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
“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
“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]
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 201–300 of 1,013
PICALM and Alzheimer’s Disease: An Update and Perspectives
Ando, K., Nagaraj, S., Küçükali, F., de Fisenne, M.-A., Kosa, A.-C., Doeraene, E., Lopez Gutierrez, L., Brion, J.-P., & Leroy, K. (2022) · Cells · doi:10.3390/cells11243994 · Cited by 104
Heterogeneity of microglia and their differential roles in white matter pathology
Lee, J. C. L., Hamanaka, G., Lo, E. H., & Arai, K. (2019) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.13266 · Cited by 105
Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders
Hu, B., Won, H., Mah, W., Park, R. B., Kassim, B., Spiess, K., Kozlenkov, A., Crowley, C. A., Pochareddy, S., The PsychENCODE Consortium, et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24243-0 · Cited by 103
Self-citationShigemizu, D., Mori, T., Akiyama, S., Higaki, S., Watanabe, H., Sakurai, T., Niida, S., & Ozaki, K. (2020) · Alzheimer's Research & Therapy · doi:10.1186/s13195-020-00654-x · Cited by 103
Signalling, sorting and scaffolding adaptors for Toll-like receptors
Luo, L., Lucas, R. M., Liu, L., & Stow, J. L. (2019) · Journal of Cell Science · doi:10.1242/jcs.239194 · Cited by 103
Sex differences in the genetic architecture of cognitive resilience to Alzheimer’s disease
Eissman, J. M., Dumitrescu, L. C., Mahoney, E. R., Smith, A. N., Mukherjee, S., Lee, M. L., Scollard, P., Choi, S. E., Bush, W. S., Engelman, C. D., et al. (2022) · Brain · doi:10.1093/brain/awac177 · Cited by 103
Self-citationShared heritability of human face and brain shape
Naqvi, S., Sleyp, Y., Hoskens, H., Indencleef, K., Spence, J. P., Bruffaerts, R., Radwan, A., Eller, R. J., Richmond, S., Shriver, M. D., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00827-w · Cited by 100
Ou, Y., Yang, Y., Deng, Y., Zhang, C. M., Hu, H., Wu, B., Liu, Y., Wang, Y., Zhu, Y., Suckling, J., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01251-6 · Cited by 100
Self-citationThe 100-plus Study of cognitively healthy centenarians: rationale, design and cohort description
Holstege, H. H., Beker, N., Dijkstra, T., Pieterse, K., Wemmenhove, E., Schouten, K., Thiessens, L., Horsten, D., Rechtuijt, S., Sikkes, S. A. M., et al. (2018) · European Journal of Epidemiology · doi:10.1007/s10654-018-0451-3 · Cited by 103
Resting State fMRI and Improved Deep Learning Algorithm for Earlier Detection of Alzheimer’s Disease
Guo, H., & Zhang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3003424 · Cited by 99
Evaluation of CD33 as a genetic risk factor for Alzheimer’s disease
Estus, S., Shaw, B. C., Devanney, N. A., Katsumata, Y., Press, E., & Fardo, D. W. (2019) · Acta Neuropathologica · doi:10.1007/s00401-019-02000-4 · Cited by 98
Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97
Matoba, N., Liang, D., Sun, H., Aygün, N., McAfee, J. C., Davis, J. E., Raffield, L. M., Qian, H., Piven, J., Li, Y., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00953-9 · Cited by 97
del Campo, M., Peeters, C. F. W., Johnson, E. C. B., Vermunt, L., Hok-A-Hin, Y. S., van Nee, M., Chen-Plotkin, A., Irwin, D. J., Hu, W. T., Lah, J. J., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00300-1 · Cited by 95
Exploring the genetic overlap between twelve psychiatric disorders
Romero, C., Werme, J., Jansen, P. R., Gelernter, J. E., Stein, M. B., Levey, D. F., Polimanti, R. A., Program, V. M. V., de Leeuw, C., Posthuma, D., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01245-2 · Cited by 97
