Meta-Analysis of 74,046 Individuals Identifies 11 New Susceptibility Loci for Alzheimer’s Disease
Open accessAbstract
Eleven susceptibility loci for late-onset Alzheimer's disease (LOAD) were identified by previous studies; however, a large portion of the genetic risk for this disease remains unexplained. We conducted a large, two-stage meta-analysis of genome-wide association studies (GWAS) in individuals of European ancestry. In stage 1, we used genotyped and imputed data (7,055,881 SNPs) to perform meta-analysis on 4 previously published GWAS data sets consisting of 17,008 Alzheimer's disease cases and 37,154 controls. In stage 2, 11,632 SNPs were genotyped and tested for association in an independent set of 8,572 Alzheimer's disease cases and 11,312 controls. In addition to the APOE locus (encoding apolipoprotein E), 19 loci reached genome-wide significance (P < 5 × 10(-8)) in the combined stage 1 and stage 2 analysis, of which 11 are newly associated with Alzheimer's disease.
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More 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 [1]
The Abstract (page not provided) reports that 19 loci in addition to APOE reached genome-wide significance in the combined analysis, including 11 newly associated loci. That count is contrary to the attributed figure of more than 90; the paper studies late-onset Alzheimer’s disease rather than the broader ADRD category. (LLM, conf=0.85)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredOver 70 loci are associated with conferring risk for late-onset Alzheimer's disease.
“In recent years, genome wide association studies (GWASs) and analyses of rare-coding variants have identified over 70 loci associated with conferring risk for late-onset AD (LOAD).”
From Microglial function, INPP5D/SHIP1 signaling, and NLRP3 inflammasome activation: implications for Alzheimer’s disease (2023) · cites this paper as [9]
The Abstract reports 19 loci reaching genome-wide significance in the combined analysis, in addition to APOE, with 11 newly associated loci. Its summary lists those 11 alongside the previously known susceptibility genes; it does not report over 70 risk loci for late-onset Alzheimer’s disease. (LLM, conf=0.88)
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 [6]
The Abstract identifies previously known loci associated with Alzheimer’s disease risk, supporting the background point for which this paper is cited. It does not establish associations with related dementias. The 39 signals in 33 loci are presented as the citing authors’ own result, not as a finding attributed to this source. (LLM, conf=0.83)
Experimental — accuracy not yet measuredGWAS and sequencing studies have found associations between AD and genes expressed in myeloid cells, including TREM2, ABCA7, and CD33.
“At the genetic level, GWAS and sequencing studies have found associations between AD and genes expressed in myeloid cells, including TREM2, ABCA7, and CD331,2,5,15–17.”
From A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease (2017) · cites this paper as [1]
The Abstract describes ABCA7 and CD33 as previously identified GWAS genes associated with Alzheimer’s disease and reports a rare TREM2 risk variant. It does not say that all three genes are expressed in myeloid cells. It also notes that this study did not replicate the CD33 association in stage 2 (P = 0.61). Page numbers are not available in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredNearly 30 late-onset Alzheimer's disease susceptibility loci are known, and risk is significantly polygenic.
“Nearly 30 LOAD susceptibility loci2-12 are known, and risk is significantly polygenic13.”
From Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease (2017) · cites this paper as [2]
The Abstract reports 11 previously identified LOAD susceptibility loci and 11 newly identified loci, supporting this paper’s contribution to the cited group of susceptibility-locus findings. It does not establish the sentence’s total of nearly 30 loci. The separate claim that risk is significantly polygenic is attached to citation 13, not to this paper. (LLM, conf=0.85)
Experimental — accuracy not yet measured
SPI1 is a central transcription factor in microglial activation, has a significant gene-wide association with Alzheimer's disease, and lies near genome-wide significant signals from IGAP.
“SPI1 is a central transcription factor in microglial activation state that has a significant gene-wide association with AD5 and is in the proximity of GWS signals identified by IGAP2.”
From Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease (2017) · cites this paper as [2]
The paper’s Table 2 (Statistical analysis; page unavailable) reports a genome-wide significant Alzheimer’s disease signal at CELF1. Our copy does not identify SPI1 or establish that it lies near that signal; searches for SPI1 and PU.1 found no relevant passage. The microglial-activation and gene-wide-association parts of the citing sentence carry a different citation, so this verdict concerns the IGAP signal claim. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe greatest non-modifiable risk factors for sporadic Alzheimer's disease are age, the APOE-ε4 allele, and gender.
“Several studies suggest that the greatest non-modifiable risk factors for SAD are age, the APOE-ε4 allele, and gender [4,88].”
From Alzheimer's Disease: Treatment Strategies and Their Limitations. (2022) · cites this paper as [88]
The Abstract (page unavailable) says Alzheimer’s disease primarily affects the elderly and identifies APOE as a major genetic risk factor; its references specifically identify the APOE-ε4 allele. The paper’s data table records participants’ sex, but our copy does not identify gender as one of the greatest risk factors or rank these three factors together. (LLM, conf=0.86)
Experimental — accuracy not yet measuredGWAS of Alzheimer's disease patients revealed deregulated expression of genes PICALM, BIN-1, and sorLA, which are essentially involved in clathrin-mediated endocytosis.
“The genome-wide association studies (GWAS) of AD patients revealed that the deregulated expression of genes PICALM, BIN-1, and sorLA, which are essentially involved in clathrin-mediated endocytosis [156,157].”
From Insights of Endocytosis Signaling in Health and Disease (2023) · cites this paper as [157]
The Abstract identifies BIN1 and PICALM as known Alzheimer’s susceptibility genes and reports a genome-wide significant association at SORL1. It does not report deregulated expression of these genes or establish that all three are involved in clathrin-mediated endocytosis; the study tested genetic associations, not gene expression. (LLM, conf=0.88)
Experimental — accuracy not yet measuredRecently identified variants in genes such as BIN1 and CLU extend the genetic landscape beyond the classical amyloid and tau pathways.
“In addition, recently identified variants in genes such as bridging integrator 1 (BIN1) and clusterin (CLU) highlight the multifactorial nature of the disease and extend the genetic landscape beyond the classical amyloid and tau pathways128,150.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [150]
The paper’s Abstract (page not provided) identifies BIN1 and CLU as established Alzheimer’s susceptibility genes and describes associated loci in pathways beyond APP and tau, including immune response and inflammation. But it does not present BIN1 and CLU as the newly identified variants that extend those pathways; it describes BIN1 as potentially connected to tau pathology. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe APOE ε4 allele significantly increases susceptibility to AD.
“The APOE gene remains the prominent genetic risk factor, with the ε4 allele significantly increasing susceptibility.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [150]
The opening passage (indexed as Abstract; page unavailable) calls APOE a major genetic risk factor for Alzheimer’s disease. Its cited reference to the type 4 allele links that allele to risk, but this paper does not itself report an ε4-specific increase in susceptibility or its statistical significance. (LLM, conf=0.82)
Experimental — accuracy not yet measuredGWAS 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 [150]
The Abstract reports GWAS susceptibility loci and links several candidate genes to immune-response and inflammation pathways. It reports 11 previously identified loci and 11 new loci, not more than 40, and does not establish the full stated set of lipid-metabolism, immune-regulation, and endosomal-trafficking pathways. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAPOE4 is the strongest genetic risk factor for Alzheimer's disease.
“APOE4 is the strongest genetic risk factor for Alzheimer’s disease (AD)1–3.”
From APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes (2022) · cites this paper as [1]
The paper states that the APOE gene is a major genetic risk factor for Alzheimer’s disease (Abstract; page not available), and its references identify the APOE type 4 allele as associated with risk (References; page not available). Our copy does not establish that APOE4, specifically, is the strongest genetic risk factor. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAmong those at risk of sporadic LOAD, patients may carry a unique set of numerous genetic changes with greater risk for developing the disease, including CLU, TREM2, and APOE variants.
“Among those at risk of sporadic late-onset AD (LOAD), patients may carry a unique set of numerous genetic changes with greater risk for developing the disease, including clusterin (CLU), triggering receptor expressed on myeloid cells 2 (TREM2), and apolipoprotein E (APOE) variants (6).”
From Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets (2021) · cites this paper as [6]
The Abstract (page unavailable) identifies APOE as a major genetic risk factor, lists CLU among known LOAD-associated genes, and reports a rare TREM2 variant associated with increased Alzheimer’s risk. It supports the role of multiple susceptibility variants, but does not establish that individual patients carry a unique set of them or that the three named variants occur together. (LLM, conf=0.84)
Experimental — accuracy not yet measuredGWAS have identified multiple loci associated with AD, including variants in PICALM, CLU, ABCA7, SORL1, CR1, TREM2, and BIN1.
“Genome-wide association studies (GWAS) have identified multiple loci associated with AD including to name a few, variants in PICALM , CLU , ATP-binding cassette transporter A7 ( ABCA7 ), sortilin related receptor-1 ( SORL1 ), complement receptor 1 ( CR1 ), triggering receptor expressed on myeloid cells 2 ( TREM2 ), and bridging integrator 1 ( BIN1 ) genes (219, 236, 254, 300, 343, 344, 528, 543).”
From Blood-Brain Barrier: From Physiology to Disease and Back (2019) · cites this paper as [344]
The Abstract lists PICALM, CLU, ABCA7, CR1, and BIN1 as previously GWAS-defined genes and reports a genome-wide significant association at SORL1. It describes the TREM2 variant as a previously reported rare susceptibility variant; this paper’s GWAS found only suggestive evidence near TREM2, so it does not support attributing that variant’s identification to GWAS. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe association of PICALM polymorphisms with late-onset AD has been reported by the majority of GWAS studies.
“The association of PICALM polymorphisms with late-onset AD has been reported by the majority of GWAS studies (94, 119, 236, 265, 298, 344, 528).”
From Blood-Brain Barrier: From Physiology to Disease and Back (2019) · cites this paper as [344]
Table 2 (Statistical analysis; page unavailable) reports a significant association between a PICALM SNP and Alzheimer’s disease in the meta-analysis, and the paper describes PICALM as a previously known GWAS-defined gene. It does not establish that a majority of GWAS studies individually reported the association; pooling four GWAS datasets cannot show that proportion. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe risk alleles of rs3851179 and rs10792832 lead to lower expression of PICALM isoform 2 in the frontal and temporal cortex in the human brain on eQTLs.
“the risk alleles of rs3851179 (236) and rs10792832 (344) lead to lower expression of PICALM isoform 2 in the frontal and temporal cortex in the human brain on the expression quantitative trait loci (eQTLs) (586), indicating that lower PICALM levels may increase the risk for AD.”
From Blood-Brain Barrier: From Physiology to Disease and Back (2019) · cites this paper as [344]
Table 2 (Statistical analysis; page unavailable) associates rs10792832 near PICALM with Alzheimer’s disease risk. The paper’s eQTL discussion (Additional information; page unavailable) describes a search of published expression data, but the passages found do not report that either named allele lowers PICALM isoform 2 expression in frontal or temporal cortex. The claim’s expression-specific finding is therefore unsupported by this source. (LLM, conf=0.85)
Experimental — accuracy not yet measuredSome additional highly replicated AD risk genes include CR1, BIN1, and TREM2.
