, Joshua Hoffman & Karen S. King

Merck, Kenilworth, NJ, USA

Caroline Fox, Dorothee Diogo, Vinay Mehta, Padhraig Gormley, Audrey Chu, Andrey Loboda, Aparna Chhibber, Anna Podgornaia & Emily Holzinger

Pfizer, New York, NY, USA

Anders Malarstig, Catherine Marshall, Xinli Hu, Kari Linden, Christopher Whelan, Kirsi Kalpala, Melissa Miller, Nan Bing, Jaakko Parkkinen, Heli Lehtonen, Stefan McDonough, Xing Chen & Åsa Hedman

Sanofi, Paris, France

Kathy Klinger, Kathy Call, Matthias Gossel, Anthony Muslin, Marika Crohns, Clarence Wang, Ethan Xu, Franck Auge, Clement Chatelain, Deepak Rajpal, Dongyu Liu, Katherine Call & Tai-he Xia

Maze Therapeutics, San Francisco, CA, USA

Tim Behrens, Robert Graham & Matt Brauer

Janssen Biotech, Beerse, Belgium

Patrick Loerch & Beryl Cummings

HiLIFE, University of Helsinki, Helsinki, Finland

Tomi Mäkelä

Auria Biobank, University of Turku, Hospital District of Southwest Finland, Turku, Finland

Petri Virolainen, Kari Pulkki, Johanna Schleutker & Antti Karlsson

THL Biobank, The National Institute of Health and Welfare Helsinki, Helsinki, Finland

Terhi Kilpi, Markus Perola, Kati Kristiansson, Päivi Laiho, Tuuli Sistonen, Essi Kaiharju, Markku Laukkanen, Elina Järvensivu, Sini Lähteenmäki, Lotta Männikkö, Regis Wong, Hannele Mattsson, Tero Hiekkalinna & Teemu Paajanen

Finnish Red Cross Blood Service, Finnish Hematology Registry and Clinical Biobank, Helsinki, Finland

Jukka Partanen & Mikko Arvas

Helsinki Biobank, Helsinki University and Hospital District of Helsinki and Uusimaa, Helsinki, Finland

Anne Pitkäranta, Olli Carpén, Miika Koskinen & Anu Loukola

Northern Finland Biobank Borealis, University of Oulu, Northern Ostrobothnia Hospital District, Oulu, Finland

Riitta Kaarteenaho, Seppo Vainio, Miia Turpeinen, Raisa Serpi, Reetta Hinttala, Johannes Kettunen, Katri Pylkäs, Marita Kalaoja, Minna Karjalainen & Tuomo Mantere

Oxford Healthy Aging Project, Clinical Trial Service Unit, University of Oxford, Oxford, UK

Tarja Laitinen

Finnish Clinical Biobank Tampere, University of Tampere, Pirkanmaa Hospital District, Tampere, Finland

Johanna Mäkelä & Eeva Kangasniemi

Biobank of Eastern Finland, University of Eastern Finland / Northern Savo Hospital District, Kuopio, Finland

Veli-Matti Kosma, Arto Mannermaa & Sami Heikkinen

Central Finland Biobank, University of Jyväskylä, Central Finland Health Care District, Jyväskylä, Finland

Urho Kujala, Jari Laukkanen & Eija Laakkonen

Business Finland, Helsinki, Finland

Outi Tuovila, Minna Hendolin & Raimo Pakkanen

Northern Savo Hospital District, Kuopio, Finland

Valtteri Julkunen, Anne Remes, Reetta Kälviäinen, Markku Laakso, Jussi Pihlajamäki, Kai Kaarniranta, Ilkka Harvima, Maria Siponen, Liisa Suominen & Päivi Mäntylä

Pirkanmaa Hospital District, Tampere, Finland

Jukka Peltola, Hannu Kankaanranta, Annika Auranen, Hannu Uusitalo & Hannele Uusitalo-Järvinen

Hospital District of Helsinki and Uusimaa, Helsinki, Finland

Pentti Tienari, Martti Färkkilä, Sampsa Pikkarainen, Kari Eklund, Paula Kauppi, Juha Sinisalo, Marja-Riitta Taskinen, Tiinamaija Tuomi, Heikki Joensuu, Tuomo Meretoja, Lauri Aaltonen, Johanna Mattson, Joni A. Turunen, Terhi Ollila, Sanna Seitsonen, Katariina Hannula-Jouppi, Sirkku Peltonen, Leena Koulu, Pirkko Pussinen, Aino Salminen, Tuula Salo, David Rice, Pekka Nieminen & Ulla Palotie

Hospital District of Southwest Finland, Turku, Finland

Juha Rinne, Markku Voutilainen, Antti Palomäki, Laura Pirilä, Markus Juonala, Kaj Metsärinne, Klaus Elenius, Vesa Aaltonen & Ulvi Gursoy

Airi Jussila, Pia Isomäki, Mika Kähönen & Teea Salmi

Northern Ostrobothnia Hospital District, Oulu, Finland

Timo Blomster, Johanna Huhtakangas, Terttu Harju, Juhani Junttila, Peeter Karihtala, Saila Kauppila, Päivi Auvinen, Marja Luodonpää, Nina Hautala, Kaisa Tasanen, Laura Huilaja, Vuokko Anttonen & Kirsi Sipilä

Mikko Kiviniemi, Oili Kaipiainen-Seppänen & Margit Pelkonen

The National Institute of Health and Welfare Helsinki, Helsinki, Finland

Veikko Salomaa & Teemu Niiranen

Bristol Myers Squibb, New York, NY, USA

Samir Wadhawan, Erika Kvikstad & Minal Caliskan

Broad Institute, Cambridge, MA, USA

Wei Zhou & Masahiro Kanai

University of Stanford, Stanford, CA, USA

Manuel Rivas

University of Helsinki, Helsinki, Finland

Kimmo Palin

University of Tampere, Tampere, Finland

Javier Garcia-Tabuenca, Harri Siirtola & Javier Gracia-Tabuenca

Finnish Red Cross Blood Service, Helsinki, Finland

Kati Hyvärinen & Jarmo Ritari

University of Turku, Turku, Finland

Csilla Sipeky, Samuel Heron, Dhanaprakash Jambulingam & Venkat Subramaniam Rathinakannan

Sanders-Brown Center on Aging, Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY, USA

Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA

Perrie M. Adams, Munro Cullum & Linda S. Hynan

Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, TX, USA

Alyssa Aguirre, Gayle Ayres & David Paydarfar

Department of Neurology, Johns Hopkins University, Baltimore, MD, USA

Marilyn S. Albert

Department of Neurology, University of Michigan, Ann Arbor, MI, USA

Roger L. Albin & Henry L. Paulson

Geriatric Research, Education and Clinical Center (GRECC), VA Ann Arbor Healthcare System (VAAAHS), Ann Arbor, MI, USA

Roger L. Albin

Michigan Alzheimer’s Disease Center, University of Michigan, Ann Arbor, MI, USA

Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA

Mariet Allen, Minerva M. Carrasquillo, Dennis W. Dickson, Nilüfer Ertekin-Taner & Steven G. Younkin

Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA

Lisa Alvarez, Robert C. Barber, Neill R. Graff-Radford & James Hall

Departments of Neurology, Radiology, and Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA

Liana G. Apostolova

Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA

Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Steven E. Arnold

Geriatric Research, Education and Clinical Center (GRECC), University of Wisconsin, Madison, WI, USA

Sanjay Asthana, Craig S. Atwood & Nathaniel A. Chin

Department of Medicine, University of Wisconsin, Madison, WI, USA

Sanjay Asthana, Craig S. Atwood, Cynthia M. Carlsson, Nathaniel A. Chin & Mark A. Sager

Wisconsin Alzheimer’s Disease Research Center, Madison, WI, USA

Sanjay Asthana, Craig S. Atwood & Cynthia M. Carlsson

Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA

Lisa L. Barnes, David Bennett, Julie A. Schneider & Lei Yu

Department of Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA

Lisa L. Barnes

Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA

Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Phoenix, AZ, USA

Thomas G. Beach

Departments of Psychiatry, Neurology, and Psychology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

James T. Becker & M. Ilyas Kamboh

National Alzheimer’s Coordinating Center, University of Washington, Seattle, WA, USA

Duane Beekly

Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA

Department of Psychiatry and Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University School of Medicine, St. Louis, MO, USA

Bruno A. Benitez & Victoria Fernandez-Hernandez

Department of Psychiatry, University of Texas at Austin/Dell Medical School, Austin, TX, USA

John Bertelson & Martin Woon

Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

Flanagan E. Margaret, Marsel Mesulam & Robert Vassar

Cognitive Neurology and Alzheimer’s Disease Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

VA Puget Sound Health Care System/GRECC, Seattle, WA, USA

Thomas D. Bird & Debby W. Tsuang

Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA

Deborah Blacker

Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA

Deborah Blacker & Mark Logue

Department of Neurology, Mayo Clinic, Rochester, MN, USA

Bradley F. Boeve & Ronald C. Petersen

Swedish Medical Center, Seattle, WA, USA

James D. Bowen

Department of Neurology, University of California San Francisco, San Francisco, CA, USA

Adam Boxer, Anna M. Karydas, Howard J. Rosen & William W. Seeley

Department of Neurosciences, University of California San Diego, La Jolla, CA, USA