Self-citationTsartsalis, S., Sleven, H., Fancy, N., Wessely, F., Smith, A. M., Willumsen, N., Cheung, T. K. D., Rokicki, M. J., Chau, V., Ifie, E., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46630-z · Cited by 95
Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing
Fang, J., Pieper, A. A., Nussinov, R., Lee, G., Bekris, L. M., Leverenz, J. B., Cummings, J. L., & Cheng, F. (2020) · Medicinal Research Reviews · doi:10.1002/med.21709 · Cited by 96
Insulin sensitivity predicts cognitive decline in individuals with prediabetes
Willmann, C., Brockmann, K., Wagner, R. J., Kullmann, S., Preissl, H. T., Schnauder, G., Maetzler, W., Gasser, T., Berg, D., Eschweiler, G. W., et al. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-001741 · Cited by 95
Antigen-presenting innate lymphoid cells orchestrate neuroinflammation
Grigg, J. B., Shanmugavadivu, A., Regen, T., Parkhurst, C. N., Ahmed, A., Joseph, A. M., Mazzucco, M. R., Gronke, K., Diefenbach, A., Eberl, G., et al. (2021) · Nature · doi:10.1038/s41586-021-04136-4 · Cited by 95
Association of common genetic variants with brain microbleeds: a genome-wide association study
Knol, M. J., Lu, D., Traylor, M., Adams, H. H. H., Romero, J. R. J., Smith, A. V., Fornage, M., Hofer, E., Liu, J., Hostettler, I. C., et al. (2020) · Neurology · doi:10.1212/wnl.0000000000010852 · Cited by 94
Genomics of Alzheimer’s disease implicates the innate and adaptive immune systems
Li, Y., Laws, S. M., Miles, L. A., Wiley, J. S., Huang, X., Masters, C. L., & Gu, B. J. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-021-03986-5 · Cited by 96
Taylor, X., Cisternas, P., You, Y., You, Y., Xiang, S., Marambio, Y. G., Zhang, J., Vidal, R., & Lasagna‐Reeves, C. A. (2020) · Journal of Neuroinflammation · doi:10.1186/s12974-020-01900-7 · Cited by 93
SnapHiC: a computational pipeline to identify chromatin loops from single-cell Hi-C data
Yu, M., Abnousi, A., Zhang, Y., Li, G., Lee, L., Chen, Z., Fang, R., Lagler, T. M., Yang, Y., Wen, J., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01231-2 · Cited by 92
Zeng, B., Bendl, J. M., Kosoy, R., Fullard, J. F., Hoffman, G. E., & Roussos, P. (2022) · Nature Genetics · doi:10.1038/s41588-021-00987-9 · Cited by 92
Visser, P. J., Reus, L. M., Gobom, J., Jansen, I. E., Dicks, E., van der Lee, S. J., Tsolaki, M., Verhey, F. R. J., Popp, J., Martínez‐Lage, P., et al. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00521-3 · Cited by 92
Self-citationHujoel, M. L. A., Gazal, S., Loh, P., Patterson, N. J., & Price, A. L. (2020) · Nature Genetics · doi:10.1038/s41588-020-0613-6 · Cited by 91
Raghavan, N. S., Dumitrescu, L. C., Mormino, E. C., Mahoney, E. R., Lee, A., Gao, Y., Bilgel, M., Goldstein, D. B., Harrison, T. M., Engelman, C. D., et al. (2020) · JAMA Neurology · doi:10.1001/jamaneurol.2020.1760 · Cited by 91
Self-citationPlasma IL-12/IFN-γ axis predicts cognitive trajectories in cognitively unimpaired older adults
Yang, H., Zhang, C., Carlyle, B. C., Zhen, S. Y., Trombetta, B. A., Schultz, A. P., Pruzin, J. J., Fitzpatrick, C. D., Yau, W. W., Kirn, D. R., et al. (2022) · Alzheimer's & Dementia · doi:10.1002/alz.12399 · Cited by 90
RNAAgeCalc: A multi-tissue transcriptional age calculator
Ren, X., & Kuan, P. F. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237006 · Cited by 90
Lee, A., Kim, C., Park, S.-W., Joo, J., Choi, B., Yang, D., Jun, K., Eom, J., Lee, S., Chung, S. J., et al. (2023) · Science Advances · doi:10.1126/sciadv.abo2467 · Cited by 90
White matter microglia heterogeneity in the CNS
Amor, S., McNamara, N. B., Gerrits, E., Marzin, M. C., Kooistra, S. M., Miron, V. E., & Nutma, E. (2022) · Acta Neuropathologica · doi:10.1007/s00401-021-02389-x · Cited by 89