“Some additional highly replicated risk genes include CR1 , BIN1 , and TREM2 (94, 119, 219, 300, 344, 543).”
From Blood-Brain Barrier: From Physiology to Disease and Back (2019) · cites this paper as [344]
The paper’s summary (section labeled Abstract; page unavailable) lists BIN1 and CR1 among already known Alzheimer’s susceptibility genes. It also reports a rare TREM2 risk variant, citing two studies, but its own nearby signal had only suggestive evidence of association. The copy therefore supports TREM2 as a reported risk gene, not the description of it as highly replicated. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPolymorphisms in genes encoding microglial markers HLA-DR, CD33 and TREM2 have been implicated as risk factors for late-onset AD.
“Polymorphisms in genes encoding microglial markers HLA-DR, CD33 and TREM2 have been implicated as risk factors for late-onset AD,9, 10, 103”
From Markers of microglia in post-mortem brain samples from patients with Alzheimer’s disease: a systematic review (2018) · cites this paper as [9]
The Abstract (page unavailable) reports an Alzheimer’s disease association at the HLA-DRB5–DRB1 region, identifies CD33 as a previously implicated locus, and reports a rare TREM2 risk variant. It does not identify all three as microglial markers, cannot determine which gene drives the HLA-region signal, and notes that CD33 did not replicate in its stage 2 analysis. (LLM, conf=0.82)
Experimental — accuracy not yet measuredRecent GWAS have identified several genes associated with AD that are predominantly or exclusively expressed in myeloid cells, including INPP5D.
“Recent genome-wide association studies (GWAS) have identified several genes associated with AD that are predominantly or exclusively expressed in myeloid cells, including INPP5D (inositol polyphosphate 5-phosphatase 1), which encodes SHIP17–10.”
From INPP5D/SHIP1 regulates endo-lysosomal function and cargo-selective phagocytosis in human microglia (2025) · cites this paper as [7]
The paper identifies an Alzheimer’s disease–associated locus at INPP5D (Abstract; Table 2, Statistical analysis). It says INPP5D is expressed at low levels in the brain, but does not establish that it or the other associated genes are predominantly or exclusively expressed in myeloid cells. (LLM, conf=0.87)
Experimental — accuracy not yet measuredA non-coding variant rs35349669 in INPP5D increases the risk for Alzheimer's disease (OR = 1.08, 95% CI = 1.06-1.11).
“a non-coding variant in inositol polyphosphate-5-phosphatase (INPP5D) has been shown to increase AD risk (rs35349669; odds ratio [OR] = 1.08, 95% confidence interval [CI] = 1.06-1.11).5,7”
From <i>INPP5D</i> deficiency attenuates amyloid pathology in a mouse model of Alzheimer's disease (2023) · cites this paper as [5]
Table 2 (indexed under “Statistical analysis”; page not given) identifies rs35349669 at the INPP5D locus and reports an overall Alzheimer’s disease association with OR 1.08. Its 95% CI is 1.05–1.11, not the claimed 1.06–1.11. The cited passage identifies INPP5D as the closest gene but does not establish that this variant is non-coding or causally increases risk. (LLM, conf=0.91)
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 Lambert et al., 2013
The Abstract (page not given) identifies APOE as a major genetic risk factor for Alzheimer’s disease and reports an AD-associated TREM2 variant. It does not establish a TREM2–TYROBP pathway, describe these pathways as microglia-specific, or show that they are critical determinants of other neurodegenerative diseases. (LLM, conf=0.87)
Experimental — accuracy not yet measuredGWAS have not identified any direct link between PCSK9 variants and Alzheimer's disease risk.
“GWAS have not identified any direct link between PCSK9 variants and Alzheimer’s risk [25].”
From The Roles of PCSK9 in Alzheimer’s Disease: A Systematic Review of Clinical, Genetic, and Preclinical Evidence (2025) · cites this paper as [25]
The paper’s Abstract reports 19 loci reaching genome-wide significance in its GWAS meta-analysis and lists the associated loci without PCSK9. This supports the narrower point that this study did not identify a PCSK9 association, but it does not establish that no GWAS has found a direct link between PCSK9 variants and Alzheimer’s risk. (LLM, conf=0.78)
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 [1]
The Abstract supports that GWAS identified genome-wide significant LOAD loci using P < 5 × 10−8. It reports 19 such loci in the combined analysis, in addition to APOE, not 75; this source does not support the cited total. (LLM, conf=0.90)
Experimental — accuracy not yet measuredLoci associated with Alzheimer's disease risk are enriched in genes expressed in microglia.
“Recent genetic studies on Alzheimer’s disease (AD) have brought microglia under the spotlight, as loci associated with AD risk are enriched in genes expressed in microglia.”
From Microglial function, INPP5D/SHIP1 signaling, and NLRP3 inflammasome activation: implications for Alzheimer’s disease (2023) · cites this paper as [9]
The passage labeled Abstract reports Alzheimer’s risk loci and says several candidate genes fall in immune-response and inflammation pathways enriched for GWAS association signals. It does not say that risk loci are enriched in genes expressed in microglia; the enrichment described is for pathways, not microglial gene expression. (LLM, conf=0.83)
Experimental — accuracy not yet measuredGWASs have identified three single nucleotide polymorphisms, rs35349669, rs10933431, and rs7597763, at the INPP5D locus that are associated with risk for AD.
“GWASs have identified three single nucleotide polymorphisms (SNPs), rs35349669, rs10933431, and rs7597763, at the INPP5D locus that are associated risk for AD [8–10].”
From Microglial function, INPP5D/SHIP1 signaling, and NLRP3 inflammasome activation: implications for Alzheimer’s disease (2023) · cites this paper as [9]
Table 2 (Statistical analysis; page unavailable) reports an association between rs35349669 at the INPP5D locus and Alzheimer’s disease risk. Searches for rs10933431 and rs7597763 found no passages identifying either SNP, so this source supports only one of the three named variants. (LLM, conf=0.89)
Experimental — accuracy not yet measuredINPP5D/SHIP1 is a myeloid-expressed gene genetically associated with Alzheimer's disease.
“Inositol polyphosphate-5-phosphatase D (INPP5D/SHIP1) is a myeloid-expressed gene genetically-associated with AD.”
From INPP5D regulates inflammasome activation in human microglia (2023) · cites this paper as [16]
The paper identifies an Alzheimer’s disease-associated locus at INPP5D (Abstract; Table 2, page not available). It says INPP5D is expressed at low levels in the brain, but does not state that it is myeloid-expressed; that part of the claim is unsupported by our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredMost of the genes associated with the ~40 loci identified by genome-wide association (GWA) analysis or by rare variant sequencing studies are expressed in glial cells (e.g., APOE, TREM2, BIN1, CD33, INPP5D, CTSB, CTSD, and PICALM).
“Most of the genes associated with the ~40 loci identified by genome-wide association (GWA) analysis or by rare variant sequencing studies are expressed in glial cells (e.g., APOE, TREM2, BIN1, CD33, INPP5D, CTSB, CTSD, and PICALM).”
From The Major Risk Factors for Alzheimer’s Disease: Age, Sex, and Genes Modulate the Microglia Response to Aβ Plaques (2019) · cites this paper as Lambert et al., 2013
The Abstract (page unavailable) identifies Alzheimer’s-associated loci including APOE, BIN1, PICALM and INPP5D, and reports a previously identified rare TREM2 risk variant. It does not establish that there are approximately 40 such loci or that most associated genes are expressed in glial cells; the searched passages also do not substantiate the CTSB and CTSD examples. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe ε4 allele is a major genetic risk factor for late onset Alzheimer disease.
“The ε4 allele is a major genetic risk factor for late onset Alzheimer disease.”
From Plasma levels of apolipoprotein E, APOE genotype and risk of dementia and ischemic heart disease: A review (2016) · cites this paper as [98]
The Abstract states that APOE is a major genetic risk factor for Alzheimer’s disease in the context of a late-onset Alzheimer’s study. The paper’s own statement concerns the gene, not specifically the ε4 allele; ε4 appears in a cited reference title about risk in late-onset families. (LLM, conf=0.82)
Experimental — accuracy not yet measuredRecent genome-wide association studies of LOAD identified several common variants of new genes, but with the exception of variants of triggering receptor expressed on myeloid cells 2, no rare variants were identified.
“These studies have identified several common variants of several new genes to be associated with LOAD, but, with the exception of variants of triggering receptor expressed on myeloid cells 2, no rare variants were identified (49,50).”
From Molecular Basis for Increased Risk for Late-onset Alzheimer Disease Due to the Naturally Occurring L28P Mutation in Apolipoprotein E4 (2014) · cites this paper as [49]
The paper’s Abstract (page unavailable) reports newly associated LOAD loci and notes a previously identified rare susceptibility variant in TREM2. It does not establish the broader claim that no rare variants other than TREM2 variants had been identified. (LLM, conf=0.85)
Experimental — accuracy not yet measuredMore 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 [84]
The paper reports 19 loci reaching genome-wide significance in the combined analysis, in addition to APOE (Abstract; page unavailable). It also reports 13 further loci with only suggestive evidence of association (Abstract; page unavailable). Together these exceed 30 loci of interest, but the source does not establish all of them as associated with Alzheimer’s disease risk. (LLM, conf=0.84)
Experimental — accuracy not yet measuredABI3 is co-expressed with INPP5D (P=2.2×10-10), a gene previously implicated in late-onset Alzheimer's disease risk.
“ABI3 is co-expressed with INPP5D (P=2.2×10-10), a gene previously implicated in LOAD risk2.”
From Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease (2017) · cites this paper as [2]
A different suggestive common variant signal in another late-onset Alzheimer's disease case-control study was observed (P=6.3×10-7).
“We also observed a different suggestive common variant signal in another LOAD case-control study (P=6.3×10-7)2.”
From Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease (2017) · cites this paper as [2]
Additional genetic risk factors for AD hint at a plurality of mechanisms through which AD is developed.
“Besides APOE, additional genetic risk factors for AD, such as those identified by linkage analysis and GWAS in the IGAP study, hint at a plurality of mechanisms through which AD is developed (6, 44, 45).”
From Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets (2021) · cites this paper as [6]
Genome-wide association studies have identified polymorphisms in inflammation-associated genes as risk factors for the development of Alzheimer's disease.
“This is supported by genome-wide association studies that have identified polymorphisms in inflammation-associated genes as risk factors for the development of AD.9, 10, 11”
From Markers of microglia in post-mortem brain samples from patients with Alzheimer’s disease: a systematic review (2018) · cites this paper as [9]
INPP5D has emerged as a genetic risk factor for Alzheimer's disease.
“its encoding gene, INPP5D, has emerged as a genetic risk factor for Alzheimer’s disease7.”
From INPP5D/SHIP1 regulates endo-lysosomal function and cargo-selective phagocytosis in human microglia (2025) · cites this paper as [7]
Data pooling in genomics research has enabled knowledge of disease-related loci to be expanded substantially.
“A relevant example is evident in genomics research, in which data pooling has, e.g., enabled knowledge of disease-related loci to be expanded substantially [e.g.Lambert et al. (2013); Manolio et al. (2008); Schizophrenia Working Group of the Psychiatric Genomics (2014)], findings which would not have been possible without the sample sizes offered through data-sharing.”