James Brewer, Douglas R. Galasko & Eliezer Masliah

Department of Medicine, Duke University, Durham, NC, USA

James R. Burke & Kathleen A. Welsh-Bohmer

University of Kansas Alzheimer’s Disease Center, University of Kansas Medical Center, Kansas City, KS, USA

Jeffrey M. Burns & Russell H. Swerdlow

Department of Pathology and Immunology, Washington University, St. Louis, MO, USA

Nigel J. Cairns & John C. Morris

USF Health Byrd Alzheimer’s Institute, University of South Florida, Tampa, FL, USA

Chuanhai Cao, Ashok Raj & Amanda G. Smith

Fred Hutchinson Cancer Research Center, Seattle, WA, USA

Christopher S. Carlson & Andrew N. McDavid

Mental Health and Behavioral Science Service, Bruce W. Carter VA Medical Center, Miami, FL, USA

Regina M. Carney

Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Scott Chasse & Kirk C. Wilhelmsen

Neurogenetics Program, University of California, Los Angeles, Los Angeles, CA, USA

Marie-Francoise Chesselet

Department of Neurology, University of Southern California, Los Angeles, CA, USA

Helena C. Chui, John M. Ringman, Lon S. Schneider & Harry V. Vinters

Section of Gerontology and Geriatric Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA

Suzanne Craft & Benjamin Williams

Department of Neurology, University of California, Irvine, Irvine, CA, USA

David H. Cribbs & Aimee Pierce

Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA

Elizabeth A. Crocco & Amanda J. Myers

NeuroGenomics and Informatics, Washington University in St. Louis, St. Louis, MO, USA

Carlos Cruchaga & Daniel H. Geschwind

Department of Psychiatry, Washington University in St. Louis, St Louis, MO, USA

Carlos Cruchaga, Oscar Harari, Laura Ibanez & John C. Morris

Alzheimer’s Disease and Memory Disorders Center, Baylor College of Medicine, Houston, TX, USA

Eveleen Darby, Rachelle S. Doody, Aisha Khaleeq, Paul Massman, Valory Pavlik & Monica Rodriguear

Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA

Barbara Davis, Joan S. Reisch & Janet P. Smith

Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA

Philip L. De Jager

Department of Neurology, University of California, Davis, Sacramento, CA, USA

Charles DeCarli

Departments of Neurology, Pharmacology and Neuroscience, Texas Tech University Health Science Center, Lubbock, TX, USA

John DeToledo, Michelle Hernandez, Kim Johnson, Victoria Perez, Henrik Wilms & Chuang-Kuo Wu

Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, Irvine, CA, USA

Malcolm Dick & Wayne W. Poon

Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA

Ranjan Duara

Department of Neurology, Mayo Clinic, Jacksonville, FL, USA

Nilüfer Ertekin-Taner, Neill R. Graff-Radford & Steven G. Younkin

Rush Institute for Healthy Aging, Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA

Denis A. Evans

Office of Strategy and Measurement, University of North Texas Health Science Center, Fort Worth, TX, USA

Thomas J. Fairchild

Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA

Kenneth B. Fallon

Department of Neurology, Indiana University, Indianapolis, IN, USA

Martin R. Farlow

Department of Psychiatry, New York University, New York, NY, USA

Steven Ferris, Arjun Masurkar & Barry Reisberg

C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, Charlestown, MA, USA

Matthew P. Frosch

Department of Neuroscience, Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Brian Fulton-Howard

Department of Health Behavior and Health Systems, University of North Texas Health Science Center, Fort Worth, TX, USA

Adriana Gamboa, Leigh Johnson, Janice Knebl & Douglas Mains

Department of Health Management and Policy, School of Public Health, University of North Texas Health Science Center, Fort Worth, TX, USA

Adriana Gamboa & Douglas Mains

Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA

Marla Gearing

Emory Alzheimer’s Disease Center, Emory University, Atlanta, GA, USA

Department of Pathology and Laboratory Medicine, Indiana University, Indianapolis, IN, USA

Bernardino Ghetti & Salvatore Spina

Department of Radiology, University of Washington, Seattle, WA, USA

Thomas J. Grabowski

Center for Spatial and Functional Genomics, Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

Struan F. A. Grant

Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Struan F. A. Grant & Jennifer E. Phillips-Cremins

Division of Genetics, Department of Medicine and Partners Center for Personalized Genetic Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

Robert C. Green

Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA

John H. Growdon, Bradley T. Hyman & Rudolph E. Tanzi

Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

Hakon Hakonarson & Yuanchao Zhang

Division of Human Genetics, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Hakon Hakonarson

Department of Pathology (Neuropathology), University of Pittsburgh, Pittsburgh, PA, USA

Ronald L. Hamilton

Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA

Lindy E. Harrell, Daniel C. Marson, John C. Morris & Erik D. Roberson

Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine, CA, USA

Elizabeth Head, Ronald Kim & Edwin S. Monuki

Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA

Victor W. Henderson

Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA

Vanderbilt Memory and Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA

Timothy Hohman

Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA

Ryan M. Huebinger

Neurogenomics Division, Translational Genomics Research Institute, Phoenix, AZ, USA

Matthew J. Huentelman & Eric M. Reiman

Department of Pathology, Duke University, Durham, NC, USA

Christine M. Hulette

Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA

Linda S. Hynan, Trung Nguyen & Roger N. Rosenberg

Department of Neurological Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA

Linda S. Hynan

Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University School of Medicine, St. Louis, MO, USA

Laura Ibanez

Department of Genome Sciences, University of Washington, Seattle, WA, USA

Gail P. Jarvik & Ellen M. Wijsman

Department of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, CA, USA

Lee-Way Jin & Joshua W. Miller

Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA

M. Ilyas Kamboh

Alzheimer’s Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA

Department of Neurology, Albert Einstein College of Medicine, New York, NY, USA

Mindy J. Katz & Richard B. Lipton

Department of Neurology, Oregon Health and Science University, Portland, OR, USA

Jeffrey A. Kaye & Joseph F. Quinn

Department of Neurology, Portland Veterans Affairs Medical Center, Portland, OR, USA

Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA

C. Dirk Keene & Joshua A. Sonnen

Department of Pathology, Boston University, Boston, MA, USA

Neil W. Kowall & Ann C. McKee

Department of Neuropsychology, University of California San Francisco, San Francisco, CA, USA

Joel H. Kramer

Department of Neurobiology and Behavior, University of California Irvine, Irvine, CA, USA

Frank M. LaFerla

Department of Neurology, Emory University, Atlanta, GA, USA

James J. Lah, Allan I. Levey & Thomas S. Wingo

Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA

Eric B. Larson

Cleveland Clinic Lou Ruvo Center for Brain Health, Cleveland Clinic, Cleveland, OH, USA

James B. Leverenz

Department of Pathology, University of Michigan, Ann Arbor, MI, USA

Andrew P. Lieberman

National Center for PTSD at Boston VA Healthcare System, Boston, MA, USA

Department of Psychiatry, Johns Hopkins University, Baltimore, MD, USA

Constantine G. Lyketsos

Department of Medicine (Pulmonary), New York University, New York, NY, USA

Frank Martiniuk

Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA

Deborah C. Mash

Department of Pathology, University of California San Diego, La Jolla, CA, USA

Eliezer Masliah

School of Nursing Northwest Research Group on Aging, University of Washington, Seattle, WA, USA

Susan M. McCurry

Pfizer Worldwide Research and Development, New York, NY, USA

Stefan McDonough

Weill Institute for Neurosciences, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA

Bruce L. Miller

Department of Pathology, University of Southern California, Los Angeles, CA, USA

Carol A. Miller

Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

Thomas J. Montine

Department of Neurology, Washington University at St. Louis, St. Louis, MO, USA

John C. Morris

Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX, USA

Sid O’Bryant

Center for Mind and Brain and Department of Neurology, University of California, Davis, Sacramento, CA, USA

John M. Olichney

Center for Population Health and Aging, Texas A&M University Health Science Center, Lubbock, TX, USA

Marcia Ory & Alan B. Stevens

Department of Family and Community Medicine, University of Texas Health Science Center San Antonio, San Antonio, TX, USA

Raymond Palmer & Donald R. Royall

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

Joseph E. Parisi

Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA

Elaine Peskind, Murray Raskind & Debby W. Tsuang

Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

Jennifer E. Phillips-Cremins

Alzheimer’s Disease Center, New York University, New York, NY, USA

Marsha Polk & Barry Reisberg

Department of Neurology, University of Colorado School of Medicine, Aurora, CO, USA

Huntington Potter

Department of Internal Medicine and Geriatrics, University of North Texas Health Science Center, Fort Worth, TX, USA

Mary Quiceno

Department of Medical Education, TCU/UNTHSC School of Medicine, Fort Worth, TX, USA

Arizona Alzheimer’s Consortium, Phoenix, AZ, USA

Eric M. Reiman

Banner Alzheimer’s Institute, Phoenix, AZ, USA

Department of Psychiatry, University of Arizona, Phoenix, AZ, USA

Tanz Centre for Research in Neurodegenerative Disease, University of Toronto, Toronto, Ontario, Canada

Ekaterina Rogaeva

Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA

Andrew J. Saykin

Department of Pathology (Neuropathology), Rush University Medical Center, Chicago, IL, USA

Julie A. Schneider

Department of Psychiatry, University of Southern California, Los Angeles, CA, USA

