The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk
Open accessAbstract
Alzheimer's disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (r g = 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“To better understand AD genetics and the microglia interactome, we distinguished likely causal variants from those in linkage disequilibrium by applying fine mapping and identified 261 credible set variants (18).”
From Brain cell type–specific enhancer–promoter interactome maps and disease - risk association (2019) · cites this paper as [18]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe cited source doesn’t support the claim as written.
In the citing paper
“More than 90 genetic loci are associated with Alzheimer’s disease and related dementia (ADRD).”
From Consensus meta-analysis of genome-wide association studies for Alzheimer’s disease and related dementias (2026) · cites this paper as [3]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“this approach enabled us to define 39 signals in 33 loci already known to be associated with the risk of developing ADD3,5–10”
From New insights into the genetic etiology of Alzheimer’s disease and related dementias (2022) · cites this paper as [3]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In contrast, AD SNP heritability was most highly enriched in microglia regulatory elements (Fig. 1D), specifically microglia enhancers (18-23).”
From Brain cell type–specific enhancer–promoter interactome maps and disease - risk association (2019) · cites this paper as [19]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In contrast, AD SNP heritability was most highly enriched in microglia regulatory elements (Fig. 1D), specifically microglia enhancers (18-23).”
From Brain cell type–specific enhancer–promoter interactome maps and disease - risk association (2019) · cites this paper as [18]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Missense variants with predicted deleterious effects on protein function have been identified by GWAS in TREM2, CR1, MS4A2, MS4A6A, TOMM40, IQCK, and CD33, among others [97, 113]”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [113]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Over the last two decades, genome-wide association studies (GWAS) have identified more than 40 risk loci associated with pathways including lipid metabolism, immune regulation, and endosomal trafficking128,129,150,151.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [129]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We performed single-cell polygenic risk enrichment for AD risk in the microglia trajectory,46,52 and identified a significant increase throughout the trajectory, revealing an enrichment of AD genetic risk single-nucleotide polymorphisms (SNPs) in DAMs (Figure 6H; STAR Methods; Data S3).”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [46]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Microglia, the resident immune cells of the brain, are implicated in the pathology and genetic risk of several CNS diseases, including AD.46,47,48,49”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [46]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“while 12 known ADRD loci are now GWS after focusing on clinically diagnosed AD cases compared with refs. 3,4,6 (NCK2, MME, ANKH/OTULIN, ABCA1, BLNK, IGH, ATP8B4, SNX1/CIAO2A, IL34/MTSS2, SIGLEC11, RBCK1 and APP).”
From Consensus meta-analysis of genome-wide association studies for Alzheimer’s disease and related dementias (2026) · cites this paper as [3]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“All of these loci are novel pleiotropic loci between AD and the respective cardiometabolic traits, but have previously been identified as genome-wide significant for AD in recent single-trait AD GWAS [4, 17, 31].”
From Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits (2021) · cites this paper as [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“microglia-specific pathways, such as Trem2-Tyrobp and ApoE, are critical determinants in AD and other neurodegenerative diseases”
From Cross-Species Single-Cell Analysis Reveals Divergence of the Primate Microglia Program (2019) · cites this paper as Jansen et al., 2019
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“GWAS analyses have identified 75 genome-wide significant (P < 5 × 10−8) loci for LOAD1–3.”
From PICALM Alzheimer’s risk allele causes aberrant lipid droplets in microglia (2025) · cites this paper as [2]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Genome -wide association meta -analysis studies (GWASs) 1,2 and interactome investigation 3 identified microglia gene expression patterns as strongly correlated with late-onset Alzheimer's disease (AD).”
From APOE4 impairs the microglial response in Alzheimer’s disease by inducing TGFβ-mediated checkpoints (2023) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The HLA class II locus encom passing HLA-DRB1/HLA-DQA1 is also a well established AD risk locus, although extensive linkage disequilibrium across this region makes it difficult to pin down the specific causal gene.”
From Is there a role for B cells in Alzheimer's disease? (2026) · cites this paper as [27]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“SCIMP is expressed accross myeloid and B cell lineages and has been identified as an AD-associated locus.”
From Is there a role for B cells in Alzheimer's disease? (2026) · cites this paper as [27]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Indeed, several AD GWAS loci implicate B cell relevant genes as well, albeit with varying strength of evidence and confidence in causal gene assignment. These include SCIMP , BLNK , PLCG2 , HLA class II region and IGHG1 [26 -32].”
From Is there a role for B cells in Alzheimer's disease? (2026) · cites this paper as [27]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Within the APOE region, Jansen et al. [23] identified 8 independent SNPs, in addition to APOE*4 and APOE*2, to be associated with AD risk.”
From Genome-wide analysis identifies novel loci influencing plasma apolipoprotein E concentration and Alzheimer’s disease risk (2023) · cites this paper as [23]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We show here that in addition to the established and opposite associations of APOE*4 and APOE*2 with both AD risk and ApoE level, multiple additional independent AD-associated SNPs reported in the APOE region [23] as well as reported SNPs in some known non-APOE AD loci [5], are also associated with plasma ApoE level.”
From Genome-wide analysis identifies novel loci influencing plasma apolipoprotein E concentration and Alzheimer’s disease risk (2023) · cites this paper as [23]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, a meta-analysis of GWAS colocalized genes showed strong enrichment in genes active in immune-related tissues, including the microglia5, the resident macrophages of the central nervous system (CNS).”
From Variant-to-function mapping of late-onset Alzheimer’s disease GWAS signals in human microglial cell models implicates RTFDC1 at the CASS4 locus (2024) · cites this paper as [5]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Genetic studies have highlighted a prominent role for microglia in susceptibility to different neurodegenerative diseases, particularly Alzheimer’s disease (AD)10–12.”
From Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease (2020) · cites this paper as [10]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Microglia and immune cells showed consistently high scores across regions, with the top scores for the microglia TPT1+ subtype and macrophages in the HC, thalamus and AG (Extended Data Fig. 9c).”