From New directions in hypnosis research: strategies for advancing the cognitive and clinical neuroscience of hypnosis (2017) · cites this paper as Lambert et al. 2013
Research in other fields has benefited substantially from data-sharing initiatives.
“Research in other fields has benefited substantially from datasharing initiatives.”
From New directions in hypnosis research: strategies for advancing the cognitive and clinical neuroscience of hypnosis (2017) · cites this paper as Lambert et al. 2013
Fifty-two sex-specific KNDs of AD were also known AD genetic risk factors based on the AlzGene database and the genes identified from a large-scale meta-analysis carried out by the International Genomics of Alzheimer's Project (IGAP).
“Fifty-two sex-specific KNDs of AD were also known AD genetic risk factors based on the AlzGene database and the genes identified from a large-scale meta-analysis carried out by the International Genomics of Alzheimer’s Project (IGAP)”
From Sex specific molecular networks and key drivers of Alzheimer’s disease (2023) · cites this paper as [47]
Apoe, the main genetic risk factor for AD, is strongly upregulated in ARMs, representing the major Aβ-response population.
“Strikingly, Apoe, the main genetic risk factor for AD (Corder et al., 1993; Lambert et al., 2013), is strongly upregulated in ARMs, representing the major Aβ-response population (Figures 1D and 1E).”
From The Major Risk Factors for Alzheimer’s Disease: Age, Sex, and Genes Modulate the Microglia Response to Aβ Plaques (2019) · cites this paper as Lambert et al., 2013
The CASS4 gene locus has consistently been identified in GWAS studies to be associated with an increased risk of developing AD.
“The CASS4 gene locus has consistently been identified in GWAS studies to be associated with an increased risk of developing AD (Lambert et al., 2013; Lin et al., 2017; Ramos de Matos et al., 2018; Schwartzentruber et al., 2021; Wang et al., 2015b).”
From Alzheimer’s genetic risk factor CASS4 controls microglial focal adhesion biology and amyloidosis (2026) · cites this paper as Lambert et al., 2013
Focal adhesion signaling has recently been linked to AD through both genetic risk factors and Aβ-associated signaling events.
“Interestingly, focal adhesion signaling has re cently been linked to AD through both genetic risk factors and Aβ-associated signaling events (Grace and Busciglio, 2003; Lambert et al., 2013; Lin et al., 2017; Manocha et al., 2015; Ramos de Matos et al., 2018; Saleh et al., 2022; Schwartzentruber et al., 2021; Wang et al., 2015b)”
From Alzheimer’s genetic risk factor CASS4 controls microglial focal adhesion biology and amyloidosis (2026) · cites this paper as Lambert et al., 2013
Mutations in the focal adhesion gene PTK2B have been linked to increased AD risk in GWAS studies.
“Moreover, mutations in the focal adhesion gene PTK2B , which encodes the protein PYK2 and is a suggested CASS4binding partner (Barrett et al., 2013; Beck et al., 2014), have also been linked to increased AD risk in GWAS studies (Lambert et al., 2013; Lin et al., 2017; Ramos de Matos et al., 2018; Schwartzentruber et al., 2021; Wang et al., 2015b).”
From Alzheimer’s genetic risk factor CASS4 controls microglial focal adhesion biology and amyloidosis (2026) · cites this paper as Lambert et al., 2013
Recent genetic studies implicate inflammatory genes and pathways (CD33, TREM2, HLA-DRB5-DRB1) in late-onset disease pathology.
“Third, recent genetic studies implicate inflammatory genes and pathways (CD33, TREM2, HLA-DRB5-DRB1) in late-onset disease pathology (112–115).”
From The Inflammatory Continuum of Traumatic Brain Injury and Alzheimer’s Disease (2018) · cites this paper as [115]
Previous analysis of normal brain co-expression networks identified 4 gene modules enriched for common variants associated with late-onset Alzheimer's disease risk.
“Previous analysis of normal brain co-expression networks identified 4 gene modules that are enriched for common variants associated with LOAD risk2,3711.”
From Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease (2017) · cites this paper as [2]
The paper’s Abstract discusses pathways enriched for Alzheimer’s disease GWAS association signals, but does not describe an analysis of normal-brain co-expression networks or identify four gene modules. Searches for “co-expression modules,” “four gene modules,” and “brain expression modules” found no passage establishing the attributed finding. (LLM, conf=0.76)
Experimental — accuracy not yet measuredMicroglial TREM2 variants are partial loss of function with impaired transport of APOE-containing lipoproteins.
“Microglial TREM2 variants (300, 344) are partial loss of function with impaired transport of APOE-containing lipoproteins (633).”
From Blood-Brain Barrier: From Physiology to Disease and Back (2019) · cites this paper as [344]
The Abstract reports a TREM2 variant associated with Alzheimer’s disease risk, but does not discuss its function. Searches for partial loss of function and transport of APOE-containing lipoproteins found no passage addressing the mechanism attributed to this source by citation 633. (LLM, conf=0.78)
Experimental — accuracy not yet measuredCR1 is predominantly expressed by erythrocytes.
“CR1 is predominantly expressed by erythrocytes (343, 344) and A 𝛃 -C3b-CR1-mediated interactions sequester A 𝛃 42 to promote clearance to periphery (180, 338), suggesting that CR1 variants can increase free blood A 𝛃 levels (343), which in turn can promote RAGE-mediated A 𝛃 re-entry across the BBB into the brain.”
From Blood-Brain Barrier: From Physiology to Disease and Back (2019) · cites this paper as [344]
The Statistical analysis table lists CR1 as an Alzheimer’s disease-associated locus but does not discuss where CR1 is expressed. Searches for erythrocytes, red blood cells, and CR1 expression found no passage in our copy addressing the claim. (LLM, conf=0.74)
Experimental — accuracy not yet measuredBIN1 is broadly expressed in brain cell types.
“BIN1 is broadly expressed in brain cell types (344), but the effect of BIN1 variants on cerebrovascular function has not been explored.”
From Blood-Brain Barrier: From Physiology to Disease and Back (2019) · cites this paper as [344]
The paper lists BIN1 as an Alzheimer’s disease susceptibility locus (Table 2) and discusses its possible connection to tau pathology (Abstract), but neither passage describes BIN1 expression across brain cell types. A search for cell-type-specific BIN1 expression found no passage addressing the cited claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredrs1057233 and SPI1 expression showed a dominant inheritance pattern, in which donors with two copies of the risk-associated A allele showed slightly increased SPI1 expression compared with heterozygous carriers and non-carriers.
“On inspection of the data, we noted that rs1057233 and SPI1 expression showed a dominant inheritance pattern, in which donors with two copies of the risk-associated A allele showed slightly increased SPI1 expression compared with heterozygous carriers and non-carriers.”
From Lymphoid gene expression supports neuroprotective microglia function (2025) · cites this paper as [108]
The Statistical analysis table (page unavailable) reports Alzheimer’s disease associations for SNPs, while the Imputation and SNP selection section (page unavailable) describes analyses of genotype dosage. Searches for rs1057233, SPI1, and the stated expression pattern found no passage comparing SPI1 expression across A-allele genotypes in our copy. (LLM, conf=0.74)
Experimental — accuracy not yet measuredGenetic analyses including GWAS and Mendelian randomization generally failed to identify a causal link between PCSK9 and Alzheimer's disease risk.
“By contrast, genetic analyses—including GWAS and Mendelian randomization—generally failed to identify a causal link between PCSK9 and disease risk [23,25,26].”
From The Roles of PCSK9 in Alzheimer’s Disease: A Systematic Review of Clinical, Genetic, and Preclinical Evidence (2025) · cites this paper as [25]
The paper’s GWAS results (Abstract; Table 2, page unavailable) identify Alzheimer’s disease susceptibility loci, but the passages found do not assess a PCSK9–Alzheimer’s disease link or report Mendelian randomization. Searches for PCSK9 and Mendelian randomization found no passage supporting the attributed conclusion; PCSK9’s absence from the reported loci does not establish it. (LLM, conf=0.70)
Experimental — accuracy not yet measuredIn IGAP Stage I, rs62256378 showed no association with AD risk in either the complete analysis (P=0.7, OR=0.98, 95% CI 0.91-1.06) or the APOE-ε4 noncarrier stratum (P=0.7, OR=1.02, 95% CI 0.91-1.13).
“Here, the SNP rs62256378 showed no association with AD risk in either the complete Stage I analysis (P ¼ 0.7, OR ¼ 0.98, 95% CI 0.91 -1.06) or the stratum of APOE-14 noncarrier (P ¼ 0.7, OR ¼ 1.02, 95% CI 0.91 -1.13).”
From SUCLG2 identified as both a determinator of CSF Aβ1–42 levels and an attenuator of cognitive decline in Alzheimer's disease (2014) · cites this paper as [6]
The paper describes the IGAP stage 1 meta-analysis in the Abstract and Statistical analysis section (pages unavailable), but the passages found do not report results for rs62256378. Searches for that SNP, the quoted odds ratios and confidence intervals, and an APOE-ε4 noncarrier analysis did not locate either set of claimed statistics in our copy. (LLM, conf=0.74)
Experimental — accuracy not yet measuredStage 1 discovery meta-analysis produced 12 loci with genome-wide significance (P ≤ 5 × 10−8), all previously described.
“The Stage 1 discovery meta-analysis produced 12 loci with genome-wide significance (P ≤ 5 × 10−8) (Table 1), all of which are previously described1,4–11.”
From Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing (2019) · cites this paper as [1]
Previous work identified 19 genome-wide-significant common variant signals in addition to APOE that influence LOAD risk.
“Our previous work identified 19 genome-wide-significant common variant signals in addition to APOE that influence risk for LOAD (onset age > 65 years)1.”
From Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing (2019) · cites this paper as [1]
Meta-analysis of Stages 1 and 2 produced 21 genome-wide-significant associations (P ≤ 5 × 10−8), 18 of which were previously reported as genome-wide significant in Lambert et al.
“Meta-analysis of Stages 1 and 2 produced 21 genome-wide-significant associations (P ≤ 5 × 10−8) (Table 1 and Fig. 1), 18 of which were previously reported as genome-wide significant in Lambert et al.1.”
From Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing (2019) · cites this paper as [1]
The CLU intronic variant rs9331896 is associated with Alzheimer's disease.
“In a meta-analysis of >74,000 individuals in 2013, rs9331896, located in a CLU intron and also in strong LD with rs11136000 (r2=0.925) and rs7982 (r2=0.889), was also associated with AD (p=2.8×10−25) (Lambert, et al., 2013)”
From A potential endophenotype for Alzheimer's disease: cerebrospinal fluid clusterin (2016) · cites this paper as Lambert, et al., 2013
Neuronal FcγRIIb binds Aβ42 with high affinity and mediates neurotoxicity and memory impairment triggered by Aβ.
“Neuronal FcγRIIb can bind to Aβ42 with a high affinity, and mediate neurotoxicity and memory impairment triggered by Aβ [240, 376–380].”
From Molecular and cellular mechanisms underlying the pathogenesis of Alzheimer’s disease (2020) · cites this paper as [378]
GWAS link immune genes CR1, CD33, and TREM2 to increased Alzheimer's disease risk.
