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.
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Fine mapping produced 261 variants in credible sets.
“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 <b>-</b> risk association (2019) · cites this paper as [18]
The Results section, under “Functional interpretation of genetic variants” (page unavailable), confirms that the authors used fine mapping to identify credible sets. But it reports 345 plausible causal SNPs in the HLA-DRB1 locus alone, which contradicts a total of 261 credible-set variants. (LLM, conf=0.87)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredMore than 90 genetic loci are associated with Alzheimer's disease and related dementia (ADRD).
“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]
The Results (Genome-wide meta-analysis for AD status; page not provided) report 94 lead SNPs located in 29 distinct loci, not more than 90 loci. The Abstract likewise reports 29 risk loci for Alzheimer’s disease; the paper does not present the 94 SNPs as loci associated with ADRD. (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe study defined 39 signals in 33 loci already known to be associated with the risk of developing Alzheimer's disease and related dementias.
“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]
The Results (‘Genome-wide meta-analysis for AD status’) report 29 loci associated with Alzheimer’s disease, including 16 that overlapped previously identified regions. The cited paper therefore supports the background claim that some loci were already associated with Alzheimer’s risk, but it does not establish their association with related dementias. The 39 signals in 33 loci are presented as the citing authors’ own result, not a finding attributed to this source. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAD SNP heritability showed its strongest enrichment in microglial regulatory elements, especially microglial enhancers.
“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 <b>-</b> risk association (2019) · cites this paper as [19]
The Results, under “Functional interpretation of genetic variants” (page unavailable), report AD SNP-heritability enrichment in H3K27ac marks and super enhancers, but do not identify these as microglial elements or say microglial enhancers had the strongest enrichment. The separate “Gene-sets implicated in AD and AD-by-proxy” section reports a microglial gene-expression association, not enrichment of SNP heritability in microglial regulatory elements. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAD SNP heritability showed its strongest enrichment in microglial regulatory elements, especially microglial enhancers.
“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 <b>-</b> risk association (2019) · cites this paper as [18]
The Results section on functional interpretation (page unavailable) reports AD SNP-heritability enrichment in H3K27ac marks and super enhancers, but does not identify these as microglial elements or say microglial enhancers had the strongest enrichment. The separate gene-set Results (page unavailable) report a suggestive microglia association, not microglial enhancer heritability enrichment. (LLM, conf=0.86)
Experimental — accuracy not yet measured
GWAS have identified more than 40 risk loci associated with pathways including lipid metabolism, immune regulation, and endosomal trafficking.
“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]
The Results (page unavailable) report 29 distinct Alzheimer’s disease risk loci, not more than 40. The Abstract and Discussion link the findings to lipid-related processes and immune biology, but the retrieved passages do not establish endosomal trafficking as an implicated pathway. (LLM, conf=0.87)
Experimental — accuracy not yet measuredSingle-cell polygenic risk enrichment for AD risk identified a significant increase throughout the microglia trajectory, revealing an enrichment of AD genetic risk single-nucleotide polymorphisms in DAMs.
“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]
The Results sections report AD-associated SNPs and an association between AD genetic risk and microglial gene expression. They do not examine a microglia trajectory or disease-associated microglia (DAMs). The trajectory increase and DAM enrichment are presented in the citing sentence as the citing authors’ own findings, rather than results of this source. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMicroglia are implicated in the pathology and genetic risk of several central nervous system diseases, including Alzheimer's disease.
“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]
The Results section (no page provided) reports suggestive associations between microglial gene expression and Alzheimer’s disease risk, and the Discussion links microglia to a hypothesis about AD pathogenesis. The study does not establish the same implication for several other CNS diseases; its microglia associations also did not survive the stringent Bonferroni correction. (LLM, conf=0.85)
Experimental — accuracy not yet measured12 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).
“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]
The Results section (‘Genome-wide meta-analysis for AD status’; page unavailable) reports a clinically diagnosed AD analysis with 18 genome-wide-significant loci, so this paper can serve as a prior-study comparator. It does not report that the 12 loci named in the citing sentence became genome-wide significant after a later analysis restricted to clinically diagnosed cases. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThese loci have previously been identified as genome-wide significant for AD in recent single-trait AD GWAS.
“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]
The Results section (‘Genome-wide meta-analysis for AD status’; page not provided) reports 29 genome-wide-significant AD loci, supporting the claim that loci had been identified in an AD GWAS. The citing sentence does not name ‘these loci,’ so our copy cannot establish that every locus in that group appears among the 29 reported here. (LLM, conf=0.74)
Experimental — accuracy not yet measuredMicroglia-specific pathways such as Trem2-Tyrobp and ApoE are critical determinants in Alzheimer's disease and other neurodegenerative diseases.
“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
The Results section (‘Gene-sets implicated in AD and AD-by-proxy’; page not supplied) reports suggestive microglial associations with AD risk, partly dependent on APOE, but says they did not survive the stringent Bonferroni correction. The paper identifies APOE as a strong AD risk locus, but the passages do not establish a TREM2–TYROBP pathway as a critical determinant or extend the finding to other neurodegenerative diseases. (LLM, conf=0.88)
Experimental — accuracy not yet measuredGWAS analyses have identified 75 genome-wide significant loci for LOAD.
“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]
The Results section (Genome-wide meta-analysis for AD status; page unavailable) reports genome-wide significant associations in 29 distinct loci, supporting the general point that GWAS identified loci associated with Alzheimer’s disease. It does not support the attributed total of 75 loci; the sentence also cites other sources, which are not assessed here. (LLM, conf=0.88)
Experimental — accuracy not yet measuredMicroglia gene expression patterns are strongly correlated with late-onset Alzheimer's disease.
“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]
The Results section (page unavailable) reports associations between Alzheimer’s disease risk genes and microglia-specific expression in both mouse and human datasets. But the associations did not survive the stringent Bonferroni correction, and the paper does not report a strong correlation between microglial expression patterns themselves and late-onset Alzheimer’s disease. (LLM, conf=0.87)
Experimental — accuracy not yet measuredHLA class II locus encompassing HLA-DRB1/HLA-DQA1 is a well established AD risk locus, although extensive linkage disequilibrium makes it difficult to pin down the specific causal gene.
“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]
The Results sections on functional interpretation and implicated genes identify an AD-associated HLA-DRB1 locus, list HLA-DRB1 and HLA-DQA among implicated genes, and nominate 345 of 434 variants there as plausible causal SNPs. This supports difficulty resolving the causal signal, but the passages do not specifically say that extensive linkage disequilibrium across the HLA region prevents identification of the causal gene. Page numbers are unavailable in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSCIMP is expressed across myeloid and B cell lineages and has been identified as an AD-associated locus.
“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]
The Results section (‘Genome-wide meta-analysis for AD status’; page unavailable) identifies SCIMP among loci previously discovered in AD genetic studies. Our copy does not state that SCIMP is expressed across myeloid and B-cell lineages; its cell-expression discussion does not establish that gene-specific claim. (LLM, conf=0.87)
Experimental — accuracy not yet measuredSeveral AD GWAS loci implicate B cell relevant genes including SCIMP, BLNK, PLCG2, HLA class II region and IGHG1.