From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [54]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“This method has been used successfully in the past3”
From New insights into the genetic etiology of Alzheimer’s disease and related dementias (2022) · cites this paper as [3]
The cited source says what the claim attributes to it.
In the citing paper
“Next, we determined PLAC-interactions between active promoters and AD-risk credible set variants (19) and identified forty-one genes that were linked to these variants across cell types (fig. S8).”
From Brain cell type–specific enhancer–promoter interactome maps and disease - risk association (2019) · cites this paper as [19]
The cited source says what the claim attributes to it.
In the citing paper
“A broader set of 134 putative risk genes were identified by applying the same analysis to all genome-wide significant variants found in two AD GWASs (fig. S8, S9A, B and Table S7) (18, 19).”
From Brain cell type–specific enhancer–promoter interactome maps and disease - risk association (2019) · cites this paper as [18]
The cited source says what the claim attributes to it.
In the citing paper
“A broader set of 134 putative risk genes were identified by applying the same analysis to all genome-wide significant variants found in two AD GWASs (fig. S8, S9A, B and Table S7) (18, 19).”
From Brain cell type–specific enhancer–promoter interactome maps and disease - risk association (2019) · cites this paper as [19]
The cited source says what the claim attributes to it.
In the citing paper
“Proxy risk for AD, defined as a parental history of AD, exhibits strong genetic correlation with AD [113] and can thus be combined with existing GWAS datasets to enhance statistical power to find true genetic associations with disease.”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [113]
The cited source says what the claim attributes to it.
In the citing paper
“Many of these variants have modest individual effect sizes, indicating a polygenic model in which numerous low-to moderate-impact alleles collectively modulate disease risk129.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [129]
The cited source says what the claim attributes to it.
In the citing paper
“among which a gene set by Jansen et al.46 provided the set of genes associated with AD.”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as Jansen et al.46
The cited source says what the claim attributes to it.
In the citing paper
“human genome-wide association studies (GWASs) support a causal role for immune mechanisms (Jansen et al., 2019; Kunkle et al., 2019; Raj et al., 2014).”
From Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models (2020) · cites this paper as Jansen et al., 2019
The cited source says what the claim attributes to it.
In the citing paper
“discovered as an AD association in Jansen et al. [17]”
From Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits (2021) · cites this paper as Jansen et al. [17]
The cited source says what the claim attributes to it.
In the citing paper
“also discovered in Jansen et al. [17] AD GWAS”
From Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits (2021) · cites this paper as Jansen et al. [17]
The cited source says what the claim attributes to it.
In the citing paper
“The Jansen et al. [17] AD GWAS and many of the cardiometabolic trait GWAS we used included individuals from the UK Biobank dataset.”
From Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits (2021) · cites this paper as [17]
The cited source says what the claim attributes to it.
In the citing paper
“It is possible that some of the pleiotropic loci we detected are due to vascular or mixed dementia patients being included in this AD cohorts, particular since Jansen et al. include some Proxy-AD patients [17] .”
From Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits (2021) · cites this paper as [17]
The cited source says what the claim attributes to it.
In the citing paper
“Previously, genes expressed by microglia have been implicated in AD in human GWASs”
From Cross-Species Single-Cell Analysis Reveals Divergence of the Primate Microglia Program (2019) · cites this paper as Jansen et al., 2019
The cited source says what the claim attributes to it.
In the citing paper
“The importance of microglia in Alzheimer’s disease (AD) pathogenesis has been demonstrated by large-scale genetic association studies, which have identified AD risk variants in a growing list of microglia-related genes1–4.”
From Somatic cancer variants enriched in Alzheimer’s disease microglia-like cells drive inflammatory and proliferative states (2026) · cites this paper as [2]
The cited source says what the claim attributes to it.
In the citing paper
“Very recent GWASs have strengthened the importance of APP metabolism by directly pointing out two of its main actors as AD genetic risk factors: ADAM10, which carries out the main α-secretase activity in the brain, and APH1B, which is part of the γ-secretase complex [59, 76].”
From The new genetic landscape of Alzheimer’s disease: from amyloid cascade to genetically driven synaptic failure hypothesis? (2019) · cites this paper as [59]
The cited source says what the claim attributes to it.
In the citing paper
“since the seminal GWAS papers published in 2009 [47, 83], more than 30 loci of interest have been identified to be associated with AD risk (Suppl. Table) [50, 59, 67, 76, 84, 97, 116, 120].”
From The new genetic landscape of Alzheimer’s disease: from amyloid cascade to genetically driven synaptic failure hypothesis? (2019) · cites this paper as [59]
The cited source says what the claim attributes to it.
In the citing paper
“We calculated the disease-relevance score of each cell in the dataset against a recent Alzheimer’s GWAS, using scDRS (based on MAGMA)54,55,116. For the scDRS results, we counted the fraction of cells with significant scDRS scores (FDR < 0.05) in each cell type, subtype and region. To test for overlap with microglia/immune modules, we compared the set of immune cells with significant expression of”
From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [54]
The cited source says what the claim attributes to it.
In the citing paper
“The ECHDC3 locus has been associated with AD risk in several GWAS23,46,49.”
From Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies (2022) · cites this paper as [49]
The cited source says what the claim attributes to it.
In the citing paper
“However, the association of this rs13500 CH25H promoter polymorphism was not found in two subsequent studies [87, 88] nor has an association between CH25H and AD risk be observed in several large GWA studies, making this association “suggestive” at best [89–91].”
From 25-Hydroxycholesterol amplifies microglial IL-1β production in an apoE isoform-dependent manner (2020) · cites this paper as [90]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The induction of MGnD genes in microglia with deletions in Itgb8 , Tgfbr2 , Nrros or Smad2 / Smad3 (Extended Data Fig. 9e-h) is associated with the suppression of AD risk factor genes, including Bin1 , Havcr2 and Inpp5d 1,2 (Fig. 8j, Extended Data Fig. 9i-l and Supplementary Table 8).”
From APOE4 impairs the microglial response in Alzheimer’s disease by inducing TGFβ-mediated checkpoints (2023) · cites this paper as [1]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“IGHG1 , in contrast, is less well established as a causal AD gene and is more accurately described as residing within an AD associated locus rather than as a validated risk gene.”