“recent GWAS link several immune genes, particularly complement receptor-1 (CR1), CD33, and triggering receptor expressed on myeloid cells-2 (TREM2), with increased AD risk (55).”
From Microglia in Alzheimer's disease (2017) · cites this paper as [55]
Allelic variants of TREM2 confer high risk for developing sporadic Alzheimer's disease.
“Allelic variants of TREM2 confer high risk for developing sporadic AD; therefore, research into TREM2's function has gained momentum.”
From Microglia in Alzheimer's disease (2017) · cites this paper as [55]
The 21 common AD susceptibility alleles are strong risk factors yet explain only a minor portion of variance in AD-related traits.
“consistent with the observation noted earlier that, while these susceptibility alleles are robust risk factors, they capture only a small fraction of the variance in these AD-related traits.”
From A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease (2018) · cites this paper as [4]
Mutations in CR1, HLA-DRB1, CD33, MS4A6A, and BIN1 are associated with more modest AD risks.
“Mutations in other microglial genes, such as CR1, HLA–DRB1, CD33, MS4A6A, and BIN1, were associated with more modest AD risks34.”
From Microglia in neurodegeneration (2018) · cites this paper as [34]
Trem2 mutations are associated with a 3.0- to 4.5-fold increased AD risk, almost as high as that associated with ApoE ε4.
“Mutations in triggering receptor expressed on myeloid cells 2 (Trem2) were associated with a 3.0- to 4.5-fold increased AD risk, almost as high as that associated with ApoE ε434,35.”
From Microglia in neurodegeneration (2018) · cites this paper as [34]
The analysis of the genetic network expressed by amyloid-responsive microglia identified five genes within the central portion of mouse microglial network whose human orthologues were associated with Alzheimer's disease from original IGAP data.
“The analysis of the genetic network expressed by amyloid-responsive microglia identified five genes within the central portion of mouse microglial network whose human orthologues were associated with Alzheimer’s disease from the original IGAP data [described in Salih et al. (2018), using the IGAP data from Lambert et al. (2013)].”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Lambert et al. (2013)
The immune module of genes showed a remarkable positive correlation to amyloid deposition and contained orthologues of established Alzheimer's disease risk genes such as Abca7 and Ms4a6d.
“This immune module of genes showed a remarkable positive correlation to amyloid deposition (Matarin et al., 2015), and contained orthologues of other already established Alzheimer’s disease risk genes such as Abca7 and Ms4a6d (Lambert et al., 2013).”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Lambert et al., 2013
When repeated using updated IGAP data, four of the five identified genes from the centre of the co-expression network were still strongly associated with human Alzheimer's disease.
“When we repeated the analysis using the updated IGAP data (Kunkle et al., 2019) containing 29.2% more cases and 12.9% more controls, and 62.7% more SNPs as compared to Lambert et al. (2013), four of the five identified genes from the centre of the co-expression network in mice were still strongly associated with the orthologues containing variants in human Alzheimer’s disease.”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Lambert et al. (2013)
A genome-wide association study identified a novel association for late-onset Alzheimer's disease in the HLA-DRB4-DRB1 region.
“a genome-wide association study (GWAS) identified a novel association for late onset Alzheimer’s disease (LOAD) in the HLA-DRB4-DRB1 region”
From Microglial dysfunction in brain aging and Alzheimer's disease (2014) · cites this paper as [18]
The same loci may contribute to both EOAD and LOAD.
“Common variants near SORL1 have also been identified as associated with disease through GWAS [96], suggesting the same loci may contribute to both EOAD and LOAD.”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [96]
Common variants near SORL1 have been identified as associated with disease through GWAS.
“Common variants near SORL1 have also been identified as associated with disease through GWAS [96], suggesting the same loci may contribute to both EOAD and LOAD.”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [96]
A PRS derived from human LOAD GWAS showed significant association with cognitive outcomes across genetically diverse AD mice at 14 months of age.
“a PRS derived from human LOAD GWAS [96] showed significant association with cognitive outcomes across genetically diverse AD mice at 14 months of age [288]”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [96]
GWAS have identified several single-nucleotide polymorphisms in immune genes as independent risk factors for late-onset AD, directly implicating microglia-mediated mechanisms in sporadic AD progression.
“Likewise, genome-wide association studies (GWAS) have identified several single-nucleotide polymorphisms (SNPs) in immune genes as independent risk factors for LOAD [52–55], directly implicating microglia-mediated mechanisms in sporadic AD progression.”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [52]
One of the most unique modules contained strong enrichment in AD risk loci identified by the IGAP GWAS, correlated with tau tangle burden, and contained more glial than neuronal proteins.
“One of the most unique modules contained strong enrichment in AD risk loci identified by the International Genetics of Alzheimer’s Project (IGAP) GWAS [12], correlated with tau tangle burden, and contained more glial than neuronal proteins.”
From Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease (2018) · cites this paper as [12]
GWAS have collectively identified over 20 genetic loci associated with AD at genome-wide significance, and many other loci that fall below genome-wide significance.
“Genetic variants that impart increased or decreased risk for AD have been mapped by genome wide association studies (GWAS), which collectively have identified over 20 genetic loci associated with AD at genome-wide significance, and many other loci that fall below genome-wide significance [12, 25].”
From Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease (2018) · cites this paper as [12]
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 [42]
rs6733839 is a non-coding risk allele for Alzheimer's disease that lies within an enhancer-like region approximately 25 kb upstream of the BIN1 transcriptional start site, suggesting regulatory function.
“rs6733839 is a non-coding risk allele for AD (59) that lies within an enhancer-like region approximately 25 kb upstream of the BIN1 transcriptional start site, suggesting regulatory function.”
From An environment-dependent transcriptional network specifies human microglia identity (2017) · cites this paper as [59]
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Cites
Part of this paper’s reference list could not be split into single entries.
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089 · see citing works
Corder, E. H., Saunders, A. M., Strittmatter, W. J., Schmechel, D. E., Gaskell, P. C., Small, G. W., Roses, A. D., Haines, J. L., & Pericak-Vance, M. A. (1993) · Science · doi:10.1126/science.8346443 · Cited by 9,429 · see citing works
Self-citationPopulation Structure and Eigenanalysis
Patterson, N., Price, A. L., & Reich, D. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020190 · Cited by 5,280 · see citing works
METAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205 · see citing works
Howie, B. N., Donnelly, P., & Marchini, J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000529 · Cited by 3,928 · see citing works
Harold, D., Abraham, R., Hollingworth, P., Sims, R., Gerrish, A., Hamshere, M. L., Pahwa, J. S., Moskvina, V., Dowzell, K., Williams, A., et al. (2009) · Nature Genetics · doi:10.1038/ng.440 · Cited by 3,326 · see citing works
Self-citation<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-citationLocusZoom: regional visualization of genome-wide association scan results
Pruim, R. J., Welch, R. P., Sanna, S., Teslovich, T. M., Chines, P. S., Gliedt, T. P., Boehnke, M., Abecasis, G. R., & Willer, C. J. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq419 · Cited by 2,707 · see citing works
Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis
The International Multiple Sclerosis Genetics Consortium & The Wellcome Trust Case Control Consortium 2 (2011) · Nature · doi:10.1038/nature10251 · Cited by 2,697 · see citing works
A new multipoint method for genome-wide association studies by imputation of genotypes
Marchini, J., Howie, B., Myers, S., McVean, G., & Donnelly, P. (2007) · Nature Genetics · doi:10.1038/ng2088 · Cited by 2,609 · see citing works
Variant 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-citationGenome-wide association study identifies variants at CLU and CR1 associated with Alzheimer's disease
the European Alzheimer's Disease Initiative Investigators, Lambert, J.-C., Heath, S., Even, G., Campion, D., Sleegers, K., Hiltunen, M., Combarros, O., Zelenika, D., Bullido, M. J., et al. (2009) · Nature Genetics · doi:10.1038/ng.439 · Cited by 2,234 · see citing works
Self-citationInitiative, T. A. D. N., Hollingworth, P., CHARGE consortium, EADI1 consortium, Harold, D., Sims, R., Gerrish, A., Lambert, J.-C., Carrasquillo, M. M., Abraham, R., et al. (2011) · Nature Genetics · doi:10.1038/ng.803 · Cited by 1,997 · see citing works
Self-citationNaj, A. C., Jun, G., Beecham, G. W., Wang, L.-S., Vardarajan, B. N., Buros, J., Gallins, P. J., Buxbaum, J. D., Jarvik, G. P., Crane, P. K., et al. (2011) · Nature Genetics · doi:10.1038/ng.801 · Cited by 1,901 · see citing works
Self-citationMaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes
Li, Y., Willer, C. J., Ding, J., Scheet, P., & Abecasis, G. R. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20533 · Cited by 1,843 · see citing works
Genome-wide Analysis of Genetic Loci Associated With Alzheimer Disease
Seshadri, S. (2010) · JAMA · doi:10.1001/jama.2010.574 · Cited by 1,219 · see citing works
Self-citationThe neuronal sortilin-related receptor SORL1 is genetically associated with Alzheimer disease
Rogaeva, E., Meng, Y., Lee, J. H., Gu, Y., Kawarai, T., Zou, F., Katayama, T., Baldwin, C. T., Cheng, R., Hasegawa, H., et al. (2007) · Nature Genetics · doi:10.1038/ng1943 · Cited by 1,167 · see citing works
Self-citationAlzheimer’s Disease Risk Gene CD33 Inhibits Microglial Uptake of Amyloid Beta
Griciuc, A., Serrano-Pozo, A., Parrado, A. R., Lesinski, A. N., Asselin, C. N., Mullin, K., Hooli, B., Choi, S. H., Hyman, B. T., & Tanzi, R. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.04.014 · Cited by 1,004 · see citing works
Self-citationNalls, M., Plagnol, V., Hernandez, D., Sharma, M., Sheerin, U., Saad, M., Simón-Sánchez, J., Schulte, C., Lesage, S., Sveinbjörnsdóttir, S., et al. (2011) · The Lancet · doi:10.1016/s0140-6736(10)62345-8 · Cited by 986 · see citing works
Self-citationThe occurrence of lung cancer in man.