“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]
The Results sections on AD status and implicated genes identify SCIMP and HLA-DRA/HLA-DRB1 among AD-associated loci or genes. The Discussion supports an immune-system connection, but our copy does not characterize these loci as B-cell-specific or identify BLNK or IGHG1; PLCG2 appears in a cited study’s title rather than among this paper’s reported loci. (LLM, conf=0.87)
Experimental — accuracy not yet measuredJansen et al. identified 8 independent SNPs in addition to APOE*4 and APOE*2 associated with Alzheimer's disease risk.
“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]
In Results > Genome-wide meta-analysis for AD status (page unavailable), Jansen et al. report 60 lead SNPs in the APOE locus but say these likely represent a single association signal; conditional analysis suggested multiple *possible* causal signals. The paper supports an association in the APOE region, but not the claim that it identified eight independent SNPs in addition to APOE*4 and APOE*2. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIn 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 and reported SNPs in some known non-APOE AD loci are also associated with plasma ApoE level.
“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]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredA meta-analysis of GWAS colocalized genes showed strong enrichment in genes active in immune-related tissues, including microglia.
“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 <i>RTFDC1</i> at the <i>CASS4</i> locus (2024) · cites this paper as [5]
The Results section ('Gene-sets implicated in AD and AD-by-proxy'; page not provided) reports associations with immune-related tissues and microglia, but calls the tissue associations suggestive and says none survived correction across all gene-sets. The Methods describe gene-based association and gene-set analyses, not an analysis of GWAS-colocalized genes. Thus 'strong enrichment' and 'colocalized genes' overstate or misdescribe the reported analysis. (LLM, conf=0.90)
Experimental — accuracy not yet measuredThe UK Biobank proxy-AD method has been used successfully in the past.
“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]
Across cell types, 41 genes were connected to AD-risk credible-set variants.
“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 <b>-</b> risk association (2019) · cites this paper as [19]
Applying the same approach to all genome-wide significant variants from two AD GWASs yielded a larger set of 134 candidate risk genes.
“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 <b>-</b> risk association (2019) · cites this paper as [18]
Applying the same approach to all genome-wide significant variants from two AD GWASs yielded a larger set of 134 candidate risk genes.
“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 <b>-</b> risk association (2019) · cites this paper as [19]
Many AD risk variants have modest individual effect sizes, indicating a polygenic model in which numerous low-to-moderate-impact alleles collectively modulate disease risk.
“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]
A gene set by Jansen et al. provided the set of genes associated with Alzheimer's disease.
“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
Human genome-wide association studies support a causal role for immune mechanisms in AD.
“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 ADAM10 locus was discovered as an AD association in Jansen et al. [17].
“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 ADAMTS4 locus was also discovered in Jansen et al. [17] AD GWAS.
“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 Jansen et al. AD GWAS and many of the cardiometabolic trait GWAS used included individuals from the UK Biobank dataset.
“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]
Some of the pleiotropic loci detected may be due to vascular or mixed dementia patients being included in the AD cohorts, particularly since Jansen et al. include some Proxy-AD patients.
“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]
Genes expressed by microglia have been implicated in Alzheimer's disease in human GWASs.
“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
Large-scale genetic association studies have identified Alzheimer's disease risk variants in a growing list of microglia-related genes.
“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]
ADAM10 and APH1B are AD genetic risk factors.
“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]
More than 30 loci of interest have been identified as associated with AD risk since the 2009 GWAS papers.
“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 induction of MGnD genes in microglia with deletions in Itgb8, Tgfbr2, Nrros or Smad2/Smad3 is associated with the suppression of AD risk factor genes, including Bin1, Havcr2 and Inpp5d.
“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]
The Results sections on implicated genes and microglial gene sets discuss genetic associations with Alzheimer’s disease, but do not identify Bin1, Havcr2 or Inpp5d as risk-factor genes in the passages retrieved. Searches for those genes and for the specified deletions found no passage addressing their suppression or its association with MGnD induction. The cited paper’s copy therefore does not verify the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredIGHG1 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.
“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]
The Results sections on implicated genes and AD-associated loci distinguish putative causal genes from locus-level associations, but they do not identify IGHG1 or place it within an AD-associated locus. Searches for IGHG1 and the immunoglobulin heavy-chain locus found no passage addressing its status as an AD risk gene. (LLM, conf=0.72)
Experimental — accuracy not yet measuredBLNK is a central adaptor downstream of BCR, and PLCG2 is a major BCR proximal signaling node implicated in germinal center and memory B cell formation.
“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]
The Results section on Alzheimer’s disease loci names CLNK, not BLNK, and the Discussion describes broad immune-system involvement. Searches for BLNK, PLCG2, B-cell receptor signaling, and germinal-center or memory B-cell formation found no passage in our copy addressing the claimed roles of BLNK or PLCG2. No page numbers were available for these passages. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe observed association between age at dementia onset and variants in TSPAN14 and TSPAN10, both with a deleterious correlation, may result from disruption of a protective role of ADAM10.
“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 <i>PSEN1 E280A</i> Colombian kindred (2023) · cites this paper as [25]
The latest GWAS studies published during 2019 identified 9 novel risk loci from 71,880 Alzheimer's disease cases and 5 novel risk loci from 35,274 clinically assessed Alzheimer's disease cases in the updated IGAP study.
“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
Several genes associated with AD incidence (APOE, CLU, ABCA7, SORL1, INPP5D, and PLCG2) function in lipid transport or signaling.
“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
Proxy approaches show substantial genetic overlap with previous case-control GWAS and nominate a number of novel loci associated with proxy risk for Alzheimer's disease.
“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]
Proxy risk for Alzheimer's disease, defined as a parental history of AD, exhibits strong genetic correlation with AD and can be combined with existing GWAS datasets to enhance statistical power to find true genetic associations with disease.
“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]
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.
“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]
AD risk genes are highly expressed in postnatal brain samples and microglia.
“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]
The GWAS results include candidate genes VAMP4 and NOD2 from the endocytic pathway.
“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]
Genetic studies have highlighted a prominent role for microglia in susceptibility to different neurodegenerative diseases, particularly Alzheimer's disease.
“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]
Microglia and immune cells showed consistently high GWAS scores across regions, with the top scores for the microglia TPT1+ subtype and macrophages in the HC, thalamus and AG.
“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]
Disease-relevance scores were calculated per cell against a recent Alzheimer's GWAS using scDRS, and overlap with microglia/immune modules was tested by hypergeometric test with Benjamini-Hochberg correction.
“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 ECHDC3 locus has been associated with AD risk in several GWAS.
“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]
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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 · see citing works
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 14,380 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Integrative 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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
The global prevalence of dementia: a systematic review and metaanalysis.