Lon S. Schneider

Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK

Peter St George-Hyslop

Faculty of Medicine, Department of Medicine (Neurology), University of Toronto, Toronto, Ontario, Canada

Center for Applied Health Research, Baylor Scott & White Health, Temple, TX, USA

Alan B. Stevens

College of Medicine, Texas A&M University Health Science Center, College Station, TX, USA

Program in Cellular Neuroscience, Neurodegeneration and Repair, Yale University School of Medicine, New Haven, CT, USA

Stephen M. Strittmatter

Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, USA

David Sultzer

Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Jeffrey L. Tilson

Department of Pathology, Johns Hopkins University, Baltimore, MD, USA

Juan C. Troncoso

Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, USA

Harry V. Vinters

Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

Sandra Weintraub

Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA

Kathleen A. Welsh-Bohmer

Department of Biostatistics, University of Washington, Seattle, WA, USA

Ellen M. Wijsman

Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA

Thomas Wisniewski

Center for Cognitive Neurology and Departments of Neurology and Pathology, New York University Grossman School of Medicine, New York, NY, USA

Department of Pathology, Oregon Health and Science University, Portland, OR, USA

Randall L. Woltjer

Evelyn F. McKnight Brain Institute, Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA

Clinton B. Wright

Department of Pathology, Case Western Reserve University, Cleveland, OH, USA

Xiongwei Zhu

Centre for Genomic and Precision Medicine, College of Medicine, UI, Ibadan, Nigeria

Rufus O. Akinyemi

Washington University at St. Louis, St. Louis, MO, USA

Muhammad Ali

Mathematics and Statistics, Curtin University, Perth, Western Australia, Australia

Nicola Armstrong

Departments of Psychiatry, Neurology, and Psychology, University of Pittsburgh, Pittsburgh, PA, USA

James T. Becker

Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

Monique Breteler & Dan Liu

Brigham and Women’s Hospital, Harvard University, Boston, MA, USA

Daniel Chasman

INSERM U1219, University of Bordeaux, Bordeaux, France

Ganesh Chauhan

University of Edinburgh, Edinburgh, UK

Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK

Gail Davies

Department of Neurology and Center for Neuroscience, University of California, Davis, Davis, CA, USA

Charles S. DeCarli

Bordeaux Population Health Research Center, Team VIN-TAGE, UMR 1219, University of Bordeaux, INSERM, Bordeaux, France

Marie-Gabrielle Duperron, Quentin Le Grand, Aniket Mishra, Hema Sekhar & Reddy Rajula

University of Pittsburgh, Pittsburgh, PA, USA

Frank Fan & Mary Ganguli

Department of Family Medicine, University of Washington, Seattle, WA, USA

Annette Fitzpatrick

University Medical Center Utrecht, Utrecht, the Netherlands

Mirjam Geerlings

Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab), Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA

Stephen J. Glatt

University of California, San Diego, San Diego, CA, USA

Hector M. Gonzalez

Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany

Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

Mohamad Habes

Susan R. Heckbert

Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria

Edith Hofer

University of Maryland, College Park, MD, USA

Elliot Hong & Jeffrey O’Connell

Department of General Medicine, University of Texas Health Science Center, San Antonio, TX, USA

Tiffany F. Kautz

Monash University Clayton Campus, Mebourne, Victoria, Australia

Paul Lacaze, Moeen Riaz & Stephen Rich

Jari Lahti & Katri Raikkonen-Talvitie

University of Colorado Anschutz Medical Center, Aurora, CO, USA

Elizabeth Litkowski

Medical University of Graz, Graz, Austria

Marisa Loitfelder

Massachusetts General Hospital, Harvard University, Cambridge, MA, USA

Alisa Manning

Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology, University of California, Davis, Davis, CA, USA

Pauline Maillard

University of Staffmail, Edinburgh, UK

Riccardo Marioni

University of Bordeaux, IMN, Bordeaux, France

Bernard Mazoyer

University of Mississippi Medical Center, Jackson, MS, USA

GeneSTAR Research Program, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Paul Nyquist & Lisa Yanek

University of Toronto, Toronto, Ontario, Canada

Departments of Psychiatry & Neuroscience, Centre Hospitalier Universitaire Saint-Justine, University of Montreal, Montreal, Quebec, Canada

Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada

Zdenka Pausova

Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, USA

Jerome Rotter

Boston Medical Center, Boston, MA, USA

Jose Romero

Gottfried Schatz Research Center, Department of Molecular Biology and Biochemistry, Medical University of Graz, Graz, Austria

Yasaman Saba, Murali Sargurupremraj & Helena Schmidt

Reinhold Schmidt

Departments of Neurology, Molecular & Human Genetics, and Neuroscience and Program in Developmental Biology, Baylor College of Medicine, Houston, TX, USA

Joshua M. Shulman

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA

Jennifer Smith

Jeannette Simino, Eeva Sliz & Adrienne Tin

Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany

Alexander Teumer

University of North Carolina, Chapel Hill, NC, USA

Alvin Thomas

University of Texas, Austin, TX, USA

Elliot Tucker-Drob

Galit Weinstein

Karolinska Institute, Stockholm, Sweden

Dylan Williams

German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany

Katharina Wittfeld

Institute of Molecular Medicine, University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, USA

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Contributions

EADB coordination: K. Mather, F.J., M.T., R.F.-S., J. Clarimon, J.-F. Deleuze, O.A.A., M.I., M. Hiltunen, K.S., C.M.v.D., R.S., W.M.v.d.F., A. Ruiz, A. Ramirez and J.-C.L. Data analyses: C. Bellenguez, F.K., I.E.J., L.K., S.M.-G., N.A., R.C., B.G.-B., V. Andrade, P.A.H., R.C.-M., V.D., S.J.v.d.L., M.R.C., T.K., I.R., J. Chapuis and P.G.-G. ADGC analysis and coordination: A.C.N., W.S.B., L.A.F., J.L.H., K.L.H.-N., P.P.K., B.W.K., C.-Y.L. and E.R.M., R. Mayeux, M.A.P.-V., J.S., L.-S.W., Y.Z. and G.D.S. Charge analysis and coordination: Q.Y., J.C.B., A.D.S., C.S., B.M.P., R.W., O. Lopez. and S. Seshadri. FinnGen analysis: T.K. and M. Hiltunen. Rotterdam analysis: A.Y., I.P.-N, M. Ghanbari and M.A.I. Sample contribution: S. Ahmad, V. Giedraitis, D. Aarsland, P.V.Ã., D.G.-G., C. Abdelnour, E.A.-M., D. Alcolea, M. Alegret, I. Alvarez, V. Alvarez, N.J.A., A. Tsolaki, C. Antúnez, I. Appollonio, M. Arcaro, S. Archetti, A.A.P., B.A., L.A., H. Bailly, N.B., M. Baquero, S. Barral, A. Beiser, A.B.P., J.E.B., P. Benchek, L.B., C. Berr, C. Besse, V. Bessi, G. Binetti, A. Bizarro, R.B., M. Boada, E.B., B.B., S. Boschi, P. Bossù, G. Bråthen, J.B., C. Bresner, H. Brodaty, K.J.B., L.I.B., D.B.-R., K.B., V. Burholt, W.S.B., M.C., L.B.C., G.C., J. Chung, M.L.C., Ã.C., R.C., L.C.-C., C. Charbonnier, H.-H.C., C. Chilotti, S.C., J.A.C., C. Clark, E. Conti, A.C.-G., E. Costantini, C. Custodero, D.D., M.C.D., A. Daniele, E. Dardiotis, J-F. Dartigues, P.P.d.D., K.d.P.L., L.D.d.W., S. Debette, J.D., T.d.S., N.D., A. DeStefano, M.D., J.D.-S., M.D.-F., P.D.R., S. Djurovic, E. Duron, E. Duzel, C.D., G.E., S.E., V.E.-P., A.E., M.E., K.M.F., T. Fabrizio, S.F.N., D.W.F., L. Farotti, C.F., M.F.-F., R.F., C.B.F., E.F., B. Fin, P.F., T. Fladby, K.F., B. Fongang, M.F., J.F., T.M.F., S.F., N.C.F., E.F.-M., M.J.B., A.F.-G., L. Froelich, B.F.-H., D.G., J.M.G.-A., S.G.-M., G.G.-R., R.G., I.G., G. Giorgio, A.M.G., O.G., D.G.-F., A.G.-P., C.G., G. Grande, E. Green, T.G., E. Grunblatt, M. Grunin, V. Gudnason, T.G.-B., A.H., G.H., J.L.H., K.L.H.-N., H. Hampel, O.H., J. Hardy, A.M.H., L.H., J. Harwood, S.H.-H., S.H., M.T.H., I.H., M.J.H., P.H., C.H., H. Holstege, R.H.V., M. Hulsman, J. Humphrey, G.J.B., X.J., C.J., G.R.J., Y.K., J. Kauwe, P.G.K., L. Kilander, A.K.S., M.K., A.K., J. Kornhuber, M.H.K., W.A.K., P.P.K., B.W.K., A.B.K., C.L., E.J.L., L. Launer, A. Lauria, C.-Y.L., J.L., O.Ler., A. Lleó, W.L.J., O. Lopez, A.L.d.M., S.L., M.L., L. Luckcuck, K.L.L., Y.M., J.M., C.A.M., W.M., F. Mangialasche, M. Spallazzi, M. Marquié, R. Marshall, E.R.M., A.M.M., C.M.R., C. Masullo, R. Mayeux, S. Mead, P. Mecocci, M. Menéndez-González, A.M., S. Mehrabian, S. Mendoza, M.M.-G., P. Mir, S. Moebus, M. Mol, L.M.-P., L. Montrreal, L. Morelli, F. Moreno, K. Morgan, T. Mosley, M.M.N., C. Muchnik, S. Mukherjee, B.N., T.N., G.N., B.G.N., R.O., A.O., M.O., G.O., A.P., C. Paollo., G. Papenberg, L.P., F.P., P. Pastor, G. Peloso, A.P.-C., J.P.-T., P. Pericard, O.P., Y.A.P., J.A.P., G.P.-R., C. Pisanu, T.P., J. Popp, D.P., J. Priller, R.P., O.Q., I.Q., J.Q.T., A. Rábano, I. Rainero, F.R., I. Ramakers, L.M.R., M.J.R., C.R., D.R.-D., P. Ridge, S.R.-H., P. Riederer, N.R., E.R.-R., A. Rongve, I.R.A., M.R.-R., J.L.R., E.R., D.R., M.E.S., P. Sakka, I.S., Ã.S., M.B.S.-A., F.S.-G., P.S.J., R.S.-V., S.B.S., C.S., C.L.S., M. Scamosci, N. Scarmeas, E. Scarpini, P. Scheltens, N. Scherbaum, M. Scherer, M. Schmid, A. Schneider, J.M.S., G. Selbæk, D.S., M. Serrano, J.S., A.A.S., O.S., S. Slifer, G.J.L.S., H.S., V.S., A. Solomon, Y.S., S. Sorbi, O.S.-G., G. Spalletta, A. Spottke, A. Squassina, E. Stordal, J.P.T., L. Tárraga, N.T., A. Thalamuthu, T.T., G.T., L. Traykov, L. Tremolizzo, A.T.-H., A. Uitterlinden, A. Ullgren, I.U., S.V., O.V., C.V.B., J. Vance, B.N.V., A.v.d.L., J.V.D., J.v.R., J.v.S., R.V., F.V., J.-S.V., J. Vogelgsang, M.V., M.W., D.W., L.-S.W., R.W., L.W., J. Wiltfnag, G.W., B.W., M.Y., H.Z., Y.Z., X.Z., C.Z., M.Z., L.A.F., B.M.P., M. Ghanbari, T.R., P. Sachdev, K. Mather, F.J., M.A.I., A.d.M., J. Hort, M.T. and M.A.P.-V. Core writing group: C. Bellenguez, F.K., V. Andrade, B.G.-B., P.A.H., R.C.-M., L.K., S.J.v.d.L., K.S., A. Ruiz, A. Ramirez and J.-C.L.