From Is there a role for B cells in Alzheimer's disease? (2026) · cites this paper as [27]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Two further loci provide more direct mechanistic links to B cell receptor (BCR) signaling: BLNK is a central adaptor downstream of the BCR, and PLCG2 is a major BCR proximal signaling node implicated in germinal center and memory B cell formation.”
From Is there a role for B cells in Alzheimer's disease? (2026) · cites this paper as [27]
Experimental — accuracy not yet measuredIn the citing paper
“the basis of the observed association between age of dementia onset and these variants in TSPAN14 and TSPAN10 (both with a deleterious correlation) may result from disruption of a protective role of ADAM10.”
From Genetic associations with age at dementia onset in the PSEN1 E280A Colombian kindred (2023) · cites this paper as [25]
In the citing paper
“The latest GWAS studies published during 2019 mark the largest of their kind for Alzheimer’s disease featuring 71 880 Alzheimer’s disease cases to identify 9 novel risk loci (Jansen et al., 2019), and 35 274 clinically assessed Alzheimer’s disease cases to identify 5 novel risk loci from the updated IGAP study (Kunkle et al., 2019).”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Jansen et al., 2019
In the citing paper
“Several genesassoci-ated with AD incidence (APOE, CLU, ABCA7, SORL1, INPP5D, and PLCG2)(Jansen et al., 2019; Kunkle et al., 2019; Marioni et al., 2018) also function in lipid transport or signaling.”
From Alzheimer’s Patient Microglia Exhibit Enhanced Aging and Unique Transcriptional Activation (2020) · cites this paper as Jansen et al., 2019
In the citing paper
“these approaches show substantial genetic overlap with previous case-control GWAS and nominate a number of novel loci associated with proxy risk for AD [112–114].”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [113]
In the citing paper
“Consistent with previous studies16,42,49, these genes were highly expressed in the postnatal brain samples (Fig. 4d) and microglia (Fig. 4e).”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [49]
In the citing paper
“Our GWAS results also include candidate genes VAMP4 and NOD2 from the endocytic pathway (38).”
From Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies (2019) · cites this paper as [38]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
Wang, K., Li, M., & Hakonarson, H. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq603 · Cited by 14,724
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728
Second-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709
Biological insights from 108 schizophrenia-associated genetic loci
Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) · Nature · doi:10.1038/nature13595 · Cited by 7,577
Self-citationIntegrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368
A general framework for estimating the relative pathogenicity of human genetic variants
Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., & Shendure, J. (2014) · Nature Genetics · doi:10.1038/ng.2892 · Cited by 6,183
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559
Self-citationThe Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062
Cited by
Sorted by citations
Items 401–500 of 1,013
Role of primary aging hallmarks in Alzheimer´s disease
Zhao, J., & Huai, J. (2022) · Theranostics · doi:10.7150/thno.79535 · Cited by 55
What does heritability of Alzheimer’s disease represent?
Baker, E. A., Leonenko, G., Schmidt, K. M., Hill, M., Myers, A., Shoai, M., de Rojas, I., Tesi, N., Holstege, H. H., van der Flier, W. M., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281440 · Cited by 55
Self-citationModifiable cardiovascular risk factors and genetics for targeted prevention of dementia
Rasmussen, I. J., & Frikke‐Schmidt, R. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad293 · Cited by 55
Yang, Z., Wen, J., Abdulkadir, A., Cui, Y., Erus, G., Mamourian, E., Melhem, R., Srinivasan, D., Govindarajan, S. T., Chen, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44271-2 · Cited by 55
Understanding microglial diversity and implications for neuronal function in health and disease
Schepper, S. D., Crowley, G., & Hong, S. Y. (2020) · Developmental Neurobiology · doi:10.1002/dneu.22777 · Cited by 54
Initiative*, T. A. D. N., Zettergren, A., Lord, J., Ashton, N. J., Benedet, A. L., Karikari, T. K., Lantero‐Rodriguez, J., Snellman, A., Suárez‐Calvet, M., Proitsi, P., et al. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-020-00754-8 · Cited by 55
Self-citationMIND Dietary Pattern and Its Association with Cognition and Incident Dementia in the UK Biobank
Cornelis, M. C., Agarwal, P., Holland, T. M., & van Dam, R. M. (2022) · Nutrients · doi:10.3390/nu15010032 · Cited by 54
Neurodegeneration cell per cell
Balusu, S., Praschberger, R., Lauwers, E., Strooper, B. D., & Verstreken, P. (2023) · Neuron · doi:10.1016/j.neuron.2023.01.016 · Cited by 53
Jin, L., Lucente, J. D., Mendiola, U. R., Suthprasertporn, N., Tomilov, A. A., Cortopassi, G. A., Kim, K., Ramsey, J. J., & Maezawa, I. (2023) · The FASEB Journal · doi:10.1096/fj.202301254r · Cited by 54
Brain Lipids and Lipid Droplet Dysregulation in Alzheimer’s Disease and Neuropsychiatric Disorders