Ml, L. (1953) · PubMed · Cited by 945 · see citing works
Genetic determinants of hair, eye and skin pigmentation in Europeans
Sulem, P., Gudbjartsson, D. F., Stacey, S. N., Helgason, A., Rafnar, T., Magnusson, K. P., Manolescu, A., Karason, A., Palsson, A., Thorleifsson, G., et al. (2007) · Nature Genetics · doi:10.1038/ng.2007.13 · Cited by 750 · see citing works
APOE and Alzheimer disease: a major gene with semi-dominant inheritance
Genin, E., Hannequin, D., Wallon, D., Sleegers, K., Hiltunen, M., Combarros, O., Bullido, M. J., Engelborghs, S., De Deyn, P., Berr, C., et al. (2011) · Molecular Psychiatry · doi:10.1038/mp.2011.52 · Cited by 714 · see citing works
Self-citationJones, L., Holmans, P. A., Hamshere, M. L., Harold, D., Moskvina, V., Ivanov, D., Pocklington, A., Abraham, R., Hollingworth, P., Sims, R., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013950 · Cited by 606 · see citing works
Self-citationGWAMA: software for genome-wide association meta-analysis
Mägi, R., & Morris, A. P. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-288 · Cited by 603 · see citing works
A Genome-Wide Association Study of Hypertension and Blood Pressure in African Americans
Adeyemo, A., Gerry, N., Chen, G.-J., Herbert, A., Doumatey, A., Huang, H.-X., Zhou, J., Lashley, K., Chen, Y.-X., Christman, M., et al. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000564 · Cited by 412 · see citing works
Increased expression of BIN1 mediates Alzheimer genetic risk by modulating tau pathology
GERAD consortium, Chapuis, J., Hansmannel, F., Gistelinck, M., Mounier, A., Van Cauwenberghe, C., Kolen, K. V., Geller, F., Sottejeau, Y., Harold, D., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2013.1 · Cited by 407 · see citing works
Self-citationProbABEL package for genome-wide association analysis of imputed data
Aulchenko, Y. S., Struchalin, M. V., & van Duijn, C. M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-134 · Cited by 396 · see citing works
GWAS of Cerebrospinal Fluid Tau Levels Identifies Risk Variants for Alzheimer’s Disease
Cruchaga, C., Kauwe, J. S. K., Harari, O., Jin, S. C., Cai, Y., Karch, C. M., Benitez, B. A., Jeng, A. T., Skorupa, T., Carrell, D., et al. (2013) · Neuron · doi:10.1016/j.neuron.2013.02.026 · Cited by 383 · see citing works
Self-citationGenetic insights in Alzheimer's disease
Bettens, K., Sleegers, K., & Broeckhoven, C. V. (2012) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70259-4 · Cited by 326 · see citing works
Self-citationPottier, C., Hannequin, D., Coutant, S., Rovelet-Lecrux, A., Wallon, D., Rousseau, S., Legallic, S., Paquet, C., Bombois, S., Pariente, J., et al. (2012) · Molecular Psychiatry · doi:10.1038/mp.2012.15 · Cited by 305 · see citing works
Self-citationCAKβ/Pyk2 Kinase Is a Signaling Link for Induction of Long-Term Potentiation in CA1 Hippocampus
Huang, Y.-Q., Lu, W.-Y., Ali, D. W., Pelkey, K. A., Pitcher, G. M., Lu, Y. M., Aoto, H., Roder, J. C., Sasaki, T., Salter, M. W., et al. (2001) · Neuron · doi:10.1016/s0896-6273(01)00220-3 · Cited by 274 · see citing works
Meur, N. L., Holder‐Espinasse, M., Jaillard, S., Goldenberg, A., Joriot, S., Amati‐Bonneau, P., Guichet, A., Barth, M., Charollais, A., Journel, H., et al. (2009) · Journal of Medical Genetics · doi:10.1136/jmg.2009.069732 · Cited by 228 · see citing works
Brouwers, N., Van Cauwenberghe, C., Engelborghs, S., Lambert, J.-C., Bettens, K., Le Bastard, N., Pasquier, F., Montoya, A. G., Peeters, K., Mattheijssens, M., et al. (2012) · Molecular Psychiatry · doi:10.1038/mp.2011.24 · Cited by 227 · see citing works
Self-citationDuriez, B., Duquesnoy, P., Escudier, E. F., Bridoux, A.-M., Escalier, D., Rayet, I., Marcos, É., Vojtek, A.-M., Bercher, J., & Amselem, S. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0611405104 · Cited by 198 · see citing works
Miyashita, A., Koike, A., Jun, G., Wang, L.-S., Takahashi, S., Matsubara, E., Kawarabayashi, T., Shoji, M., Tomita, N., Arai, H., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058618 · Cited by 196 · see citing works
Self-citationRIN3: a novel Rab5 GEF interacting with amphiphysin II involved in the early endocytic pathway
Kajiho, H., Saito, K., Tsujita, K., Kontani, K., Araki, Y., Kurosu, H., & Katada, T. (2003) · Journal of Cell Science · doi:10.1242/jcs.00718 · Cited by 191 · see citing works
Shulman, J. M., Imboywa, S., Giagtzoglou, N., Powers, M. P., Hu, Y., Devenport, D., Chipendo, P., Chibnik, L. B., Diamond, A., Perrimon, N., et al. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddt478 · Cited by 167 · see citing works
CMS: An adapter molecule involved in cytoskeletal rearrangements
Kirsch, K. H., Georgescu, M.-M., Ishimaru, S., & Hanafusa, H. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.11.6211 · Cited by 162 · see citing works
Implication of the Immune System in Alzheimer's Disease: Evidence from Genome-Wide Pathway Analysis
Lambert, J.-C., Grenier-Boley, B., Chouraki, V., Heath, S., Zelenika, D., Fievet, N., Hannequin, D., Pasquier, F., Hanon, O., Brice, A., et al. (2010) · Journal of Alzheimer's Disease · doi:10.3233/jad-2010-100018 · Cited by 144 · see citing works
Self-citationVillar, K. D., & Miller, C. A. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307349101 · Cited by 135 · see citing works
Genetics of Alzheimer's disease: new evidences for an old hypothesis?
Lambert, J., & Amouyel, P. (2011) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2011.02.002 · Cited by 132 · see citing works
Self-citationPandey, P., Avraham, S., Kumar, S., Nakazawa, A., Place, A. E., Ghanem, L. R., Rana, A., Kumar, V., Majumder, P. K., Avraham, H. K., et al. (1999) · Journal of Biological Chemistry · doi:10.1074/jbc.274.15.10140 · Cited by 132 · see citing works
Structural Insight into the Zinc Finger CW Domain as a Histone Modification Reader
He, F., Umehara, T., Saito, K., Harada, T., Watanabe, S., Yabuki, T., Kigawa, T., Takahashi, M., Kuwasako, K., Tsuda, K., et al. (2010) · Structure · doi:10.1016/j.str.2010.06.012 · Cited by 121 · see citing works
In Vivo Analysis of MEF2 Transcription Factors in Synapse Regulation and Neuronal Survival
Akhtar, M. W., Kim, M.-S., Adachi, M., Morris, M. J., Qi, X., Richardson, J. A., Bassel‐Duby, R., Olson, E. N., Kavalali, E. T., & Monteggia, L. M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034863 · Cited by 113 · see citing works
The integrin coactivator Kindlin-2 plays a critical role in angiogenesis in mice and zebrafish
Pluskota, E., Dowling, J. J., Gordon, N., Golden, J. A., Szpak, D., West, X. Z., Nestor, C., Ma, Y., Białkowska, K., Byzova, T. V., et al. (2011) · Blood · doi:10.1182/blood-2010-11-321182 · Cited by 81 · see citing works
NYAP: a phosphoprotein family that links PI3K to WAVE1 signalling in neurons
Yokoyama, K., Tezuka, T., Kotani, M., Nakazawa, T., Hoshina, N., Shimoda, Y., Kakuta, S., Sudo, K., Watanabe, K., Iwakura, Y., et al. (2011) · The EMBO Journal · doi:10.1038/emboj.2011.348 · Cited by 75 · see citing works
Larsson, M., Duffy, D. L., Zhu, G., Liu, J. Z., MacGregor, S., McRae, A. F., Wright, M. J., Sturm, R. A., Mackey, D. A., Montgomery, G. W., et al. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.07.011 · Cited by 71 · see citing works
A robust clustering algorithm for identifying problematic samples in genome-wide association studies
Bellenguez, C., Strange, A., Freeman, C., Donnelly, P., & Spencer, C. C. A. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr599 · Cited by 66 · see citing works
Self-citationWilliamson, R., van Aalten, L., Mann, D. M. A., Platt, B., Plattner, F., Bedford, L., Mayer, J., Howlett, D. R., Usardi, A., Sutherland, C. D., et al. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-110617 · Cited by 64 · see citing works
Bao, M., Hanabuchi, S., Facchinetti, V., Du, Q., Bover, L., Plumas, J., Chaperot, L., Cao, W., Qin, J., Sun, S.-C., et al. (2012) · The Journal of Immunology · doi:10.4049/jimmunol.1200887 · Cited by 49 · see citing works
The role of CELF proteins in neurological disorders
Gallo, J., & Spickett, C. (2010) · RNA Biology · doi:10.4161/rna.7.4.12345 · Cited by 41 · see citing works
Brauer, H., Strauss, J., Wegner, W., Müller-Tidow, C., Horstmann, M., & Jücker, M. (2012) · Cellular Signalling · doi:10.1016/j.cellsig.2012.07.017 · Cited by 33 · see citing works
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Verbanck, M., Chen, C.-Y., Neale, B., & Do, R. (2018) · Nature Genetics · doi:10.1038/s41588-018-0099-7 · Cited by 8,591 · see citing works
The amyloid hypothesis of Alzheimer's disease at 25 years
Selkoe, D. J., & Hardy, J. (2016) · EMBO Molecular Medicine · doi:10.15252/emmm.201606210 · Cited by 6,164 · see citing works
Self-citationThe GTEx Consortium atlas of genetic regulatory effects across human tissues
The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547 · see citing works
Self-citationNeuroinflammation and microglial activation in Alzheimer disease: where do we go from here?
Leng, F., & Edison, P. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-020-00435-y · Cited by 3,179 · see citing works
Alzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946 · see citing works
Self-citationNew 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
“To prioritize candidate genes in the new loci, we systematically searched for evidence for these genes in seven different domains: (1) variant annotation, (2) eQTL-GWAS integration, (3) sQTL-GWAS integration, (4) protein QTL (pQTL)-GWAS integration, (5) mQTL-GWAS integration, (6) histone acetylation QTL (haQTL)-GWAS i…”
MethodSelf-citationJansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, I. K., Hägg, S., Athanasiu, L., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0311-9 · Cited by 2,277 · see citing works
Self-citationThe amyloid state and its association with protein misfolding diseases.