Corresponding authors

Correspondence to Céline Bellenguez or Jean-Charles Lambert .

Ethics declarations

Competing interests.

H. Hampel is an employee of Eisai. The present article was initiated and prepared as part of his academic position at Sorbonne University (Paris, France), and it reflects entirely and exclusively his own opinion. He serves as Senior Associate Editor for the Alzheimers & Dementia journal and has not received any fees or honoraria since May 2019. Before May 2019, H. Hampel received lecture fees from Servier, Biogen and Roche; research grants from Pfizer, Avid and MSD Avenir (paid to the institution); travel funding from Eisai, Functional Neuromodulation, Axovant, Eli Lilly and Company, Takeda, Zinfandel Pharmaceuticals, GE Healthcare and Oryzon Genomics; and consultancy fees from Qynapse, Jung Diagnostics, Cytox, Axovant, Anavex, Takeda, Zinfandel Pharmaceuticals, GE Healthcare, Oryzon Genomics and Functional Neuromodulation. He served as a scientific advisory board member for Functional Neuromodulation, Axovant, Eisai, Eli Lilly and Company, Cytox, GE Healthcare, Takeda and Zinfandel, Oryzon Genomics and Roche Diagnostics. The remaining authors declare no competing interests

Peer review

Peer review file.

Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary information

Supplementary information.

Supplementary Note, Methods, Results and Figures 1–50.

Reporting Summary.

Peer review file., supplementary tables.

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Bellenguez, C., Küçükali, F., Jansen, I.E. et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat Genet 54 , 412–436 (2022). https://doi.org/10.1038/s41588-022-01024-z

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Huang X, editor. Alzheimer’s Disease: Drug Discovery [Internet]. Brisbane (AU): Exon Publications; 2020 Dec 18. doi: 10.36255/exonpublications.alzheimersdisease.2020.ch1

Alzheimer’s Disease: Drug Discovery [Internet].

Chapter 1 alzheimer’s disease: etiology, neuropathology and pathogenesis.

Olivia Sheppard and Michael Coleman .

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Alzheimer’s disease is the most common form of dementia and the most common neurodegenerative disease. It manifests as a decline in short-term memory and cognition that impairs daily behavior. Most cases of Alzheimer’s disease are sporadic, but a small minority of inherited forms allow gene identification which, together with neuropathology, yields important clues about the wider causes. Environmental and metabolic risk factors, including inflammation and vascular impairment, play a role in disease onset and progression. While neuronal atrophy and a loss of synapses occur throughout the cerebral cortex, we lack a full understanding of how this arises. The known hallmarks of Alzheimer’s disease include amyloid-β plaques and neurofibrillary tau tangles and while extensive research has been carried out throughout the past few decades, the exact role of these protein aggregates in the disease remains elusive. In this chapter, we discuss mechanisms that have been implicated, including inflammation, mitochondrial dysfunction, oxidative stress and changes in protein clearance.

• INTRODUCTION

Around 50 million people worldwide suffer from dementia ( 1 ). About two thirds have Alzheimer’s disease (AD) ( 2 ), an irreversible neurodegenerative disorder involving a decline in memory and executive function, and personality change ( 3 ). It is named after Alois Alzheimer who first characterized AD in 1906 ( 4 ). AD results in synapse loss and neuronal atrophy predominately throughout the hippocampus and cerebral cortex. It is characterized by amyloid plaques and neurofibrillary tau tangles (NFTs), aggregates of misfolded proteins, throughout the brain. Both genetics and environmental factors are believed to play a role in AD. While there are a small number of cases due to dominant genetic mutations ( 5 – 7 ), a majority of AD cases are sporadic and have no single genetic cause. Environmental and metabolic risk factors such as diabetes, cerebrovascular disease, poor diet, head injury and stress are linked to increased dementia risk. The leading hypothesis as to how AD begins and progresses, the amyloid hypothesis, though quite widely accepted, leaves many questions. In particular it remains unclear “what is the best drug target?” and “what lies upstream of the rise in amyloid-β (Aβ) in sporadic cases?.” We still lack a fundamental understanding of how AD comes to fruition, and therapies to help individuals fight the disease. AD is a chronic disease manifesting as loss of memory, language, cognition and problem-solving skills, changes in behavior and ultimately death. While the primary signs are memory loss and executive dysfunction, they are often preceded by changes in language and vision ( 8 ). Additionally, not all types of memory are equally affected. People with AD have severely impaired episodic, semantic and working memory, yet long-term memory, such as procedural memory, tends to remain intact ( 9 , 10 ). Clinically, AD is classified into seven stages ( Table 1 ) ( 11 ). Patients often die 3–10 years after onset of symptoms ( 12 ) with complications arising from immobility, such as pneumonia or blood clots ( 13 , 14 ).

The seven clinical stages of Alzheimer’s disease (Global Deterioration Scale) (11).

Both genetic and environmental risk factors play a role in the manifestation of AD. The greatest risk factor is age. At age 65, the likelihood of having AD is about 3%, rising to over 30% by age 85 ( 15 ). The incidence of AD under the age of 65 is less certain, but estimates suggest that this age group accounts for around 3% of AD cases ( 15 ). Although overall numbers are increasing with the ageing population, age-specific incidence appears to be falling in several countries ( 16 – 18 ).

AD can be classified by when the disease manifests, and whether it is inherited. Early-onset Alzheimer’s disease (EOAD) occurs before age 65, whereas late-onset Alzheimer’s disease (LOAD) accounts for over 95% of cases ( 19 ) and manifests beyond age 65. Familial AD shows Mendelian (usually dominant) inheritance, while sporadic AD shows no simple familial link ( 20 ). Nearly all EOAD are familial as these cases are due to mutations in APP , PSEN1 or PSEN2 , and a vast majority of LOAD are sporadic. Genome wide association studies (GWAS) and sequencing have now provided more than 20 risk loci in total that contribute to sporadic cases ( 21 ), but often there is no identifiable genetic cause.

Aβ precursor protein

Aβ precursor protein (APP) was the first gene shown to have autosomal dominant mutations causing AD. As the precursor of the aggregated peptide in amyloid plaques, its discovery in 1991 by John Hardy and colleagues ( 5 ) led to the “amyloid hypothesis,” which states that the toxic build-up of Aβ starts a cascade of events, leading to neuronal death and disease ( 22 , 23 ). There are now over 50 known APP mutations, accounting for approximately 10% of familial cases. Widely studied ones include the London (V717I) ( 24 ), Swedish (KM670/671NL) ( 25 ), Indiana (V717F) ( 26 ) and Artic (E693G) ( 27 ) mutations, and most cluster around cleavage sites for β and γ-secretase ( 28 ). Research suggests that many of these mutations increase Aβ production, or the Aβ 42:40 ratio, leading to increased amyloid accumulation. In very rare instances, APP duplication or promoter mutations can cause AD ( 29 , 30 ). Interestingly, studies have also found that there is an APP mutation (Icelandic—A673T) which lowers Aβ and protects against AD ( 31 ).