Zhao, X., Zhang, S., Sanders, A. R., & Duan, J. (2023) · Complex Psychiatry · doi:10.1159/000535131 · Cited by 54
Wojtas, A., Dammer, E. B., Guo, Q., Ping, L., Shantaraman, A., Duong, D. M., Yin, L., Fox, E. J., Seifar, F., Lee, E. B., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13821 · Cited by 57
Liu, Y., Xiao, X., Yang, Y., Yao, R., Yang, Q., Zhu, Y., Yang, X., Zhang, S., Shen, L. H., & Jiao, B. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14049 · Cited by 54
Fröhlich, A. S., Gerstner, N., Gagliardi, M., Ködel, M., Yusupov, N., Matosin, N., Czamara, D., Sauer, S., Roeh, S., Murek, V., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01742-z · Cited by 54
Wu, Y., Broadaway, K. A., Raulerson, C. K., Scott, L. J., Pan, C., Ko, A., He, A., Tilford, C. A., Fuchsberger, C., Locke, A. E., et al. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz263 · Cited by 54
Sherva, R., Gross, A., Mukherjee, S., Koesterer, R., Amouyel, P., Bellenguez, C., Dufouil, C., Bennett, D. A., Chibnik, L., Cruchaga, C., et al. (2020) · Alzheimer's & Dementia · doi:10.1002/alz.12106 · Cited by 54
PICALM Alzheimer’s risk allele causes aberrant lipid droplets in microglia
Kozlova, A., Zhang, S., Sudwarts, A., Zhang, H., Smirnou, S., Byeon, S. K., Thapa, C., Sun, X., Stephenson, K., Zhao, X., et al. (2025) · Nature · doi:10.1038/s41586-025-09486-x · Cited by 53
Mirabnahrazam, G., Ma, D., Beaulac, C., Lee, S., Popuri, K., Lee, H., Cao, J., Galvin, J. E., Wang, L., & Beg, M. F. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.10.005 · Cited by 53
Integrative genetic analysis illuminates ALS heritability and identifies risk genes
Megat, S., Mora, N., Sanogo, J., Roman, O., Catanese, A., Alami, N. O., Freischmidt, A., Mingaj, X., de Calbiac, H., Muratet, F., et al. (2023) · Nature Communications · doi:10.1038/s41467-022-35724-1 · Cited by 53
Wu, Y., Qi, T., Wray, N. R., Visscher, P. M., Zeng, J., & Yang, J. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100344 · Cited by 53
Polygenic risk score as clinical utility in psychiatry: a clinical viewpoint
Ikeda, M., Saito, T., Kanazawa, T., & Iwata, N. (2020) · Journal of Human Genetics · doi:10.1038/s10038-020-0814-y · Cited by 53
Scieszka, D. P., Hunter, R., Begay, J., Bitsui, M., Lin, Y., Galewsky, J., Morishita, M., Klaver, Z. M., Wagner, J. G., Harkema, J. R., et al. (2021) · Toxicological Sciences · doi:10.1093/toxsci/kfab147 · Cited by 53
Lin, J.-R., Sin‐Chan, P., Napolioni, V., Torres, G. G., Mitra, J., Zhang, Q., Jabalameli, M. R., Wang, Z., Nguyen, N., Gao, T., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00108-5 · Cited by 52
Lynall, M.-E., Soskic, B., Hayhurst, J. D., Schwartzentruber, J., Levey, D. F., Pathak, G. A., Polimanti, R. A., Gelernter, J. E., Stein, M. B., Trynka, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33885-7 · Cited by 53
Chen, W., Feng, J., Jiang, S., Guo, J., Zhang, X., Zhang, X., Wang, C., Ma, Y., & Dong, Z. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.07.044 · Cited by 52
T cell infiltration mediates neurodegeneration and cognitive decline in Alzheimer's disease
Zeng, J., Liao, Z., Yang, H., Wang, Q., Wu, Z., Hua, F., & Zhou, Z. (2024) · Neurobiology of Disease · doi:10.1016/j.nbd.2024.106461 · Cited by 53
Massively parallel techniques for cataloguing the regulome of the human brain
Townsley, K. G., Brennand, K., & Huckins, L. M. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00740-1 · Cited by 52
PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics
Zhao, Z., Yi, Y., Song, J., Wu, Y., Zhong, X., Lin, Y., Hohman, T. J., Fletcher, J. M., & Lu, Q. (2021) · Genome biology · doi:10.1186/s13059-021-02479-9 · Cited by 52
Self-citationA human brain vascular atlas reveals diverse cell mediators of Alzheimer’s disease risk
Yang, A. C., Vest, R. T., Kern, F., Lee, D. P., Maat, C. A., Losada, P. M., Chen, M. B., Agam, M., Schaum, N., Khoury, N., et al. (2021) · doi:10.1101/2021.04.26.441262 · Cited by 48
Liu, H., Hu, Y., Zhang, Y., Zhang, H., Gao, S., Wang, L., Wang, T., Han, Z., Sun, B.-L., & Liu, G. (2022) · Alzheimer's Research & Therapy · doi:10.1186/s13195-022-00963-3 · Cited by 51
Aging, Senescence, and Dementia
Behfar, Q., Zuniga, A. R., & Adami, P. V. M. (2022) · The Journal of Prevention of Alzheimer s Disease · doi:10.14283/jpad.2022.42 · Cited by 52
Yuan, S., Ma, W., Yang, R., Xu, F., Han, D., Huang, T., Peng, M., Xu, A., & Lyu, J. (2022) · BMC Geriatrics · doi:10.1186/s12877-022-03298-8 · Cited by 51
Tipping points in neurodegeneration
Simons, M., Levin, J., & Dichgans, M. (2023) · Neuron · doi:10.1016/j.neuron.2023.05.031 · Cited by 52
Yang, H., Teng, L., Kang, D., Menon, V., Ge, T., Finucane, H. K., Schultz, A. P., Properzi, M. J., Klein, H., Chibnik, L. B., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43132-2 · Cited by 56
Self-citationPeripheral blood DNA methylation differences in twin pairs discordant for Alzheimer’s disease
Konki, M., Malonzo, M., Karlsson, I., Lindgrén, N., Ghimire, B. R., Smolander, J., Scheinin, N. M., Ollikainen, M., Laiho, A., Elo, L. L., et al. (2019) · Clinical Epigenetics · doi:10.1186/s13148-019-0729-7 · Cited by 51
Self-citationAge-dependent effect of APOE and polygenic component on Alzheimer's disease
Bellou, E., Baker, E. A., Leonenko, G., Bracher‐Smith, M., Daunt, P., Menzies, G. E., Williams, J. H., & Escott‐Price, V. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.04.024 · Cited by 51