Knowles, T. P. J., Vendruscolo, M., & Dobson, C. M. (2014) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3810 · Cited by 2,382 · see citing works
Blood-Brain Barrier: From Physiology to Disease and Back
Sweeney, M. D., Zhao, Z., Montagne, A., Nelson, A. R., & Zlokovic, B. V. (2019) · Physiological Reviews · doi:10.1152/physrev.00050.2017 · Cited by 2,294 · see citing works
Immunopathology of multiple sclerosis
Dendrou, C. A., Fugger, L., & Friese, M. A. (2015) · Nature Reviews Immunology · doi:10.1038/nri3871 · Cited by 2,215 · see citing works
Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R., Palmer, T., Schooling, C. M., Wallace, C., Zhao, Q., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00092-5 · Cited by 2,229 · see citing works
ADHD Working Group of the Psychiatric Genomics Consortium (PGC), Demontis, D., Early Lifecourse & Genetic Epidemiology (EAGLE) Consortium, 23andMe Research Team, Walters, R. K., Martin, J., Mattheisen, M., Als, T. D., Agerbo, E., Baldursson, G., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0269-7 · Cited by 2,203 · see citing works
Self-citationAnalysis 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-citationThe Cellular Phase of Alzheimer’s Disease
De Strooper, B., & Karran, E. (2016) · Cell · doi:10.1016/j.cell.2015.12.056 · Cited by 1,990 · see citing works
Amyloid Beta: Structure, Biology and Structure-Based Therapeutic Development
Chen, G.-F., Xu, T.-H., Yan, Y., Zhou, Y.-R., Jiang, Y., Melcher, K., & Xu, H. E. (2017) · Acta Pharmacologica Sinica · doi:10.1038/aps.2017.28 · Cited by 1,965 · see citing works
The 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-citationMicroglia in Alzheimer’s disease
Hansen, D. V., Hanson, J. E., & Sheng, M. (2018) · Journal of Cell Biology · doi:10.1083/jcb.201709069 · Cited by 1,935 · see citing works
Preclinical Alzheimer’s Disease: Definition, Natural History, and Diagnostic Criteria
Dubois, B., Hampel, H., Feldman, H. H., Scheltens, P., Aisen, P., Andrieu, S., Bakardjian, H., Benali, H., Bertram, L., Blennow, K., et al. (2016) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.02.002 · Cited by 1,925 · see citing works
Self-citationMicroglia emerge as central players in brain disease
Salter, M. W., & Stevens, B. (2017) · Nature Medicine · doi:10.1038/nm.4397 · Cited by 1,767 · see citing works
Clearance systems in the brain—implications for Alzheimer disease
Tarasoff-Conway, J. M., Carare, R. O., Osorio, R. S., Glodzik, L., Butler, T., Fieremans, E., Axel, L., Rusinek, H., Nicholson, C., Zlokovic, B. V., et al. (2015) · Nature Reviews Neurology · doi:10.1038/nrneurol.2015.119 · Cited by 1,678 · see citing works
Defeating Alzheimer's disease and other dementias: a priority for European science and society
Winblad, B., Amouyel, P., Andrieu, S., Ballard, C., Brayne, C., Brodaty, H., Cedazo-Minguez, A., Dubois, B., Edvardsson, D., Feldman, H., et al. (2016) · The Lancet Neurology · doi:10.1016/s1474-4422(16)00062-4 · Cited by 1,676 · see citing works
Self-citationMicroglia in neurodegeneration
Hickman, S., Izzy, S., Sen, P., Morsett, L., & El Khoury, J. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0242-x · Cited by 1,710 · see citing works
GERAD1 Consortium, Pardiñas, A. F., CRESTAR Consortium, Holmans, P., Pocklington, A. J., Escott-Price, V., Ripke, S., Carrera, N., Legge, S. E., Bishop, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0059-2 · Cited by 1,552 · see citing works
Self-citationDisease-Associated Microglia: A Universal Immune Sensor of Neurodegeneration
Deczkowska, A., Keren-Shaul, H., Weiner, A., Colonna, M., Schwartz, M., & Amit, I. (2018) · Cell · doi:10.1016/j.cell.2018.05.003 · Cited by 1,474 · see citing works
Apolipoprotein E and Alzheimer disease: pathobiology and targeting strategies
Yamazaki, Y., Zhao, N., Caulfield, T. R., Liu, C.-C., & Bu, G. (2019) · Nature Reviews Neurology · doi:10.1038/s41582-019-0228-7 · Cited by 1,425 · see citing works
Gene-Wide Analysis Detects Two New Susceptibility Genes for Alzheimer's Disease
Escott-Price, V., Bellenguez, C., Wang, L.-S., Choi, S.-H., Harold, D., Jones, L., Holmans, P., Gerrish, A., Vedernikov, A., Richards, A., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094661 · Cited by 1,398 · see citing works
Self-citationAlzheimer’s Disease Risk Genes and Mechanisms of Disease Pathogenesis
Karch, C. M., & Goate, A. M. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.05.006 · Cited by 1,357 · see citing works
Self-citationCorces, M. R., Buenrostro, J. D., Wu, B., Greenside, P. G., Chan, S. M., Koenig, J. L., Snyder, M. P., Pritchard, J. K., Kundaje, A., Greenleaf, W. J., et al. (2016) · Nature Genetics · doi:10.1038/ng.3646 · Cited by 1,151 · see citing works
Role of pro-inflammatory cytokines released from microglia in Alzheimer's disease.
(2015) · Europe PMC (PubMed Central) · doi:10.3978/j.issn.2305-5839.2015.03.49 · Cited by 1,288 · see citing works
Age and Age-Related Diseases: Role of Inflammation Triggers and Cytokines
Rea, I. M., Gibson, D. S., McGilligan, V., McNerlan, S. E., Alexander, H. D., & Ross, O. A. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.00586 · Cited by 1,267 · see citing works
Religious Orders Study and Rush Memory and Aging Project
Bennett, D. A., Buchman, A. S., Boyle, P. A., Barnes, L. L., Wilson, R. S., & Schneider, J. A. (2018) · Journal of Alzheimer's Disease · doi:10.3233/jad-179939 · Cited by 1,260 · see citing works
Self-citationAn environment-dependent transcriptional network specifies human microglia identity
Gosselin, D., Skola, D., Coufal, N. G., Holtman, I. R., Schlachetzki, J. C. M., Sajti, E., Jaeger, B. N., O’Connor, C., Fitzpatrick, C., Pasillas, M. P., et al. (2017) · Science · doi:10.1126/science.aal3222 · Cited by 1,220 · see citing works
“rs6733839 is a noncoding risk allele for AD (59) that lieswithinanenhancer-like region~25kbupstream of the BIN1 transcriptional start site, suggesting regulatory function.”
BackgroundAlzheimer disease: Epidemiology, diagnostic criteria, risk factors and biomarkers
Reitz, C., & Mayeux, R. P. (2014) · Biochemical Pharmacology · doi:10.1016/j.bcp.2013.12.024 · Cited by 1,180 · see citing works
Self-citationDetection and interpretation of shared genetic influences on 42 human traits
Pickrell, J. K., Berisa, T., Liu, J. Z., Ségurel, L., Tung, J. Y., & Hinds, D. A. (2016) · Nature Genetics · doi:10.1038/ng.3570 · Cited by 1,151 · see citing works
Ageing, neurodegeneration and brain rejuvenation
Wyss-Coray, T. (2016) · Nature · doi:10.1038/nature20411 · Cited by 1,147 · 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-citationLin, Y.-T., Seo, J., Gao, F., Feldman, H. M., Wen, H.-L., Penney, J., Cam, H. P., Gjoneska, E., Raja, W. K., Cheng, J., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.05.008 · Cited by 1,125 · see citing works
Comprehensive functional genomic resource and integrative model for the human brain
Wang, D., Liu, S., Warrell, J., Won, H., Shi, X., Navarro, F. C. P., Clarke, D., Gu, M., Emani, P., Yang, Y. T., et al. (2018) · Science · doi:10.1126/science.aat8464 · Cited by 965 · see citing works
Gene 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-citationFinucane, H. K., Reshef, Y. A., Anttila, V., Slowikowski, K., Gusev, A., Byrnes, A., Gazal, S., Loh, P.-R., Lareau, C., Shoresh, N., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0081-4 · Cited by 1,097 · see citing works
Molecular and cellular mechanisms underlying the pathogenesis of Alzheimer’s disease
Guo, T., Zhang, D., Zeng, Y., Huang, T. Y., Xu, H., & Zhao, Y. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00391-7 · Cited by 994 · see citing works
Wilson's disease and other neurological copper disorders
Bandmann, O., Weiss, K. H., & Kaler, S. G. (2014) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70190-5 · Cited by 1,040 · see citing works
Microglia-derived ASC specks cross-seed amyloid-β in Alzheimer’s disease
Venegas, C., Kumar, S., Franklin, B. S., Dierkes, T., Brinkschulte, R., Tejera, D., Vieira-Saecker, A., Schwartz, S., Santarelli, F., Kummer, M. P., et al. (2017) · Nature · doi:10.1038/nature25158 · Cited by 983 · see citing works
Sala Frigerio, C., Wolfs, L., Fattorelli, N., Thrupp, N., Voytyuk, I., Schmidt, I., Mancuso, R., Chen, W.-T., Woodbury, M. E., Srivastava, G., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.03.099 · Cited by 926 · see citing works
Wightman, 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-citationJohnson, E. C. B., Dammer, E. B., Duong, D. M., Ping, L., Zhou, M., Yin, L., Higginbotham, L. A., Guajardo, A., White, B., Troncoso, J. C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0815-6 · Cited by 1,001 · see citing works
Self-citationAmyloid β-based therapy for Alzheimer’s disease: Challenges, successes and future
Zhang, Y., Chen, H., Li, R., Sterling, K., & Song, W. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01484-7 · Cited by 988 · see citing works
“RIN3, CLU and PTK2B) are linked to Aβ degradation.(33,34) Extensive genetic studies have supported the causative role of Aβ accumulation in AD pathogenesis.”
BackgroundAlzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci
De Jager, P. L., Srivastava, G., Lunnon, K., Burgess, J., Schalkwyk, L. C., Yu, L., Eaton, M. L., Keenan, B. T., Ernst, J., McCabe, C., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3786 · Cited by 956 · see citing works
Self-citationGrubman, A., Chew, G., Ouyang, J. F., Sun, G., Choo, X. Y., McLean, C., Simmons, R. K., Buckberry, S., Vargas-Landin, D. B., Poppe, D., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0539-4 · Cited by 956 · see citing works
Innate immune memory in the brain shapes neurological disease hallmarks
Wendeln, A.-C., Degenhardt, K., Kaurani, L., Gertig, M., Ulas, T., Jain, G., Wagner, J., Häsler, L. M., Wild, K., Skodras, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0023-4 · Cited by 956 · see citing works
Association of Lifestyle and Genetic Risk With Incidence of Dementia
Lourida, I., Hannon, E., Littlejohns, T. J., Langa, K. M., Hyppönen, E., Kuzma, E., & Llewellyn, D. J. (2019) · JAMA · doi:10.1001/jama.2019.9879 · Cited by 881 · see citing works
Microglia in Alzheimer's disease
Sarlus, H., & Heneka, M. T. (2017) · Journal of Clinical Investigation · doi:10.1172/jci90606 · Cited by 911 · see citing works
Integrative functional genomic analysis of human brain development and neuropsychiatric risks
Li, M., Santpere, G., Imamura Kawasawa, Y., Evgrafov, O. V., Gulden, F. O., Pochareddy, S., Sunkin, S. M., Li, Z., Shin, Y., Zhu, Y., et al. (2018) · Science · doi:10.1126/science.aat7615 · Cited by 810 · see citing works
Self-citationAdvancing the understanding of autism disease mechanisms through genetics
de la Torre-Ubieta, L., Won, H., Stein, J. L., & Geschwind, D. H. (2016) · Nature Medicine · doi:10.1038/nm.4071 · Cited by 827 · see citing works
The Amyloid-β Oligomer Hypothesis: Beginning of the Third Decade
Cline, E. N., Bicca, M. A., Viola, K. L., & Klein, W. L. (2018) · Journal of Alzheimer's Disease · doi:10.3233/jad-179941 · Cited by 870 · 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
Self-citationNeutrophils promote Alzheimer's disease–like pathology and cognitive decline via LFA-1 integrin
Zenaro, E., Pietronigro, E., Bianca, V. D., Piacentino, G., Marongiu, L., Budui, S., Turano, E., Rossi, B., Angiari, S., Dusi, S., et al. (2015) · Nature Medicine · doi:10.1038/nm.3913 · Cited by 865 · see citing works
The genetic landscape of Alzheimer disease: clinical implications and perspectives
Van Cauwenberghe, C., Van Broeckhoven, C., & Sleegers, K. (2016) · Genetics in Medicine · doi:10.1038/gim.2015.117 · Cited by 863 · see citing works
Self-citationThe Gut Microbiota and Alzheimer’s Disease
Jiang, C., Li, G., Huang, P., Liu, Z., & Zhao, B. (2017) · Journal of Alzheimer's Disease · doi:10.3233/jad-161141 · Cited by 852 · see citing works
TREM2 Is a Receptor for β-Amyloid that Mediates Microglial Function
Zhao, Y., Wu, X., Li, X., Jiang, L.-L., Gui, X., Liu, Y., Sun, Y., Zhu, B., Piña-Crespo, J. C., Zhang, M., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.01.031 · Cited by 763 · see citing works
A 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
López, J. A., González, H. M., & Léger, G. C. (2019) · Handbook of clinical neurology · doi:10.1016/b978-0-12-804766-8.00013-3 · Cited by 834 · see citing works
Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits
Wu, Y., Zeng, J., Zhang, F., Zhu, Z., Qi, T., Zheng, Z., Lloyd-Jones, L. R., Marioni, R. E., Martin, N. G., Montgomery, G. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03371-0 · Cited by 685 · see citing works
TREM2 — a key player in microglial biology and Alzheimer disease
Ulland, T. K., & Colonna, M. (2018) · Nature Reviews Neurology · doi:10.1038/s41582-018-0072-1 · Cited by 735 · see citing works
MahmoudianDehkordi, S., Arnold, M., Nho, K., Ahmad, S., Jia, W., Xie, G., Louie, G., Kueider‐Paisley, A., Moseley, M. A., Thompson, J. W., et al. (2019) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.07.217 · Cited by 706 · see citing works
Self-citationHistory and progress of hypotheses and clinical trials for Alzheimer’s disease
Liu, P.-P., Xie, Y., Meng, X.-Y., & Kang, J.-S. (2019) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-019-0063-8 · Cited by 723 · see citing works
Readhead, B., Haure-Mirande, J.-V., Funk, C. C., Richards, M. A., Shannon, P., Haroutunian, V., Sano, M., Liang, W. S., Beckmann, N. D., Price, N. D., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.05.023 · Cited by 708 · see citing works
Microglia in Neurological Diseases: A Road Map to Brain-Disease Dependent-Inflammatory Response
Bachiller, S., Jiménez-Ferrer, I., Paulus, A., Yang, Y., Swanberg, M., Deierborg, T., & Boza‐Serrano, A. (2018) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2018.00488 · Cited by 706 · see citing works
Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine
Gormley, P., Anttila, V., Winsvold, B. S., Palta, P., Esko, T., Pers, T. H., Farh, K.-H., Cuenca-León, E., Muona, M., Furlotte, N. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3598 · Cited by 681 · see citing works
Self-citationPsychiatric Genomics: An Update and an Agenda
Sullivan, P. F., Agrawal, A., Bulik, C. M., Andreassen, O. A., Børglum, A. D., Breen, G., Cichon, S., Edenberg, H. J., Faraone, S. V., Gelernter, J., et al. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2017.17030283 · Cited by 676 · see citing works
Self-citationMacrophages in health and disease
Park, M. D., Silvin, A., Ginhoux, F., & Merad, M. (2022) · Cell · doi:10.1016/j.cell.2022.10.007 · Cited by 644 · see citing works
Common genetic variation drives molecular heterogeneity in human iPSCs
Kilpinen, H., Goncalves, A., Leha, A., Afzal, V., Alasoo, K., Ashford, S., Bala, S., Bensaddek, D., Casale, F. P., Culley, O. J., et al. (2017) · Nature · doi:10.1038/nature22403 · Cited by 586 · see citing works
Genetic identification of brain cell types underlying schizophrenia
Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Skene, N. G., Bryois, J., Bakken, T. E., Breen, G., Crowley, J. J., Gaspar, H. A., Giusti-Rodriguez, P., Hodge, R. D., Miller, J. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0129-5 · Cited by 651 · see citing works
Self-citationSingle-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
Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression
Bai, B., Wang, X., Li, Y., Chen, P.-C., Yu, K., Dey, K. K., Yarbro, J. M., Han, X., Lutz, B. M., Rao, S., et al. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.015 · Cited by 632 · see citing works
“, 2013; Lutz and Peng, 2018), genome-wide association analysis (Lambert et al., 2013), transcriptome (Allen et al.”
BackgroundMethodInfluentialMicroglial dysfunction in brain aging and Alzheimer's disease
Mosher, K. I., & Wyss-Coray, T. (2014) · Biochemical Pharmacology · doi:10.1016/j.bcp.2014.01.008 · Cited by 618 · see citing works
Molecular 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
Self-citationBrain-Derived Neurotrophic Factor in Alzheimer’s Disease and Its Pharmaceutical Potential
Gao, L., Zhang, Y., Sterling, K., & Song, W. (2022) · Translational Neurodegeneration · doi:10.1186/s40035-022-00279-0 · Cited by 606 · see citing works
Pluripotent stem cells in disease modelling and drug discovery
Avior, Y., Sagi, I., & Benvenisty, N. (2016) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2015.27 · Cited by 592 · see citing works
New insights on the role of microglia in synaptic pruning in health and disease
Hong, S., Dissing-Olesen, L., & Stevens, B. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.12.004 · Cited by 557 · see citing works
Vascular contributions to cognitive impairment and dementia including Alzheimer's disease
Snyder, H. M., Corriveau, R. A., Craft, S., Faber, J. E., Greenberg, S. M., Knopman, D., Lamb, B. T., Montine, T. J., Nedergaard, M., Schaffer, C. B., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.10.008 · Cited by 603 · see citing works
Self-citationAlzheimer's Disease: Treatment Strategies and Their Limitations.
Passeri, E., Elkhoury, K., Morsink, M., Broersen, K., Linder, M., Tamayol, A., Malaplate, C., Yen, F. T., & Arab-Tehrany, E. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232213954 · Cited by 592 · see citing works
“manifest AD, which indicates that other as yet to be identified determinants (genetic or otherwise) may be involved in AD development [88].”
BackgroundMicroglia in Neuroinflammation and Neurodegeneration: From Understanding to Therapy
Muzio, L., Viotti, A., & Martino, G. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.742065 · Cited by 581 · see citing works
Animal models of neurodegenerative diseases
Dawson, T. M., Golde, T. E., & Lagier-Tourenne, C. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0236-8 · Cited by 576 · see citing works
Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease
Gjoneska, E., Pfenning, A. R., Mathys, H., Quon, G., Kundaje, A., Tsai, L.-H., & Kellis, M. (2015) · Nature · doi:10.1038/nature14252 · Cited by 576 · see citing works
Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer's disease
Lunnon, K., Smith, R., Hannon, E., De Jager, P. L., Srivastava, G., Volta, M., Troakes, C., Al-Sarraj, S., Burrage, J., Macdonald, R., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3782 · Cited by 574 · see citing works
Self-citationCommon brain disorders are associated with heritable patterns of apparent aging of the brain
Kaufmann, T., van der Meer, D., Doan, N. T., Schwarz, E., Lund, M. J., Agartz, I., Alnæs, D., Barch, D. M., Baur‐Streubel, R., Bertolino, A., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0471-7 · Cited by 559 · see citing works
Self-citationRaja, W. K., Mungenast, A. E., Lin, Y.-T., Ko, T., Abdurrob, F., Seo, J., & Tsai, L.-H. (2016) · PLOS ONE · doi:10.1371/journal.pone.0161969 · Cited by 569 · see citing works
Hopperton, K. E., Mohammad, D., Trépanier, M. O., Giuliano, V, & Bazinet, R. P. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.246 · Cited by 567 · see citing works
A Quarter Century of APOE and Alzheimer’s Disease: Progress to Date and the Path Forward
Belloy, M. E., Napolioni, V., & Greicius, M. D. (2019) · Neuron · doi:10.1016/j.neuron.2019.01.056 · Cited by 570 · see citing works
The road to restoring neural circuits for the treatment of Alzheimer's disease
Canter, R. G., Penney, J., & Tsai, L.-H. (2016) · Nature · doi:10.1038/nature20412 · Cited by 565 · 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
Self-citationLate onset Alzheimer’s disease genetics implicates microglial pathways in disease risk
Efthymiou, A. G., & Goate, A. M. (2017) · Molecular Neurodegeneration · doi:10.1186/s13024-017-0184-x · Cited by 562 · see citing works
Self-citationA genetic cause of Alzheimer disease: mechanistic insights from Down syndrome
Wiseman, F. K., Al-Janabi, T., Hardy, J., Karmiloff-Smith, A., Nizetic, D., Tybulewicz, V. L. J., Fisher, E. M. C., & Strydom, A. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3983 · Cited by 562 · see citing works
Self-citationNeurovascular dysfunction and neurodegeneration in dementia and Alzheimer's disease
Nelson, A. R., Sweeney, M. D., Sagare, A. P., & Zlokovic, B. V. (2016) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2015.12.016 · Cited by 559 · see citing works
Wang, S., Mustafa, M., Yuede, C. M., Salazar, S. V., Kong, P., Long, H., Ward, M., Siddiqui, O., Paul, R., Gilfillan, S., et al. (2020) · Journal of Experimental Medicine · doi:10.1084/jem.20200785 · Cited by 549 · see citing works
Seyfried, N. T., Dammer, E. B., Swarup, V., Nandakumar, D., Duong, D. M., Yin, L., Deng, Q., Nguyen, T., Hales, C. M., Wingo, T., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2016.11.006 · Cited by 477 · see citing works
Zuber, V., Grinberg, N. F., Gill, D., Manipur, I., Slob, E. A. W., Patel, A., Wallace, C., & Burgess, S. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.04.001 · Cited by 532 · see citing works
Sequential regulatory activity prediction across chromosomes with convolutional neural networks
Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., & Snoek, J. (2018) · Genome Research · doi:10.1101/gr.227819.117 · Cited by 537 · 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
- Authors
- (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., Jun, G., DeStefano, A. L., Bis, J. C., Beecham, G. W., Grenier-Boley, B., Russo, G., Thornton-Wells, T. A., Jones, N., Smith, A. V., Chouraki, V., Thomas, C., Ikram, M. A., Zelenika, D., Vardarajan, B. N., Kamatani, Y., Lin, C.-F., Gerrish, A., Schmidt, H., Kunkle, B., Dunstan, M. L., Ruiz, A., Bihoreau, M.-T., Choi, S.-H., Reitz, C., Pasquier, F., Hollingworth, P., Ramirez, A., Hanon, O., Fitzpatrick, A. L., Buxbaum, J. D., Campion, D., Crane, P. K., Baldwin, C., Becker, T., Gudnason, V., Cruchaga, C., Craig, D., Amin, N., Berr, C., Lopez, O. L., De Jager, P. L., Deramecourt, V., Johnston, J. A., Evans, D., Lovestone, S., Letenneur, L., Morón, F. J., Rubinsztein, D. C., Eiriksdottir, G., Sleegers, K., Goate, A. M., Fiévet, N., Huentelman, M. J., Gill, M., Brown, K., Kamboh, M. I., Keller, L., Barberger-Gateau, P., McGuinness, B., Larson, E. B., Green, R., Myers, A. J., Dufouil, C., Todd, S., Wallon, D., Love, S., Rogaeva, E., Gallacher, J., St George-Hyslop, P., Clarimon, J., Lleo, A., Bayer, A., Tsuang, D. W., Yu, L., Tsolaki, M., Bossù, P., Spalletta, G., Proitsi, P., Collinge, J., Sorbi, S., Sanchez-Garcia, F., Fox, N. C., Hardy, J., Naranjo, M. C. D., Bosco, P., Clarke, R., Brayne, C., Galimberti, D., Mancuso, M., Matthews, F., Moebus, S., Mecocci, P., Del Zompo, M., Maier, W., Hampel, H., Pilotto, A., Bullido, M., Panza, F., Caffarra, P., Nacmias, B., Gilbert, J. R., Mayhaus, M., Lannfelt, L., Hakonarson, H., Pichler, S., Carrasquillo, M. M., Ingelsson, M., Beekly, D., Alvarez, V., Zou, F., Valladares, O., Younkin, S. G., Coto, E., Hamilton-Nelson, K. L., Gu, W., Razquin, C., Pastor, P., Mateo, I., Owen, M. J., Faber, K. M., Jonsson, P. V., Combarros, O., O'Donovan, M. C., Cantwell, L. B., Soininen, H., Blacker, D., Mead, S., Mosley, T. H., Bennett, D. A., Harris, T. B., Fratiglioni, L., Holmes, C., de Bruijn, R. F. A. G., Passmore, P., Montine, T. J., Bettens, K., Rotter, J. I., Brice, A., Morgan, K., Foroud, T. M., Kukull, W. A., Hannequin, D., Powell, J. F., Nalls, M. A., Ritchie, K., Lunetta, K. L., Kauwe, J. S. K., Boerwinkle, E., Riemenschneider, M., Boada, M., Hiltunen, M., Martin, E. R., Schmidt, R., Rujescu, D., Wang, L.-S., Dartigues, J.-F., Mayeux, R., Tzourio, C., Hofman, A., Nöthen, M. M., Graff, C., Psaty, B. M., Jones, L., Haines, J. L., Holmans, P. A., Lathrop, M., Pericak-Vance, M. A., Launer, L. J., Farrer, L. A., van Duijn, C. M., Van Broeckhoven, C., Moskvina, V., Seshadri, S., Williams, J., Schellenberg, G. D., & Amouyel, P.