Presenilins

Presenilin 1 ( PSEN1 ) and Presenilin 2 ( PSEN2 ) encode the catalytic components of γ-secretase, an enzyme complex involved in APP processing ( 32 ). Presenilin mutations cause autosomal dominant AD, with PSEN1 variants being the most commonly known Mendelian genetic cause, estimated to account for around 30–50% of familial EOAD cases ( 33 , 34 ). Research shows that PSEN1 and PSEN2 mutations alter Aβ production, similar to APP mutations ( 35 ) but paradoxically tend to confer loss of function, raising questions as to how this fits the amyloid hypothesis ( 36 , 37 ).

Other genetic risk factors

Other genes known to have variants associated with AD risk include TREM2 ( 38 ), APOE ( 39 ), CLU ( 40 – 42 ), SORL1 ( 43 ), BIN1 ( 42 ) and PICALM ( 40 , 42 ). APOE (apolipoprotein E) is a protein involved in fat metabolism, and its E4 allele is the most common genetic risk factor for AD with an allele frequency of ~13.7% ( 44 , 45 ). Heterozygosity for this allele increases the risk 3-fold ( 39 ). Although rarer, the variant TREM2 R47H (triggering receptor expressed on myeloid cells 2) has a similar effect size ( 46 ). TREM2 is a receptor expressed on multiple cell types of the immune response, and its association supports a role for inflammation in AD pathogenesis.

Down syndrome

By age 65, up to 80% of Down syndrome (DS) individuals develop dementia ( 47 ). As with other instances of EOAD, amyloid and tau pathology begin much earlier than in LOAD, even at <40 years of age ( 48 – 50 ). DS results from the trisomy of chromosome 21, where the APP gene is located, and having three copies of this gene is sufficient to increase Aβ levels. However, the increased risk of developing the disease may also be due in part to triplication of other genes on chromosome 21 ( 47 , 51 , 52 ).

Inflammation

Sporadic AD often results from a combination of genetic and environmental risk factors, with cerebral hypoperfusion ( 53 ) and inflammation ( 54 ) being among the most common. Inflammation due to trauma, sepsis and infection has been linked to both short- and long-term cognitive impairment ( 55 – 57 ). Traumatic brain injury, and even bone fractures in the elderly, are implicated in dementia risk ( 58 , 59 ). Higher levels of inflammatory markers such as interleukin 6 (IL-6) associate with greater risk of AD and vascular dementia ( 60 ). AD patients often have higher levels of certain inflammatory markers and activated microglia and astrocytes in the brain, which tend to surround plaques and tangles ( 61 , 62 ). Finally, higher levels of these markers are associated with faster cognitive decline ( 63 ).

Cerebral, cardiovascular disease and diabetes

There is a strong link between vascular disease and dementia. Cardiovascular disease, including high blood pressure and heart attack, and cerebrovascular disease such as ischemia are associated with increased risk of AD ( 64 ). Metabolic and lifestyle risk factors for developing vascular diseases, including poor diet, obesity, high cholesterol and sedentary lifestyle, are also risk factors for dementia ( 65 , 66 ). Poor diet and high cholesterol can produce metabolic changes both systemically and in the brain, and alter oxygen levels ( 67 ). Additionally, type 2 diabetes approximately doubles the risk for dementia ( 68 – 70 ).

Other environmental risk factors

The list of environmental and metabolic risk factors discussed here is not intended to be comprehensive, especially as the nature of epidemiology in populations with diverse genetics and lifestyle means that important mechanisms will not always generate conclusive evidence. Other risk factors implicated include pollution, stress and heavy metal exposure ( 71 – 76 ). Many of these risk factors share some common characteristics with one another which can thus make it difficult to determine how their presence affects the brain. Some may act through similar mechanisms, such as inflammation or oxidative stress, which will be discussed later in this chapter.

• NEUROPATHOLOGY

AD is characterised by synapse loss, followed by the atrophy of neurons throughout the cerebral cortex, with the medial temporal lobe being the most severely affected ( 77 – 79 ). Pathology appears to start within the hippocampus and entorhinal regions and spreads subsequently throughout the fronto-temporal cortices. It reaches as far as the striatum and thalamus, usually with sparing of the cerebellum ( 80 – 83 ). On a macroscale level, MRI scans show shrinkage of these regions ( 84 ). In particular, pyramidal cells of the CA1 of the hippocampus are vulnerable to morphological changes and cell death, consistent with the main symptom of memory loss ( 85 , 86 ). The appearance of Aβ plaques and NFTs precedes clinical symptoms suggesting that by symptom onset, there have been years of pathological changes making early intervention difficult.

Aβ plaques

Senile plaques are primarily made of a variety of 36–43 residue-long amyloid peptides that undergo fibrilization to form Aβ sheets that are resistant to degradation ( 87 ). They often co-localize with neuronal debris and activated microglia and astrocytes ( 88 ), and first appear in the frontal, temporal and occipital lobes of the neocortex. They spread throughout neocortical areas as well as the hippocampal formation and entorhinal region, and eventually spread further throughout the cerebral cortex to the striatum and thalamus ( 83 ) ( Figure 1 ). Amyloid pathology appears to precede that of tau, with NFTs only being found in regions where amyloid was already present. Numerous studies have shown that cognitively unimpaired elderly individuals can also have significant Aβ deposition ( 89 – 91 ), while on the contrary, others have reported a correlation of deposition to cognitive decline ( 92 ) and dementia severity ( 93 ). A recent study has more specifically shown that differences in Aβ oligomer concentration may be a better correlate of disease ( 94 , 95 ). It is likely that differences in methodology are responsible for the varying conclusions from these studies. It has also been suggested that cognitively normal persons with high plaque levels may have “prodromal” disease, with Aβ pathology that precedes cognitive changes ( 96 , 97 ).

Amyloid and tau pathology . (A) Thioflavin S staining of Aβ plaques in the cortex of a CRND8 APP transgenic mouse. (B) AT8 staining of neurofibrillary tau tangles (NFTs) within an aged human CA1 region of the hippocampus. (C) The spread of amyloid (more...)

Neuronal fibrillary tau tangles

NFTs are intraneuronal aggregates of hyperphosphorylated tau protein, encoded by the microtubule associated protein tau ( MAPT ) gene ( 98 ) ( Figure 1 ). NFTs are composed of paired helical fragments (PHFs) of tau fibrils approximately 20 nm in diameter ( Figure 2 ). Like plaques, they spread throughout the brain as disease progresses, beginning near the entorhinal cortex. Braak staging is commonly used as a means of defining the progression of disease as determined by tau pathology. In stages I–II, tangles appear in the trans-entorhinal region; in stages III–IV, tangles have spread to the limbic system and start to show in the neocortex; in stages V–VI, pathology is present throughout the neocortex ( 83 ) ( Figure 1 ). In addition to AD, several other neurodegenerative diseases are classified as tauopathies due to the presence of NFTs; these include Parkinson’s disease, progressive supranuclear palsy, corticobasal degeneration and frontotemporal dementia (FTD) ( 99 ). While aggregates of amyloid and tau have both been associated with neuronal loss and toxicity, they have a poor correlation with cognitive decline as AD progresses. On the contrary, the loss of synapses is one of the strongest correlates to cognitive decline in AD ( 100 ). Familial cases and PET imaging have allowed us to identify changes in both Aβ and tau prior to changes in brain structure and symptom onset ( 101 ). A combination of psychological and cognitive testing, scans and CSF and blood tests (to rule out other neurological disorders) are required to obtain the diagnosis of AD. Ultimately though, definitive confirmation of the disease requires post-mortem histopathology.

Microtubule-associated protein Tau (MAPT) aggregation results in the accumulation of neurofibrillary tau tangles (NFTs). Tau is believed to play a role in the stabilization of microtubules. Hyperphosphorylated tau polymerization leads to the creation (more...)

• PATHOGENESIS

The mechanism of AD pathology and neuronal loss remains elusive. The roles of both Aβ and tau have been extensively researched in the past few decades, yet we are still unsure of their role in disease. A variety of mechanisms have been proposed to explain what occurs in the pathogenesis of AD. It is possible that different combinations of risk factors in different patients activate the disease in different ways, and that these converge on a common pathway of degeneration.