Milind, N., Preuß, C., Haber, A., Ananda, G., Mukherjee, S., Cai, J., Shapley, S. M., Tyler, A. L., Logsdon, B. A., Crane, P. K., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008775 · Cited by 51
Chai, A. B., Hartz, A. M. S., Gao, X., Yang, A., Callaghan, R., & Gelissen, I. C. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms22010246 · Cited by 51
Patel, D., Zhang, X., Farrell, J. J., Chung, J., Stein, T. D., Lunetta, K. L., & Farrer, L. A. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01373-z · Cited by 51
The aging immune system in Alzheimer’s and Parkinson’s diseases
Heavener, K. S., & Bradshaw, E. M. (2022) · Seminars in Immunopathology · doi:10.1007/s00281-022-00944-6 · Cited by 51
Differential microRNA expression analyses across two brain regions in Alzheimer’s disease
Dobricic, V., Schilling, M., Schulz, J., Zhu, L.-S., Zhou, C.-W., Fuß, J., Franzenburg, S., Zhu, L.-Q., Parkkinen, L., Lill, C. M., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02108-4 · Cited by 48
Predictive classification of Alzheimer’s disease using brain imaging and genetic data
Sheng, J., Xin, Y., Zhang, Q., Wang, L., Yang, Z., & Yin, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-06444-9 · Cited by 50
Mirabnahrazam, G., Initiative, T. A. D. N., Ma, D., Lee, S., Popuri, K., Lee, H., Cao, J., Wang, L., Galvin, J. E., & Beg, M. F. (2022) · Journal of Alzheimer's Disease · doi:10.3233/jad-220021 · Cited by 50
Cui, Y., Arnold, F. J., Peng, F., Wang, D., Li, J. S., Michels, S., Wagner, E. J., Spada, A. R. L., & Li, W. (2023) · Nature Communications · doi:10.1038/s41467-023-36311-8 · Cited by 50
Progress and trends in neurological disorders research based on deep learning
Iqbal, M. S., Heyat, M. B. B., Parveen, S., Hayat, M. A. B., Roshanzamir, M., Alizadehsani, R., Akhtar, F., Sayeed, E., Hussain, S., Hussein, H. S., et al. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102400 · Cited by 50
HENA, heterogeneous network-based data set for Alzheimer’s disease
Sügis, E., Dauvillier, J., Leontjeva, A., Adler, P., Hindie, V., Moncion, T., Collura, V., Daudin, R., Loe-Mie, Y., Hérault, Y., et al. (2019) · Scientific Data · doi:10.1038/s41597-019-0152-0 · Cited by 50
Alzheimer’s disease-related transcriptional sex differences in myeloid cells
Coales, I., Tsartsalis, S., Fancy, N., Weinert, M., Clode, D., Owen, D., & Matthews, P. M. (2022) · Journal of Neuroinflammation · doi:10.1186/s12974-022-02604-w · Cited by 49
Tu, L. M. T. P. P. L. M., Hoang, T. X., Thu, V. T. A., Pham, H. L., Le, H. T. N., Chinnadayyala, S. R., Kim, J. Y., Lee, S.-M., Cho, W. W., Kim, Y. H., et al. (2021) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2021.1887732 · Cited by 49
snpXplorer: a web application to explore human SNP-associations and annotate SNP-sets
Tesi, N., van der Lee, S. J., Hulsman, M., Holstege, H. H., & Reinders, M. J. T. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab410 · Cited by 49
Physiological clearance of Aβ by spleen and splenectomy aggravates Alzheimer‐type pathogenesis
Yu, Z., Chen, D., Tan, C., Zeng, G., He, C.-Y., Wang, J., Bu, X., & Wang, Y. (2021) · Aging Cell · doi:10.1111/acel.13533 · Cited by 49
Self-citationCary, G. A., Wiley, J. C., Gockley, J., Keegan, S., Amirtha Ganesh, S. S., Heath, L., Butler, R. R., Mangravite, L. M., Logsdon, B. A., Longo, F. M., et al. (2024) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1002/trc2.12461 · Cited by 49
Zhuang, Z., Gao, M., Yang, R., Liu, Z., Cao, W., & Huang, T. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.10.022 · Cited by 49
Pleiotropic Roles of P2X7 in the Central Nervous System
Kanellopoulos, J. M., & Delarasse, C. (2019) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2019.00401 · Cited by 48
Phosphoproteomics identifies microglial Siglec‐F inflammatory response during neurodegeneration
Morshed, N., Ralvenius, W. T., Nott, A., Watson, L. A., Rodriguez, F. H., Akay, L. A., Joughin, B. A., Pao, P., Penney, J., LaRocque, L. M., et al. (2020) · Molecular Systems Biology · doi:10.15252/msb.20209819 · Cited by 48
Hu, Y., Zhang, Y., Zhang, H., Gao, S., Wang, L., Wang, T., Han, Z., Sun, B., & Liu, G. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01695-4 · Cited by 48
The Role of MEF2 Transcription Factor Family in Neuronal Survival and Degeneration
Lisek, M., Przybyszewski, O., Żylińska, L., Guo, F., & Boczek, T. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24043120 · Cited by 48
Genetic architecture of brain age and its causal relations with brain and mental disorders
Leonardsen, E. H., Dídac Vidal-piñeiro, A., Roe, J. M., Frei, O., Shadrin, A. A., Iakunchykova, O. P., de Lange, A. G., Kaufmann, T., Taschler, B., Smith, S. M., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02087-y · Cited by 48
Self-citationTranscriptomic landscape, gene signatures and regulatory profile of aging in the human brain
González-Velasco, Ó., Papy-García, D., Douaron, G. L., Sánchez-Santos, J. M., & Rivas, J. D. L. (2020) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2020.194491 · Cited by 48
Mahootchi, E., Homaei, S. C., Kleppe, R., Winge, I., Hegvik, T., Pérez, R., Totland, C., Mogavero, F., Baumann, A., Glennon, J. C., et al. (2020) · Science Advances · doi:10.1126/sciadv.abb3713 · Cited by 48