Prince, M., Bryce, R., Albanese, E., Wimo, A., Ribeiro, W., & Ferri, C. P. (2013) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2012.11.007 · Cited by 4,941 · see citing works
Goate, A., Chartier-Harlin, M.-C., Mullan, M., Brown, J., Crawford, F., Fidani, L., Giuffra, L., Haynes, A., Irving, N., James, L., et al. (1991) · Nature · doi:10.1038/349704a0 · Cited by 4,659 · see citing works
Self-citationMeta-Analysis of 74,046 Individuals Identifies 11 New Susceptibility Loci for Alzheimer’s Disease
(EADI), E. A. D. I., Lambert, J.-C., (GERAD), G. A. E. R. I. A. D., Alzheimer's Disease Genetic Consortium (ADGC), (CHARGE), C. F. H. A. A. R. I. G. E., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., et al. (2013) · Nature Genetics · doi:10.1038/ng.2802 · Cited by 4,336 · see citing works
Self-citationAn atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255 · see citing works
Functional mapping and annotation of genetic associations with FUMA
Watanabe, K., Taskesen, E., van Bochoven, A., & Posthuma, D. (2017) · Nature Communications · doi:10.1038/s41467-017-01261-5 · Cited by 4,083 · see citing works
Self-citationCloning of a gene bearing missense mutations in early-onset familial Alzheimer's disease
Sherrington, R., Rogaev, E. I., Liang, Y., Rogaeva, E. A., Levesque, G., Ikeda, M., Chi, H., Lin, C., Li, G., Holman, K., et al. (1995) · Nature · doi:10.1038/375754a0 · Cited by 4,051 · see citing works
Fagerberg, L., Hallström, B. M., Oksvold, P., Kampf, C., Djureinovic, D., Odeberg, J., Habuka, M., Tahmasebpoor, S., Danielsson, A., Edlund, K., et al. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.035600 · Cited by 3,665 · see citing works
MAGMA: Generalized Gene-Set Analysis of GWAS Data
de Leeuw, C. A., Mooij, J. M., Heskes, T., & Posthuma, D. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004219 · Cited by 3,653 · see citing works
Self-citationCognitive reserve in ageing and Alzheimer's disease
Stern, Y. (2012) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70191-6 · Cited by 3,557 · see citing works
Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy
Liu, C.-C., Kanekiyo, T., Xu, H., & Bu, G. (2013) · Nature Reviews Neurology · doi:10.1038/nrneurol.2012.263 · Cited by 3,459 · see citing works
<i>TREM2</i> Variants in Alzheimer's Disease
Guerreiro, R., Wojtas, A., Bras, J., Carrasquillo, M., Rogaeva, E., Majounie, E., Cruchaga, C., Sassi, C., Kauwe, J. S. K., Younkin, S., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1211851 · Cited by 3,020 · see citing works
Self-citationAnnotation of functional variation in personal genomes using RegulomeDB
Boyle, A. P., Hong, E. L., Hariharan, M., Cheng, Y., Schaub, M. A., Kasowski, M., Karczewski, K. J., Park, J., Hitz, B. C., Weng, S., et al. (2012) · Genome Research · doi:10.1101/gr.137323.112 · Cited by 2,722 · see citing works
Partitioning heritability by functional annotation using genome-wide association summary statistics
ReproGen Consortium, Finucane, H. K., Schizophrenia Working Group of the Psychiatric Genomics Consortium, The RACI Consortium, Bulik-Sullivan, B., Gusev, A., Trynka, G., Reshef, Y., Loh, P.-R., Anttila, V., et al. (2015) · Nature Genetics · doi:10.1038/ng.3404 · Cited by 2,604 · see citing works
Self-citationVariant of <i>TREM2</i> Associated with the Risk of Alzheimer's Disease
Jonsson, T., Stefansson, H., Steinberg, S., Jonsdottir, I., Jonsson, P. V., Snaedal, J., Bjornsson, S., Huttenlocher, J., Levey, A. I., Lah, J. J., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1211103 · Cited by 2,558 · see citing works
Self-citationChromHMM: automating chromatin-state discovery and characterization
Ernst, J., & Kellis, M. (2012) · Nature Methods · doi:10.1038/nmeth.1906 · Cited by 2,489 · see citing works
Karran, E., Mercken, M., & Strooper, B. D. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3505 · Cited by 2,188 · see citing works
Analysis of shared heritability in common disorders of the brain
The Brainstorm Consortium, Anttila, V., Bulik-Sullivan, B., Finucane, H. K., Walters, R. K., Bras, J., Duncan, L., Escott-Price, V., Falcone, G. J., Gormley, P., et al. (2018) · Science · doi:10.1126/science.aap8757 · Cited by 1,924 · see citing works
Self-citationExpansion of the Gene Ontology knowledgebase and resources
The Gene Ontology Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1108 · Cited by 1,850 · see citing works
Role of Genes and Environments for Explaining Alzheimer Disease
Gatz, M., Reynolds, C. A., Fratiglioni, L., Johansson, B., Mortimer, J. A., Berg, S., Fiske, A., & Pedersen, N. L. (2006) · Archives of General Psychiatry · doi:10.1001/archpsyc.63.2.168 · Cited by 1,798 · see citing works
Self-citationGenetic Investigation of ANthropometric Traits (GIANT) Consortium, Yang, J., DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Ferreira, T., Morris, A. P., Medland, S. E., Madden, P. A. F., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2012) · Nature Genetics · doi:10.1038/ng.2213 · Cited by 1,712 · see citing works
Systematic identification of trans eQTLs as putative drivers of known disease associations
Westra, H.-J., Peters, M. J., Esko, T., Yaghootkar, H., Schurmann, C., Kettunen, J., Christiansen, M. W., Fairfax, B. P., Schramm, K., Powell, J. E., et al. (2013) · Nature Genetics · doi:10.1038/ng.2756 · Cited by 1,658 · see citing works
Savage, J. E., Jansen, P. R., Stringer, S., Watanabe, K., Bryois, J., de Leeuw, C. A., Nagel, M., Awasthi, S., Barr, P. B., Coleman, J. R. I., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0152-6 · Cited by 1,443 · see citing works
Self-citationPRSice: Polygenic Risk Score software
Euesden, J., Lewis, C. M., & O’Reilly, P. F. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu848 · Cited by 1,149 · see citing works
ARUK Consortium, Sims, R., GERAD/PERADES, C., van der Lee, S. J., Naj, A. C., Bellenguez, C., Badarinarayan, N., Jakobsdottir, J., Kunkle, B. W., Boland, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3916 · Cited by 1,126 · see citing works
Self-citationGene expression elucidates functional impact of polygenic risk for schizophrenia
Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091 · see citing works
Self-citationMassively parallel single-nucleus RNA-seq with DroNc-seq
Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4407 · Cited by 1,069 · see citing works
Incidence of dementia over three decades in the Framingham heart study
Satizabal, C. L., Beiser, A. S., Chouraki, V., Chêne, G., Dufouil, C., & Seshadri, S. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1504327 · Cited by 1,051 · see citing works
Causal associations between risk factors and common diseases inferred from GWAS summary data
Zhu, Z., Zheng, Z., Zhang, F., Wu, Y., Trzaskowski, M., Maier, R., Robinson, M. R., McGrath, J. J., Visscher, P. M., Wray, N. R., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02317-2 · Cited by 951 · see citing works
Zheng, J., Erzurumluoglu, A. M., Elsworth, B. L., Kemp, J. P., Howe, L., Haycock, P. C., Hemani, G., Tansey, K., Laurin, C., Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium, et al. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw613 · Cited by 869 · see citing works
A compendium of chromatin contact maps reveals spatially active regions in the human genome
Schmitt, A. D., Hu, M., Jung, I., Xu, Z., Qiu, Y., Tan, C. L., Li, Y., Lin, S., Lin, Y., Barr, C. L., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.10.061 · Cited by 866 · see citing works