Aβ and APP

The amyloid hypothesis remains the dominant hypothesis in AD research due to the causal mutations found in both APP and presenilin genes. APP is processed via either the amyloidogenic or non-amyloidogenic pathway. For Aβ, APP is sequentially cleaved by the β- and γ-secretases, releasing the peptide into the cytosol ( Figure 3 ). Functions of APP and Aβ are largely unknown, but they are thought to play a role in signal transduction for neuronal development, growth and survival ( 102 , 103 ). While genetic mutations may explain Aβ accumulation in EOAD, it is still unclear how this occurs in LOAD. Aβ accumulation has been proposed to cause neuronal death via a number of mechanisms, including excitotoxicity, synaptic disruption, oxidative stress and mitochondrial dysfunction. Excitotoxicity can occur when NMDA receptors are continually activated, either by Aβ directly or by a downstream mechanism. In conjunction with synapse loss, both AD patients and animal models show reductions in the synaptic proteins synaptophysin and PSD-95 ( 104 – 108 ). Aβ oligomers accumulating in an AD brain ( 109 ) may be even more toxic than fibrils or plaques. Soluble oligomers appear to amass in a different manner compared to plaques and appear early in pathogenesis ( 110 ). Oligomers can disrupt cognitive function ( 111 ) and inhibit long-term potentiation (LTP) ( 112 ) in vivo , and can be neurotoxic ( 113 ) in vitro . Interestingly, oligomers tend to cluster near synapses ( 114 ) and can induce synapse loss and dysfunction ( 115 ). It has also been suggested that changes in another APP processing product could be a contributor to AD ( 103 ). Though many APP mouse models present with aspects of AD pathology, most fail to fully recapitulate the neurodegeneration seen in the human AD brain. While this most likely reflects inter-species differences, it also raises questions about the relative importance of APP/Aβ in driving dementia ( 116 ).

Post-translational processing of A β precursor protein (APP) is thought to occur at the cell surface or within endosomes. It includes cleavage by either α- then γ-secretase (non-amyloidogenic), or β- then γ-secretase (more...)

NFTs and Tau

While no MAPT mutations are associated with AD, causal mutations in tau have been found for other neurodegenerative diseases such as FTD, suggesting that tau dysfunction and aggregation can be neurotoxic. Tau’s major role is thought to be that of a cytoskeletal protein, interacting with tubulin to help assemble and stabilize microtubules ( 117 ). In humans there are six isoforms of tau generated by alternative splicing of exons 2, 3 and 10. The incorporation of exon 10 leads to four microtubule-binding repeats (4R tau) instead of three (3R tau), altering how tightly the protein binds to microtubules and its propensity to aggregate ( 118 ). Healthy adult humans express similar amounts of 3R and 4R tau. Research has shown that the ratio between the two may impact disease, with higher 4R isoforms leading to greater degeneration. In AD, there is a higher ratio of 4R to 3R, and reported downstream consequences include transcriptional alterations in the Wnt signaling pathway ( 119 ) and altered axonal transport ( 120 ). Prior to NFT formation, tau becomes hyperphosphorylated, and tau phosphorylation not only plays a large role in regulating tau function, but could be the key change resulting in the accumulation, and potential toxicity, of this protein. In fact, multiple tauopathy mutations cause tau to be more readily phosphorylated ( 117 ).

Mutant tau mouse models have shown that mutations in this gene can result in severe neurological phenotypes ( 121 , 122 ). Tau has been hypothesized to induce neurotoxicity via loss of function, gain of function and/or mis-localization. Loss of function of tau occurs when tau is no longer able to stabilize microtubes having an impact on neuronal cytoskeleton, and similarly could lead to deficiencies in axonal transport ( 123 , 124 ). Higher levels of tau have also been shown to inhibit vesicle and organelle trafficking, including those carrying APP, and increase levels of oxidative stress ( 125 ), as well as have an effect on axonal transport ( 126 ). The mis-localization of tau to dendritic spines has been shown to effect cognition and synapses in vivo ( 127 , 128 ). As with APP, it remains unclear as to exactly how tau influences disease progression, but interestingly, Aβ induced toxicity and impairment in LTP has been found to be a requirement for the presence of endogenous tau ( 129 , 130 ). It has also been suggested that tau and Aβ work together to result in transcriptional deficits ( 131 ) and synaptic changes ( 132 ) in AD.

Mitochondrial dysfunction and oxidative stress

One of the many processes that is compromised in AD is mitochondrial function. Alterations in mitochondrial morphology, number and transport, reduced cytochrome oxidase activity, deficiencies in metabolic proteins, changes in mitochondrial membrane potential and an increase in oxidative stress have been observed in AD ( 133 , 134 ). Neurons are highly dependent on mitochondria, and mitochondria accumulate at synapses, helping to power their high metabolic demand. The high level of ROS production which occurs at synapses, in conjunction with insufficient antioxidants, can lead to oxidative stress ( 134 ). In addition, the brain is composed of high levels of cholesterols, which are also very vulnerable to oxidative damage ( 135 ). Thus, the high energy demands of the brain and its high lipid concentration naturally put it at risk for oxidative damage. Rather than aging driving amyloid pathology, as in the case of the amyloid hypothesis, the mitochondrial cascade hypothesis proposes that genetic and environmental factors determine the rate of mitochondrial decline, which in turn determines the rate of aging and subsequently AD ( 133 ). In terms of EOAD, APP or Aβ induces mitochondrial deficits, inducing an increase in the rate of aging, thus making some people susceptible to AD. This has been suggested as a potential link between EOAD and LOAD pathogenesis ( 136 ). Supporting this hypothesis, Thy-1-APP mice show reduced mitochondrial membrane potential and ATP synthesis and increased ROS production ( 137 ). Similarly, transgenic APP mice have shown an increase in Aβ within synaptic mitochondria, leading to dysfunction and oxidative stress prior to plaque accumulation ( 138 ). Paradoxically, oxidative stress, a by-product of mitochondrial deficiency, has been known to affect β-secretase activity ( 139 ), which in turn could alter Aβ production.

Insulin resistance and a decrease in insulin receptors have been observed in the AD brain ( 140 ). Late stages of diabetes also result in insulin resistance in the brain. As cells are heavily dependent upon glucose metabolism for energy production, this can lead to energy deficiencies, potentially leading to oxidative stress. It has also been shown that insulin plays a role in neurotransmission ( 141 ) and can be neuroprotective during insults such as ischemia ( 142 ). Additionally, it has been reported that insulin and metabolic inhibitors result in increased levels of β-secretase in both wild-type and Tg2576 mice (an APP transgenic model). In Tg2576 mice, this also resulted in an increase in Aβ levels ( 143 ). Yet, as others report a protective role of insulin, it is likely that there is a certain level of this hormone which allows the brain to function optimally.

Hypoglycemia and vascular dysfunction

In addition to insulin resistance, the link between diabetes and AD could be due to changes in metabolic proteins, glucose receptors/transporters or even hypoglycemia due to over-medication. Glucose metabolism decreases in the normal aging brain ( 144 ) and even further in the AD brain ( 145 ). It has also been reported that there is a decline in the expression of glucose transporter at the blood brain barrier (BBB) in both AD patients and animal models of AD ( 146 , 147 ), as well as in aged wild-type mice ( 147 , 148 ). In addition, insulin-induced hypoglycemia has also been shown to cause neuronal death in vitro and in vivo ( 149 ). Glucose deprivation can elevate tau levels in vitro ( 150 ), and hypoglycemia has also been linked to increases in oxidative stress ( 151 ). Hypoglycemia could also be the link between cardiovascular and cerebral-vascular diseases and dementia, but whether it be hypoglycemia, hypoxia, a change in another blood component or a combination of these which increases one’s risk of disease is still unknown. Finally, abnormal angiogenesis and alterations of vasculature, including changes in blood flow, have been shown in AD patients and animal models of the disease ( 152 – 154 ).

The role of inflammation is a more recent topic of interest in the AD field. As discussed previously, people with inflammation are more likely to develop dementia, and dementia patients with higher levels of inflammatory markers tend to deteriorate more rapidly. Studies in animal models have shown that inflammation can result in cognitive impairment ( 155 ), as well as neuronal damage and synaptic loss in vivo and in vitro ( 156 – 159 ). Although inflammation and the activation of microglia are thought to play a neuroprotective role in acute circumstances, in the long term, this may lead to neurotoxicity, and an increase in Aβ load ( 155 , 160 , 161 ). Aβ itself is thought to activate microglia, attracting them to plaques and enhancing phagocytosis ( 162 – 164 ). Potentially, microglial response to Aβ is protective, but after chronic activation, the microglia begin to play a detrimental role, resulting in a feed-forward loop of degradation ( 54 ). Similarly, it has been shown that increased ROS levels increase inflammatory markers, and that immune cells influence the production of ROS ( 165 – 168 ), demonstrating the complex interplay between Aβ, oxidative stress and inflammation.

Tau pathology also appears to be influenced by ( 169 , 170 ), and have an effect upon ( 171 , 172 ), inflammation. Research looking at the ability of microglia to phagocytose tau aggregates is conflicting, potentially due to microglia playing an initial role in clearance, but losing their ability to maintain this over extended periods ( 173 ). And finally, it has been reported that altering expression of TREM2, which plays a role in inflammation, may have an effect on Aβ levels and plaque-associated macrophages ( 174 ).

Ubiquitin-proteasome system

The ubiquitin-proteasome system (UPS) is involved in the degradation of misfolded and excess proteins. It is particularly important for synapse function, where there is high protein turnover ( 175 ). Proteins to be degraded go through an enzymatic process where they are labelled with a polyubiquitin chain which is recognized by the proteasome ( 176 ), and subsequently broken down. The proteasome targets monomeric proteins, so is not thought to break down plaques or tangles, but both have been shown to potentially inhibit proteasome activity ( 177 ). This could lead to a toxic build-up of excess and misfolded proteins in the brain, and more specifically synapses.