Genome-wide epistasis analysis for Alzheimer’s disease and implications for genetic risk prediction
Wang, H., Bennett, D. A. A., Jager, P. L. D., Zhang, Q., & Zhang, H. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00794-8 · Cited by 48
Review of multi-omics data resources and integrative analysis for human brain disorders
Dong, X., Liu, C., & Dozmorov, M. (2021) · Briefings in Functional Genomics · doi:10.1093/bfgp/elab024 · Cited by 47
Socioeconomic Deprivation, Genetic Risk, and Incident Dementia
Klee, M., Leist, A. K., Veldsman, M., Ranson, J. M., & Llewellyn, D. J. (2023) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2023.01.012 · Cited by 47
Bottero, V., & Potashkin, J. A. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20215403 · Cited by 47
From beta amyloid to altered proteostasis in Alzheimer’s disease
Bruni, A. C., Bernardi, L., & Gabelli, C. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101126 · Cited by 47
Patient-Derived Midbrain Organoids to Explore the Molecular Basis of Parkinson's Disease
Galet, B., Cheval, H., & Ravassard, P. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.01005 · Cited by 47
Intellectual disability: dendritic anomalies and emerging genetic perspectives
Quach, T., Stratton, H. J., Khanna, R., Kolattukudy, P. E., Honnorat, J., Meyer, K., & Duchemin, A. (2020) · Acta Neuropathologica · doi:10.1007/s00401-020-02244-5 · Cited by 47
Glinos, D. A., Soskic, B., Williams, C., Kennedy, A., Jostins, L., Sansom, D. M., & Trynka, G. (2020) · Genes and Immunity · doi:10.1038/s41435-020-00118-0 · Cited by 47
Stefanovski, L., Meier, J. M., Pai, R. K., Triebkorn, P., Lett, T. A. P., Martin, L., Bülau, K., Hofmann‐Apitius, M., Solodkin, A., McIntosh, A. R., et al. (2021) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2021.630172 · Cited by 47
Zhang, L., Rietz, E. D., Kuja‐Halkola, R., Dobrosavljevic, M., Johnell, K., Pedersen, N. L., Larsson, H., & Chang, Z. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.12462 · Cited by 47
Self-citationCell type-specific potential pathogenic genes and functional pathways in Alzheimer’s Disease
Wang, X., & Li, L. (2021) · BMC Neurology · doi:10.1186/s12883-021-02407-1 · Cited by 47
Wei, Y., de Lange, S. C., Pijnenburg, R., Scholtens, L. H., Ardesch, D. J., Watanabe, K., Posthuma, D., & van den Heuvel, M. P. (2021) · Human Brain Mapping · doi:10.1002/hbm.25711 · Cited by 47
Self-citationParp mutations protect from mitochondrial toxicity in Alzheimer’s disease
Yu, Y., Fedele, G., Celardo, I., Loh, S. H. Y., & Martins, L. M. (2021) · Cell Death & Disease · doi:10.1038/s41419-021-03926-y · Cited by 46
Homann, J., Osburg, T., Ohlei, O., Dobricic, V., Deecke, L., Bos, I., Vandenberghe, R., Gabel, S., Scheltens, P., Teunissen, C. E., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.840651 · Cited by 46
Self-citationTwo macrophages, osteoclasts and microglia: from development to pleiotropy
Lee, J., Lee, I., Iimura, T., & Kong, S. W. (2021) · Bone Research · doi:10.1038/s41413-020-00134-w · Cited by 46
Panyard, D. J., McKetney, J., Deming, Y. K., Morrow, A. R., Ennis, G. E., Jonaitis, E. M., Hulle, C. A. V., Yang, C., Sung, Y. J., Ali, M., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13130 · Cited by 48
Petralla, S., Panayotova, M., Franchina, E., Fricker, G., & Puris, E. (2024) · Pharmaceutics · doi:10.3390/pharmaceutics16070948 · Cited by 46
Kikuchi, M., Sekiya, M., Hara, N., Miyashita, A., Kuwano, R., Ikeuchi, T., Iijima, K., & Nakaya, A. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddz320 · Cited by 46
Wu, Y., Sun, Z., Zheng, Q., Miao, J., Dorn, S., Mukherjee, S., Fletcher, J. M., & Lu, Q. (2024) · Nature Genetics · doi:10.1038/s41588-024-01963-9 · Cited by 45
Neuroimmunometabolism: A New Pathological Nexus Underlying Neurodegenerative Disorders
Mitra, S., Banik, A., Saurabh, S., Maulik, M., & Khatri, S. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0998-21.2022 · Cited by 45
Shi, H., Yin, Z., Koronyo, Y., Fuchs, D.-T. S., Sheyn, J., Davis, M. R., Wilson, J. W., Margeta, M. A., Pitts, K. M., Herron, S., et al. (2022) · Acta Neuropathologica Communications · doi:10.1186/s40478-022-01439-z · Cited by 45
Ma, Q., Shams, H., Didonna, A., Baranzini, S. E., Cree, B., Hauser, S. L., Henry, R. G., & Oksenberg, J. R. (2023) · Communications Biology · doi:10.1038/s42003-023-04713-5 · Cited by 45
Tang, X., Tena, J., Lucente, J. D., Maezawa, I., Harvey, D. J., Jin, L., Lebrilla, C. B., & Zivkovic, A. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-34787-4 · Cited by 45
Multiscale proteomic modeling reveals protein networks driving Alzheimer’s disease pathogenesis
Wang, E., Yu, K., Cao, J., Wang, M., Katsel, P., Song, W., Wang, Z., Li, Y., Wang, X., Wang, Q., et al. (2025) · Cell · doi:10.1016/j.cell.2025.08.038 · Cited by 45
Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power
Glasser, M. F., Coalson, T. S., Bijsterbosch, J. D., Harrison, S. J., Harms, M. P., Anticevic, A., Essen, D. C. V., & Smith, S. M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.046 · Cited by 45
Genetic Dissection of Alzheimer’s Disease Using Drosophila Models
Jeon, Y., Lee, J., Choi, B., Won, S.-Y., & Cho, K. S. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21030884 · Cited by 46
Impact of glucose on risk of dementia: Mendelian randomisation studies in 115,875 individuals