Liang, D., Elwell, A. L., Aygün, N., Krupa, O., Wolter, J. M., Kyere, F. A., Lafferty, M. J., Cheek, K. E., Courtney, K. P., Yusupova, M., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00858-w · Cited by 91
Sleep, major depressive disorder, and Alzheimer disease
Huang, J. V., Zuber, V., Matthews, P. M., Elliott, P., Tzoulaki, I., & Dehghan, A. (2020) · Neurology · doi:10.1212/wnl.0000000000010463 · Cited by 88
Ali, A. M., & Kunugi, H. (2020) · Antioxidants · doi:10.3390/antiox9100937 · Cited by 88
Blood–brain barrier endothelial cells in neurodegenerative diseases: Signals from the “barrier”
Yuan, Y., Sun, J., Dong, Q., & Cui, M. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1047778 · Cited by 90
Alzheimer disease risk genes: 29 and counting
Bertram, L., & Tanzi, R. E. (2019) · Nature Reviews Neurology · doi:10.1038/s41582-019-0158-4 · Cited by 88
Wang, J., Sun, B.-L., Xiang, Y., Tian, D.-Y., Zhu, C., Li, W.-W., Liu, Y.-H., Bu, X.-L., Shen, L.-L., Jin, W.-S., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00918-y · Cited by 87
Self-citationInnovations in 3D Tissue Models of Human Brain Physiology and Diseases
Lovett, M. L., Nieland, T. J. F., Dingle, Y. L., & Kaplan, D. L. (2020) · Advanced Functional Materials · doi:10.1002/adfm.201909146 · Cited by 87
Walker, R. M., Vaher, K., Bermingham, M. L., Morris, S. W., Bretherick, A. D., Zeng, Y., Rawlik, K., Amador, C., Campbell, A., Haley, C., et al. (2021) · Genome Medicine · doi:10.1186/s13073-020-00808-4 · Cited by 87
Plasma high-density lipoprotein cholesterol and risk of dementia: observational and genetic studies
Kjeldsen, E. W., Thomassen, J. Q., Rasmussen, I. J., Nordestgaard, B. G., Tybjærg‐Hansen, A., & Frikke‐Schmidt, R. (2021) · Cardiovascular Research · doi:10.1093/cvr/cvab164 · Cited by 87
The role of Alzheimer's disease risk genes in endolysosomal pathways
Szabo, M. P., Mishra, S., Knupp, A., & Young, J. E. (2021) · Neurobiology of Disease · doi:10.1016/j.nbd.2021.105576 · Cited by 87
Inferring protein expression changes from mRNA in Alzheimer’s dementia using deep neural networks
Tasaki, S., Xu, J., Avey, D. R., Johnson, L., Petyuk, V., Dawe, R. J., Bennett, D. A., Wang, Y., & Gaiteri, C. (2022) · Nature Communications · doi:10.1038/s41467-022-28280-1 · Cited by 89
Self-citationStep by step: towards a better understanding of the genetic architecture of Alzheimer’s disease
Lambert, J.-C., Ramirez, A., Grenier-Boley, B., & Bellenguez, C. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02076-1 · Cited by 87
Dendritic Spines: Mediators of Cognitive Resilience in Aging and Alzheimer’s Disease
Walker, C. K., & Herskowitz, J. H. (2021) · The Neuroscientist · doi:10.1177/1073858420945964 · Cited by 86
Immune checkpoint TIM-3 regulates microglia and Alzheimer’s disease
Kimura, K., Subramanian, A., Yin, Z., Khalilnezhad, A., Wu, Y., He, D., Dixon, K. O., Chitta, U. K., Ding, X., Adhikari, N., et al. (2025) · Nature · doi:10.1038/s41586-025-08852-z · Cited by 85
Luo, M., Lee, L. K. C., Peng, B., Choi, C. H. J., Tong, W. Y., & Voelcker, N. H. (2022) · Advanced Science · doi:10.1002/advs.202201740 · Cited by 85
de Vries, L. E., Huitinga, I., Kessels, H. W. H. G., Swaab, D. F., & Verhaagen, J. (2024) · Molecular Neurodegeneration · doi:10.1186/s13024-024-00719-7 · Cited by 85
Shifting paradigms: The central role of microglia in Alzheimer’s disease
Schwabe, T., Srinivasan, K., & Rhinn, H. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104962 · Cited by 84
Richardson, T. G., Hemani, G., Gaunt, T. R., Relton, C. L., & Smith, G. D. (2020) · Nature Communications · doi:10.1038/s41467-019-13921-9 · Cited by 84
Luningham, J. M., Chen, J., Tang, S., Jager, P. L. D., Bennett, D. A. A., Buchman, A. S., & Yang, J. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.08.022 · Cited by 87