Autophagy lysosome pathway

Autophagy and lysosomal dysfunction are also proposed mechanisms of AD pathogenesis. Autophagy is involved in tau clearance ( 178 ), and plays a role in both the generation and clearance of Aβ. APP amyloidogenic processing involves trafficking through the endo-lysosomal pathway ( 179 ). Several genes implicated in AD including BIN1 , SORL1 and PICALM are involved in endosomal recycling, and studies have reported that each may directly play a role in APP endosomal processing ( 95 , 180 , 181 ).

Cholinergic hypothesis

The cholinergic hypothesis was one of the first proposed theories on the manifestation of AD ( 182 , 183 ). This came to fruition due to abnormal levels of acetylcholine in the AD brain. Cholinergic neurons of the basal forebrain are one of the earliest affected by AD and there is a decrease in choline acetyltransferase (ChAT) transcription and activity in remaining neurons. Studies have also shown a relationship between acetylcholinesterase (AChE) and Aβ accumulation ( 182 ). However, as the AD field has moved forward there has been difficulty in linking acetylcholine with other AD pathologies. Indeed, pyramidal neurons are lost in greatest numbers in regions with plaques and tangles and these are, for the most part, glutamatergic neurons ( 184 ).

Although we have amassed a vast amount of knowledge in the search for a central, unifying mechanism behind dementia and AD, we are still lacking suitable therapies to help slow down the progression of disease. The amyloid hypothesis remains the dominant theory, yet drugs aimed at lowering Aβ levels have been largely unsuccessful. The possibility of NFT and plaque-load being correlative rather than causative with disease progression is entirely possible. There is much overlap between many of the risk factors, both genetic and environmental, and the known pathogenesis, highlighting the complexity of dementia. Similarly, we lack a firm understanding of how familial EOAD and sporadic LOAD ultimately produce the same neurodegenerative outcome. By enhancing our understanding of AD etiology, pathology and pathogenesis, we hope to one day find an effective therapy.

Acknowledgements: Olivia Sheppard is currently funded by an Alzheimer’s Research UK project grant. The authors thank Dr. Robert Adalbert for contributing the Aβ plaque image in Figure 1A . The authors also thank Dr. Antonina Kouli, Dr. Caroline Williams-Gray and the Cambridge Brain Bank for the NFT image in Figure 1B . The Cambridge Brain Bank is supported by the NIHR Cambridge Biomedical Research Centre.

Conflict of Interest: The authors declare no potential conflicts of interest with respect to research, authorship and/or publication of this chapter.

Copyright and Permission: To the best of our knowledge, the materials included in this chapter do not violate copyright laws. All original sources have been appropriately acknowledged and/or referenced. Where relevant, appropriate permissions have been obtained from the original copyright holder(s).

Doi: https://doi ​.org/10.36255 ​/exonpublications ​.alzheimersdisease.2020.ch1

Licence: This open access article is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). https://creativecommons.org/licenses/by-nc/4.0/

• Cite this Page Sheppard O, Coleman M. Alzheimer’s Disease: Etiology, Neuropathology and Pathogenesis. In: Huang X, editor. Alzheimer’s Disease: Drug Discovery [Internet]. Brisbane (AU): Exon Publications; 2020 Dec 18. Chapter 1. doi: 10.36255/exonpublications.alzheimersdisease.2020.ch1
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A review of the roles of pathogens in Alzheimer's disease

Affiliation.

• 1 Department of Neurology, Neuroscience Center, The First Hospital of Jilin University, Jilin University, Changchun, China.
• PMID: 39224577
• PMCID: PMC11366636
• DOI: 10.3389/fnins.2024.1439055

Alzheimer's disease (AD) is one of the leading causes of dementia and is characterized by memory loss, mental and behavioral abnormalities, and impaired ability to perform daily activities. Even as a global disease that threatens human health, effective treatments to slow the progression of AD have not been found, despite intensive research and significant investment. In recent years, the role of infections in the etiology of AD has sparked intense debate. Pathogens invade the central nervous system through a damaged blood-brain barrier or nerve trunk and disrupt the neuronal structure and function as well as homeostasis of the brain microenvironment through a series of molecular biological events. In this review, we summarize the various pathogens involved in AD pathology, discuss potential interactions between pathogens and AD, and provide an overview of the promising future of anti-pathogenic therapies for AD.

Keywords: Alzheimer’s disease; bacterial infection; fungal infection; pathogen; viral infection.

Copyright © 2024 Zhao, Wang, Shen, Wei, Zhang and Sun.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Mechanisms of pathogens invade the…

Mechanisms of pathogens invade the brain and pathogens drive AD pathology. Pathogens invade…

Anti-pathogenic/anti-inflammation therapies for AD. Pathogens…

Anti-pathogenic/anti-inflammation therapies for AD. Pathogens (virus, bacteria, and fungi) act as risk factors…

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• Volume 73, Issue 10
• Presenilins: the hidden guardians of gut health in Alzheimer’s disease
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• http://orcid.org/0000-0001-9939-7324 Julian Schwärzler 1 ,
• http://orcid.org/0000-0003-3898-7093 Bram Verstockt 2 , 3
• 1 Department of Internal Medicine I, Gastroenterology, Hepatology, Endocrinology, and Metabolism , Medical University of Innsbruck , Innsbruck , Austria
• 2 University Hospitals Leuven, Department of Gastroenterology and Hepatology , KU Leuven , Leuven , Belgium
• 3 Department of Chronic Diseases and Metabolism , KU Leuven , Leuven , Belgium
• Correspondence to Dr Julian Schwärzler, Department of Internal Medicine I, Gastroenterology, Hepatology, Endocrinology & Metabolism, Medical University of Innsbruck, Innsbruck, Austria; julian.schwaerzler{at}i-med.ac.at ; Professor Bram Verstockt, University Hospitals Leuven, Department of Gastroenterology and Hepatology, KU Leuven, Leuven, Belgium; bram.verstockt{at}uzleuven.be

https://doi.org/10.1136/gutjnl-2024-332677

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• GUT INFLAMMATION
• BRAIN/GUT INTERACTION

Inflammatory bowel diseases (IBD) comprise a spectrum of chronic intestinal inflammatory diseases, mainly ulcerative colitis (UC) and Crohn’s disease (CD), with an increasing incidence worldwide. 1 2 Scientific efforts and technological advances led to a profound understanding of IBD pathophysiology, helping to recognise the complex nature and heterogeneity of the IBD spectrum. In recent years, IBD has been increasingly appreciated as a systemic disease, associated with disorders affecting various organs of the body. Moreover, the complex relationship between the gut and the central nervous system (CNS) (termed ‘the gut-brain axis’) is substantially being investigated, exploring the potential causal link between many intestinal and neurological diseases. 3

Several factors might explain the remarkable connection of the gut, and in particular IBD, with neurological diseases. First, chronic intestinal inflammation might damage the CNS through a systemic inflammatory response but also by dysregulating peripheral neurons in the gut. 4 5 Second, disturbed interactions between luminal factors in the gut—particularly microorganisms and their metabolites—the enteric nervous system and the CNS are implicated in IBD, which impair neuron function in the CNS. 6 Third, pathophysiological traits and disease mechanisms might be shared between IBD and neurologic diseases, as suggested earlier in the case of Parkinson’s disease. 7

In Gut, Erkert et al …

JS and BV contributed equally.

Contributors JS and BV contributed equally to the submitted comment.

Funding This study was funded by Tiroler Wissenschaftsförderung (F.45107), Austrian Society for Gastroenterology and Hepatology (ÖGGH), KU Leuven.

Competing interests BV reports research support from AbbVie, Biora Therapeutics, Landos, Pfizer, Sossei Heptares and Takeda; speaker’s fees from Abbvie, Biogen, Bristol Myers Squibb, Celltrion, Chiesi, Falk, Ferring, Galapagos, Janssen, Lily, MSD, Pfizer, R-Biopharm, Sandoz, Takeda, Tillots Pharma, Truvion and Viatris; consultancy fees from Abbvie, Alfasigma, Alimentiv, Applied Strategic, Astrazeneca, Atheneum, BenevolentAI, Biora Therapeutics, Boxer Capital, Bristol Myers Squibb, Galapagos, Guidepont, Landos, Lily, Merck, Mylan, Nxera, Inotrem, Ipsos, Janssen, Pfizer, Progenity, Sandoz, Sanofi, Santa Ana Bio, Sapphire Therapeutics, Sosei Heptares, Takeda, Tillots Pharma and Viatris; stock options Vagustim.

Provenance and peer review Commissioned; internally peer reviewed.