Large-scale whole-genome sequencing of the Icelandic population
Gudbjartsson, D. F., Helgason, H., Gudjonsson, S. A., Zink, F., Oddson, A., Gylfason, A., Besenbacher, S., Magnusson, G., Halldorsson, B. V., Hjartarson, E., et al. (2015) · Nature Genetics · doi:10.1038/ng.3247 · Cited by 791 · see citing works
Self-citationGenetic variability in the regulation of gene expression in ten regions of the human brain
UK Brain Expression Consortium, Ramasamy, A., North American Brain Expression Consortium, Trabzuni, D., Guelfi, S., Varghese, V., Smith, C., Walker, R., De, T., Coin, L., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3801 · Cited by 644 · see citing works
Self-citationMolecular genetics of early-onset Alzheimer’s disease revisited
Cacace, R., Sleegers, K., & Van Broeckhoven, C. (2016) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.01.012 · Cited by 617 · see citing works
GWAS on family history of Alzheimer’s disease
Marioni, R. E., Harris, S. E., Zhang, Q., McRae, A. F., Hagenaars, S. P., Hill, W. D., Davies, G., Ritchie, C. W., Gale, C. R., Starr, J. M., et al. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0150-6 · Cited by 564 · see citing works
Practical aspects of imputation-driven meta-analysis of genome-wide association studies
de Bakker, P. I. W., Ferreira, M. A. R., Jia, X., Neale, B. M., Raychaudhuri, S., & Voight, B. F. (2008) · Human Molecular Genetics · doi:10.1093/hmg/ddn288 · Cited by 531 · see citing works
An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome
Ng, B., White, C. C., Klein, H.-U., Sieberts, S. K., McCabe, C., Patrick, E., Xu, J., Yu, L., Gaiteri, C., Bennett, D. A., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4632 · Cited by 452 · see citing works
Identification of context-dependent expression quantitative trait loci in whole blood
Zhernakova, D. V., Deelen, P., Vermaat, M., van Iterson, M., van Galen, M., Arindrarto, W., van 't Hof, P., Mei, H., van Dijk, F., Westra, H.-J., et al. (2017) · Nature Genetics · doi:10.1038/ng.3737 · Cited by 441 · see citing works
Actionable exomic incidental findings in 6503 participants: challenges of variant classificationPreprint
Amendola, L. M., Dorschner, M. O., Robertson, P. D., Salama, J. S., Hart, R., Shirts, B. H., Murray, M. L., Tokita, M., Gallego, C. J., Kim, D. S., et al. (2015) · Genome Research · doi:10.1101/gr.183483.114 · Cited by 361 · see citing works
Alzheimer's disease associated with mutations in presenilin 2 is rare and variably penetrant.
Sherrington, R., Froelich, S., Sorbi, S., Campion, D., Chi, H., Rogaeva, E., Lévesque, G., Rogaev, E., Lin, C.-P., Liang, Y., et al. (1996) · Human Molecular Genetics · doi:10.1093/hmg/5.7.985 · Cited by 335 · see citing works
Skene, N., & Grant, S. G. N. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00016 · Cited by 332 · see citing works
Self-citationLoss-of-function variants in ABCA7 confer risk of Alzheimer's disease
DemGene, Steinberg, S., Stefansson, H., Jonsson, T., Johannsdottir, H., Ingason, A., Helgason, H., Sulem, P., Magnusson, O. T., Gudjonsson, S. A., et al. (2015) · Nature Genetics · doi:10.1038/ng.3246 · Cited by 324 · see citing works
Self-citationSteinthórsdóttir, V., Þorleifsson, G., Sulem, P., Helgason, H., Grarup, N., Sigurðsson, Á., Helgadottir, H. T., Johannsdottir, H., Magnússon, Ó. Þ., Gudjonsson, S. A., et al. (2014) · Nature Genetics · doi:10.1038/ng.2882 · Cited by 306 · see citing works
Novel genetic loci underlying human intracranial volume identified through genome-wide association
Adams, H., Hibar, D., Chouraki, V., Stein, J., Nyquist, P., Rentería, M. E., Trompet, S., Arias-Vásquez, A., Seshadri, S., Desriviéres, S., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4398 · Cited by 238 · see citing works
Self-citationCase–control association mapping by proxy using family history of disease
Liu, J. Z., Erlich, Y., & Pickrell, J. K. (2017) · Nature Genetics · doi:10.1038/ng.3766 · Cited by 238 · see citing works
A general framework for estimating the relative pathogenicity of human genetic variants
Kircher, M., Witten, D., Cooper, G. M., & Shendure, J. (2014) · F1000Research · doi:10.7490/f1000research.1095265.1 · Cited by 234 · see citing works
Suh, J., Choi, S. H., Romano, D. M., Gannon, M. A., Lesinski, A. N., Kim, D. Y., & Tanzi, R. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.08.035 · Cited by 217 · see citing works
AddNeuroMed—The European Collaboration for the Discovery of Novel Biomarkers for Alzheimer's Disease
Lovestone, S., Francis, P. T., Kłoszewska, I., Mecocci, P., Simmons, A., Soininen, H. S., Spenger, C., Tsolaki, M., Vellas, B. J., Wahlund, L., et al. (2009) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2009.05064.x · Cited by 211 · see citing works
Polygenic risk score analysis of pathologically confirmed Alzheimer disease
Escott‐Price, V., Myers, A., Huentelman, M. J., & Hardy, J. (2017) · Annals of Neurology · doi:10.1002/ana.24999 · Cited by 178 · see citing works
Self-citationCausal associations between risk factors and common diseases inferred from GWAS summary dataPreprint
Zhu, Z., Zheng, Z., Zhang, F., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/168674 · Cited by 160 · see citing works
Abbott, R. D., White, L. R., Ross, G. W., Petrovitch, H., Masaki, K., Snowdon, D. A., & Curb, J. D. (1998) · PEDIATRICS · doi:10.1542/peds.102.3.602 · Cited by 150 · see citing works
Self-citationHead Circumference as a Measure of Cognitive Reserve
Graves, A. B., Mortimer, J. A., Larson, E. B., Wenzlow, A. T., Bowen, J. D., & McCormick, W. C. (1996) · The British Journal of Psychiatry · doi:10.1192/bjp.169.1.86 · Cited by 141 · see citing works
Polygenic Overlap Between C-Reactive Protein, Plasma Lipids, and Alzheimer Disease
Desikan, R. S., Schork, A. J., Wang, Y., Thompson, W. K., Dehghan, A., Ridker, P. M., Chasman, D. I., McEvoy, L. K., Holland, D., Chen, C.-H., et al. (2015) · Circulation · doi:10.1161/circulationaha.115.015489 · Cited by 135 · see citing works
Self-citationCommon variants at 6q22 and 17q21 are associated with intracranial volume
Ikram, M. A., Taal, H. R., Mook‐Kanamori, D. O., Struchalin, M., Albert Hofman, D., Vernooij, M. W., Jaddoe, V. W. V., van Duijn, C. M., Breteler, M. M. B., Bergen, N. E., et al. (2012) · Nature Genetics · doi:10.1038/ng.2245 · Cited by 130 · see citing works
Dumitriu, A., Golji, J., Labadorf, A. T., Gao, B., Beach, T. G., Myers, R. H., Longo, K. A., & Latourelle, J. C. (2015) · BMC Medical Genomics · doi:10.1186/s12920-016-0164-y · Cited by 129 · see citing works
Assembly, Maturation, and Trafficking of the γ-Secretase Complex in Alzheimers Disease
Dries, D. R., & Yu, G. (2008) · Current Alzheimer Research · doi:10.2174/156720508783954695 · Cited by 120 · see citing works
Lipid metabolism and neuroinflammation in Alzheimer’s disease: a role for liver X receptors
Kang, J., & Rivest, S. (2012) · Endocrine Reviews · doi:10.1210/er.2011-1049 · Cited by 94 · see citing works
Incidence of Dementia over Three Decades in the Framingham Heart Study
Hinterberger, M., Fischer, P., & Zehetmayer, S. (2016) · New England Journal of Medicine · doi:10.1056/nejmc1604823 · Cited by 94 · see citing works
Erratum: Common variants at 6q22 and 17q21 are associated with intracranial volume
Ikram, M. A., Fornage, M., Smith, A. V., Seshadri, S. S., Schmidt, R., Debette, S., Vrooman, H. A., Sigurdsson, S. O., Ropele, S., Taal, H. R., et al. (2012) · Nature Genetics · doi:10.1038/ng0612-732c · Cited by 64 · see citing works
[ECG examination of calves with nutritional muscular degeneration].