Benn, M., Nordestgaard, B. G., Tybjærg‐Hansen, A., & Frikke‐Schmidt, R. (2020) · Diabetologia · doi:10.1007/s00125-020-05124-5 · Cited by 46
Genomic mechanisms in Alzheimer's disease
Bertram, L., & Tanzi, R. E. (2020) · Brain Pathology · doi:10.1111/bpa.12882 · Cited by 45
Cuddy, L. K., Prokopenko, D., Cunningham, E. P., Brimberry, R., Song, P., Kirchner, R., Chapman, B., Hofmann, O., Hide, W., Procissi, D., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aaz2541 · Cited by 45
Bothongo, P. L., Jitlal, M., Parry, E., Waters, S., Foote, I. F., Watson, C. J., Cuzick, J. M., Giovannoni, G., Dobson, R., Noyce, A. J., et al. (2022) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2022.100321 · Cited by 45
Self-citationRegulation of the Late Onset alzheimer’s Disease Associated HLA-DQA1/DRB1 Expression
Zhang, X., Zou, M., Wu, Y., Jiang, D., Wu, T., Zhao, Y., Wu, D., Cui, J., & Li, G. (2022) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/15333175221085066 · Cited by 45
Adherence to MIND Diet, Genetic Susceptibility, and Incident Dementia in Three US Cohorts
Vu, T. T., Beck, T. L., Bennett, D. A., Schneider, J. A., Hayden, K. M., Shadyab, A. H., Rajan, K. B., Morris, M. C., & Cornelis, M. C. (2022) · Nutrients · doi:10.3390/nu14132759 · Cited by 45
Population-level variation in enhancer expression identifies disease mechanisms in the human brain
Dong, P., Hoffman, G. E., Apontes, P., Bendl, J. M., Rahman, S., Fernando, M. B., Zeng, B., Vicari, J. M., Zhang, W., Girdhar, K., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01170-4 · Cited by 45
Prokopenko, D., Hecker, J., Kirchner, R., Chapman, B., Hofmann, O., Mullin, K., Hide, W., Bertram, L., Laird, N. M., DeMeo, D. L., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-61883-6 · Cited by 44
Drug repositioning for psychiatric and neurological disorders through a network medicine approach
Dias, T. L., Schuch, V., Beltrão-Braga, P. C. B., Martins‐de‐Souza, D., Brentani, H., Franco, G. R., & Nakaya, H. I. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0827-5 · Cited by 44
Schoentgen, B., Gagliardi, G. P., & Defontaines, B. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01814 · Cited by 44
Choudhuri, A., Trompouki, E., Abraham, B. J., Colli, L. M., Kock, K. H., Mallard, W., Yang, M.-L., Vinjamur, D. S., Ghamari, A., Sporrij, A., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-00738-2 · Cited by 44
Marshe, V. S., Maciukiewicz, M., Hauschild, A.-C., Islam, F., Li, Q., Tiwari, A. K., Sibille, E. L., Blumberger, D. M., Karp, J. F., Flint, A. J., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01248-3 · Cited by 44
Uncovering the impact of noncoding variants in neurodegenerative brain diseases
Frydas, A., Wauters, E., van der Zee, J., & Broeckhoven, C. V. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.08.010 · Cited by 44
Soliai, M. M., Kato, A., Helling, B. A., Stanhope, C. T., Norton, J. E., Naughton, K. A., Klinger, A. I., Thompson, E. E., Clay, S. M., Kim, S., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00967-y · Cited by 44
Crossing borders in Alzheimer’s disease: A T cell’s perspective
van Olst, L., Coenen, L., Nieuwland, J. M., Rodriguez-Mogeda, C., de Wit, N. M., Kamermans, A., Middeldorp, J., & de Vries, H. E. (2022) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2022.114398 · Cited by 45
Martin, S., Tyrrell, J., Thomas, E. L., Bown, M. J., Wood, A. R., Beaumont, R. N., Tsoi, L. C., Stuart, P. E., Elder, J. T., Law, P., et al. (2022) · eLife · doi:10.7554/elife.72452 · Cited by 43
Metadata
- Authors
- Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, I. K., Hägg, S., Athanasiu, L., Voyle, N., Proitsi, P., Witoelar, A., Stringer, S., Aarsland, D., Almdahl, I. S., Andersen, F., Bergh, S., Bettella, F., Bjornsson, S., Brækhus, A., Bråthen, G., de Leeuw, C., Desikan, R. S., Djurovic, S., Dumitrescu, L., Fladby, T., Hohman, T. J., Jonsson, P. V., Kiddle, S. J., Rongve, A., Saltvedt, I., Sando, S. B., Selbæk, G., Shoai, M., Skene, N. G., Snaedal, J., Stordal, E., Ulstein, I. D., Wang, Y., White, L. R., Hardy, J., Hjerling-Leffler, J., Sullivan, P. F., van der Flier, W. M., Dobson, R., Davis, L. K., Stefansson, H., Stefansson, K., Pedersen, N. L., Ripke, S., Andreassen, O. A., & Posthuma, D.
- Affiliations
- Amsterdam Neuroscience (NL)
- Karolinska Institutet (SE)
- deCODE Genetics (Iceland) (IS)
- Vanderbilt University (US)
- Oslo University Hospital (NO)
- King's College London (GB)
- University of Oslo (NO)
- UiT The Arctic University of Norway (NO)
- Innlandet Hospital Trust (NO)
- Norwegian University of Science and Technology (NO)
- University of California, San Francisco (US)
- Vanderbilt University Medical Center (US)
- University of Cambridge (GB)
- Fonna Hospital Trust (NO)
- UCL Queen Square Institute of Neurology (GB)
- University of North Carolina at Chapel Hill (US)
- University of London (GB)
- Broad Institute (US)
- ORCID
- Jansen, Iris E.
- Savage, Jeanne E.
- Watanabe, Kyoko
- Bryois, Julien
- Williams, Dylan M.
- Steinberg, Stacy
- Sealock, Julia
- Karlsson, Ida K.
- Hägg, Sara
- Athanasiu, Lavinia
- Proitsi, Petroula
- Witoelar, Aree
- Stringer, Sven
- Aarsland, Dag
- Almdahl, Ina S.
- Bergh, Sverre
- Bettella, Francesco
- Bråthen, Geir
- de Leeuw, Christiaan
- Desikan, Rahul S.