Wainberg, M., Andrews, S. J., & Tripathy, S. J. (2023) · Alzheimer s Research & Therapy · doi:10.1186/s13195-023-01244-3 · Cited by 83
Gamache, J., Yun, Y., & Chiba-Falek, O. (2020) · Disease Models & Mechanisms · doi:10.1242/dmm.045211 · Cited by 84
Anderson, A. G., Rogers, B. B., Loupe, J. M., Rodriguez-Nunez, I., Roberts, S. C., White, L. M., Brazell, J. N., Bunney, W. E., Bunney, B. G., Watson, S. J., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100263 · Cited by 82
Self-citationTau at the interface between neurodegeneration and neuroinflammation
Didonna, A. (2020) · Genes & Immunity · doi:10.1038/s41435-020-00113-5 · Cited by 82
Apolipoprotein E4 and meningeal lymphatics in Alzheimer disease: a conceptual framework
Mentis, A. A., Dardiotis, E., & Chrousos, G. P. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0731-7 · Cited by 83
Hartman, R. J. G., Kapteijn, D., Haitjema, S., Bekker, M. N., Mokrý, M., Pasterkamp, G., Civelek, M., & den Ruijter, H. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-69451-8 · Cited by 82
Micali, N., Ma, S., Li, M., Kim, S., Mato-Blanco, X., Sindhu, S. K., Arellano, J. I., Gao, T., Shibata, M., Gobeske, K. T., et al. (2023) · Science · doi:10.1126/science.adf3786 · Cited by 82
Adewuyi, E. O., O’Brien, E. K., Nyholt, D. R., Porter, T., & Laws, S. M. (2022) · Communications Biology · doi:10.1038/s42003-022-03607-2 · Cited by 82
Chronic effects of inflammation on tauopathies
Langworth-Green, C., Patel, S., Jaunmuktane, Z., Jabbari, E., Morris, H. R., Thom, M., Lees, A. J., Hardy, J., Zandi, M. S., & Duff, K. (2023) · The Lancet Neurology · doi:10.1016/s1474-4422(23)00038-8 · Cited by 85
Self-citationCARMA is a new Bayesian model for fine-mapping in genome-wide association meta-analyses
Yang, Z., Wang, C., Liu, L., Khan, A., Lee, A., Vardarajan, B. N., Mayeux, R. P., Kiryluk, K., & Ionita‐Laza, I. (2023) · Nature Genetics · doi:10.1038/s41588-023-01392-0 · Cited by 81
Multi-ancestry meta-analysis and fine-mapping in Alzheimer’s disease
Lake, J., Solsberg, C. W., Kim, J. J., Acosta‐Uribe, J., Makarious, M. B., Li, Z., Levine, K. S., Heutink, P., Alvarado, C. X., Vitale, D., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02089-w · Cited by 81
Integrating non-mammalian model organisms in the diagnosis of rare genetic diseases in humans
Yamamoto, S., Kanca, O., Wangler, M. F., & Bellen, H. J. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00633-6 · Cited by 81
Circular RNAs in the human brain are tailored to neuron identity and neuropsychiatric disease
Dong, X., Bai, Y., Liao, Z., Gritsch, D., Liu, X., Wang, T., Borges-Monroy, R., Ehrlich, A., Serrano, G. E., Feany, M. Β., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40348-0 · Cited by 83
Shigemizu, D., Mitsumori, R., Akiyama, S., Miyashita, A., Morizono, T., Higaki, S., Asanomi, Y., Hara, N., Tamiya, G., Kinoshita, K., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01272-3 · Cited by 80
Li, Q. S., Sun, Y., & Wang, T. (2020) · Clinical Epigenetics · doi:10.1186/s13148-020-00944-z · Cited by 84
Sterenborg, R. B. T. M., Steinbrenner, I., Li, Y., Bujnis, M. N., Naito, T., Marouli, E., Galesloot, T. E., Babajide, O., Andreasen, L., Astrup, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-44701-9 · Cited by 79
Shigemizu, D., Akiyama, S., Higaki, S., Sugimoto, T., Sakurai, T., Boroevich, K. A., Sharma, A. K., Tsunoda, T., Ochiya, T., Niida, S., et al. (2020) · Alzheimer s Research & Therapy · doi:10.1186/s13195-020-00716-0 · Cited by 79
Beyond Haemostasis and Thrombosis: Platelets in Depression and Its Co-Morbidities