Nĕmecek, L., & Kursa, J. (1971) · PubMed · Cited by 52 · see citing works
Brain xQTL map: Integrating the genetic architecture of the human brain transcriptome and epigenomePreprint
Ng, B., White, C. C., Klein, H., Sieberts, S., Aubin, C., Patrick, E., Xu, J., Yu, L., Gaiteri, C., Bennett, D. A. A., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/142927 · Cited by 39 · see citing works
Pathophysiological function of ADAMTS enzymes on molecular mechanism of Alzheimer’s disease
Gurses, M. S., Ural, M. N., Gulec, M. A., Akyol, O., & Akyol, S. (2016) · Aging and disease · doi:10.14336/ad.2016.0111 · Cited by 35 · see citing works
Beta-Amyloid Monomer and Insulin/IGF-1 Signaling in Alzheimer's Disease
Giuffrida, M. L., Tomasello, M. F., Caraci, F., Chiechio, S., Nicoletti, F., & Copani, A. (2012) · Molecular Neurobiology · doi:10.1007/s12035-012-8313-6 · Cited by 34 · see citing works
Suzuki, H., Hanada, K., & Fukuhara, T. (1974) · PubMed · Cited by 15 · see citing works
[Contribution to the problem of acute circulatory arrest].
Hoflehner, G., & Grundner, H. (1968) · PubMed · Cited by 7 · see citing works
Inflammation in Alzheimer's Disease: Cross-talk between Lipids and Innate Immune Cells of the Brain
Ai, L., & Me, C. (2015) · Cited by 2 · see citing works
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Scheltens, P., De Strooper, B., Kivipelto, M., Holstege, H., Chételat, G., Teunissen, C. E., Cummings, J., & van der Flier, W. M. (2021) · The Lancet · doi:10.1016/s0140-6736(20)32205-4 · Cited by 5,465 · see citing works
Self-citationThe neuropathological diagnosis of Alzheimer's disease
DeTure, M. A., & Dickson, D. W. (2019) · Molecular Neurodegeneration · doi:10.1186/s13024-019-0333-5 · Cited by 3,329 · see citing works
“Other risk factors for LOAD including TREM2, ADAM10 and PLD3 have been identified using genome wide association studies to implicate nearly 30 genes that not only affect APP and tau directly but also modulate cholesterol metabolism, endocytosis and immune response, among those with known functions [16, 17].”
BackgroundNew insights into the genetic etiology of Alzheimer’s disease and related dementias
Bellenguez, C., Küçükali, F., Jansen, I. E., Kleineidam, L., Moreno-Grau, S., Amin, N., Naj, A. C., Campos-Martin, R., Grenier-Boley, B., Andrade, V., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01024-z · Cited by 2,498 · see citing works
“The results were similar when we (1) computed indices for other follow-up time points, (2) applied a random effects meta-analysis, (3) considered conversion to all-cause-dementia as the outcome and (4) excluded the Framingham Heart Study (FHS), as it was part of the stage II of the GWAS from which ORs for PRS computat…”
BackgroundMethodResultInfluentialSelf-citationNalls, M. A., Blauwendraat, C., Vallerga, C. L., Heilbron, K., Bandres-Ciga, S., Chang, D., Tan, M., Kia, D. A., Noyce, A. J., Xue, A., et al. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30320-5 · Cited by 2,454 · see citing works
Self-citationThe Amyloid-β Pathway in Alzheimer’s Disease
Hampel, H., Hardy, J., Blennow, K., Chen, C., Perry, G., Kim, S. H., Villemagne, V. L., Aisen, P., Vendruscolo, M., Iwatsubo, T., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01249-0 · Cited by 1,952 · see citing works
Self-citationWightman, D. P., Jansen, I. E., Savage, J. E., Shadrin, A. A., Bahrami, S., Holland, D., Rongve, A., Børte, S., Winsvold, B. S., Drange, O. K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00921-z · Cited by 1,011 · see citing works
Self-citationThe genetic architecture of Parkinson's disease
Blauwendraat, C., Nalls, M. A., & Singleton, A. B. (2020) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30287-x · Cited by 1,114 · see citing works
Clinical diagnosis of Alzheimer’s disease: Recommendations of the International Working Group
Dubois, B., Villain, N., Frisoni, G. B., Rabinovici, G. D., Sabbagh, M., Cappa, S., Bejanin, A., Bombois, S., Epelbaum, S., Teichmann, M., et al. (2021) · The Lancet Neurology · doi:10.1016/s1474-4422(21)00066-1 · Cited by 1,051 · see citing works
Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease
Chen, W.-T., Lu, A., Craessaerts, K., Pavie, B., Sala Frigerio, C., Corthout, N., Qian, X., Laláková, J., Kühnemund, M., Voytyuk, I., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.038 · Cited by 949 · see citing works
The genetic architecture of the human cerebral cortex
Grasby, K. L., Jahanshad, N., Painter, J. N., Colodro-Conde, L., Bralten, J., Hibar, D. P., Lind, P. A., Pizzagalli, F., Ching, C. R. K., McMahon, M. A. B., et al. (2020) · Science · doi:10.1126/science.aay6690 · Cited by 876 · see citing works
Self-citationA human brain vascular atlas reveals diverse mediators of Alzheimer’s risk
Yang, A. C., Vest, R. T., Kern, F., Lee, D. P., Agam, M., Maat, C. A., Losada, P. M., Chen, M. B., Schaum, N., Khoury, N., et al. (2022) · Nature · doi:10.1038/s41586-021-04369-3 · Cited by 797 · see citing works
Brain cell type–specific enhancer–promoter interactome maps and disease <b>-</b> risk association
Nott, A., Holtman, I. R., Coufal, N. G., Schlachetzki, J. C. M., Yu, M., Hu, R., Han, C. Z., Pena, M., Xiao, J., Wu, Y., et al. (2019) · Science · doi:10.1126/science.aay0793 · Cited by 823 · see citing works
hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data
Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E., & Swarup, V. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100498 · Cited by 773 · see citing works
Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease
Morabito, S., Miyoshi, E., Michael, N., Shahin, S., Martini, A. C., Head, E., Silva, J., Leavy, K., Perez-Rosendahl, M., & Swarup, V. (2021) · Nature Genetics · doi:10.1038/s41588-021-00894-z · Cited by 643 · see citing works
Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease
Olah, M., Menon, V., Habib, N., Taga, M. F., Ma, Y., Yung, C. J., Cimpean, M., Khairallah, A., Coronas-Samano, G., Sankowski, R., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19737-2 · Cited by 722 · see citing works
Amyloid-β-independent regulators of tau pathology in Alzheimer disease
van der Kant, R., Goldstein, L. S. B., & Ossenkoppele, R. (2020) · Nature reviews. Neuroscience · doi:10.1038/s41583-019-0240-3 · Cited by 580 · see citing works
Schwartzentruber, J., Cooper, S., Liu, J. Z., Barrio-Hernandez, I., Bello, E., Kumasaka, N., Young, A. M. H., Franklin, R. J. M., Johnson, T., Estrada, K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00776-w · Cited by 532 · see citing works
The probabilistic model of Alzheimer disease: the amyloid hypothesis revised
Frisoni, G. B., Altomare, D., Thal, D. R., Ribaldi, F., van der Kant, R., Ossenkoppele, R., Blennow, K., Cummings, J., van Duijn, C., Nilsson, P. M., et al. (2022) · Nature Reviews Neuroscience · doi:10.1038/s41583-021-00533-w · Cited by 513 · see citing works
Chia, R., Sabir, M. S., Bandres-Ciga, S., Saez-Atienzar, S., Reynolds, R. H., Gustavsson, E., Walton, R. L., Ahmed, S., Viollet, C., Ding, J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00785-3 · Cited by 438 · see citing works
Self-citationCross-Species Single-Cell Analysis Reveals Divergence of the Primate Microglia Program
Geirsdottir, L., David, E., Keren-Shaul, H., Weiner, A., Bohlen, S. C., Neuber, J., Balic, A., Giladi, A., Sheban, F., Dutertre, C.-A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.010 · Cited by 461 · see citing works
Siglecs as Immune Cell Checkpoints in Disease
Duan, S., & Paulson, J. C. (2020) · Annual Review of Immunology · doi:10.1146/annurev-immunol-102419-035900 · Cited by 452 · see citing works
Alzheimer’s Patient Microglia Exhibit Enhanced Aging and Unique Transcriptional Activation
Srinivasan, K., Friedman, B. A., Etxeberria, A., Huntley, M. A., van der Brug, M. P., Foreman, O., Paw, J. S., Modrusan, Z., Beach, T. G., Serrano, G. E., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107843 · Cited by 427 · see citing works
Distinct amyloid-β and tau-associated microglia profiles in Alzheimer’s disease
Gerrits, E., Brouwer, N., Kooistra, S. M., Woodbury, M. E., Vermeiren, Y., Lambourne, M., Mulder, J., Kummer, M., Möller, T., Biber, K., et al. (2021) · Acta Neuropathologica · doi:10.1007/s00401-021-02263-w · Cited by 441 · see citing works
Astrocyte contribution to dysfunction, risk and progression in neurodegenerative disorders
Brandebura, A. N., Paumier, A., Onur, T. S., & Allen, N. J. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-022-00641-1 · Cited by 427 · see citing works
Peripheral and central immune system crosstalk in Alzheimer disease — a research prospectus
Bettcher, B. M., Tansey, M. G., Dorothée, G., & Heneka, M. T. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-021-00549-x · Cited by 427 · see citing works
The complex genetic architecture of Alzheimer's disease: novel insights and future directions
Andrews, S. J., Renton, A. E., Fulton-Howard, B., Podlesny-Drabiniok, A., Marcora, E., & Goate, A. M. (2023) · eBioMedicine · doi:10.1016/j.ebiom.2023.104511 · Cited by 377 · see citing works
Corces, M. R., Shcherbina, A., Kundu, S., Gloudemans, M. J., Frésard, L., Granja, J. M., Louie, B. H., Eulalio, T., Shams, S., Bagdatli, S. T., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-00721-x · Cited by 358 · see citing works
Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores
de Rojas, I., Moreno-Grau, S., Tesi, N., Grenier-Boley, B., Andrade, V., Jansen, I. E., Pedersen, N. L., Stringa, N., Zettergren, A., Hernández, I., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22491-8 · Cited by 324 · see citing works
Self-citationImmune Signaling in Neurodegeneration
Hammond, T. R., Marsh, S. E., & Stevens, B. (2019) · Immunity · doi:10.1016/j.immuni.2019.03.016 · Cited by 336 · see citing works
Single-cell multiregion dissection of Alzheimer’s disease
Mathys, H., Boix, C. A., Akay, L. A., Xia, Z., Davila-Velderrain, J., Ng, A. P., Jiang, X., Abdelhady, G., Galani, K., Mantero, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07606-7 · Cited by 343 · see citing works
Interpretation of risk loci from genome-wide association studies of Alzheimer's disease
Andrews, S. J., Fulton-Howard, B., & Goate, A. (2020) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30435-1 · Cited by 314 · see citing works
Menon, M., Mohammadi, S., Davila-Velderrain, J., Goods, B. A., Cadwell, T. D., Xing, Y., Stemmer-Rachamimov, A., Shalek, A. K., Love, J. C., Kellis, M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12780-8 · Cited by 331 · see citing works
Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans
Thrupp, N., Sala Frigerio, C., Wolfs, L., Skene, N. G., Fattorelli, N., Poovathingal, S., Fourne, Y., Matthews, P. M., Theys, T., Mancuso, R., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108189 · Cited by 329 · see citing works
Self-citationSingle-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320 · see citing works
Sey, N. Y. A., Hu, B., Mah, W., Fauni, H., McAfee, J. C., Rajarajan, P., Brennand, K. J., Akbarian, S., & Won, H. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0603-0 · Cited by 297 · see citing works
Wan, Y.-W., Al-Ouran, R., Mangleburg, C. G., Perumal, T. M., Lee, T. V., Allison, K., Swarup, V., Funk, C. C., Gaiteri, C., Allen, M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107908 · Cited by 295 · see citing works
The path forward in Alzheimer's disease therapeutics: Reevaluating the amyloid cascade hypothesis
Tolar, M., Abushakra, S., & Sabbagh, M. (2020) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2019.09.075 · Cited by 275 · see citing works
Lopes, K. D. P., Snijders, G. J. L., Humphrey, J., Allan, A., Sneeboer, M. A. M., Navarro, E., Schilder, B. M., Vialle, R. A., Parks, M., Missall, R., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00976-y · Cited by 273 · see citing works
Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture
Zhang, Q., Sidorenko, J., Couvy-Duchesne, B., Marioni, R. E., Wright, M. J., Goate, A. M., Marcora, E., Huang, K.-L., Porter, T., Laws, S. M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18534-1 · Cited by 272 · see citing works
The human connectome in Alzheimer disease — relationship to biomarkers and genetics
Yu, M., Sporns, O., & Saykin, A. J. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-021-00529-1 · Cited by 265 · see citing works
Cellular senescence and Alzheimer disease: the egg and the chicken scenario
Saez-Atienzar, S., & Masliah, E. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0325-z · Cited by 257 · see citing works
Tau: Enabler of diverse brain disorders and target of rapidly evolving therapeutic strategies
Chang, C.-W., Shao, E., & Mucke, L. (2021) · Science · doi:10.1126/science.abb8255 · Cited by 263 · see citing works
Synergistic insights into human health from aptamer- and antibody-based proteomic profiling
Pietzner, M., Wheeler, E., Carrasco-Zanini, J., Kerrison, N. D., Oerton, E., Koprulu, M., Luan, J., Hingorani, A. D., Williams, S. A., Wareham, N. J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27164-0 · Cited by 234 · see citing works
Genetic analyses identify widespread sex-differential participation bias
Pirastu, N., Cordioli, M., Nandakumar, P., Mignogna, G., Abdellaoui, A., Hollis, B., Kanai, M., Rajagopal, V. M., Parolo, P. D. B., Baya, N., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00846-7 · Cited by 251 · see citing works
Novikova, G., Kapoor, M., TCW, J., Abud, E. M., Efthymiou, A. G., Chen, S. X., Cheng, H., Fullard, J. F., Bendl, J., Liu, Y., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21823-y · Cited by 214 · see citing works
Microglia in Brain Development, Homeostasis, and Neurodegeneration
Bohlen, C. J., Friedman, B. A., Dejanovic, B., & Sheng, M. (2019) · Annual Review of Genetics · doi:10.1146/annurev-genet-112618-043515 · Cited by 212 · see citing works
A Map of Transcriptional Heterogeneity and Regulatory Variation in Human Microglia
Young, A. M. H., Kumasaka, N., Calvert, F., Hammond, T. R., Knights, A., Panousis, N., Park, J. S., Schwartzentruber, J., Liu, J., Kundu, K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00875-2 · Cited by 213 · see citing works
Large-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk
Walker, K. A., Chen, J., Zhang, J., Fornage, M., Yang, Y., Zhou, L., Grams, M. E., Tin, A., Daya, N., Hoogeveen, R. C., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00064-0 · Cited by 212 · see citing works
Logan, T., Simon, M. J., Rana, A., Cherf, G. M., Srivastava, A., Davis, S. S., Low, R. L. Y., Chiu, C.-L., Fang, M., Huang, F., et al. (2021) · Cell · doi:10.1016/j.cell.2021.08.002 · Cited by 233 · see citing works
Cholesterol and Alzheimer’s Disease; From Risk Genes to Pathological Effects
Feringa, F. M., & van der Kant, R. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.690372 · Cited by 226 · see citing works
Genetics of Alzheimer’s disease: where we are, and where we are going
Bellenguez, C., Grenier-Boley, B., & Lambert, J.-C. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.11.024 · Cited by 215 · see citing works
Endo-lysosomal dysregulations and late-onset Alzheimer’s disease: impact of genetic risk factors
Van Acker, Z. P., Bretou, M., & Annaert, W. (2019) · Molecular Neurodegeneration · doi:10.1186/s13024-019-0323-7 · Cited by 222 · see citing works
Tran, M. N., Maynard, K. R., Spangler, A., Huuki, L. A., Montgomery, K. D., Sadashivaiah, V., Tippani, M., Barry, B. K., Hancock, D. B., Hicks, S. C., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.001 · Cited by 219 · see citing works
Gene–environment interactions in Alzheimer disease: the emerging role of epigenetics
Migliore, L., & Coppedè, F. (2022) · Nature Reviews Neurology · doi:10.1038/s41582-022-00714-w · Cited by 217 · see citing works
Co-expression patterns of microglia markers Iba1, TMEM119 and P2RY12 in Alzheimer’s disease
Kenkhuis, B., Somarakis, A., Kleindouwel, L. R. T., van Roon-Mom, W. M. C., Höllt, T., & van der Weerd, L. (2022) · Neurobiology of Disease · doi:10.1016/j.nbd.2022.105684 · Cited by 188 · see citing works
Lee, S., Devanney, N. A., Golden, L. R., Smith, C. T., Schwartz, J. L., Walsh, A. E., Clarke, H. A., Goulding, D. S., Allenger, E. J., Morillo-Segovia, G., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112196 · Cited by 203 · see citing works
Early Alzheimer’s disease pathology in human cortex involves transient cell states
Gazestani, V., Kamath, T., Nadaf, N. M., Dougalis, A., Burris, S. J., Rooney, B., Junkkari, A., Vanderburg, C., Pelkonen, A., Gomez-Budia, M., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.005 · Cited by 209 · see citing works
Diverse human astrocyte and microglial transcriptional responses to Alzheimer’s pathology
Smith, A. M., Davey, K., Tsartsalis, S., Khozoie, C., Fancy, N., Tang, S. S., Liaptsi, E., Weinert, M., McGarry, A., Muirhead, R. C. J., et al. (2022) · Acta Neuropathologica · doi:10.1007/s00401-021-02372-6 · Cited by 209 · see citing works
“Microglia also were highly enriched in genes associated previously with genetic risk for AD (27/65) [3, 25, 31] (Extended Data Fig.”
BackgroundAPOE4 impairs the microglial response in Alzheimer’s disease by inducing TGFβ-mediated checkpoints
Yin, Z., Rosenzweig, N., Kleemann, K. L., Zhang, X., Brandão, W., Margeta, M. A., Schroeder, C., Sivanathan, K. N., Silveira, S., Gauthier, C., et al. (2023) · Nature Immunology · doi:10.1038/s41590-023-01627-6 · Cited by 202 · see citing works
“Genome ‐ wide association meta ‐ analysis studies (GWASs) 1,2 and inter-actome investigation 3 identified microglia gene expression patterns as strongly correlated with late-onset Alzheimer’s disease (AD).”