- Djurovic, Srdjan
- Dumitrescu, Logan
- Fladby, Tormod
- Hohman, Timothy J.
- Kiddle, Steven J.
- Rongve, Arvid
- Saltvedt, Ingvild
- Sando, Sigrid B.
- Selbæk, Geir
- Shoai, Maryam
- Skene, Nathan G.
- Stordal, Eystein
- Wang, Yunpeng
- Hardy, John
- Hjerling-Leffler, Jens
- Sullivan, Patrick F.
- Dobson, Richard
- Davis, Lea K.
- Stefansson, Kari
- Pedersen, Nancy L.
- Ripke, Stephan
- Andreassen, Ole A.
- Posthuma, Danielle
- Venue
- Nature Genetics
- Published
- 2019-01-07
- Volume
- 51
- Issue
- 3
- Pages
- 404-413
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC6836675
- OpenAlex
- W2908002252
- Cited by
- 2,277
- References
- 74
- Citations by year
- 2026: 178 · 2025: 290 · 2024: 337 · 2023: 348 · 2022: 350 · 2021: 387 · 2020: 282 · 2019: 102
- FWCI
- 197.10
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-02-06)
- Open access
- green
- research.vu.nl
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- VU Research Portal (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Pure Amsterdam UMC (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Hirsla (Landspítali University Hospital) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Apollo
- Funders
- National Science Foundation (FP7/2007-2013, U01AG016976)
- Alzheimer's Association (U01 AG016976, U01 AG032984, ADGC-10–196728, U24AG021886, AG033193)
- Case Western Reserve University
- National Institute for Social Care and Health Research (MR/K006584/1)
- Wellcome Trust (FP7/2007–2013, MR/K006584/1, 082604/2/07/Z, 108726, 082604, /Z/15/Z, MR/K006584, 115372, 108726/Z/15/Z)
- EU Joint Programme – Neurodegenerative Disease Research (237250)
- European Federation of Pharmaceutical Industries and Associations (FP7/20072013, 115372)
- Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease
- Université de Lille
- Cancer Research UK (FP7/2007-2013, 12100, 115372)
- National Institute for Health and Care Research (115372, FP7/2007–2013)
- British Heart Foundation (115372)
- University College London
- European Commission (2007–2013, FP7 2007-2013, ANR-13, 286213, FP7/2007, 248778, FP6-2004-LIFESCIHEALTH-5, 115372)
- Agence Nationale de la Recherche (ANR-13-JPRF-0001)
- Institut National de la Santé et de la Recherche Médicale
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (FP7/2007-2013, 15435)
- Vrije Universiteit Amsterdam (FP7/2007-2013)
- Bundesministerium für Bildung und Forschung (01GI0102, 01GI0711, 01GI0420)
- Austrian Science Fund (P20545, P 13180)
- Erasmus Medisch Centrum
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (480- 05-003, FP7 2007-2013, VICI 453-14-005, NWO VICI 453-14-005, 453-14-005)
- University of Toronto
- Hjärnfonden
- Oesterreichische Nationalbank (15435)
- Vetenskapsrådet (FP7/2007-2013)
- Österreichische Forschungsförderungsgesellschaft (FP7/2007-2013, 827462)
- Norges Forskningsråd (223273, #251134, 213837, 237250, 248778, #225989)
- Karl-Franzens-Universität Graz
- Medizinische Universität Graz
- Hjartavernd (N01-AG-1-2100)
- Nasjonalforeningen for Folkehelsen
- Centre hospitalier régional universitaire de Lille (503480, 503176, 082604/2/07/Z)
- National Institutes of Health (AG081220, HHSN268201100012C, FP7/2007-2013, HHSN268201100008C, HHSN268201100006C, 223273, N01-AG-1-2100, HHSN26820- 1100005C, HHSN268201100007C, -0001, HHSN268201100011C, HHSN268201100010C, HHSN268201100009C, 213837, NIA AG081220, AG016976, 2U01-HL-096902, 2U01-HL-096899)
- Medical Research Council (FP7/20072013, MR/K006584/1, 503480, K006584/1, 108726/Z/15/Z, MC_PC_17228, MR/P021573/1, MR/L011859/1, MC_PC_17214)
- Engineering and Physical Sciences Research Council (FP7/2007–2013)
- Economic and Social Research Council (FP7/2007-2013)
- National Institute on Aging (AG 016976, N01-AG12100, R01AG059716, AG037985, AG081220, U01AG016976, 2U01HL096899, NIA AG081220, K01AG049164, U01AG032984, AG033193, U01 2U01HL096812, AG032984, 2U01HL096902, R01AG033193, AG021886, U24AG021886)
- National Heart, Lung, and Blood Institute (HHSN268201 100012C, HHSN268201100007C, HHSN2682 01100008C, 2U01HL096899, HH-SN-268201100006C, HHSN268201100011C, HL-105756, U01 2U01HL096812, HHSN268201100005C, HHSN268201100010C, U01HL096814, HHSN26820 1100009C, R01HL105756, U01HL096917, AG033193, 2U01HL096902, RC2HL102419, U01HL096812)
- National Institute of Mental Health (U01MH109536)
- National Institute on Deafness and Other Communication Disorders (2U01HL096899, U01 2U01HL096812, 2U01HL096902)
- National Institute of General Medical Sciences (T32GM080178)
- National Institute of Diabetes and Digestive and Kidney Diseases (DK066134, U01DK066134, 2U01HL096899, U01 2U01HL096812, 2U01HL096902)
- National Institute of Neurological Disorders and Stroke (AG033193, HHSN268201100005C, 2U01HL096899, 2U01HL096902, FP7/2007-2013, U012U01HL096812)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- Alzheimer's disease, genetic susceptibility loci, genetic correlation, microglia, lipid metabolism, cognitive ability, Mendelian randomization, gene set enrichment analysis
- MeSH terms
- Adult, Alzheimer Disease, Female, Humans, Male, Middle Aged, Risk, Case-Control Studies, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome-Wide Association Study, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