- Affiliations
- Inserm (FR)
- Cardiff University (GB)
- Université de Lille (FR)
- Boston University (US)
- University of Washington (US)
- University of Miami (US)
- University of Zurich (CH)
- Institut Pasteur de Lille (FR)
- University of Iceland (IS)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Fondation Jean Dausset-CEPH (FR)
- Medical University of Graz (AT)
- Columbia University Irving Medical Center (US)
- University of Bonn (DE)
- Hôpital Broca (FR)
- Queen's University Belfast (GB)
- Washington University in St. Louis (US)
- University of Pittsburgh (US)
- Brigham and Women's Hospital (US)
- Rush University Medical Center (US)
- King's College London (GB)
- Icelandic Heart Association (IS)
- University of Cambridge (GB)
- University of Antwerp (BE)
- Translational Genomics Research Institute (US)
- University of Nottingham (GB)
- Frenchay Hospital (GB)
- University of Toronto (CA)
- Universitat Autònoma de Barcelona (ES)
- Aristotle University of Thessaloniki (GR)
- MRC Prion Unit (GB)
- Hospital Universitario de Gran Canaria Doctor Negrín (ES)
- UCL Queen Square Institute of Neurology (GB)
- Oasi Maria SS (IT)
- University of Milan (IT)
- University of Pisa (IT)
- University of Perugia (IT)
- University of Cagliari (IT)
- Goethe University Frankfurt (DE)
- Casa Sollievo della Sofferenza (IT)
- University of Parma (IT)
- University of Florence (IT)
- Universitätsklinikum des Saarlandes (DE)
- Uppsala University (SE)
- Children's Hospital of Philadelphia (US)
- Mayo Clinic (US)
- Hospital Universitario Central de Asturias (ES)
- Universidad de Cantabria (ES)
- Harvard University (US)
- University of Eastern Finland (FI)
- Karolinska University Hospital (SE)
- Cedars-Sinai Medical Center (US)
- Centre National de la Recherche Scientifique (FR)
- National Institutes of Health (US)
- Brigham Young University (US)
- Baylor College of Medicine (US)
- Columbia University (US)
- Kaiser Permanente Washington Health Research Institute (US)
- Vanderbilt University (US)
- Centre Hospitalier Universitaire de Lille (FR)
- University of Pennsylvania (US)
- Corresponding author
- Williams, Julie
- Amouyel, Philippe
- ORCID
- Lambert, Jean-Charles
- Harold, Denise
- Naj, Adam C
- Sims, Rebecca
- Bellenguez, Céline
- Jun, Gyungah
- DeStefano, Anita L
- Bis, Joshua C
- Beecham, Gary W
- Russo, Giancarlo
- Smith, Albert V
- Chouraki, Vincent
- Thomas, Charlene
- Ikram, M Arfan
- Vardarajan, Badri N
- Lin, Chiao-Feng
- Gerrish, Amy
- Schmidt, Helena
- Kunkle, Brian
- Dunstan, Melanie L
- Pasquier, Florence
- Hanon, Olivier
- Buxbaum, Joseph D
- Campion, Dominique
- Crane, Paul K
- Becker, Tim
- Gudnason, Vilmundur
- Cruchaga, Carlos
- Craig, David
- Amin, Najaf
- Berr, Claudine
- Deramecourt, Vincent
- Evans, Denis
- Lovestone, Simon
- Letenneur, Luc
- Morón, Francisco J
- Rubinsztein, David C
- Sleegers, Kristel
- Goate, Alison M
- Huentelman, Matthew J
- Gill, Michael
- Kamboh, M Ilyas
- McGuinness, Bernadette
- Larson, Eric B
- Green, Robert
- Myers, Amanda J
- Dufouil, Carole
- Todd, Stephen
- Wallon, David
- Love, Seth
- Rogaeva, Ekaterina
- Gallacher, John
- Tsuang, Debby W
- Yu, Lei
- Tsolaki, Magda
- Bossù, Paola
- Spalletta, Gianfranco
- Proitsi, Petroula
- Collinge, John
- Sorbi, Sandro
- Fox, Nick C
- Hardy, John
- Bosco, Paolo
- Clarke, Robert
- Brayne, Carol
- Galimberti, Daniela
- Mancuso, Michelangelo
- Matthews, Fiona
- Moebus, Susanne
- Mecocci, Patrizia
- Maier, Wolfgang
- Hampel, Harald
- Bullido, Maria
- Panza, Francesco
- Caffarra, Paolo
- Nacmias, Benedetta
- Gilbert, John R
- Hakonarson, Hakon
- Carrasquillo, Minerva M
- Ingelsson, Martin
- Valladares, Otto
- Gu, Wei
- Owen, Michael J
- Faber, Kelley M
- Soininen, Hilkka
- Blacker, Deborah
- Mead, Simon
- Mosley, Thomas H
- Bennett, David A
- Harris, Tamara B
- Fratiglioni, Laura
- Holmes, Clive
- Passmore, Peter
- Montine, Thomas J
- Rotter, Jerome I
- Brice, Alexis
- Morgan, Kevin
- Foroud, Tatiana M
- Kukull, Walter A
- Hannequin, Didier
- Powell, John F
- Ritchie, Karen
- Lunetta, Kathryn L
- Boerwinkle, Eric
- Hiltunen, Mikko
- Martin, Eden R
- Schmidt, Reinhold
- Rujescu, Dan
- Wang, Li-San
- Dartigues, Jean-François
- Mayeux, Richard
- Tzourio, Christophe
- Nöthen, Markus M
- Graff, Caroline
- Psaty, Bruce M
- Jones, Lesley
- Haines, Jonathan L
- Holmans, Peter A
- Lathrop, Mark
- Launer, Lenore J
- Farrer, Lindsay A
- Seshadri, Sudha
- Williams, Julie
- Schellenberg, Gerard D
- Amouyel, Philippe
- Venue
- Nature Genetics
- Published
- 2013-10-27
- Volume
- 45
- Issue
- 12
- Pages
- 1452-1458
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC3896259
- OpenAlex
- W2115779804
- Cited by
- 4,336
- References
- 52
- Citations by year
- 2026: 190 · 2025: 264 · 2024: 337 · 2023: 330 · 2022: 394 · 2021: 437 · 2020: 463 · 2019: 421 · 2018: 389 · 2017: 336 · 2016: 330 · 2015: 267 · 2014: 165 · 2013: 10 · 2012: 1
- FWCI
- 28.36
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- PubMed Central (acceptedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- HAL (submittedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- Florence Research (University of Florence) (submittedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion)
- Explore Bristol Research (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Hirsla (Landspítali University Hospital) (submittedVersion)
- Funders
- Alzheimer's Association (U01 AG032984, AG033193)
- Vanderbilt University
- University of Miami
- University of Pennsylvania
- School of Medicine, Boston University
- University of Washington
- Dr. John T. MacDonald Foundation
- Wellcome Trust (100140, 089703, 095317)
- Llywodraeth Cymru
- Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease
- National Institute for Health and Care Research (NF-SI-0611-10084, NF-SI-0508-10123)
- Cardiff University
- Agence Nationale de la Recherche (ANR-11-LABX-0009)
- Institut National de la Santé et de la Recherche Médicale
- Erasmus Medisch Centrum
- Háskóli Íslands
- Universität Zürich
- Karl-Franzens-Universität Graz
- Medizinische Universität Graz
- Versus Arthritis
- Hjartavernd
- National Institutes of Health (UL1TR000124)
- Perelman School of Medicine, University of Pennsylvania
- Medical Research Council (MR/K013041/1, MC_G1000734)
- National Institute on Aging (R01AG017917, K25AG041906, U01AG049505, R01AG037212, U01AG016976, P50AG005131, P50AG005138, P50AG016573, R01AG041232, R01AG015819, U01AG032984, AG033193, P30AG008051, R01AG041797, P50AG008702, R01AG016495, AG032984, P30AG013846, P50AG005134, R01AG023629, R01AG033193, R01AG008122, U24AG056270, U24AG021886, P30AG062421)
- National Heart, Lung, and Blood Institute (R01HL105756)
- National Human Genome Research Institute (U01HG006375)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK063491)
- National Institute of Neurological Disorders and Stroke (R01NS080820)
- National Center for Advancing Translational Sciences
- 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, late-onset Alzheimer's disease, genetic susceptibility loci, genome-wide association study, apolipoprotein E, genetic risk, European ancestry, single nucleotide polymorphisms
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Female, Humans, Male, Middle Aged, Cohort Studies, Case-Control Studies, Age of Onset, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Genetic Loci
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