BackgroundMicroglial metabolism is a pivotal factor in sexual dimorphism in Alzheimer's disease
Guillot-Sestier, M.-V., Araiz, A. R., Mela, V., Gaban, A. S., O’Neill, E., Joshi, L., Chouchani, E. T., Mills, E. L., & Lynch, M. A. (2021) · Communications Biology · doi:10.1038/s42003-021-02259-y · Cited by 182 · see citing works
Multiscale causal networks identify VGF as a key regulator of Alzheimer’s disease
Beckmann, N. D., Lin, W.-J., Wang, M., Cohain, A. T., Charney, A. W., Wang, P., Ma, W., Wang, Y.-C., Jiang, C., Audrain, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17405-z · Cited by 172 · see citing works
Self-citationIron loading is a prominent feature of activated microglia in Alzheimer’s disease patients
Kenkhuis, B., Somarakis, A., de Haan, L., Dzyubachyk, O., IJsselsteijn, M. E., de Miranda, N. F. C. C., Lelieveldt, B. P. F., Dijkstra, J., van Roon-Mom, W. M. C., Höllt, T., et al. (2021) · Acta Neuropathologica Communications · doi:10.1186/s40478-021-01126-5 · Cited by 184 · see citing works
A comparative atlas of single-cell chromatin accessibility in the human brain
Li, Y. E., Preissl, S., Miller, M., Johnson, N. D., Wang, Z., Jiao, H., Zhu, C., Wang, Z., Xie, Y., Poirion, O., et al. (2023) · Science · doi:10.1126/science.adf7044 · Cited by 173 · see citing works
Dourlen, P., Kilinc, D., Malmanche, N., Chapuis, J., & Lambert, J.-C. (2019) · Acta Neuropathologica · doi:10.1007/s00401-019-02004-0 · Cited by 168 · see citing works
Genetics of the human microglia regulome refines Alzheimer’s disease risk loci
Kosoy, R., Fullard, J. F., Zeng, B., Bendl, J., Dong, P., Rahman, S., Kleopoulos, S. P., Shao, Z., Girdhar, K., Humphrey, J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01149-1 · Cited by 168 · see citing works
Absence of microglia promotes diverse pathologies and early lethality in Alzheimer’s disease mice
Kiani Shabestari, S., Morabito, S., Danhash, E. P., McQuade, A., Sanchez, J. R., Miyoshi, E., Chadarevian, J. P., Claes, C., Coburn, M. A., Hasselmann, J., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110961 · Cited by 167 · see citing works
Haure-Mirande, J.-V., Audrain, M., Ehrlich, M. E., & Gandy, S. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00552-w · Cited by 159 · see citing works
Lipid lowering and Alzheimer disease risk: A mendelian randomization study
Williams, D. M., Finan, C., Schmidt, A. F., Burgess, S., & Hingorani, A. D. (2020) · Annals of Neurology · doi:10.1002/ana.25642 · Cited by 157 · see citing works
Self-citationLagomarsino, V. N., Pearse, R. V., Liu, L., Hsieh, Y.-C., Fernandez, M. A., Vinton, E. A., Paull, D., Felsky, D., Tasaki, S., Gaiteri, C., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.003 · Cited by 151 · see citing works
Human iPSC‐derived microglia: A growing toolset to study the brain's innate immune cells
Hasselmann, J., & Blurton‐Jones, M. (2020) · Glia · doi:10.1002/glia.23781 · Cited by 145 · see citing works
Nazir, S., Jankowski, V., Bender, G., Zewinger, S., Rye, K.-A., & van der Vorst, E. P. C. (2020) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2020.10.006 · Cited by 140 · see citing works
The Alzheimer’s disease drug development landscape
van Bokhoven, P., de Wilde, A., Vermunt, L., Leferink, P. S., Heetveld, S., Cummings, J., Scheltens, P., & Vijverberg, E. G. B. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00927-z · Cited by 123 · see citing works
Ewers, M., Biechele, G., Suárez‐Calvet, M., Sacher, C., Blume, T., Morenas‐Rodriguez, E., Deming, Y., Piccio, L., Cruchaga, C., Kleinberger, G., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.202012308 · Cited by 133 · see citing works
Investigational BACE inhibitors for the treatment of Alzheimer’s disease
Imbimbo, B. P., & Watling, M. (2019) · Expert Opinion on Investigational Drugs · doi:10.1080/13543784.2019.1683160 · Cited by 132 · see citing works
Fang, J., Zhang, P., Wang, Q., Chiang, C.-W., Zhou, Y., Hou, Y., Xu, J., Chen, R., Zhang, B., Lewis, S. J., et al. (2022) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00951-z · Cited by 140 · see citing works
Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants
Zhang, S., Zhang, H., Zhou, Y., Qiao, M., Zhao, S., Kozlova, A., Shi, J., Sanders, A. R., Wang, G., Luo, K., et al. (2020) · Science · doi:10.1126/science.aay3983 · Cited by 127 · see citing works
Genetic architecture of Alzheimer's disease
Neuner, S. M., TCW, J., & Goate, A. M. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104976 · Cited by 126 · see citing works
“Interpretation of GWAS hits is made more straightforward when GWAS SNPs themselves or nearby variants in high LD are located within coding regions of the genome, although this happens relatively infrequently (Kunkle et al., 2019; Jansen et al., 2019).”
BackgroundResultInfluentialMosquera, J. V., Auguste, G., Wong, D., Turner, A. W., Hodonsky, C. J., Alvarez-Yela, A. C., Song, Y., Cheng, Q., Lino Cardenas, C. L., Theofilatos, K., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113380 · Cited by 123 · see citing works
Non-coding variability at the APOE locus contributes to the Alzheimer’s risk
Zhou, X., Chen, Y., Mok, K. Y., Kwok, T. C. Y., Mok, V. C. T., Guo, Q., Ip, F. C., Chen, Y., Mullapudi, N., Initiative, A. D. N., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10945-z · Cited by 120 · see citing works
Self-citationGenetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk
Salih, D. A., Bayram, S., Guelfi, S., Reynolds, R. H., Shoai, M., Ryten, M., Brenton, J. W., Zhang, D., Matarin, M., Botia, J. A., et al. (2019) · Brain Communications · doi:10.1093/braincomms/fcz022 · Cited by 110 · see citing works
Self-citationXu, J., Mao, C., Hou, Y., Luo, Y., Binder, J. L., Zhou, Y., Bekris, L. M., Shin, J., Hu, M., Wang, F., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111717 · Cited by 115 · see citing works
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 · see citing works
Self-citationImaging transcriptomics of brain disorders
Arnatkeviciute, A., Fulcher, B. D., Bellgrove, M. A., & Fornito, A. (2022) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2021.10.002 · Cited by 113 · see citing works
Physiological β-amyloid clearance by the liver and its therapeutic potential for Alzheimer’s disease
Cheng, Y., He, C.-Y., Tian, D.-Y., Chen, S.-H., Ren, J.-R., Sun, H.-L., Xu, M.-Y., Tan, C.-R., Fan, D.-Y., Jian, J.-M., et al. (2023) · Acta Neuropathologica · doi:10.1007/s00401-023-02559-z · Cited by 105 · see citing works
Self-citationGenetic control of the human brain proteome
Robins, C., Wingo, A. P., Fan, W., Duong, D. M., Meigs, J., Gerasimov, E. S., Dammer, E. B., Cutler, D. J., Jager, P. L. D., Bennett, D. A., et al. (2019) · doi:10.1101/816652 · Cited by 104 · see citing works
Kaji, S., Berghoff, S. A., Spieth, L., Schlaphoff, L., Sasmita, A. O., Vitale, S., Büschgens, L., Kedia, S., Zirngibl, M., Nazarenko, T., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.09.014 · Cited by 104 · see citing works
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 · see citing works
Tsartsalis, 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 · see citing works
Plasma 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 · see citing works
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 · see citing works
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 · see citing works
Self-citationRen, 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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
“Thus, NYAP1 may be related to AD pathology.”
BackgroundInfluentialInterleukin-10 deficiency exacerbates inflammation-induced tau pathology
Weston, L. L., Jiang, S., Chisholm, D., Jantzie, L. L., & Bhaskar, K. (2021) · Journal of Neuroinflammation · doi:10.1186/s12974-021-02211-1 · Cited by 69 · see citing works
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 · see citing works
Self-citation
- 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 · 2018: 1
- 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
- Health Sciences › Medicine › Physiology › 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
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