Izzi, B., Tirozzi, A., Cerletti, C., Donati, M. B., de Gaetano, G., Hoylaerts, M., Iacoviello, L., & Gialluisi, A. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21228817 · Cited by 79
Nitta, Y., & Sugie, A. (2022) · Fly · doi:10.1080/19336934.2022.2087484 · Cited by 81
Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction
Zhou, X., Chen, Y., Ip, F. C. F., Jiang, Y., Cao, H., Lv, G., Zhong, H., Chen, J., Tao, Y., Chen, Y., et al. (2023) · Communications Medicine · doi:10.1038/s43856-023-00269-x · Cited by 79
Hillary, R. F., McCartney, D. L., Harris, S. E., Stevenson, A. J., Seeboth, A., Zhang, Q., Liewald, D. C., Evans, K. L., Ritchie, C. W., Tucker-Drob, E. M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11177-x · Cited by 78
Accelerated evolution of oligodendrocytes in the human brain
Berto, S., Mendizabal, I., Usui, N., Toriumi, K., Chatterjee, P., Douglas, C., Tamminga, C. A., Preuss, T. M., Yi, S. V., & Konopka, G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907982116 · Cited by 78
The Ubiquitin System in Alzheimer’s Disease
Harris, L. D., Jasem, S., & Licchesi, J. D. F. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-38266-7_8 · Cited by 79
Higher CSF sTREM2 attenuates ApoE4-related risk for cognitive decline and neurodegeneration
Franzmeier, N., Suárez‐Calvet, M., Frontzkowski, L. K., Moore, A. M., Hohman, T. J., Morenas‐Rodríguez, E., Nuscher, B., Shaw, L. M. J., Trojanowski, J. Q., Dichgans, M., et al. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00407-2 · Cited by 78
Self-citationPhysical and Mental Activity, Disease Susceptibility, and Risk of Dementia
Zhu, J., Ge, F., Zeng, Y., Qu, Y., Chen, W., Yang, H., Yang, L., Fang, F., & Song, H. (2022) · Neurology · doi:10.1212/wnl.0000000000200701 · Cited by 77
Human microglia maturation is underpinned by specific gene regulatory networks
Han, C. Z., Li, R. Z., Hansen, E., Trescott, S., Fixsen, B. R., Nguyen, C. T., Mora, C. M., Spann, N. J., Bennett, H. R., Poirion, O., et al. (2023) · Immunity · doi:10.1016/j.immuni.2023.07.016 · Cited by 84
The CD33 short isoform is a gain-of-function variant that enhances Aβ1–42 phagocytosis in microglia
Bhattacherjee, A., Jung, J., Zia, S., Ho, M. F. S., Eskandari‐Sedighi, G., Laurent, C. D. S., McCord, K. A., Bains, A., Sidhu, G., Sarkar, S., et al. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00443-6 · Cited by 77
Remembering your A, B, C's: Alzheimer's disease and ABCA1
Lewandowski, C. T., Laham, M. S., & Thatcher, G. R. J. (2022) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2022.01.011 · Cited by 76
Kim, M., Haney, J. R., Zhang, P., Hernandez, L. M., Wang, L.-K., Pérez‐Cano, L., Loohuis, L. M. O., de la Torre-Ubieta, L., & Gandal, M. J. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00847-z · Cited by 76
HDL Cholesterol and Non-Cardiovascular Disease: A Narrative Review
Kjeldsen, E. W., Nordestgaard, L. T., & Frikke‐Schmidt, R. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22094547 · Cited by 76
Genetic Evidence Supporting a Causal Role of Depression in Alzheimer’s Disease
Harerimana, N. V., Liu, Y., Gerasimov, E. S., Duong, D. M., Beach, T. G., Reiman, E. M., Schneider, J. A., Boyle, P. A., Lori, A., Bennett, D. A., et al. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.11.025 · Cited by 76
Fruhwürth, S., Zetterberg, H., & Paludan, S. R. (2024) · Journal of Neuroimmunology · doi:10.1016/j.jneuroim.2024.578342 · Cited by 77
The necroptosis cell death pathway drives neurodegeneration in Alzheimer’s disease
Balusu, S., & Strooper, B. D. (2024) · Acta Neuropathologica · doi:10.1007/s00401-024-02747-5 · Cited by 76
Niculescu, A. B., Le-Niculescu, H., Roseberry, K., Wang, S., Hart, J. E., Kaur, A., Robertson, H., Jones, T., Strasburger, A. M., Williams, A., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0602-2 · Cited by 76