Linked Articles

• Inflammatory bowel disease Alzheimer’s disease-related presenilins are key to intestinal epithelial cell function and gut immune homoeostasis Lena Erkert Reyes Gamez-Belmonte Melanie Kabisch Lena Schödel Jay V Patankar Miguel Gonzalez-Acera Mousumi Mahapatro Li-Li Bao Christina Plattner Anja A Kühl Jie Shen Lutgarde Serneels Bart De Strooper TRR241 IBDome Consortium Markus F Neurath Stefan Wirtz Christoph Becker TRR241 IBDome Consortium Imke Atreya Raja Atreya Petra Bacher Christoph Becker Christian Bojarski Nathalie Britzen-Laurent Caroline Bosch-Voskens Hyun-Dong Chang Andreas Diefenbach Claudia Günther Ahmed N Hegazy Kai Hildner Christoph SN Klose Kristina Koop Susanne Krug Anja A Kühl Moritz Leppkes Rocío López-Posadas Leif S-H Ludwig Clemens Neufert Markus Neurath Jay Patankar Christina Plattner Magdalena Prüß Andreas Radbruch Chiara Romagnani Francesca Ronchi Ashley Sanders Alexander Scheffold Jörg-Dieter Schulzke Michael Schumann Sebastian Schürmann Britta Siegmund Michael Stürzl Zlatko Trajanoski Antigoni Triantafyllopoulou Maximilian Waldner Carl Weidinger Stefan Wirtz Sebastian Zundler Gut 2024; 73 1618-1631 Published Online First: 29 Apr 2024. doi: 10.1136/gutjnl-2023-331622

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iPSC-Based Neuromuscular and Neuronal Models for ALS and Drug Discovery

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Human-induced pluripotent stem cells (iPSCs) can be utilized to study complex health conditions in in vitro platforms relevant to humans. It is possible to reprogram human iPSC lines from patient blood cells and fibroblasts and differentiate them into endpoint cells relevant to the central and peripheral nervous systems, the cardiovascular system, and skeletal muscle alike.

These iPSC-derived cells can be gathered from patients with complex conditions such as Amyotrophic Lateral Sclerosis (ALS), Alzheimer’s Disease, and Parkinson’s Disease, and used in in vitro models for research and discovering new drugs. 1, 2 Merging these cells with micro-physiological systems (MPS) can improve mimicking of the relationship among human cell types in vivo, delivering more comprehensive in vitro representations of human disease.

In the following article, Axol Bioscience demonstrates how multiple MPS platforms with human iPSC-derived neuronal cell types can be used to develop models of neurodegenerative conditions and pain. The compatibility of iPSC-derived neuronal cell types and MPS platforms is essential for the success of instituting complex biological interactions. Axol Bioscience outlines a range of important parameters for making a successful culture using the MPS platforms. Targeted and established biocompatibility is required for approaches that assess cell-to-cell interactions using multimodal endpoints, such as immunocytochemistry, cytokine release, neurite outgrowth, and multi-electrode array (MEA).

This article provides examples of complex human iPSC-based models. These examples include 2D and 3D neuromuscular junction (NMJ) models that make use of iPSC-derived skeletal muscle and motor neurons (to research specific disease-relevant targets in ALS) and simple monoculture systems that use iPSC-derived motor neurons or sensory neurons for axotomy, pain and itch models for discovering new drugs.

Microphysiological systems and 3D organoid models

Multiple systems are now available to build a given tissue model. This article focuses on using 3 distinct systems: standard microfluidic molded devices, 3D bio-printed scaffolds to operate with multi-electrode array instruments , and complex microfluidic systems with integrated MEA technology.

Figure 1. Overview of MPS platforms we currently use: 2D models using standard microfluidic commercial devices, 3D MEA using Axol Bioscience’s proprietary cell scaffold in combination with the Axion Maestro Pro MEA system, and 2D MEA, using the NETRI Neurofluidics TM DuaLink device with the Axion Maestro Pro MEA system. Image Credit: Axol Bioscience Ltd

Key considerations for bio-compatibility

The biocompatibility of the MPS and certain cell types is a crucial point to consider. It is critical for customizing the iPSC-derived neuronal cell types and can improve the reliability and consistency of in vitro models.

Factors for consideration include:

Source: Axol Bioscience Ltd

Extracellular matrix evaluation

Figure 2. Comparison of different matrix coatings for both axoCells sensory neurons and motor neurons, displaying how different coatings can influence neuronal morphology and formation. PDL= Poly-D-Lysine, PLO= Poly-L-Ornithine. Image Credit: Axol Bioscience Ltd

Model surface chemistry

Figure 3.  Specific treatments to the surface chemistry of an MPS system can change the flow dynamics and surface tension of a cellular solution inserted into the device. Treatment with UV and or PVA solution on PDMS microfluidic molds completely alters the surface tension and flow of a solution by converting a highly hydrophobic surface to hydrophilic. PDMS = polydimethylsiloxane. Image Credit: Axol Bioscience Ltd

Assessment of cell performance: MPS compatibility

Image Credit: Axol Bioscience Ltd

Establishment of measurable criteria

Figure 4.   (A) Strategic approach to measuring cell performance across cell culture platforms or MPS devices. On this matrix, a score of 3.5+ is needed to pass each criterion.  (B) A potential model for measurable criteria associated with the neuronal cells and the platform to validate a batch of cells against. Image Credit: Axol Bioscience Ltd

Standard MPS platforms with platform-specific, operationally validated neuronal systems

Properly validated and highly functional cell types can be utilized to develop sophisticated in vitro models with multifaceted end-point readouts. Building these as mono-, co-or even tri-culture with utility in multiple platforms is possible.

Axol Bioscience has utilized this method to build biological models in diverse MPS platforms with rapid maturation.

Figure 5. 2D model: NETRI Microfluidic devices built with axoCells motor neuron and sensory neuron cultures. (A) axoCells iPSC-derived motor neurons at day 10. Red = Beta tubulin marker, yellow = ChAT marker.  (B) axoCells iPSC-derived sensory neurons at day 20. Green = Beta tubulin marker, yellow = Nav 1.7 marker, blue = Dapi.  Image Credit: Axol Bioscience Ltd

Figure 6. 2D model: Neuromuscular junction (NMJ) built with axoCells motor neurons and skeletal muscle in co-culture.  Xona microfluidics co-culture of axoCells human iPSC-derived motor neurons and skeletal muscle as an NMJ model. The motor neurons (stained yellow using NeuN) completely overlap the postsynaptic acetylcholine receptors (stained green using fluorescent α bungarotoxin conjugates) and skeletal muscle (stained red using Titin). Image Credit: Axol Bioscience Ltd

Figure 7. 3D Model: MEA NMJ model measuring motor neuron-induced skeletal muscle contraction for ALS. (A) The MEA platform measures skeletal muscle contraction driven by innervated motor neurons from the scaffold above, where neurites span a 50um fluidic space between the cell types. (B) The C9orf72 hyperexcitability phenotype of the ALS motor neurons is demonstrated here by the increased number of driven contractions (“beats”) per minute, BPM, compared to the healthy control-derived motor neurons. (C) Schematic timeline of the protocol used to generate the assay-ready platform in 15 days. Image Credit: Axol Bioscience Ltd

Developing helpful iPSC –based in vitro models for several platforms necessitates well-verified cell types that have been evaluated with multiple parameters for optimum functionality in the selected platform. This article has discussed key parameters to consider when validating human iPSC-derived neuronal cells and data on potential in vitro models that can be developed using these cells.

Building isolated 2D and 3D culture environments provides distinct approaches for assessing intracell interactions, such as an in vitro neuromuscular junction model. Fueling these culture environments with functional human iPSC-derived cell types has allowed researchers to generate more sophisticated in vitro models relevant to humans for research and discovering new drugs, paving the way for more effective therapies for patients worldwide.

References and further reading

• Nicholson, M., Ting, C., Chan., et al. Utility of iPSC derived cells for disease modelling, drug development, and cell therapy. Cells 11, 1853 (2022). https://ncbi.nlm.nih/pmc/articles/PMC9180434/
• enney, J. et al. Modelling Alzheimer’s disease with iPSC-derived brain cells. Mol Psychiatry 25, 148–167 (2020). https://doi.org/10.1038/s41380-019-0468-3

About AXOL Bioscience

The first choice for high-quality, functionally relevant iPSC-derived cells .

With over a decade of experience, we’ve developed the manufacturing capabilities to produce high-quality, functional iPSC-derived cells with excellent consistency.

Your research can benefit from our quality-focused approach, with our catalog of robust, highly relevant iPSC-derived neurons and cardiomyocytes developed at our ISO 9001:2015-accredited production facility.

Our leading neuronal cell types include: cortical excitatory neurons, striatal neurons, cortical inhibitory interneurons, microglia, astrocytes, sensory neurons and motor neurons. We also provide high-quality atrial cardiomyocytes and ventricular cardiomyocytes, as well as made-to-order myotubes.

Sponsored Content Policy: News-Medical.net publishes articles and related content that may be derived from sources where we have existing commercial relationships, provided such content adds value to the core editorial ethos of News-Medical.Net which is to educate and inform site visitors interested in medical research, science, medical devices and treatments.

Last updated: Sep 16, 2024 at 9:57 AM

Please use one of the following formats to cite this article in your essay, paper or report:

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Axol Bioscience Ltd. "iPSC-Based Neuromuscular and Neuronal Models for ALS and Drug Discovery". News-Medical . 16 September 2024. <https://www.news-medical.net/whitepaper/20240916/iPSC-Based-Neuromuscular-and-Neuronal-Models-for-ALS-and-Drug-Discovery.aspx>.

Axol Bioscience Ltd. "iPSC-Based Neuromuscular and Neuronal Models for ALS and Drug Discovery". News-Medical. https://www.news-medical.net/whitepaper/20240916/iPSC-Based-Neuromuscular-and-Neuronal-Models-for-ALS-and-Drug-Discovery.aspx. (accessed September 16, 2024).

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