Integrated analysis of the aging brain transcriptome and proteome in tauopathy
Mangleburg, C. G., Wu, T., Yalamanchili, H. K., Guo, C., Hsieh, Y.-C., Duong, D. M., Dammer, E. B., De Jager, P. L., Seyfried, N. T., Liu, Z., et al. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00405-4 · Cited by 75
Hauberg, M. E., Creus‐Muncunill, J., Bendl, J. M., Kozlenkov, A., Zeng, B., Corwin, C., Chowdhury, S., Kranz, H. D., Hurd, Y. L., Wegner, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19319-2 · Cited by 75
Causal effect of COVID‐19 on Alzheimer's disease: A Mendelian randomization study
Baranova, A. V., Cao, H., & Zhang, F. (2022) · Journal of Medical Virology · doi:10.1002/jmv.28107 · Cited by 75
Altendorfer, B., Unger, M. S., Poupardin, R., Hoog, A., Asslaber, D., Gratz, I. K., Mrowetz, H., Benedetti, A., de Sousa, D. M. B., Greil, R., et al. (2022) · The Journal of Immunology · doi:10.4049/jimmunol.2100737 · Cited by 77
Enhancer variants associated with Alzheimer’s disease affect gene expression via chromatin looping
Kikuchi, M., Hara, N., Hasegawa, M., Miyashita, A., Kuwano, R., Ikeuchi, T., & Nakaya, A. (2019) · BMC Medical Genomics · doi:10.1186/s12920-019-0574-8 · Cited by 74
“Thus, NYAP1 may be related to AD pathology.”
BackgroundInfluentialDesplats, P., Gutierrez, A. M., Antonelli, M. C., & Frasch, M. G. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.10.013 · Cited by 74
Phospholipase D3 degrades mitochondrial DNA to regulate nucleotide signaling and APP metabolism
Acker, Z. P. V., Perdok, A., Hellemans, R., North, K., Vorsters, I., Cappel, C. C., Dehairs, J., Swinnen, J. V., Sannerud, R., Bretou, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38501-w · Cited by 76
Massively parallel characterization of regulatory elements in the developing human cortex
Deng, C., Whalen, S., Steyert, M., Ziffra, R., Przytycki, P. F., Inoue, F., Pereira, D. A., Capauto, D., Norton, S., Vaccarino, F. M., et al. (2024) · Science · doi:10.1126/science.adh0559 · Cited by 79
Drug repurposing for Alzheimer’s disease and other neurodegenerative disorders
Cummings, J. L., Zhou, Y., Stone, A. V., Cammann, D., Tonegawa‐Kuji, R., Cacho, J. R. F., & Cheng, F. (2025) · Nature Communications · doi:10.1038/s41467-025-56690-4 · Cited by 80
Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer’s disease
Morabito, S., Miyoshi, E., Michael, N., & Swarup, V. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa182 · Cited by 74
Genomics and Functional Genomics of Alzheimer's Disease
Kamboh, M. I. (2022) · Neurotherapeutics · doi:10.1007/s13311-021-01152-0 · Cited by 73
Button, E. B., Boyce, G. K., Wilkinson, A., Stukas, S. K., Hayat, A., Fan, J., Wadsworth, B. J., Robert, J., Martens, K. M., & Wellington, C. L. (2019) · Alzheimer s Research & Therapy · doi:10.1186/s13195-019-0497-9 · Cited by 73
Xue, F., & Du, H. (2021) · Cells · doi:10.3390/cells10020321 · Cited by 73
Shade, L. M. P., Katsumata, Y., Abner, E. L., Aung, K. Z., Claas, S. A., Qiao, Q., Heberle, B. A., Brandon, J. A., Page, M. L., Hohman, T. J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01939-9 · Cited by 72
Self-citationDelbridge, A. R. D., Huh, D., Brickelmaier, M., Burns, J. C., Roberts, C., Challa, R., Raymond, N., Cullen, P., Carlile, T. M., Ennis, K. A., et al. (2020) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2020.592005 · Cited by 72
The role of the immune system in driving neuroinflammation
Ní Chasaide, C., & Lynch, M. A. (2020) · Brain and Neuroscience Advances · doi:10.1177/2398212819901082 · Cited by 72
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
